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2024 NDIA Michigan Chapter Ground Vehicle Systems engineering and Technology Symposium, GVSETS 2024

作者： Muriithi, Grace Papari, Behnaz Arsalan, Ali Khan, Asif Buraimoh, Elutunji Ozkan, Gokhan Timilsina, Laxman Edrington, Christopher Dept. Automotive Engineering Clemson University SC United States Dept. Electrical and Computer Engineering Clemson Universit United States

In the age of advancing digitalization and integrating complex electronics within modern vehicles, hybrid tracked vehicles (HTVs) are increasingly becoming susceptible to cybersecurity threats. Particularly vulnerable are the control units, which have become prime targets for adversarial exploitation due to their pivotal role in vehicle functionality. To address these security concerns, this study demonstrates two distinct yet equally harmful scenarios using replay and DoS attacks to uncover and evaluate vulnerabilities within the Energy Management System (EMS) of HEVs. In the replay attack scenario, the adversary surreptitiously alters the SoC control messages emanating from the Battery Management System (BMS). The attack is calibrated to strategically time and sequence the message replays across various operational states using reinforcement learning, thereby maintaining apparent legitimacy within expected SoC ranges to evade detection. The DoS attack, however, presents a more direct threat by targeting the generator's revolution speed sensors. By compromising these sensors, the attack disrupts the generator and causes subsequent engine shutdowns, compelling the battery to singularly meet the vehicle's power supply demands. Formal attack models are developed and subsequently deployed on a simulated HTV platform within MATLAB/Simulink to analyze the impacts of these cyberattacks. The simulation results illustrate the impacts of the simulated cyberattacks, further reinforcing the importance of resilient and adaptive security measures for the control layer. © Rights reserved by the National Defense Industrial Association (NDIA) Michigan Chapter, authors and their respected organizations.

关键词： Battery management systems

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COVID-19 Image Retrieval Using Siamese Deep Neural Network and Hashing Technique 14

COVID-19 Image Retrieval Using Siamese Deep Neural Network a...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Boroujeni, Farsad Zamani Afshari, Doryaneh Hossien Mahmoodi, Fatemeh Science and Research Branch Dept. of Computer Engineering Tehran Iran Branch Dept. of Computer Engineering Isfahan Iran

ISBN: (纸本)9798350349412

Medical images are crucial for early diagnosis and are increasingly important in modern medicine. This study presents a medical image retrieval system using a Siamese neural network with 13 layers. The system uses the Minimum Redundancy Maximum Relevance (mRMR) technique to extract deep features from the Siamese, and then uses binary hashing to retrieve similar images using Hamming distance. The experimental results on Covid-19 dataset show that the proposed method improves lung image retrieval by over 6% compared to previous methods, achieving an average accuracy of 93.83% and 92.73% in 5 and 10 retrieved images, respectively. © 2023 IEEE.

关键词： Image enhancement

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AI-based Visual Attention Scenario Identification Model in Military Environment 4

AI-based Visual Attention Scenario Identification Model in M...

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4th International Conference on Advances in electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sapariya, Ankur Ravikumar, R.N. Bhatt, Urvi Singh, Suraj Prakash Wanglen, Soram Singh, Sushil Kumar Marwadi University Dept. of Computer Engineering Rajkot India School of Management Sciences Dept. of Computer Engineering Varanasi India

ISBN: (纸本)9798350343670

In response to the evolving landscape of modern military operations, where drone technology now constitutes an estimated 70% of Indian military missions, as evidenced by recent statistics, our proposed AI-based Visual Attention Scenario Identification Model takes center stage. This groundbreaking model is meticulously designed to augment the safety and efficiency of military endeavors in challenging terrains. Our model harnesses advanced deep-learning CNN techniques by processing real-time images from various sources, including drones, strategically focusing on critical details within these visuals. This approach enables the timely detection of potential dangers, even in complex environments, with the primary objective of significantly enhancing situational awareness for military personnel. Positioned as a valuable asset in the military's toolkit, our model, with 85% accuracy, proves particularly effective in identifying potential threats in forested and mountainous regions, ultimately minimizing risks faced by soldiers on the ground. © 2024 IEEE.

关键词： CNN Deep Learning Image Processing Military Operations Visual Attention AI model

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Heart disease Classification Utilising Machine Learning Techniques 3

Heart disease Classification Utilising Machine Learning Tech...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Dhondiyal, Shiv Ashish Verma, Himanshu Kumar, Rohit Sawan, Vikash Uniyal, Ishita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Haridwar University Roorkee Dept. of Computer Science and Engineering India GLA University Department of Computer Engineering & Applications Mathura India Era Hill University Department of Electrical Engineering Graphic India

ISBN: (纸本)9798350354171

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world's biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. Data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various Data Mining and Machine Learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994. © 2024 IEEE.

关键词： Random forests

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SmartCitrus: An Efficient Deep Learning Approach for Real-Time Detection and Classification of Citrus Leaf Diseases

SmartCitrus: An Efficient Deep Learning Approach for Real-Ti...

