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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

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Stochastic Planning of Solar PV/Wind Turbine/Micro- Hydro/Biomass Microgrids Integrating Fuel Cell Electric Vehicles 11

Stochastic Planning of Solar PV/Wind Turbine/Micro- Hydro/Bi...

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11th International Conference on Smart Grid and Clean Energy Technologies, ICSGCE 2023

作者： Mohseni, Soheil Brent, Alan C School of Engineering and Computer Science Victoria University of Wellington Sustainable Energy Systems Wellington Faculty of Engineering Wellington6140 New Zealand

ISBN: (纸本)9798350305364

This paper presents a novel approach for stochastic planning of multi-dimensional microgrids (MGs) integrating solar photovoltaic panels, wind turbines, a micro-hydro power plant, a biomass power plant, battery storage, a super-capacitor bank, hydrogen storage, and fuel cell electric vehicles (FCEVs). More specifically, the paper introduces a stochastic planning framework that incorporates uncertainties in renewable energy generation, energy demand, and wholesale electricity price dynamics. An advanced metaheuristic optimization algorithm is applied to optimize the MG planning, considering the stochastic nature of renewable energy sources and dynamic component interactions. The proposed approach aims to improve the reliability and robustness of MGs under uncertain conditions. The performance of the proposed approach is evaluated through simulation studies and compared with standard deterministic methods to demonstrate its effectiveness in addressing uncertainties whilst optimizing multi-dimensional MGs. Importantly, the results indicate percentage changes of -22%, +4%, and +23% in the whole-life system cost in the best-case, most likely case, and worst-case stochastic scenarios compared to the deterministic methods. © 2023 IEEE.

关键词： Hydroelectric power plants

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Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

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computer systems science & engineering 2023年第9期46卷 2651-2666页

作者： A.M.Hafiz M.Hassaballah Abdullah Alqahtani Shtwai Alsubai Mohamed Abdel Hameed Department of Electronics&Communication Engineering Institute of TechnologyUniversity of KashmirSrinagarJ&K190006India Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkhrjKSA Department of Computer Science Faculty of Computers and InformationLuxor UniversityLuxorEgypt

With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world *** the scope of this area,various techniques are found in the *** such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each *** learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully *** more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance *** a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL *** uses multiple binary action DQNs having shared state and *** benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based *** approach can potentially be extended to any type of DQN by forming its *** extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent *** proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.

关键词： Deep Q-networks ensemble learning reinforcement learning OpenAI Gym environments

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Answer Code Validation Program with Test Data Generation for Code Writing Problem in Java Programming Learning Assistant System

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engineering Letters 2024年第5期32卷 981-994页

作者： Wai, Khaing Hsu Funabiki, Nobuo Aung, Soe Thandar Lu, Xiqin Jing, Yanhui Kyaw, Htoo Htoo Sandi Kao, Wen-Chung Department of Information and Communication Systems Okayama University Okayama Japan Department of Computer and Information Science Tokyo University of Agriculture and Technology Tokyo Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

In order to support the learning of novice students in Java programming, the web-based Java Programming Learning Assistant System (JPLAS) has been developed. JPLAS offers several types of exercise problems to foster code reading and writing skills at different levels. In JPLAS, the code writing problem (CWP) asks a student to write a source code that will pass the test code given in the assignment where the correctness is verified by running them on JUnit. In this paper, to reduce the teacher’s workload during the marking process, we present the answer code validation program that verifies all the source codes from a large number of students for each assignment and reports the number of passing tests for each source code in the CSV file. Besides, to test a source code with various input data, we implement the test data generation algorithm that identifies the data type, generates new data, and replaces it for each test data in the test code. Furthermore, to verify the correctness of the implemented procedures in the source code, we introduce the intermediate state testing in the test code. For evaluations, we applied the proposal to source codes and test codes in a Java programming course in Okayama university, Japan, and confirmed the validity and effectiveness. © 2024, International Association of Engineers. All rights reserved.

关键词： Students

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Comparative Analysis of HVAC and HVDC Green Corridors for Photovoltaic Integration Overcoming Geographical Constraints 8

Comparative Analysis of HVAC and HVDC Green Corridors for Ph...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Saadeh, Mahmood Saadeh, Osama Sba, Baher Abu Hamdan, Mohammad A. Dalala, Zakariya Department of Electrical Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Energy Engineering Department German Jordanian University Amman Jordan Electrical Engineering Department Faculty of Engineering and Technology Applied Science Private University Amman Jordan Energy Systems Engineering Department College of Engineering Energy Abdullah Al Salem University Khaldiya Kuwait

ISBN: (纸本)9798350382150

The optimal integration of photovoltaic (PV) systems into existing power grids is a complex issue. While geographical constraints have traditionally posed challenges to optimal PV integration for power system planners and operators, innovative strategies can turn challenges into opportunities. This study takes the IEEE 5-bus system as a case study, introducing an illustrative geographical constraint by allowing a PV installation only near Bus Two. From this constrained scenario, the research investigates broader integration strategies employing the concept of "green corridors" – dedicated transmission lines that facilitates the efficient transfer of renewable energy from areas with abundance of renewable energy to demand centers, bypassing the immediate geographical limitations. This research evaluates a direct PV connection to Bus Two and contrasts it with eight alternative scenarios. In these scenarios, the PV system, while physically near Bus Two, is electrically connected to each of the other buses in the system. For each bus connection beyond Bus Two, two integration methods are investigated. Once through an HVAC transmission line green corridor and again using an HVDC transmission line green corridor. Both LCC-HVDC and VSC-HVDC transmission technologies have been considered for the HVDC green corridor. The primary objective is to determine the overall power system losses associated with each integration method, and for each configuration. By overcoming geographical constraints, green corridors allow power system planners and operators a higher degree of flexibility when integrating renewable energy systems into power grids. ©2024 IEEE.

