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arXiv 2023年

作者： Stokke, Jakob S. Mitra, Koondanibha Storvik, Erlend Both, Jakub W. Radu, Florin A. Center for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Norway Computational Mathematics Group Hasselt University Belgium Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway

Flow in variably saturated porous media is typically modelled by the Richards equation, a nonlinear elliptic-parabolic equation which is notoriously challenging to solve numerically. In this paper, we propose a robust and fast iterative solver for Richards’ equation. The solver relies on an adaptive switching algorithm, based on rigorously derived a posteriori indicators, between two linearization methods: L-scheme and Newton. Although a combined L-scheme/Newton strategy was introduced previously in [1], here, for the first time we propose a reliable and robust criteria for switching between these schemes. The performance of the solver, which can be in principle applied to any spatial discretization and linearization methods, is illustrated through several numerical *** Codes 65M12, 65J08, 65M50, 76Sxx © 2023, CC BY.

关键词： Nonlinear equations

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Effect of Particle Image Velocimetry processing on CT Dicom images in a deep learning based pipeline for lung nodule segmentation✱ 22

Effect of Particle Image Velocimetry processing on CT Dicom ...

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Proceedings of the Thirteenth Indian Conference on computer Vision, Graphics and Image Processing

作者： R Jenkin Suji W Wilfred Godfrey Dhar ABV Indian Institute of Information Technology and Management Gwalior IN Department of Computer Science and Engineering ABV Indian Institute of Information Technology and Management Gwalior IN Department of Applied Sciences ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9781450398220

Segmentation of lung nodules is critical to computer-aided diagnosis systems for lung cancer diagnosis. In recent times, with the application of deep learning in medical image processing, novel architectures have been proposed for segmentation. However, most of these architectures work on a single modality and work on a single image or whole image volume. In this work, we present Particle Image Velocimetry approach as a preprocessing technique by processing sequence of two input images. We used with and without PIV processed images on a deep learning-based pipeline to extract features from these images for candidate lung nodule detection. The results show that the use of particle image velocimetry processing significantly improves the performance of the deep learning pipeline.

关键词： CAD System

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CAPTAIN: A Testbed for Co-Simulation of Scalable Serverless Computing Environments for AIoT Enabled Predictive Maintenance in Industry 4.0

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IEEE Internet of Things Journal 2024年

作者： Golec, Muhammed Wu, Huaming Ozturac, Ridvan Parlikad, Ajith Kumar Cuadrado, Felix Gill, Sukhpal Singh Uhlig, Steve Queen Mary University of London School of Electronic Engineering and Computer Science United Kingdom Abdullah Gul University Kayseri Turkey Tianjin University Center for Applied Mathematics Tianjin China Trendyol Group Istanbul34485 Turkey University of Cambridge Institute for Manufacturing Department of Engineering Cambridge United Kingdom School of Telecommunications Engineering Madrid Spain

The massive amounts of data generated by the Industrial Internet of Things (IIoT) require considerable processing power, which increases carbon emissions and energy usage, and we need sustainable solutions to enable flexible manufacturing. Serverless computing shows potential for meeting this requirement by scaling idle containers to zero energy-efficiency and cost, but this will lead to a cold start delay. Most solutions rely on idle containers, which necessitates dynamic request time forecasting and container execution monitoring. Furthermore, Artificial Intelligence of Things (AIoT) can provide autonomous and sustainable solutions by combining IIoT with Artificial Intelligence (AI) to solve this problem. Therefore, we develop a new testbed, CAPTAIN, to facilitate AI-based co-simulation of scalable and flexible serverless computing in IIoT environments. The AI module in the CAPTAIN framework employs Random Forest (RF) and Light Gradient-Boosting Machine (LightGBM) models to optimize cold start frequency and prevent cold starts based on their prediction results. The proxy module additionally monitors the client-server network and constantly updates the AI module training dataset via a message queue. Finally, we evaluated the proxy module's performance using a predictive maintenance-based real-world IIoT application and the AI module's performance in a realistic serverless environment using a Microsoft Azure dataset. The AI module of the CAPTAIN outperforms baselines in terms of cold start frequency, computational time with 0.5 milliseconds, energy consumption with 1161.0 joules, and CO2 emissions with 32.25e-05 gCO2. The CAPTAIN testbed provides a co-simulation of sustainable and scalable serverless computing environments for AIoT-enabled predictive maintenance in Industry 4.0. © 2014 IEEE.

