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IEEE International Conference on systems, Man and Cybernetics

作者： Wenpeng He Xin Chen Hao Fu Yipu Sun School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan P. R. China

ISBN: (纸本)9781665442084

This paper investigates the consensus problems of heterogeneous discrete-time (DT) nonlinear multi-agent systems (MASs) with unknown dynamics and switching typology. By adding a virtual model to each agent and using the model reference protocol to the actual model, the consensus problems of unknown nonlinear MASs are transformed into consensus problems of known virtual linear MASs, and a distributed control law is designed for the virtual model to make the virtual linear MASs achieve consensus under the switching typology. Two numerical simulations with nonidentical nonlinear dynamics and switching typology are given to prove the effectiveness of the proposed method.

关键词： Protocols Conferences Switches Consensus control Numerical simulation Numerical models Nonlinear dynamical systems

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An Intelligent Deterministic Scheduling Method for Ultra-Low Latency Communication in Edge Enabled Industrial Internet of Things

arXiv

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arXiv 2022年

作者： Lu, Yinzhi Yang, Liu Yang, Simon X. Hua, Qiaozhi Sangaiah, Arun Kumar Guo, Tan Yu, Keping The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada The Computer school Hubei University of Arts and Science Xiangyang441000 China The National Yunlin University of Science and Technology Taiwan The Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan The RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, Time Sensitive Network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then a non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultra-low latency services and guarantee efficient bandwidth utilization. © 2022, CC BY.

关键词： Scheduling

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CLAWER: Context-aware Cloud-Fog based Workflow Management Framework for Health Emergency Services

CLAWER: Context-aware Cloud-Fog based Workflow Management Fr...

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Shreya Ghosh Jaydeep Das Soumya K Ghosh Rajkumar Buyya Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

With the major development of sensor technologies and advancements of communication network infrastructures, there is a growing interest to add more intelligence in the e-health monitoring for facilitating an effective healthcare system. While IoT devices are capable of continuous health-parameter sensing and providing notifications to the user, an effective business process management (BPM) facilitates effective system integration and data processing workflow. This paper proposes an efficient framework for managing emergency situations (specifically, health-related) through the analysis of heterogeneous data sources. The proposed framework, named CLAWER (CLoud-Fog bAsed Workflow for Emergency seRvice) aims to bridge the gap between process management and data analytics by providing an automated workflow for personalized health-monitoring and efficient recommendation system. Here, the IoT devices are used for collecting the movement and health data. The smart phone can act as an edge device to acquire data with user movement information. The accumulated data is initially processed inside the fog device, and finally the analysis and recommendations are generated by the cloud. In this paper the indoor health-status of the users are analysed in small cell cloud enhanced eNode B, which is used as fog device. The generated recommendations are stored in the fog device to provide the recommendations to the users with low latency and in timely manner. The experimental analysis of CLAWER yields better precision and recall values than the existing methods.

关键词： Cloud computing Roads Medical services Markov processes Monitoring Delays Biomedical monitoring

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Accurate grid keypoint learning for efficient video prediction

arXiv

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arXiv 2021年

作者： Gao, Xiaojie Jin, Yueming Dou, Qi Fu, Chi-Wing Heng, Pheng-Ann The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The CUHK T Stone Robotics Institute Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Video prediction methods generally consume substantial computing resources in training and deployment, among which keypoint-based approaches show promising improvement in efficiency by simplifying dense image prediction to light keypoint prediction. However, keypoint locations are often modeled only as continuous coordinates, so noise from semantically insignificant deviations in videos easily disrupt learning stability, leading to inaccurate keypoint modeling. In this paper, we design a new grid keypoint learning framework, aiming at a robust and explainable intermediate keypoint representation for long-term efficient video prediction. We have two major technical contributions. First, we detect keypoints by jumping among candidate locations in our raised grid space and formulate a condensation loss to encourage meaningful keypoints with strong representative capability. Second, we introduce a 2D binary map to represent the detected grid keypoints and then suggest propagating keypoint locations with stochasticity by selecting entries in the discrete grid space, thus preserving the spatial structure of keypoints in the long-term horizon for better future frame generation. Extensive experiments verify that our method outperforms the state-of-the-art stochastic video prediction methods while saves more than 98% of computing resources. We also demonstrate our method on a robotic-assisted surgery dataset with promising results. Our code is available at https://***/ xjgaocs/Grid-Keypoint-Learning. Copyright © 2021, The Authors. All rights reserved.

