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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Guler, Naki Bagheri, Farzaneh Komurcugil, Hasan Bayhan, Sertac Gazi University Electrical and Electronic Engineering Technology Faculty Ankara Turkey Antalya Bilim University Department of Electrical and Electronic Engineering Antalya Turkey Eastern Mediterranean University Department of Computer Engineering FamagustaVia Mersin 10 Turkey Qatar Environment and Energy Research Institute Doha Qatar

ISBN: (纸本)9798350306262

This paper proposes a single-phase bidirectional battery charger which comprises a three-level F-type converter and a bidirectional dc-dc converter. The proposed charger is suitable for electric vehicles and battery energy storage systems. The bidirectional dc-dc converter is integrated into the system to prevent the charge current oscillations caused by double-line frequency voltage ripples of the F-type converter when operates as a single-phase rectifier. The F-type converter operates as rectifier and inverter during charging and discharging modes, respectively. The power flow of the battery is controlled with a bidirectional dc-dc converter. The current control of both F-type and bidirectional dc-dc converters is achieved by the model predictive control (MPC) method. The control strategy is designed by considering the power balance theory. For this purpose, the voltage of the common connection point of both converters (dc-link) is regulated. The grid current reference of the F-type converter is obtained by using a proportional-integral (PI) controller whose input is the dc-link voltage error. Thus, bidirectional power flow control is achieved. The effectiveness of the proposed power converter structure as well as the control strategy is investigated by simulation studies performed on MATLAB/Simulink environments. The results show that the proposed system is capable of controlling the grid and battery currents in both power directions. © 2024 IEEE.

关键词： Model predictive control

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Polarization Insensitive Broadband Zero Indexed Nano-Meta Absorber for Optical Region Applications

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computers, Materials & Continua 2022年第4期71卷 993-1009页

作者： Ismail Hossain Md Samsuzzaman Ahasanul Hoque Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mohammad Tariqul Islam Space Science Center(ANGKASA) Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityDumki8602PatuakhaliBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia

Broadband response metamaterial absorber(MMA)remains a challenge among researchers.A nanostructured new zero-indexed metamaterial(ZIM)absorber is presented in this study,constructed with a hexagonal shape resonator for optical region *** design consists of a resonator and dielectric layers made with tungsten and quartz(Fused).The proposed absorbent exhibits average absorption of more than 0.8972(89.72%)within the visible wavelength of 450–600 nm and nearly perfect absorption of 0.99(99%)at 461.61 *** on computational analysis,the proposed absorber can be characterized as *** developments of ZIM absorbers have demonstrated plasmonic resonance characteristics and a perfect impedance *** incidence obliquity in typically the range of 0◦–90◦both in TE and TM mode with maximum absorbance is more than 0.8972(∼89.72%),and up to 45◦angular stability is suitable for solar cell applications,like exploiting solar *** proposed structure prototype is designed and simulated by studying microwave technology numerical computer simulation(CST)*** finite integration technique(FIT)based simulator CST and finite element method(FEM)based simulator HFSS also helps validate the numerical data of the proposed ZIM *** proposed MMA design is appropriate for substantial absorption,wide-angle stability,absolute invisible layers,magnetic resonance imaging(MRI),color images,and thermal imaging applications.

关键词： Hexagonal resonator loaded star shaped polarization insensitive compact broadband metamaterial absorber optical region applications

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Algorithmic Trading Model for Stock Price Forecasting Integrating Forester with Golden Ratio Strategy 12

Algorithmic Trading Model for Stock Price Forecasting Integr...

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12th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2024

作者： Govindan, Niranjana Baskaran, Bharathi Azizan, Azizul Yakub, Fitri Dhamanti, Inge Dhaya, C. SRMIST Faculty of Engineering & Technology Department of Computing Technologies Kattankulathur Chennai India Faculty of Intelligence Kuala Lumpur Malaysia Malaysia-Japan International Institute of Technology UTM Electronic System Engineering Department Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia Adhiparasakthi Engineering College Department of Computer Science and Technology Melmaruvathur India

ISBN: (纸本)9798350340204

Algorithmic trading has emerged as a powerful tool for making data-driven trading decisions in financial markets. In this paper, we present an innovative algorithmic trading model for stock price forecasting that integrates forest-based machine learning algorithms with the Golden Ratio strategy. Our proposed approach aims to leverage the predictive power of machine learning while incorporating insights from technical analysis to enhance trading performance and profitability. The methodology involves data collection and preprocessing, feature engineering, model development, and performance evaluation. Historical stock price data, financial indicators, and sentiment analysis data are gathered and pre-processed to ensure data quality. Feature engineering techniques are applied to extract relevant features, and forest-based machine learning algorithms are trained on the data to forecast stock prices. The Golden Ratio strategy is integrated into the trading model to identify the entry point and exit point. Empirical validation and performance evaluation demonstrate the effectiveness and robustness of the proposed approach. Through back testing and simulation-based testing, we evaluate the model's accuracy, profitability, and risk-adjusted returns. Our findings contribute to the advancement of algorithmic trading research and practice, offering insights into the potential benefits of combining machine learning algorithms with established trading strategies. The proposed hybrid model incorporates Golden Ratio Strategy, integrating Random Forest (RF). The results of the proposed random forest with Golden Ratio Strategy are compared with the results of individual model. This comparison helps in proving the efficiency of the proposed model. © 2024 IEEE.

