Running, jumping and walking are physical activities that are performed by humans in a simple and efficient way. However, these types of movements are difficult to perform by humanoid robots. Humans perform these activities without difficulty thanks to their ability to absorb the ground impact force. The absorption of the impact force is based on the human ability to vary muscles stiffness. The principal objective of this dissertation is to study vertical jumps in order to reduce the impact force in the landing phase of the jump motion of humanoid robots. Additionally, the impact force reduction is applied to an arm-oriented movement with the objective of preserving the integrity of falling humanoid robot. This dissertation focuses on researching vertical jump motions by designing, implementing and testing variable stiffness control strategies based on Computed-Torque Control while tracking desired trajectories calculated using the Zero Moment Point (ZMP) and the Center of Mass (CoM) conditions. Variable stiffness method is used to reduce the impact force during the landing phase. The variable stiffness approach was previously presented by Pratt et al. in [1], where they proposed that full stiffness is not always required. In this dissertation, the variable stiffness capability is implemented without the integration of any springs or dampers. All the actuators in the robot are DC Motors and the lower stiffness is achieved by the design and implementation of PID gain values in the PID controller for each motor. The current research proposes two different approaches to generate variable stiffness. The first approach is based on an optimal control theory where the linear quadratic regulator is used to calculate the gain values of the PID controller. The second approach is based on Fuzzy logic theory and it calculates the proportional gain (KP) of the PID controller. Both approaches are based on the idea of computing the PID gains to allow for the displacement of the DC motor positions with respect to the target positions during the landing phase. While a DC motor moves from the target position, the robot CoM changes towards a lower position reducing the impact force. The Fuzzy approach uses an estimation of the impact velocity and a specified desired soft landing level at the moment of impact in order to calculate the P gain of the PID controller. The optimal approach uses the mathematical model of the motor and the factor, which affects the Q matrix of the Linear Quadratic Regulator (LQR), in order to calculate the new PID values. A One-legged robot is used to perform the jump motion verification in this research. In addition, repeatability experiments were also successfully performed with both the optimal control and the Fuzzy logic methods. The results are evaluated and compared according to the impact force reduction and the robot balance during the landing phase. The impact force calculation is based on the displacement of the CoM during the landing phase. The impact force reduction is accomplished by both methods; however, the robot balance shows a considerable improvement with the optimal control approach in comparison to the Fuzzy logic method. In addition, the Optimal Variable Stiffness method was successfully implemented and tested in Falling Robots. The robot integrity is accomplished by applying the Optimal Variable Stiffness control method to reduce the impact force on the arm joints, shoulders and elbows.

The global importance of and need for sustainable development demand an informed decision-making process from the built environment to ensure optimum service life, which depends on the ability to quantify changes in condition of building materials over time. The objective of this thesis is to develop a model, which translates expert knowledge and reasoning into probability values through the application of Fuzzy Logic Artificial Intelligence to supplement limited historical performance data on degradation of building materials for the development of Markov Chain transitional probability matrices to predict service life, condition changes over time, and consequences of maintenance levels on service life of buildings. The Markov Chain methodology, a stochastic approach used for simulating the transition from one condition to another over time, has been identified as the preferred method for service life prediction by a number of studies. Limited availability of historic performance data on degradation and durability of building materials, required to populate the Markovian transition probability matrices, however restricts the application of the Markov Chain methodology. The durability and degradation factors, defined as design and maintenance levels, material and workmanship quality, external and internal climate, and operational environment, similar to the factors identified in the state-of-the–art ‘Factor Method’ for service life prediction, and current condition are rated on a uniform colour-coded five-point rating system and used to develop “IF-THEN” rules based on expert knowledge and reasoning. Fuzzy logic artificial intelligence is then used to translate these rules into crisp probability values to populate the Markovian transitional probability matrices. Historic performance data from previous condition assessments of six academic hospitals are used to calibrate and test the model. There is good correlation between the transitional probability matrices developed for the proposed model and other Markov applications in concrete bridge deck deterioration and roof maintenance models, based on historic performance data collected over extended periods, which makes the correlation more significant. Proof is presented that the Markov Chain can be used to calculate the estimated service life of a building or component, quantify changes in condition over time and determine the effect of maintenance levels on service life. It is also illustrated that the limited availability of historic performance data on degradation of building materials can be supplemented with expert knowledge, translated into probability values through the application of Fuzzy Logic Artificial Intelligence, to develop transition probability matrices for the Markov Chain. The proposed model can also be used to determine the estimated loss of or gain in service life of a building or component for various levels of maintenance. / Thesis (PhD(Civil Engineering))--University of Pretoria, 2007. / Civil Engineering / unrestricted