关键词： Location

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Temporal memory relation network for workflow recognition from surgical video

arXiv

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arXiv 2021年

作者： Jin, Yueming Long, Yonghao Chen, Cheng Zhao, Zixu Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering T Stone Robotics Institute Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automatic surgical workflow recognition is a key component for developing context-aware computer-assisted systems in the operating theatre. Previous works either jointly modeled the spatial features with short fixed-range temporal information, or separately learned visual and long temporal cues. In this paper, we propose a novel end-to-end temporal memory relation network (TMRNet) for relating long-range and multi-scale temporal patterns to augment the present features. We establish a long-range memory bank to serve as a memory cell storing the rich supportive information. Through our designed temporal variation layer, the supportive cues are further enhanced by multi-scale temporal-only convolutions. To effectively incorporate the two types of cues without disturbing the joint learning of spatio-temporal features, we introduce a non-local bank operator to attentively relate the past to the present. In this regard, our TMRNet enables the current feature to view the long-range temporal dependency, as well as tolerate complex temporal extents. We have extensively validated our approach on two benchmark surgical video datasets, M2CAI challenge dataset and Cholec80 dataset. Experimental results demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin (e.g., 67.0% v.s. 78.9% Jaccard on Cholec80 dataset). Copyright © 2021, The Authors. All rights reserved.

关键词： Surgery

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An avoiding overlap method between acoustic shadow and organ for automated ultrasound diagnosis and treatment

An avoiding overlap method between acoustic shadow and organ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Momoko Matsuyama Norihiro Koizumi Yu Nishiyama Ryosuke Tsumura Hiroyuki Tsukihara Kazushi Numata Department of Mechanical and Intelligent Systems Engineering The University of Electro-Communications Chofu Japan Department of Computer and Network Engineering The University of Electro-Communications Chofu Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology line 4 Tsukuba Japan Department of Cardiothoracic Surgery The University of Tokyo Bunnkyo-Ku Japan Gastroenterological Center Yokohama City University Medical Center Yokohama Japan

ISBN: (纸本)9781665492331

Ultrasound diagnosis and treatment have attracted attention because they are noninvasive and allow real-time observation of lesions. However, it is difficult to accurately estimate the location of the treatment area because of information loss due to acoustic shadows caused by the reflection of sound waves from hard tissues such as bone. In this study, we propose a probe manipulation method that uses deep learning to avoid overlap between organs and shadows for automated diagnosis.

关键词： Deep learning Ultrasonic imaging Acoustics Real-time systems Reflection Reliability Lesions

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Cashew Apple Nutrients Prediction from Optical Spectroscopic Streaming Data Using Machine Learning-Based Approach

SSRN

引用

SSRN 2024年

作者： Ishaya, Jeremiah Ayock Adoni, Wilfried Yves Hamilton Zoueu, Jérémie Thouakesseh Assoi, Koko Eliane Mutombo, Franck Kalala Krichen, Moez Abdalzaher, Mohamed S. African Institute for Mathematical Sciences Senegal Academic City University College Accra Ghana Helmholtz Zentrum Dresden Rossendorf Center for Advanced Systems Understanding Görlitz Germany Helmholtz Zentrum Dresden Rossendorf Helmholtz Institute Freiberg for Resource Technology Freiberg Germany University of San Pedro San-Pedro Ivory Coast National Polytechnic Institute - Felix Houphouet Boigny Yamoussoukro Ivory Coast Faculty of Computer Science and Information Technology Al-Baha University Al Baha Saudi Arabia ReDCAD Laboratory National School of Engineers of Sfax Sfax Tunisia University of Lubumbashi Lubumbashi Democratic Republic Congo Department of Seismology National Research Institute of Astronomy and Geophysics Cairo Egypt African Institute for Mathematical Sciences Ghana African Institute for Mathematical Sciences Rwanda

