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Functionalised B-Cu@F-Ag@Au trimetallic nanomaterials with long term stability for rapid and highly sensitive in situ SERS detection of furfural in transformer oil

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High Voltage 2023年第2期8卷 293-304页

作者： Yu Lei Changding Wang Weiping Kong Mingliang Wang Xinyuan Zhang Abdolmaleki Nima Caisheng Wang Weigen Chen Fu Wan School of Electrical Engineering Chongqing UniversityChongqingChina State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina Electrical and Computer Engineering Wayne State UniversityWayneNebraskaUSA

The surface-enhanced Raman scattering(SERS)substrates enable a highly sensitive detection of furfural in the transformer ***,detection substrates with long-term stability are still extremely *** this work,we anchored the thiol-containing coupling agents 2,5-dimercapto-1,3,4-thiadiazole(DMTD)and 1,4-benzenedithiol(BDT)on the surface of bubble copper(B-Cu)and flower-like silver nanoparticles(FAg),*** three-dimensional SERS detection substrates with long-term stability by using a combination of chemical reduction and self-assembly methods were *** substrate has a minimum detection limit of 10^(−9) M for rhodamine B in oil with an enhancement factor of up to 2.23×10^(7).Importantly,the three-crystal BCu@F-Ag_(1)@Au_(5) substrate was used for the detection of furfural in the transformer oil with a detection limit of 2 mg/L and a relative standard deviation value of 2.46%.After 60 days of a simulated operation,the detection signal of furfural in the transformer oil samples at 75℃ and still reached the initial value of 77.53%,indicating that the substrate has a good long-term *** triple frame structured SERS detection platform shows great potential in tracking furfural in the aging transformer oil mixing systems.

关键词： stability. stability SERS

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Fast Parallel Recovery for Transactional Stream Processing on Multicores 40

Fast Parallel Recovery for Transactional Stream Processing o...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhao, Jianjun Liu, Haikun Zhang, Shuhao Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Yu School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Transactional stream processing engines (TSPEs) have gained increasing attention due to their capability of processing real-time stream applications with transactional semantics. However, TSPEs remain susceptible to system failures and power outages. Existing TSPEs mainly focus on performance improvement, but still face a significant challenge to guarantee fault tolerance while offering high-performance services. We revisit commonly-used fault tolerance approaches in stream processing and database systems, and find that these approaches do not work well on TSPEs due to complex data dependencies. In this paper, we propose a novel TSPE called MorphStreamR to achieve fast failure recovery while guaranteeing low performance overhead at runtime. The key idea of MorphStreamR is to record intermediate results of resolved dependencies at runtime, and thus eliminate data dependencies to improve task parallelism during failure recovery. MorphStreamR further mitigates the runtime overhead by selectively tracking data dependencies and incorporating workload-aware log commitment. Experimental results show that MorphStreamR can significantly reduce the recovery time by up to 3.1 x while experiencing much less performance slowdown at runtime, compared with other applicable fault tolerance approaches. © 2024 IEEE.

关键词： Fault tolerance

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GCA-Net: Global Cross Aware Network for salient object detection 23

GCA-Net: Global Cross Aware Network for salient object detec...

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8th International Conference on Multimedia and Image Processing, ICMIP 2023

作者： Zhang, Zixuan Shi, Fan Geng, Xinbo Tao, Yi He, Jing Key Laboratory of Computer Vision and System Ministry of Education School of Computer Science and Engineering Tianjin University of Technology China

ISBN: (纸本)9781450399586

In recent years, significant progress has been made in salient object detection. Nevertheless, there remains a need for further improvements in the effective combination of local and global perspectives. Combining global perception with the local focus can enhance the ability to capture the integrity of protruding objects while achieving accurate segmentation. In this paper, we introduce a newly proposed salient object detection network named GCA-Net, which is built upon three essential components. Firstly, we utilize atrous convolution to effectively aggregate contextual information and refine high-level semantic information via spatial pooling. Secondly, we employ 1-dim crossover operations to achieve global perception while minimizing computational effort. Lastly, We have innovatively integrated a contour-aware loss into our salient object detection task to constrain our model's predictions, leading to more accurate segmentation. After evaluating our model on the established salient object detection benchmark dataset, we conducted extensive experiments to demonstrate the exceptional performance of our method. As a result, our method achieved 1st in both Sm and MAE. © 2023 ACM.

