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3rd Information technology to Enhance e-Learning and Other Application, IT-ELA 2022

作者： Haji, Lailan M Zeebaree, Subhi R. M. Ageed, Zainab Salih Ahmed, Omar M. Sadeeq, Mohammed A. M. Shukur, Hanan M. University of Zakho Faculty of Science Computer Science Dep. Duhok Iraq Technical College of Engineering Duhok Polytechnic University Energy Eng. Dept. Duhok Iraq College of Science Nawroz University Computer Science Dept. Duhok Iraq Zakho Technical Institute Duhok Polytechnic University Information Technology Dept. Duhok Iraq Technical College of Administration Duhok Polytechnic University ITM Dept. Duhok Iraq Al-Kitab University Computer Engineering Dept. Kirkuk Iraq

ISBN: (纸本)9798350334968

The field of research known as 'parallel processing' examines architectural and algorithmic methods for increasing the efficiency and other desirable qualities (such as cost-effectiveness and reliability) of computers by exploiting many forms of concurrency. The development of multi-core CPUs has opened the door to novel strategies for boosting system performance via parallelism. Increasing the number of concurrent threads greatly affects the time cost. While increased parallelism might reduce calculation time, the associated synchronization costs would likely be prohibitive for most applications. In this article, we'll go through the first half of the process, which involves gathering requirements and designing a prototype;the second half, which involves monitoring and managing a program that can run on a wide range of multicore system architectures, including those with (2, 4, and 8 CPUs). A shared-memory parallel processing method was proposed and built to show off the system's full potential. The implications of multi-core systems on overall program execution time and parallel processing in shared-memory systems provide the backbone of this investigation. © 2022 IEEE.

关键词： Cost effectiveness

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Dynamic Programming to Solve the Optimal Production Model

Dynamic Programming to Solve the Optimal Production Model

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Applied Physics and Computing (ICAPC), International Conference on

作者： Xiao Chen Pengbei Zhang Zeming Zhen Wenqian Liang Baijie Deng Computer Engineering Technical College Guangdong Institute of Science and Technology Zhuhai China School of International Cooperation Guangdong Institute of Science and Technology Zhuhai China

This article mainly investigates the demand for small batch materials. It establishes a multi-objective dynamic programming model aimed at maximizing service level, minimizing inventory, and shortages by listing corre...

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DiffusionREC: Diffusion Model with Adaptive Condition for Referring Expression Comprehension 39

DiffusionREC: Diffusion Model with Adaptive Condition for Re...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ke, Jingcheng Wong, Waikeung Wang, Jia Li, Mu Fei, Lunke Wen, Jie School of Computer Science and Technology Guangdong University of Technology GuangZhou China School of Fashion and Textiles The Hong Kong Polytechnic University Hong Kong College of Medical Information Engineering Guangdong Pharmaceutical University China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)157735897X

The objective of referring expression comprehension (REC) is to accurately identify the object in an image described by a given expression. Existing REC methods, including transformer-based and graph-based approaches among others, have shown robust performance in REC tasks. In this study, we present a groundbreaking framework named DiffusionREC for REC task. This framework reimagines the REC task as a text-guided bounding box denoising diffusion process, through which noisy bounding boxes are refined and distilled to pinpoint the target box. Throughout the training process, the bounding box of the target object diffuses from its ground-truth position towards a random distribution. Simultaneously, a filtering-based object decoder is introduced to reverse this diffusion of noise, conditional on the provided expression, the result from previous denoised step and the interaction between the expression and the image. At the inference stage, we begin by randomly generating a collection of boxes. Subsequently, the filtering-based object decoder is iteratively employed to refine and prune these bounding boxes, taking into account aforementioned conditions. Extensive experiments on five datasets show that DiffusionREC not only effectively addresses the limitations of existing REC methods but also surpasses them, delivering superior performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Efficient Photothermal Catalytic Oxidation Enabled by Three-Dimensional Nanochannel Substrates

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Environmental science and technology 2024年第11期58卷 5153-5161页

作者： Li, Yifei Zhang, Qianpeng Chong, Yanan Huang, Wei-Hsiang Chen, Chi-Liang Jin, Xiaojing Chen, Guangxu Fan, Zhiyong Qiu, Yongcai Ye, Daiqi School of Environment and Energy State Key Laboratory of Luminescent Materials and Devices Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control South China University of Technology Guangdong Guangzhou510000 China State Key Laboratory of Photovoltaic Science and Technology Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai200433 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong National Synchrotron Radiation Research Center 101 Hsin-Ann Road Hsinchu Science Park Hsinchu30076 Taiwan Taipei10607 Taiwan College of Light Chemical Industry and Materials Engineering Shunde Polytechnic Foshan528333 China

