• Steel and Composite Structures
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• v.21 no.6
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• pp.1183-1210
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• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.139-149
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• 2024

In this paper, we propose the standard architecture for the IFF interface SW in naval combat management system(CMS). The proposed standard interface architecture is a method designed to reduce modification efforts and man-month of reliability test for the existing the IFF interface SW of 11 types. We identified highly dependent CMS and GFE information, leading to the redefinition of standard requirements and functions, and proceeded with the initial design applying the Naval Shield Component Platform(NSCP). Subsequently, using the Feature Model, we derived additional common and variable elements for the interface of multiple CMS and GFE. Considering the S.O.L.I.D principles, we designed the final architecture. The proposed IFF Interface SW, based on the standard architecture, is expected to enhance management efficiency through a common architecture, increase code reusability and scalability, and reduce development costs by shortening reliability testing times.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.563-568
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• 2007

This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.112-124
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• 2004

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.4
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• pp.329-339
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• 2003

A neural network based adaptive controller design method is proposed for reconfigurable flight control systems in the presence of variations in aerodynamic coefficients or control effectiveness decrease caused by control surface damage. The neural network based adaptive nonlinear controller is developed by making use of the backstepping technique for command following of the angle of attack, sideslip angle, and bank angle. On-line teaming neural networks are implemented to guarantee reconfigurability and robustness to the uncertainties caused by aerodynamic coefficients variations. The main feature of the proposed controller is that the adaptive controller is designed with assumption that not any of the nonlinear functions of the system is known accurately, whereas most of the previous works assume that only some of the nonlinear functions are unknown. Neural networks loam through the weight update rules that are derived from the Lyapunov control theory. The closed-loop stability of the error states is also investigated according to the Lyapunov theory. A nonlinear dynamic model of an F-16 aircraft is used to demonstrate the effectiveness of the proposed control law.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• Korea Journal of Hospital Management
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• v.9 no.1
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• pp.1-21
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• 2004

The United States has a unique health care system, which is unlikely any other health care systems in the world. The major part of basic functional components of the system -financing, insurance, delivery, and payment- is in private hands. A market-oriented economy invites the participation of numerous private entities that are interested in carrying out the key functions of health systems. Due to this central feature, U.S.health care is not delivered through a network of interrelated components designed to work together coherently. For lack of standardization, the various components of the system fit together only loosely. The involvement of numerous players in the key functions leads to duplication, overlap, inadequacy, inconsistency, and waste, which add to the complexity and also make the system inefficient. Hence, cost containment remains an elusive goals. Moreover, the system falls short of delivering equitable services to all americans, though consumption of health care services is the largest in the world. On the other hand, United States leads the world in the latest and the best in medical technology, medical training, and research. It offers some of the most sophisticated institutions, products, and processes of health care delivery. This article discuss the characteristic features of the U.S. health care system. and its performance, trying to seek its implication on Korean health care system.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.211-218
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• 2001

This paper introduces a method of constructing expert systems in an integrated environment for automatic software design. This integrated environment may be applicable from top-level system architecture design, data flow diagram design down to flow chart and coding. The system is integrated with three CASE tools, FSD (Functional Structure Diagram), DFD (Data Flow Diagram) and structured chart PAD (Problem Analysis Diagram), and respective expert systems with automatic design capability by reusing past design. The construction way of these expert systems is based on systematic acquisition of design knowledge stemmed from a systematic design work process of well-matured developers. The design knowledge is automatically acquired from respective documents and stored in the respective knowledge bases. By reusing it, a similar software system may be designed automatically. In order to develop these expert systems in a short period, these design knowledge is expressed by the unified frame structure, functions of th expert system units are partitioned mono-functions and then standardized components. As a result, the design cost of an expert system can be reduced to standard work procedures. Another feature of this paper is to introduce the integrated environment for automatic software design. This system features an essentially zero start-up cost for automatic design resulting in substantial saving of design man-hours in the resulting in substantial saving of design man-hours in the design life cycle, and the expected increase in software productivity after enough design experiences are accumulated.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.501-505
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• 2007

Recently KISTI has developed the KnowlegeMatrix which is a stand-alone type bibliographic data analysis software. In this paper, we try to benchmark test on the performance level of KnowledgeMatrix with well-known S/Ws such as VantagePoint and BibTechMon. We compare distinctive functions and usability of each S/Ws on comparative categories including Data, Matrix, Analysis, Visualization and Preprocessing. Test results show that all S/Ws have differentiated specific feature, but there is some performance gaps. KnowledgeMatrix overally shows better performance than others.

• Journal of Sasang Constitutional Medicine
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• v.23 no.4
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• pp.458-470
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• 2011

1. Objectives Vocal characteristics are commonly considered as an important factor in determining the Sasang constitution and the health condition. We have tried to find out the classification procedure to distinguish the constitution objectively and quantitatively by analyzing the characteristics of subject's voice without noise and error. 2. Methods In this study, we extract the vocal features from voice selected with prior information, remove outliers, minimize the correlated features, correct the features with normalization according to gender and age, and make the discriminant functions that are adaptive to gender and age from the features for improving diagnostic accuracy. 3. Results and Conclusions Finally, the discriminant functions produced about 45% accuracy to classify the constitution for every age interval and every gender, and the diagnostic accuracy was meaningful as the result from only the voice.