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Utilising Machine Learning for Precise State-of-Charge Prediction in Li-Ion Batteries 12

Utilising Machine Learning for Precise State-of-Charge Predi...

引用

12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Khatri, Avinash Lota, Jaswinder Nepal, Prasanna Amirhosseini, Mohammad Hossein School of Architecture Computing and Engineering University of East London London United Kingdom Nepal Engineering College Department of Electrical and Electronics Engineering Bhaktapur Nepal

ISBN: (纸本)9798350350982

As the popularity of electric propulsion using batteries rises alongside the demand for renewable energy, effective battery management and monitoring are crucial for sustain ability and efficiency in electric vehicles (EVs). The battery monitoring system (BMS) employs IoT/sensor networks to estimate crucial battery metrics like remaining useful life (RUL), state-of-health (SoH), and state-of-charge (SoC), using data on current status, temperature, and voltage. Machine learning (ML) and artificial intelligence (AI) are increasingly utilized to enhance BMS accuracy, addressing challenges like real-time data processing and the accuracy of estimations. This paper investigates the effectiveness of Linear Regression and Random Forest models in estimating SoC. During the hyperparameter tuning phase, the models were optimized using the Grid Search method, and their performance was evaluated at various temperatures: 25°C, 10°C, 0°C, and -10°C. The findings indicate that the models' effectiveness enhances as the temperature increases. The Random Forest model exhibited superior performance at 25°C, achieving an R2 score of 0.99646 and an RMSE score of 0.000264. This paper not only contributes to advancing Li-ion battery monitoring system, but also empowers professionals in this field to harness machine learning capabilities effectively. © 2024 IEEE.

关键词： State of charge

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Risk Management for 5G-Enabled Internet of Things by Using Machine Learning: A Survey

Risk Management for 5G-Enabled Internet of Things by Using M...

引用

2024 Arab ICT Conference, AICTC 2024

作者： Alfaw, Aysha Elmedany, Wael Sharif, Mhd Saeed College of Information Technology University of Bahrain Sakhir Bahrain School of Architecture Computing and Engineering Department Computer Science and Digital Technologies UEL London United Kingdom

ISBN: (纸本)9798350342086

Nowadays, there is a noticeable increase in the development and use of the Internet of Things(loT). With this rapid increase, IoT devices may face several challenges when used in the real world through applications. Using 5G with large numbers of Internet-connected devices of IoT puts many devices at risk and requires risk management. The growing of IoT leads to the growth of cyber-attackers and allow them to expose the vulnerabilities. This paper will present the risk management of 5G-enabled IoT technology and the use of machine learning to mitigate the risk and reduce the attacks on these technologies, and finding solutions through previous researches. This research aims to identify and eliminate potential risks in the IoT based 5G by machine learning. The paper also aims to present a solution to the security problems and risks faced when integrating the fifth-generation network and the Internet of Things. With 5G-enabled IoT, the risk management helps organizations use emerging technologies effectively while mitigating potential underlying risks such as security breaches, and data loss. Authentication,encryption, access control, and communication security are essential for making security. Machine learning algorithms have the potential to remove many obstacles to implementing the security of the Internet of Things, paving the door for the use of sophisticated technology like 5G. With new 5G networks, it is expected that the current IoT will be significantly expanded, which will improve cellular operations and the security of IoT, as well as push the future of the Internet to its edges. Machine learning (ML) creates a secure and intelligent system and provides a robust security mechanism and dynamic for 5G networks. Therefore, this time will also present previous solutions with machine learning against the risks to IoT and 5G. This paper will present a set of previous studies related to insurance of risk management for the 5G-enabled IoT, Which aims to find previ

关键词： Adversarial machine learning

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iNUMAlloc: Towards Intelligent Memory Allocation for AI Accelerators with NUMA 21

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Yuanchao Qian, Ruyi Wang, Yida Huo, Qirun Capital Normal University College of Information Engineering Beijing China Skl of Computer Architecture Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798350329223

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time. © 2023 IEEE.

关键词： Deep neural networks

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EagerReuse: An Efficient Memory Reuse Approach for Complex Computational Graph 29

EagerReuse: An Efficient Memory Reuse Approach for Complex C...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qian, Ruyi Cao, Bojun Gao, Mengjuan Shi, Qinwen Wang, Yida Xu, Yuanchao Huo, Qirun Qiu, Keni Capital Normal University College of Information Engineering Beijing China Institute of Computing Technology Cas Skl of Computer Architecture Beijing China

ISBN: (纸本)9798350330717

Memory reuse is a promising approach for deep neural network (DNN) to reduce memory consumption because it does not introduce any additional runtime overhead. We observe that existing memory reuse algorithms consider only the effect of an individual data feature (either tensor size or tensor lifetime) on memory reuse and ignore the relative position relationship (RPR) among tensors. As computational graphs grow slightly more complex, the mining of memory reuse becomes insufficient. To address this issue, we propose a new memory reuse algorithm - EagerReuse, which can exploit more memory reuse opportunities by analyzing RPR among tensors and reusing them as quickly as possible. We evaluated the algorithms with inference models in TensorFlow Model Garden, and the results show that the EagerReuse outperforms the state-of-the-art algorithms in three out of seven cases. For more complex computational graphs, EagerReuse can achieve better memory usage with slightly higher but acceptable overhead. © 2023 IEEE.

