Clarence Wilfred de Silva


Relevant Thesis-Based Degree Programs

Affiliations to Research Centres, Institutes & Clusters


Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Sensor fusion for predictive control of human-prosthesis-environment dynamics in assistive walking (2022)

Powered prostheses are effective for helping amputees walk on level ground, but these devices are inconvenient to use in complex environments. To address the above issue, the present thesis develops a sensor fusion system in a complete human-prosthesis-environment loop to recognize the environmental context, predict the motion intent of different subjects, and control the motion of the prosthesis.There are challenges for realizing this objective:1. Complex information in the prosthesis-environment loop.2. User-dependent and subjective information in the human-prosthesis loop.3. Noisy decisions.4. Disconnected neural system of the user.5. Infeasibility of continuous control.6. No accommodation of navigation in rough terrains.To resolve the mentioned problems, the present thesis develops several methods. First, the present thesis designs a vision-motion sensor fusion system and a side-view convolutional neural network, to recognize the environmental context. To resolve the user-dependent issue, the present thesis proposes an unsupervised cross-subject adaptation method to recognize the locomotion intent for target subjects at 95% accuracy even if the associated data are not labeled. Then the thesis fuses the sequential decisions based on a hidden Markov model to smooth the decisions. Next, the thesis fuses the sensing and control system of the prosthesis to control the powered prosthesis predictively. To improve the authority of the prosthesis user, the present thesis studies two more challenging areas: 1) Transformation from discrete-state control to continuous-phase control. The present thesis estimates the gait phase of human locomotion in complex terrains with 80% lower error than traditional methods; 2) Transformation from structured terrains to rough terrains. The thesis fuses sequential 3D gaze and the environmental context to predict the foot placements of the wearer in rough terrains. The developed methodology is implemented in a physical prototype, and the performance is validated using both computer simulation and experimentation. The developed system and algorithms can recognize the environmental context and predict the motion intent of different amputee users accurately (at an accuracy = 97%) and efficiently (computing time
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Design optimization of a mechatronic device in the presence of quantitative and qualitative design criteria and multiple objectives (2020)

Mechatronic devices and multi-domain (multi-physics) systems are widely used in modern industry and other engineering applications. Mechatronic engineering focuses on developing a design solution that integrates multiple domains, particularly electrical and mechanical systems. For a successful product, these systems require to be accurate, fast, reliable, flexible, minimalist, easy to use and cost effective. Such design demands are diverse, can interact with each other, and might be characterized quantitatively, qualitatively, or both. This might require different scales, units, and physical representations between multiple criteria or objectives. Interacting criteria or objectives might be conflicting, e.g., improving one requirement might deteriorate another requirement. This requires reaching a compromise between objectives, a trade-off decision. The present dissertation addresses the multi-objective design optimization problem that involves quantitative and qualitative design criteria and objectives, in a mechatronic system. The methods developed in this thesis are applied to the design of a wearable sleep monitoring system. For the benefit of that application, a design optimization framework is proposed for sensor placement on a human body to improve the wearablity and reliability of a monitoring system that contains the sensors. The developed framework assists the designer in selecting the type and location of the sensors, and the pertinent wiring. The framework uses fuzzy sets and numbers to reduce the subjectivity that arises with qualitative criteria. To describe the qualitative objective comfort, fuzzy measures and the Choquet integral are used, particularly for combining multiple criteria and handling model interactions. Furthermore, fuzzy measures with the Choquet integral and the decision-making method VIKOR are introduced to make a relatively less subjective trade-off decision between conflicting objectives. Finally, a comparison is made between an improved VIKOR method and a fuzzy measure and Choquet integral approach, related to their optimization trade-off decisions. This study leads to a synthesis of all presented results and concludes that the proposed methods provide comparable results and are effective strategies for trade-off decisions. In this manner, the present investigation significantly contributes to the development of a more effective approach for solving a multi-objective design problem with quantitative and qualitative design criteria.

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Development of a multi-layer V-model design process and computational tools for mechatronic conceptual design (2020)

Conceptual design is a crucial phase in the Design Development Process (DDP) of complex mechatronic systems. Yet, the available design support is not adequate for the Conceptual Design Development Process (CDDP), let alone the entire DDP. Typically, linear methodology, called the V-model, is used in the software development life cycle (SDLC) for the DDP. The developed DDP can as well aid in addressing the customer requirements properly according to the degree of detail that is sought. Also, a Conceptual Integrated Model (CIM) can describe products from different viewpoints and can be developed to aid the simulation-based design.The primary focus of the present thesis is the conceptual design phase. The thesis proposes a hierarchical DDP, where the V-model process is expanded into multiple layers. These layers assist in providing increased flexibility to the DDP, in which each design phase is subjected to a separate and independent integration and evaluation. Through this approach, the required functions can be realized, and the lengthy iteration loops, due to incompatible subsystems, are avoided. The second key objective of the present thesis is to develop a CIM for formal concept modeling using the modeling language SysML with generic design functional libraries. The first set of design libraries are the FB libraries, which aid in the development of the functional structure. The second set of design libraries are Amesim simulation software elements, which help establish the concept simulation models. Also, the challenges of the transformation and exchange of information between a descriptive modeling language – SysML – and a multi-physics modeling language – Amesim – are explored. The last key objective of the present thesis is the use of fuzzy measures and fuzzy integrals for the evaluation of the non-functional requirements of the conceptual design phase, where Sugeno lamda-measures are employed to address the uncertainty of the requirements. The present research is conducted starting with a descriptive study for the development of a design concept model, concept simulation, and concept evaluation of an industrial fish cutting machine, which falls into the category of complex mechatronic systems. The evaluation of the approach focuses on improving the quality of the conceptual design.

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Energy efficient schemes in a mobile sensor network with application in automated monitoring of water quality (2020)

The present thesis addresses the power management issues in a mobile sensor network, with application in automated water quality monitoring. A water quality monitoring platform typically involves a wireless sensor network (WSN), in which a number of mobile sensor nodes (SN) are deployed in the water body to constantly collect the water-related sensory data such as the dissolved oxygen, pH value, temperature, oxidation-reduction potential, and electrical conductivity. This data is used to compute water quality index values, transmit them via some routing schemes, and eventually make them accessible to the water quality professionals, governing agencies, or the public. Power management is nontrivial in the monitoring of a remote environment, especially when long-term monitoring is anticipated. However, constrained by the limited energy supply and internal characteristics of the devices, without proper power management, the devices may become nonfunctional within the networked monitoring system, and as a consequence, the data or events captured during the monitoring process will become inaccurate or non-transmittable. Research is proposed here to develop three distinct approaches for energy conservation in a sensor network, and apply them for automated monitoring of the quality of water in an extensive and remote aquatic body. This thesis analytically develops and applies several energy efficient schemes for power management in the automated spatiotemporal monitoring of the quality of water in an extensive and remote aquatic environment. In general, the schemes for power management of a sensor network can be investigated from a number of aspects and schemes. Those schemes typically range from physical layer optimization to network layer solutions. Meanwhile, depending on the specific applications, some energy efficient methodologies are custom-designed, and thus have limitations when used in other applications. Given this background, three energy efficient methods are proposed in this thesis for conserving energy within a WSN. Those proposed three methods, including DDASA, Hybrid DPS and GCVD, are studied on both the sensor node level and the system level, which energy-efficiently reduce the energy consumption and save extra energy thereby prolonging the life of the WSN. It is expected that the proposed methods will be applicable in other spatiotemporal monitoring applications.

