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Process map for collision avoidance based on information exchange for autonomous navigation of vessel

IOP Conference Series: Materials Science and engineering 2020年第1期929卷

作者： Ho Namgung Jung Sik Jeong Kwangil Lee Sun-Young Kim Graduate school of Maritime Transportation System Mokpo National Maritime University Mokpo Republic of Korea Department of Maritime Transportation Science Mokpo National Maritime University Mokpo Republic of Korea Department of Control and Automation Engineering Korea Maritime and Ocean University Busan Republic of Korea Maritime Safety and Environmental Research Division Korea Research Institute of Ships & Ocean Engineering Daejeon Republic of Korea jsjeong@mmu.ac.kr

A reliable collision avoidance method is essential when operating an autonomous vessel. For this to become a reality, a number of researches have been conducted in order that the autonomous vessel could take an action for collision avoidance by herself. However, it may lead to dangerous situation without knowing the navigation intention of target-ships. In order to solve the issue, we developed the process map for collision avoidance based on information exchange via the communication relay system as follows: (i) the navigation situation analysis of the autonomous vessel was conducted based on manned ship voyage definition; (ii) the sequence diagram for information exchange was developed based on the autonomous vessel's navigation context and the communication relay system, and (iii) the process map for collision avoidance based on information exchange was proposed based on the developed sequence diagram and the existing collision avoidance method. It would be expected that the autonomous vessel could avoid collision more safely than taking an action by herself.

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One-Dimensional EEG Artifact Removal Network Based on Convolutional Neural Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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Robust medical instrument segmentation challenge 2019

arXiv

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arXiv 2020年

作者： Roβ, Tobias Reinke, Annika Full, Peter M. Wagner, Martin Kenngott, Hannes Apitz, Martin Hempe, Hellena Filimon, Diana Mindroc Scholz, Patrick Tran, Thuy Nuong Bruno, Pierangela Arbelaéz, Pablo Bian, Gui-Bin Bodenstedt, Sebastian Bolmgren, Jon Lindström Bravo-Sánchez, Laura Chen, Hua-Bin González, Cristina Guo, Dong Halvorsen, Pål Heng, Pheng-Ann Hosgor, Enes Hou, Zeng-Guang Isensee, Fabian Jha, Debesh Jiang, Tingting Kirtac, Kadir Jin, Yueming Kletz, Sabrina Leger, Stefan Li, Zhixuan Maier-Hein, Klaus H. Ni, Zhen-Liang Riegler, Michael A. Schoeffmann, Klaus Shi, Ruohua Speidel, Stefanie Stenze, Michael Twick, Isabell Wang, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Zhang, Yujie Zhou, Yan-Jie Zhu, Lei Wiesenfarth, Manuel Kopp-Schneider, Annette Müller-Stich, Beat P. Maier-Hein, Lena Heidelberg Germany University of Heidelberg Germany Heidelberg Germany Department of General Heidelberg University Hospital Visceral and Transplant Surgery Heidelberg Germany caresyntax Berlin Germany University of Chinese Academy Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SimulaMet Oslo Norway Tromsø Norway Oslo Norway School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Partner Site Dresden Heidelberg Germany Faculty of Medicine and University Hospital Carl Gustav Carus Technische Universität Dresden Dresden Germany Dresden Germany Universidad de Los Andes Bogotá Colombia Peking University Peking China Department of Computer Science School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong German Cancer Research Center Division of Biostatistics Heidelberg Germany Institute of Information Technology Klagenfurt University Austria

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization;algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts). Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

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Increase in accuracy forecast for electrical energy consumption of the WEM subjects using fuzzy neural networks

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IOP Conference Series: Materials Science and engineering 2020年第1期791卷

作者： Ya P Fedorov V L Burkovsky A L Rutskov Corporate finance department and corporate management of the Financial University under the Government of the Russian Federation Leningradsky prospect 49 Moscow 125993 Russia Department of electric drive automation and control in an engineering system Voronezh State Technical University Moscow prospect 179 Voronezh 394066 Russia Forecasting department of electricity balance power and loss analysis branch office OJSC «IDGC Centre» - «Voronezhenergo» Arzamaskaya 2 Voronezh 394033 Russia

This paper covers in depth functioning essence of the balancing electricity market in the Russian Federation in accordance with the current economic stimuli. Significant role of the prediction process for energy resources consumption has been noted, which has a direct influence on a pricing policy within the marked trade area. Analysis of different factors, that impact execution accuracy of the one day in advance model, was conducted. Problem of the weakly defined and unknown parameters, that influence the performance of the balancing electricity market, is suggested to resolve by means of fuzzy neural controller. The paper presents its structure and settings, and possibility to scale it to use not only as an analyzer but as an executive element in the subsystems of electric power systems. High accuracy performance of the fuzzy neural controller was shown - around (0,5-3) %, that is much higher than methods already in use (for instance, based on a coefficient of decline/growth of power consumption).

