Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
Not a member yet
785 research outputs found
Sort by
Fuzzy Fault-Tolerant Control Applied on Two Inverted Pendulums with Nonaffine Nonlinear Actuator Failures
Get PDFThis paper deals with the problem of fault-tolerant control for a class of perturbed nonlinear systems with nonlinear non-affine actuator faults. Fuzzy systems are integrated into the design of the control law to get rid of the system nonlinearities and the considered actuator faults. Two adaptive controllers are proposed in order to reach the control objective and ensure stability. The first term is an adaptive controller involved to mollify the system uncertainties and the considered actuator faults. Therefore, the second term is known as a robust controller introduced for the purpose of dealing with approximation errors and exogenous disturbances. In general, the designed controller allows to deal automatically with the exogenous disturbances and actuator faults with the help of an online adaption protocol. A Butterworth low-pass filter is utilized to avoid the algebraic loop issue and allows a reliable approximation of the ideal control law. A stability study is performed based on Lyapunov's theory. Two inverted pendulum example is carried out to prove the accuracy of the controller
Fish Swarmed Kalman Filter for State Observer Feedback of Two-Wheeled Mobile Robot Stabilization
Get PDFOver the past few decades, there have been significant technological advancements in the field of robots, particularly in the area of mobile robots. The performance standards of speed, accuracy, and stability have become key indicators of progress in robotic technology. Self-balancing robots are designed to maintain an upright position without toppling over. By continuously adjusting their center of mass, they can maintain stability even when disturbed by external forces. This research aims to achieving and maintaining balance is a complex task. Self-balancing robots must accurately sense their orientation, calculate corrective actions, and execute precise movements to stay upright. Eliminating disturbances and measurement noise in self-balancing robot can enhance the accuracy of their output. One common technique for achieving this is by using Kalman filters, which are effective in addressing non-stationary linear plants with unknown input signal strengths that can be optimized through filter poles and process covariances. Additionally, advanced Kalman filter methods have been developed to account for white measurement noise. In this research, state estimation was conducted using the Fish Swarm Optimization Algorithm (FSOA) to provide feedback to the controller to overcome the effects of disturbances and noise in the measurements through the designed filter. FSOA mimics the social interactions and coordinated movements observed in fish groups to solve optimization problems. FSOA is primarily used for optimization tasks where finding the global optimal solution is desired. The results show that the use of an optimized Kalman filter with FSOA on a two-wheeled mobile robot to handle system stability reduces noise values by 38.37%, and the system reaches a steady state value of 3.8 s with a steady error of 0.2%. In addition, by using the proposed method, filtering disturbances and measurement noise in self-balancing robot can help improve the accuracy of the self balancing robot’s output. System response becomes faster towards stability compared to other methods which are also applied to two-wheeled mobile robots
Exploring Blockchain Data Analysis and Its Communications Architecture: Achievements, Challenges, and Future Directions: A Review Article
Get PDFBlockchain technology is relatively young but has the potential to disrupt several industries. Since the emergence of Bitcoin, also known as Blockchain 1.0, there has been significant interest in this technology. The introduction of Ethereum, or Blockchain 2.0, has expanded the types of data that can be stored on blockchain networks. The increasing popularity of blockchain technology has given rise to new challenges, such as user privacy and illicit financial activities, but has also facilitated technical advancements. Blockchain technology utilizes cryptographic hashes of user input to record transactions. The public availability of blockchain data presents a unique opportunity for academics to analyze it and gain a better understanding of the challenges in blockchain communications. Researchers have never had access to such an opportunity before. Therefore, it is crucial to highlight the research problems, accomplishments, and potential trends and challenges in blockchain network data analysis and communications. This article aims to examine and summarize the field of blockchain data analysis and communications. The review encompasses the fundamental data types, analytical techniques, architecture, and operations related to blockchain networks. Seven research challenges are addressed: entity recognition, privacy, risk analysis, network visualization, network structure, market impact, and transaction pattern recognition. The latter half of this section discusses future research directions, opportunities, and challenges based on previous research limitations
Clustering of business strategies among Indonesian manufacturing firms
