• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.271-280
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• 2011

Heart Rate Variability (HRV) analysis is a convenient tool to assess Myocardial Ischemia (MI). The analysis methods of HRV can be divided into time domain and frequency domain analysis. This paper uses wavelet transform as frequency domain analysis in contrast to time domain analysis in short term HRV analysis. ST-T and normal episodes are collected from the European ST-T database and the MIT-BIH Normal Sinus Rhythm database, respectively. An episode can be divided into several segments, each of which is formed by 32 successive RR intervals. Eighteen HRV features are extracted from each segment by the time and frequency domain analysis. To diagnose MI, the Neural Network with Weighted Fuzzy Membership functions (NEWFM) is used with the extracted 18 features. The results show that the average accuracy from time and frequency domain features is 75.29% and 80.93%, respectively.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.101-111
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• 1993

Many processes such as machining, injection-moulding and metal-forming are usually operated by hydraulic servo-systems. The dynamic characteristics of these systems are complex and highly non-linear and are often subjected to the uncertain external disturbances associated with the processes. Consequently, the conventional approach to the controller design for these systems may not guarantee accurate tracking control performance. An effective neuro-fuzzy controller is proposed to realize an accurate hydraulic servo-system regardless of the uncertainties and the external disturbances. For this purpose, first, we develop a simplified fuzzy logic controller which have multidimensional and unsymmetric membership functions. Secondly, we develop a neural network which consists of the parameters of the fuzzy logic controller. It is show that the neural network has both learning capability and linguistic representation capability. The proposed controller was implemented on a hydraulic servo-system. Feedback error learning architecture is adopted which uses the feedback error directly without passing through the dynamics or inverse transfer function of the hydraulic servo-system to train the neuro-fuzzy controller. A series of simulations was performed for the position-tracking control of the system subjected to external disturbances. The results of simulations show that regardless of inherent non-linearities and disturbances, an accuracy tracking-control performance is obtained using the proposed neuro-fuzzy controller.

• Journal of Sensor Science and Technology
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• v.12 no.2
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• pp.79-87
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• 2003

In this paper, we propose a method to obtain a linear characteristic using soft computing for systems which have array sensors of nonlinear characteristics. Also a procedure utilizing the pattern information of array sensors without additional sensors is proposed to reduce disturbance effects. For a typical example, even a single CdS cell for CdS array has nonlinear characteristics. Overall linear characteristic for CdS array is obtained using fuzzy logic for each cell and overlapped portion. In addition, further improvement for linearization is obtained applying genetic algorithms for the parameters of membership functions. Also the effect of disturbing external light changes to the CdS array can be reduced without using any additional sensors for calibration. The proposed method based on fuzzy logic shows improvements for position measurements and disturbance reduction to external light changes due to the fuzziness of the shadow boundary as well as the inherent nonlinearity of the CdS array. This improvement is shown by applying the proposed method to the ball position measurements of a magnetic levitation system.

• Journal of Life Science
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• v.8 no.2
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• pp.226-226
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• 1998

Discovery of molecular chaperone has stimulate cell biologists and thus made it possible to re-examine the processes whereby proteins achieve and maintain their functional conformations within living cells. the term ‘Molecular chaperone’ was first coined to describe one particular protein involved in the assembly of nucleosomes, but the term has now been extended to describe the function of a wide variety of proteins that assist protein transport across membranes, folding of nascent polypeptide, the assembly and disassembly of oligomeric structures, and the recovery or removal of proteins damaged by various environmental stresses including heat shock. Progress of molecular chaperone research is still limited by the lack of 3-dimensional structural information and detailed interacts with taget proteins in the cell. However, several laboratories around the world are attempting to extend our knowledge on the functions of molecular chaperone, and such efforts seem justified to finally provide the answers to the most burning questions shortly.

