检索结果-内蒙古大学图书馆

Relation Model of Burden Operation and State Variables of Blast Furnace Based on Low Frequency Feature Extraction*

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2017年第1期50卷 13796-13801页

作者： Zhang, Kexin Wu, Min An, Jianqi Cao, Weihua Liu, Zhentao Ning, Fulong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Faculty of Engineering China University of Geosciences wuhan430074 China Department of Computer Science School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

In the blast furnace ironmaking process, the running state of the blast furnace can be directly adjusted by the burden operation. Therefore, the explicit relation between the burden operation and the running state variables is vital for the blast furnace operators. However, the state variables of the blast furnace are affected by many factors and the burden matrix is complicated. In this paper, a support vector regression(SVR) predicting model based on low frequency feature extraction is proposed to solve these problems. First, the low frequency components of state variables time series which are mostly affected by the burden operation are extracted. Then, the dimensions of the burden matrix are reduced by a devised simple expression method. Finally, the relation model based on SVR is proposed to predict the values of the expected state variables according to the simplified burden matrix and the value of initial state variables. The simulation on real factory data indicates that the model reflects the accurate quantitative relation between the burden operation and the state variables. © 2017

关键词： Blast furnaces Blast furnace practice Extraction Feature extraction Support vector regression Blast furnace iron making Blast furnace operators burden operation Initial state variables Low frequency components Relation models State variables Support vector regression (SVR)

Simulation of wood biomass combustion in hot water boiler

学校读者我要写书评

暂无评论

IOP Conference Series: Materials Science and Engineering 2020年第1期776卷

作者： I Klačková I Zajačko R Lenhard I Gritsuk D Wiecek Department of Automation and Production systems Faculty of Mechanical Engineering University of Žilina Univerzitná 1 010 26 Žilina Slovak Republic Department of Power Engineering Faculty of Mechanical Engineering University of Žilina Univerzitná 1 010 26 Žilina Slovak Republic Center RUDN University 117198 Moscow Miklukho-Maklaya str.6 Moscow Russia Faculty of Mechanical Engineering and Computer Science University of Bielsko-Biala ul. Willowa 2 43-309 Bielsko-Biala Poland

Calculation methods based on finite volume methods (CFD method) have proven to be very useful in optimizing (efficiency, emission) heat sources for wood burning. Such heat sources also include gasification hot-water boilers for the combustion of dry wood. One of the important parts of the entire process of converting the primary energy contained in the fuel to the transfer of heat to the heat carrier is the gasification of the fuel in the feed chamber and the subsequent burning of wood gas. The paper presents CFD simulation of wood gasification process in the filling chamber and subsequent burning of wood gas on a model heat source. As a model heat source, was selected a hot-water boiler with lower fuel firing with a flue gas-water heat exchanger tubular heat exchanger. It is a gasification hot water boiler for the combustion of dry wood. The interior of the boiler consists of a filling chamber where the fuel is dried and fused. The wood gas then passes through the nozzle to the combustion chamber, where it burns with the aid of secondary air. The flue gases pass their heat in the heat exchanger through the walls of the pipes into the water.

关键词：

The learning of fuzzy cognitive maps with noisy data: A rapid and robust learning method with maximum entropy

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Feng, Guoliang Lu, Wei Pedrycz, Witold Yang, Jianhua Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116023 China School of Automation Engineering Northeast Electric Power University Jilin132012 China School of Control Science and Engineering Dalian University of Technology Dalian116023 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Fuzzy rules

Patient specific model based induction of hypnosis using fractional order control

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2017年第1期50卷 15097-15102页

作者： Copot, Dana Muresan, Cristina De Keyser, Robin Ionescu, Clara Ghent University Research Group on Dynamical Systems and Control Technologiepark 914 GhentB9052 Belgium Technical University of Cluj-Napoca Faculty of Automation and Computer Science G. Baritiu 26-28 Cluj Napoca400027 Romania

Optimal and safe control of drug delivery systems with continuous infusion protocol is of key importance to avoid over- or under-dosing of the patient. By implementing close-loops one is able to optimize the amount of drug given to the patient. In this paper a robust control methodology is presented. Emerging tools from fractional calculus have been considered and a fractional order PI controller for drug dosing during hypnosis has been designed. In this paper a robust fractional order control of hypnosis is proposed. The controller has been evaluated on an artificial data set of 24 patients and the results indicate that such a control strategy is robust to uncertainty stemming from the inter- and intra-patient variability. © 2017

关键词： Controlled drug delivery Calculations Control engineering Controllers Robust control Robustness (control systems) Targeted drug delivery Uncertainty analysis Continuous infusions Control methodology Drug delivery system Fractional order control hypnosis Patient specific model Personalized healthcare uncertainty

Applying d-RSA with login system to speed up decryption process in client side 3

学校读者我要写书评

暂无评论

Applying d-RSA with login system to speed up decryption proc...

