检索结果-内蒙古大学图书馆

International Universities Power Engineering Conference (UPEC)

作者： Mircea N. Lancranjan Alexandru Muresan Dacian I. Jurj Alexandru Berciu Alexis Polycarpou Levente Czumbil Dan D. Micu Department Electrical Engineering Technical University of Cluj-Napoca Cluj-Napoca Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9798350379730

ISBN: (纸本)9798350379747

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated.

关键词： Load monitoring Costs Scalability Signal processing algorithms Hardware Smart meters Energy efficiency Transient analysis Time complexity Load modeling

来源：评论

学校读者我要写书评

暂无评论

Identification of personalized driver genes for individuals using graph convolution network

Identification of personalized driver genes for individuals ...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Peng, Wei Yu, Piaofang Dai, Wei Fu, Xiaodong Liu, Li Pan, Yi Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665468190

The correct identification of the driver genes that lead to cancer development is essential for understanding the mechanisms of cancer and developing drugs to treat it. Currently, most computational methods for identifying cancer driver genes are based on a cohort of patients. However, due to the heterogeneity of cancers, patients diagnosed with the same cancers may have different genomic characteristics and present varied clinical symptoms. It requires devising effective methods to identify personalized cancer driver genes in an individual. This work developed a novel method to predict personalized cancer driver genes of a single sample based on graph convolution networks, namely pDriverGCN. pDriverGCN constructed a mutant gene-sample heterogeneous network according to the known driver genes of samples. Then it employed two separate graph convolution network models to learn feature representations for genes and samples by gathering the features of themselves and their neighbors. Finally, pDriverGCN used the feature representations to reconstruct the association matrix between genes and samples through a linear correlation coefficient decoder. We apply our model to identify personalized driver genes of samples on the TCGA datasets. The experimental results show that our model outperforms state-of-the-art methods being evaluated at both population and individual levels. © 2022 IEEE.

关键词： Genes

来源：评论

学校读者我要写书评

暂无评论

SIF-STGDAN: A Social Interaction Force Spatial-Temporal Graph Dynamic Attention Network for Decision-Making of Connected and Autonomous Vehicles

SIF-STGDAN: A Social Interaction Force Spatial-Temporal Grap...

引用

IEEE Symposium on Intelligent Vehicle

作者： Qi Liu Yujie Tang Xueyuan Li Fan Yang Xin Gao Zirui Li School of Mechanical Engineering Beijing Institute of Technology Beijing China Faculty of Computer Science Dalhousie University Halifax Canada Chair of Traffic Process Automation "Friedrich List" Faculty of Transport and Traffic Sciences TU Dresden Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

The collaborative decision-making technology of connected and autonomous vehicles (CAVs) is critical in today’s autonomous driving. Recently, graph reinforcement learning (GRL)-based methods have demonstrated exemplary performance in solving decision-making problems by implementing graphic technologies. However, current GRL-based research faces the challenge of modeling the interaction completely and extracting driving features efficiently. To address these issues, this paper proposes a social interaction force (SIF) spatial-temporal graph dynamic attention network (SIF-STGDAN) to solve the decision-making of CAVs. First, a SIF model is established to better represent the mutual effect between vehicles; an on-ramp merging scenario is then constructed and modeled by graph representation. Then, the SIF-STGDAN is proposed by combining the temporal convolutional network (TCN) and graph dynamic attention network to extract the graphic features of the on-ramp scenario efficiently, and the double deep q-learning (DDQN) algorithm is utilized to generate the optimized driving strategies for CAVs. Finally, experiments are conducted, and results show that our proposed SIF-STGDAN outperforms the baselines in terms of safety, efficiency, and model stability.

关键词： Graphics Heuristic algorithms Decision making Merging Force Feature extraction Stability analysis

来源：评论

学校读者我要写书评

暂无评论

Analysis of Electric Arc Furnace Influence on Power Quality in Low and Medium Voltage Networks

Analysis of Electric Arc Furnace Influence on Power Quality ...

