检索结果-内蒙古大学图书馆

Short-term net load forecasting using gated spiking neural P systems and generative adversarial network

学校读者我要写书评

暂无评论

Journal of Membrane Computing 2025年 1-16页

作者： Guo, Yuanshuo Wang, Jun Zhong, Yan Sui, Zeyuan Peng, Hong School of Electrical Engineering and Electronic Information Xihua University No. 999 Jinzhou Road Sichuan Chengdu 610039 China Sichuan Province Key Laboratory of Power Electronics Energy-Saving Technologies and Equipment Xihua University No. 999 Jinzhou Road Sichuan Chengdu 610039 China School of Computer and Software Engineering Xihua University No. 999 Jinzhou Road Sichuan Chengdu 610039 China

To address the issues of non-linearity, non-stationarity, and low prediction accuracy of power net load, this paper proposes a basic framework for future load forecasting researches in sustainable energy systems under extreme weather events and provides new direction for membrane computing model in terms of power net load forecasting. Based on gated spiking neural P systems and generative adversarial network (GAN), in this study, we proposed a novel combined forecasting model. Initially, with considering the impact of factors such as radiation and environmental temperature on the net load, the Kendall correlation coefficient method is employed for feature selection of power system net load samples, eliminating redundant features to construct the high coupling feature combination matrix. Subsequently, the conditional label matrices of related factors and random noise are inputted into the GAN, the generator produces synthetic samples which approximate real samples and the discriminator extracts non-linear behaviors of real data. Then high-quality samples are input into gated spiking neural P model for training. Finally, simulation results demonstrate that the proposed model surpasses other deep learning models in terms of forecasting accuracy under different scenarios. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Composite forecasting Correlation analysis GSNP model Power net load Sample generation

Band Prompting Aided SAR and Multi-Spectral Data Fusion Framework for Local Climate Zone Classification

学校读者我要写书评

暂无评论

Band Prompting Aided SAR and Multi-Spectral Data Fusion Fram...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haiyan Lan Shujun Li Mingjie Xie Xuanjia Zhao Hongning Liu Pengming Feng Dongli Xu Guangjun He Jian Guan College of Computer Science and Technology Harbin Engineering University Harbin China School of Astronautics Beihang University Beijing China School of Software Dalian University of Technology Dalian China State Key Laboratory of Space-Ground Integrated Information Technology Beijing China Independent Researcher

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Local climate zone (LCZ) classification is of great value for understanding the complex interactions between urban development and local climate. Recent studies have increasingly focused on the fusion of synthetic aperture radar (SAR) and multi-spectral data to improve LCZ classification performance. However, it remains challenging due to the distinct physical properties of these two types of data and the absence of effective fusion guidance. In this paper, a novel band prompting aided data fusion framework is proposed for LCZ classification, namely BP-LCZ, which utilizes textual prompts associated with band groups to guide the model in learning the physical attributes of different bands and semantics of various categories inherent in SAR and multi-spectral data to augment the fused feature, thus enhancing LCZ classification performance. Specifically, a band group prompting (BGP) strategy is introduced to align the visual representation effectively at the level of band groups, which also facilitates a more adequate extraction of semantic information of different bands with textual information. In addition, a multivariate supervised matrix (MSM) based training strategy is proposed to alleviate the problem of positive and negative sample confusion by completing the supervised information. The experimental results demonstrate the effectiveness and superiority of the proposed data fusion framework.

关键词： Training Visualization Semantics Data integration Signal processing Robustness Data mining Speech processing Synthetic aperture radar Meteorology

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Liu, Jia Jinguo, Cheng Fang, Xia Ma, Zhenyuan Wu, Yuankai School of Software Engineering Sichuan University Chengdu China National Key Laboratory of Fundamental Science of Synthetic Vision Sichuan University Chengdu China School of Mechanical Engineering Sichuan University Chengdu China China Electric Power Research Institute Beijing China School of Computer Science Sichuan University Chengdu China

Time series foundation models excel at diverse time series forecasting tasks, but their capacity for continuous improvement through incremental learning remains unexplored. We present the first comprehensive study investigating these models’ temporal plasticity—their ability to progressively enhance performance through continual learning while maintaining existing capabilities. Through experiments on real-world datasets exhibiting distribution shifts, we evaluate both conventional deep learning models and foundation models using a novel continual learning framework. Our findings reveal that while traditional models struggle with performance deterioration during incremental fine-tuning, foundation models like Time-MoE and Chronos demonstrate sustained improvement in predictive accuracy. This suggests that optimizing foundation model fine-tuning strategies may be more valuable than developing domain-specific small models. Our research introduces new evaluation methodologies and insights for developing foundation time series models with robust continuous learning capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： engineering education

A Novel HDL Code Generator for Effectively Testing FPGA Logic Synthesis Compilers