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MK-92 FIRE-control system

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NAVAL ENGINEERS JOURNAL 1979年第4期91卷 69-76页

作者： RYAN, FN BERLIN, J Mr. Floyd N. Ryan graduated from Frostburg State College and has performed graduate studies in Technology of Management at American University and University of Southern California. He is presently Executive Director of the Surface Missile Systems Sub-Group in the Naval Sea Systems Command. He was previously Deputy Project Manager of the MK-92 FCS/MK-75 Gun Project Deputy Systems Engineering Manager in the AEGIS Project and Surface Missile Systems Branch Head in the Naval Ordnance Systems Command. His earlier experience with the Department of the Navy included the Engineering Interface Management Office in the Bureau of Ships the Foreign Ship Systems Engineering Office and Switching Systems Design Office at RCA Service Company and he served for four years in the U.S. Navy as an Electronic Technician Petty Officer. In addition to ASNE he is a member of the Association of Scientists and Engineers and is the Executive Director (NAVSEA) for the 1978-79 term. In addition to several outstanding performance awards and citations for achievement Mr. Ryan received the Navy Meritorious Civilian Service Award for exceptional contribution to the development of the AEGIS Weapon System. Mr. Jack Berlin received his Bachelor's degree in Electrical Engineering from the City College of New York and his M.S. degree in Electrical Engineering from Columbia University. He spent eight years in the Radar and Microwave Electronics Section of the Naval Material Laboratory. Subsequently. he has had extensive experience in Radar and Fire Control Systems. This has included responsibility for the design and field evaluation of the GFCS MK 87 Radar. From 1972 to 1973 he was the Sperry Program Manager for the Fire Control System (FCS) MK 92. His current assignment is Assistant Manager for New Developments of Missile and Gun Fire Control Systems for the U.S. Navy. He is a member of the Tau Beta Pi and Eta Kappa Nu Honorary Societies.

The MK 92 Fire control system (FCS) & a new, integrated, highly reliable and light-weight U.S. Navy Fire control system for missile and gun control. This system, which is in production for the FFG, PCG, PGG and PHM Ship Classes, provides the detection and automation required for modern naval warfare. Search radar data & presented at a very high rate at the operator's console, a highly integrated man-machine interface. Utilization of monopulse and “track-while-scan” techniques result in multiple target tracking capability. The system console(s) offer a self-contained command and control capability and, in addition, standard digital computer channels provide the versatile interface with the ship's command and control, integrating the complete engagement process. Error cancellation techniques are employed to obtain high performance accuracy even under severe environmental conditions. The low manning requirement for both operation and maintenance is a key system attribute for all applications. Comprehensive “at-sea” evaluations, performed by the U.S. Navy, demonstrated successful system operation in all modes of surveillance, multi-target tracking and simultaneous missile and gun engagements. The “at-sea” performance record of the FCS MK 92 was cited by the Chief of Naval Operations to have established new standards for Naval Surface Weapons systems.

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Enhancing BEM simulations of a stalled wind turbine using a 3D correction model

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Journal of Physics: Conference Series 2018年第1期974卷