Get PDFThis study aims to to classify business strategies adopted by manufacturing firms. Data were acquired by sending questionnaires to Indonesian manufacturing firms in Java. Factor and cluster analysis were performed on 435 survey data. Using principal component analysis and varimax rotation, the factor analysis identified two distinct business strategies based on Porter's generic strategy: cost leadership and differentiation. Hierarchical and K-means clustering conducted sequentially enabled the identification of three clusters of business strategy: cost leadership, differentiation, and no strategy. According to the findings, the majority of Indonesian manufacturing firms utilize the differentiation strategy, followed by the cost leadership strategy. Only a small percentage of firms employ no strategy at all. The descriptive analysis of each cluster shows that the number of firms adopting the differentiation strategy is significantly higher than those pursuing the cost leadership strategy in a number of products, including food and beverage, computers, electronic, and optical products, as well as machinery and electrical equipment. For some products, such as automotive and furniture, the percentage of firms using cost leadership or differentiation strategies is comparable. Large firms dominate both clusters. Nevertheless, there are more large firms in the differentiation cluster than in the cost leadership cluster. The findings of this study enrich the body of knowledge in the domains of strategic management and manufacturing by classifying the business strategies that help firms achieve their business objectives, particularly in the context of emerging countries
Performance Investigation of Low-Cost Dual-Axis Solar Tracker using Light Dependent Resistor
Get PDFTo generate power, the solar tracker mechanism can be mounted on a stationary or mobile platform. Moving platforms include boats, ground vehicles, and aerial vehicles. The solar tracker must be a mechanism that can keep the solar panel perpendicular to the direction of the sun at an appropriate level of precision in order to be more effective. Therefore, this research is going to investigate the performance of a low-cost dual-axis solar tracker (parallel mechanism) installed on a moving platform. This work describes the simulation and experiment of a dual-axis solar tracker that is mounted on a rotating support plate with rotational axis. This simulation uses the method of controlling linear actuators to adjust the solar panel perpendicular to the direction of sunlight. Both actuators were controlled by proportional and integral controllers (PI), which will make the system have a faster response time. The tracker is equipped with a type of low-cost sunlight sensor to provide the information for determining the orientation of the sunlight vector with respect to the solar panel. The sunlight sensor was designed and fabricated on our own by adding four light-dependent resistors in the four different quadrants. For the purpose of tracking the sun, the mathematical models of the tracker mechanism, sun sensor, and control architecture are defined. The results of simulation and experiment demonstrate that the tracker control system can follow the sun with some tracking error (about 2 degrees) at its final alignment. In real-time applications, solar trackers can be used on vehicles or boats to adjust solar panels on their surfaces and increase their exposure to sunlight and electrical output
Semi-supervised labelling of chest x-ray images using unsupervised clustering for ground-truth generation
Get PDFSupervised classifiers require a lot of data with accurate labels to learn to recognize chest X-ray images (CXR). However, manually labeling an extensive collection of CXR images is time-consuming and costly. To address this issue, a method for the semi-supervised labelling of extensive collections of CXR images is proposed leveraging unsupervised clustering with minimum expert knowledge to generate ground truth images. The proposed methodology entails: using unsupervised clustering techniques such as K-Means and Self-Organizing Maps. Second, the images are fed to five different feature vectors to utilize the potential differences between features to their full advantage. Third, each data point gets the label of the cluster’s center to which it belongs. Finally, a majority vote is used to decide the ground truth image. The number of clusters created by the method chosen strictly limits the amount of human involvement. To evaluate the effectiveness of the proposed method, experiments were conducted on two publicly available CXR datasets, namely VinDR-CXR and Montgomery datasets. The experiments showed that, for a KNN classifier, manually labeling only 1% (VinDr-CXR), or 10% (Montgomery) of the training data, gives a similar performance as labeling the whole dataset. The proposed methodology efficiently generates ground-truth images from publicly available CXR datasets. To our knowledge, this is the first study to use the VinDr-CXR and Montgomery datasets for ground truth image generation. Extensive experimental analysis using machine learning and statistical techniques shows that the proposed methodology efficiently generates ground truth images from CXR datasets
Dynamic Model of a Robotic Manipulator with One Degree of Freedom with Friction Component
Get PDFThis research aims to develop a dynamic model of a robotic manipulator with one degree of freedom by incorporating the LuGre friction model. The study combines a mathematical model with experimental data analysis, using the Stribeck curve and Non-linear Least Square method for Parameter Identification. The purpose of the study is to improve the accuracy of the model and enhance the performance of robotic manipulators. The LuGre model is chosen for its ability to capture the nonlinear behavior of friction, which is a significant source of error in robot control systems. The effectiveness of the proposed representation is evaluated by comparing the simulation results of the dynamic model with experimental data obtained from a prototype. The results indicate that the model accurately captures the nonlinear behavior of friction, and the proposed approach can be used to develop more accurate models for control purposes