Photothermal catalysis exhibits promising prospects to overcome the shortcomings of high-energy consumption of traditional thermal catalysis and the low efficiency of photocatalysis. However, there is still a challenge to develop catalysts with outstanding light absorption capability and photothermal conversion efficiency for the degradation of atmospheric pollutants. Herein, we introduced the Co3O4 layer and Pt nanoclusters into the three-dimensional (3D) porous membrane through the atomic layer deposition (ALD) technique, leading to a Pt/Co3O4/AAO monolithic catalyst. The 3D ordered nanochannel structure can significantly enhance the solar absorption capacity through the light-trapping effect. Therefore, the embedded Pt/Co3O4 catalyst can be rapidly heated and the O2 adsorbed on the Pt clusters can be activated to generate sufficient O2- species, exhibiting outstanding activity for the diverse VOCs (toluene, acetone, and formaldehyde) degradation. Optical characterization and simulation calculation confirmed that Pt/Co3O4/AAO exhibited state-of-the-art light absorption and a notable localized surface plasmon resonance (LSPR) effect. In situ diffuse reflectance infrared Fourier transform spectrometry (in situ DRIFTS) studies demonstrated that light irradiation can accelerate the conversion of intermediates during toluene and acetone oxidation, thereby inhibiting byproduct accumulation. Our finding extends the application of AAO’s optical properties in photothermal catalytic degradation of air pollutants. © 2024 American Chemical Society.

关键词： Catalysis

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Optimal Location of Renewable Energy Generators in Transmission and Distribution System of Deregulated Power Sector:A Review

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Energy engineering 2025年第3期122卷 823-859页

作者： Digambar Singh Najat Elgeberi Mohammad Aljaidi Ramesh Kumar Rabia Emhamed Al Mamlook Manish Kumar Singla Department Electrical Engineering Government Polytechnic CollegeBTE LucknowEtah207120India University of Nevada RenoNV 89557USA Department of Computer Science Zarqa UniversityZarqa13110Jordan Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering&TechnologyChitkara UniversityChandigarh160002India Jadara University Research Center Jadara UniversityIrbid00962Jordan Department of Industrial Engineering Al Zawiya UniversityAl-ZawiyaP.O.Box 16418Libya Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Biosciences Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India

The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in *** study focuses on the most relevant publications directly addressing the research problem at ***,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission ***,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively *** demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution *** presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and ***,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy *** paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.

关键词： Optimization of RESs distributed generation modeling and selection of RESs hybrid systems standalone systems optimal location

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Evolving Equation Learner For Symbolic Regression

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Dong, Junlan Zhong, Jinghui Liu, Wei-Li Zhang, Jun School of Computer Science and Engineering South China University of Technology Guangzhou China Wei-Li Liu with the School of Computer Science Guangdong Polytechnic Normal University Guangzhou China College of Artificial Intelligence Nankai University Tianjing China

Symbolic regression, a multifaceted optimization challenge involving the refinement of both structural components and coefficients, has gained significant research interest in recent years. The Equation Learner (EQL), a neural network designed to optimize both equation structure and coefficients through gradient-based optimization algorithms, has emerged as an important topic of concern within this field. Thus far, several variations of EQL have been introduced. Nevertheless, these existing EQL methodologies suffer from a fundamental constraint that they necessitate a predefined network structure. This limitation imposes constraints on the complexity of equations and makes them ill-suited for high-dimensional or high-order problem domains. To tackle the aforementioned shortcomings, we present a novel approach known as the evolving Equation Learner (eEQL). eEQL introduces a unique network structure characterized by automatically defined functions (ADFs). This new architectural design allows for dynamic adaptations of the network structure. Moreover, by engaging in self-learning and self-evolution during the search process, eEQL facilitates the generation of intricate, high-order, and constructive sub-functions. This enhancement can improve the accuracy and efficiency of the algorithm. To evaluate its performance, the proposed eEQL method has been tested across various datasets, including benchmark datasets, physics datasets, and real-world datasets. The results have demonstrated that our approach outperforms several well-known methods. IEEE

关键词： Neural networks

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Addressing Domain Discrepancy: A Dual-branch Collaborative Model to Unsupervised Dehazing

arXiv

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arXiv 2024年

作者： Fan, Shuaibin Xue, Minglong Ning, Aoxiang Zhong, Senming College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China Industrial Training Center Guangdong Polytechnic Normal University Guangzhou510665 China