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Information-based sampling for spatiotemporal field estimation and reconstruction in environmental monitoring (2019)

This dissertation addresses the near-optimal deployment problem of robot-sensory nodes in a spatiotemporal field. With limited resources, monitoring of a complex environment may face serious challenges in providing sufficient information for spatiotemporal signal estimation and reconstruction. It is therefore essential to retrieve most useful information from sampling locations while using a small number of sensor nodes. In this dissertation, three aspects are investigated to overcome the shortcomings of the existing information-based sampling methods.First, a sensor node deployment method is designed to find the minimum number of sensor deployment locations while achieving near-optimal field estimation error. To this end, a sampling-based field exploration method is used to find near-optimal sampling locations over an infinite horizon. Moreover, spatiotemporal correlations of the sampling data are studied to find redundant signals. The corresponding sampling locations of the redundant signals are eliminated concerning the network connectivity.Second, a deep reinforcement learning approach is proposed to accelerate the field exploration. Typically, field exploration methods are heavily dependent on random sampling, which has low efficiency. To avoid unnecessary or redundant sampling locations, observations from the sampling locations are utilized. Then a model-based information gain determination of the sampling locations is developed to evaluate the effectiveness of the approach. The proposed method can determine the informativeness of the spatiotemporal field by learning the information gain from the sampled area. The mobile sensory agents are then encouraged to take more samples in the area of higher information gain. Consequently, the spatiotemporal field can be efficiently explored. Moreover, the selected sampling locations can near-optimally reconstruct the spatiotemporal field using statistical methods.Third, a deep learning framework is designed to provide accurate reconstruction and prediction of the spatiotemporal field, using a limited number of observations. Nonlinear mapping from limited observations to the entire spatiotemporal field is needed in a sufficiently large spatiotemporal field. Hence, a deep learning method is proposed to extract sparse representations of the field and their nonlinear mappings. It is also proven that the proposed framework obeys Lipschitz continuity and that the observations collected by sparse representations are sufficient for spatiotemporal field reconstruction.

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Robotic path planning for environmental field estimation and its application in aquatic monitoring (2019)

Recent advances in the technologies of sensing, robotics, and sensor networks have led to significant progress in environmental telemonitoring. Robotic systems have been widely developed and deployed in the field by using their capabilities of mobile sensing, autonomous navigation, and wireless communication. In particular, robotic monitoring and data sampling at locations of interest may be utilized to characterize and interpret the environmental phenomena of a study area. However, in real-world robotic sensing applications, the limitations of on-board resources will limit the coverage of the monitored area and the extent of acquired data, which will hinder the performance of field estimation and mapping. Meanwhile, the constraints of computational capability of the system components call for a computationally efficient framework to schedule and control the robotic sensing missions.This dissertation investigates and develops systematic sensor scheduling and path planning schemes for environmental field estimation through robotic sampling, and their application in aquatic monitoring. First, a hexagonal grid-based sampling frame is designed to distribute spatially balanced sampling locations over the monitored field. Two novel hexagonal grid-based survey planners are developed to generate energy-efficient sampling paths for the exploratory survey using mobile sensing robots, which can be executed in a computationally efficient manner. Second, an energy-constrained survey planner is developed, which achieves optimal coverage density for sampling, with a limit on the energy budget. The generated survey mission guides the robots to collect data samples for estimation and mapping of an unknown field under a Gaussian Process (GP) model. Third, a hierarchical planning framework with a built-in Gaussian Markov Random Field (GMRF) model is developed to provide informative path planning and adaptive sampling for efficient spatiotemporal monitoring. Fourth, the development of a cost-effective, rapidly deployable, and easily maintainable Wireless Mobile Sensor Network (WMSN) for on-line monitoring of surface water is presented. A novel On-Line Water Quality Indexing (OLWQI) scheme is developed and implemented to interpret the large volume of on-line measurements.The experimental results in the present dissertation demonstrate the effectiveness and efficiency of the proposed planning schemes and their application in aquatic environmental monitoring.

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A biology-inspired attention model with application in robot navigation (2018)

At present, robots are applied to specific situations and needs, so special methods are adopted for determining and controlling their movements. This thesis investigates the attention and navigation control of a mobile robot for carrying out dynamically challenging tasks involving humans. Since the robot environment can be complex, dynamic, and unpredictable, the required capacities are defined as those where the robot is required to pay attention to the needed object, plan the best path to the goal location and avoid obstacles. This thesis makes significant contributions in the development of an integrated model of top-down and bottom-up visual attention with self-awareness, and a biology-inspired method of robot planning and obstacle avoidance for a mobile robot. Specifically, an integrated model of top-down and bottom-up visual attention with self-awareness for robot is developed for selecting the highest saliency area in robot view. For mimicking the human attention processes, a robot self-awareness model with a fuzzy decision making system is developed and utilized, which is an important improvement over the existing robot attention models. Inspired by a mammal’s spatial awareness and navigation capabilities, a path planning method based on biological recognition is proposed for navigation tasks of a mobile robot in an unstructured and dynamic environment. An episodic cognitive map that encapsulates the information of scene perception, state neurons and pose perception is built to realize the environment cognition of the robot. An algorithm of the event sequence planning is presented for real-time navigation using the minimum distance between events. The method can choose the optimal planning path based on the tasks. A visual navigation algorithm that has the scale invariant feature transform (SIFT) feature is presented. By using the SIFT feature information, the horizontal coordinate of the matched feature pairs is considered to achieve the purpose of visual navigation. An approach of obstacle avoidance for visual navigation is presented based on a risk function and feasible paths. It can choose an optimal path for obstacle avoidance and then return to path planning through the interaction with the surrounding environment. The developed methodologies are evaluated using both simulation and experimentation.

