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

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

SDN-based VANET routing: A comprehensive survey on architectures, protocols, analysis, and future challenges

As the automotive and telecommunication industries advance, more vehicles are becoming connected, leading to the realization of intelligent transportation systems (ITS). Vehicular ad-hoc network (VANET) supports various ITS services, including safety, convenience, and infotainment services for drivers and passengers. Generally, such services are realized through data sharing among vehicles and nearby infrastructures or vehicles over multi-hop data routing mechanisms. Vehicular data routing faces many challenges caused by vehicle dynamicity, intermittent connectivity, and diverse application requirements. Consequently, the software-defined networking (SDN) paradigm offers unique features such as programmability and flexibility to enhance vehicular network performance and management and meet the quality of services (QoS) requirements of various VANET services. Recently, VANET routing protocols have been improved using the multilevel knowledge and an up-to-date global view of traffic conditions offered by SDN technology. The primary objective of this study is to furnish comprehensive information regarding the current SDN-based VANET routing protocols, encompassing intricate details of their underlying mechanisms, forwarding algorithms, and architectural considerations. Each protocol will be thoroughly examined individually, elucidating its strengths, weaknesses, and proposed enhancements. Also, the software-defined vehicular network (SDVN) architectures are presented according to their operation modes and controlling degree. Then, the potential of SDN-based VANET is explored from the aspect of routing and the design requirements of routing protocols in SDVNs. SDVN routing algorithms are uniquely classified according to various criteria. In addition, a complete comparative analysis will be achieved to analyze the protocols regarding performance, optimization, and simulation results. Finally, the challenges and upcoming research directions for developing such protocols are widely stated here. By presenting such insights, this paper provides a comprehensive overview and inspires researchers to enhance existing protocols and explore novel solutions, thereby paving the way for innovation in this field

A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions

Recent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients. DL needs more data and diversity to build a more accurate system. To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges. To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed. FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy. This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcare; (2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiency; and (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications. An extensive review of the literature on FL from a data-centric perspective was conducted. We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and January 2023. A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers. In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification. We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies

Vibration signal for bearing fault detection using random forest

Based on the chosen properties of an induction motor, a random forest (RF) classifier, a machine learning technique, is examined in this study for bearing failure detection. A time-varying actual dataset with four distinct bearing states was used to evaluate the suggested methodology. The primary objective of this research is to evaluate the bearing defect detection accuracy of the RF classifier. First, run four loops that cycle over each feature of the data frame corresponding to the daytime index to determine the bearing states. There were 465 repetitions of the inner race fault and the roller element fault in test 1, 218 repetitions of the outer race fault in test 2, and 6324 repetitions of the outer race in test 3. Secondly, the task is to find the data for the typical bearing data procedure to differentiate between normal and erroneous data. Out of 3 tests, (22-23) % normal data was obtained since every bearing beginning to degrade usually exhibits some form of a spike in many locations, or the bearing is not operating at its optimum speed. Thirdly, to display and comprehend the data in a 2D and 3D environment, Principal Component Analysis (PCA) is performed. Fourth, the RF algorithm classifier recognized the data frame's actual predictions, which were 99% correct for normal bearings, 97% accurate for outer races, 94% accurate for inner races, and 97% accurate for roller element faults. It is thus concluded that the proposed algorithm is capable to identify the bearing faults

Construction novel highly active photocatalytic H2 evolution over noble-metal-free trifunctional Cu3P/CdS nanosphere decorated g-C3N4 nanosheet

Hydrogen energy possesses immense potential in developing a green renewable energy system. However, a significant problem still exists in improving the photocatalytic H2 production activity of metal-free graphitic carbon nitride (g-C3N4) based photocatalysts. Here is a novel Cu3P/CdS/g-C3N4 ternary nanocomposite for increasing photocatalytic H2 evolution activity. In this study, systematic characterizations have been carried out using techniques like X-ray diffraction (XRD), scanning electron microscopy (SEM), high resolution transmission electron microscopy (HR-TEM), Raman spectra, UV–Vis diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy (XPS), surface area analysis (BET), electrochemical impedance (EIS), and transient photocurrent response measurements. Surprisingly, the improved 3CP/Cd-6.25CN photocatalyst displays a high H2 evolution rate of 125721 μmol h−1 g−1. The value obtained exceeds pristine g-C3N4 and Cu3P/CdS by 339.8 and 7.6 times, respectively. This could be the maximum rate of hydrogen generation for a g–C3N4–based ternary nanocomposite ever seen when exposed to whole solar spectrum and visible light (λ > 420 nm). This research provides fresh perspectives on the rational manufacture of metal-free g-C3N4 based photocatalysts that will increase the conversion of solar energy. By reusing the used 3CP/Cd/g-C3N4 photocatalyst in five consecutive runs, the stability of the catalyst was investigated, and their individual activity in the H2 production activity was assessed. To comprehend the reaction mechanisms and emphasise the value of synergy between the three components, several comparison systems are built