关键词： Semantics

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Multiobjective Optimization for Traveling Salesman Problem: A Deep Reinforcement Learning Algorithm via Transfer Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 896-908页

作者： Gao, Le-Yang Wang, Rui Jia, Zhao-Hong Liu, Chuang Anhui University School of Computer Science and Technology Hefei230039 China National University of Defense Technology College of System Engineering Changsha410073 China Xiangjiang Laboratory Changsha410205 China Anhui University School of Internet Hefei230039 China

A wide range of real applications can be modelled as the multiobjective traveling salesman problem (MOTSP), one of typical combinatorial optimization problems. Meta-heuristics can be used to address MOTSP. However, due to involving iteratively searching large solution space, they often entail significant computation time. Recently, deep reinforcement learning (DRL) algorithms have been employed in generating approximate optimal solutions to the single objective traveling salesman problems, as well as MOTSPs. This study proposes a multiobjective optimization algorithm based on DRL, called multiobjective pointer network (MOPN), where the input structure of the pointer network is redesigned to be applied to MOTSP. Furthermore, a training strategy utilizing a representative model and transfer learning is introduced to enhance the performance of MOPN. The proposed MOPN is insensitive to problem scale, meaning that a trained MOPN can address MOTSPs with different scales. Compared to meta-heuristics, MOPN takes much less time on forward propagation to obtain the pareto front. To verify the performance of our model, extensive experiments are conducted on three different MOTSPs to compare the MOPN with two state-of-the-art DRL models and two multiobjective meta-heuristics. Experimental results demonstrate that the proposed MOPN obtains the best solution with the least training time among all the compared DRL methods. © 2024 IEEE.

关键词： Traveling salesman problem

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Neighborhood Combination Search for Single-Machine Scheduling with Sequence-Dependent Setup Time

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Journal of computer Science & Technology 2024年第3期39卷 737-752页

作者：刘晓路徐宏云陈嘉铭苏宙行吕志鹏丁俊文 College of System Engineering National University of Defense TechnologyChangsha 410015China School of Artificial Intelligence Jianghan UniversityWuhan 430056China Xi’an Satellite Control Center Xi’an 710043China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China CCF

In a local search algorithm,one of its most important features is the definition of its neighborhood which is crucial to the algorithm's *** this paper,we present an analysis of neighborhood combination search for solv-ing the single-machine scheduling problem with sequence-dependent setup time with the objective of minimizing total weighted tardiness(SMSWT).First,We propose a new neighborhood structure named Block Swap(B1)which can be con-sidered as an extension of the previously widely used Block Move(B2)neighborhood,and a fast incremental evaluation technique to enhance its evaluation ***,based on the Block Swap and Block Move neighborhoods,we present two kinds of neighborhood structures:neighborhood union(denoted by B1UB2)and token-ring search(denoted by B1→B2),both of which are combinations of B1 and ***,we incorporate the neighborhood union and token-ring search into two representative metaheuristic algorithms:the Iterated Local Search Algorithm(ILSnew)and the Hybrid Evolutionary Algorithm(HEA_(new))to investigate the performance of the neighborhood union and token-ring ***-sive experiments show the competitiveness of the token-ring search combination mechanism of the two *** on the 120 public benchmark instances,our HEA_(new)has a highly competitive performance in solution quality and computational time compared with both the exact algorithms and recent *** have also tested the HEA,new algorithm with the selected neighborhood combination search to deal with the 64 public benchmark instances of the single-machine scheduling problem with sequence-dependent setup *** is able to match the optimal or the best known results for all the 64 *** particular,the computational time for reaching the best well-known results for five chal-lenging instances is reduced by at least 61.25%.

关键词： single-machine scheduling sequence-dependent setup time neighborhood combination search token-ring search hybrid evolutionary algorithm

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Combinatorial Kinetic Dualistic Auction Model for Metaverse Service Allocation 1

Combinatorial Kinetic Dualistic Auction Model for Metaverse ...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Singh, Raj Sarkar, Pritam Bandyopadhyay, Anjan Swain, Sujata Sardar, Tanvir Habib Pandey, Bishwajeet School of Computer Engineering KIIT 751024 India Department of Computer Science GITAM University Bengaluru India Department of Intelligent System and Cybersecurity Astana IT University Kazakhstan

ISBN: (纸本)9798350364378

The focus of this research work revolves around the utilization of a specialized"Combinatorial Kinetic Dualistic Auction Model" designed specifically for"metaverse Services." The main goal is to improve the allocation of resources in this ever-changing environment and enhance the overall experience for end-users. Through the exploration of metaverse services, we aim to further develop the comprehension and practical application of the Combinatorial Kinetic Dualistic Auction Model, ultimately contributing to a more immersive and responsive virtual experience. ©2024 IEEE |

关键词： Resource allocation

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Malware Detection system Based on Static-Dynamic Preprocessing Techniques Combined in an Ensemble Model 15th

Malware Detection System Based on Static-Dynamic Preprocessi...

