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

作者： Lihua Zhang Quan Liu Xiongzhen Zhang Yapeng Xu School of Computer Science and Technology Soochow University Suzhou China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

We study the problem of imitation learning in automated decision systems, in which a learner is trained to imitate an expert demonstrator. A widely used method is adversarial imitation learning that alternately optimizes a generator (learner) and a discriminator (reward function). However, the discriminator is biased during the initial and intermediate training stages. Consequently, the gradient descent direction of the learner is misguided, which leads to unstable training and sample complexity. In this paper, we propose deep imitation learning through a guidance-based policy distillation (GIL) algorithm. First, GIL proposes a teacher model, the guidance-based variational autoencoder, which is pre-trained with expert demonstrations. Then, GIL proposes a perturbation-based policy distillation method that uses the teacher model to guide the learner in the correct optimization direction, enabling the learner to imitate the expert policy with fewer detours. The experimental results show that our approach achieve higher sample efficiency compared with multiple baselines.

关键词： Training Legged locomotion Signal processing algorithms Grasping Signal processing Generators Task analysis

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Data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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ChatGPT: potential, prospects, and limitations

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Frontiers of information technology & Electronic Engineering 2024年第1期25卷 6-11页

作者： Jie ZHOU Pei KE Xipeng QIU Minlie HUANG Junping ZHANG School of Computer Science Fudan UniversityShanghai 200433China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433China

Recently,OpenAI released Chat Generative Pre-trained Transformer(ChatGPT)(Schulman et al.,2022)(https://***),which has attracted considerable attention from the industry and academia because of its impressive *** is the first time that such a variety of open tasks can be well solved within one large language *** better understand ChatGPT,we briefly introduce its history,discuss its advantages and disadvantages,and point out several potential ***,we analyze its impact on the development of trustworthy artificial intelligence,conversational search engine,and artificial general intelligence.

关键词： artificial limitations intelligence

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Topic-BiGRU-U-Net for Document-level Relation Extraction from Biomedical Literature

Topic-BiGRU-U-Net for Document-level Relation Extraction fro...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Yali Yan, Rong Inner Mongolia University College of Computer Science Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (纸本)9798350337488

Document-level biomedical relation extraction refers to extract relationship facts from unstructured biomedical literature. Due to the fact that many relationship facts span multiple sentences and involve complex interactions between entities, it requires models with strong logical reasoning capabilities. However, previous methods have had limitations in terms of logical reasoning, such as lack logical reasoning and the entities merely based on a single granularity. Although other efforts are based on multiple granularity, including mention pairs and entity pairs, the inference mechanism is weak. In this paper, we propose a module to enhance the model's logical reasoning capabilities from four kinds of granularity (mention, entity, mention pairs, and entity pairs). Specifically, we encode the entities and mentions separately using BiGRU to capture contextual features. Simultaneously, we employ a U-Net network to model entity pairs and mention pairs, and enhance inter-sentence reasoning abilities. Additionally, to support the model's reasoning capabilities, we introduce topic nodes into the traditional heterogeneous graph to fully extract document information. Experimental results on CDR and GDA datasets in the biomedical field demonstrate the outstanding performance of the proposed model. © 2023 IEEE.

关键词： BiGRU document-level relation extraction information extraction U-Net

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Insulator Defect Detection Based on YOLOv4-tiny with Improved Feature Fusion 12

Insulator Defect Detection Based on YOLOv4-tiny with Improve...

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12th International Conference on information technology in Medicine and Education, ITME 2022

作者： Zhong, Yuning Hu, Rong Li, Zuoyong Cai, Yuanzheng Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Insulator exposed to a wild environment for long-term running usually suffers from the damage of natural calamities that makes them become one of the most fragile components in the whole power system. In this case, even a slight fault will seriously affect the power supply safety of the power grid. Therefore, it is critical to find and identify the faulty insulator efficiently and accurately. However, an insulator is quite a small component in comparison with a huge power system which makes it hard to visible with naked eyes. This characteristic leads to poor recognition accuracy for existing detection methods. In order to solve the above problems, we propose an insulator defect detection algorithm based on an improved YOLOv4-tiny model. It differs from existing insulator detection models, our algorithm applies the effective channel attention network to the feature extraction network of YOLOv4-tiny, which significantly enhances the feature extraction ability of the backbone network. These two-way features of the pyramid can be fully fused in the two-way feature fusion stages seamlessly. Experimental results show that compared with, our proposed algorithm outperforms Faster RCNN and YOLOv3 in average classification accuracy, missed detection, and false detection. © 2022 IEEE.

