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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Kuo-Chi Chang Ze Xu Kai-Chun Chu Fu-Hsiang Chang Hsiao-Chuan Wang Tsui-Lien Hsu Yuh-Chung Lin Yuwen Zhou North Borneo University College Sabah Malaysia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Fujian University of Technology Fuzhou China Shih-Hsin University Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan

At the same time, the burning of a large amount of fossil fuels has aggravated environmental pollution and the greenhouse effect. In order to solve the problems of energy shortage and environmental pollution, electric vehicle technology has been highly valued by many countries. Electric vehicles are the most promising new energy vehicles. Therefore, on the basis of fully considering the charging load characteristics of electric vehicles, how to better establish charging facilities and adjust the impact of a large number of random loads on the grid. Therefore, it is of great significance and application value to study the charging strategy and control method of electric vehicles to improve the stable operation level of the power grid and the ability to accept new energy. Here, the frequency modulation control strategy of electric vehicles considering the restriction of electricity price is studied to ensure the regulation ability and charging demand of electric vehicles.

关键词： Industries Pollution Frequency modulation Greenhouse effect Electric vehicles Regulation Fossil fuels

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CAFI: News Recommendation with Candidate Perception of Fine-Grained Interaction Information

CAFI: News Recommendation with Candidate Perception of Fine-...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Xiaoyang Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The most critical task in personalized news recommendation is to perform exact matching between candidate news and users’ interests. Existing news recommendation methods usually model users’ interests from historical clicked news items without considering candidate news, which makes it difficult to precisely match candidate news with users’ interests. Moreover, it is challenging to distinguish similar news when forming a vector representation. Simply adding candidate news to the user interest modeling process without considering word-level information does not provide sufficient discrimination for recommending appropriate news. In this paper, we propose a news recommendation model with candidate-aware fine-grained interaction information (CAFI). In our approach, we propose a fine-grained interaction module that matches candidate news items and text fragments of each historical news item at each semantic granularity to achieve interaction between the two at the word level and help similar historical news form more specific and accurate representations. In addition, our proposed gated self-attention mechanism utilizes the candidate news features as channel moderation gates to implement the process of filtering out information that is irrelevant to the candidate news for learning user interest representations, thereby better matching the candidate news to specific user interests. Experiments conducted on the large real-world Microsoft News dataset (MIND) show that our model significantly outperforms the previously developed models in terms of all metrics. Our code is published at the following URL: https://***/liyiweneven/CAFI.

关键词： Uniform resource locators Measurement Codes Filtering Federated learning Semantics Focusing

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LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yuchen Liu Hu Liang Ran Qiu Shengrong Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network’s lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments.

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Efficient meta neural heuristic for multi-objective combinatorial optimization 23

Efficient meta neural heuristic for multi-objective combinat...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jinbiao Chen Jiahai Wang Zizhen Zhang Zhiguang Cao Te Ye Siyuan Chen School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China School of Computing and Information Systems Singapore Management University Singapore

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time.

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TSFE: Research on Transformer and Feature Enhancement Methods for Image-Text Retrieval

TSFE: Research on Transformer and Feature Enhancement Method...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jinxiao Yang Xingang Wang Qingao Wang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, cross-modal retrieval has a wide range of applications in fields such as information retrieval and multimedia processing, where the key is to find relevant data in another modality through one modal query. In cross-modal graphic retrieval, mutual attention mechanisms are commonly used to interact image and text features. However, this interaction mechanism makes it difficult to extract separate visual and textual features for subsequent indexing steps, especially in large-scale retrieval tasks. Furthermore, in image representation, non-semantic information in image features can lead to inaccuracies in the alignment of image and text features. Therefore this paper proposes a Transformer and Feature Enhancement Method (TSFE) for Image-Text Retrieval. The method better fuses the underlying features and the higher level features by using a hierarchical Transformer coding structure on two separate branches, image and text, to proofread and localise the final output features. The Feature Enhanced Attention (FEA) module is designed in order to infer non-semantic information in the image and enhance the features of salient regions. Channel-space attention and max pooling is introduced on top of Transformer encoder self-attention to filter redundant and interfering information from channel and spatial dimensions. Experiments on Flickr30k dataset and MS-COCO dataset in this paper show improved performance in cross-modal retrieval, thus validating the effectiveness and sophistication of the proposed method.

