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IEEE International Conference on robotics and Automation (ICRA)

作者： Zhaofeng Zuo Xiaoming Liu Zhuo Chen Yuyang Li Xiaoqing Tang Dan Liu Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and School of Mechatronical Engineering Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micromanipulation techniques that can achieve controlled fine operations at the micro scale play an important role in biomedical fields including embryo engineering, gene engineering, drug screening, and cell analysis. However, micromanipulation of biological micro-objects, such as cells and micro tissues, suffers from mechanical damage and low efficiency. Several techniques have been introduced to manipulate cells more easily, but most of them are restricted by expensive devices, limited work area, and potential damage to cellular structure. Here we develop a hydrodynamic manipulation method to rotate and transport mouse oocytes, which utilizes acoustic waves and micropipette to generate acoustic radiation force and excite microstreaming. This method can accomplish rotational and translational operations precisely and controllably. We tested the process of trapping, rotation, and transportation of the mouse oocytes, and measured rotational and translational speed with a range of applied voltage. The method was able to shorten the cost time of delivery and posture adjustment before oocyte injection. Our study provides an easy-to-use technique for oocyte manipulation without contact, and it has the potential to be universally applied in many cellular studies.

关键词： Visualization Voltage measurement Three-dimensional displays Force Transportation Hydrodynamics Biology

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Graph-to-Text Generation Combining Directed and Undirected Structural Information in Knowledge Graphs*

Graph-to-Text Generation Combining Directed and Undirected S...

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Natural Language Processing (ICNLP), International Conference on

作者： Hongda Gong Shimin Shan Hongkui Wei Software School Dalian University of Technology Dalian University of Technology DaLian China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China

Graph-to-text generation task is transforms knowledge graphs into natural language. In current research, pretrained language models(PLMs) have shown better performance than structured graph encoders in the generation task. Currently, PLMs serialise knowledge graphs mostly by transforming them into undirected graph structures. The advantage of an undirected graph structure is that it provides a more comprehensive representation of the information in knowledge graph, but it is difficult to capture the dependencies between entities, so the information represented may not be accurate. Therefore, We use four types of positional embedding to obtain both the directed and undirected structure of the knowledge graph, so that we can more fully represent the information in knowledge graph, and the dependencies between entities. We then add a semantic aggregation module to the Transformer layer of PLMs, which is used to obtain a more comprehensive representation of the information in knowledge graph, as well as to capture the dependencies between entities. Thus, our approach combines the advantages of both directed and undirected structural information. In addition, our new approach is more capable of capturing generic knowledge and can show better results with small samples of data.

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Flapping-Wing Robot Achieves Multi-Domain Locomotion Using Transformable Wheel Mechanism

Flapping-Wing Robot Achieves Multi-Domain Locomotion Using T...

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IEEE International Conference on robotics and Biomimetics

作者： Xiangxiao Chai Shihua Wang Erzhen Pan Wenfu Xu School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong Shenzhen China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The difficulty of moving from aquatic to aerial environment is one of the major challenges limiting the development of trans-medium multi-domain locomotion vehicles. In this paper, the concept of the transformable wheel, a feature commonly found in autonomous land vehicles (AL V s), is introduced into the design. Additionally, a flapping-wing robot equipped with multi-domain locomotion capability, which can not only realize aerial flight and surface navigation on water, but also ascend from water to land for subsequent takeoff is presented. The paper first introduces the system design of the robot and proposes the corresponding multi-domain locomotion strategy. Subsequently, the design of the transformable wheel mechanism is described in detail and the force analysis for the water-exit is conducted. The feasibility of the proposed strategy is experimentally validated, confirming the prototype's multi-domain locomotion performance finally. This research explores the feasibility of introducing the transformable wheel mechanism into trans-medium multi-domain vehicle platforms and provides a design example.

关键词： Legged locomotion Torque Limiting Navigation Propellers Roads Wheels Prototypes Motors system analysis and design

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A Missing Data Imputation Method for Electricity Consumption Data Based on TCN-Attention with Mask Tokens

A Missing Data Imputation Method for Electricity Consumption...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Yifeng Mao Ming Yang Peng Li Zhujian Ou Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China State Grid Nantong Power Supply Company State Grid Jiangsu Electric Power CO. LTD Nantong China

With the popularization of smart meters, power companies can collect massive amounts of data from users for non-invasive load detection, electricity theft detection, etc. However, due to faults in smart meters and abnormal communication, the dataset often has missing values, making it difficult for data-driven methods to be widely applied. In this paper, we propose an unsupervised learning method based on TCN-Attention to fill in customers' electricity consumption data. Firstly, we extract electricity consumption features through TCN. In order to improve the feature extraction capability, we introduce a channel attention mechanism to TCN. The attention mechanism makes TCN focus on the effective features in the data by assigning weights. Meanwhile, we use mask tokens to cover a certain proportion of data, forcing the proposed model to learn potential feature representations of the data during the training process. Finally, the features are restored to data through feedforward neural networks. An example is given to verify the effectiveness of the proposed method.