关键词： Renewable energy

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Optimal Deep Belief Network Enabled Malware Detection and Classification Model

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Intelligent Automation & Soft Computing 2023年第3期35卷 3349-3364页

作者： P.Pandi Chandran N.Hema Rajini M.Jeyakarthic Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of CSE Alagappa Chettiar Government College of Engineering and TechnologyKaraikudi630003India Department of Computer and Information Science Annamalai UniversityChidambaram608002India

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily *** Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading *** recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify *** this motivation,this study focuses on the design of mayﬂy optimization with a deep belief network for PDF malware detection and classiﬁcation(MFODBN-MDC)*** major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the *** proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature *** addition,Adamax optimizer with the DBN model is used for PDF malware detection and classiﬁ*** design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-ﬁcation demonstrates the novelty of the *** demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various *** comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

关键词： PDF malware data classiﬁcation security deep learning feature selection metaheuristics

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Crop Yield Prediction Using Multimodal Meta-Transformer and Temporal Graph Neural Networks

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 545-553页

作者： Sarkar, Somrita Dey, Anamika Pradhan, Ritam Sarkar, Upendra Mohan Chatterjee, Chandranath Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Industrial and Systems Engineering Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Agricultural and Food Engineering Kharagpur721302 India Indian Institute of Technology Patna Department of Computer Science and Engineering Patna801106 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red-green-blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction. © 2024 IEEE.

关键词： Distribution transformers

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Analysis of the Possibility of Using Eye-tracking in Evaluating the Work of an Airport Security Control Operator

Analysis of the Possibility of Using Eye-tracking in Evaluat...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Kierzkowski, A. Kisiel, T. Mardeusz, E. Ryczyński, J. Department of Technical Systems Operation and Maintenance Faculty of Mechanical Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9798350386097

The security screening system is inextricably linked to the human factor. In the current systems at airports, it is not verified that an operator marks an alarm haphazardly when he evaluates an X-ray scan image of items carried in air transportation. This condition can distort the actual value of screening operators' evaluation indicators. The article presents the preliminary results of ongoing research on the implementation of eye-tracking technology for evaluating the performance of airport screening operators. The research focuses on using eye-tracking technology to track eye focus points. The results of the research indicate that there is an error when pointing the eye to a reference location. The results presented in the article are a prelude to further work on estimating the level of error that should have been considered in the security screening system. © 2024 IEEE.

关键词： Airport security

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Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

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computers, Materials & Continua 2023年第1期74卷 897-914页

作者： Sandeep Kumar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdul Khader Jilani Saudagar Abdullah AlTameem Mohammed AlKhathami Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Social media,like Twitter,is a data repository,and people exchange views on global issues like the COVID-19 *** media has been shown to influence the low acceptance of *** work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to *** work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 *** paper introduces a sigmoidal particle swarm optimization(SPSO)***,the performance of SPSO is measured on a set of 12 benchmark problems,and later it is deployed for selecting optimal text features and categorizing *** proposed method uses TextBlob and VADER for sentiment analysis,CountVectorizer,and term frequency-inverse document frequency(TF-IDF)vectorizer for feature extraction,followed by SPSO-based feature *** Covid-19 vaccination tweets dataset was created and used for training,validating,and *** proposed approach outperformed considered algorithms in terms of ***,we augmented the newly created dataset to make it balanced to increase performance.A classical support vector machine(SVM)gives better accuracy for the augmented dataset without a feature selection *** shows that augmentation improves the overall accuracy of tweet *** the augmentation performance of PSO and SPSO is improved by almost 7%and 5%,respectively,it is observed that simple SVMwith 10-fold cross-validation significantly improved compared to the primary dataset.

关键词： Twitter data analysis sentiment analysis social media analytics swarm intelligence COVID-19 vaccine

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Simulation of a Pick and Place System for Electronic Cards Using a YuMi Cobot 10

Simulation of a Pick and Place System for Electronic Cards U...

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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Sze, Derrick L. Reyes, Rosula S.J. Abu, Patricia Angela R. Computer and Communications Engineering Department of Electronics United States Ateneo de Manila University Department of Information Systems and Computer Science Quezon City Philippines

ISBN: (纸本)9798350394245

Collaborative Robots are one of the main drivers of Industry 4.0, which started as a vision focusing on industrial production. It addresses several challenges in the current manufacturing industry such as performing repetitive work and requiring highly skilled workers. The goal of the research is to be able to simulate a pick and place environment with electronic cards using a YuMi cobot and mobile platforms in Coppeliasim. The mobile robot is responsible for transporting the electronic cards to the target location through path planning implemented using the OMPL plug-in. After arriving at the target location, YuMi will then perform the pick and place task from vertically stacked cards through inverse kinematics. Use cases were considered on the chosen mobile platform and robotic arms to determine the capabilities and limitations of the hardware involved. In addition, the total power consumption was obtained for energy assessment when the setup is performed in a real-world setting. For this particular setup, the fourth joint starting from the base consumed the most power and is therefore the primary target for optimization. © 2024 IEEE.

关键词： Motion planning

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