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Application of machine health monitoring in design optimization of mechatronic systems (2017)

Mechatronic systems are widely used in modern manufacturing. The key machinery of a manufacturing system should be reliable, flexible, intelligent, less complex, and cost effective, which indeed are distinguishing features of a mechatronic system. To achieve these goals, continuous or on-demand design improvements should be incorporated rapidly and effectively, which will address new design requirements or resolve existing weaknesses of the original design.With the advances in sensor technologies, wireless communication, data storage, and data mining, machine health monitoring (MHM) has achieved significantcapabilities to monitor the performance of an operating machine. The extensive data from the MHM system can be employed in design improvement of the monitored system. In that context, the present dissertation addresses several challenges in applying MHM in design optimization of a mechatronic system.First, this dissertation develops a systematic framework for continuous design evolution of a mechatronic system with MHM. Possible design weaknesses of themonitored system are detected using the information from MHM. The proposed method incorporates an index to identify a possible design weakness by evaluatingthe performance, detecting failures and estimating the health status of the system.Second, improved approaches of intelligent machine fault diagnosis (IMFD) that can be applied to more general machinery and faults, are presented. Thisdissertation develops an IMFD approach based on deep neural networks (DNN). It uses the massive unlabeled MHM data to learn representative features. Usingvery few items of labeled data, this approach can achieve superior diagnosis performance. The dissertation presents another IMFD approach, which uses the convolutional neural networks (CNN) and sensor fusion and has increased diagnosis accuracy and reliability. The end-to-end learning capability of the two approaches enables diagnosis of fault types or machines for which limited prior knowledge is available.Third, a hierarchical DNN-based method of remaining useful life (RUL) prediction is developed. It achieves high accuracy of RUL prediction by modeling thesystem degradation on different health stages. This method generates a better estimate of the system RUL, which provides accurate information for the evaluation of system design.

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Estimation distribution algorithms based on extreme elitism and their application in engineering optimization problems (2017)

This dissertation modifies several estimation distribution algorithms (EDAs) and implements them in engineering optimization problems. The EDAs are population-based evolutionary algorithms, which employ extreme elitism selection. The main work of the present study is outlined below. First, an approach of extreme elitism selection is developed for EDAs. This selection highlights the effect of a few top solutions and advances EDAs to form a primary evolutionary direction. Simultaneously, this selection can also maintain population diversity to make EDAs avoid premature convergence. EDAs with the new selection approach are tested using a set of benchmark low-dimensional and high-dimensional optimization problems. The experimental results show that the EDA based on univariate marginal Gaussian distribution (UMGD) with extreme elitism selection can outperform some other classical evolution algorithms for most problems. Second, the EDA based on UMGD with extreme elitism is implemented for solving the inverse displacement problem (IDP) of a robotic manipulator. This EDA is compared with the EDAs with other selection methods in solving the IDP of a 4-degree-of-freedom (DOF) robotic arm. Next the algorithm is integrated with differential mutation to solve the IDP of a 7-DOF robotic arm. After that, the proposed algorithm is used to search for satisfactory solutions as a continuous curve. The simulation results show this algorithm can reach real time speeds, in practical applications. Third, EDAs based on five different Gaussian distributions are proposed to solve optimization problems with various types of constraints like equality, inequality, linear, nonlinear, continuous or discontinuous. It is found the EDA based on a single multivariate Gaussian distribution with extreme elitism selection can outperform other EDAs. Besides, this EDA has good performance for four engineering design problems. Fourth, EDA is combined with differential mutation to solve multi-objective optimization problems (MOPs). The hybrid algorithm seeks to find the Pareto optimal front for MOPs. EDAs guide the search direction in the evolution while differential mutation keeps a diversified population. A new sampling method that uses more Gaussian models to generate offspring is specially designed for the EDAs for MOPs. In light of no-free-lunch theorem, different probabilistic models and programing codes are adopted for different MOPs.

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Learning image-based localization (2017)

Image-based localization plays a vital role in many tasks of robotics and computervision, such as global localization, recovery from tracking failure, and loop closuredetection. Recent methods based on regression forests for camera relocalizationdirectly predict 3D world locations for 2D image locations to guide camera poseoptimization. During training, each tree greedily splits the samples to minimizethe spatial variance. This thesis develops techniques to improve the performancecamera pose estimation based on regression forests method and extends its application domains. First, random features and sparse features are combined so thatthe new method only requires an RGB image in the testing. After that, a label-freesample-balanced objective is developed to encourage equal numbers of samplesin the left and right sub-trees, and a novel backtracking scheme is developed toremedy the incorrect 2D-3D correspondence in the leaf nodes caused by greedysplitting. Furthermore, the methods based on regression forests are extended to uselocal features in both training and test stages for outdoor applications, eliminatingtheir dependence on depth images. Finally, a new camera relocalization method isdeveloped using both points and lines. Experimental results on publicly availableindoor and outdoor datasets demonstrate the efficacy of the developed approaches,showing superior or on-par accuracy with several state-of-the-art baselines.Moreover, an integrated software and hardware system is presented for mo-bile robot autonomous navigation in uneven and unstructured indoor environments.This modular and reusable software framework incorporates capabilities of perception and autonomous navigation. The system is evaluated are in both simulationand real-world experiments, demonstrating the efficacy and efficiency of the developed system.

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Haptic Teleoperation with Impedance Control Based on Learned Inverse Dynamics with Application in Homecare Robotics (2016)

Bilateral teleoperation allows a human operator to interact with a remote environment using the superior actuation and sensing skills of a robot and the unmatched cognitive skills of a human operator. It has shown promising results in applications such as telemedicine, telesurgery, and access to hazardous or remote environments. In all of these applications, the robot has to co-exist with humans and other delicate objects in the environment and therefore has to behave in a compliant (“soft”) manner. Moreover, in order to improve the task performance, the interaction force must be fed back to the operator to feel. In this backdrop, the present thesis focuses on the application of bilateral teleoperation in a homecare environment.In view of the underlying challenges involved with bilateral teleoperation, this dissertation focuses on the development of a complete teleoperation system that can effectively perform in real-time. A primary objective here is to use the impedance control approach to design local controllers for master and slave manipulators where the dynamic relationship between the applied forces and the resulting positions of the manipulators during interaction, is controlled. Impedance control requires the identification of the robot inverse dynamic model that can be computed in real-time and can adapt to changes in the actual dynamics of the robot. A complete data-driven learning-based technique called Locally Weighted Projection Regression (LWPR) is therefore used, which does not assume any a-priori knowledge of the inertial parameters of the robot. Performance of the system is improved by using online estimation of impedance of the unknown environment with which the slave manipulator interacts. A method of admittance control is designed. This method overcomes the shortcomings of the standard impedance control, as observed during experimentation. In the end, a method is developed to improve the transparency and position synchronization of the popular approach of wave-variables, which ensures stability under time delay that is induced by the communication channel during the exchange of information between the master and the slave ends. The effectiveness of the present developments is validated in an environment of homecare robotics, through simulation and experimentation, and the results are discussed.

