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Improving internet of vehicles research: A systematic preprocessing framework for the VeReMi dataset

Data in Brief 2025年 60卷

作者： Aparup Roy Debotosh Bhattacharjee Ondrej Krejcar Bachelor of Science (B.S.) in Data Science and Applications (Pursuing) Indian Institute of Technology Madras BS Degree Office 3rd Floor ICSR Building IIT Madras Chennai 600036 India Department of Computer Science & Engineering Jadavpur University 188 Raja S.C. Mallick Rd Kolkata 700032 West Bengal India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove 50003 Czech Republic Research Center Skoda Auto University Na Karmeli 1457 293 01 Mlada Boleslav Czech Republic

The Vehicular Reference Misbehavior Dataset (VeReMi) is a vital resource for advancing Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV). However, its large size (∼7 GB) and inherent class imbalance pose significant challenges for machine learning model development. This paper presents a preprocessing framework to enhance VeReMi’s usability and relevance. Through 10 % down-sampling, the dataset was reduced to ∼724MB, making it computationally manageable. Biases were addressed by balancing benign and malicious samples through synthesis and identifying benign instances using predefined criteria. A refined feature set, including key attributes like rcvTime, pos_0, pos_1, and attack_type (renamed attacker_type ), was selected to improve machine learning compatibility. This preprocessing pipeline effectively maintains data integrity and preserves the representativeness of malicious patterns. The optimized dataset is well-suited for ITS and IoV applications, such as anomaly detection and network security, underscoring the crucial role of preprocessing in overcoming real-world constraints and enhancing model performance.

关键词： Vehicular reference misbehavior dataset (VeReMi) Intelligent transportation systems (ITS) Internet of vehicles (IoV) Intrusion detection systems (IDS) Data preprocessing Dataset optimization Anomaly detection Network security Machine learning (ML) Cybersecurity

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Social Sensing for Monuments Recognition using Convolutional Neural Networks: A Case Study

Social Sensing for Monuments Recognition using Convolutional...

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IEEE Symposium on computers and Communications (ISCC)

作者： Giovanni Delnevo Arber Kazazi Elio Amadori Silvia Mirri Department of Computer Science and Engineering University of Bologna Bologna Italy Bachelor Degree in Computer Science and Engineering University of Bologna Cesena Italy Romagna Tech S.C.P.A ForlÌ Italy

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Social media are revolutionizing various areas of our society, including the cultural heritage one. The same goes for artificial intelligent strategies and machine learning algorithms, which are exploited in many contexts, and could play an interesting role in promoting and sharing cultural heritage and touristic activities and points of interests in urban environments. An interesting application of these techniques in the cultural and touristic domain could be represented by the automatic recognition and classification of monuments and points of interest. In this work, we present a study on monuments recognition, starting from pictures taken by users by means of their mobile devices, while visiting an urban environment. In particular, we employed images collected by the citizens and tourists with the aim of contributing to valorize monuments or places in a specific case study, that is the city of Cesena (Italy). In this paper, we compared several architectures of convolutional neural networks, that are able to discriminate among a set of outdoor monuments with an accuracy of over 90%.

关键词： computers Machine learning algorithms Social networking (online) Urban areas computer architecture Mobile handsets Sensors

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Deep convolutional neural network model for short-term electricity price forecasting

arXiv

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arXiv 2020年

作者： Cheng, Hsu-Yung Kuo, Ping-Huan Shen, Yamin Huang, Chiou-Jye Department of Computer Science and Information Engineering National Central University Computer and Intelligent Robot Program for Bachelor Degree National Pingtung University School of Electrical Engineering and Automation Jiangxi University of Science and Technology

In the modern power market, electricity trading is an extremely competitive industry. More accurate price forecast is crucial to help electricity producers and traders make better decisions. In this paper, a novel method of convolutional neural network (CNN) is proposed to rapidly provide hourly forecasting in the energy market. To improve prediction accuracy, we divide the annual electricity price data into four categories by seasons and conduct training and forecasting for each category respectively. By comparing the proposed method with other existing methods, we find that the proposed model has achieved outstanding results, the mean absolute percentage error (MAPE) and root mean square error (RMSE) for each category are about 5.5% and 3, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Power markets

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PERFORMANCE TESTING OF SHIPBOARD ELECTRONIC SYSTEMS

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NAVAL ENGINEERS JOURNAL 1993年第3期105卷 118-124页

作者： HARDER, G MARTIN, RA PE George Harder:is an electronics engineer for Naval Undersea Warfare Center Detachment Norfolk's Electromagnetic Engineering Branch located at the Norfolk Shipboard Electronic Systems Evaluation Facility (SESEF) site. He is responsible for fleet electromagnetic readiness (FEMR) inspections on board Navy ships and provides computer programming support to SESEF. Mr. Harder has a bachelor of science degree in electrical engineering from the University of Florida and has qualified for his professional engineering license in the state of Virginia. Robert A. Martin:is a supervisor electronics engineer employed by the Naval Undersea Warfare Center Detachment Norfolk. He functions as the branch head for the Electromagnetic Engineering Office which is responsible for the operation of the Norfolk Shipboard Electronic Systems Evaluation Facility (SESEF) and the Fleet Electromagnetic Readiness (FEMR) Program. Mr. Martin received a bachelor of science degree in electrical engineering from the University of Akron.