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Distributed Photovoltaic Power Forecasting Method Based on AP Clustering and Transfer Learning

Distributed Photovoltaic Power Forecasting Method Based on A...

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IEEE/IAS Industrial and Commercial Power system Asia (I&CPS Asia)

作者： Baolong Yang Ming Yang Qiangsheng Bu Chuanqi Wang Yuanhe Zhang Menglin Li Fei Luo Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China Research Institute State Grid Jiangsu Electric Power CO. LTD. Nanjing China

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

Highly accurate photovoltaic (PV) power forecast technology can provide a scientific basis for grid planning to ensure safe, economical, and efficient grid operation. The investment cost for a specialized power prediction system for each PV plant is high. Consequently, a distributed PV (DPV) power forecast method based on affinity propagation (AP) clustering and transfer learning is proposed in this paper. First, the dynamic time warping (DTW) algorithm determines the similarity of power curves between DPV plants; on this basis, multiple DPV power plants in the area to be forecasted are divided into station clusters by using AP clustering; and the PV power plants in each station cluster are divided into source and target domain by transfer learning, combined with long-short term memory (LSTM) network, the power forecasting model is established, and enabling transfer of power models from source to target domain. Finally, data from 20 DPV plants in China are used to demonstrate the effectiveness of the approach. Compared to separate modeling, the proposed method reduces the average training time by 58% while maintaining the forecasting accuracy.

关键词： Photovoltaic systems Training Accuracy Costs Heuristic algorithms Transfer learning Weather forecasting Predictive models Forecasting Long short term memory

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Modeling of Doubly Fed Wind Power Generation system and Simulation Study on Grid-Connection Stability 4

Modeling of Doubly Fed Wind Power Generation System and Simu...

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4th International Conference on intelligent Power and systems, ICIPS 2024

作者： Zhang, Jingyue Zhang, Jie Hu, Yufeng Lu, Yuanhong Huang, Libin Zhao, Yanjun Guo, Haiping China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China China Southern Power Grid National Energy Power Grid Technology R & D Centre Guangzhou Guangzhou510663 China China Southern Power Grid Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou Guangzhou510663 China China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China National Energy Power Grid Technology R & D Centre Guangzhou Guangzhou510663 China

ISBN: (纸本)9798331532765

Addressing the stability challenges posed by the unpredictability and intermittent nature of wind power output during grid integration, and aiming to enhance the understanding of factors influencing grid stability upon wind power system connection and to implement efficient control measures, a grid- connected model of a doubly fed induction generator (DFIG) was established utilizing a customized version of the RTRES (Real-Time Power system Simulation) software, specifically designed for emerging power systems and aligned with wind farm realities. Stability simulations for grid connection were conducted under both voltage regulation mode and reactive power regulation mode across a range of wind speeds, revealing distinct technical behaviors. The findings suggest that the generator's terminal voltage, terminal current, active power, reactive power, and rotor speed exhibit strong correlations with variations in wind speed, and that all parameters are capable of stabilizing following initial disturbances. © 2024 IEEE.

关键词： Windmill

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Short-Term Load Prediction of EV Charging Station Based on LSTM Recursion

Short-Term Load Prediction of EV Charging Station Based on L...

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Power Science and Technology (ICPST), IEEE International Conference on

作者： Ziyu Xu Kuan Cao Yutian Liu Chunyi Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Development Planning Department State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

With the rapidly increasing market share of electric vehicles (EV) in recent years, the randomness and uncertainty of EV charging load have posed challenges to the operation of distribution networks. To improve accuracy in load prediction of EV charging station, a short-term load prediction method based on the long short-term memory (LSTM) recursive neural network is proposed in the paper. First, the Canopy-based K-means clustering algorithm is improved to divide the similarly daily load sets of EV charging station. Then, the characteristic information of load days, such as meteorological data, is processed to obtain the typical feature vector of each load set. Next, the bidirectional long short-term memory (BiLSTM) models corresponding to each load set are trained based on the sliding window principle and phase space reconstruction. By calculating the Euclidean distance of the feature vectors between the predicted day and each load set, the most suitable pre-trained BiLSTM model is selected to predict the charging station load. Finally, simulation results demonstrate that the proposed method can effectively improve the load prediction accuracy.

