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A Penalty Groups-Assisted Iterated Greedy Integrating Idle Time Insertion:Solving the Hybrid Flow Shop Group Scheduling with Delivery Time Windows

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Complex System Modeling and Simulation 2024年第2期4卷 137-165页

作者： Qianhui Ji Yuyan Han Yuting Wang Biao Zhang Kaizhou Gao the School of Computer Science Liaocheng UniversityLiaocheng 252059China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

The hybrid flow shop group scheduling problem(HFGSP)with the delivery time windows has been widely studied owing to its better flexibility and suitability for the current just-in-time production ***,there are several unresolved challenges in problem modeling and algorithmic design tailored for *** our study,we place emphasis on the constraint of ***,this paper first constructs a mixed integer linear programming model of HFGSP with sequence-dependent setup time and delivery time windows to minimize the total weighted earliness and tardiness(TWET).Then a penalty groups-assisted iterated greedy integrating idle time insertion(PG IG ITI)is proposed to solve the above *** the PG IG ITI,a double decoding strategy is proposed based on the earliest available machine rule and the idle time insertion rule to calculate the TWET ***,to reduce the amount of computation,a skip-based destruction and reconstruction strategy is designed,and a penalty groups-assisted local search is proposed to further improve the quality of the solution by disturbing the penalized groups,i.e.,early and tardy ***,through comprehensive statistical experiments on 270 test instances,the results prove that the proposed algorithm is effective compared to four state-of-the-art algorithms.

关键词： hybrid flow shop group scheduling iterated greedy algorithm delivery time windows sequence-dependent setup time

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Public Datasets for Cloud Computing: A Comprehensive Survey

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ACM Computing Surveys 2025年第8期57卷 1-38页

作者： Liu, Guozhi Lin, Weiwei Zhang, Haotong Lin, Jianpeng Peng, Shaoliang Li, Keqin School of Computer Science and Technology South China University of Technology Guangzhou China South China University of Technology Guangzhou China Pengcheng Laboratory Shenzhen China Hunan University Changsha China Department of Computer Science State University of New York New Paltz United States

Publicly available datasets are vital to researchers because they permit the testing of new algorithms under a variety of conditions and ensure the verifiability and reproducibility of scientific experiments. In cloud computing research, there is a particular dependence on obtaining load traces and network traces from real cloud computing clusters, which are used for designing energy efficiency prediction, workload analysis, and anomaly detection solutions. To address the current lack of a comprehensive overview and thorough analysis of cloud computing datasets and to gain insight into their current status and future trends, in this article, we provide a comprehensive survey of existing publicly cloud computing datasets. First, we utilize a systematic mapping approach to analyze 968 scientific papers from 6 scientific databases, resulting in the retrieval of 42 datasets related to cloud computing. Second, we categorize these datasets based on 11 characteristics to assist researchers in quickly finding datasets suitable for their specific needs. Third, we provide detailed descriptions of each dataset to assist researchers in gaining a clearer understanding of their characteristics. Fourth, we select 12 mainstream datasets and conduct a comprehensive analysis and comparison of their characteristics. Finally, we discuss the weaknesses of existing datasets, identify challenges, provide recommendations for long-term dataset maintenance and updates, and outline directions for the future creation of new cloud computing datasets. Related resources are available at https://***/ACAT-SCUT/Awesome-CloudComputing-Datasets. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cloud platforms

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MFR-CNN: A modified faster R-CNN approach based on bounding box and reliable score for cloth image retrieval

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Multimedia Tools and Applications 2024年 1-29页

作者： Kaur, Navneet Pandey, Shreelekha Kalra, Nidhi Department of Computer Science and Engineering Thapar Institute of Engineering & ampTechnology Punjab Patiala147004 India School of Computer Science and Engineering RV University South Karnataka Bangalore560059 India