The eulachon (Thaleichthys pacificus), a small anadromous smelt (Family Osmeridae) found only along the Northwest Pacific Coast, is poorly understood. Many spawning populations have suffered declines but as their historic status is relatively unknown and the fisheries poorly documented, it is difficult to study the contributing factors. This thesis provides a survey of eulachon fisheries throughout its geographical range and three analyses aimed at improving our understanding of past and present fisheries, coast-wide abundance status, and the factors which may be impacting these populations. An in-depth view of the Nuxalk Nation eulachon fishery on the Bella Coola River, Central Coast, BC, is provided. The majority of catches were used for making eulachon grease, a food item produced by First Nations by fermenting, then cooking the fish to release the grease. Catch statistics were kept yearly from 1945-1989 but have since, rarely been recorded. Using traditional and local ecological knowledge, catches were reconstructed based on estimated annual grease production. Run size trends were also created using local Fisheries Officers and Nuxalk interview comments. A fuzzy logic expert system was designed to estimate the relative abundance of fifteen eulachon systems. The expert system uses catch data to determine the exploitation status of a fishery and combines it with other data sources (e.g., CPUE) to estimate an abundance status index. The number of sources depended on the existing data and varied from one to eight. Using designed heuristic rules and by adjusting weighting parameters a final index was produced. Results suggest that there have been recent and extended declines in several eulachon rivers particularly the Klamath, California; Bella Coola, BC; Wannock, BC; and Kitimat, BC. Seven of the fifteen abundance time-series were used to evaluate the potential relationships between the declines and some of the factors that impact eulachon. Results suggest increases in shrimp and hake catches, seal and sea lion abundance, and sea surface temperatures were weakly associated with the declines. But contrary to expectations, adult hake biomass showed a positive association with four eulachon relative abundance time-series, suggesting that common environmental factors influenced both species. / Science, Faculty of / Resources, Environment and Sustainability (IRES), Institute for / Graduate

No description available.

The bioreactor landfill is an emerging concept for solid waste management that has gained significant attention in the last decade. This technology employs specific operational practices to enhance the microbial decomposition processes in landfills. However, the unsupervised management and lack of operational guidelines for the bioreactor landfill, specifically leachate manipulation and recirculation processes, usually results in less than optimal system performance. Therefore, these limitations have led to the development of SMART (Sensor-based Monitoring and Remote-control Technology), an expert control system that utilizes real-time monitoring of key system parameters in the management of bioreactor landfills. SMART replaces conventional open-loop control with a feedback control system that aids the human operator in making decisions and managing complex control issues. The target from this control system is to provide optimum conditions for the biodegradation of the refuse, and also, to enhance the performance of the bioreactor in terms of biogas generation. SMART includes multiple cascading logic controllers and mathematical calculations through which the quantity and quality of the recirculated solution are determined. The expert system computes the required quantities of leachate, buffer, supplemental water, and nutritional amendments in order to provide the bioreactor landfill microbial consortia with their optimum growth requirements. Soft computational methods, particularly fuzzy logic, were incorporated in the logic controllers of SMART so as to accommodate the uncertainty, complexity, and nonlinearity of the bioreactor landfill processes. Fuzzy logic was used to solve complex operational issues in the control program of SMART including: (1) identify the current operational phase of the bioreactor landfill based on quantifiable parameters of the leachate generated and biogas produced, (2) evaluate the toxicological status of the leachate based on certain parameters that directly contribute to or indirectly indicates bacterial inhibition, and (3) predict biogas generation rates based on the operational phase, leachate recirculation, and sludge addition. The later fuzzy logic model was upgraded to a hybrid model that employed the learning algorithm of artificial neural networks to optimize the model parameters. SMART was applied to a pilot-scale bioreactor landfill prototype that incorporated the hardware components (sensors, communication devices, and control elements) and the software components (user interface and control program) of the system. During a one-year monitoring period, the feasibility and effectiveness of the SMART system were evaluated in terms of multiple leachate, biogas, and waste parameters. In addition, leachate heating was evaluated as a potential temperature control tool in bioreactor landfills. The pilot-scale implementation of SMART demonstrated the applicability of the system. SMART led to a significant improvement in the overall performance of the BL in terms of methane production and leachate stabilization. Temperature control via recirculation of heated leachate achieved high degradation rates of organic matter and improved the methanogenic activity.