关键词： Recurrent neural networks Accuracy Uncertainty Simulation Predictive models Feature extraction Electric vehicle charging

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A hybrid algorithm for underwater image restoration based on color correction and image sharpening

A hybrid algorithm for underwater image restoration based on...

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作者： Meng, Haiyang Yan, Yongjie Cai, Chengtao Qiao, Renjie Wang, Feng College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Ministry of Education Harbin150001 China Harbin150001 China State Key Laboratory of Air Traffic Management System and Technology Nanjing China

In this paper, an effective method is proposed for restoring underwater image based on color correction and image sharpening. The main purpose of this method is to improve the visibility of underwater images. Traditional methods generally adopt color balancing method to restore the images. However, we found that color balancing has a poor effectiveness on underwater image when the values of blue channel are greater than other two channels, specially, red channel is great of small than blue channel. Therefore, we propose a hybrid method for color correction process based on a principle that exploits the relationship of three channels (red channel, green channel, and blue channel). On the other words, color balancing will be employed to restore the images when the values of red channel approximate to blue channel, while the DCP-base method will be used if otherwise. To enhance the sharpness of underwater image, we employed a sharpening process based on Maximum a Posteriori (MAP) method when color correction has finished. The proposed method also been validated through carrying out experiments on several underwater image datasets which are provided by previous researchers. Our validation has proved that the proposed method has a better performance than the state-of-the-art methods. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image reconstruction

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Unsupervised Attention Regularization Based Domain Adaptation for Oracle Character Recognition

arXiv

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arXiv 2024年

作者： Wang, Mei Deng, Weihong Hu, Jiani Su, Sen The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

The study of oracle characters plays an important role in Chinese archaeology and philology. However, the difficulty of collecting and annotating real-world scanned oracle characters hinders the development of oracle character recognition. In this paper, we develop a novel unsupervised domain adaptation (UDA) method, i.e., unsupervised attention regularization network (UARN), to transfer recognition knowledge from labeled handprinted oracle characters to unlabeled scanned data. First, we experimentally prove that existing UDA methods are not always consistent with human priors and cannot achieve optimal performance on the target domain. For these oracle characters with flip-insensitivity and high inter-class similarity, model interpretations are not flip-consistent and class-separable. To tackle this challenge, we take into consideration visual perceptual plausibility when adapting. Specifically, our method enforces attention consistency between the original and flipped images to achieve the model robustness to flipping. Simultaneously, we constrain attention separability between the pseudo class and the most confusing class to improve the model discriminability. Extensive experiments demonstrate that UARN shows better interpretability and achieves state-of-the-art performance on Oracle-241 dataset, substantially outperforming the previously structure-texture separation network by 8.5%. © 2024, CC BY.

关键词： Character recognition

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A Pilot Study on Fabric-based Pneumatic Soft Gloves for Assisting Patients with Severe Brachial Plexus Injury

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Feng, Miao Yang, Dezhi Sun, Jianing Lao, Jie Rui, Jing Gu, Guoying Shanghai Jiao Tong University Robotics Institute State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Key Laboratory of Intelligent Robotics Meta Robotics Institute Shanghai200240 China Southwest Technology and Engineering Research Institute Chongqing400039 China Fudan University Department of Hand Surgery Huashan Hospital China NHC Key Laboratory of Limbs Reconstruction Shanghai Key Laboratory of Peripheral Nerve and Microsurgery Shanghai200040 China

Objective: robotic gloves show promise in hand assistance due to their wearability and home-based potential, yet empirical research remains limited. This pilot study presents a fabric-based pneumatic soft glove, aiming to identify its potential and challenges in clinical practice by evaluating its effectiveness in assisting patients with severe brachial plexus injury (BPI). Methods: The glove integrates a thumb abduction actuator and four bidirectional fabric-based pneumatic actuators (FPAs) with asymmetric chambers for high output force. Sixteen healthy volunteers and five individuals with BPI, all of whom lacked active hand and wrist movements, were recruited. Participants performed object grasping across 25 cm. The healthy group performed seven tasks using objects weighing up to 2 kg, with muscle activities recorded for analysis. The BPI group further performed tasks with eight objects from the action research arm test (ARAT) and twelve objects for activities of daily living (ADLs), encompassing various sizes, weights, and geometries. Results: In the healthy group, sEMG showed a decrease in 89.3% of trials, with 56.0% of these decreases being significant (p © 1964-2012 IEEE.

关键词： Pneumatic actuators

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