Energy Monitoring and Control of Automatic Transfer Switch between Grid and Solar Panel for Home System
Get PDFIn this digital age, most aspects of life require an electric supply. The availability of electrical power is very critical to maintaining the continuity of the system in various applications. To maintain system continuity, the Energy Monitoring and Control of an Automatic Transfer Switch (ATS) between the Grid and Solar Panel is proposed. The system consists of an Automatic Transfer Switch, Home Solar Power Plant, and Automatic Charging. The function of the monitoring system is to monitor the voltage, current, and power across the device, the operation mode of the ATS, and the State of Charge (SoC) of the battery. The control system is to control the operation mode of ATS according to the energy source selected by the user on the Internet of Things (IoT) interface. The results show that the system can successfully monitor solar panel conditions, AC output, and battery's State of Charge through Blynk IoT. The ATS works automatically with a switching delay of 20ms to 26ms, while on the user's command, the average switching delay is 303.33ms to activate the relay and 185ms to deactivate the relay
Monte Carlo Simulations on 2D LRF Based People Tracking using Interactive Multiple Model Probabilistic Data Association Filter Tracker
Get PDFConsistency of tracking filter such as Interactive Multiple Model Probabilistic Data Association Filter (IMMPDAF) is the most important factor in targets tracking. Inaccurate tracking capability will lead to poor tracking performance when dealing with multiple people's interactions and occlusions. In order to validate the consistency, Normalized Estimation Error Squared (NEES) and Normalized Innovation Squared (NIS) were evaluated and tested using Monte Carlo experiments for 50 runs. These simulations has proven that the tracker is conditionally consistent on targets tracking despite the fact that it has difficulties on handling occlusions and maneuvering people. NEES requires ground truth of tracking data and predicted data, whereas NIS requires observation and predicted data for Monte Carlo simulations. In NEES simulations, the result emphasizes that state estimation errors of IMMPDAF tracker are inconsistent with filter-calculated covariances especially when dealing with sudden turns in zig-zag motion where quite a large number of points fall outside 95\% probability region. In NIS simulations, IMMPDAF tracker is confirmed to have difficulties to handle multiple targets with a short period of occlusion although a small number of points falls outside of 95\% probability region. Filter tracker is considered mismatched when dealing with zig-zag motion; however, it deemed to be optimistic when dealing with occlusions. As a result, the IMMPDAF tracker has limited capability in monitoring sharp turns under occlusion conditions, although it is acceptable when dealing with occlusions only
Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization
Get PDFThe management of chemical process liquid levels poses a significant challenge in industrial process control, affecting the efficiency and stability of various sectors such as food processing, nuclear power generation, and pharmaceutical industries. While Proportional-Integral-Derivative (PID) control is a widely-used technique for maintaining liquid levels in tanks, its efficacy in optimizing complex and nonlinear systems has limitations. To overcome this, researchers are exploring the potential of metaheuristic algorithms, which offer robust optimization capabilities. This study introduces a novel approach to liquid level control using the Prairie Dog Optimization (PDO) algorithm, a metaheuristic algorithm inspired by prairie dog behavior. The primary objective is to design and implement a PID-controlled three-tank liquid level system that leverages PDO to regulate liquid levels effectively, ensuring enhanced stability and performance. The performance of the proposed system is evaluated using the ZLG criterion, a time domain metric-based objective function that quantifies the system's efficiency in maintaining desired liquid levels. Several analysis techniques are employed to understand the behavior of the system. Convergence curve analysis assesses the PDO-controlled system's convergence characteristics, providing insights into its efficiency and stability. Statistical analysis determines the algorithm's reliability and robustness across multiple runs. Stability analysis from both time and frequency response perspectives further validates the system's performance. A comprehensive comparison study with state-of-the-art metaheuristic algorithms, including AOA-HHO, CMA-ES, PSO, and ALC-PSODE, is conducted to benchmark the performance of PDO. The results highlight PDO's superior convergence, stability, and optimization capabilities, establishing its efficacy in real-world industrial applications. The research findings underscore the potential of PDO in PID control applications for three-tank liquid level systems. By outperforming benchmark algorithms, PDO demonstrates its value in industrial control scenarios, contributing to the advancement of metaheuristic-based control techniques and process optimization. This study opens avenues for engineers and practitioners to harness advanced control solutions, thereby enhancing industrial processes and automation