引用

International Conference on Environment and Electrical Engineering (EEEIC)

作者： Dariusz Grabowski Maciej Klimas Andrzej Lange Department of Electrical Engineering and Computer Science Faculty of Electrical Engineering Silesian University of Technology Gliwice Poland Department of Electrical and Power Eng. Electronics and Automation Faculty of Technical Sciences University of Warmia and Mazury Olsztyn Poland

ISBN: (数字)9798350355185

ISBN: (纸本)9798350355192

This paper focuses on analyzing power quality issues resulting from electric arc furnace operation. It highlights the challenges posed by the stochastic nature of the arc and proposes strategies to mitigate its impact on the power grid. Through measurements carried out on the medium and low voltage side of the furnace, fluctuations in parameters such as voltage, current, and power factor were observed, indicating significant disturbances in power quality. Mitigation strategies, including e.g. installing compensators, are suggested to stabilize the network and improve power quality, enhancing efficiency and reducing energy consumption and electrode usage.

关键词： Electrodes Economics Reactive power Energy consumption Fluctuations Power measurement Furnaces Current measurement Power quality Standards

来源：评论

学校读者我要写书评

暂无评论

Multi-objective performance prediction based on a self-constructed Al-Si alloy dataset

Multi-objective performance prediction based on a self-const...

引用

Information Systems and Computing Technology (ISCTech), International Conference on

作者： Dan Hu Yingli Liu Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China

The combination of data-driven approaches and machine learning techniques has changed the design approach for new materials, propelling materials science research into the realm of the fourth paradigm. Researchers in the field of materials science have increasingly focused on utilizing machine learning methods to uncover the underlying nonlinear relationships within material data. However, there is currently limited research in the field of aluminum-silicon alloys regarding the prediction of alloy performance based on the composition-process-structure relationship. Additionally, there is a lack of data sharing in the aluminum-silicon alloy domain. Therefore, this study proposes a methodology. Firstly, an aluminum-silicon alloy dataset is constructed by mining relevant information from materials literature. Then, high-dimensional features in the dataset are subjected to feature selection techniques for dimensionality reduction. Subsequently, a multilayer perceptron model is applied to investigate the relationship between input features such as alloy composition, preparation process, and microstructural parameters, and output features such as yield strength, ultimate tensile strength, and elongation, enabling performance prediction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach with Fusion Edge Prediction Strategy for Table Structure Recognition

A Novel Approach with Fusion Edge Prediction Strategy for Ta...

引用

Information Systems and Computing Technology (ISCTech), International Conference on

作者： Yingli Liu Guangtao Zhang Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China

Table, as an information carrier with high information density and direct display form, are widely used in scientific literature and social production documents. Accurately identifying tables from various documents will significantly promote the intelligent application of documents. The table’s structural information is crucial for comprehending how the text content in the table relates to one another, but teaching a computer to fully understand a table’s structure is still a very difficult undertaking. In this research work, we provided a unique edge-based method to recognize table structures. Specifically, we define the inter-cell relationships as an edge prediction problem and leverage graph convolutional networks to learn representations of the cells. These learned representations are then utilized to directly predict the connections between the cells. The experimental results conducted on three publicly available datasets for table structure recognition provide compelling evidence supporting the feasibility and usefulness of our presented methodology.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Essential proteins identification based on weak consensus model and neighborhood aggregation centrality

Essential proteins identification based on weak consensus mo...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Zhipeng Li, Gaoshi Wu, Jingli Luo, Xinlong Liu, Jiafei Peng, Wei Zhu, Xiaoshu Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security Guilin541004 China Guangxi Normal University College of Computer Science and Engineering Guilin541004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Guilin University of Electronic Science and Technology School of Computer and Information Security & School of Software Engineering Guilin China

ISBN: (纸本)9798350337488

Essential proteins play an essential role in cell survival and replication. Currently, more and more computational methods are developed to identify essential proteins, which overcome the time-consuming, costly and inefficient shortcomings with biological experimental methods. In order to improve the recognition rate, some new methods by fusing multiple features are developed, but they seldom consider the connection among features. After analyzing a large number of methods based on multi-feature fusion, a weak consensus model to fuse features is proposed in this paper. Then, this paper uses the weak consensus model to fuse protein-protein interaction network, gene expression data, and orthologous data, thus proposing a new method, WOL. Then experiments are performed on one *** dataset. The results show that compared with WDC, PeC, ION, JDC, NCCO and E_POC, WOL has a higher recognition rate. © 2023 IEEE.

