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2024 IEEE Sustainable Power and Energy Conference, iSPEC 2024

作者： Li, Shunfeng Wang, Peng Zhu, Xiuli Li, Ning Yan, Weiwu Shanghai Jiao Tong University Department of Automation Shanghai China College of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350395075

With the the vigorous development of new power system, energy and carbon coupling trading has emerged as an effective way to promote the renewable generation and reduce carbon footprint. It is a challenge to design an effective market framework for joint electricity and carbon trading to achieve better strategies. This paper introduces a peer-to-peer electricity-carbon coupling trading (ECCT) market and develops a carbon-electricity virtual battery (CEVB) model to represent the relationship between dynamic carbon credits and energy consumption. The trading optimization problem is formulated as a partially observable Markov decision process, then multi-agent double soft actor-critic (MADSAC) algorithm is introduced to solve the problem. The algorithm makes the method more exploratory, protects personal privacy and solves the overestimation problems effectively. Result studies demonstrate the effectiveness of the proposed market and algorithm in optimizing total cost and reducing carbon emissions. © 2024 IEEE.

关键词： Electricity-carbon coupling trading market Multi-agent reinforcement learning Peer-to-peer

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Visualizing Attitudinal Dynamics of Moving Objects via Android: Capturing Pitch, Roll, and Yaw Parameters 8

Visualizing Attitudinal Dynamics of Moving Objects via Andro...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Musa, Purnawarman Suhartini, Suhartini Senjaya, Remi Sularsih, Puji Aryunani, Witari Novrina, Novrina Gunadarma University depart. of Electrical Engineering Depok Indonesia Gunadarma University depart of Computer Science Depok Indonesia

ISBN: (纸本)9798350342604

In recent times, smartphones can be said to have become a necessity for users. From children to old age, everyone knows what a Smartphone is. The smartphone has various uses according to its users and is equipped with hardware with various sensors. The sensors that each smartphone owns also vary depending on the original factory. Some of the sensors owned by smartphones have beneficial uses;for example, magnetometer sensors do use to determine the wind direction. Then there is the Accelerometer and Gyroscope sensor, which will form an orientation sensor to discover the changes the smartphone feels at an angle, which developed in an application. This study emphasizes the value of the Pitch, Roll, Azimuth, and Angle sensors, which will form a graph according to the behavior of the smartphone. The overall experimental outcomes were obtained by conducting a comprehensive comparison against applications available on the Play Store, ensuring that the margin of error remained below 5 degrees. The orientation sensor is intended to get the value of the attitude change by the smartphone, whose value will then form a graph in real-time. The values preserved results will storing in the Cloud Firebase. © 2023 IEEE.

关键词： Smartphones

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Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3167-3181页

作者： Mesfer Al Duhayyim Hadeel Alsolai Siwar Ben Haj Hassine Jaber SAlzahrani Ahmed SSalama Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent *** remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic *** order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and ***,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on *** this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing *** presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing *** accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature ***,Elman neural network(ENN)model is applied to allot proper class labels into the input ***,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this *** experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct *** comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

关键词： Hyperspectral images remote sensing deep learning hurricane optimization algorithm crop classification parameter tuning

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Optimal Utilization of Electric Vehicles in the Parking Lot of the Faculty Building to Increase Social Welfare 6

Optimal Utilization of Electric Vehicles in the Parking Lot ...

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6th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2023

作者： Misljenovic, Nemanja Fekete, Kresimir Knezevic, Goran Pleskalt, Martin Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Faculty of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350328752

This paper strives to investigate the impact of integrating charging stations in the parking lot of the faculty building with an installed photovoltaic power plant (PV) and battery energy storage system (BESS) to maximize social welfare. The integration of charging stations in the parking lot of business buildings would enable savings for owners of electric vehicles (EVs) who live in residential buildings and have to charge their EVs at public charging stations. On the other hand, by integrating charging stations into the parking lot, business buildings could reduce their electricity costs because they use EVs as BESS. Accordingly, the optimization model was developed in the GAMS environment by applying MILP formulation. The research was conducted according to the current market conditions and for a newly proposed market model for charging and discharging batteries of EVs in the parking lot of business buildings. The results show the need to find the optimal prices for charging and discharging batteries of EVs by applying multi-level optimization. © 2023 IEEE.

关键词： Energy management systems

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SMAKAP: Secure Mutual Authentication and Key Agreement Protocol for RFID Systems 17

SMAKAP: Secure Mutual Authentication and Key Agreement Proto...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Naziri, Shayesta Wang, Xu Yu, Guangsheng Xu, Jian Shrestha, Sudhir Liang, Christy Jie School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia School of Computer Science University of Technology SydneyNSW2007 Australia

ISBN: (纸本)9798331509736

Radio Frequency Identification (RFID) is a crucial technology in the Internet of Things (IoT), enabling seamless wireless communication and data exchange. However, these technologies can pose significant security chal-lenges if not implemented with proper attention to security protocols-especially in communication, where pre-shared keys are not used between active tags and readers for device authentication. Some recent authentication protocols rely solely on a hash function, nonce, and single public kay agreement, which can lead to failure to implement robust security and proper authentication or ineffective for high security application environments. To effectively address these challenges this paper proposes a secure Elliptic Curve Cryptography (ECC) based lightweight mutual authentication protocol utilizing a hybrid key agreement protocol between active tag and reader for secure communication in RFID-enabled devices in the IoT environments. The informal analysis demonstrates a secure communication environment for data privacy and flexibility through effective key management. This protocol is adaptable to various applications by addressing specific requirements and limitations. © 2024 IEEE.