关键词： Visualization Semantics Pipelines Noise Image representation Transformers Feature extraction

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lichao Ding Jing Zhao Kai Lu Zenghao Hao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets.

关键词： Tensors Stacking knowledge graphs Predictive models Transformers Vectors Decoding Standards Mutual information Periodic structures

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可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

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Science Bulletin 2023年第7期68卷 740-749页

作者：曹敏田雅吴凯陈旻陈玉胡雪孙中昶左丽君林键骆磊朱瑞许振赐 Temenoujka Bandrova Milan Konecny 袁文平郭华东林珲闾国年 Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China School of Geography and Environment Jiangxi Normal UniversityNanchang 330022China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China The Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Sierra Nevada Research Institute University of CaliforniaMerced CA 95348USA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Geography the University of Hong KongHong Kong 999077China Laboratory on Cartography University of ArchitectureCivil Engineering and GeodesySofia 1164Bulgaria Laboratory on Geoinformatics and Cartography Institute of GeographyMasaryk UniversityBrno 60177Czech Republic School of Atmospheric Sciences Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Sun Yat-sen UniversityZhuhai 519082China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of EducationJiangxi Normal UniversityNanchang 330022China

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其... 详细信息

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

关键词： Sustainable development goals(SDGs) Land-use projection Carbon pool Scenarios System dynamics model Cellular automata

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Topic-based Multi-layer knowledge Filtering for Emotion Recognition in Conversation

Topic-based Multi-layer Knowledge Filtering for Emotion Reco...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiaoyang Li Zhenyu Yang Yiwen Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Emotion recognition in conversation (ERC) is a prominent research topic in natural language processing, widely applicable in various scenarios. However, accessing external commonsense knowledge and related dialogue topics, along with the complexity of fusing utterances, presents significant challenges. In this paper, we introduce a Topic-based Multilayer knowledge Filtering (TMKF) model to enhance dialog emotion recognition accuracy. TMKF acquires commonsense knowledge for each utterance and employs two Variational Autoencoder (VAE) modules to extract global and speaker-level dialogue topics. We utilize Global knowledge Filtering (GKF) and Local knowledge Filtering (LKF) to obtain topic-specific knowledge representations after filtering commonsense information through global and local topic hierarchies. Subsequently, we leverage relational graphs to convolve commonsense knowledge with utterances, resulting in knowledge-constrained utterance representations for classification. Our proposed model is evaluated through extensive experiments on four widely used benchmark datasets for conversational emotion recognition. The results underscore the effectiveness of TMKF, which significantly outperforms other methods in standard metric evaluations.

关键词： Measurement Emotion recognition Filtering Convolution Oral communication Benchmark testing Nonhomogeneous media

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Research on long and short-term social recommendation based on convolutional and gated recurrent units

Research on long and short-term social recommendation based ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zihe Jia Peng Xue Zhiqiang Dai Qian Gao Xiaomeng Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users’ long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data; secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier; finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved.

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Design of Primary System for 110kV Smart Substation

Design of Primary System for 110kV Smart Substation

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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Kuo-Chi Chang Hongkai Chen Kai-Chun Chu Fu-Hsiang Chang Hsiao-Chuan Wang Tsui-Lien Hsu Yuh-Chung Lin Yuwen Zhou North Borneo University College Sabah Malaysia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Fujian University of Technology Fuzhou China Shih-Hsin University Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan

Substation is the bridge between power plant and load, and it is the place where high-quality energy is transported. And China's power technology has been in the world's top level, because of the complex terrain in China, so the design of power station should consider the impact of terrain factors, economic factors, and future development factors and so on. Therefore, smart substation design is worthy of our research and innovation. Installation of lightning arrester can effectively reduce the threat of lightning.

关键词： Wiring Arresters Substations Short-circuit currents Lightning Stability analysis Circuit stability

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