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Impedance Control without Force Sensors with Application in Homecare Robotics (2015)

This thesis addresses the problem of interaction control between robot manipulator and the manipulated object in a homecare project. This project aims to use homecare robots at the elderly or disabled people’s home to provide necessary aid and assistance. The robot manipulator is to be operated in autonomous mode or teleoperation mode. The possible first aid or assistance requires direct interaction between the remote side robot manipulator and the human body. To guarantee the compliant interaction between the manipulator and the human body, impedance control was applied. In impedance control, neither the force nor the actual motion of the manipulator is controlled. The dynamic relationship between the interaction force and the resulting motion is controlled so that the interaction force will be monitored and kept at an acceptable range.To shape the mechanical impedance to any desired value as we wish, the remote side interaction force sensing is required. The interaction force could be sensed by a force sensor. Force sensors have a lot of inherent limitations such as narrow bandwidth, sensing noise, and high cost. To avoid a force sensor due to its limitations, sliding mode observers will be applied to estimate the interaction force. The estimated interaction force will be used in the impedance control algorithms. The observer and controller framework will be formulated and the solvability will be discussed thoroughly. In addition, the proposed approach will be compared with some available approaches to show its advantages over others. Bilateral impedance control will be applied in a teleoperation system. The master side impedance controller is to ensure the robust stability of the teleoperation system. The remote slave side impedance controller is used so that the interaction force will be monitored and kept at some acceptable range. Desired impedance parameters selection will be discussed considering the compromise between robust stability and performance. Also, in order to deal with the uncertainties in operator and environment dynamics, a robust performance guaranteed controller synthesis approach will be proposed. Gain-scheduling control could guarantee the stability and the robust performance under those uncertainties.

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Robot Learning for Autonomous Navigation in a Dynamic Environment (2015)

This dissertation addresses autonomous navigation of robots in a dynamic environment where the existence of moving and/or unknown objects leads to more serious challenges for the robots than those when operating in a traditional stationary environment. Therefore, the use of learning capabilities to facilitate proper robotic operation in a dynamic environment has become an important research area in the past decade. This dissertation proposes several novel learning-based methods to overcome the shortcomings in the existing approaches of autonomous navigation. Three aspects are addressed in the present work. First, a real-time path planning method is designed for autonomous navigation that can generate a path that avoids stationary and moving obstacles. To this end, learning ability is imparted to the robot. The present framework incorporates the statistical planning approach called probabilistic roadmap (PRM), Q-learning together with regime-switching Markov decision process (RSMDP) due to its beneficial characteristics, to form a robust Q-learning. Consequently, the initial path can be improved through robust Q-learning during interaction with a dynamic environment. Second, motion planning under constraints is investigated. Specifically, a closed-form piecewise affine control law, called piecewise affine-extended linear quadratic regulator (PWA-ELQR), for nonlinear-nonquadratic control problems with constraints is proposed. Through linearization and quadratization in the vicinity of the nominal trajectories, nonlinear-nonquadratic control problems can be approximated to linear-quadratic problems where the closed-form results can be derived relatively easily. Third, people detection is integrated into the autonomous navigation task. A classifier trained by a multiple kernel learning-support vector machine (MKL-SVM) is proposed to detect people in sequential images of a video stream. The classifier uses multiple features to describe a person, and learn its parameter values rapidly with the assistance of multiple kernels. In addition to the methodology development, the present research involves computer simulation and physical experimentation. Computer simulation is used to study the feasibility and effectiveness of the developed methodologies of path planning, motion planning and people detection. The experimentation involves autonomous navigation of a homecare robot system. The performance of the developed system is rigorously evaluated through physical experimentation and is improved by refining the developed methodologies.

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Topologies and design methods for folding kinetic structures: Expanding the architectural paradigm (2014)

Built environments are the most prominent and important part of our material culture.Although they are vital for accommodating the exponentially growing and increasinglyurbanized population under the challenging conditions of severe climatic changes anddestabilized global societies, researchers note that the methods of both their design andconstruction need to be significantly improved. The construction industry is the largestsource of waste and remains inefficient, while the architectural profession is beingchallenged by digital technologies, conflicting paradigms, and adverse market realities.What then are the obstacles in improving buildings? Is it the lack of viable ideas? Or, is itthe social reluctance to accept novel ideas? How can architects be the socially relevantforce contributing viable concepts? This thesis builds upon the current theories assertingthe importance of human behavior and intentionality for understanding built environments,thus considering the complexity of both the technical and cultural circumstances. Itestablishes that although architecture is usually considered as a solid and invariable staticform, it never has been a static shell merely delimiting discrete spaces. Through all times,buildings comprised both fixed structures and adjustable devices, which were theinteractive interfaces between the static structures and the transiency of human action. Thisstudy focuses on rigidly foldable kinetic structures as they exemplify the potentialadvantages and challenges of novel architecture; and they are a logical expansion of thetraditional adjustable architectural elements. For decades, theorists expected kineticarchitecture to address the shortcomings of the traditional buildings. However, solvingfolding kinetic geometries is difficult and is hindered by the unintuitive nature of thecurrent digital tools. Furthermore, kinetic environments challenge the traditionalexpectations of occupants. In response, the present thesis investigates the evolving,influenced by digital technologies, paradigms of public spaces, and human reasoningdrivendesign tools, while incorporating such human-centric considerations as socialdynamics, history, and culture into the engineering and architectural design methods forbuilt environments. It is concluded that architecture, its design and construction areprimarily a social endeavor. Therefore understanding the cognitive barriers of design tools and negotiating the social expectations are essential when advancing new technologies forarchitecture.

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Analysis and design of isolated bidirectional DC-DC converter with novel triple phase-shift control (2012)

The bidirectional DC-DC converter is widely used in automobiles, energy storage systems, uninterruptible power supplies and aviation power systems. At present, there are three main problems in this area. The first problem concerns stability of the bidirectional converter when parameters change; the second is maintaining high efficiency of the bidirectional converter over wide load range; the third concerns the sensitivity of the efficiency of the bidirectional converter to parameter changes. This thesis presents a new method to determine the stability of the bidirectional converter using the Lyapunov function method under arbitrary parameter changes. As another new contribution, the stability analysis with eigenvalue method is presented when only the input voltage changes. Although these two methods are used in this thesis to determine the stability of bidirectional dual full bridge DC-DC converter with triple phase-shift control, they can be used to determine the stability of other power converters composed of various power switches and controlled with different control methods. A novel triple phase-shift control method is developed in this thesis to make the bidirectional converter operate at high efficiency and make it robust to parameters changes and output power variations. Simulation results illustrate that the novel control method is better than several other commonly used control methods for the bidirectional converter when component parameters and output power change. The working theory of the bidirectional converter with novel triple phase-shift control method is comprehensively described in the thesis. As another new contribution, the maximum output power of the bidirectional converter is analyzed in detail in the thesis. Simulation studies of this project have provided satisfactory results. Conclusions are made on the presented work and possible future directions in continuing the work are indicated.