作者： Galih Bangga Go Hutomo Taurista Syawitri Tri Kusumadewi Winda Oktavia Ahmad Sabila Herlambang Setiadi Muhamad Faisal Yongki Hendranata Dwi Lastomo Louis Putra Stefanus Kristiadi Ilmi Bumi Institute of Aerodynamics and Gas Dynamics University of Stuttgart Stuttgart Germany Mechanical Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia Toyota SO Department Astra International Ltd. Indonesia Mechanical Engineering Department Universitas Muhammadiyah Surakarta Surakarta Indonesia School of Manufacturing System and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Thailand Project Support and Cost Control Division Indoturbine Ltd. Jakarta Indonesia Civil Engineering Department Universitas Brawijaya Malang Indonesia School of Information Technology & Electrical Engineering The University of Queensland Brisbane Australia Non-Installation Top Range Department Berca Schindler Lifts Ltd. Indonesia Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taiwan Mechanical Engineering Department Texas A&M University College Station United States PUI-PT Mechatronics and Industrial Automation Research Center Intitut Teknologi Sepuluh Nopember Surabaya Indonesia Mechanical Engineering Department Politecnico di Milano Milan Italy Aeronautics Department Imperial College of Science Technology and Medicine London United Kingdom Materials Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Nowadays wind turbine rotors are usually employed with pitch control mechanisms to avoid deep stall conditions. Despite that, wind turbines often operate under pitch fault situation causing massive flow separation to occur. Pure Blade Element Momentum (BEM) approaches are not designed for this situation and inaccurate load predictions are already expected. In the present studies, BEM predictions are improved through the inclusion of a stall delay model for a wind turbine rotor operating under pitch fault situation of -2.3° towards stall. The accuracy of the stall delay model is assessed by comparing the results with available Computational Fluid Dynamics (CFD) simulations data.

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Developing a prototype concurrent design tool for composite topside structures

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NAVAL ENGINEERS JOURNAL 1997年第3期109卷 279-290页

作者： Dirlik, S Hambric, S Azarm, S Marquardt, M Hellman, A Bartlett, S Castelli, V Steve Dirlik:began his career at the Naval Surface Warfare Center Carderock Division in 1981 as an aerospace engineer co-op student. He completed his bachelor of science degree in aeropace and ocean engineering from Virginia Polytechnic Institute and State University in 1985 and his master of science degree in aerospace engineering from the University of Maryland in 1990. Mr. Dirlik recently completed a master of science program in applied physics at The Johns Hopins University and currently works in the Radar Cross Section and Target Physics Branch of the Signatures Directorate at the Naval Surface Warfare Center Corderoke Division. He has worked on Navy low observable programs since 1990. Stephen Hambric:is a research associate at the Applied Research Laboratory at The Pennsylvania State University. He received his B. S. and M.S. degree in mechanical engineering from Virginia Polytechnic Institute and State University and his D. Sc. in mechnical engineering from the George Washington University. He has worked on several computer aided multidiscikplinary design and optimization projects over the years including an automated propeller design system and a structural acoustic optimization capability. Dr. Shpour Azarm:is currently working as an associate professor with the Design and Manufacturing Group of the Department of Mechanical Engineering at the University of maryland at College Park. Dr Azarm's expertise is in the areas of optimization-based designed and concurrent design and optimization of multidisciplinary systems. He was a consultant at Black & Decker Corporation (summer 1996) and worked as a Navy senior summer faculty fellow (summer 1995) and a NASA summer faculty fellow (summer 1994). He was a visiting scientist at NASA Langley Research Center for Multidisciplinary Analysis and Applied Structural Optimzatiom at the University of Siegen in Germany (spring 1992) and the Design Institute of the Technical University of Denmark (summer 1990). Dr Azarm was an associate technical editor of the ASME Jour

A prototype concurrent engineering tool has been developed for the preliminary design of composite topside structures for modern navy warships. This tool, named GELS for the Concurrent engineering of Layered Structures, provides designers with an immediate assessment of the impacts of their decisions on several disciplines which are important to the performance of a modern naval topside structure, including electromagnetic interference effects (EMI), radar cross section (RCS), structural integrity, cost, and weight. Preliminary analysis modules in each of these disciplines are integrated to operate from a common set of design variables and a common materials database. Performance in each discipline and an overall fitness function for the concept are then evaluated. A graphical user interface (GUI) is used to define requirements and to display the results from the technical analysis modules. Optimization techniques, including feasible sequential quadratic programming (FSQP) and exhaustive search are used to modify the design variables to satisfy all requirements simultaneously. The development of this tool, the technical modules, and their integration are discussed noting the decisions and compromises required to develop and integrate the modules into a prototype conceptual design tool.