关键词： Authentication Protocol

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Secure and Efficient Federated Learning Against Model Poisoning Attacks in Horizontal and Vertical Data Partitioning

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10913-10927页

作者： Chong Yu Zhenyu Meng Wenmiao Zhang Lei Lei Jianbing Ni Kuan Zhang Hai Zhao Department of Computer Science University of Cincinnati Cincinnati OH USA Department of Electrical and Computer Engineering University of Nebraska-Lincoln Lincoln NE USA School of Engineering University of Guelph Guelph ON Canada Department of Electrical and Computer Engineering Queen’s University Kingston ON Canada School of Computer Science and Engineering Northeastern University Shenyang China

In distributed systems, data may partially overlap in sample and feature spaces, that is, horizontal and vertical data partitioning. By combining horizontal and vertical federated learning (FL), hybrid FL emerges as a promising solution to simultaneously deal with data overlapping in both sample and feature spaces. Due to its decentralized nature, hybrid FL is vulnerable to model poisoning attacks, where malicious devices corrupt the global model by sending crafted model updates to the server. Existing work usually analyzes the statistical characteristics of all updates to resist model poisoning attacks. However, training local models in hybrid FL requires additional communication and computation steps, increasing the detection cost. In addition, due to data diversity in hybrid FL, solutions based on the assumption that malicious models are distinct from honest models may incorrectly classify honest ones as malicious, resulting in low accuracy. To this end, we propose a secure and efficient hybrid FL against model poisoning attacks. Specifically, we first identify two attacks to define how attackers manipulate local models in a harmful yet covert way. Then, we analyze the execution time and energy consumption in hybrid FL. Based on the analysis, we formulate an optimization problem to minimize training costs while guaranteeing accuracy considering the effect of attacks. To solve the formulated problem, we transform it into a Markov decision process and model it as a multiagent reinforcement learning (MARL) problem. Then, we propose a malicious device detection (MDD) method based on MARL to select honest devices to participate in training and improve efficiency. In addition, we propose an alternative poisoned model detection (PMD) method considering model change consistency. This method aims to prevent poisoned models from being used in the model aggregation. Experimental results validate that under the random local model poisoning attack, the proposed MDD method can

关键词： Computational modeling Data models Training Servers Accuracy Analytical models Costs Energy consumption Predictive models Federated learning

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Ocean Swell Height Estimation From Spaceborne GNSS-R Data Using Tree Model Based Machine Learning Methods 13

Ocean Swell Height Estimation From Spaceborne GNSS-R Data Us...

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13th IEEE/OES Current, Waves and Turbulence Measurement, CWTM 2024

作者： Bu, Jinwei Wang, Qiulan Liu, Xinyu Li, Linghui Zhang, Yongfeng Huang, Weimin Kunming University of Science and Technology Faculty of Land Resources Engineering Kunming China Memorial University of Newfoundland Department of Electrical and Computer Engineering St. John's Canada

ISBN: (纸本)9798350393910

Global navigation satellite system-reflectometry (GNSS-R), as an emerging observation method, has recently been applied to the retrieval of swell height. Existing research typically uses feature observables extracted from delay Doppler maps (DDMs) to construct empirical geophysical model functions (GMFs) for estimating swell height. However, using multiple variable parameters as model inputs poses significant challenges. For this purpose, we study the development of a multivariate regression model for swell height estimation using spaceborne GNSS-R data from the CYGNSS satellite and machine learning methods based on tree models (i.e. Random Forest (RF), Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)). To test the performance of the proposed model, ERA5 and Wavewatch III (WW3) data were used as reference data. The results showed that when ERA5 was used as the reference data, the proposed RF model performed the best among the five models, with root mean square error (RMSE) and correlation coefficient (CC) being better than 0.411 m and 0.83, respectively. When WW3 was used as reference, the RMSE and CC are better than 0.496 m and 0.84 m, respectively. The method proposed in this article provides a new approach for inverting global swell heights using CYGNSS data. © 2024 IEEE.