Technology such as television, computers, and video games are often in the line for reasons of why people lack physical activity and tend to gain weight and become obese. In the case of video games, with the advent of the so called “serious games initiative”, a new breed of video games have come into place. Such games are called “exergames” and they are intended to motivate the user to do physical activity. Although there is some evidence that some types of Exergames are more physically demanding than traditional sedentary games, there is also evidence that suggests that such games are not really providing the intensity of exert that is at the recommended levels for a daily exercise. Currently, most exergames have a passive approach. There is no real tracking of the players progress, there is no assessment of his/her level of exert, no contextual information, and there is no adaptability on the game itself to change the conditions of the game and prompt the desired physiological response on the player. In this thesis we present research work done towards the design and development of an architecture and related systems that support a shift in the exertion game paradigm. The contributions of this work are enablers in the design and development of exertion games with a strict serious game approach. Such games should have “exercising” as the primary goal, and a game engine that has been developed under this scheme should be aware of the exertion context of the player. The game should be aware of the level of exertion of the player and adapt the gaming context (in-game variables and exertion interface settings) so that the player can reach a predefined exertion rate as desired. To support such degree of adaptability in a multimedia, multimodal system, we have proposed a system architecture that lays down the general guidelines for the design and development of such systems.

HTTP Adaptive Streaming (HAS) has now become ubiquitous, and it accounts for a large proportion of multimedia delivery over the Internet. Consequently, it poses new challenges for content providers and network operators. In this study, we aim to improve the user’s Quality of Experience (QoE) for HAS using from two main approaches including client centric approach and network assisted approach. In the client centric approach, we address the issue of enhancing the client’s QoE by proposing a fuzzy logic–based video bitrate adaptation and prediction mechanism for Dynamic Adaptive Streaming over HTTP (DASH) players. This adaptation mechanism allows HAS players to take appropriate actions sooner than existing methods to prevent playback interruptions caused by buffer underrun and reduce the ON-OFF traffic phenomena, which causes instability and unfairness among competing players. Our results show that compared to other studied methods, our proposed method has two advantages: better fairness among multiple competing players by almost 50% on average and as much as 80% as indicated by Jain’s fairness index, and better perceived quality of video by almost 8% on average and as much as 17%, according to the eMOS model. In the network assisted approach, we propose a novel mechanism for HAS stream adaptation in the context of wireless mobile networks. The proposed mechanism leverages recent advances in the 3GPP DASH specification, including the optional feature of QoE measurement and reporting for DASH clients. As part of the proposed mechanism, we formulate a utility-maximization problem that incorporates factors influencing QoE to specify the optimum value of Quality of Service (QoS)-related parameters for HAS streams within a wireless mobile network. The results of our simulations demonstrate that our proposed system results in better perceived quality of video, measured by Mean Opinion Score (MOS), by almost 7% on average, while lowering the freezing period by almost 20% on average across HAS users when compared to other approaches where HAS users only rely on local adaptation logics.