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Control of Mobile Manipulation with Networked Sensing (2012)

This thesis addresses the manipulation control of a mobile robot with the support of a sensor network, for carrying out dynamically challenging tasks. Such tasks are defined as those where the robot is required to first identify objects, approach and grasp the needed objects, and transport them to goal locations in an environment that is dynamic, unstructured and only partially known. In the present work, a robotic system with these capabilities is developed and implemented for use in tasks of search and rescue, and homecare robotics. To this end, this thesis makes significant original contributions in developing a scheme of adaptive nonlinear model predictive control (ANMPC) and a sensor network with dynamic clustering capability for mobile manipulation under challenging conditions. Two object tracking algorithms for color tracking and feature tracking are developed for object identification and tracking. A system that uses Q-learning is developed for mobile robot navigation, which allows the robot to learn and operate in an unknown and unstructured dynamic environment. A traditional approach of image-based visual servo control is developed and demonstrated. The scheme of ANMPC is developed, which incorporates a multi-input multi-output (MIMO) control system that can accommodate constraints, including environmental constraints and physical constraints of the robots. In implementing ANPC scheme, the nonlinear and time-variant model is linearized on line with respect to the current position of the image feature and robot joints, using an adaptive approach. The corresponding control architecture predicts the system outputs and generates optimized control actions according to a cost function. In order to extend the mobile manipulation system to a wider workspace such as that found in cities and home scenarios, a sensor network is designed and developed employing PFSA (Probabilistic Finite State Automata). The developed PFSA is utilized in both modeling the sensor data and organizing and representing the sensor network. An application of object identification and tracking is presented; and a heterogeneous sensor network is developed along with a simulation platform in MATLAB. A self-organized and clustered sensor network, which is based on PFSA, is demonstrated. In conclusion, directions for further research and development are indicated.

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A modified particle swarm optimization and its application in thermal management of an electronic cooling system (2011)

Particle Swarm Optimization (PSO) is an evolutionary computation technique, which has been inspired by the group behavior of animals such as schools of fish and flocks of birds. It has shown its effectiveness as an efficient, fast and simple method of optimization. The applicability of PSO in the design optimization of heat sinks is studied in this thesis. The results show that the PSO is an appropriate optimization tool for use in heat sink design.PSO has common problems that other evolutionary methods suffer from. For example, in some cases premature convergence can occur where particles tend to be trapped at local optima and not able to escape in seeking the global optimum. To overcome these problems, some modifications are suggested and evaluated in the present work. These modifications are found to improve the convergence rate and to enhance the robustness of the method. The specific modifications developed for PSO and evaluated in the thesis are: (1) Chaotic Acceleration Factor (2) Chaotic Inertia Factor (3) Global Best Mutation The performance of these modifications is tested through benchmarks problems, which are commonly found and used in the optimization literature. Detailed comparative analysis of the modifications to the classical PSO approach is made, which demonstrates the potential performance improvements. In particular, the modified PSO algorithms are applied to problems with nonlinear constraints. The non-stationary, multi-stage penalty method (PFM) is implemented to handle nonlinear constraints. Pressure vessel optimization and welded beam optimization are two common engineering problems that are used for testing the performance of optimization algorithms and are used here as benchmark testing examples. It is found that the modified PSO algorithms, as developed in this work, outperform many classical and evolutionary optimization algorithms in solving nonlinear constraint problems. The modified PSO algorithm is applied in heat sink design and detailed results are presented. The commercially available software package Ansys Icepak is used in the present work to solve the heat and flow equations in implementing the optimal design variables resulting from the modified PSO algorithms. The main contributions the work are summarized and suggestions are made for possible future work.

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Robust and Autonomous Multi-Robot Cooperation Using an Artificial Immune System (2010)

This thesis investigates autonomous and fault-tolerant cooperative operation and intelligent control of multi-robot systems in a dynamic, unstructured, and unknown environment. It makes significant original contributions pertaining to autonomous robot cooperation, dynamic task allocation, system robustness, and real-time performance. The thesis develops a fully autonomous and fault tolerant distributed control system framework based on an artificial immune system for cooperative multi-robot systems. The multi-robot system consists of a team of heterogeneous mobile robots which cooperate with each other to achieve a global goal while resolving conflicts and accommodating full and partial failures in the robots. In this framework, the system autonomously chooses the appropriate number of robots required for carrying out the task in an unknown and unpredictable environment. An artificial immune system (AIS) approach is incorporated into the multi-robot system framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. Based on the structure of the human immune system, immune response, immune network theory, and the mechanisms of interaction among antibody molecules, the robots in the team make independent decisions, coordinate, and if required cooperate with each other to accomplish a common goal. As needed for application in cooperative object transportation by mobile robots, the thesis develops a new method of object pose estimation. In this method, a CCD camera, optical encoders, and a laser range finder are the sensors used by the robots to estimate the pose of the detected object. The thesis also develops a market-based algorithm for autonomous multi-robot cooperation, which is then used for comparative evaluation of the performance of the developed AIS-based system framework. In order to validate the developed techniques, a Java-based simulation system and a physical multi-robot experimental system are developed. This practical system is intended to transport multiple objects of interest to a goal location in a dynamic and unknown environment with complex static and dynamic obstacle distributions. The approaches developed in this thesis are implemented in the prototype system in our laboratory and rigorously tested and validated through both computer simulation and physical experimentation.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Attention-based deep neural networks for the forecasting and reconstruction of an environmental image series (2022)

Over the past decade, climate change has become a serious issue, and it has received tremendous attention from investigators in different fields and also from the general public. In order to learn and provide immediate and appropriate actions with respect to climate variations, researchers have proposed various techniques for learning the underlying dynamic behavior in natural environments. In particular, in associated engineering research, convolutional long short-term memory (ConvLSTM) has been utilized to process spatiotemporal observations from the environment and predict the possible future measurements. Although the convolutional layer in this network can capture the local spatial dependencies, the network fails to extract global interactions among hidden features in the date. Moreover, in order to interpret the associated environmental field, a Kriging approach may be utilized in the analysis of the data collected from the monitored area and the estimation of an unknown region, through a Gaussian Process (GP) model. However, the performance of the field mapping is limited by the computationally expensive operation of determining the optimal sensor placement.To improve the accuracy and efficiency for the indicated goal of environmental monitoring and assessment, the thesis exploits a deep neural network for forecasting an environmental image series and reconstructing an environmental field, based on the acquired environmental data. First, an attention-based ConvLSTM model is developed to perform multi-step image series forecasting. Specifically, a convolutional self-attention (CSA) is designed to learn long-range dependencies within the latent variables during the training procedure. Second, an attention-based deep residual neural network is proposed to speed up the process of selecting the optimal monitoring locations. The proposed methodologies are evaluated with a real-world dataset. From this evaluation and the obtained experiment results, it is found that the proposed forecasting approach outperforms the existing methods in a reliable and accurate manner. The MSE (mean squared error) of the proposed model is approximately 7% lower than that of the existing methods. Moreover, the proposed model accelerates the process of finding the optimal sensor placements. Specifically, it is shown to achieve approximately 73% improvement in speed and an accuracy of about 90%.