关键词： Testbeds

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Scalable Multi-Objective Optimization for Robust Traffic Signal Control in Uncertain Environments

arXiv

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arXiv 2024年

作者： Guo, Weian Li, Wuzhao Zhang, Zhiou Zhang, Lun Li, Li Li, Dongyang The Sino-German College of Applied Sciences Tongji University Shanghai China The Yongjia College Wenzhou Polytechnic Zhejiang Wenzhou China The School of Computer Science and Engineering University of New South Wales Sydney Australia The Department of Transportation Tongji University Shanghai China The Department of Electrics and Information Engineering Tongji University Shanghai China

Intelligent traffic signal control is essential to modern urban management, with important impacts on economic efficiency, environmental sustainability, and quality of daily life. However, in current decades, it continues to pose significant challenges in managing large-scale traffic networks, coordinating intersections, and ensuring robustness under uncertain traffic conditions. This paper presents a scalable multi-objective optimization approach for robust traffic signal control in dynamic and uncertain urban environments. A multi-objective optimization model is proposed in this paper, which incorporates stochastic variables and probabilistic traffic patterns to capture traffic flow dynamics and uncertainty. We propose an algorithm named Adaptive Hybrid Multi-Objective Optimization Algorithm (AHMOA), which addresses the uncertainties of city traffic, including network-wide signal coordination, fluctuating patterns, and environmental impacts. AHMOA simultaneously optimizes multiple objectives, such as average delay, network stability, and system robustness, while adapting to unpredictable changes in traffic. The algorithm combines evolutionary strategies with an adaptive mechanism to balance exploration and exploitation, and incorporates a memory-based evaluation mechanism to leverage historical traffic data. Simulations are conducted in different cities including Manhattan, Paris, São Paulo, and Istanbul. The experimental results demonstrate that AHMOA consistently outperforms several state-of-the-art algorithms and the algorithm is competent to provide scalable, robust Pareto optimal solutions for managing complex traffic systems under uncertain environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Stress-Relieving Video Game and Its Effects: A POMS Case Study

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Computational Intelligence and Neuroscience 2022年第0期2022卷 4239536页

作者： Ajmal, Abdullah Aldabbas, Hamza Amin, Rashid Ibrar, Sundas Alouffi, Bader Gheisari, Mehdi Department of Computer Science University of Engineering and Technology Taxila Pakistan Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Al-Salt Jordan Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science Islamic Azad University Tehran Iran

Stress is the response or a change in our bodies to environmental factors like challenges or demands that are physical and emotional. The main cause of stress is illnesses and it is gaining more interest, a hot topic for many researchers. Stress can be brought about by a wide range of normal life occasions that are hard to avoid. Stress generally refers to two things: first, the psychological perception of pressure and the body's response to it. On the other hand, it involves multiple systems, from metabolism to muscles to memory. Many methods and tools are being developed to reduce stress in humans. Stress can be a short-term issue or a long-term problem, depending on what changes in your life. The emphasis of this article is to reduce the effects of stress by developing a stress-releasing game and verifying its results through the Profile of Mood States (POMS) and POMS-2 survey. Games are associated with stress levels;hence, parameters like sounds, visuals, and colors associated with reducing stress are used to develop a game for the stress reduction in the players. The survey research aims to determine that the purpose-built game will affect the player's stress level using a reliable psychological survey paper. The survey collected a variety of information from its participants over six months. Different aspects of a person's psychology and reactions are recorded in this scenario by calculating the mean, standard deviation, degree of freedom, zero-error, and probability-value%. The POMS and POMS-2 results are obtained from the custom-built game, and these are found to be effective in reducing stress. © 2022 Abdullah Ajmal et al.

关键词： Surveys

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6-regular partitions: New combinatorial properties, congruences, and linear inequalities

arXiv

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arXiv 2023年

作者： Ballantine, Cristina Merca, Mircea Department of Mathematics and Computer Science College of the Holy Cross WorcesterMA01610 United States Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center University Politehnica of Bucharest BucharestRO-060042 Romania

We consider the number of the 6-regular partitions of n, b6(n), and give infinite families of congruences modulo 3 (in arithmetic progression) for b6(n). We also consider the number of the partitions of n into distinct parts not congruent to ±2 modulo 6, Q2(n), and investigate connections between b6(n) and Q2(n) providing new combinatorial interpretations for these partition functions. In this context, we discover new infinite families of linear inequalities involving Euler's partition function p(n). Infinite families of linear inequalities involving the 6-regular partition function b6(n) and the distinct partition function Q2(n) are proposed as open *** Codes 11P81, 11P82, 05A19, 05A20 © 2023, CC BY.

