• 대한전기학회논문지:시스템및제어부문D
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• 제50권7호
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.212-220
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• 2024

A recently the advancement of society, AI technology has made significant strides, especially in the fields of computer vision and voice recognition. This study introduces a system that leverages these technologies to recognize users through a camera and relay commands within a vehicle based on voice commands. The system uses the YOLO (You Only Look Once) machine learning algorithm, widely used for object and entity recognition, to identify specific users. For voice command recognition, a machine learning model based on spectrogram voice analysis is employed to identify specific commands. This design aims to enhance security and convenience by preventing unauthorized access to vehicles and IoT devices by anyone other than registered users. We converts camera input data into YOLO system inputs to determine if it is a person, Additionally, it collects voice data through a microphone embedded in the device or computer, converting it into time-domain spectrogram data to be used as input for the voice recognition machine learning system. The input camera image data and voice data undergo inference tasks through pre-trained models, enabling the recognition of simple commands within a limited space based on the inference results. This study demonstrates the feasibility of constructing a device management system within a confined space that enhances security and user convenience through a simple real-time system model. Finally our work aims to provide practical solutions in various application fields, such as smart homes and autonomous vehicles.

• 한국항행학회논문지
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• 제17권6호
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• pp.804-809
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• 2013

현재 구축되어 있는 반도체 공정에서의 상태감시 시스템은 센서 데이터를 수동으로 수집하는 방식으로써 복합 장애 검출이나 실시간 감시에서 한계가 존재한다. 본 논문에서는 영역 온톨로지를 구성하여 시간에 따른 관계망을 형성하는 상황인지 알고리즘을 설계하고 이를 통해 반도체 공정에서 위험요소가 발견되는 부분에 대해서 이벤트를 생성하여 사용자에게 서비스하는 시스템을 제안하며, 이를 구현하기 위해 상황 추론을 위한 다중센서 노드를 설계하고 이를 실험하였다. 실험 결과, 다수의 수집된 데이터에서 시간에 대한 관계가 형성된 내용에 대해서는 시간적 규칙추론이 적용된 이벤트가 발생하였으며 오작동 및 외부의 시간적 요인에서 발생되는 이벤트는 Log로만 데이터를 제공하는 것을 확인할 수 있었다.

• 대한전기학회논문지
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• 제43권4호
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• pp.627-637
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• 1994

In this study, a predictive fuzzy control algorithm to supervise the elevator system with plural cars is developed and its performance is evaluated. The proposed algorithm is based on fuzzy in-ference system to cope with multiple control objects and uncertainty of system state. The control objects are represented as linguistic predictive fuzzy rules and simplified reasoning method is utilized as a fuzzy inference method. Real-time simulation is performed with respect o all possible modes of control, and the resultant controls ard predicted. The predicted rusults are then utilized as the control in-puts of the fuzzy rules. The feasibility of the proposed control algorithm is evaluated by graphic simulator on computer. Finallu, the results of graphic simulation is compared with those of a conventional group control algorighm.

• 한국정보통신학회논문지
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• 제12권9호
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• pp.1689-1695
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• 2008

저전력 무선 통신의 발전과 다기능, 저가의 스마트 센서는 원격에서 상태정보를 감지할 수 있는 센서네트워크의 실현을 가능하게 하였다. 센서 노드는 소형 배터리를 사용해 에너지를 공급받는데 일반적으로 배터리 교환이 용이하지 않은 위치에 설치되기 때문에 센서 노드의 평균 소모 전력을 최소화할 필요가 있다. 알려진 바에 따르면 센서 노드의 전체 소모 전력의 20-60%를 무선 통신에 사용하는 RF 모듈이 차지하고 있다. 본 논문에서는 센싱된 데이터의 송신에 소비되는 에너지를 개선하기 위해 센싱 데이터의 변동 특성에 실시간 적응하여 확률적 계산값이 임의의 랜덤값보다 클 경우 기지국 노드로 전송하는 확률추론 휴리스틱기반 비주기적 전송 방법을 제안한다. 제안하는 전송 방법에서는 확률추론 휴리스틱 알고리즘에 따라 센싱된 데이터와 직전 센싱된 데이터를 평가하여 전송 여부를 결정하며 알고리즘에 필요한 계수값은 알고리즘 검증 데이터의 재현율을 통하여 결정한다.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Earthquakes and Structures
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• 제22권1호
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• pp.53-64
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• 2022