关键词： computational graph memory optimization memory reuse memory usage

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Analysis of Influence Degree of Fear Based on Different Musical Characteristics

Analysis of Influence Degree of Fear Based on Different Musi...

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2024 International Conference on Culture-Oriented Science and Technology, CoST 2024

作者： Li, Muzi Lee, Zheng Long Kua, Yee Shan Zhu, Jiaxing Huang, Yilun Tianjin University College of Intelligence and Computing 300072 China China Conservatory of Music Department of Composition Beijing100101 China Beijing Jiaotong University School of Architecture and Design 100044 China

ISBN: (纸本)9798350380347

In this paper, the effects of four factors in music such as pitch, length, the degree of consonance and loudness fluctuation frequency on human's fear were investigated experimentally. Further, we analyzed the difference of fear perception between professional music learners and nonprofessional music learners with different degree of consonance, as well as the influence of different personalities on fear perception. We carried out the listening experiment employing a method of subjective evaluation. The experimental results shown that pitch, length, loudness fluctuation frequency and the degree of consonance all had significant effects on fear perception, and their contribution degree decreased in turn. In addition, professional music learners had more significant fear perception at different degree of consonance. At the same time, the research also found that individuals with different personalities had significant differences in fear perception. © 2024 IEEE.

关键词： fear perception musical factors sound synthesis subjective evaluation

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Tropical Cyclone Analysis and Accumulated Precipitation Predictive Model Using Regression Machine Learning Algorithm 3rd

Tropical Cyclone Analysis and Accumulated Precipitation Pred...

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3rd International Conference on Computer and Communication Engineering, CCCE 2023

作者： Abalos, Maribel S. Fajardo, Arnel C. College of Engineering Architecture and Technology Isabela State University Isabela Ilagan Philippines College of Computing Studies Information and Communication Technology Isabela State University Isabela Cauyan City Philippines

ISBN: (纸本)9783031352980

The Philippines is susceptible to tropical cyclones due to its geographical location which generally produces heavy rains and flooding of a large area that result in heavy casualties to human life and destruction of crops and properties. In the past 4 years, the Cagayan Valley region is hit by almost 20 Tropical Cyclones with different strengths and attributes. It is of utmost importance to have sufficient knowledge of such maritime phenomena for beneficial purposes. This study analyzed the Tropical Cyclones that hit Cagayan Valley using Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA) dataset from 2017–2021. Different attributes were considered to create a framework, statistical analysis, and comparison matrix. Also, the dataset is analyzed using four Regression Machine Learning Algorithms namely MultilayerPerceptron, Linear Regression, SMOReg, and Gaussian Processes, and compared their performances where the best-performed algorithm is used to develop a predictive model for rainfall accumulation. Results of the study found that Cagayan Valley accumulated 219.857 mm of rainfall and an average Tropical Cyclone Maximum Sustained winds of 123.438 kph. Tropical Cyclone Ulysses reached almost the highest value in all the attributes as evidence of its devastating nature last November 2020. Also, the Linear Regression algorithm revealed a better performance which displayed a higher correlation coefficient and lower error estimates and took less time in developing the model. Lastly, the results can be considered to be a basis of the impact of a Tropical Cyclone based on their attributes and can be implemented into technological platforms for predictive application which helps foresee the prediction rate of rain accumulation to develop techniques and preventive measures to target reduction of damage on properties and the number of casualties. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tropical cyclone

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Rough Rice Grading in the Philippines Using Infrared Thermography 3rd

Rough Rice Grading in the Philippines Using Infrared Thermog...

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3rd International Conference on Computer and Communication Engineering, CCCE 2023

作者： Bejarin, Orfel L. Fajardo, Arnel C. College of Engineering Architecture and Technology Isabela State University Isabela Ilagan3300 Philippines College of Computing Studies Information and Communication Technology Isabela State University Isabela Cauayan City3305 Philippines

ISBN: (纸本)9783031352980

The Rough rice grading is a set of standard methods and processes for evaluating quality that are important to marketing and process controls. IR thermography uses the electromagnetic spectrum’s infrared region to detect heat that has been emitted. FLIR ONE thermal camera was used to acquire images of the sample. In order to represent each sample, a thermal index was generated using the average value of the pixels in the thermal image. In this study, the grading of rough rice was evaluated using infrared thermography and image processing techniques. Using infrared thermography, the results of the study indicated that detecting moisture content was 87.30% accurate and detecting foreign matter was 95.04% accurate. The results showed that infrared thermography evaluation using spectral analysis could accurately determine the moisture content of rough rice. Farmers, rice millers, and other related government agencies can greatly benefit from the application of non-contact, non-destructive methods for determining the purity and moisture content of rough rice. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Moisture

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Classifying Fake News in Filipino Language using Improved DistilBERT Model

Classifying Fake News in Filipino Language using Improved Di...

