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38th Chinese Control Conference (CCC)

作者： Wan, Youhong Cao, Weitao Wang, Lu Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210003 Peoples R China

ISBN: (纸本)9789881563972

The load prediction of electric vehicles (EVs) is the basis for the planning of distribution network facilities. The assumptions of the charging conditions of EVs are not reasonable enough in the previous research. Aiming at this problem, this paper proposes a novel method to predict the distribution of EV charging load in time and space based on fuzzy inference algorithm. First, compared with the previous research, a trip chain model is established to describe the dynamic process of EVs more comprehensively. Then, in view of the influence of traffic factors, we simulate the actual travel situation of EVs through the traffic congestion factor, and the probabilistic density functions of space-time variables in the trip chain are modeled. Next, considering three factors which affect the charging behavior of EVs, a three-input-one-output fuzzy inference system (FIS) is proposed to calculate the charging probability, instead of assuming that the charging behavior takes place at a certain time or satisfies a certain formula in the traditional method. Finally, the load distribution curves of EVs in different charging regions are obtained by Monte Carlo simulation. The simulation results verify the validity and accuracy of the proposed prediction method.

关键词： Electric Vehicles (EVs) fuzzy inference algorithm Trip Chain Charging Load Prediction Monte Carlo Simulation

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A Prediction Method for EV Charging Load Based on fuzzy inference algorithm

A Prediction Method for EV Charging Load Based on Fuzzy Infe...

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第三十八届中国控制会议

作者： Youhong Wan Weitao Cao Lu Wang College of Automation Nanjing University of Posts and Telecommunications

The load prediction of electric vehicles(EVs) is the basis for the planning of distribution network facilities. The assumptions of the charging conditions of EVs are not reasonable enough in the previous research. Aiming at this problem,this paper proposes a novel method to predict the distribution of EV charging load in time and space based on fuzzy inference algorithm. First, compared with the previous research, a trip chain model is established to describe the dynamic process of EVs more comprehensively. Then, in view of the influence of traffic factors, we simulate the actual travel situation of EVs through the traffic congestion factor, and the probabilistic density functions of space-time variables in the trip chain are modeled. Next,considering three factors which affect the charging behavior of EVs, a three-input-one-output fuzzy inference system(FIS) is proposed to calculate the charging probability, instead of assuming that the charging behavior takes place at a certain time or satisfies a certain formula in the traditional method. Finally, the load distribution curves of EVs in different charging regions are obtained by Monte Carlo simulation. The simulation results verify the validity and accuracy of the proposed prediction method.

关键词： Electric Vehicles(EVs) fuzzy inference algorithm Trip Chain Charging Load Prediction Monte Carlo Simulation

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DECOMPOSITION OF THE fuzzy inference SYSTEM FOR IMPLEMENTATION IN THE FPGA STRUCTURE

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2013年第2期23卷 473-483页

作者： Wyrwol, Bernard Hrynkiewicz, Edward Silesian Tech Univ Inst Elect PL-44101 Gliwice Poland

The paper presents the design and implementation of a digital rule-relational fuzzy logic controller. Classical and decomposed logical structures of fuzzy systems are discussed. The second allows a decrease in the hardware cost of the fuzzy system and in the computing time of the final result (fuzzy or crisp), especially when referring to relational systems. The physical architecture consists of IP modules implemented in an FPGA structure. The modules can be inserted into or removed from the project to get a desirable fuzzy logic controller configuration. The fuzzy inference system implemented in FPGA can operate with a much higher performance than software implementations on standard microcontrollers.

关键词： fuzzy logic fuzzy inference algorithm decomposition digital fuzzy logic controller FPGA

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An Optimal Hardware Implementation for Active Learning Method Based on Memristor Crossbar Structures

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IEEE SYSTEMS JOURNAL 2014年第4期8卷 1190-1199页

作者： Afrakoti, Iman Esmaili Paeen Shouraki, Saeed Bagheri Haghighat, Bahar Sharif Univ Technol Dept Elect Engn Tehran 113659363 Iran

This paper presents a new inference algorithm for active learning method (ALM). ALM is a pattern-based algorithm for soft computing, which uses the ink drop spread (IDS) algorithm as its main engine for feature extraction. In this paper, a fuzzy number is extracted from each IDS plane rather than from the narrow path and the spread, as in previous approaches. This leads to a significant reduction in the hardware required to implement the inference part of the algorithm and real-time computation of the implemented hardware. A modified version of the memristor crossbar structure is used to solve the problem of varying shapes of the ink drops, as reported in previous studies. In order to compare performance of the algorithm and the proposed hardware with the one proposed in our previous work, two functions that are widely used in literature are modeled as the benchmark. Simulation results show that the proposed algorithm is as effective as the previous one in modeling with reduced hardware complexity.

关键词： Active learning method (ALM) fuzzy inference algorithm ink drop spread (IDS) memristor crossbar

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Development of a monitoring system for quality prediction in laser marking using fuzzy theory

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JOURNAL OF LASER APPLICATIONS 2007年第1期19卷 55-63页

作者： Park, Young Whan Kim, Taehyung Rhee, Sehun Hanyang Univ Dept Mech Engn Seoul 133791 South Korea

When laser marking on a silicon wafer is performed during chip scale packaging processing, predicting the quality of the marked region may be necessary to increase productivity. Research to estimate marking line geometry after marking has been carried out. The methods to evaluate the line geometry after marking have limited applicability in production. Therefore, in this study, a process monitoring system was applied to laser micromaterial processing and an algorithm for quality estimation was developed using fuzzy theory. The monitoring system consisted of a sensor which measured the plasma light generated during laser marking by means of a coaxial monitoring method. Relationships between marking linewidth and sensor signals as a function of laser power were analyzed. In order to determine marking quality, a fuzzy inference algorithm used estimated marking widths by predicted neural network model and the feature values extracted from sensor signals. A quality monitoring program was developed using the proposed algorithm. (c) 2007 Laser Institute of America.

