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SSRN 2024年

作者： Wang, Mujiangshan Xu, Shuhao Jiang, Jincheng Xiang, Dong Hsieh, Sun-Yuan Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Software Tsinghua University Beijing100084 China Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Guangdong Academy of Agricultural Sciences China

In the research field of system-level diagnosis, people used to focus on studying different diagnosabilities with restrictions. By studying this, reliability measured by these diagnosabilities of various of networks is obtained. However, for the decentralized parallel computing systems, there are always upper bonds for diagnosabilities, which means there exists an undiagnosable subnetwork cut off by a faulty vertex cut if the subnetwork is large enough. In this paper, we set up a 2-stage plan for the global reliable diagnosis of networks combining Self-Comparative diagnosis (SC) model and the g-good-neighbor property, which means every vertex in the networks is diagnosed with high accuracy. Six algorithms are proposed for the realization of the plan and diagnosis model for g≥2. In the end, we code a simulator for the plan and test how well it works and the codes are provided via Github link for readers' check. © 2024, The Authors. All rights reserved.

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Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Ullah, Kaleem Shakir, Daniyal Abid, Usama Alahmari, Saad Aslam, Sheraz Ullah, Zahid US-Pakistan Centre for Advanced Studies in Energy University of Engineering and Technology Peshawar Peshawar Pakistan Sr. Business Analyst S&P Global Islamabad Islamabad Pakistan Department of Computer Science and Information Technology School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science CTL eurocolege Limassol Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Lombardia Italy

Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Convolutional neural networks

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A Dual-mode Local Search Algorithm for Solving the Minimum Dominating Set Problem

arXiv

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arXiv 2023年

作者： Zhu, Enqiang Zhang, Yu Wang, Shengzhi Strash, Darren Liu, Chanjuan Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Department of Computer Science Hamilton College ClintonNY13323 United States School of Computer Science and Technology Dalian University of Technology Dalian116024 China

Given a graph, the minimum dominating set (MinDS) problem is to identify a smallest set D of vertices such that every vertex not in D is adjacent to at least one vertex in D. The MinDS problem is a classic NP-hard problem and has been extensively studied because of its many disparate applications in network analysis. To solve this problem efficiently, many heuristic approaches have been proposed to obtain a good solution within an acceptable time limit. However, existing MinDS heuristic algorithms are always limited by various tie-breaking cases when selecting vertices, which slows down the effectiveness of the algorithms. In this paper, we design an efficient local search algorithm for the MinDS problem, named DmDS—a dual-mode local search framework that probabilistically chooses between two distinct vertex-swapping schemes. We further address limitations of other algorithms by introducing vertex selection criterion based on the frequency of vertices added to solutions to address tie-breaking cases, and a new strategy to improve the quality of the initial solution via a greedy-based strategy integrated with perturbation. We evaluate DmDS against the state-of-the-art algorithms on seven datasets, consisting of 346 instances (or families) with up to tens of millions of vertices. Experimental results show that DmDS obtains the best performance in accuracy for almost all instances and finds much better solutions than state-of-the-art MinDS algorithms on a broad range of large real-world graphs. Copyright © 2023, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Optimally Tracking Labels on an Evolving Tree

arXiv

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arXiv 2022年

作者： Acharya, Aditya Mount, David M. Department of Computer Science University of Maryland College ParkMD United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD United States

Motivated by the problem of maintaining data structures for a large sets of points that are evolving over the course of time, we consider the problem of maintaining a set of labels assigned to the vertices of a tree. We study the problem in the evolving data framework, where labels continuously change over time due to the action of an agent called the evolver. An algorithm, which can only track these changes by explicitly probing the individual vertices, is tasked with maintaining an approximate sketch of the underlying tree. Such a framework necessitates an algorithm which is fast enough to keep up with the changes, while simultaneously being accurate enough to maintain a close approximation. We present an algorithm that allows for both randomized and adversarial evolution of the data, subject to allowing a constant speedup factor over the evolver. We show that in the limit, it is possible to maintain labels to within an average distance of O(1) of their actual locations. We also present nearly matching lower bounds, both on the distance, and the speed-up factor. © 2022, CC BY.