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Automatic sleep stage classification using multitaper spectral estimation, wavelet transform and neural network (2021)

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.

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Unsupervised domain adaptation and generative model for intelligent engineering systems (2020)

Data-driven approaches have been widely applied to intelligent engineering systems. Their performance has been improved significantly by deep neural networks, which take advantage of large quantities of labeled data. However, the discriminative model that is trained using one dataset typically performs poorly when another different but related dataset is applied. In other words, it is difficult to design a discriminative model that can perform consistently well in different application scenarios without using any adaptation strategies. To tackle this problem, a research field called unsupervised domain adaptation, which aims at transferring knowledge from a label-rich source domain to an unlabeled target domain becomes active, has emerged. It is known that a discriminative model that is independent of the users or the environments, can be generalized by using unsupervised domain adaptation algorithms. However, there is a problem with all existing unsupervised domain adaptation methods. They cannot always learn a common representation space for the features from the two domains, making it difficult for the target domain to take advantage of the discriminative source features for its classification. To tackle this problem, specifically for engineering applications, two novel approaches, namely discriminative feature alignment and mutual variational inference, are proposed in the thesis. The proposed discriminative feature alignment can ensure that the features from the two domains can be properly constructed in a single distribution space, which is the space of a predefined Gaussian prior distribution where the target input samples can maximally take advantage of the discriminative source features for their classification tasks. The proposed mutual variational inference can indirectly transfer the knowledge learned from the source domain to the target domain via variational inference and mutual-information optimization. The developments are implemented on practical engineering problems and the performance is carefully evaluated to validate them.

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Multisensor data fusion for sleep apnea monitoring and classification (2018)

Sleep apnea is the most common type of sleep disorder that is related to breathing, amongst the adult population. Although laboratory polysomnography is the gold standard for the detection of apnea, wearable sleep monitoring devices are preferred due to many reasons such as comfort, monitoring in a familiar sleep environment, accessibility without delay, and low cost. It is important then to extract suitable features for the classification of apneic events as suitable for a wearable sleep monitoring device while maintaining the same accuracy levels as in laboratory polysomnography. This thesis first identifies suitable preprocessing and feature extraction techniques for standard biomedical signals monitored in laboratory polysomnography. Then it develops a feature-extraction technique and designs and implements a neural network for sleep apnea detection and classification of sleep stages. Composite Multiscale Sample Entropy (CMSE) is used as the feature extraction technique, in view of its desirable characteristics. The performance of the developed methodology is evaluated using true clinical data of sleep monitoring. The designed neural networks in the present work is found to have the ability to process and classify apneic events and sleep stages while maintaining the accuracy levels of sleep scoring in clinical polysomnography, which is the existing gold standard of sleep monitoring and scoring. The neural network used for classification of sleep stages may be subsequently incorporated as the input to a neural network for classifying apneic events. In addition, the thesis demonstrates the extent to which each individual signal that is monitored in polysomnography has the ability to independently detect apneic events. This would be useful in the implementation of a portable wearable device with clinical capability for sleep monitoring, which is the end objective of the current project.

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Adaptive wireless sensor network for real-time monitoring of water quality (2017)

Water quality problems have appeared in many places all around the world, and have caused severe public health problems. In identify the quality of different aquatic environments, wireless sensor networks have been used for monitoring large geographic areas of interest (AOI). Among the challenges of using wireless sensor networks for water quality monitoring in large areas, sensor node deployment strategy is a key consideration since an optimal sensor node deployment strategy can ensure the most appropriate utilization of the limited monitoring resources (sensor node, incorporated sensors, power supply, monitoring rates, etc.). To tackle such problems, we in the Industrial Automation Laboratory (IAL) of the Department of Mechanical Engineering, the University of British Columbia (UBC) have developed a mobile wireless sensor network for water quality monitoring. It has mobile (dynamic) sensor nodes, which can move to best sensing locations, and the ability to sense key water quality attributes. The developed platform is equipped with multiple nodes each of which having basic water quality detecting sensor probes, supports up to six propellers, and has upgradeable wireless communication boards. Besides, we have also proposed an optimal sensor node deployment strategy called “Rapid Random exploring tree with Linear Reduction” (RRLR) for this mobile wireless sensor network. The proposed method removes redundant sensor nodes depending on the linear dependence of sensor readings at the current deployment location without losing information. In this manner, spatial-temporal correlation of sensor node deployment in large geographic AOI can be minimized. The developed platform is demonstrated to have good performance even when moving against water flow and has low packet loss rate (0.85%) while transmitting data under different types of obstacles in real-world tests. Furthermore, the developed optimal sensor node deployment strategy, RRLR, requires nearly 60% fewer sensor nodes to achieve the same estimation error as our benchmark.

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Dynamic compensation and sensor fusion for a GSM-based water quality monitoring network (2016)

With the increasing demand for water, access to clean water is becoming a more challenging problem for people, in both rural and urban communities. The quantity and quality of fresh water resources, both surface water and ground water, are of major concern worldwide. A continuous water quality monitoring system with access to accurate real-time data can play an important role in water quality tracking and environmental protection. However, evaluation of water quality is complicated; on the one hand, a great number of physical, chemical and biological parameters are usually involved. Hence, multi-sensors network is often deployed for collecting a variety of useful water quality information, such as pH value, ammonia concentration, oxidation-reduction potential, temperature, electrical conductivity, turbidity, and the concentration of dissolved oxygen. On the other hand, objectives of in situ testing are complex and dynamic, and the testing environment in the field is also dynamic and harsh. This thesis develops a wireless data transmission platform to solve the communication problem between the monitoring sensor nodes in the field and the base station. What’s more, an individual sensor is only able to make a judgment using a single parameter as evidence. Simplex information is neither sufficient nor reliable, and some parameters also have mutual interference with each other to some extent. Specifically, there should be a systematic way to integrate information from multiple sensors to obtain more accurate and reliable water quality information. Furthermore, allowance has to be made for the variation in the conditions of a sensor, which will affect the sensor accuracy. Therefore, compensation and fusion of sensory data from disparate sources are very necessary to secure a reliable, accurate, and comprehensive monitoring result. By applying Dempster-Shafer theory and Euclidean Distance, this thesis presents a method of assigning four different parameters in the same scale, and combining them into an integrated and reliable quality evaluation result. The necessary methodologies are systematically presented. They are applied to realistic sensory data to illustrate their application and effectiveness.