Although synthetic data can alleviate acquisition challenges in image dehazing tasks, it also introduces the problem of domain bias when dealing with small-scale data. This paper proposes a novel dual-branch collaborative unpaired dehazing model (DCM-dehaze) to address this issue. The proposed method consists of two collaborative branches: dehazing and contour constraints. Specifically, we design a dual depthwise separable convolutional module (DDSCM) to enhance the information expressiveness of deeper features and the correlation to shallow features. In addition, we construct a bidirectional contour function to optimize the edge features of the image to enhance the clarity and fidelity of the image details. Furthermore, we present feature enhancers via a residual dense architecture to eliminate redundant features of the dehazing process and further alleviate the domain deviation problem. Extensive experiments on benchmark datasets show that our method reaches the state-of-the-art. This project code will be available at https://***/Fan-pixel/DCM-dehaze. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Analysis of E-commerce Review Text Based on Capsule Network

Analysis of E-commerce Review Text Based on Capsule Network

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Frontiers technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Hui Shao Computer Engineering Technology College (Artificial Intelligence College) Guangdong Polytechnic of Science and Technology Zhuhai China

For traditional models such as CNN, BIGRU, lacks the representation of sentence system features and lacks the ability to extract deep features and other problems. This paper took the e-commerce Chinese reviews as the research objects, in the BERT word vector and attention mechanism, an sentiment analysis is proposed based on the capsule network model. First, the model uses BERT to obtain a vector, then uses CNN to extract deep-level phrase features, then uses BIGRU to extract global features deep-learning phrase system features to obtain the features of the sentence system; and then uses the increase of the attention layer to filter the effective features. Finally, the model uses a richer expression of text sentiment information to improve the overall abstraction ability of the model. After comparing experiments on the dataset, this method is better than the control group, indicating that the proposed model can effectively improve the accuracy of text classification.

关键词： Sentiment analysis Analytical models Computational modeling Bit error rate Text categorization Feature extraction Information filters

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Efficient Retrieval Method of Malicious Information in Multimedia Big Data Network Based on Human-computer Interaction 4th

Efficient Retrieval Method of Malicious Information in Multi...

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4th EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2020

作者： Gong, Jia-ju Xie, Wen-da Wang, Jing-hua Jiangmen Polytechnic Electronic and Information Technology Jiangmen529030 China Guangdong Polytechinc of Science and Technology Computer Engineering Technical College Guangdong Zhuhai519090 China College of Electronic and Optical Engineering & College of Microelectronics Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9783030678708

In order to solve the phenomenon that the malicious information retrieval in the traditional method is not comprehensive and the precision is not high, an efficient retrieval method of malicious information in multimedia big data network based on human-computer interaction is proposed and designed. On the basis of analyzing the principle of information retrieval, the human-computer interaction form is used to divide the metrics of malicious information. On this basis, the human-computer interaction retrieval logic is established, and the malicious information clustering method is used to realize the multimedia big data network. Efficient retrieval of malicious information. Through the method of experimental argumentation analysis, the validity of the human-computer interaction retrieval method is determined. The results show that the method has a high recall rate of 28.31% compared with the traditional method, and the retrieval accuracy is extremely high. In this paper, the method of network malicious information retrieval is effective and suitable for popularization. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Big data

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Artificial Intelligence assisted Electric Vehicles Fast Charging Station Design using Grid Distribution Network Model 4

Artificial Intelligence assisted Electric Vehicles Fast Char...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Anitha, C. Chitra, K. Vijayalakshmi, G. Kumaran, G. Vijetha, T. Ormila, K. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Cmr Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bengaluru India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Cse Tamilnadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad Dundigal India A.M.K. Technological Polytechnic College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

The electric vehicle charging station is a game-changer that supplants the older kind of charging stations. Our futuristic electric vehicle charging station may be installed virtually anywhere, takes minimal space, and operates independently of the government's power source, in contrast to the current practice of reserving these stations for major urban areas. Among the many global industries that rely on fossil fuels, transportation ranks high. The development of electric vehicles (EVs) and other forms of transportation electrification are thus essential in the fight against carbon-dioxide emissions and the over-reliance on traditional energy sources that are harmful to the environment. At the same time, electricity distribution, use, and supply have been accompanied by massive amounts of data due to the fast digitization of electrical networks and EVs. These datasets may be used to inform choices and insights by using artificial intelligence (AI) algorithms. Despite a number of recent studies in this area, no one has explicitly examined the usefulness of AI for charge-demand profiling, data augmentation, demand forecasting, explainability of demand, and charge optimization of the EVI. For the purpose of efficiently delivering the fast charging function, this study suggested a novel logic called Grid Distributed Fast EV Charging (GDFEVC). To evaluate the proposed model's efficiency, it was cross-validated with the standard Fast EV Charging Model (FEVCM). This AI framework has been empirically tested on a real-world EVI case study, and the results show that it helps with the new problems with dispersed energy supplies that are coming up with EV adoption. © 2024 IEEE.

关键词： Charging stations

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