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2024 International Conference on Advances in Computing, Communication, electrical, and Smart Systems, iCACCESS 2024

作者： Emon, Shaharear Hossain Islam, Iftea Khairul Nahin, Tasfia Jahan Ahmed, Ahnaf Mahdin Orchi, Nabiha Tasnim Alam, Md Ashraful Dipto, Shakib Mahmud Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Cvis Research Lab Dept. of Computer Science and Engineering Dhaka Bangladesh University of Liberal Arts Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Cvis Research Lab Dhaka Bangladesh

ISBN: (纸本)9798350350289

Bangladesh is a prominent citrus exporter. Annually, the country has been exporting citrus fruits to over 60 countries. Distinguishing various diseases affecting citrus leaves requires a significant investment of time, effort, and specialized knowledge. Consequently, it is essential to create an innovative method for detecting citrus diseases. In this study, we have devised a valuable methodology by employing CNN models to identify diseases in citrus leaves. By employing a distinctive ensemble strategy, we successfully trained the model using varying numbers of classes in each stage. In reality, it allowed us to utilize suitable varieties of leaves for various ailments. Furthermore, it has enhanced the rate at which models learn during the later stages. In addition, it has reduced the level of model intricacy in comparison to frequently employed ensemble models. The identification of plant diseases in the present study involved the utilization of leaf photographs and algorithms for segmentation and feature extraction. Ultimately, we have successfully attained a 96 % accuracy rate for each class, signifying a substantial potential for mitigating production losses. © 2024 IEEE.

关键词： Citrus fruits

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BSCIAM: A Blockchain based Secure Cloud Identity and Access Management Framework

BSCIAM: A Blockchain based Secure Cloud Identity and Access ...

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2024 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2024

作者： Das, Swatisipra Sahil, Mohammad Pandit, Naveen Kumar Priyadarshini, Rojalina Gochhayat, Sarada Prasad Fakir Mohan University Postgraduate Dept. of Computer Science Odisha India C.V. Raman Global University Dept. of Computer Science and Engineering Odisha India Indian Institute of Technology Jammu Dept. of Computer Science and Engineering J&K India

ISBN: (纸本)9798350348460

Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security problems arising in cloud identity management techniques such as, dependency on a third-party token provider, leakage of user's details, single point of failure, etc., decentralized cloud identity management systems came into the picture. Blockchain is a decentralized database providing immutability and transparency to recorded transaction data. The current token-based decentralized cloud identity management systems have limitations, including the absence of data access management procedures and a lack of security features. This paper suggests a Blockchain based Secure Cloud Identity and Access Management (BSCIAM) model for secure identity and access management, utilizing encryption and key sharing techniques. The proposed model has been implemented using Ethereum smart contracts for token-based identity management. © 2024 IEEE.

关键词： access management blockchain cloud identity management

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Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction 19

Integrating Temporal Analysis with Hybrid Machine Learning a...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed M. Le, Duc-Tai Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

Air pollution remains a critical issue, adversely affecting public health and the environment. In this study, we utilize the Air Quality index dataset from Kaggle to analyze temporal and seasonal variations of key pollutants, specifically Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Benzene (C6H6 ). Building upon this analysis, we predict Absolute Humidity (AH), a vital meteorological factor influencing pollutant dispersion, using Machine Learning (ML) and Deep Learning (DL) techniques. Three ML techniques, Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR), and three DL techniques, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are employed for predicting AH. The results indicate that while the RF model achieved the lowest Mean Absolute Error (MAE) among ML methods (0.02), the CNN model, despite having an MAE of 0.04, demonstrated statistical superiority through paired t-tests and Wilcoxon signed-rank tests (p © 2025 IEEE.

关键词： Convolutional neural networks

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Battery Degradation Heuristics for Predictive Energy Management in Shipboard Power Systems 50

Battery Degradation Heuristics for Predictive Energy Managem...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Vedula, Satish Olajube, Ayobami Omiloli, Koto Anubi, Olugbenga Moses Dept. of Electrical and Computer Engineering FAMU-FSU College of Engineering Tallahassee United States

ISBN: (纸本)9781665464543

The presence of Pulse Power Loads (PPLs) in the Notional Shipboard Power System (SPS) presents a challenge in the form of meeting their high ramp rate requirements. Considering the ramp rate limitations on the generators, this might hinder the power flow in the grid. Failure to meet the ramp rate requirements might cause instability. Aggregating generators with energy storage elements usually addresses the ramp requirements while ensuring the power demand is achieved. This paper proposes an energy management strategy that adaptively splits the power demand between the generators and the batteries while simultaneously considering the battery degradation and the generator’s efficient operation. Since it is challenging to incorporate the battery degradation model directly into the optimization problem due to its complex structure and the degradation time scale which is not practical for real-time implementation, two reasonable heuristics in terms of minimizing the absolute battery power and minimizing the battery state of charge are proposed and compared to manage the battery degradation. A model predictive energy management strategy is then developed to coordinate the power split considering the generator efficiency and minimizing the battery degradation based on the two heuristic approaches. The designed strategy is tested via a simulation of a lumped notional shipboard power system. The results show the impact of the battery degradation heuristics for energy management strategy in mitigating battery degradation and its health management. © 2024 IEEE.

关键词： Battery management systems

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in computer science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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Inverter Control of Scheme for IEEE1547-2018 Category3 in Fuel Cell Systems

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Transactions of the Korean Institute of electrical Engineers 2022年第1期71卷 90-97页

作者： Won, Su-Yeon Lee, Kyo-Beum Dept. of Electrical and Computer Engineering Ajou University Korea Republic of

This paper presents an inverter control scheme for fuel cell systems in IEEE1547-2018 Category3. The current configuration of Grid-Connected fuel cell system has limitations Category3's LVRT (Low Voltage Ride Through) reponse, additional inverter are needed. The inverter operates in an abnormal state where the grid voltage is unstable, and normally supplies the voltage to BOP (Balance of Plant) of the fuel cell system. Simulated verification of the inverter control scheme was performed to change the voltage source to the BOP from grid to inverter during abnormal conditions. © 2022 Korean Institute of electrical Engineers. All rights reserved.

关键词： Fuel cells

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