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Dynamic Programming-Based Optimal Charging Scheduling for Electric Vehicles

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Dynamic Programming-Based Optimal Charging Scheduling for El...

IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Kuan Li Ying Zhang Chenglie Du Tao You Lu Bai Jiaming Wu School of Software Northwestern Polytechnical University Xi’an China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Architecture and Civil Engineering Chalmers University of Technology Gothenburg Sweden

The charging scheduling of electric vehicles not only affects the operation of the grid system, but also affects the charging cost of vehicle users. This paper studies the charging scheduling problem of electric vehicles from the user’s perspective by considering the grid’s as well as the user’s needs. On the basis of real-time electricity prices, an electric vehicle charging scheduling system aiming at the lowest charging cost is designed and an optimal charging scheduling method based on dynamic programming is established. In order to reduce the unnecessary computational expenses, this paper optimizes the dynamic programming algorithm, and defines the state transition limit of the dynamic programming search space according to the actual constraints, which reduces the computational complexity. The simulation results show that, on the premise of satisfying the power constraints and user charging demands, the electric vehicles charging scheduling system can effectively reduce the charging cost and time. In addition, the charging process has little impact on the power system from the perspective of grid fluctuation.

关键词： Fuzzy logic Vehicle-to-grid Costs Fluctuations Processor scheduling Simulation Dynamic scheduling

Design of Subsea storage tanks for Arctic conditions - heat treatment of materials

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Materials Today: Proceedings 2023年

作者： Kishor Tirumanur Shanmugavelu, Arvind Muraliraja, R. Shanmugam, Ragavanantham Pal Singh Pawar, Mayur Vishwakarma, Rajoo Sarkar, Proshanta Department of Naval Architecture and Offshore Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 Tamil Nadu India Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 Tamil Nadu India Advanced Manufacturing Engineering Technology Navajo Technical University NM 87313 United States Department of Civil Engineering IES College of Technology Bhopal 462044 Madhya Pradesh India Department of Mechanical Engineering Suyash Institute of Information Technology Gorakhpur 273016 Uttar Pradesh India Department of Computer Science Kalinga University Naya Raipur 492101 Chhattisgarh India

The Arctic region contains a plethora of recoverable hydrocarbon wealth in the form of oil and gas in existing conventional wells and other undiscovered fields. The main challenges faced in the Arctic region are both environmental and operational. Some environmental challenges are low ambient temperature, winterization, severity and unpredictability of the weather. However, the issue of multi-phase flow in the current operation coupled with the decline of the reservoir pressure are the major concerns in this gas field. In the early stages of field life, the reservoir pressure is sufficient to allow the natural flow from reservoir formation to surface without additional supporting compression. Unfortunately, the pressure naturally declines throughout the entire life cycle of the field. To achieve a better production profile in the arctic region the idea is to implement a Subsea processing concept employing a Subsea Storage Tank (SST) in order to maximize production from additional infield wells, introducing a novel step in this field development phase that will change the entire oil and gas industry especially for subsea processing field architecture. The incorporation of SST in the subsea system will enhance the production rate as well as it avoids the transportation problems through pipe lines. By this work, the production rate is increased by 50% and decrease the oil spill accidents considerably. © 2023 Elsevier Ltd. All rights reserved.

关键词： Heat transfer

A Hybrid Intrusion Detection System Approach Using PCA and SMOTE with Advanced Ensemble Models

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A Hybrid Intrusion Detection System Approach Using PCA and S...