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International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE)

作者： Jan Mar Ruel Espiritu Ralph John E. Policarpio Aliah Chavy B. Sabado Vince Marc B. Sabado Hajah T. Sueno College of Engineering Architecture and Computing Notre Dame of Marbel University Koronadal City Philippines

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

Detecting misinformation in the Filipino language poses unique challenges, especially when working with limited data resources. This study explored the use of transformer models, specifically pre-trained models like BERT, to construct a fake news classification model for Filipino. However, BERT's large size and computation hinder its deployment in resource-constrained environments. To address this, the study focuses on developing a lightweight fake news classifier using the DistilBERT architecture, fine-tuned for Filipinos. The model's performance was evaluated against BERT based on accuracy, precision, recall, F1 score, inference time, memory requirement, and parameter size. Results indicate that while BERT exhibits slightly better classification performance, DistilBERT offers significant advantages in efficiency with faster inference time and smaller resource requirements.

关键词： Electrical engineering Accuracy Computational modeling Memory management Transformers Fake news Information technology

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Automated Solar-Powered Corn Planting Robot

Automated Solar-Powered Corn Planting Robot

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IEEE Symposium on Wireless Technology and Applications (ISWTA)

作者： Michael Adrian P. Agustin Ferdinand H. Lopez Adrean Rey L. Tiu Huan Jenith F. Guaro Joriel S. Tiba College of Engineering Architecture and Computing Notre Dame of Marbel University Koronadal City Philippines

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

The Philippine economy is based primarily on agriculture. Nowadays, there's a bigger need than ever for efficient and automated farming methods. Slow and poor production has been one of the major problems of agriculture. The best approach to deal with these problems is for the agricultural industry to become more automated. This leads to more systematic farming, which raises productivity rates. The purpose of this study is to design and create an automated robot that plants corn seeds automatically. To make the most of the tropical climate in the area, this automated robot is also integrated with a solar power. The Automated Solar-Powered Corn Planting Robot (ASP-CPR) was designed by the researchers with the following functions: it can drill farmed soil with a regular depth and distance and release a standard amount of corn seeds on the drilled soil automatically that follows the standard set-up of the Department of Agriculture. Upon multiple testing, results revealed the accuracy on releasing the standard number of seeds is 94%, 98 % for the depth of drilled soil, and 92% accuracy for the distance taken by ASP-CPR between planted seeds. Additionally, the researchers integrated a notification system that notifies the user if the robot experiences low power or immovability, or if the seed container is empty. Moreover, the researchers conducted a product quality evaluation ISO/IEC 25010 for 20 respondents where the gathered data and results revealed an overall weighted mean of 4.13 which indicates a strong agreement amongst respondents that ASP-CPR adhere to the quality model.

关键词： Productivity Accuracy Service robots Soil Message services Agriculture Seeds (agriculture)

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Cognitive computing Applications in Real-Time Financial Risk Management

Cognitive Computing Applications in Real-Time Financial Risk...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Josyula, Hari Prasad Landge, Sunil Rajaram Ranjan, Piyush Gajjar, Kishan D. Kumar, P. Anand Bdair, Mohammad Jamal 203 Quail Crk PrincetonNJ08540 United States PSG College of Arts & Science Civil Aerodrome Post Sitra Coimbatore641014 India School of Architecture Computing and Engineering University of East London United Kingdom

ISBN: (纸本)9798350374933

The incorporation of cognitive computing into real-time financial threat operations, to highlight the revolutionary potential of this technology for the financial sector. Cognitive computing, which is a combination of artificial intelligence, machine literacy, and natural language processing, improves the capability to reuse enormous quantities of financial data quickly and directly. The purpose of this study is to investigate how cognitive systems can recognise, evaluate, and mitigate financial risks in real-time. These systems offer greater prophetic analytics and decision-making support in comparison to traditional methods. Cognitive computing makes it possible to conduct more accurate threat assessments, detect implicit dangers earlier, and improve nonsupervisory compliance. This is accomplished through the use of sophisticated algorithms and data analytics. Case studies illustrate the practical applications of these technologies through the use of colourful financial scripts, demonstrating how beneficial they are in improving threat operating methods. In the final section of the study, the author discusses the counterarguments that cognitive computing poses for the future of fiscal threat operation, as well as the implicit issues that are associated with its widespread abandonment. © 2024 IEEE.

关键词： Decentralized finance

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