The fleshy portion of the cashew fruit that is joined to the nut is known as the cashew apple. It is an economically important fruit, rich in minerals, vitamins, carbohydrates, organic acids, and antioxidants. The cashew apple is also renowned for its antiscorbutic, pharmacological, and therapeutic characteristics due to its health benefits, and can be consumed fresh or processed into candies, alcohol, and syrup. Due to its economic importance, measuring its quality enables good management and enhances its olfactory, dietary, and sanitary qualities. The main criterion for harvesting the cashew apple is its maturity, and this is usually made known by some specific parameters such as pH, water, sugar, drought measure, and acidity (cashew apple nutrients). However, determining the quality of the cashew apple using these parameters is always tedious, time-consuming, and requires expert knowledge. In this study, we proposed two different approaches (single target and multi-output) used in predicting the parameters using four different machine learning algorithms (Cashew Multiple Linear Regression (CMLR), Cashew Ridge Regression (CRR), Cashew K-Nearest Neighbor (CKNN), and Cashew Lasso Regression (CLAR) for the predictions. The performance was measured using Mean Square Error(MSE) and Average Root-Mean-Square Error (aRMSE), where we obtained promising results. In the single target prediction results, it is observed that the CRR model (optimized) mse performs better than the other algorithms for pH and water prediction, having a mse of 0.126009, and 3.787282 respectively. Furthermore, for sugar, and drought measures, the optimization of the model’s hyperparameters did not have any significant difference with the baseline model, with the CLAR model outperforming the other models having 4.00923, and 6.364642 mse respectively. For acidity, the CMLR-optimized model outperformed the other algorithms with 0.000670 mse. In the multi-output prediction results, it is observed that

关键词： Forecasting

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High sensitivity interferometric fiber optic gyroscopes for 6-component seismograph 27

High sensitivity interferometric fiber optic gyroscopes for ...

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27th International Conference on Optical Fiber Sensors, OFS 2020

作者： Cao, Yuwen He, Dong Zhang, Dingfan Zhang, Wentao Li, Li Li, Zhengbin State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China State Key Laboratory of Transducer Technology Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Institute of Geophysics China Earthquake Administration Beijing100081 China

The principle of dual polarized fiber optic gyroscope insensitive to the temperature and magnetic field variance is presented, and several novel experiments have been demonstrated, including earthquake detection, envi... 详细信息

ISBN: (纸本)9781557523075

关键词： Fiber optics

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Simulation of Radio Tomographic Imaging for Measurement Rice Moisture Content 10

Simulation of Radio Tomographic Imaging for Measurement Rice...

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10th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2020

作者： Abdullah, Mohammed Saeed Moqbel Kamarudin, Latifah Munirah Rahiman, Mohd Hafiz Fazalul Mohamed, Latifah Zakaria, Ammar Universiti Malaysia Perlis School of Mechatronic Engineering Perlis Malaysia Universiti Malaysia Perlis School of Computer and Communication Engineering Perlis Malaysia Universiti Malaysia Perlis Centre of Excellence for Advanced Sensor Technology School of Mechatronic Engineering Perlis Malaysia Universiti Malaysia Perlis School of Electrical Systems Engineering Perlis Malaysia

ISBN: (纸本)9781728172439

Radio Tomographic Imaging (RTI) is an emerging technology for imaging the attenuation caused by physical objects in wireless networks that perform wireless receive signal strength (RSS) measurements obtain a reconstruction of objects inside an area of interest to know the different moisture content of rice in the silo. The simulation results analysis has been performed. The image of the phantoms was reconstructed by the selected image reconstruction algorithms, which are Linear Back Projection (LBP), Filtered Back Projection (FBP), and Gaussian. Evaluation of this work was assessed by using three image quality assessment techniques Mean Structural Similarity Index (MSSIM). MSSIM was used to analyze the reconstructed images. Among the three proposed images reconstruction algorithms linear back projection, filtered back projection, and Gaussian algorithm. Gaussian seems to be a more reliable option for reconstructing the image of moisture content of rice in a silo by using 20 RF nodes in the RTI system. This paper discusses in detail the use of shadowing losses on links between RF sensors in a wireless community to image the attenuation of moisture content inside the wi-fi network vicinity. © 2020 IEEE.

关键词： Moisture

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CryoNet:atomic model building from Cryo-EM density map by deep neural network

CryoNet:atomic model building from Cryo-EM density map by de...

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第十九次中国暨国际生物物理大会

作者： Kui Xu Jingle Xu Zhe Wang Zhuoer Dong Qiangfeng Cliff Zhang MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural Biology &Frontier Research Center for Biological StructureCenter for Synthetic and Systems BiologyTsinghuaPeking Joint Center for Life SciencesSchool of Life SciencesTsinghua University Department of Computer Science and Technology Tsinghua University

Model buildingfromCryo-ElectronMicroscopy(Cryo-EM) density map is a final key step in macromolecular structure determination using Cryo-EM single particle *** have evolved from manual construction to template matching,which is error-prone,time-consuming,and extremely *** this paper,we present CryoNet,an end to enddeep learning method to automatically build atomic modelsfromCryo-EMdensity maps.

关键词： CryoNet:atomic model building from Cryo-EM density map by deep neural network

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