International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Khaled Mahmud Sujon Nusrat Jahan Someyo Kamal Utsho Mahadi Hasan Shuvo Md Najmus Sakib Razin Department of Software Engineering Universiti Teknologi Malaysia (UTM) Johor Bahru Johor Malaysia Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Software Engineering Universiti Teknologi Malaysia Johor Bahru Johor Malaysia Department of Architecture American International University-Bangladesh Dhaka Bangladesh Department of Electrical & Electronics Engineering Universiti Teknologi Malaysia Johor Bahru Johor Malaysia

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A persistent issue of class imbalance in intrusion detection systems (IDS) is addressed in this research, particularly in the context of cyber-physical systems and IoT devices. Current IDS approaches often struggle to detect rare attack patterns due to the dominance of normal traffic in imbalanced datasets. To address this, we present a unique strategy that combines Principal Component Analysis (PCA) for selecting features with the Synthetic Minority Oversampling Technique (SMOTE) to deal with class imbalance, applied to four powerful classifiers: Random Forest (RF), Decision Trees (DT), XGBoost (XGB), and AdaBoost. By reducing the feature space with PCA and balancing the dataset with SMOTE, our methodology significantly enhances model performance. Evaluating on the KDDCup ’99 dataset, we observe notable improvements: for instance, Random Forest achieves 96.42% accuracy, 98.52% precision, 94.68% recall 96.55% F1-score, and 93.87% MCC. Similarly, XGBoost achieves 94.78% accuracy, 96.72% precision, 92.94% recall, 94.81% F1-score, and 92.36% MCC. These results demonstrate that the integration of SMOTE and PCA effectively addresses class imbalance and enhances the detection of minority class attacks. Our findings contribute to the advancement of machine learning techniques in IDS and provide a foundation for further research into combining oversampling methods and dimensionality reduction for robust cybersecurity solutions. Future work will focus on exploring alternative oversampling methods, incorporating deep learning models, and assessing the practical deployment of the proposed methodology in real-time network environments.

关键词： Dimensionality reduction Deep learning Accuracy Computational modeling Intrusion detection Real-time systems Decision trees Random forests Principal component analysis Testing

Classification of Malicious Code Based on Grayscale Texture Fingerprint 7

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Classification of Malicious Code Based on Grayscale Texture ...

7th International Conference on Information science and Control Engineering, ICISCE 2020

作者： Li, Jun Liu, Shengao Niu, Yan Zhang, Hui Hubei University of Technology Department of Computer Science Wuhan China Hubei University of Technology School of Civil Engineering Architecture and Environment Wuhan China

ISBN: (纸本)9781728164069

Traditional malicious code classification technology faces a challenge to do with increasingly deformed malicious code, whose problems include having low detection rate and being time-consuming. In order to solve the above-mentioned problems, the traditional malicious code classification technology combines feature extraction of texture fingerprints on the image with the deep learning neural network classification method and proposes a malicious code classification method based on gray texture features. This method first converts the binary file of the malicious code into a grayscale image. It uses the grayscale co-occurrence matrix to extract the features in the malicious code, and the extracted features are in turn substituted into an improved neural network model. Classification training is then performed to reduce the probability of feature loss in the pooling layer. The experimental results indicate that the malicious code classification based on the above method have higher accuracy. Through the use of features extracted by the gray level co-occurrence matrix as output of the pooling layer, the learning time can also be effectively shortened hence making this method best for classification detection usage. © 2020 IEEE.

关键词： Textures

Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside"

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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： Robotics

Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey

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arXiv 2024年

作者： Sohail, Shahab Saquib Himeur, Yassine Kheddar, Hamza Amira, Abbes Fadli, Fodil Atalla, Shadi Copiaco, Abigail Mansoor, Wathiq School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates LSEA Laboratory Department of Electrical Engineering University of Medea 26000 Algeria Department of Computer Science University of Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning (DL). However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL), which decreases dependency and costs by utilizing knowledge gained from a source data/task in training a target data/task, has been widely investigated. Numerous DTL frameworks have been suggested for aligning point clouds obtained from several scans of the same scene. Additionally, DA, which is a subset of DTL, has been modified to enhance the point cloud data’s quality by dealing with noise and missing points. Ultimately, fine-tuning and DA approaches have demonstrated their effectiveness in addressing the distinct difficulties inherent in point cloud data. This paper presents the first review shedding light on this aspect. it provides a comprehensive overview of the latest techniques for understanding 3DPC using DTL and domain adaptation (DA). Accordingly, DTL’s background is first presented along with the datasets and evaluation metrics. A well-defined taxonomy is introduced, and detailed comparisons are presented, considering different aspects such as different knowledge transfer strategies, and performance. The paper covers various applications, such as 3DPC object detection, semantic labeling, segmentation, classification, registration, downsampling/upsampling, and denoising. Furthermore, the article discusses the advantages and limitations of the presented frameworks, identifies open challenges, and suggests potential research directions. © 2024, CC BY.

