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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Liu, He Liu, Shi-Qi Xie, Xiao-Liang Li, Ye Zhou, Xiao-Hu Feng, Zhen-Qiu Li, Guo-Tao Ma, Xi-Yao Hou, Zeng-Guang Yuan, Yong-Yi Cui, Yong Tian, Yu-Hong Dai, Pu Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institutes of Advanced Technology Shenzhen518055 China The Sixth Medical Center of Pla General Hospital College of Otolaryngology Head and Neck Surgery Beijing100853 China The 983th Hospital of Joint Logistic Support Force Department of Otolaryngology Tianjin300142 China

ISBN: (纸本)9798350325706

Nasotracheal intubation is a crucial emergency procedure that can improve the survival rate of injured patients. Compared to orotracheal intubation, nasotracheal intubation offers greater stability and comfort. However, due to the large curvature of the cavity, the procedure is highly challenging and requires skilled medical staff, which can be a shortage in emergencies and delay patient rescue. To solve this problem, a flexible and portable robot for nasotracheal intubation has been developed in this paper. The robot incorporates a continuum catheter, allowing the shape of the endotracheal tube's distal section to be controllable and making it easier to pass through the nasal cavity into the trachea. An endoscope is integrated into the distal section of the catheter to make the intubation process more intuitive, allowing doctors to observe the injuries through endoscopic images and adjust the treatment plan. Additionally, the robot is designed with a quick-detachable structure, enabling aseptic and low-cost intubation procedures. The structure and circuitry of the robot are designed for portability, with a smaller size and lower power consumption, which can work for a long time just powered by batteries. Finally, experiments on a mannequin validate that the robotic system effectively reduces the difficulty of tracheal intubation, enabling even non-medically trained operators to complete the procedure with the assistance of the robot. © 2023 IEEE.

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Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs

arXiv

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arXiv 2022年

作者： Xu, Shaoxian Shao, Zhiyuan Yang, Ci Liao, Xiaofei Jin, Hai The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

The emerging Graph Convolutional Network (GCN) has been widely used in many domains, where it is important to improve the efficiencies of applications by accelerating GCN trainings. Due to the sparsity nature and exploding scales of input real-world graphs, state-of-the-art GCN training systems (e.g., GNNAdvisor) employ graph processing techniques to accelerate the message exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these systems treat both the aggregation stages of forward and backward propagation phases as all-active graph processing procedures that indiscriminately conduct computations on all vertices of an input graph. In this paper, we first point out that in a GCN training problem with a given training set on an input graph, its aggregation stages of backward propagation phases (called as backward aggregations in this paper) can be equivalently converted to partially-active graph processing procedures, which conduct computations on only partial vertices of the input graph. By leveraging such a finding, we propose an execution path preparing method that collects and coalesces the graph data used during different training layers of backward aggregations, and constructs their corresponding sub-graphs (called as execution paths in this paper) as inputs to conduct the backward training on GPUs. Further, we propose a structural-aware strategy for the execution paths to compute their optimal group sizes, so as to gain as high as possible performances on GPUs during the backward aggregations. The experiment results by conducting GCN training in typical real-world graphs show that compared with GNNAdvisor, our approach improves the performance of backward aggregations by up to 5.68x on NVIDIA P100 GPU, and up to 6.57x on NVIDIA V100S GPU. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphics processing unit

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SoLar: sinkhorn label refinery for imbalanced partial-label learning 22

SoLar: sinkhorn label refinery for imbalanced partial-label ...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Haobo Wang Mingxuan Xia Yixuan Li Yuren Mao Lei Feng Gang Chen Junbo Zhao Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University Department of Computer Sciences University of Wisconsin-Madison College of Computer Science Chongqing University and Center for Advanced Intelligence Project RIKEN

ISBN: (纸本)9781713871088

Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling. In this work, we first identify the major reasons that the prior work failed. We subsequently propose SoLar, a novel Optimal Transport-based framework that allows to refine the disambiguated labels towards matching the marginal class prior distribution. SoLar additionally incorporates a new and systematic mechanism for estimating the long-tailed class prior distribution under the PLL setup. Through extensive experiments, SoLar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods.

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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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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

arXiv

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arXiv 2025年

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi’an China

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method’s superior performance over existing diffusion and discriminative models, validating its efficacy. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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A Privacy Preserving Federated Learning Aggregation Algorithm for Noise Label

A Privacy Preserving Federated Learning Aggregation Algorith...