3rd IEEE International Conference on Engineering Technologies and Social Sciences, ICETSS 2017

作者： Somsuk, Kritsanapong Thammawongsa, Nopparat Department of Computer and Communication Engineering Faculty of Technology Udon Thani Rajabhat University Udon Thani Thailand Department of Electronic and Automation Systems Faculty of Technology Udon Thani Rajabhat University Udon Thani Thailand

ISBN: (纸本)9781538616116

Login System is the important process to identify the right for accessing the application by identifying and authenticating. Usually, username and password are the personal details of user for accessing the system. However, if both of them are stolen over the insecure channel, then the system is broken. With this problem, some of cryptographic algorithms are chosen to apply with the login system to avoid breaking the system. In the beginning of this year, the strong login system was proposed by applying with two different cryptographic algorithms, RSA and One Time Pad (OTP). RSA which is the asymmetric key cryptosystem is chosen for exchanging the OTP's key. One the other hand, user's password will be protected by using OTP. Although the improved login system is very strong and is very difficult for attackers to trap the information, the speed becomes very slow especially the decryption process of RSA taking very high computation time. In this paper, the new modified login system is proposed to speed up the login process with the same security level when it is compared with the login system applying with RSA and OTP. In fact, d-RSA is chosen instead of RSA by choosing the new private key with the lower Hamming weight in comparison to the private key. In particularly, with the lower Hamming weight, it implies that the new modified login system applying with d-RSA and OTP is faster than the login system with RSA and OTP. The experimental results show that the new modified system can speed up the login system although it becomes slower in registration process. The reason is that in registration process, it must also take time to find the new private key with the lower Hamming weight. However, the registration system is the process that will be implemented only the first time for user to sign up the application. © 2017 IEEE.

关键词： Authentication

Robust medical instrument segmentation challenge 2019

学校读者我要写书评

暂无评论

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

Combined heat and power economic dispatch solution using iterative cultural algorithm

学校读者我要写书评

暂无评论

2017 International Conference on Artificial Intelligence, ICAI 2017 at 2017 World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2017

作者： Shayanfar, Heidarali Bonab, Reza Dadashzadeh Tabrizi, Navid Mehdinejad, Mehdi Center of Excellence for Power Systems Automation and Operation School of Electrical Engineering Iran University of Science and Technology Tehran Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (纸本)9781601324603

With increasing the environmental concerns and fuel prices of the power industry, the optimal utilization of Combined Heat and Power (CHP) systems has become one of the essential subjects in power systems. Different artificial intelligence methods are used for optimization of CHP operation. Cultural Algorithm (CA) is based on some theories originated in archaeology and sociology which try to model cultural evolution. The basic disadvantage of the original culture algorithm is the fact that, it can’t give better optimal solution rather than most of other artificial intelligent methods. In this paper an iterative cultural algorithm is proposed to solve Combined Heat And Power Economic Dispatch (CHPED) problem. The effect of valve-point that make the CHPED problem as a non-convex issue is considered in the cost function and formulated in the conventional polynomial cost function as an absolute sinusoidal term. The proposed method is applied to three small (with three different test cases), medium and large test systems in order to evaluate its efficiency and feasibility. From obtained results, it is seen that proposed method converged to a better and feasible solution in all cases in comparison with some recently published papers. © 2017 CSREA Press. All rights reserved.

关键词： Cost functions

Common Limitations of Image Processing Metrics: A Picture Story

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

PID2018 benchmark challenge: Model-based feedforward compensator with a conditional integrator ??

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Yuan, Jie Ates, Abdullah Dehghan, Sina Zhao, Yang Fei, Shumin Chen, YangQuan School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey Mechatronics Embedded Systems and Automation Lab University of California MercedCA95340 United States School of Science and Engineering Shandong University Jinan250061 China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China

Since proportional-integral-derivative (PID) controllers absolutely dominate the control engineering, numbers of different control structures and theories have been developed to enhance the effciency of PID controllers. Thus, it is essential and inspiring to operate different PID control strategies to the PID2018 Benchmark Challenge. In this paper, a novel control strategy is designed for this refrigeration system, where a feedforward compensator and a conditional integrator are utilized to compensate the disturbances and remove the steady-state error in the benchmark problem, respectively. The simulation results given in the benchmark problem show the straightforward effectiveness of the proposed control structure compared with the existing control methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Disturbance rejection