关键词： Adversarial machine learning

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Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第3期2卷 392-406页

作者： Li, Jinhao Wang, Changlong Zhang, Yanru Wang, Hao Monash University Department of Data Science and AI Faculty of IT and Monash Energy Institute MelbourneVIC3800 Australia Monash University Department of Civil Engineering MelbourneVIC3800 Australia University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

The battery energy storage system (BESS) has immense potential for enhancing grid reliability and security through its participation in the electricity market. BESS often seeks various revenue streams by taking part in multiple markets to unlock its full potential, but effective algorithms for joint-market participation under price uncertainties are insufficiently explored in the existing research. To bridge this gap, we develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services (FCAS) markets. Our approach leverages a transformer-based temporal feature extractor to effectively respond to price fluctuations in seven markets simultaneously and helps DRL learn the best BESS bidding strategy in joint-market participation. Additionally, unlike conventional 'black-box' DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS in the dynamic electricity market. We validate our method using realistic market prices from the Australian National Electricity Market. The results show that our strategy outperforms benchmarks, including both optimization-based and other DRL-based strategies, by substantial margins. Our findings further suggest that effective temporal-aware bidding can significantly increase profits in the spot and contingency FCAS markets compared to individual market participation. © 2023 IEEE.

关键词： Real time systems

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Single Block Encoder-Decoder Transformer Model for Multi-Step Traffic Flow Forecasting 6

Single Block Encoder-Decoder Transformer Model for Multi-Ste...

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6th IEEE Symposium on computers and Informatics, ISCI 2024

作者： Omar, Mas Yakub, Fitri Talip, Mohamad Sofian Abu Maslan, Mohd Nazmin Sinha, Vijay Kumar Muljono, Muljono Universiti Teknologi Malaysia Wee Laboratory Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Department of Electronic System Engineering Kuala Lumpur Malaysia University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Universiti Teknikal Malaysia Faculty of Industrial and Manufacturing Technology and Engineering Melaka Malaysia Chitkara University School of Engineering & Technology Department of Computer Science & Engineering Himachal Pradesh India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350353853

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this purpose, the potential of Transformer models remains underexplored. This is particularly true for the simplest form of a single block encoder-decoder Transformer model, which can be finely tuned through optimised hyperparameters. This paper examines the performance of a singular horizon-step forecasting method for multi-step traffic flow forecasting using a proposed Single Block Encoder-Decoder Transformer model optimised with a Grid Search algorithm. Results demonstrate that this model can enhance forecasting accuracy compared to the state-of-the-art LSTM model typically used for multi-step forecasting. The model effectively captures long-range temporal dependencies within a single road traffic flow dataset. It was tested on hourly traffic flow data to forecast the next 24 hours for the I5-North freeway in California, sourced from the Caltrans Performance Measurement System. The optimal configuration included an embedding dimension of 32, a feed-forward dimension of 128, and 8 attention heads. Results show a significant improvement, with a 4.7% reduction in Root Mean Squared Error compared to an LSTM model with two hidden layers of 100 neurons each, showcasing the potential of Single Block Encoder-Decoder Transformer models for real-world traffic prediction applications. © 2024 IEEE.

关键词： Mean square error

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A Deep and Systematic Review of the Intrusion Detection Systems Based on Machine Learning and Deep Learning Techniques 10

A Deep and Systematic Review of the Intrusion Detection Syst...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Guhan, N.K. Ravindran, Sindhu Ramachandran, M. Vijean, Vikneswaran Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Universiti Malaysia Perlis Kampus Alam UniMAP Faculty of Electronic Engineering & Technology Arau Pauh Putra Perlis Malaysia

ISBN: (纸本)9798350384369

Intrusion detection systems are the primitive elements in safeguarding vital infrastructures, which are able to identify malicious activity on hosts or networks. The efficient IDS must be able to handle a variety of threats is a difficult security cum threat related problems. Numerous strategies have been put out in the literature as of right now to increase intrusion detection efficiency while taking into account some of these restrictions, like mobility and resource limitations. This review paper discusses about the early research works on performed on IDS using Deep Learning and Machine Learning techniques. This paper provides a qualitative assessment of these methods and deeper insights about the analysis as well as a summary of the field's current themes and the methods used to train and implement IDSs. We address the overall limitations of these solutions, namely the types of attacks that these approaches are unable to identify and the setup limitations that are specific to each of these works, even though they offer insightful information and solutions for certain aspects of these constraints. © 2024 IEEE.