学校读者我要写书评

暂无评论

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, Zhihao Guo, Shikai Zhao, Guilin Zou, Peiyu Li, Xiaochen Jiang, He Southeast University School of Information Science and Technology Dalian Maritime University School of Computer Science and Engineering Dalian China School of Computer Science and Engineering Nanjing China Dalian Maritime University Key Laboratory of Artificial Intelligence of Dalian School of Information Science and Technology Dalian China Key Laboratory of Artificial Intelligence of Dalian School of Information Science and Technology Dalian China Dalian University of Technology School of Software Dalian China

Field Programmable Gate Array (FPGA) logic synthesis compilers (e.g., Vivado, Iverilog, Yosys, and Quartus) are widely applied in Electronic Design Automation (EDA), such as the development of FPGA programs. However, defects (e.g. incorrect synthesis) in logic synthesis compilers may lead to unexpected behaviors in target applications, posing security risks. Therefore, it is crucial to thoroughly test logic synthesis compilers to eliminate such defects. Despite several Hardware Design Language (HDL) code generators (e.g., Verismith) having been proposed to find defects in logic synthesis compilers, the effectiveness of these generators is still limited by the simple code generation strategy and the monogeneity of the generated HDL code. This paper proposes EvoHDL, a novel method to generate syntax-valid HDL code for comprehensively testing FPGA logic synthesis compilers. EvoHDL can generate more complex and diverse defect-triggering HDL code (e.g., Verilog, VHDL, and SystemVerilog) by leveraging the guidance of abstract syntax tree and the extensive function block libraries of cyber-physical systems. Extensive experiments show that the diversity and defect-triggering capability of HDL code generated by EvoHDL are significantly better than the state-of-the-art method (i.e., Verismith). In three months, EvoHDL has reported 20 new defects–many of which are deep and important;16 of them have been confirmed. © 1982-2012 IEEE.

关键词： Hardware-software codesign

Weighted Density for The Win: Accurate Subspace Density Clustering

学校读者我要写书评

暂无评论

Weighted Density for The Win: Accurate Subspace Density Clus...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Maixuan Peng Yuyang Wu Yang Lu Mengke Li Yiqun Zhang Yiu-Ming Cheung School of Computer Science and Technology Guangdong University of Technology Guangzhou China Faculty of Science Wilfrid Laurier University Brantford Canada Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

k-clustering typically struggles with the detection of irregular-distributed clusters due to the natural bias, while density clustering usually cannot well-adapt to different datasets and clustering tasks as it is not an oriented optimization process. This paper, therefore, proposes to perform density clustering in dynamically learned subspaces. To exploit the irregular-distributed clusters obtained by density clustering for the subspace determination, we design a new strategy to appropriately evaluate the importance of attributes. It turns out that the proposed Weighted Density-based Subspace Clustering (WDSC) algorithm inherits the unbiased merits of density clustering, and also upgrades the unlearning density clustering to be learnable under the subspace learning paradigm of k-clustering. A comprehensive evaluation including significance tests, ablation studies, qualitative comparisons, etc., shows the superiority of WDSC.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Clustering algorithms Signal processing Acoustics Speech processing Optimization

NSPG-Miner: Mining Repetitive Negative Sequential Patterns

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Hebei Key Laboratory of Big Data Computing 300401 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this paper explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner, but also verify that NSPG-Miner can discover more valuable information than the state-ofthe-art algorithms. Algorithms and datasets can be downloaded from https://***/wuc567/PatternMining/tree/master/NSPG-Miner. Copyright © 2025, The Authors. All rights reserved.

关键词： Miners

MindScore: quantifying human preference for text-to-image generation through multi-view lens

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2025年第06期 72-85页

作者： Yiqi TONG Jiarui ZHANG Shaohang WEI Wei GUO Fuzhen ZHUANG Deqing WANG Xi YANG Richeng XUAN School of Artificial Intelligence Beihang University School of Computer Science and Engineering Beihang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Computer Science Peking University State Key Laboratory of Complex & Critical Software Environment Beihang University Beijing Academy of Artificial Intelligence

Understanding and quantifying the capabilities of foundation models, particularly in text-to-image(T2I) generation, is crucial for verifying their alignment with human expectations and practical requirements. However, evaluating T2I foundation models presents significant challenges due to the complex, multi-dimensional psychological factors that influence human preferences for generated images. In this work, we propose MindScore, a multi-view framework for assessing the generation capacity of T2I models through the lens of human preference. Specifically, MindScore decomposes the evaluation into four complementary modules that align with human cognitive processing of images: matching, faithfulness, quality,and realness. The matching module quantifies the semantic alignment between generated images and prompt text, while the faithfulness module measures how accurately the images reflect specific prompt details. Furthermore, we incorporate quality and realness modules to capture deeper psychological preferences, recognizing that unpleasant or distorted images often trigger adverse human responses. Extensive experiments on three T2I datasets with human preference annotations clearly validate the superiority of our proposed MindScore over various state-of-the-art baselines. Our case studies further reveal that MindScore offers valuable insights into T2I generation from a human-centric perspective.

关键词：

Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation