Reinforcement learning-based target tracking for unmanned aerial vehicle with achievement rewarding and multistage traning

Target tracking using an unmanned aerial vehicle (UAV) is a challenging robotic problem. It requires handling a high level of nonlinearity and dynamics device. The aim is to enable accurate target tracking by UAV with responding to the dynamic generated by the target such as sudden trajectory change using reinforcement learning which is proved to learn dynamic effectively. In this thesis, the Twin Delayed Deep Deterministic Policy Gradient Algorithm (TD3), as one recent and composite architecture of reinforcement learning (RL), has been explored as a tracking agent for the problem of UAV-based target tracking. This involved several improvements on the original TD3. First, the proportional-differential controller was used to boost the exploration of the TD3 in training. Second, a novel reward formulation for the UAV-based target tracking was proposed to enable a careful combination of the various dynamic variables in the reward functions. This was accomplished by incorporating two exponential functions to limit the effect of velocity and acceleration to prevent the deformation in the policy function approximation. Third, the concept of multistage training based on the dynamic variables was proposed as an opposing concept to one-stage combinatory training. Fourth, an enhancement of the rewarding function by including piecewise decomposition was used to enable more stable learning behaviour of the policy and move out from the linear reward to the achievement formula. Fifth, a novel agent selection algorithm was developed to enable the selection of the best agent and avoid under-fitting and over-fitting. For the purpose of evaluating the performance of the control system, flight testing was conducted based on three types of target trajectories, namely fixed, square, and blinking. The evaluation was performed in both simulation and real-world experiments. The results showed that the multistage training achieved the best-accomplished performance with both exponential and achievement rewarding for a fixed trained agent with a fixed and square moving target and for a combinatorial agent with both exponential and achievement rewarding for a fixed trained agent in the case of a blinking target. With respect to the traditional proportional differential (PD) controller, the maximum error reduction rate is 86%. The developed achievement rewarding and the multistage training opens the door to various applications of RL in target tracking

Effect of Nitrogen Fertilization and Liming on Rye-Ryegrass Yield and Soil pH Dynamics

Using ammonium based nitrogen fertilizers in crop production has been shown to acidify soils. Lime used to correct soil pH is an important cost to producers. Recommendations of the optimal level of nitrogen to apply typically ignore the cost of lime created by nitrogen fertilization. This study was aimed to estimate soil pH change in response to nitrogen and lime application, and determine the effect of considering the cost of lime on recommendations about the optimal level of nitrogen. Yield response and pH functions were estimated and used to determine optimal levels of inputs. The effect of the cost of lime on recommendations about the optimal level of nitrogen was found to be marginal. Nitrogen acidification was found to be more severe with nitrogen application amounts above recommended rates than with nitrogen that is used by the plant.Lime, Nitrogen, Soil pH, Rye-ryegrass, Crop Production/Industries, Production Economics,

Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models

The parameters of yield response functions can vary by year. Past studies usually assume yield functions are nstochastic ‘‘limited’’ stochastic. In this study, we estimate rye– ryegrass yield functions in which all parameters are random. The three functional forms considered are the linear response plateau, the quadratic, and the Spillman-Mitscherlich. Nonstochastic yield models are rejected in favor of stochastic parameter models. Quadratic functional forms fit the data poorly. Optimal nitrogen application recommendations are calculated for the linear response plateau and Spillman-Mitscherlich. The stochastic models lead to smaller recommended levels of nitrogen, but the economic benefits of using fully stochastic crop yield functions are small because expected profit functions are relatively flat for the stochastic yield functions. Stochastic crop yield functions provide a way of incorporating production, uncertainty into input decisions.cereal rye–ryegrass, Monte Carlo, nitrogen, random parameters, stochastic plateau, Production Economics, Q10, C12, D24,

Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models

The parameters of yield response functions can vary by year. Past studies usually assume yield functions are nonstochastic or ‘limited’ stochastic. In this study, we estimate rye-ryegrass yield functions where all parameters are random. Optimal nitrogen rates are calculated for two yield response functions: linear response plateau and quadratic. Nonstochastic models are rejected in favor of stochastic parameter models. However, the economic benefits of using fully stochastic models are small since optimal nitrogen rates do not differ greatly between stochastic and nonstochastic models.Linear response plateau, Monte Carlo, nitrogen, random parameters, Agricultural and Food Policy, Crop Production/Industries, Farm Management, Production Economics,

