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2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023

作者： Gong, Jing Zhong, Siyu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

When using wavelet for voltage interruption detection, improper selection can lead to detection failure due to the differences in wavelet characteristics and the diversity of the generating function. Based on the analysis of wavelet characteristics, the modulus maximum principle is used to compare the detail coefficients after wavelet decomposition, and a method that should be followed for selecting the generating function is proposed. Here are four important points to consider: First, choose orthogonal wavelets that can decrease redundancy. Second, symmetry does not have a major effect. Third, higher-order vanishing moments and longer support lengths result in better singularity detection. Finally, the order of vanishing moments is more important than the support length. We establish a disturbance signal model with interruption occurring at the time of voltage zero crossing and select six types of wavelets: db1, db3, db6, db8, coif2, and coif3 for four-scale wavelet decomposition. The experimental results demonstrate the accuracy of the proposed method in this paper. © Published under licence by IOP Publishing Ltd.

关键词： Wavelet decomposition

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Research on Backward Cloud Transformation Algorithm Based on Adding Cloud Drops 9

Research on Backward Cloud Transformation Algorithm Based on...

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9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Xu, Changlin Kong, Lingzhuo School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ning Xia Province Yinchuan750021 China

ISBN: (纸本)9781643684703

As a tool for studying uncertain information, cloud model is of great significance in uncertain artificial intelligence and data mining. Among them, the backward cloud transformation method is one of the important algorithms of cloud model, which can realize the conversion from quantitative data to qualitative concepts. In this paper, a dynamic incremental backward cloud transformation algorithm is proposed to solve the problem that the estimated value of hyper entropy is imaginary when the backward cloud transformation method is used to calculate hyper entropy. First, according to the formation characteristics of cloud drops, the generated samples and the original samples are fused as new samples by dynamically and randomly generating samples, and then the hyper entropy is estimated until the estimated value of hyper entropy is real. Secondly, the stability and convergence of the algorithm proposed in this paper are analyzed through simulation experiments. The experimental results show that the new dynamic incremental backward cloud transformation algorithm solves the problem that the hyper entropy estimation value is imaginary while the estimation error is small and the stability is good. Finally, the algorithm is applied to brain CT segmentation, and the results show that the algorithm is effective and practical. © 2023 The authors and IOS Press.

关键词： data mining

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FBOD: An Outlier Detection Algorithm based on data Features Suitable for Processing Large-scale datasets on Distributed Platforms 10

FBOD: An Outlier Detection Algorithm based on Data Features ...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Zhao, Jinbo He, Jinling Song, Hu Cheng, Xinyun Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

The primary objective of outlier detection is to identify values that are significantly different from other data in the dataset. However, most of the current algorithms are effective for small-scale data and their performance is highly dependent on the choice of parameters. To this, the paper proposes an innovative outlier detection algorithm, named FBOD, which is based on data features. Specifically, FBOD utilizes the number of combinations of attribute values in each tuple across the dataset as a key feature of the tuple. This feature reflects the degree of differentiation of each data tuple from the overall dataset. By identifying data features that are significantly different from objects in the entire dataset, FBOD can accurately determine outliers. Based on the knowledge of attribute importance in Rough Set Theory, FBOD can efficiently reduce the data dimensionality, so high-dimensional datasets can be handled by FBOD. Furthermore, this paper employs a distributed parallelism approach in the algorithm. The performance of FBOD is evaluated on several datasets, including standard datasets and artificial datasets, and is compared with common outlier detection algorithms. Experimental results indicate that, compared to existing typical algorithms, FBOD achieves higher outlier detection accuracy without relying on a prior knowledge of datasets or parameter selection. It also proves the suitability of FBOD for large-scale dataset processing on distributed platforms. © 2024 IEEE.

关键词： Anomaly detection

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Research on Electricity Consumption Model of Library Building Based on data Mining

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Energy Engineering 2022年第6期119卷 2407-2429页

作者： Jiaming Dou Hongyan Ma Rong Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing102616China Institute of Distributed Energy Storage Safety Big Data Beijing University of Civil Engineering and ArchitectureBeijing102616China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing102616China

With the exponential development of Chinese population,the massive energy consumption of buildings has recently become an interest *** much research has been conducted on residential buildings,heating ventilation and air conditioning(HVAC),little research has been conducted on the relationship between student’s behavior,campus buildings,and their *** classical seasonal decomposition,hierarchical clustering,and apriori algorithm,this paper aims to provide an empirical model for consumption data in campus *** meter data from a library in Beijing,China,is adopted in this *** electricity consumption patterns are investigated on an hourly/daily/monthly *** to the monthly analysis,electricity consumption peaks each year around June and December due to teaching programs,social exams,and outdoor *** data analysis revealed a relatively stable consumption *** shows three different types of daily load *** data analysis demonstrated a high relationship between HVAC consumption and building total consumption,with a lift value of ***,links between temperature and subsystems were also *** a case study of library,this study provides a unique insight into campus electricity *** results could help to develop operational strategies for campus facilities.

