• Journal of Power Electronics
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• v.9 no.2
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• pp.207-214
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• 2009

This paper presents a modem approach for speed control of a PMSM using the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the PI-Controller. The overall system simulated under various operating conditions and an experimental setup is prepared. The use of PSO as an optimization algorithm makes the drive robust, with faster dynamic response, higher accuracy and insensitive to load variation. Comparison between different controllers is achieved, using a PI controller which is tuned by two methods, firstly manually and secondly using the PSO technique. The system is tested under variable operating conditions. Implementation of the experimental setup is done. The simulation results show good dynamic response with fast recovery time and good agreement with experimental controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.108-113
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• 2005

This paper suggests a control method for an efficient topology/parameter evolution in a bond graph-based GP design framework that automatically synthesizes designs for multi-domain, lumped parameter dynamic systems. We adopt a hierarchical breeding control mechanism with fitness-level-dependent differences to obtain better balancing of topology/parameter search - biased toward topological changes at low fitness levels, and toward parameter changes at high fitness levels. As a testbed for this approach in bond graph synthesis, an eigenvalue assignment problem, which is to find bond graph models exhibiting minimal distance errors from target sets of eigenvalues, was tested and showed improved performance for various sets of eigenvalues.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.1-12
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• 2016

In this paper, we propose an approach that efficiently builds regional hazardous weather prediction models based on past weather data. Doing so requires finding the proper weather attributes that strongly affect hazardous weather for each region, and that requires a large number of experiments to build and test models with different attribute combinations for each kind of hazardous weather in each region. Using our proposed method, we reduce the number of experiments needed to find the correct weather attributes. Compared to the traditional method, our method decreases the number of experiments by about 45%, and the average prediction accuracy for all hazardous weather conditions and regions is 79.61%, which can help forecasters predict hazardous weather. The Korea Meteorological Administration currently uses the prediction models given in this paper.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.210-217
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• 2010

This paper presents integrated path planning and collision avoidance for an omni-directional mobile robot. In this scheme, the autonomous mobile robot finds the shortest path by the descendent gradient of a navigation function to reach a goal. In doing so, the robot based on the proposed approach attempts to overcome some of the typical problems that may pose to the conventional robot navigation. In particular, this paper presents a set of analysis for an omni-directional mobile robot to avoid trapped situations for two representative scenarios: 1) Ushaped deep narrow obstacle and 2) narrow passage problem between two obstacles. The proposed navigation scheme eliminates the nonfeasible area for the two cases by the help of the descendent gradient of the navigation function and the characteristics of an omni-directional mobile robot. The simulation results show that the proposed navigation scheme can effectively construct a path-planning system in the capability of reaching a goal and avoiding obstacles despite possible trapped situations under uncertain world knowledge.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.164-170
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• 2013

Recent years have been higher demands for automatic speech recognition (ASR) systems that are able to operate robustly in an acoustically noisy environment. This paper proposes an improved product hidden markov model (HMM) used for bimodal speech recognition. A two-dimensional training model is built based on dependently trained audio-HMM and visual-HMM, reflecting the asynchronous characteristics of the audio and video streams. A weight coefficient is introduced to adjust the weight of the video and audio streams automatically according to differences in the noise environment. Experimental results show that compared with other bimodal speech recognition approaches, this approach obtains better speech recognition performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.173-180
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• 2016

Fault detection and diagnosis is a task to monitor the occurrence of faults and pinpoint the exact location of faults in the system. Fault detection and diagnosis is gaining importance in development of efficient, advanced and safe industrial systems. Three phase inverter is one of the most common and excessively used power electronic system in industries. A fault diagnosis system is essential for safe and efficient usage of these inverters. This paper presents a fault detection technique and fault classification algorithm. A new feature extraction approach is proposed by using three-phase load current in three-dimensional space and neural network is used to diagnose the fault. Neural network is responsible of pinpointing the fault location. Proposed method and experiment results are presented in detail.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.208-215
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• 2016

The lithography hotspot detection process is crucial for semiconductor design development process. But, the lithography hotspot detection using optical simulation method takes much time and it slowdown the layout design development cycle. Though the geometry based approach is introduced as an alternative, it still revealed low detection performance and sophisticated framework. To solve this problem, we introduce a deep convolutional neural network based hotspot detection method. Our method made better results in ICCCAD 2012 dataset. To reach this score, we used lots of technical effort to improve the result in addition to just utilizing the nature of convolutional neural network. Inspection region reduction, data augmentation, DBSCAN clustering helped our work more stable and faster.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.347-352
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• 2005

PD(Partial discharges) are small electrical sparks that occur within the electric insulation of cables, transformers and windings on motors. PD analysis is a proactive diagnostic approach that uses PD measurements to evaluate the integrity of this equipment. Recently, several diagnostic algorithms for classifying the type of PD and locating the defect position have been developed. In this work, a new PD recognition system is proposed, which utilizes approximate coefficients of wavelet transform as a feature vector, furthermore, introduces bank of Elman networks to recognize the various PD phenomena. In order to verify the performance of the proposed scheme, it is applied to the simulated PD data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.47-51
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• 2006

Electrical resistance tomograpy (ERT) maps resistivity values of the soil subsurface and characterizes buried objects. The characterization includes location, size, and resistivity of buried objects. In this paper, truncated least squares (TLS) is presented for the solution of the ERT image reconstruction. Results of numerical experiments in ERT solved by the TLS approach is presented and compared to that obtained by the Gauss-Newton method.