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International Conference on Computer and Communications (ICCC)

作者： Weiguang Han Xishan Yan Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China College of Computer and Cyber Secutiry Fujian Normal University Fuzhou China University of Chinese Academy of Sciences Beijing China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fuzhou China

To solve the problem that a large number of noise labels lead to the reduction of the efficiency of existing aggregation Algorithmic efficiency and the difficulty of extending existing methods to the privacy protection framework, a noise label oriented privacy protection Federated learning aggregation algorithm PP-ASNL is proposed. This algorithm is mainly divided into two parts: a client-side noise label sample detection model and a server-side model rating aggregation mechanism. Simulation results show that the proposed algorithm has higher model accuracy and Rate of convergence than the existing methods.

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research on vehicle routing problem based on improved dragonfly algorithm

Research on vehicle routing problem based on improved dragon...

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Chinese Automation Congress (CAC)

作者： Fan Yang Yiyi Cao Xu Zhang YTO Express Co. LTD Nanjing Jiangsu China Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing Jiangsu China Anhui post valley Express intelligent technology Co. LTD Wuhu Anhui China

Aiming at the low carrying rate and high fuel consumption cost of vehicle routing optimization, a vehicle routing model with time window is constructed. An improved dragonfly algorithm integrated with random learning strategy is designed and applied to solve the problem. The real-value calculation form of the five behaviors of the original dragonfly algorithm is changed into random learning target position pairs and applied to vehicle routing problem with time windows. By comparing the results with those of other three algorithms, the effectiveness and feasibility of improved effective dragon algorithm are further verified, which can reasonably allocate logistics capacity and human resources, and realize the cost reduction and efficiency increase of enterprise business.

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for computing Machinery.

关键词： Ethanol

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Effects of Personality Traits on Travelers’ Heterogeneous Preferences Toward Air Itinerary Choice: Analysis from Urumqi, China

SSRN

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SSRN 2021年

作者： Pan, Xiaofeng Zuo, Zhi Intelligent Transportation Systems Research Center Wuhan University of Technology China Big Data Research Institute Liaoning University of International Business and Economics China

Personality is a psychological concept which reflects people’s characteristic patterns of thoughts, feelings, and behavior. In this sense, it is straightforward to state that people’s behavior is fundamentally affected by their personalities. However, the concept of personality is rarely considered in most travel behavior studies. Given this fact, this paper aims to investigate the effect of personality traits on travelers’ heterogeneous preferences in the context of air itinerary choice. To this end, travelers’ stated choices for air itinerary and information reflecting travelers’ personality traits were collected. After defining specific personality traits based on the collected data, a hybrid choice model with interaction effects between travelers’ personality traits/socio-demographic characteristics and alternative-specific attributes were established and estimated and next the contributions of personality traits and socio-demographic characteristics to travelers’ choice behavior were compared and analyzed. The results confirmed the effects of personality traits on travelers’ heterogeneous preferences. However, the results also revealed that the magnitudes of such effects are not as great as the effects of socio-demographic characteristics. © 2021, The Authors. All rights reserved.

关键词： Population statistics

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Sub-Loc:predicting protein sub-mitochondrial localization based on sequence embedding

Sub-Loc:predicting protein sub-mitochondrial localization ba...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoting Wang Juan Wang Haodong Bian Maozu Guo School of Computer Science Inner Mongolia University and Ecological Big Data Engineering Research Center of the Ministry of Education Hohhot China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing P.R. China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing P.R. China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Mitochondria are subcellular organelles existing in most eukaryotic organisms. They have a pivotal role in lots of bio-chemical processes for cells. Proteins in different compartments of mitochondria have their transport routes. Locating proteins in mitochondria can provide a solid foundation for mitochondrial pathologies. So far, there have been several computational methods for solving the issue. However, their accuracy is so low that they do not identify the localization precisely. We develop an unsupervised learning model to represent mitochondrial proteins as n-dimensional vectors, called sequence embedding, which can learn the global and context information from mitochondrial proteins. We design a new model, called Sub-Loc, to predict the sub-mitochondrial localization of proteins using the SVM classifier and the sequence embedding method. The sequence embedding method and the Sub-Loc are tested by experiments. Experimental results show the sequence embedding method remarkably enhances the performance of prediction for sub-mitochondrial localization of proteins compared with other feature representations. The Sub-Loc outperforms other approaches for predicting sub-mitochondrial localization of proteins.

关键词： Proteins Location awareness Support vector machines Deep learning Pathology Predictive models Feature extraction

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