关键词： Essential proteins orthologous data protein-protein interaction weak consensus model

来源：评论

学校读者我要写书评

暂无评论

Informatization as a condition for the development of academic disciplines by students of a higher educational institution 1

Informatization as a condition for the development of academ...

引用

1st International Conference on Technology Enhanced Learning in Higher Education, TELE 2021

作者： Burlakova, Elena Kachalova, Svetlana Muzyleva, Inna Yazykova, Liubov Lipetsk State Technical University Faculty of Humanities and Social Sciences and Law Lipetsk Russia Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia

ISBN: (纸本)9781665443968

The article enlightens the role of information technology in the educational process of a modern higher educational institution. The authors of the work talk about the need to apply new methods and models of communication in their work. The authors investigate the forms of communication of participants in the educational process used in distance learning, suggesting that information resource portals are a promising area of educational communications. The purpose of the study is to analyze effective forms of e-learning. The authors note the prospects for the development of e-learning. The conclusions made by the authors of the article make it possible to assert the need to apply new educational models and technologies in the activities of teachers of higher educational institutions. © 2021 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Residual and Attention Mechanism-Based Network for Aluminum-Silicon Alloy Microstructure Image Classification

Residual and Attention Mechanism-Based Network for Aluminum-...

引用

Information Systems and Computing Technology (ISCTech), International Conference on

作者： Xiao Yang Tao Shen Jiancheng Yin Yingli Liu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Faculty of Materials Science and Engineering Kunming University of Science and Technology Kunming China

To enhance the accuracy of aluminum-silicon alloy microstructure image classification, we propose an aluminum-silicon alloy microstructure image classification model based on an improved residual network, incorporating attention mechanisms and multi-scale thinking. The model is built upon the ResNet18 architecture, utilizing enhanced basic residual blocks to select informative features. Additionally, a multi-scale attention module is incorporated into the network to fuse multi-scale features and further extract effective features. Experimental results on an aluminum-silicon alloy microstructure image classification dataset demonstrate that compared to the best-performing model currently available, the proposed model achieves an increase in classification accuracy, precision, recall, and F1 score by 3.17%, 0.74%, 5.53%, and 3.26%, respectively, reaching 94.97%, 96.39%, 93.97%, and 95.17%. These results indicate that the proposed model exhibits strong capabilities in classifying aluminum-silicon alloy microstructure images and can facilitate further research on the properties of aluminum-silicon alloys.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Data Transmission Approach Based on Ant Colony Optimization and Threshold Proxy Re-encryption in WSNs

引用

Journal of Artificial Intelligence and Technology 2022年第1期2卷 23-31页

作者： Jing Liu Zenghui Liu Chenyu Sun Junxi Zhuang College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China College of Automation Engineering Beijing PolytechnicBeijing 100176China

Wireless sensor networks(WSNs)have become increasingly popular due to the rapid growth of the Internet of *** open wireless transmission media are easy to attack,security is one of the primary design concerns for *** solutions consider routing and data encryption as two isolated issues,providing incomplete ***,in this paper,we divide the WSN communication process into a data path selection phase and a data encryption *** propose an improved transmission method based on ant colony optimization(ACO)and threshold proxy re-encryption for WSNs,and we named it as *** method resists internal and external attacks and ensures safe and efficient data *** the data path selection stage,the ACO algorithm is used for network *** improvement of the pheromone concentration is *** order to resist attacks from external attackers,proxy re-encryption is extended to WSN in the data encryption *** threshold secret sharing algorithm is introduced to generate a set of re-encryption key fragments composed of random numbers at the source *** confirm the performance of our model via simulation studies.

关键词： wireless sensors network ant colony optimization pheromone proxy re-encryption threshold

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：