Performance evaluation of shipboard electronic systems entails debugging the systems in a laboratory environment, placing them in service and relving on svstem operators to provide feedback. General testing can be performed at selected sites by system designers, but each site where equipment is to be installed can offer unique problems. It is impossible to predict all the scenarios. Unique problems are more the rule than the exception when equipment is destined for Navy ships. Ship deployments make for difficult logistics when sending test teams to evaluate system failures. So, out of necessity, if newly installed equipment receives the proper inputs and generates the proper outputs, it is accepted and becomes the sailor's responsibilitv to maintain. In cases where documentation is ambiguous or incomplete, it is left to the sailor's ingenuity to continue testing and training on equipment. This is generallv obtained through computer simulations and back-to-back testing which can provide results for ideal conditions, but does not take the dynamics of interference into account. Remote site testing is the only way to get a true representation of equipment performance and training problems. Electronic system operators on board Navy vessels are fortunate, thev have help. There exists an organization available near major naval ports worldwide whose existence is to test electronic systems performance. The testing utilizes electronic systems as they would normally be configured for operations. This organization is the Shipboard Electronic Svstem Evaluation Facility (SESEF).

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Reliable Data Transmission through Private CBRS Networks

arXiv

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arXiv 2023年

作者： Kuo, Hsun-Yu Liu, Szu-Yu Huang, Chin-Ya Chen, Yu-Chi Xie, Meng-Hua The Bachelor Degree Program of Applied Science and Technology National Taiwan University of Science and Technology Taiwan The Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan

We consider the use of a domain proxy assisted private citizen broadband radio service (CBRS) network and propose a Maximum Transmission Continuity (MTC) scheme to transmit Internet of Things (IoT) data reliably. MTC dynamically allocates available CBRS channels to sustain the continuity of data transmission without violating the channel access requirements. MTC allocates the granted CBRS channels according to the priority of each user, the instant channel access status, interference among users, and the fairness. The simulation results demonstrate the improvement in managing reliable IoT data transmission in the private CBRS network. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Comments on a Computation-Transferable Authenticated Key Agreement Protocol for Smart Healthcare

Comments on a Computation-Transferable Authenticated Key Agr...

引用

International Conference on Applied System Innovation (ICASI)

作者： Ya-Fen Chang Chung-Yi Tsai Wei-Liang Tai Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan

Recently, Wang et al. proposed a computationally transferable authenticated key agreement protocol for smart healthcare by adopting the certificateless public-key cryptography. They claimed that their protocol could ensure privacy, resist various attacks, and possess superior properties. After analyzing their protocol, we find that it suffers from some flaws. Firstly, user privacy is not ensured as claimed. Secondly, some statements are inaccurate or missing. Thirdly, it cannot resist DoS attack. In this paper, the details of how these flaws threaten Wang et al.’s protocol are shown.

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Comments on a Blockchain-Enabled Privacy-Preserving Scheme for Demand-Response Management in Smart Grid Environments

Comments on a Blockchain-Enabled Privacy-Preserving Scheme f...

引用

International Conference on Applied System Innovation (ICASI)

作者： Ya-Fen Chang Jia-Yen Wu Wei-Liang Tai Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

To protect privacy, Park et al. proposed a blockchain-enabled privacy-preserving scheme (BPPS) to achieve demand response in the smart grid environment. Park et al. claimed that their scheme could resist various attacks and ensure both of privacy and data integrity. However, with thorough analysis of their scheme, we find that it suffers from three flaws.

关键词： Privacy Technological innovation Resists Load management Denial-of-service attack Demand response Smart meters

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PV System Power Model Application in Smart Cities

PV System Power Model Application in Smart Cities

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IEEE International Conference on Knowledge Innovation and Invention (ICKII)

作者： Chiou-Jye Huang Min-Chan Hwang Hsin-Chuan Chen Ping-Huan Kuo School of Electrical Engineering and Automation Jiangxi University of Science and Technology Ganzhou China School of Computer Engineering University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China Computer and Intelligent Robot Program for Bachelor Degree National Pingtung University Pingtung Taiwan

ISBN: (数字)9781728101101

ISBN: (纸本)9781728101231

The photovoltaic (PV) system energy generation is closely related to climate conditions, installation site, and system design. In this paper, a simple energy generation model is proposed and established by means of module temperature and solar irradiation, module temperature coefficient, module rated power, number of modules, and three correction coefficients including temperature derating coefficient, inverter conversion loss coefficient, and other loss coefficients. The experiment results show that the estimation model proposed in this paper has high accuracy using the Mean Absolute Percentage Error (MAPE) model as the evaluation index, the estimation model MAPE is 6.75%, while the PV SYST software simulation has MAPE of 11.90%. According to international MAPE evaluation standards, the method proposed in this paper has higher accuracy level than PV SYST software. This model can be used for the estimation of energy generation when installing solar PV systems, and can be used for the reasonable assessment of PV system long-term power generation, to predict system power generation anomalies, and reduce system abnormalities caused by downtime losses, and increase investment efficiency.