关键词： laser marking ablation plasma photodiode quality prediction width estimation neural network model fuzzy inference algorithm

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Interactive Medicine Case System for Elderly Recipient

Interactive Medicine Case System for Elderly Recipient

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3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

作者： Suzuki, Takuo Nakauchi, Yasushi Univ Tsukuba Grad Sch Syst & Informat Engn Dept Intelligent Interact Technol Tsukuba Ibaraki 3058573 Japan

ISBN: (纸本)9781424477098

In Japan, the number of elderly recipients who take medicines regularly has been increasing due to population aging. Since they sometimes induce serious accidents caused by incorrect dosing, we propose iMec System to promote correct dosing. The system is composed of iMec (intelligent medicine case), Ubiquitous Sensors, iMec Server, and Home Robot. iMec recognizes the types and quantities of medicines which a recipient has picked up, estimates whether the recipient is in the living states corresponding to common dosing timings by using Ubiquitous Sensors, and evaluates the dosing adequacy about type, quantity and timing. Ubiquitous Sensors, which are distributed wireless sensors, measure the position of the recipient and the condition of house appliances. iMec Server provides a medication monitoring interface by accumulating the adequacy and send emergency e-mail to caretakers if the adequacy is incorrect. And, Home Robot assists the recipient in correct dosing by its gestures and sounds. We used background subtraction method for confirming the dosages and simple fuzzy inference algorithm for confirming the timings. We confirmed that iMec System was able to confirm the timings by experiments.

关键词： caretakers distributed wireless sensors dosing drugs elderly recipients fuzzy inference algorithm fuzzy reasoning geriatrics home robot iMec server iMec system intelligent medicine case medical computing medical robotics medication monitoring interface patient care patient monitoring telemedicine ubiquitous computing ubiquitous sensors wireless sensor networks

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RISC microcontroller built-in fuzzy logic controller for maximum power point tracking in solar-powered for battery charger

RISC microcontroller built-in fuzzy logic controller for max...

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IEEE Region 10 Conference on Analog and Digital Techniques in Electrical Engineering

作者： Khaehintung, N Pramotung, K Sirisuk, P Mahanakorn Univ Technol Dept Control & Instrumentat Engn Bangkok 10530 Thailand

ISBN: (纸本)0780385608

This paper presents the development of maximum power point tracking (MPPT) using a fuzzy logic controller (FLC). By applying the synthetic fuzzy inference algorithm, the relationship between input and output of FLC can be effectively stored in a memory-limited lookup table (LUT). As a consequence, the controller can be efficiently implemented on a low-cost 16F872 RISC microcontroller. The proposed controller is integrated with a boost converter for realization of a high performance Solar-Powered Battery Charger (SPBC). Simulations with practical parameters show that our proposed MPPT using FLC implemented by LUT outperforms the conventional MPPT controller in terms of tracking speed. Moreover, preliminary experimental results are presented.

关键词： reduced instruction set computing fuzzy control solar power battery chargers cost reduction power convertors tracking microcontrollers RISC microcontroller fuzzy logic controller maximum power point tracking fuzzy inference algorithm memory-limited lookup table 16F872 boost converter solar-powered battery charger tracking speed

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Effective Partitioning of Input Domains for ALM algorithm

Effective Partitioning of Input Domains for ALM Algorithm

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1st Iranian Conference on Pattern Recognition and Image Analysis (PRIA)

作者： Afrakoti, Iman Esmaili Paeen Ghaffari, Aboozar Shouraki, Saeed Bagheri Sharif Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781467362061;9781467362047

This paper presents a new and simple algorithm for partitioning the input domain for implementation of Active Learning Method (ALM) algorithm. ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm as its main engine for feature extraction. In this paper a simple algorithm is introduced with a few computation cost. In order to evaluate the performance of the proposed algorithm, it is applied to two applications, system modeling and pattern recognition. Simulation results show the effectiveness of our algorithm in specifying the appropriate points for dividing the inputs domains.

关键词： Active learning method (ALM) fuzzy inference algorithm ink drop spread (IDS)

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A Novel inference algorithm for Active Learning Method

A Novel Inference Algorithm for Active Learning Method

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1st Iranian Conference on Pattern Recognition and Image Analysis (PRIA)

作者： Afrakoti, Iman Esmaili Paeen Shouraki, Saeed Bagheri Ghaffari, Aboozar Sharif Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781467362061;9781467362047

This paper presents a new inference algorithm for Active Learning Method (ALM). ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm as its main engine for feature extraction. In this paper a fuzzy number is extracted from each IDS plane rather than the narrow path and spread as in previous approaches. In order to compare performance of the proposed algorithm with the original one, two functions which are widely used in literature are modeled as the benchmark. Simulation results show that the proposed algorithm is as effective as previous one in the modeling task.

关键词： Active learning method (ALM) fuzzy inference algorithm ink drop spread (IDS)

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