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15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Kim, Hae-Soo Kim, Mi-Hui School of Computer Engineering and Applied Mathematics Computer System Institute Hankyong National University 327 Jungang-ro Gyeonggi-do Anseong-si Korea Republic of

ISBN: (纸本)9789819724468

Malware distributors use persistence mechanisms in their malware to keep a PC infected for a long time. Techniques such as packing and obfuscation are often used so that the presence of malware cannot be determined by the simple executable code of the program. Static analysis of executable code, structures, and strings cannot determine obfuscated and packed malware. Therefore, dynamic analysis is required to determine the malicious behavior imposed on the system. In this paper, we used the CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) model, which preprocessed static data and dynamic data and combined them into an ensemble model to determine malicious and benign and achieved an accuracy of 97.68%, F1-Score of 97.67%, and false positive rate of 1.57%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Unlocking Security: A Comprehensive Exploration of Biometric Authentication Techniques 14

Unlocking Security: A Comprehensive Exploration of Biometric...

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Sriman, Jindam Thapar, Puneet Alyas, Arsalan Ahmed Singh, Urvashi School of Computer Science Engineering Lovely Professional University Phagwara India Wilmington University Information System Technologies United States

ISBN: (纸本)9798350344837

Biometric authentication is a rapidly evolving field in information security, providing a personalized and reliable approach to identity verification. This paper explores various biometric methods, including fingerprints, iris scans, facial recognition, voice authentication, and behavioral biometrics, highlighting their strengths and vulnerabilities. Addressing challenges like spoofing attacks, multimodal integration, and privacy concerns, the paper emphasizes the need for robust security measures. The thorough analysis considers critical factors such as accuracy, user-friendliness, and vulnerability to fraudulent activities, providing a well-rounded assessment of their strengths and weaknesses. Besides, the review delves into the convergence of diverse biometric modalities and the evolving landscape of multimodal authentication systems, underscoring the pressing need for sustained research and advancement to address security challenges and privacy issues. The ultimate objective is to support user acceptance and instill confidence in biometric authentication. By critically appraising established techniques and contemplating potential future developments, this paper aims to provide profound insights for researchers and practitioners in the field of biometric authentication. © 2024 IEEE.

关键词： Biometrics

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A Study on Improvement of EV Charging Occupancy Prediction Efficiency 15th

A Study on Improvement of EV Charging Occupancy Prediction E...

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15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Kang, Myung-Joe Kim, Mi-Hui School of Computer Engineering and Applied Mathematics Computer System Institute Hankyong National University 327 Jungang-ro Gyeonggi-do Anseong-si Korea Republic of

ISBN: (纸本)9789819724468

In this paper, we propose a hybrid model structure combining CNN-LSTM and Fully Connected to improve the prediction of electric vehicle charging station occupancy efficiently. We conducted experiments on UK and Korean datasets to evaluate the proposed model's learning efficiency and prediction accuracy compared to existing models. As a result, no loss in prediction accuracy was observed in both datasets, and our model achieved an average learning speed of approximately 1.61 times faster than the existing model on the UK dataset, and approximately 1.46 times faster than the Korean dataset. Notably, for charging stations with the largest differences, the UK dataset demonstrated a 2.04 times improvement in the view of learning speed, whereas the Korean dataset demonstrated a 1.84 times improvement. This proposed process allows for dimensionality reduction and feature extraction, ensuring only critical data is utilized in the LSTM model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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Color Image Encryption Using Genetic Recombination With Tent Mapping Based Time-Delay Chaotic 4

Color Image Encryption Using Genetic Recombination With Tent...

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4th International Conference on computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Xu, Yaoqun Liu, Jiaoyang Institute of System Engineering Harbin University of Commerce Harbin150028 China School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China

ISBN: (纸本)9781510683242

As the form of information transmission has shifted from text to images in recent years, the in-crease in information dimension has led to a growing interest in secure and efficient encryption algorithms. This paper proposes a chaotic system that combines tent mapping and a time-delay chaotic neural network, and it extracts a single neuron to test the system's chaotic properties and stability. On this basis, the randomness test of the chaotic sequence generated by the system is conducted to ensure that the new chaotic system can maintain the chaotic state more effectively. In addition, genetic recombination compensates for the restriction that DNA code cannot be confounded and diffused during the encryption procedure. In this paper, chaotic sequences are applied to genetic recombination and DNA coding, and an algorithm for image encryption is proposed. The data indicate that the algorithm has excellent anti-attack capabilities. In addition, the algorithm has passed the NIST and TestU01 randomness evaluations, guaranteeing its sensitivity and randomness. Therefore, the algorithm is secure and can be used to encrypt color images. © 2024 SPIE.

关键词： Gene encoding

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