关键词： Electricity consumption data mining load profile campus building

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Design of cloud platform for niche monitoring of forest pests based on Internet of Things 22

Design of cloud platform for niche monitoring of forest pest...

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5th International Conference on Information Management and Management Science, IMMS 2022

作者： Fan, Min Lu, Ying Xu, Quanyuan Yang, Peidong College of Big Data and Intelligent Engineering Southwest Forestry University China College of Big Data and Intelligent Engineering Southwest Forestry University China and Key Laboratory of Forest Ecology Big Data National Forestry and Grassland Administration Southwest Forestry University China Qujing Tobacco Company of Yunnan Province China

ISBN: (纸本)9781450396721

In view of the frequent occurrence of dangerous forest diseases and insect pests in China, which have caused varying degrees of damage to forest resources and ecological environment, this study designed a niche monitoring system for forest diseases and insect pests based on STM32 and the Internet of Things. The design uses STM32 as the data acquisition terminal and repeater, and transmits data to the cloud via the Internet through the W5500 module, realizing remote monitoring of the host computer's webpage. Through this monitoring system, users can understand the environmental information and historical conditions that have a greater impact on forest diseases and insect pests in the deployment area of the system at any time, including carbon dioxide concentration, oxygen concentration, soil pH, temperature and humidity, light intensity, etc., collected by the system The terminal can adapt to about 20 kinds of sensors, and can easily and comprehensively monitor and manage different forest niches. This study provides a basis for forest pest monitoring, which can efficiently and timely provide forest ecological environment information and reduce the consumption of human and material resources. It is of great significance to the scientific management of forest workers. © 2022 ACM.

关键词： Internet of things

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

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A Model robustness optimization method based on adversarial sample detection 5

A Model robustness optimization method based on adversarial ...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Sun, Jiaze Long, Siyuan Ma, Xianyan Tang, Yanmei Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Deep neural networks are extremely vulnerable due to the existence of adversarial samples. It is a challenging problem to optimize the robustness of the model to protect deep neural networks from the threat of adversarial samples. To improve the model robustness, an integrated detection model of adversarial samples is designed to detect the existence of adversarial samples, which consists of a multi-classification detector and five single-classification detectors to perform double-layer detection of adversarial samples and intercept the adversarial samples finally sent to the image classification model. The detection experiments were conducted on the CIFAR-10 dataset for the adversarial samples generated by five attack algorithms: FGSM, BIM, DeepFool, JSMA, and C&W, and the detection success rate for all types of adversarial samples reached over 98.96%. After that, secondary attack experiments were conducted on the model, and the detection success rate of the model for the second attack adversarial samples reached over 92.36%. It provides an efficient and robust optimization method for deep neural network models in adversarial environments. © 2022 ACM.

关键词： Deep neural networks

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SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement

arXiv

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arXiv 2025年

作者： Fang, Runnan Wang, Xiaobin Liang, Yuan Qiao, Shuofei Wu, Jialong Xi, Zekun Zhang, Ningyu Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

In the interaction between agents and their environments, agents expand their capabilities by planning and executing actions. However, LLM-based agents face substantial challenges when deployed in novel environments or required to navigate unconventional action spaces. To empower agents to autonomously explore environments, optimize workflows, and enhance their understanding of actions, we propose SynWorld, a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search (MCTS) exploration to effectively refine their action knowledge in the current environment. Our experiments demonstrate that SynWorld is an effective and general approach to learning action knowledge in new environments.1 Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual reality

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Travel Characteristics and Combination Choice Behavior Modeling of Passengers under the Influence of Epidemic Prevention Policies 24

Travel Characteristics and Combination Choice Behavior Model...

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24th COTA International Conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Zhang, Yue Dong, Chunjiao Li, Penghui Dong, Qianru Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong Univ. Beijing China State Key Laboratory of Intelligent Vehicle Safety Technology China Automotive Engineering Research Institute Co. Ltd. Chongqing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

The intermodal travel characteristics and combination selection behaviors of passengers are key issues to providing reasonable, efficient, and safe solutions, ensuring the traffic order, and improving quality. With the Beijing Winter Olympics as a background, by combining the Stated Preference and Revealed Preference surveys, the research develops a decision-making behavior model based on nested logit to investigate the passenger travel choices. The impacts of individual attributes, epidemic prevention policies, and preferential measures on the choice of intermodal travel, as well as the combination of departure time and travel mode, are studied. The results show that cultural and creative products have a significant impact on the combined travel choices. The cultural and creative products and consumer vouchers have a significant impact on the choice of intermodal travel. The research results can provide insights into traffic organization and management policies during large-scale events. © ASCE.

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