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Power management in a sensor network for automated water quality monitoring (2016)

Power management is crucial in remote environmental monitoring, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested for sustaining a monitoring system. Without proper power management, equipment within the monitoring system may become nonfunctional and as a consequence, the data or events captured during the monitoring process will become inaccurate as well. Based on reinforcement learning, this thesis develops and applies an adaptive sampling algorithm and duty cycling for power management in automated water quality monitoring with energy harvesting. The state of the water quality parameters in a water source such as a lake or river may change in an unpredictable manner (e.g., may remain stable or change abruptly) depending on many factors such as climate or environmental changes or those caused by humans (e.g., waste water discharge from factories, construction, farming, and litter). Ideally, the sampling rate that is used for a sensor signal should depend on the rate at which the signal changes. Hence, adaptive sampling scheme using reinforcement learning is used in the present work, for water quality monitoring. The energy consumption for signal acquisition, processing, and transmission all depend on the sampling frequency, either directly or indirectly. Hence, it is desirable for the sensor nodes to dynamically learn how to determine the best sampling frequency for a sensor signal, depending how the signal changes due to the environmental situations, and adjust the sampling rate accordingly. It is found that by dynamically changing the sampling frequency, the battery state can be maintained at an energy-neutral level. Duty cycling also contributes to achieving the same goal by scheduling the working and sleeping time of a sensor node. It is shown that by switching between the work mode and the sleep mode, a satisfactory battery state can be maintained. These two methods have different degrees of advantage and performance in power management, but it is shown that both methods can achieve the energy neutrality while maintaining a high level of accuracy in the acquired data.

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A wearable sensor jacket and enabling technologies for a telemedicine system (2015)

Telemedicine will alleviate the pressure on healthcare systems by reducing avoidable hospital visits and providing a service that can directly address the specific needs of patients, notably of the elderly and the disabled. Unlike the current healthcare services, which primarily focus on treatment of illness in a centralized manner, a telemedicine system has the promise of distributing the medical consultation, which can provide rapid and convenient healthcare particularly for under-served rural communities. This thesis develops enabling technologies for a convenient and wireless telemedicine system. The system uses a multi-sensor jacket, which a patient wears for acquiring the vital information that a medical professional would need to make accurate diagnosis of common illness. In the system that is developed in the present thesis, the sensor jacket will automatically inflate in a conformable manner when the patient wears it. The sensors are properly located to acquire the vital data. The acquired signals are wirelessly transmitted to a local computer for processing, and transmitting to the medical professional through a public communication network. A wireless telemedicine system relies more on the bandwidth of the communication network than a wired system does. Hence, size reduction of the data stream is important. This thesis proposes a new method to reduce the size of an ECG signal, for example, by determining the key attributes of the signal. The key attributes are transmitted instead of the entire ECG waveform. Then the proposed method regenerates a representative ECG waveform at the doctor’s end using the transmitted attributes. Illustrative examples show that the method is quite accurate and effective in medical diagnosis. For better mobility and easier access of the communication network, the patient end application primarily runs on a mobile device such as an iPhone. The patient end application provides live video and audio interaction between the doctor and the patient. During an active video session, the system streams vital data to the doctor in real-time. Besides receiving, storing and displaying the vital data of the patient, the doctor end can use the video conference feature to discuss the medical condition with other medical professionals.

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Autonomous and Cooperative Multi-robot System for Multi-object Transportation (2014)

This thesis investigates multi-robot cooperation in multi-robot systems (MRS) for simultaneous multi-object transportation in unknown, dynamic, and unstructured environments. Two distinct control frameworks are developed for MRS to achieve its global goal while resolving conflicts in the system.An autonomous and distributed algorithm that uses artificial immune system (AIS) is developed for multi-robot cooperation and it is validated using experimental work carried out on a team of real physical robots in the Industrial Automation Laboratory (IAL).Two deadlock handling approaches are considered to avoid shared resource conflicts in the system. One method prevents the system from getting into a deadlock situation and is so-called the prevention-based method. The other method autonomously detects the deadlocks and then recovers system from that situation, and is known as the detection-based method.Two separate deadlock resolution algorithms are developed for MRS; one based on the prevention method and the other based on the detection method. Either of these two deadlock handling algorithms is then combined with the multi-robot cooperation algorithm to generate two integrated task execution algorithms for the control frameworks of MRS.Feasibility and effectiveness of the developed control frameworks are demonstrated and evaluated through simulation of the MRS on the Webots simulation platform. Finally, using the simulation results, a comparative evaluation of the two control frameworks developed in this research is carried out with respect to task completion time, communication overhead, and the number of tasks executed.

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A Design Expert System for Guidance and Assessment of the Design Evolution of Motion Control Systems (2012)

The automation of engineering design is of great significance in the development of machinery and products in competitive industries. Using an automated and “optimal” design process to redesign the faulty components and poorly performing regions of an existing engineering system will facilitate the realization of realistic design alternatives, with benefits such as improved quality, reliability, and cost effectiveness. Motivated by such needs, this thesis develops a Design Expert System (DES) for motion control (MC) applications. The developed DES is expected to be integrated into a multi-system Evolutionary Design Framework (EDF) which is being developed in our laboratory. The EDF integrates techniques of condition monitoring, modeling, and evolutionary optimization for autonomous identification, diagnosis, and redesign of poorly performing aspects of an existing machine. Through integration with optimization routines and the use of a comprehensive knowledge base (KB) in the MC domain, the DES developed in this work is able to guide the evolution of optimal design alternatives and assess their feasibility and effectiveness. Due to the prevalence of electric motors as actuators in many industrial applications, MC design and actuator (motor) selection represent the application domain of the DES developed in the present research. The KB of the DES includes knowledge of typical mechanical structures used in industrial MC systems, common profiles of load speed or position (duty cycles), and the effect of practical issues such as s-curve profiling, geometric trajectory blending, intermittent duty cycles, rms torque, and the thermal response of motors. A systematic methodology for detailed design analysis and subsequent selection of commercially available motors, their drive systems, and transmission devices (e.g., gears) from an external database is developed. Selections by the DES are compared to those by a human designer for both hypothetical and actual designs, thereby verifying the DES procedure. To facilitate the interaction between different systems in the EDF, a graphical user interface (GUI) is created for the DES in Excel®. The DES is synchronized with Matlab® to guide optimization routines based on its built-in human expertise and heuristic design knowledge. A guided optimization case study is presented and benefits of the guidance process are discussed.

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Design evolution of engineering systems using bond graphs and genetic programming (2012)

Engineering design is a complex task, which typically involves multiple physical domains. It can benefit from a modeling tool that can represent different domains in a unified manner. For complex designs, optimization by traditional techniques (such as gradient-based methods) may not be appropriate. Evolutionary methodologies may be used in design optimization of complex engineering systems. This research is based on a framework for evolutionary design system consisting of a machine health monitoring system, model-based evolutionary design optimization system, and a design expert system. The design weaknesses and faults of an existing engineering system are identified using a machine health monitoring system. Then under supervision of the design expert system, an optimal design is evolved using genetic programming. This thesis primarily addresses the modeling and evolutionary aspects and their integration. The thesis develops the integrated system consisting of bond-graph modeling and genetic programming. The performance of the developed system is studied using both experimentation and simulation. The drawbacks of the fitness calculation methodologies that are presented in literature are identified and improved fitness functions are developed in the present work. A methodology to automatically obtain the state-space model of a system represented using bond graphs is also developed. While previous researchers have investigated the integration of bond graphs and genetic programming in design, they have not applied the method in a real engineering system. The present work specifically addresses the application of the developed method for design improvement for an industrial machine. For this purpose a linear bond graph model of the industrial fish processing machine is developed and the parameter values are identified using genetic programming. The design of the actual system is modified according to the evolved bond graph model and the results are validated using the data from the actual engineering system. The proposed method is applicable particularly to actual systems, first because the initial model can be tested by comparing its simulated results with the corresponding results from the actual system, and second because the design improvements as suggested by the evolutionary design framework may be implemented and tested against the behaviour of the corresponding model.

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New Approaches to Linear Graph Modeling of Distributed-Parameter Systems (2012)

Analytical modeling is an important fundamental step in the development of procedures such as simulation, design, control, and health monitoring of engineering systems. Typically, physical properties such as inertia, flexibility (or stiffness), capacitance, inductance, and energy dissipation (mechanical damping or electrical resistance) are spatially distributed in a physical dynamic system. Often in dynamic models, these characteristics are approximated by spatially “lumped” elements. For better accuracy, however, the true distributed nature of these parameters has to be incorporated into the model. Distributed parameter (DP) models are important in this context. This thesis concerns the representation of distributed parameter engineering systems using linear graphs (LG). Among possible approaches for modeling of engineering systems, linear graphs are used in the present work due to its numerous advantages as discussed in the thesis. An engineering system may possess physical properties in many domains such as mechanical, electrical, thermal, and fluid. Mechatronic systems are multi-domain systems, which typically possess at least electro-mechanical characteristics. Linear graphs present a domain-independent unified approach for modeling multi-domain systems. Furthermore, linear graphs have beneficial features in the development of automatic procedures for modeling and designing engineering systems, which are long-term goals of the present work. In this thesis, approaches are developed for the representation of distributed-parameter systems as LG models. Different approaches are presented for this purpose and compared. The LG modeling approach enables one to visualize the system structure before formulating the dynamic equations of the system. For example, for a DP system the structure of its LG model may possess a well-defined pattern. In this work, vector linear graphs are introduced to take advantage of these patterns. General notations and elements are defined for vector linear graphs. As a result of this development a new single element is generated for use in the modeling of distributed-parameter systems, particularly in the mechanical domain.In this thesis, a software toolbox is enhanced and presented for LG modeling, which is able to automatically extract the state space equations of a mechatronic system. This software tool is provided free for academic use and is accessible through the Internet. Throughout the thesis many comprehensive examples are provided to illustrate the developed concepts and procedures and their application.

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Design evolution of engineering systems through modeling, on-line monitoring, and evolutionary optimization (2011)

This work develops a framework of design evolution to support and automate the generation and evaluation of optimal multi-domain engineering designs. It integrates a Machine Health Monitoring System (MHMS), a Model Generation System (MGS), a Design Expert System (DES) and an Evolutionary Design Optimization System (EDOS) for aiding engineers through the redesign of an existing engineering system. First, the MHMS, while maintaining the engineering system in an operable condition by anticipating possible failures, indicates subsystems for possible design improvement. Second, the MGS which provides the capability of system modeling through the Linear Graphs approach enables representation of the current version of the system that is being designed. Third, the integration of a DES to the evolutionary framework provides automatic incorporation of expert suggestions into the system. Fourth, the EDOS automatically evolves mechatronic designs represented by Linear Graphs using Genetic Programming (GP). In addition, the Mechatronic Design Quotient has been proposed as the fitness function of the evolutionary process, as it provides an intelligent way to represent the quality of design using various design indices. Also it has proven to be a good approach to meet design constraints and do not violate the feasibility of implementation.The experimental system (Iron Butcher) is an automated industrial fish processing machine that already has a MHMS. Development of the DES is an on-going project of other researchers in our laboratory. The present thesis primarily focuses on the modeling using Linear Graph and design optimization using Genetic Programming.An algorithm which integrates GPLAB, a MATLAB toolbox for Genetic Programming, with the powerful modeling and simulation tool of Simscape is developed. Both the scheme and the design alternatives generated by the algorithm are validated using computer simulations and physical experimentation on a realistic environment. For this purpose, a state-space model of the electromechanical conveying system of the Iron Butcher is developed using Linear Graph modeling. Results show that under normal operating conditions, the response of the machine satisfactorily matches that of the state-space model. Also it is found that the new mechatronic engineering designs automatically evolved through the developed design framework successfully met the design requirements.

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Robust 3D Object Detection and Feature Extraction for Cooperative Multi-Robot Tasks (2010)

Computer vision uses image processing, image understanding, and feature extraction, which isvital in robotic tasks. This research is an integral part of a larger project on human rescue robotics where the goal is to quickly locate objects in an emergency scenario by a group of heterogeneous robots and assemble them into a useful device. Hence, the vision system should be fast and capable of working in an unstructured, dynamic, and unknown environment. Since there may be a number of variations with regard to the objects and the environment, the robustness is crucial. A novel vision system architecture is proposed and developed in this research to fulfill the vision requirements of a multi-robot system. Appropriate approaches, techniques, and structures are proposed and implemented together with appropriate existing methods and their enhancements. An approach of object modeling is proposed and used to generate object models. These models are used with a proposed object detection method to identify objects and determine usefulfeatures and parameters. Another object detection method is proposed to detect regular geometrical shaped objects. The proposed methods is able to detect multiple objects with varying object properties and environmental factors. Different types of object detection methods are employed in the proposed system according to the requirement of a robot by utilizing a real-time method selection technique, which is developed in the thesis. Achieving the expected level of performance involves a trade-off betweenspeed and accuracy, by managing the execution of the processing steps in the developed method.Properties of expected objects need to be defined as facts and constraints based on the requirements of the robots. The performance of the vision system can be enhanced, by providing more facts and constraints. The developed methodologies are implemented in an experimental system in the Industrial Automation Laboratory of the University of British Columbia. Rigorous experiments areconducted in a typical unstructured environment. Features such as invariance of scale, rotation,illumination, and occlusion are tested with different types of objects, for various methods.Generally good results have been obtained thereby validating the developed vision system foruse in the multi-robot application.

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