Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms

This thesis presents a new approach to optimize the performance of a dual beam optical scanning system in terms of its scanning combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the scanning tasks need to be segregated and assigned for each scanner head, and path planning where the best combinatorial paths for each scanner are determined in order to minimize the total motion of scanning time. The knowledge acquired by the process is interpreted and mapped into vectors, which are kept in the database and used by the system to guide its reasoning process. Also, this research involves in developing a machine-learning system and program via genetic algorithm that is capable of performing independent learning capability and optimization for scanning sequence using novel GA operators. The main motivation for this research is to introduce and evaluate an advance new customized GA. Comparison results of different combinatorial operators, and tests with different probability factors are shown. Also, proposed are the new modifications to existing genetic operator called DPPC (Dynamic Pre-Populated Crossover) together with modification of a simple method of representation, called MLR (Multi-Layered Representation). In addition, the performance of the new operators called GA_INSP (GA Inspection Module), DTC (Dynamic Tuning Crossover), and BCS (Bi-Cycle Selection Method) for a better evolutionary approach to the time-based problem has been discussed in the thesis. The simulation results indicate that the algorithm is able to segregate and assign the tasks for each scanning head and also able to find the shortest scanning path for different types of objects coordination. Besides that, the implementation of the new genetic operators helps to converge faster and produce better results. The representation approach has been implemented via a computer program in order to achieve optimized scanning performance. This algorithm has been tested and implemented successfully via a dual beam optical scanning system

Development Of Scara Robotic Arm And Control System For Laboratory Automation

Recent developments in robotic system usage for material handlings, examine the various benefits of its applications in the chemical industry environment. In order to avoid the risk factor in chemical handling, various steps can be taken. One of the prominent methods is by substituting the human hands with the robotic arm in handling dangerous and erosive chemicals. It is with these reasons that this study was conducted with the primary objective to develop a system that would contribute towards encouraging a safety way of chemical testing and processing. This thesis provides an early analysis of robotic developments in the area The objective of this project is to develop a SCARA robotic arm and the intelligent control system. The design and development of this project involves three major sections. First section concerns about software programming, while the second section involves hardware construction and the final section deals with the graphic user interfacing (GUI). The hardware design can mainly be categorised into electrical design and mechanical design. Electrical design involves proper electrical wiring of input and output devices, power distributions, safety devices, interfacing devices and control components. The mechanical design is referred to the construction of the robot arm structures. These comprise mechanical drawings, mechanical simulations, mathematical calculations, as well as parts fabrication. On the other hand the design of software will consist of input and output assignments, program flow charts, robot-learning method and MINT programming. Design of GUI will involve Visual Basic (Professional Edition) programming. Basically, this project comprises several subsystems, namely: a sensor system, which is used to obtain data about the state of the mechanism and the environment, a controller and drivers, to guide the mechanism and the sensors in a desired manner, a planning and control system that decides on the actions and also consists of a power distributions system. The specified function of the robotic system is accomplished by intelligent interpretation of sensor information and mechanical actuations in term of plan, task and model.The entire robotic system was carefully and meticulously designed, constructed and tested. From the experimental results, it is proven that the proposed robotic system was successfully developed. This robotic arm can handle hazardous tasks in lab experiment specifically regarding chemical processing. Thus, reducing the risk on human

Design of digital circuit structure based on evolutionary algorithm method

Evolutionary Algorithms (EAs) cover all the applications involving the use of Evolutionary Computation in electronic system design. It is largely applied to complex optimization problems. EAs introduce a new idea for automatic design of electronic systems; instead of imagine model, abstractions, and conventional techniques, it uses search algorithm to design a circuit. In this paper, a method for automatic optimization of the digital circuit design method has been introduced. This method is based on randomized search techniques mimicking natural genetic evolution. The proposed method is an iterative procedure that consists of a constant-size population of individuals, each one encoding a possible solution in a given problem space. The structure of the circuit is encoded into a one-dimensional genotype as represented by a finite string of bits. A number of bit strings is used to represent the wires connection between the level and 7 types of possible logic gates; XOR, XNOR, NAND, NOR, AND, OR, NOT 1, and NOT 2. The structure of gates are arranged in an m * n matrix form in which m is the number of input variables

A Comprehensive Review on Recent Developments of LED Drivers

Background: In these recent years, LED lighting has been widely implemented for household and industrial applications. By implementing the correct topology, the performance of a LED driver can be improved in terms of efficiency, power factor, lifespan, size and cost of development. Objective: This paper aims to provide a comprehensive review on the latest trends of LED driver design to serve as a useful guide for design engineers and researchers. Result: Latest research journals and conference proceedings have been reviewed. Conclusion: There are suitable converter topologies for LED drivers of varied power levels, with the flyback converter being the most suitable for applications of less than 100W. When designing the LED driver, considerations must be made on the power factor, efficiency, dimming capability, and lifespan

Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module

This research presents a new approach to optimise the performance of a multiple-beam optical scanning system in terms of its marking combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the marking tasks need to be segregated and assigned for each scanner head and path planning where the best combinatorial paths for each scanner are determined in order to minimise the total motion of marking time. The knowledge acquired by the process is interpreted and mapped into vectors, which are kept in the database and used by the system to guide its reasoning process. The main motivation for this study is to introduce and evaluate an advance new customized GA. Comparison results of different combinatorial operators and tests with different probability factors are shown. Also, proposed are the new modifications to existing crossover operator called DPPC (Dynamic Pre-Populated Crossover) together with modification of a simple crossover selection method, called BCS (Bi-Cycle Selection Method). The performance of the new operator called GA_INSP (GA Inspection Module) for a better evolutionary approach to the time-based problem has been discussed in the study. The representation approach has been implemented via a computer program in order to achieve optimized marking performance. This algorithm has been tested and implemented successfully via a dual-beam optical scanning module. © 2006 Asian Network for Scientific Information