关键词： Feature extraction

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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High spectral-efficiency,ultra-low MIMO SDM transmission over a field-deployed multi-core OAM fiber

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Photonics Research 2025年第1期13卷 18-30页

作者： JUNYI LIU SHUQIN MO ZENGQUAN XU YUMING HUANG YINING HUANG ZHENHUA LI YUYING GUO LEI SHEN SHUO XU RAN GAO CHENG DU QIAN FENG JIE LUO JIE LIU SIYUAN YU State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Yangtze Optical Fibre and Cable Joint Stock Limited Company State Key Laboratory of Optical Fibre and Cable Manufacture TechnologyWuhan 430074China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China Fiberhome Telecommunication Technologies Co. Ltd.Wuhan 430074China School of Electronics and Information Technology and Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen UniversityGuangzhou 510006China

Space-division multiplexing(SDM)systems based on few-mode multi-core fibers(FM-MCFs)utilize both spatial channels(fiber cores)and modes(optical modes per core)to maximize transmission *** laboratory FM-MCFs or field-deployed single-mode multi-core fibers(SM-MCFs),SDM transmissions over field-deployed FM-MCFs in outdoor settings have not been ***,concerns remain that environmental interference and cabling stress could worsen inter-core and intra-core modal crosstalk and impact the performance of SDM systems over *** this paper,we demonstrate successful bidirectional SDM transmission over a 5-km,field-deployed seven ring-core fiber(7-RCF)with a cladding diameter of 178μ*** measurements show no significant differences in attenuation and mode coupling compared to pre-cabling conditions,confirming the fiber’s resilience to environmental disturbances and adaptability to cable *** the field-deployed 7-RCF,bi-directional SDM transmission is implemented,achieving spectral efficiency(SE)of 2×201.6 bit/(s Hz)which sets a new record in field-deployed fiber cables that is a tenfold increase over previous ***,these results were achieved using a small-scale 4×4 multiple-input multiple-output(MIMO)scheme with a time-domain equalization(TDE)tap number not exceeding *** results demonstrate the substantial potential of using SDM techniques to significantly enhance SE and expand capacity in practical fiber-optic transmission applications.

关键词： fiber MIMO SDM

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Ultra-high definition video quality assessment method based on deep learning 9

Ultra-high definition video quality assessment method based ...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Li, Wei He, Can Zhou, MingLe Zhang, HengYu Liu, WenLong Hisense Video Technology Co. Ltd. Qing Dao China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

With the development of display technology, people's demand for high-definition video is increasing, and the requirements for video quality are also increasing. Nowadays, a large number of videos have emerged, and how to distinguish the quality of videos faces many challenges. Nowadays, deep learning is developing rapidly, and many video quality assessment (VQA) methods have emerged. These VQA methods can efficiently evaluate video quality and save a lot of manpower and material resources. To achieve the evaluation of UHD video quality, we propose an inter-frame downsampling UHD video quality assessment model (IFDU-VQA) based on deep learning. This model is a no-reference (NR) evaluation model that adopts a dual-branch structure: one branch uses a recursive network to deeply extract video inter-frame features, and combines inter-frame downsampling technology with a multi-information fusion (MIF) module to achieve different time and frequency information. Effective fusion;the other branch applies convolutional neural networks (CNN) to extract features from individual video frames. By fusing the outputs of the two branches, regression and pooling modules are used to calculate the final quality score of the video. Compared with multiple existing models, experimental results show that IFDU-VQA demonstrates superior performance on multiple standard data sets. © 2024 IEEE.

关键词： Convolutional neural networks

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Sequence-to-Sequence Knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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Architecture Design of Equipment Warehouse Scheduling System Based on Software Definition 5th

Architecture Design of Equipment Warehouse Scheduling System...

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5th International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2022

作者： Zhang, Xue Ting Pan, Yan Peng Yang, Li Jie Xue, Chen Chen Zhang, Fu Quan China Academy of Launch Vehicle Technology Beijing100076 China School of Automation Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9789819908479

The technology of software-defined changing all areas of life, but the equipment storage system in the field of application is still in its infancy. At present, the traditional equipment warehousing scheduling system is difficult to meet the new needs of rapid scheduling and flexible maintenance of large quantities of equipment. Based on the development and application characteristics of software definition technology, this paper integrates the traditional equipment storage scheduling system and presents the research on the architecture design technology of equipment storage scheduling system under software definition technology. This paper clarifies the key application technologies of the system, realizes the design of new open system architecture based on software-defined technology and the optimization design of system center service platform architecture, and improves the architecture design ability of automation, configuration ability, and easy maintenance of the existing equipment storage scheduling system. It solves the new needs of rapid deployment, intelligent upgrade, flexible configuration, and convenient service of large quantities of equipment warehouse scheduling system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Application programs

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