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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Sharma, Mohit Samanta, Akash Huynh, Alvin Williamson, Sheldon Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350331820

Rapid printed circuit board (PCB) development for modular battery management systems (BMS) can provide a flexible, scalable, and customizable solution for all lithium-ion battery-powered devices as well as in research and development. Therefore, to satisfy the increasing demand and stringent requirements of high performance, low cost, and smaller footprints, a single-stage PCB development technique using a CO2 laser, galvo scanner, and two-axis computer numerical control (CNC) router is proposed in this paper. The system can selectively cut/erode between the copper or substrate layers with only a single active laser by controlling the intensity of the laser beam. The proposed method circumvents the fabrication of a negative and the chemical processing steps of PCB manufacturing, resulting in higher production speed at a reduced cost. The technique is also environmentally friendly and suitable for small-batch production to support small industries and research. The superiority of the proposed method is also demonstrated by a comparison between the proposed and a high-density fiber laser-based method. © 2023 IEEE.

关键词： Lithium-ion batteries

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Maximum Power Point Tracking Algorithm Based on Adaptive Particle Swarm Optimization Under Partial Shading Conditions 49

Maximum Power Point Tracking Algorithm Based on Adaptive Par...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Hu, Pengcheng Ukil, Abhisek Nair, Nirmal-Kumar C University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350331820

This article introduces a way to optimize the power output of a photovoltaic (PV) system by implementing a maximum power point tracking (MPPT) technique. The algorithm is based on adaptive particle swarm optimization (APSO) under partial shading conditions (PSC). APSO aims to solve the issue that conventional MPPT algorithms cannot track the optimal global solution when the photovoltaic array's power-voltage (P-V) curve shows multiple peaks under PSC. In APSO, the algorithm adaptively adjusts the learning factor and inertia weight to optimize convergence speed and precision. The simulation results prove that APSO outperforms the conventional particle swarm optimization (PSO) algorithm by rapidly and precisely tracking the maximum power point (MPP) under uniform illumination and static or dynamic PSC. Moreover, the APSO exhibits fewer power fluctuations during the tracking process. © 2023 IEEE.

关键词： Maximum power point trackers

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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SGAN-RL: Fusion classification method for network intrusion detection based on data augmentation

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Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 569-583页

作者： Li, Lining Liu, Shuai Ren, Rong Wu, Lianhai Wang, Qian Zhang, Bing College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information Science and Engineering Yanshan University P.R. Qinhuangdao China The Key Laboratory of Software Engineering Yanshan University Qinhuangdao China

Although traditional research methods for intrusion detection can effectively prevent and mitigate issues such as data leaks to avoid severe consequences, existing intrusion detection technologies encounter limitations, including low classification accuracy and high false positive rates, particularly when dealing with high-dimensional, complex network anomaly traffic data. Additionally, the common problem of class imbalance in intrusion detection datasets exacerbates these challenges. This paper introduces a novel approach to network intrusion detection classification, termed SGAN-RL, which integrates ResNet and LSTM models based on the semi-supervised generative adversarial network (SGAN) framework. This method utilizes SGAN to generate synthetic data resembling real intrusion detection data and continually trains the discriminator to produce high-quality data, thereby enhancing data distribution balance and quality. Moreover, a fusion intrusion detection classification model is presented, leveraging the strengths of ResNet and LSTM architectures to capture spatial and temporal features, respectively. This synergistic fusion enhances the model’s capacity for comprehensive data representation, resulting in improved performance in intrusion detection data classification tasks. Experiments conducted on intrusion detection datasets NSL-KDD, UNSW-NB15, and CIC-IDS2017 demonstrate that the proposed model enhances accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, respectively, showcasing its robust performance and reliability in network intrusion detection classification. © 2025 – IOS Press. All rights reserved.

关键词： Network intrusion

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Use of Audience Response Systems to Enhance Student Engagement in Online Synchronous Environments: An Exploratory Study 27

Use of Audience Response Systems to Enhance Student Engageme...

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27th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2023

作者： Nesbit, Trevor Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (纸本)9781713880325

Many higher education institutions adapted to the Covid-19 pandemic by switching their teaching into online mode making use of online synchronous sessions using technologies such as Zoom. It was common for lecturers to find it disconcerting that many students were not turning on their cameras and microphones and how this made it difficult to ascertain whether their students were engaged in the sessions at all. This paper examines the experiences of teaching staff in eight courses relating to the use of audience response systems (ARS) to improve the experiences of students and teaching staff when conducting synchronous online teaching sessions or hybrid sessions when they had some face-face students and some online students joining sessions synchronously. Literature is examined that shows the benefits relating to the use of ARS in synchronous online teaching sessions to include anonymity of student responses;enhancement of feedback between teaching staff and students;and teaching staff getting a better sense of student engagement during a session. An analysis of the eight cases presented confirms these benefits in the literature from the perspective of the teaching staff. The findings apply irrespective of the ARS being used and will be of relevance and interest to any teaching staff seeking to improve the experiences of students and teaching staff involved in synchronous online teaching sessions. © 2023 Proceedings of World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI. All rights reserved.