关键词： Object detection

DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias

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arXiv 2022年

作者： Zheng, Yu Gao, Chen Ding, Jingtao Yi, Lingling Jin, Depeng Li, Yong Wang, Meng Department of Electronic Engineering Tsinghua University Beijing China WeChat Technical Architecture Department Tencent Inc. Shenzhen China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the impact of bias to achieve unbiased recommendation. In this work, we focus on an essential bias in micro-video recommendation, duration bias. Specifically, existing micro-video recommender systems usually consider watch time as the most critical metric, which measures how long a user watches a video. Since videos with longer duration tend to have longer watch time, there exists a kind of duration bias, making longer videos tend to be recommended more against short videos. In this paper, we empirically show that commonly-used metrics are vulnerable to duration bias, making them NOT suitable for evaluating micro-video recommendation. To address it, we further propose an unbiased evaluation metric, called WTG (short for Watch Time Gain). Empirical results reveal that WTG can alleviate duration bias and better measure recommendation performance. Moreover, we design a simple yet effective model named DVR (short for Debiased Video Recommendation) that can provide unbiased recommendation of micro-videos with varying duration, and learn unbiased user preferences via adversarial learning. Extensive experiments based on two real-world datasets demonstrate that DVR successfully eliminates duration bias and significantly improves recommendation performance with over 30% relative progress. Codes and datasets are released at https://***/tsinghua-fib-lab/WTG-DVR. © 2022, CC BY.

关键词： Watches

Hyperspectral Image Denoising via Self-Modulating Convolutional Neural Networks

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arXiv 2023年

作者： Torun, Orhan Yuksel, Seniha Esen Erdem, Erkut Imamoglu, Nevrez Erdem, Aykut Hacettepe University Institute of Science Beytepe Ankara 06800 Turkey Hacettepe University Department of Electrical and Electronics Engineering Beytepe Ankara 06800 Turkey Hacettepe University Department of Computer Engineering Beytepe Ankara 06800 Turkey National Institute of Advanced Industrial Science and Technology Digital Architecture Research Center Tokyo135-0064 Japan Koç University Department of Computer Engineering Sarıyer Istanbul34450 Turkey Koç University Is Bank AI Center Sarıyer Istanbul34450 Turkey

Compared to natural images, hyperspectral images (HSIs) consist of a large number of bands, with each band capturing different spectral information from a certain wavelength, even some beyond the visible spectrum. These characteristics of HSIs make them highly effective for remote sensing applications. That said, the existing hyperspectral imaging devices introduce severe degradation in HSIs. Hence, hyperspectral image denoising has attracted lots of attention by the community lately. While recent deep HSI denoising methods have provided effective solutions, their performance under real-life complex noise remains suboptimal, as they lack adaptability to new data. To overcome these limitations, in our work, we introduce a self-modulating convolutional neural network which we refer to as SM-CNN, which utilizes correlated spectral and spatial information. At the core of the model lies a novel block, which we call spectral self-modulating residual block (SSMRB), that allows the network to transform the features in an adaptive manner based on the adjacent spectral data, enhancing the network’s ability to handle complex noise. In particular, the introduction of SSMRB transforms our denoising network into a dynamic network that adapts its predicted features while denoising every input HSI with respect to its spatio-spectral characteristics. Experimental analysis on both synthetic and real data shows that the proposed SM-CNN outperforms other state-of-the-art HSI denoising methods both quantitatively and qualitatively on public benchmark datasets. Our code will be available at https://***/orhan-t/SM-CNN. © 2023, CC BY-NC-ND.

关键词： Convolution