In this thesis a Proactive Risk-Aware Robotic Sensor Network (RSN) is proposed for the application of Critical Infrastructure Protection (CIP). Each robotic member of the RSN is granted a perception of risk by means of a Risk Management Framework (RMF). A fuzzy-risk model is used to extract distress-based risk features and potential intrusion-based risk features for CIP. Detected high-risk events invoke a fuzzy-auction Multi-Robot Task Allocation (MRTA) algorithm to create a response group for each detected risk. Through Evolutionary Multi-Objective (EMO) optimization, a Pareto set of optimal robot configurations for a response group will be generated using the Non-Dominating Sorting Genetic Algorithm II (NSGA-II). The optimization objectives are to maximize sensor coverage of essential spatial regions and minimize the amount of energy exerted by the response group. A set of non-dominated solutions are produced from EMO optimization for a decision maker to select a single response. The RSN response group will re-organize based on the specifications of the selected response.

The widespread adoption of embedded computing systems has resulted in the realization of numerous sensing, decision, and control applications with diverse application-specific requirements. However, such embedded systems applications are becoming increasingly difficult to design, simulate, and optimize due to the multitude of interdependent parameters that must be considered to achieve optimal, or near-optimal, performance that meets design constraints. This situation is further exacerbated for data-adaptable embedded systems (DAES) applications due to the dynamic characteristics of the deployment environment and the data streams on which these systems operate. As operating conditions change, these embedded systems must continue to adapt their configuration and composition at runtime in order to meet application requirements. To assist both platform developers and application domain experts, this dissertation presents design and optimization frameworks for the synthesis of runtime adaptable embedded systems. For sensor network applications, we present an initial dynamic profiling and optimization platform that profiles network and sensor node activity to generate optimal node configurations at runtime based on designed-specified application requirements. To support a broader class of DAES applications, we present a modeling and optimization framework that supports the specification of application task flows, data types, and runtime estimation models for the runtime adaptation of task implementations and device mappings. Experimental results for these design and optimization frameworks demonstrate the benefits of dynamic optimization compared to static optimization alternatives. For the presented sensor network and video-based collision avoidance applications, dynamic configurations exhibited improvements of up to 109% and 76%, respectively. Moreover, the performance of the heuristic design space exploration (DSE) algorithms utilized by the runtime optimization frameworks is compared to exhaustive DSE implementations, resulting in speedups of up to 1662X and 544X for the same two applications, respectively.

As empresas estão buscando novas estratégias para diminuir os custos e aumentar os lucros dos acionistas e, assim, manter-se no mercado cada vez mais competitivo. O trabalho tem como objetivo apresentar o uso da lógica Fuzzy no modelo TDABC, para diminuir a subjetividade e incerteza, buscando informações mais precisas para auxiliar no planejamento e tomada de decisão. Tendo em vista que a teoria dos conjuntos Fuzzy é conhecida como uma lógica de abordagem que lida com o raciocínio de gerir a incerteza. Como o TDABC tem sido criticado por diversos autores quando se trata de situações incertas pela falta de padronização de algumas atividades, que dificultam a modelagem em termos de equação do tempo. Também são criticados pelos autores os ambientes de produção por encomenda, onde as atividades desse tipo de empresa apresentam grande imprevisibilidade, tanto com relação ao tempo de execução, quanto à intensidade do consumo de recursos. Com o uso da lógica Fuzzy no TDABC podemos reduzir a incerteza e subjetividade buscando informações mais precisas, podendo auxiliar no planejamento e na formação de preços dos produtos. / Companies are seeking new strategies to reduce costs and increase shareholder profits and thus keep the market increasingly competitive. The paper aims to present the use of fuzzy logic in the model TDABC to reduce subjectivity and uncertainty, seeking more precise information to assist in planning and decision making. Given that the theory of fuzzy sets is known as a logical approach that deals with reasoning to manage uncertainty. How TDABC has been criticized by several authors when dealing with uncertain situations by the lack of standardization of some activities that hinder the modeling in terms of the equation of time. Also, are criticized by the authors production environments on demand where activities of the corporation have great unpredictability both with respect to runtime, as the intensity of resource consumption. With the use of fuzzy logic in TDABC can reduce uncertainty and subjectivity seeking more precise information can help in the planning and pricing of products.