Today, machine learning is used in a broad variety of applications. Convolution neural networks (CNN), in particular, are widely used to analyze visual data. The fashion industry is catching up to the growing usage of machine learning in a variety of fields throughout the world. The proposed model named MFR-CNN, which is a modified Faster R-CNN approach, has been presented in this manuscript. The effectiveness of an algorithm is validated on cloth attributes by established datasets such as the Deep Fashion dataset and the Fashion Landmark dataset, alongside a new dataset created for the same manually. The proposed model detects clothes using bounding boxes and further classifies them. Apart from this, retrieval functionality has also been added. The effectiveness of the proposed model is evident through the learning rate analysis done for the modules present in the same. On the metrics of accuracy, execution time, and total loss, the model proposed here is contrast with existing techniques named R-CNN, Fast R-CNN, and Faster R-CNN. It is found that MFR-CNN is 50% more accurate in contrast to R-CNN. Also, the accuracy of MFR-CNN is 30% and 7% more accurate than the Fast R-CNN and Faster R-CNN, respectively. Moreover, MFR-CNN is quite effective in contrast to other approaches as it takes less time for execution. It consumes 33 seconds less in contrast to R-CNN. Also, the time taken by the MFR-CNN is 17 seconds and 3 seconds less in comparison to Fast R-CNN and Faster R-CNN, respectively. From the experimental results it is being inferred that the MFR-CNN approach for detecting and classifying clothes is both efficient and effective. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image retrieval

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An Enhanced Linearly Homomorphic Network Coding Signature Scheme for Secure Data Delivery in IoT Networks

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5534-5548页

作者： Huang, Hao Wang, Xiaofen Au, Man Ho Cao, Sheng Zhao, Qinglin Yu, Jiguo University of Electronic Science and Technology of China Chengdu611731 China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong Macau University of Science and Technology School of Computer Science and Engineering China

Recently, Li et al. proposed an identity-based linearly homomorphic network coding signature (IB-HNCS) scheme for secure data delivery in Internet of Things (IoT) networks, and they claimed that the IB-HNCS scheme can resist pollution attacks. However, this paper shows that the IB-HNCS scheme is vulnerable to pollution attacks, as anyone who only has the public parameter can forge a new file identifier or a valid signature on a corrupted data packet to pollute legitimate sensor data. To enhance security and performance in network coding-based IoT networks, we propose a secure and efficient certificateless linearly homomorphic network coding signature scheme for IoT data delivery, which is free of burdensome certificate management and key escrow issue. In addition, our scheme is proved to be secure against adaptive chosen identity and adaptive chosen subspace attacks under two types of adversaries in the algebraic group model and random oracle model. Therefore, our scheme can verify the validity of data packets and allow data packets to be computed, so as to resist pollution attacks. The performance evaluation demonstrates that our scheme is more efficient and practical than existing secure schemes. Specifically, for a 73-dimensional data vector, the costs of signature generation and verification in our scheme are reduced by 38.588%-86.076% and 38.570%-85.664% respectively under the symmetric bilinear pairing setting, and the costs of signature generation and verification in our scheme are reduced by 17.740%-49.752% and 29.697%-58.645% respectively under the asymmetric bilinear pairing setting. © 2005-2012 IEEE.

关键词： Internet of things

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Delving into high-quality SVBRDF acquisition: A new setup and method

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Computational Visual Media 2024年第3期10卷 523-541页

作者： Chuhua Xian Jiaxin Li Hao Wu Zisen Lin Guiqing Li School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Guangdong Shidi Intelligence Technology Ltd.Guangzhou 510000China

In this study, we present a new andinnovative framework for acquiring high-qualitySVBRDF maps. Our approach addresses the limitations of the current methods and proposes a newsolution. The core of our method is a simple hardwaresetup consisting of a consumer-level camera, LEDlights, and a carefully designed network that canaccurately obtain the high-quality SVBRDF propertiesof a nearly planar object. By capturing a ﬂexiblenumber of images of an object, our network usesdifferent subnetworks to train different property mapsand employs appropriate loss functions for each ofthem. To further enhance the quality of the maps, weimproved the network structure by adding a novel skipconnection that connects the encoder and decoder withglobal features. Through extensive experimentation usingboth synthetic and real-world materials, our resultsdemonstrate that our method outperforms previousmethods and produces superior results. Furthermore,our proposed setup can also be used to acquire physicallybased rendering maps of special materials.