关键词： Warships

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Study on data-driven PEMFC humidity mechanism soft-sensing model

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Journal of Physics: Conference Series 2019年第1期1423卷

作者： Baohua Tan Hongbin Chen Rui Quan Shuhai Quan Yuanye Zhou School of Science Hubei University of Technology Wuhan China Hubei Engineering Technology Research Center of Energy Photoelectric Device and System Hubei University of Technology Wuhan China Department of Respiratory Medicine Renmin Hospital of Wuhan University Wuhan China School of Electrical and Electronic Engineering Hubei University of Technology Wuhan China The New Energy Detection and Control Technology Research Centre Wuhan University of Technology Wuhan China

Proton exchange membrane fuel cell (PEMFC) has a special closed structure, whose humidity cannot be measured directly. This paper selects the diffusion model as the modelling and analysis basis of the humidity mechanism soft-sensing based on the experimental research object. After the study of the basic principles of the diffusion model, a one-dimensional data-driven proton exchange membrane fuel cell humidity mechanism soft-sensing model is analysed and completed. At last, the actual experimental data drives the humidity mechanism soft-sensing model and the predicted output error based on the humidity mechanism soft-sensing model can be controlled within 3%. The output results verify the effectiveness of the humidity mechanism soft-sensing model.

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CENTML control system TRAINING THROUGH STA TIC AND DYNAMIC SIMULATION

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Naval Engineers Journal 1980年第2期92卷 196-206页

作者： HALL, EDWIN E. MOSS, DONALD G. NORRIS, CLIFFORD S. PETERSON, HAROLD D. Mr. Edwin E. Hall received his Bachelor's degree in Electronics from Oklahoma City University. He also is a certified College-Level Instructor in the state of Florida and has done graduate work in Computer Science at the University of Florida. As the Technical Publications and Training Manager Simulation and Control Systems Department. General Electric Company Daytona Beach Fla. he currently is responsible for the costing. planning performance scheduling and timely completion of the Department's Technical Manuals and Technical Training Programs. These Training and Manual contracts cover Ship Systems Programs. Simulation Programs. and Communications Programs for the Armed Services and commercial customers. Mr. Hall has over twenty-years experience as a Technical Writer and Instructor and for the past seventeen years has been at the General Electric Company's Daytona Beach Facility. His experience ranges from teaching Basic Electronics and Radar Circuitry as a civilian instructor for the U.S. Army to writing Manuals Proposals Reports Specifications and Brochures for General Electric's product lines. Mr. Donald G. MOSS is a graduate of Kansas State University from which he received both his B.S. degree in Business Administration and his M.S. degree in Electrical Engineering. He is presently a Senior Systems Engineer for Control Systems in the Simulation and Control Systems Department at the General Electric Company's Daytona Beach Facility. He has been employed by General Electric for 23 years — the first seven as an Engineer and Program Manager on the Fire Control Systems for the Fleet Ballistics Missile Program the next six managing the design of control and checkout equipment on the APOLLO Program and the last ten years working on control equipment for the machinery plants of new Navy ships. Over the span of years at General Electric he has worked on propulsion control for the DD-963 propulsion electric plant and auxiliary control for the FFG-7: propulsion boiler burner electric auxiliary and car

Dynamic Simulation is defined as the hardware and software required to present to the student operator visual and audible cues and responses that are the same as those encountered when operating the control Consoles aborad ship. This type of simulation is considered essential to the development of operator skills for critical tasks. Less critical tasks can be practiced using static simulation devices. This paper discusses the utilization of these types of training devices to train Navy crews. An overview of the Machinery control systems of the FFG 7 and AO 177 Class ships is discussed to provide an understanding of state‐of‐the‐art equipment development. The training programs for these two ship Classes are then reviewed to show how simulation devices are currently used by the Training Community, and this is followed by a brief discussion of the additional benefits that also are derived. © 1980 by the American Society of Naval Engineers, Inc.

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New results on anti-synchronization of switched neural networks with time-varying delays and lag signals

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Neural networks : the official journal of the International Neural Network Society 2016年 81卷 52-8页

作者： Yuting Cao Shiping Wen Michael Z Q Chen Tingwen Huang Zhigang Zeng School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: wenshiping226@***. The Department of Mechanical Engineering The University of Hong Kong Hong Kong. Texas A & M University at Qatar Doha 5825 Qatar.

This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system as the inner states are very hard to measure. Therefore, it is necessary to investigate the controller based on the output of the neuron cell. Through theoretical analysis, it is obvious that the obtained ones improve and generalize the results derived in the previous literature. To illustrate the effectiveness, a simulation example with applications in image encryptions is also presented in the paper.

关键词： Anti-synchronization Exponential stability Switched neural networks

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