关键词： Approximation algorithms

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Quantum algorithms and the power of forgetting

arXiv

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arXiv 2022年

作者： Childs, Andrew M. Coudron, Matthew Gilani, Amin Shiraz Department of Computer Science University of Maryland Institute for Advanced Computer Studies University of Maryland Joint Center for Quantum Information and Computer Science NIST University of Maryland United States

The so-called welded tree problem provides an example of a black-box problem that can be solved exponentially faster by a quantum walk than by any classical algorithm [CCD+03]. Given the name of a special ENTRANCE vertex, a quantum walk can find another distinguished EXIT vertex using polynomially many queries, though without finding any particular path from ENTRANCE to EXIT. It has been an open problem for twenty years whether there is an efficient quantum algorithm for finding such a path, or if the path-finding problem is hard even for quantum computers. We show that a natural class of efficient quantum algorithms provably cannot find a path from ENTRANCE to EXIT. Specifically, we consider algorithms that, within each branch of their superposition, always store a set of vertex labels that form a connected subgraph including the ENTRANCE, and that only provide these vertex labels as inputs to the oracle. While this does not rule out the possibility of a quantum algorithm that efficiently finds a path, it is unclear how an algorithm could benefit by deviating from this behavior. Our no-go result suggests that, for some problems, quantum algorithms must necessarily forget the path they take to reach a solution in order to outperform classical computation. Copyright © 2022, The Authors. All rights reserved.

关键词： Trees (mathematics)

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A Hybrid Approach Integrating Deep Fuzzy Dual Support Vector Regression with Evolutionary Computation and Ensemble Learning

A Hybrid Approach Integrating Deep Fuzzy Dual Support Vector...

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Intelligent Systems and Machine Learning Conference (ISML)

作者： Jagendra Singh Neeraj Kamal Upreti Sonali Gaur A. K. Shrivastav Mohit Tiwari Computer Science Engineering Alliance School of Advanced Computing Alliance University Banglore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science CHRIST (Deemed to be University) Ghaziabad India IILM Institute for Higher Education New Delhi India Department of Mathematics & Humanities MMEC Maharishi Markandashwar (Deemed University) Ambala India Dept. of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

This research endeavors to revolutionize the precision of photovoltaic (PV) power generation predictions through the introduction of a novel Hybrid Ensembled Model. Motivated by the imperative to enhance the reliability of renewable energy technologies, the study amalgamates the strengths of fuzzy logic, support vector regression, ensemble learning, and evolutionary computation. The model's design addresses the inherent challenges of PV power generation prediction, notably uncertainties and non-linear dynamics. The investigation unfolds in distinct phases, beginning with a meticulous exploration of the significance of precise PV power generation prediction. Acknowledging the limitations of existing methodologies, the research establishes a robust foundation for the development of the Hybrid Ensembled Model. Methodologically, the incorporation of Double-Input-Fuzzy-Modules (DIFM), Extreme Learning Machine (ELM), and evolutionary computation ensures a comprehensive and adaptive framework. This research propels the discourse on renewable energy prediction models, presenting a Hybrid Ensembled Model as a pioneering solution for precise and reliable PV power generation forecasts. The implications extend beyond academia, envisioning a future where sustainable energy systems are underpinned by accurate predictions, fostering optimal resource utilization and contributing to a resilient and sustainable energy landscape.

关键词： Fuzzy logic Renewable energy sources Adaptation models Computational modeling Electricity Evolutionary computation Predictive models Hybrid power systems Reliability Ensemble learning

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Crow Search Optimization to Identify Adversary Nodes in Wireless Networks

Crow Search Optimization to Identify Adversary Nodes in Wire...

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International Conference on Edge computing and Applications (ICECAA)

作者： Gnanajeyaraman Rajaram T. Stephen Thangaraj A. Packialatha R. Deepa Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Paavai Engineering College Namakkal Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

Adversary node detection systems have become a vital component due to the malware attacks. These systems are very important for network security due to internet's widespread development and increased accessibility to global data systems. This paper presents a Crow Search Optimization Algorithm (CSOA) to detect the adversary node in a wireless network. The Rivest, Shamir, Adleman (RSA) algorithm transmits secure data at first. It uses the public key to encrypt the data and the private key to decrypt the data and the adversary nodes can't access or modify the real data. One of the significant problems is when an unknown attack occurs on the network due to the high volume of data while the detection's accuracy and false alarm rate decline. The proposed system aims to improve accuracy by identifying the adversary nodes using CSOA algorithm. From the results, it is observed that the CSOA mechanism reduces the loss ratio and improves throughput of the network.