关键词： Combinatorial mathematics

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Data Preparation for Deep Learning based Code Smell Detection: A Systematic Literature Review

arXiv

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arXiv 2024年

作者： Zhang, Fengji Zhang, Zexian Keung, Jacky Wai Tang, Xiangru Yang, Zhen Yu, Xiao Hu, Wenhua Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Sanya Science and Education Innovation Park of Wuhan University of Technology Sanya China School of Engineering & Applied Science Yale University New Haven United States School of Computer Science and Technology Shandong University Tsingtao China Wuhan University of Technology Chongqing Research Institute Chongqing China

Code Smell Detection (CSD) plays a crucial role in improving software quality and maintainability. And Deep Learning (DL) techniques have emerged as a promising approach for CSD due to their superior performance. However, the effectiveness of DL-based CSD methods heavily relies on the quality of the training data. Despite its importance, little attention has been paid to analyzing the data preparation process. This systematic literature review analyzes the data preparation techniques used in DL-based CSD methods. We identify 36 relevant papers published by December 2023 and provide a thorough analysis of the critical considerations in constructing CSD datasets, including data requirements, collection, labeling, and cleaning. We also summarize seven primary challenges and corresponding solutions in the literature. Finally, we offer actionable recommendations for preparing and accessing high-quality CSD data, emphasizing the importance of data diversity, standardization, and accessibility. This survey provides valuable insights for researchers and practitioners to harness the full potential of DL techniques in CSD. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Detection of Magnetite Rust Using Circular Microstrip Patch Antenna-based Chipless RFID Sensor

Detection of Magnetite Rust Using Circular Microstrip Patch ...

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International Electrical engineering Congress (iEECON)

作者： Sirisak Nongpromma Suwan Janin Kamol Kaemarungsi Akkarat Boonpoonga Nonchanutt Chudpooti Lakkhana Bannawat Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Engineering and Technology Faculty of Engineering Rajamangala University of Technology Lanna Lampang Lampang Thailand National Science and Technology Development Agency National Electronics and Computer Technology Center Pathumthani Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Computer Engineering Faculty of Engineering Rajamangala University of Technology Rattanakosin Nakhon Pathom Thailand

In this paper, detection of the magnetite rust is presented by using a chipless RFID sensor based on a circular microstrip patch antenna (CMPA). The sensor is designed at the resonant frequency of 4.89 GHz by using a single-layer PCB with its dielectric constant of 4.3. Simulations were conducted to verify that the designed sensor can detect the magnetite rust through shifted resonant frequency. In simulations, the sensor was mounted on the iron with its magnetite rust. The width and depth of iron rust were varied in order to investigate the effect of iron rust on the resonant frequency. Simulation results have been shown that the proposed chipless RFID sensor and RCS’ can be utilized to detect the effect of the iron rust.

关键词： Radar cross-sections Patch antennas Simulation Magnetic resonance Microstrip antennas Radar antennas Iron

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Impact of Techno-Economic Parameters on Optimal Sizing of EV Fast Charging Stations

Impact of Techno-Economic Parameters on Optimal Sizing of EV...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Anindita Golder Sheldon S. Williamson Department of Electrical and Computer Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

As the adoption for electric vehicles (EV) increases, the need for proper charging infrastructure also increases. As such, it is necessary to ensure proper charging infrastructure to meet the charging demand. Hence, studies concerning the planning of EV charging stations which include the sizing of distributed energy resources (DERs) in the station to reduce the peak demand on the grid. In this paper, EV charging stations were designed keeping in mind factors such as the EV charging load profile (on demand charging versus deferred charging), uncertainties in the load profile as well as the tariff rates. A sensitivity analysis was also carried out in order to examine the impact of several techno-economic parameters on the overall cost of the system.

关键词： Vehicle-to-grid Costs Uncertainty Sensitivity analysis Tariffs Vehicular ad hoc networks Transportation

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Ransomware Attacks Detection Methodology to Protect IoT-Enabled Critical Infrastructures

Ransomware Attacks Detection Methodology to Protect IoT-Enab...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Amit Kumar Mishra Mohammad S. Obaidat Harshit Bhajpai Pranjal Trivedi Mohammad Wazid D. P. Singh Joel J. P. C. Rodrigues Balqies Sadoun Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Instituto de Telecomunições Covilhã Portugal College of Engineering Al- Balqa' Applied University Al-Salt Jordan

Critical infrastructure is a collection of physical and cyber systems, which are essentially required to support the day-to-day operations of our daily life. In the critical infrastructure, the computing systems (i.e., Internet of Things (loT) devices) communicate through the Internet. Therefore, most of the time, critical infrastructures are targeted by hackers by launching some cyber-attack, i.e., ransomware. Hence we require some security mechanisms to protect the data and systems of critical infrastructures. This paper proposes a scheme for the detection, analysis and mitigation of ransomware attacks to protect Internet of Things (loT)-enabled critical infrastructure (in short, RADM-ICI). We also practically demonstrated RADM-ICI and computed essential performance parameters, i.e., accuracy and F1-score under different machine learning models. The conducted security analysis of RADM-ICI proved its excellent security for the ransom ware attacks. During the performance comparison of the proposed RADM-ICI and other similar competing existing schemes, it has been observed that the proposed RADM-ICI achieved better accuracy than the other existing competing schemes.

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