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AI-based computer-aided System on Long Term Care

AI-based Computer-aided System on Long Term Care

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Yi-Jui Huang Hong-Yao Ciou Yuan-Hsun Yang Chin-Yin Lin Chia-Wei Lee Yu-Hsien Chen Computer Science and Information Engineering National Taitung University Taitung Taiwan Computer Science University of Taipei Taitung Taiwan Bachelor’s Degree Program for Indigenous Peoples in Senior Health and Care Management National Taitung University Taitung Taiwan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

According to data from Taiwan’s National Development Council in 2024, Taiwan is expected to enter a super-aged society by 2025, indicating a great acceleration in population aging. With the aging population and the increasing prevalence of chronic diseases, the importance of rehabilitation for the elderly has become crucial. Rehabilitation aims not only to aid in recovering from illness or surgery but also to maintain health in daily life. For the elderly, appropriate rehabilitation exercises can improve physical strength, cardiopulmonary function, and overall quality of life. This not only helps restore physical function but also reduces the occurrence and progression of chronic diseases. The system includes contextual rehabilitation games, general rehabilitation training, accessible remote instruction, and physical fitness assessment. The contextual rehabilitation games utilize gamified interactive designs to enhance engagement and motivation. General rehabilitation training provides standardized guidance on rehabilitation movements. Accessible remote instruction allows patients who are unable to move due to various factors to receive professional guidance in hospitals or care centers, reducing the inconvenience of movement. Finally, physical fitness assessment provides accurate data support for medical personnel, while also reducing manual calculation errors and labor costs. Through this comprehensive assistive system, smart healthcare can not only improve the effectiveness and efficiency of rehabilitation but also provide a new approach for health management and rehabilitation of the elderly, helping to address the challenges brought by an aging society in the future.

关键词： Training Hospitals Surgery Games Manuals Signal processing Health and safety Personnel Older adults Diseases

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Decision-aid system for survivability of damaged conventional military submarines

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NAVAL ENGINEERS JOURNAL 2002年第4期114卷 21-30页

作者： Lee, D Lee, J Lee, KH DONGKON LEE has served as a research engineer in Korea Research Institute of Ships and Ocean Engineering(KRISO)/KORDI for 19 years. He obtained his Ph.D in naval architecture at Pusan National University Korea in 1995. His current research interests include artificial intelligence maritime safety damage survivability evacuation and computer simulation. He is a member of the American Society of Naval Engineers Society of Naval Architects of Korea Korea Information Science Society and Korea Intelligent Information System. JAEYONG LEE has served as an engineering duty officer a commander in Korea Naval Sea Systems Command for 20 years. He graduated from Korea Naval Academy in 1979 with a bachelor of science degree in electric engineering. He received a master of engineering degree in industrial engineering from Yeonse University in 1997 and a master of engineering degree in naval architecture from Chungnam National University in 2000. His current research interests include design and safety of submarine. KYUNG-HO LEE has served as a research engineer in Korea Research Institute of Ships and Ocean Engineering(KRISO)/KORDI for 11 years. He obtained his Ph.D in naval architecture at Seoul National University in 1998. His current research interests include artificial intelligence maritime safety and concurrent engineering. He is a member of the Society of Naval Architects of Korea

AEven small leakage in submarines can lead to serious consecutive damage, since they operate under high water pressure. Such leakage including damage to piping and the bull eventually result in human casualties and loss of expensive equipment as well as the loss of combat capabilities. In such cases, a decision-making system is necessary to respond immediately to the damage in order to maintain the safety or the survival of the submarine. So it is necessary to have an automatic system that can generate responses and give advice to the operator on bow to make decisions to maintain the survivability of the damaged vessel. In this paper a knowledge-based decision support system for submarine safety is developed. The domain knowledge is acquired from submarine design documents, design expertise, and interviews with operators. The knowledge consists of responses regarding damage of the pressure bull and piping systems. Expert elements are deduced to obtain the decision from the knowledge base, and the system makes recommendations on bow to cope with damage to the bull and piping and whether the submarine should stay in the sea or to surface. It is demonstrated that the developed system is well simulated to the real situation through sample applications.

关键词： Submarines

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