Issues of Designing a Model Adaptive Controller Without a State Observer

It can be challenging to develop a controller using conventional techniques for a plant with a linear or nonlinear dynamical system or model uncertainty. Model adaptive control is a new alternative to classical control techniques and a simple way to update controller parameters. Because model reference adaptive control is unable to anticipate the state in real time if the state observer is not designed with, we will review some of the most major disadvantages of the most commonly used design techniques without state observer in this work

Model of Academics Professional Development Factors for Higher Education Institutions

This paper presents a study on developing a structural equation model of factors affecting UAE Academics Professional Development (APD) programs. Data used to develop the model was collected from questionnaire survey amongst three of UAE Higher Education Institutions. The model which comprised of seven independent constructs and one dependent construct was developed and assessed using AMOS SEM software. At the initial stage, eight measurement models (which is the eight constructs altogether) were developed and assessed individually using confirmatory factor analysis (CFA) of the software until it achieves goodness of fit. Then these eight measurement models are tied up into structural model which also assessed using CFA to find the goodness of fit. Once the structural model has achieved the goodness of fit, the path analysis or known as hypotheses testing was conducted on the model. The hypotheses testing found that five constructs have significant effect to academic’s professional development (APD) which are the i) design of teaching plan, ii) teaching skills, iii) communication skills, iv) expertise skill in the lesson content, and v) technology. While, two constructs which are i) individual and occupational identity and ii) policy and strategy do not have significant relationship with quality academic professional development. This model contributed to the body of knowledge and also to the related parties that involved in developing academic professional activitie

GR-67 Representation Learning for Motion Sequence

This research project proposes a new deep learning architecture that is used to align human poses to be used in an exercise assistant system. In short, the assistant system takes a video feed of a user doing exercise, then provides visual feedback by comparing the user’s current pose to a professional trainer’s pose that is stored in the system. We design a new deep architecture to accomplish this task and show better accuracy and efficiency.Advisors(s): Project Sponsors - Dr. Ying Xie & Dr. Linh Le Project Advisor- Dr. Meng HanTopic(s): Data/Data AnalyticsIT 799

Structural Relationship of Technology Adoption and Performance Factors in UAE Manufacturing Industry

The world is rapidly changing as a result of technology, which has played an important role in organisational development by improving operations and reducing obstacles. Businesses are constantly investing in technology in order to improve their performance and gain a competitive advantage over their competitors. Technological advancements assist businesses in automating their systems and management, providing them with the impetus to efficiently target customers through low-cost business solutions. As a result, this paper examined the relationship between technology adoption and the performance of business organisations involved in manufacturing. This study was conducted quantitatively, with data collected via questionnaire survey. The collected data was used to develop the model of structural relationship between the factors using PLS-SEM approach. Based on the validated PLS-SEM model, it was found that performance expectancy, effort expectancy, social influence, and facilitating condition all have a positive relationship with technology adoption. One of the most significant benefits of increased technological use in manufacturing firms is increased revenue through improved performance. The evaluation of the mediation effects of firm size and training on the relationship between technology adoption and manufacturing firm performance in the UAE revealed that the hypotheses' outcomes were positive, indicating that firm size and training do play a role in manufacturing performance. When using technology in manufacturing, the training and firm size have a significant impact on manufacturing performance

Factors Affecting Academics Professional Development of Higher Education Institutions

Several challenging contributions linked to the standard of teaching in higher education institutions and one of them is academic professional development programs. The challenge is so obvious due to structural changes in a modern society that requires an improvement to the teaching quality. Hence, this paper presents a study on determine the factors affecting academics professional development in UAE higher learning institution. There are 63 factors that were clustered into seven groups of factors namely design teaching plan; communication skills; expertise skill in the lesson content; individual and occupational; policy and strategy; technological factors; and teaching skills. The collected data from the questionnaire survey was analysed to determine the ranking of the seven groups’ factors it was found that communication skills group of factors is the most influencing factor affecting academics professional development. The following factor is the design of teaching plan factors ranked as second and policy and strategy factors ranked as third place. The collected data was also analysed using cross tabulation approach and found that for professor concerned is communication skills and the least concerned is individual and occupational identity. While for associate professor, the concerned factor is the expertise skill in the lesson content and the least concerned is communication skills. For senior lecture, the most concerned is design of teaching plan and the least concerned is technology. Finally for lecturer, the most concerned is design of teaching plan and the least concerned is individual and occupational identity. The findings from this study will benefit related parties in formulating their professional development programs for the academics