关键词： Decision trees

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Cloud-Based Anomaly Detection for Broken Rail Track Using LSTM Autoencoders and Cross-modal Audio Analysis 1

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Rath, Smita Upadhyay, Hans Prakash, Somya Raja, Harsh Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India Department of Electrical and Electronics Engineering Siksha O Anusandhan Deemed To Be University Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha O Anusandhan Deemed To Be University Odisha Bhubaneswar India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

In an era driven by technological evolution, this paper embarks on an unprecedented journey to revolutionize rail track anomaly detection. Amidst the bustling world of transportation, our innovative approach harnesses the synergy of cloud-based processing, audio analysis, and Mel-frequency cepstral coefficients (MFCC) extraction to unveil the previously hidden secrets of rail track conditions. The hallmark of our method lies in its dynamic fusion of intricate components. It transcends traditional boundaries and converts audio data into spectrograms through the short-time Fourier transform (STFT). This visual tapestry of frequencies unfolds a tale of spectral evolution across time, acting as the precursor to the essence of rail track anomalies. The proposed method commences with the conversion of audio data into spectrograms using short-time Fourier transform (STFT), facilitating the visualization of frequency content changes over time. Through this, the study computes MFCC features by first calculating Mel frequencies and subsequently deriving coefficients using cosine functions. Embodying key spectral characteristics, these features are then stored and standardized within a data frame. Enter cloud computing—a celestial realm of limitless computational prowess. Our approach transcends conventional confines by fusing the cloud's scalable might with the agility of audio analysis. Massive datasets unravel effortlessly, invoking a novel era of real-time rail track surveillance. Machine learning models, nurtured by standardized MFCCs, become vigilant sentinels against anomalies, forging a vigilant shield of safety. With experimental applause, our approach emerges victorious. A parade of metrics—accuracy, precision, recall, the cadence of F-score, and the majestic ROC curve—testify to the unparalleled prowess of our method. Anomalies that once whispered are now boisterously identified, etching a new chapter in rail track safety. In summary, this paper pioneers an innovati

关键词： Anomaly detection

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Portable Embedded Deployment of 5G Standalone (SA) System with Software-Defined Radio 28

Portable Embedded Deployment of 5G Standalone (SA) System wi...

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28. VDE ITG-Fachtagung Mobilkommunikation: Technologien und Anwendungen - 28th VDE ITG Conference on Mobile Communication: Technologies and Applications

作者： John, Fabian Hauschild, Sebastian Hellbrück, Horst University of Applied Sciences Lübeck – Technische Hochschule Lübeck Germany Department of Electrical Engineering and Computer Science Center of Excellence CoSA Germany

ISBN: (纸本)9783800763832

We present a low-cost 5G-SA system based on edge-computing hardware with open-source components and off-the-shelf hardware in this paper. Our system is suitable for nomadic and ad-hoc 5G-SA use cases that require low-power consumption and small space. We show a complete development covering how to prepare the operating system for the installation of the 5G next-generation node B (gNB) and how to install and deploy the 5G-SA software components. We evaluate the performance, indoor coverage range, and power consumption of our system with measurements. We show that our system achieves acceptable data rates, and is very space and energy-efficient with an average power consumption of ≈ 24.81 W. We provide detailed and maintained documentation on the deployment of our system and discuss possible extensions for future work. © 2024 Mobilkommunikation: Technologien und Anwendungen - 28. VDE ITG-Fachtagung. All rights reserved.

关键词： Software radio

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ECHO: An Approach to Enhance Use Case Quality Exploiting Large Language Models 49

ECHO: An Approach to Enhance Use Case Quality Exploiting Lar...

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49th Euromicro Conference on Software engineering and Advanced Applications, SEAA 2023

作者： De Vito, Gabriele Palomba, Fabio Gravino, Carmine Di Martino, Sergio Ferrucci, Filomena Lab Department of Computer Science Italy University of Naples "federico Ii" Department of Electrical Engineering and Information Technologies Italy

ISBN: (纸本)9798350342352

UML use cases are commonly used in software engineering to specify the functional requirements of a system since they are an effective tool for interacting with stakeholders thanks to the use of natural languages. However, producing high-quality use cases can be challenging due to the lack of precise guidelines and suitable tools. This can lead to problems, e.g. inaccuracy and incompleteness, in the derived software artifacts and the final product. Recent advancements in Natural Language Processing and Large Language Models (LLMs) can provide the premises for developing tools supporting activities based on natural languages. In this paper, we propose ECHO, a novel approach for supporting software engineers in enhancing the quality of UML use cases using LLMs. Our approach consists of a co-prompt engineering approach and an iterative and interactive process with the LLM to improve the quality of use cases, based on practitioners' feedback. To prove the feasibility of the proposal, we instantiated the approach using ChatGPT and performed a controlled experiment to assess its effectiveness by involving seven software engineering professionals. Three were part of the experimental group and used ECHO to improve the quality of the use cases. Three others were the control group and enhanced the quality of use cases manually. Finally, the last participant acted as an oracle, blind w.r.t. the groups, and evaluated the quality of the enhanced use cases, both qualitatively by means of a questionnaire, and quantitatively, by means of the Use Case Points metric. Results show that ECHO can effectively support software engineers to improve use cases' quality thanks to the prompts suitably designed to interact with ChatGPT. © 2023 IEEE.

关键词： Software engineering

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