关键词： acquisition setup SVBRDF acquisition material capture global skip connection

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Task Offloading and Resource Management for IIoT with Satellite-Terrestrial Integrated Computing Power Network Based on D3QN

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IEEE Internet of Things Journal 2025年第12期12卷 21770-21783页

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex communication environments, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov Decision Process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2025 IEEE.

关键词： Energy utilization

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Influence of nano silicon powder content on the corrosion resistance of reinforced concrete

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International Journal of Microstructure and Materials Properties 2025年第1-2期18卷 73-87页

作者： Wu, Tingting Su, Xiaochao Department of Quality and Information Technology Hunan Labor and Human Resources Vocational College Hunan410000 China School of Civil Engineering Changsha University of Science and Technology Changsha410114 China

This study aims to investigate the effect of different dosages of nano silica powder on the corrosion resistance of reinforced concrete. Through electrochemical corrosion treatment experiments, the corrosion rate of steel bars, Tafel polarisation curve, electrochemical impedance and other parameters were measured, and the microstructure was analysed. The experimental results show that the dosage of nano silicon powder has a significant impact on the corrosion resistance of reinforced concrete. When the dosage is 2%, the corrosion resistance of reinforced concrete is significantly improved, the corrosion rate is significantly reduced, the Tafel polarisation curve shows good stability, and the electrochemical impedance is significantly increased. However, when the dosage increases to 4%, the corrosion resistance shows a downward trend, and the corrosion rate and negative displacement value both increase. Microscopic analysis shows that the appropriate addition of nano silicon powder can reduce the formation of corrosion products and cracks. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Electrochemical corrosion

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SEAformer: frequency domain decomposition transformer with signal enhanced for long-term wind power forecasting

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Neural Computing and Applications 2024年第33期36卷 20883-20906页

作者： Yan, Leiming Wu, Siqi Li, Shaopeng Chen, Xianyi School of Computer Science Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing China

Accurate and reliable wind power forecasting is of great importance for stable grid operation and advanced dispatch planning. Due to the complex, non-stationary, and highly volatile nature of wind power data, Transformer-based methods find it difficult to capture long-term trend features and incur high computational costs. To address these challenging problems, we propose a frequency domain decomposition Transformer architecture with signal enhanced attention mechanism (SEAformer). Firstly, we devise a frequency domain-based trend decomposition structure that enables the Transformer to extract more effective long-term trend features, thereby further improving the long-term prediction accuracy of the model. Secondly, in response to the large fluctuations and instability of wind power data, we design an internal signal enhanced substructure combined with an attention mechanism in the Transformer, which filters out high-frequency noise signals and reduces the computational cost of the Transformer. We conduct extensive experiments on the benchmark dataset, the experimental analysis demonstrates that SEAformer outperforms the baseline methods (Transformer-based, MLP-based, and Traditional methods) in both multivariate and univariate prediction tasks, exhibiting the best prediction performance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Wind forecasting

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Heart-Rate Analysis of Healthy and Insomnia Groups with Detrended Fractal Dimension Feature in Edge

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Tsinghua science and technology 2022年第2期27卷 325-332页

作者： Xuefei Wang Yichao Zhou Chunxia Zhao the School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Insomnia,whether situational or chronic,affects over a third of the general population in today’s ***,given the lack of non-contact and non-inductive quantitative evaluation approaches,most insomniacs are often unrecognized and *** Polysomnographic(PSG)is considered as one of the assessment methods,it is poorly tolerated and *** this paper,with the recent development of Internet-of-Things devices and edge computing techniques,we propose a detrended fractal dimension(DFD)feature for the analysis of heart-rate signals,which can be easily acquired by many wearables,of good sleepers and *** feature was derived by calculating the fractal dimension(FD)of detrended *** the trend component removal,we improved the null space pursuit algorithm and proposed an adaptive trend extraction *** experimental results demonstrated the efficacy of the proposed DFD index through numerical statistics and significance testing for healthy and insomnia groups,which renders it a potential biomarker for insomnia assessment and management.

关键词： insomnia fractal dimension adaptive signal separation hypothesis testing

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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