Path optimization using genetic algorithm in laser scanning system

Laser marking is superior in quality and flexibility to conventional marking techniques such as ink stamping marking. The beam deflected scanning has been commonly applied in the industries. In this paper, the genetic algorithm (GA) has been proposed to optimize the scanning sequence, thus shortening the required laser scanning path. The GA would base on the real-number representation, namely Real-Coded Genetic Algorithm (RCGA). It employs the Dynamic Variable Length Two Point Crossover (DVL-2PC). The simulation results indicating that the proposed algorithm has good convergent speed and manage to solve the sequencing problem efficiently

Support Vector Machines Study on English Isolated-Word-Error Classification and Regression

Abstract: A better understanding on word classification and regression could lead to a better detection and correction technique. We used different features or attributes to represent a machine-printed English word and support vector machines is used to evaluate those features into two class types of word: correct and wrong word. Our proposed support vectors model classified the words by using fewer words during the training process because those training words are to be considered as personalized words. Those wrong words could be replaced by correct words predicted by the regression process. Our results are very encouraging when compared with neural networks, Hamming distance or minimum edit distance technique; with further improvement in sight

Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model

Medium voltage (MV) switchgear is a vital part of modern power systems, responsible for regulating the flow of electrical power and ensuring the safety of equipment and personnel. However, switchgear can experience various types of faults that can compromise its reliability and safety. Common faults in switchgear include arcing, tracking, corona, normal cases, and mechanical faults. Accurate detection of these faults is essential for maintaining the safety of MV switchgear. In this paper, we propose a novel approach for fault detection using a hybrid model (1D-CNN-LSTM) in both the time domain (TD) and frequency domain (FD). The proposed approach involves gathering a dataset of switchgear operation data and pre-processing it to prepare it for training. The hybrid model is then trained on this dataset, and its performance is evaluated in the testing phase. The results of the testing phase demonstrate the effectiveness of the hybrid model in detecting faults. The model achieved 100% accuracy in both the time and frequency domains for classifying faults in Switchgear, including arcing, tracking, and mechanical faults. Additionally, the model achieved 98.4% accuracy in detecting corona faults in the TD. The hybrid model proposed in this study provides an effective and efficient approach for fault detection in MV switchgear. By learning spatial and temporal features simultaneously, this model can accurately classify faults in both the TD and FD. This approach has significant potential to improve the safety of MV switchgear as well as other industrial applications

Progress in research and technological developments of phase change materials integrated photovoltaic thermal systems : The allied problems and their mitigation strategies

The efficiency of solar cells and photovoltaic (PV) panels are lacking significantly due to its surface overheating by the incident solar radiation. Indeed, the generated heat energy is harnessed by integrating a thermal system into PV panel, which introduces a photovoltaic thermal (PVT) system. Phase change materials (PCM)s are a class of energy material that is intended to facilitate thermal regulations of photovoltaic (PV) panel. Despite, PVT systems are allied with numerous problems like, integration technique, increase in overall weight of the system, dust accumulation, complication of tracking etc., which are of utmost importance to be resolved. The foremost aim of the review is to analyze the current technologies and allied problems of PVT system, the impact of the overall weight of the system on the PVT systems, detailed assessment of recent advancements in soil mitigation techniques, and the economic benefit of the PVT systems. Also, this review article is specifically intended to discuss on a) concerns allied with PV and PVT system integrated with PCM for thermal regulation; b) framework intimidating the performance of PCM-integrated PVT system; and c) mitigation techniques to resolve the problems and enhanced the performance of PCM integrated PVT system. A elaborative technical exploration on common issues associated with both PV and PVT systems in terms of surface cleaning towards dust mitigation via advanced mechanisms and futuristic technologies is comprehensively presented. A new possible sustainable solution towards enhancing the performance of PV and PVT systems is also provided. A summary of numerous research works conducted on enhancing the performance of PVT system integrated with PCM at different global locations is summarized. Furthermore, this review also discusses the economic analysis of PVT system integrated with PCM along with a summary of technical challenges and future outlook of PCM integrated PVT system to boost sustainable development

Energizing the thermophysical properties of phase change material using carbon-based nano additives for sustainable thermal energy storage application in photovoltaic thermal systems

As solar energy are intermittent in nature and not predictable, researchers and scientists are actively developing efficient thermal energy storage (TES) systems intending to maximize the utilization of solar energy. Phase change materials (PCM) are potential materials that are largely accessed towards TES. However, the notable drawback of PCM is their lower thermal conductivity, leading to slower heat transfer rates and reduced thermal energy storage density. Thus, the current study focuses on developing and exploring a PCM composite by embedding paraffin wax and graphene to enhance the heat transfer mechanisms, making it a promising option for TES applications. Various aspects of the composite's performance were examined, including its microstructural behaviour, chemical stability, thermal stability, thermal conductivity, thermal reliability, and heat transfer characteristics. The findings revealed that the inclusion of graphene led to a substantial increase of up to 75.09 % in thermal conductivity while preserving the melting enthalpy of the material. The newly developed nanocomposite also demonstrated chemically and thermally stable up to a temperature of 210 °C, and the thermal stability was slightly enhanced by adding nanoparticles. This nanocomposite also exhibited improved optical absorptance and reduced transmittance, enhancing its potential for solar energy absorption. It further demonstrated durability, maintaining stability even after undergoing 500 thermal cycles. Notably, the overall efficiency of the nano-enhanced PCM integrated photovoltaic-thermal system (PVT) enhanced by 29 % and 49 % greater than the PVT system and conventional PV system. Given these exceptional characteristics and performance enhancements, this nanocomposite material holds promise for significantly advancing future sustainable TES technologies