关键词： Students

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Smart Meter Customer Data Synchronization Technique by KMean Clustering

Smart Meter Customer Data Synchronization Technique by KMean...

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2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023

作者： Bule, Leoni Elizabeth Nair, Nirmal-Kumar C The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9781728170251

The purpose of this paper is to review the methods of classification of stochastic variables in this case the customer power consumption recorded from smart meters. There are data available in the power distribution system from different sources which are often not synchronized in time. Customer smart meter data is existing and is valuable record which provides information of the parts of the power distribution network not visible to the distribution network operator. The purpose of the work is to find initialization process suitable for customer load demand within an active power distribution system and utilize the model to synchronize the customer smart meter load data with the datasets utilized in the power distribution operation such as data available in SCADA. KMean clustering techniques have been tested using actual customer smart meter data collected from ADRRESS Concept website in Australia. The customer datasets are collected for 7 typical winter days and 7 typical summer days. The KMean clustering technique has been widely used to build models from probabilistic natured variables. It is relatively effective and efficient for large datasets as well as datasets on a smaller time scale. © 2023 IEEE.

关键词： Smart meters

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Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

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Multimedia Tools and Applications 2024年 1-25页

作者： Polo-Rodríguez, A. Valera, J.C. Peral, J. Gil, D. Medina-Quero, J. Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings. © The Author(s) 2024.

关键词： Ultra-wideband (UWB)

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MCM-Llama: A Fine-Tuned Large Language Model for Real-Time Threat Detection through Security Event Correlation 4

MCM-Llama: A Fine-Tuned Large Language Model for Real-Time T...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Diakhame, Mouhamadou Lamine Diallo, Cherif Mejri, Mohamed Gaston Berger University Department of Computer Science Saint Louis Senegal Laval University Department of Computer Science & Software Engineering Quebec Canada

ISBN: (纸本)9798350395914

The correlation of security alerts is crucial for effective threat detection and mitigation in modern cybersecurity landscapes. With the exponential growth in the volume of generated alerts, there arises an imperative to continually develop new, efficient methods for alert correlation. The use of Large Language Models (LLMs) presents new avenues for enhancing real-time threat detection in the field of cybersecurity. In this paper, we introduce MCM-Llama, a fine-tuned language model aimed at improving security event correlation for real-time threat detection. Building upon our prior research, we augment the current framework by integrating MCM-Llama and introducing a novel architecture to practically implement our proposition. In doing so, we replace the correlation module previously based on Named Entity Recognition (NER) and semantic similarity with a fine-tuned Large Language Model (LLM). This transition allows us to capitalize on the advanced capabilities of LLM in comprehending and correlating security events dynamically. Through this enhancement, we aim to significantly improve the accuracy and efficiency of our threat detection system while leveraging the full potential of LLM technology in cybersecurity. Our experimental results validate the efficacy of the proposed approach by significantly improving the correlation rate, thereby reinforcing the pivotal role of Large Language Models (LLMs) in advancing security event correlation and threat detection methodologies. © 2024 IEEE.

关键词： Semantics

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Towards Interoperability of Industrial Automation Systems Using SystemJ and TwinCAT 18

Towards Interoperability of Industrial Automation Systems Us...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Kang, Owen Salcic, Zoran University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350312201

This paper proposes extending the system-level programming language SystemJ to interact with the industry control software TwinCAT to introduce dynamic behaviors into TwinCAT-based systems. The approach extends abstract SystemJ signal classes using the Automation Device Specification (ADS) protocol to access TwinCAT status and control variables, allowing new high-level concepts like dynamic reconfigurability of industrial automation systems to control systems originally designed using traditional languages and tools. We created prototypes of realistic manufacturing systems testbeds and conducted experiments to validate the proposed approach by accessing different types of TwinCAT variables. © 2023 IEEE.

关键词： Interoperability

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Bi-Directional LSTM-Based Search Engine for Source Code Retrieval 4

Bi-Directional LSTM-Based Search Engine for Source Code Retr...

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4th International Conference on Communication Technologies, ComTech 2023

作者： Bibi, Nazia Rana, Tauseef Maqbool, Ayesha National University of Sciences and Technology Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350337211

The code retrieval process looks for the most relevant code fragments. For this purpose, many code-search methods support natural-language queries. But still, there are problems with figuring out the pros and cons of each technique and choosing the best one for the task of code search. The proposed approach employs Word2Vec embedding and Bi- LSTM, a neural network model, to identify query title similarities with the existing dataset to search the code fragment. A novel model named BiD-CSE (Bi-Direction LSTM Code Search Engine) is proposed which generates source code recommendations. The proposed system assists programmers in locating appropriate implementations of sample code based on requirements specified in the query. The evaluation of the proposed framework is performed using three datasets. BiD-CSE collects and analyzes the input query which is then parsed to find the best match in the dataset. BiD-CSE finds the desired code by matching variables, functions, doc-strings, and comments. The BiD-CSE model is implemented using a web-based platform that allows users to enter a query and obtain the top ten most effective results. Evaluation results show that the performance of the model is better than the current baseline approaches. © 2023 IEEE.

关键词： Search engines

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