关键词： Network intrusion

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Binary Growth Optimizer: For Solving Feature Selection Optimization Problems 15th

Binary Growth Optimizer: For Solving Feature Selection Optim...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Chu, Shu-Chuan Dou, Zhi-Chao Pan, Jeng-Shyang Kong, Lingping Pan, Tien-Szu College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava Czech Republic Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung82445 Taiwan

ISBN: (纸本)9789819700677

The present study introduces a novel binary meta-heuristic optimizer, referred to as the Binary Growth Optimizer (BGO), which is specifically developed to address discrete optimization problems. The BGO utilizes transfer functions to convert positions in a continuous space to discrete positions. To assess the performance of the proposed algorithm, we conducted experiments on 14 publicly available datasets from the UCI Machine Learning Repository. We compared the BGO’s performance with seven state-of-the-art meta-heuristics. Based on convergence accuracy statistics, the results indicate that the BGO outperforms the seven compared meta-heuristics, providing the most favorable outcomes. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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Face image authentication scheme based on MTCNN and SLT

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Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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Leveraging Outage probability in system limited by BX fading and Co-channel interference for ChatGPT-enabled QoS adaptation 31

Leveraging Outage probability in system limited by BX fading...

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31st Telecommunications Forum, TELFOR 2023

作者： Vujović, Vuk Milić, Dejan Petrović, Nenad Marković, Filip Suljović, Suad Stefanović, Radiša MB University Belgrade Computer Science and Informatics Information Technology Belgrade Serbia University of Niš Telecommunications Faculty of Electronic Engineering Niš Serbia University of Niš Computer Science and Informatics Faculty of Electronic Engineering Niš Serbia University of Priština Computer Science and Informatics Faculty of Technical Sciences Kosovska Mitrovica Serbia Computer Science and Informatics the Academy of Applied Technical Studies Belgrade Belgrade Serbia

ISBN: (纸本)9798350303131

In this paper, we performed an analysis of the outage probability (Pout) at the output of the receiver with selection combination (SC) with L branches of the wireless communication system in the presence of Beaulieu-Xie (BX) fading and BX co-channel interference (CCI). The mathematical form of the function for Pout as a normalized signal-to-interference ratio (SIR) was derived, based on which a graph was drawn for different values of fading and CCI parameters. The second part of the paper considers applying machine learning approach relying on ChatGPT with aim to enable fading type recognition based on Pout measurements, which can be further used in order to perform Quality of Service (QoS) adaptation by tuning receiver's parameters. © 2023 IEEE.

关键词： Quality of service

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High-quality Arabidopsis thaliana Genome Assembly with Nanopore and HiFi Long Reads

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Genomics, Proteomics & Bioinformatics 2022年第1期20卷 4-13页

作者： Bo Wang Xiaofei Yang Yanyan Jia Yu Xu Peng Jia Ningxin Dang Songbo Wang Tun Xu Xixi Zhao Shenghan Gao Quanbin Dong Kai Ye MOE Key Laboratory for Intelligent Networks&Network Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China

Arabidopsis thaliana is an important and long-established model species for plant molecular biology,genetics,epigenetics,and ***,the latest version of reference genome still contains a significant number of missing ***,we reported a high-quality and almost complete Col-0 genome assembly with two gaps(named Col-XJTU)by combining the Oxford Nanopore Technologies ultra-long reads,Pacific Biosciences high-fidelity long reads,and Hi-C *** total genome assembly size is 133,725,193 bp,introducing 14.6 Mb of novel sequences compared to the TAIR10.1 reference *** five chromosomes of the Col-XJTU assembly are highly accurate with consensus quality(QV)scores>60(ranging from 62 to 68),which are higher than those of the TAIR10.1 reference(ranging from 45 to 52).We completely resolved chromosome(Chr)3 and Chr5 in a telomere-to-telomere ***4 was completely resolved except the nucleolar organizing regions,which comprise long repetitive DNA *** Chrl centromere(CEN1),reportedly around 9 Mb in length,is particularly challenging to assemble due to the presence of tens of thousands of CEN180 satellite *** the cutting-edge sequencing data and novel computational approaches,we assembled a 3.8-Mb-long CEN1 and a 3.5-Mb-long *** also investigated the structure and epigenetics of *** clusters of CEN180 monomers were detected,and the centromere-specific histone H3-like protein(CENH3)exhibited a strong preference for CEN180 Cluster ***,we observed hypomethylation patterns in CENH3-enriched *** believe that this high-quality genome assembly,Col-XJTU,would serve as a valuable reference to better understand the global pattern of centromeric polymorphisms,as well as the genetic and epigenetic features in plants.

关键词： Centromere architecture CENH3 Bacterial artificial chromosome Telomere-to-telomere Model plant

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