Bulletin of Electrical Engineering and Informatics
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Stability analysis of a Ñlosed non-linear system “FC-BM†of the electric drive of an electric vehicle
Get PDFThe article presents the analysis and stability program of the closed non-linear system frequency converter-brushless motor “(FC-BM)â€, which differs significantly from the analysis of linear systems. First of all, this is because the stability property of a nonlinear system depends on the initial conditions and external influences: for some input signals, the system will be stable, while for others it becomes unstable. Consequently, the stability criteria developed in the linear theory cannot be applied to their analysis. The stability of a non-linear automatic control system means that small changes in the input signal or disturbances, initial conditions, or plant parameters will not take the output variable beyond a sufficiently small neighborhood of the equilibrium point or limit cycle. Since several equilibrium positions can exist for a non-linear system, stability should be analyzed in the vicinity of each of them. This complicates the task of research
Implementing advance control strategies to improve the performance of a microgrid
Get PDFIntegration of flexible and non-dispatchable renewable energy production will influence the operation and future expansion of prevailing power systems. Because of the variations in performance responses between microgrid (MG) and regular generators, including renewable energy sources-based (RES-based) MG into the electrical system may have an influence on stability analysis. The reduction in switching frequency induced by these energy processors electronic interconnected electricity producing sources has a detrimental impact on the system’s structural analysis, potentially leading to stability issues. Power infusion from RES-based MG, on the other hand, increases damping efficiency, reducing transmission line congestion and power shortage. As a result, in light of expanded MG information, it is important to analyse more complex stability problems and regulate the production of a power grid. This study will examine the effect of RES-based MG on the structural analysis and controller of a multimachine multi-area device in various scenarios This paper defines the growth of a one-of-a-kind proportional-integral-derivative (PID-based) power system stabilizer (PSS) type2 fuzzy partial order based on a meta-heuristic hybrid technique for refining the efficiency and robustness of harmonic currents
Current critical review on prediction stroke using machine learning
Get PDFStrokes are a significant health problem because they often lead to long-term disabilities due to delayed diagnoses and insufficient information about the disease. The use of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), has the potential to aid in stroke diagnosis and significantly advance healthcare. This review article critically examines predictive methods for ischemic and hemorrhagic strokes. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was used to identify 79 relevant articles from five databases spanning 2012 to 2022, with IEEE having the highest number of articles and citations. China had the most authors, and the random forest (RF) algorithm showed the most accurate results. A taxonomy categorizing the implementation and usage of ML and DL for stroke prediction was created and includes five focus areas: building, system planning, evaluation, comparison, and analysis. Additional research into other disease features related to stroke is warranted. Decentralized federated learning should also be implemented to collect data from remote locations for early diagnosis and create a single training model
A review on microstrip patch antenna for wireless communication systems at 3.5 GHz
Get PDFThis article presents a review of several microstrip patch antennas for 3.5 GHz wireless applications. Different substrate materials, FR-4 (loss), FR-4 Epoxy, Rogers RT/droid 5880, TLC-30, and Rogers RT/droid 5880 LZ, are used. In recent years, wireless antenna applications have increased, including biomedical appliances, internet of things (IoT) terminals, edge devices, radars, mobile phones, and many more. In this work, several articles were reviewed and investigated, and several microstrip patch antennas with a resonance frequency of 3.5 GHz were designed using different substrate materials and shapes. This article also discussed the geometric shapes of antennas, antenna properties, sizes of substrate materials, loss tangent, thickness, return loss, bandwidth, voltage standing wave ratio (VSWR), gain, efficiency, and directivity. Several software is used for design and simulation, including computer simulation technology (CST), high-frequency simulation frequency (HFSS), and advanced design system (ADS), FEKO, and MATLAB. The main goal of this paper is to talk about different wireless application papers that work in the S-band at a frequency of 3.5 GHz and have been published in various international journals and conferences
Internet of things and radio frequency identification based embedded system to reduce shopping time in supermarkets
Get PDFDoing daily shopping in a Peruvian supermarket means a large investment of time for many people, usually due to inaccurate and faulty scanning of products by barcodes at supermarket checkout counters. For this reason, an embedded system based on internet of things (IoT) and radio frequency identification (RFID) is designed to reduce shopping time in a supermarket. The system uses an ESP32 development board with embedded hardware specialized in IoT projects and firmware development based on C language and real-time operating systems (FreeRTOS) through espressif’s IoT development framework (ESP-IDF). RFID tags were used to scan the products and IoT with message queuing telemetry transport (MQTT) communication protocol are implemented to a local database in real time. The system achieves a significant reduction in terms of scanning time compared to self-service checkouts using barcodes, which allows to statistically analyze the reduced time per quantity of products and the linear trend of the 2 samples
Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study
Get PDFRoad defect inspection is a crucial task in maintaining a good transportation infrastructure as road surface distress can impact user’s comfortability, reduce the lifetime of vehicles’ parts, and cause road casualties. In recent years, machine learning has been adapted widely in various fields, including object detection, thanks to its superior performance and the availability of high computing power which is generally needed for its model training. Many works have reported using machine-learning-based object detection algorithms to detect defects, such as cracks in buildings and roads. In this work, YOLOv5, YOLOv6 and YOLOv7 models have been implemented and trained using a custom dataset of road cracks and potholes and their performances have been evaluated and compared. Experiments on the dataset show that YOLOv7 has the highest performance with [email protected] score of 79.0% and an inference speed of 0.47 m for 255 test images
Descriptive analysis of wide area network flow control internet traffic on Metro-E 100 Mbps campus network
Get PDFQoS in computer networking is the capability to provide better service to network traffic over various technologies such as ethernet and IP networks. This paper presents a descriptive analysis of WAN flow control and internet traffic on a Metro-E campus network. Issues on network congestion and delay in network QoS where internet traffic is gradually increasing, resulting in bursts of network capacity that affect network QoS. The method implies 12 months data collection and analysis on protocol, bytes and packets inbound and correlation between parameters on the Metro-E 100 Mbps campus network. The result presents heavy-tailed distributions on an inbound packet kurtosis value of 347 and an outbound packet kurtosis value of 780. Bytes outbound and inbound are skewed at 122 and right at 17 respectively. The average amount of data inbound and outbound is 458.5 MB and 34.8 MB. Protocol 6 TCP presents the highest amount of -traffic and a weak positive correlation at 0.104 exists between the inbound and outbound packets and bytes on the network. The correlation coefficient's 95% confidence interval ranges between 0.096 and 0.111. This research is significant in the future deployment of traffic scheduling, policing, and shaping algorithms for QoS bandwidth management on the WAN Metro-E campus network
Image quality evaluation: evaluation of the image quality of actual images by using machine learning models
Get PDFEvaluating image features is a significant step in image processing in applications like number plate detection, vehicle tracking and many image processing-based applications. Image processing-based applications need accurate parts to get the best outcomes. Feature detection is done based on various feature detection techniques. The proposed system aims to get the best feature detector based on the input images by evaluating the image features. For assessing the image features, the proposed system worked on various descriptors like oriented FAST and rotated brief (ORB), learned arrangements of three patch codes (LATCH), binary robust independent elementary features (BRIEF), and binary robust invariant scalable keypoints (BRISK) to extract and evaluate the features using K-nearest neighbor (KNN)-matching and retrieve the inliers of the matching. Each descriptor produces different matching features and inliers; with the matchings and inliers, the inlier ratio calculates to show the analysis. To increase performance, we also examine adding depth information to descriptors
Performance evaluation of feature selections on some ML approaches for diagnosing the narcissistic personality disorder
Get PDFNarcissistic personality disorder (NPD) is a personality disorder that affects various aspects of life, including relationships, employment, school, and finances. Persons with NPD usually feel unhappy and disappointed when no one helps them and is not praised for their achievements. Diagnosing narcissism is generally done using a screening test that consumes time and costs a lot. This research aims to evaluate the performance of several feature selection (FS) approaches on machine learning (ML) techniques (support vector machine (SVM), random forest classifier (RFC), and Naive Bayes). Three scenarios of FS (all features, the information gain technique and the gain ratio (GR) feature technique) are used for each ML method. Several experiments using the benchmark narcissistic disorder dataset have been done. It adopts the k-fold cross-validation (10-fold cross-validation) strategy. We evaluate the method’s performance by measuring its accuracy, error rate, and processing time. It is shown that the RFC GR strategy gives the best performance with an accuracy of 100%
Image dermoscopy skin lesion classification using deep learning method: systematic literature review
Get PDFClassifying skin lesions poses a significant challenge due to the distinctive characteristics and diverse shapes they can exhibit, particularly in identifying early-stage melanoma. To address the shortcomings of the prior method, a neural network-driven strategy was introduced to differentiate between two types of skin lesions based on dermoscopic images. This new approach comprises four key stages: i) initial image processing, ii) skin lesion segmentation, iii) feature extraction, and iv) classification using deep neural networks (DNNs). Computers can also provide more accurate diagnosis results. In the review process, the articles are analyzed and summarized to contribute to developing methods or application development in skin lesion diagnosis. The stages include defining the relevant theory, input data, methods used (architecture and modules), training process, and model evaluation. This review also explores information based on trends and users, emphasizing the skin lesion segmentation process, skin lesion classification process, and minimal datasets as recommendations for future research