This study proposes an intelligent semi-active isolation system combining a variable-stiffness control device and ground motion characteristic prediction. To determine the optimal control parameter in real-time, a genetic algorithm (GA)-fuzzy control law was developed in this study. Data on various types of ground motions were collected, and the ground motion characteristics were quantified to derive a near-fault (NF) characteristic ratio by employing an on-site earthquake early warning system. On the basis of the peak ground acceleration (PGA) and the derived NF ratio, a fuzzy inference system (FIS) was developed. The control parameters were optimized using a GA. To support continuity under near-fault and far-field ground motions, the optimal control parameter was linked with the predicted PGA and NF ratio through the FIS. The GA-fuzzy law was then compared with other control laws to verify its effectiveness. The results revealed that the GA-fuzzy control law could reliably predict different ground motion characteristics for real-time control because of the high sensitivity of its control parameter to the ground motion characteristics. Even under near-fault and far-field ground motions, the GA-fuzzy control law outperformed the FPEEA control law in terms of controlling the isolation layer displacement and the superstructure acceleration.

• Communications for Statistical Applications and Methods
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• 제23권2호
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• pp.131-146
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• 2016

In research on behavioral studies, significant attention has been paid to the stage-sequential process for longitudinal data. Latent class profile analysis (LCPA) is an useful method to study sequential patterns of the behavioral development by the two-step identification process: identifying a small number of latent classes at each measurement occasion and two or more homogeneous subgroups in which individuals exhibit a similar sequence of latent class membership over time. Maximum likelihood (ML) estimates for LCPA are easily obtained by expectation-maximization (EM) algorithm, and Bayesian inference can be implemented via Markov chain Monte Carlo (MCMC). However, unusual properties in the likelihood of LCPA can cause difficulties in ML and Bayesian inference as well as estimation in small samples. This article describes and addresses erratic problems that involve conventional ML and Bayesian estimates for LCPA with small samples. We argue that these problems can be alleviated with a small amount of prior input. This study evaluates the performance of likelihood and MCMC-based estimates with the proposed prior in drawing inference over repeated sampling. Our simulation shows that estimates from the proposed methods perform better than those from the conventional ML and Bayesian method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.442-445
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• 1997

We present a hybrid self-tuning method of fuzzy inference systems with hyper elliptic Gaussian membership functions using genetic algorithm(GA) and back-propagation algorithm. The proposed self-tuning method has two phases : one is the coarse tuning process based on GA and the other is the fine tuning process based on back-propagation. But the parameters which is obtained by a GA are near optimal solutions. In order to solve the problem in GA applications, it uses a back-propagation algorithm, which is one of learning algorithms in neural networks, to finely tune the parameters obtained by a GA. We provide Box-Jenkins time series to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.

• Journal of information and communication convergence engineering
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• 제4권2호
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• pp.71-74
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• 2006

A basic access method about IEEE 802.11 MAC layer protocol using IEEE 802.11 wireless LANs is the DCF thatis based on the CSMA/CA. But, cause of IEEE 802.11 MAC layer uses original backoff algorithm (exponential backoff method), when collision occurred, the size of contention windows increases the double size Also, a time of packet transmission delay increases and efficienty is decreased by original backoff scheme. In this paper, we have analyzed TCP packet transmission time of IEEE 802.11 MAC DCF protocol for wireless LANs a proposed enhanced backoff algorithm. It is considered the transmission time of transmission control protocol (TCP) packet on the orthogonal frequency division multiplexing (OFDM) in additive white gaussian noise (A WGN) and Rician fading channel. From the results, a proposed enhanced backoff algorithm produces a better performance improvement than an original backoff in wireless LAN environment. Also, in OFDM/quadrature phase shift keying channel (QPSK), we can achieve that the transmission time in wireless channel decreases as the TCP packet size increases and based on the data collected, we can infer the correlation between packet size and the transmission time, allowing for an inference of the optimal packet size in the TCP layer.