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DSCT:a novel deep-learning framework for rapid and accurate spatial transcriptomic cell typing

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National science Review 2025年第5期12卷 123-138页

作者： Yiheng Xu Bin Yu Xuan Chen Aibing Peng Quyuan Tao Youzhe He Yueming Wang Xiao-Ming Li Department of Neurology and Department of Psychiatry the Second Affiliated HospitalZhejiang University School of Medicine NHC and CAMS Key Laboratory of Medical Neurobiology MOE Frontier Center of Brain Science and Brain-machine Integration School of Brain Science and Brain MedicineZhejiang University College of Computer Science and Technology Zhejiang University Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province Institute of brain and cognitive science Hangzhou City University School of Medicine Department of Neurobiology Affiliated Mental Health Center & Hangzhou Seventh People's HospitalZhejiang University School of Medicine BGI Research Qiushi Academy for Advanced Studies Zhejiang University The Nanhu Brain-computer Interface institute

Unraveling complex cell-type-composition and gene-expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep Neural Network-based Spatial Cell Typing(DSCT)—an innovative framework for spatial cell typing within spatial transcriptomic data sets. This approach utilizes a synergistic integration of an enhanced gene-selection strategy and a lightweight deep neural network for data training, offering a more rapid and accurate solution for the analysis of spatial transcriptomic data. Based on comprehensive analysis, DSCT achieved exceptional accuracy in cell-type identification across various brain regions, species and spatial transcriptomic *** also performed well in mapping finer cell types, thereby showcasing its versatility and adaptability across diverse data sets. Strikingly, DSCT exhibited high efficiency and remarkable processing speed, with fewer computational resource demands. As such, this novel approach opens new avenues for exploring the spatial organization of cell types and gene-expression patterns, advancing our understanding of biological functions and pathologies within the nervous system.

关键词： DSCT spatial cell typing spatial transcriptomics deep-learning neural network

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Stitch-avoiding Global Routing for Multiple E-Beam Lithography

Stitch-avoiding Global Routing for Multiple E-Beam Lithograp...

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International Conference on VLSI Design

作者： Kritanta Saha Sudipta Paul Pritha Banerjee Susmita Sur-Kolay Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India Department of Computer Science and Engineering University of Calcutta Kolkata India

ISBN: (数字)9781665485050

ISBN: (纸本)9781665496612

In Multiple E-Beam Lithography (MEBL), a layout of an IC is partitioned into vertical stripes whose boundaries are called stitch-lines. Routing patterns in each stripe are written by different beams and/or in different writing passes. However, patterns cut by stitch-lines suffer from overlay errors resulting in severe pattern distortions. These distortions are mainly due to Via violation, Vertical Routing violation, or Short Polygon. In this paper, we have proposed a Stitch-avoiding Global Router. The proposed method selects the best possible global routing path for each net so that even a traditional detailed router can generate a Detailed Routing solution with fewer violations. Experimental results show that our proposed global router followed by a conventional detailed router generates solutions with 17.3%, 6.21% and 1.89% reduction in Via violations, Short polygon violations and routed wirelength respectively with no vertical routing violations, compared to those by traditional global routing.

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Developing mapping and allotment in volunteer cloud systems using reliability profile algorithms in a virtual machine 2

Developing mapping and allotment in volunteer cloud systems ...

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2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

作者： Jeyalaksshmi, S. Nidhya, M.S. Suseendran, G. Pal, Souvik Akila, D. Information Technology Vels Institute of Science Technology Advanced Studies Chennai India Periyar Maniammai Institute of Science Technology Dept of Software Engineering Vallam Thanjavur India Global Institute of Management and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9781728194912

While the placement of the Virtual computer was It remains an open challenge for Volunteer Cloud computing, which reveals many divergent gaps in conventional cloud computing contexts, and has been thoroughly studied. Features, including sporadic usability of nodes and Infrastructure that's inefficient. In this article, we are modeling the Virtual In Volunteer Cloud computing, computer positioning dilemma As a multi-dimensional, constrained 0-1 knapsack issue and Built algorithms to satisfy the basic aims and shortcomings of Volunteer Cloud computing. The proof of a dedicated Cloud computing Volunteer, The competitive success outcomes of these test beds are illustrated With algorithm. © 2021 IEEE.

关键词： Cloud computing

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