• International Journal of Naval Architecture and Ocean Engineering
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• v.5 no.2
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• pp.188-198
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• 2013

In constructing a collision avoidance system, it is important to determine the time for starting collision avoidance maneuver. Many researchers have attempted to formulate various indices by applying a range of techniques. Among these indices, collision risk obtained by combining Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) information with fuzzy theory is mostly used. However, the collision risk has a limit, in that membership functions of DCPA and TCPA are empirically determined. In addition, the collision risk is not able to consider several critical collision conditions where the target ship fails to take appropriate actions. It is therefore necessary to design a new concept based on logical approaches. In this paper, a collision ratio is proposed, which is the expected ratio of unavoidable paths to total paths under suitably characterized operation conditions. Total paths are determined by considering categories such as action space and methodology of avoidance. The International Regulations for Preventing Collisions at Sea (1972) and collision avoidance rules (2001) are considered to solve the slower ship's dilemma. Different methods which are based on a constant speed model and simulated speed model are used to calculate the relative positions between own ship and target ship. In the simulated speed model, fuzzy control is applied to determination of command rudder angle. At various encounter situations, the time histories of the collision ratio based on the simulated speed model are compared with those based on the constant speed model.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.179-187
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• 2008

In this paper, we apply a set of algorithms to classily normal and cancer nucleus from uterine cervical pap-smear images. First, we use lightening compensation algorithm to restore color images that have defamation through the process of obtaining $1{\times}400$ microscope magnification. Then, we remove the background from images with the histogram distributions of RGB regions. We extract nucleus areas from candidates by applying histogram brightness, Kapur method, and our own 8-direction contour tracing algorithm. Various binarization, cumulative entropy, masking algorithms are used in that process. Then, we are able to recognize normal and cancer nucleus from those areas by using three morphological features - directional information, the size of nucleus, and area ratio - with fuzzy membership functions and deciding rules we devised. The experimental result shows our method has low false recognition rate.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.52-62
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• 2006

In a fuzzy control system to process fuzzy data in high-speed for intelligent systems, one of the important problems is the improvement of the execution speed in the fuzzy inference and defuzzification stages. Especially, it is more important to have high-speed operations in the consequent part and defuzzification stage. Therefore, in this paper, to improve the speedup of the fuzzy controllers for intelligent systems, we propose an integer line mapping algorithm using only integer addition to convert [0,1] real values in the fuzzy membership functions in the consequent part to integer grid pixels $(400{\times}30)$. This paper also shows a novel defuzzification algorithm without multiplications. Also we apply the proposed system to the truck backer-upper control system. As a result, this system shows a real-time very high speed fuzzy control as compared as the conventional methods. This system will be applied to the real-time high-speed intelligent systems such as robot arm control.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.29-37
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• 2017

This papers focuses on remanufacturing processes in a closed loop supply chain. The remanufacturing processes is considered as one of the effective strategies for enterprises' sustainability. For this reason, a lot of companies have attempted to apply remanufacturing related methods to their manufacturing processes. While many research studies focused on the return rate for remanufacturing parts as a control parameter, the relationship with demand certainties has been studied less comparatively. This paper considers a closed loop supply chain environment with remanufacturing processes, where highly fluctuating demands are embedded. While other research studies capture uncertainties using probability theories, highly fluctuating demands are modeled using a fuzzy logic based ambiguity based modeling framework. The previous studies on the remanufacturing have been limited in solving the actual supply chain management situation and issues by analyzing the various situations and variables constituting the supply chain model in a linear relationship. In order to overcome these limitations, this papers considers that the relationship between price and demand is nonlinear. In order to interpret the relationship between demand and price, a new price elasticity of demand is modeled using a fuzzy based nonlinear function and analyzed. This papers contributes to setup and to provide an effective price strategy reflecting highly demand uncertainties in the closed loop supply chain management with remanufacturing processes. Also, this papers present various procedures and analytical methods for constructing accurate parameter and membership functions that deal with extended uncertainty through fuzzy logic system based modeling rather than existing probability distribution based uncertainty modeling.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.4
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• pp.14-22
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• 2001

In this paper, we design the Adaptive Neuro-Fuzzy Precompensator(ANFP) for the suppression of low-frequency oscillation and the improvement of system stability. Here, ANFP is designed to compensate the conventional Power System Stabilizer(PSS). This design technique has the structural merit that is easily implemented by adding ANFP to an existing PSS. Firstly, the Fuzzy Precompensator with Loaming ability is constructed and is directly learned from the input and output data of the generating unit. Because the ANFP has the property of learning, fuzzy rules and membership functions of the compensator can be automatically tuned by teaming algorithm Loaming is based on the minimization of the ems evaluated by comparing the output of the ANFP and a desired controller. Case studies show the 7posed schema can be provided the good damping of the power system over the wide range of operating conditions and improved dynamic performance of the system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.