• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.121-130
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• 2012

This paper proposes an intuitive real-time robot control system using human body movement. Recently, it has been developed that motion generation for humanoid robots with reflecting human body movement, which is measured by a motion capture. However, in the existing studies about robot control system by human body movement, the detailed structure information of a robot, for example, degrees of freedom, the range of motion and forms, must be examined in order to calculate inverse kinematics. In this study, we have proposed Associative Motion Generation as humanoid robot motion generation method which does not need the detailed structure information. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis and Jordan recurrent neural network, and the associative motion is generated with the following three steps. First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are extracted for associating a new motion from an unfamiliar indication using nonlinear principal component analysis. Last, the robot generates a new motion through calculation by Jordan recurrent neural network using the associative values. In this paper, we propose a real-time humanoid robot control system based on Associative Motion Generation, that enables user to control motion intuitively by human body movement. Through the task processing and subjective evaluation experiments, we confirmed the effective usability and affective evaluations of the proposed system.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.51-59
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• 2019

Due to the exponential growth of access information on the web, the need for predicting web users' next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.489-509
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• 2024

Slope stability analysis and prediction are of critical importance to geotechnical engineers, given the severe consequences associated with slope failure. This research endeavors to forecast the factor of safety (FOS) for slopes through the implementation of six distinct ML techniques, including back propagation neural networks (BPNN), feed-forward neural networks (FFNN), Takagi-Sugeno fuzzy system (TSF), gene expression programming (GEP), and least-square support vector machine (Ls-SVM). 344 slope cases were analyzed, incorporating a variety of geometric and shear strength parameters measured through the PLAXIS software alongside several loss functions to assess the models' performance. The findings demonstrated that all models produced satisfactory results, with BPNN and GEP models proving to be the most precise, achieving an R² of 0.86 each and MAE and MAPE rates of 0.00012 and 0.00002 and 0.005 and 0.004, respectively. A Pearson correlation and residuals statistical analysis were carried out to examine the importance of each factor in the prediction, revealing that all considered geomechanical features are significantly relevant to slope stability. However, the parameters of friction angle and slope height were found to be the most and least significant, respectively. In addition, to aid in the FOS computation for engineering challenges, a graphical user interface (GUI) for the ML-based techniques was created.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• International Journal of Advanced Culture Technology
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• v.2 no.2
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• pp.7-10
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• 2014

In order to develop smart grid communications infrastructure, a high level of interconnectivity and reliability among its nodes is required. Sensors, advanced metering devices, electrical appliances, and monitoring devices, just to mention a few, will be highly interconnected allowing for the seamless flow of data. Reliability and security in this flow of data between nodes is crucial due to the low latency and cyber-attacks resilience requirements of the Smart Grid. In particular, Artificial Intelligence techniques such as Fuzzy Logic, Bayesian Inference, Neural Networks, and other methods can be employed to enhance the security gaps in conventional IDSs. A distributed FPGA-based network with adaptive and cooperative capabilities can be used to study several security and communication aspects of the smart grid infrastructure both from the attackers and defensive point of view. In this paper, the vital issue of security in the smart grid is discussed, along with a possible approach to achieve this by employing FPGA based Radial Basis Function (RBF) network intrusion.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.155-161
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• 2003

출입국 관리 절차를 간소화하는 방안의 하나로 퍼지 신경망을 이용한 여권 인식 시스템을 제안한다. 제안된 여권 인식 방법은 소벨 연산자와 수평 스미어링, 윤곽선 추적 알고리즘을 적용하여 코드의 문자열 영역을 추출한다 여권의 문자열 영역은 OCR 문자 서체로 구성되어 있고, 명도 차이가 다양하게 나타난다. 따라서 추출된 문자열 영역을 블록 이진화와 평균 이진화를 각각 수행하고 그 결과들을 AND 비트 연산을 취하여 적응적으로 이진화한다. 이진화된 문자열 영역에 대해서 개별 코드의 문자들을 복원하기 위하여 CDM(Conditional Dilation Morphology) 마스크를 적용한 후, 역 CDM마스크와 HEM(Hit Erosion Morphology)마스크를 적용하여 잡음을 제거한다 잡음이 제거된 문자열 영역에 대해 수직 스미어링을 적용하여 개별 코드의 문자를 추출한다. 추출된 개별 코드의 인식은 퍼지 ART 알고리즘을 개선하여 RBF 네트워크의 중간층으로 적용하는 퍼지 RBF 네트워크와 개선된 퍼지 ART 알고리즘과 지도 학습을 결합한 퍼지 자가 생성 지도 학습 알고리 즘을 각각 제안하여 여권의 개별 코드 인식에 적용한다. 제안된 방법의 성능을 확인하기 위해서 실제 여권 영상을 대상으로 실험한 결과, 제안된 추출 및 인식 방법이 여권 인식에서 우수한 성능이 있음을 확인하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.367-370
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• 2005

본 논문에서는 정보 입자화와 유전자 알고리즘을 기반으로 최적 퍼지 다항식 뉴럴네트워크를 제안하고, 유전자 알고리즘을 사용하여 종합적인 설계방법을 개발한다. 제안된 모델은 기존의 진화론적 퍼지 다항식 뉴럴네트워크의 구조를 정보입자화를 통해 좀 더 빠르게 최적의 해공간에 접근시키는데 그 목적이 있다. 퍼지 관계기반 다항식 뉴럴네트워크는 퍼지 다항식 뉴론이 기초가 되어 가능한 구조적이고 요소적으로 모델의 성능을 향상 시켜준다. 퍼지 다항식 뉴런의 최적 구조를 위해 유전자 알고리즘을 이용하여 입력변수의 수와 후반부 다항식의 차수 입력변수 수에 따른 입력변수 그리고 멤버쉽 함수의 수를 동조한다. 여기서, 클러스터링의 하나의 방법인 HCM에 의해 퍼지 규칙 각각의 전반부와 후반부에 데이터 중심값을 이용하여 다항식함수의 파라미터값을 결정한다. 제안된 유전론적 퍼지 관계 다항식 뉴럴네트워크의 성능평가는 기존 퍼지 모델링에서 이용된 표준 데이터를 활용하여 평가한다.

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.2
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• pp.189-207
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• 1997

Recently, we have witnessed a host of emerging tools in the management support systems (MSS) area including the data warehouse/multidimensinal databases (MDDB), data mining, on-line analytical processing (OLAP), intelligent agents, World Wide Web(WWW) technologies, the Internet, and corporate intranets. These tools are reshaping MSS developments in organizations. This article reviews a set of emerging data management technologies in the knowledge discovery in databases(KDD) process and analyzes their implications for decision support. Furthermore, today's MSS are equipped with a plethora of AI techniques (artifical neural networks, and genetic algorithms, etc) fuzzy sets, modeling by example , geographical information system(GIS), logic modeling, and visual interactive modeling (VIM) , All these developments suggest that we are shifting the corporate decision making paradigm form information-driven decision making in the1980s to knowledge-driven decision making in the 1990s.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1958-1959
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• 2011

RBFNNs(Radial Basis Function Neural Networks) 모델의 경우 Min-Max, HCM(Hard C-means)클러스터링 그리고 FCM(Fuzzy C-means)클러스터링 중 한가지를 통해 데이터 입자는 로드 규칙을 생성한 후 퍼지 공간을 분할 및 가우시안 함수의 정점을 정의한다. 본 논문은 기존의 방법과는 다르게 Min-Max와 FCM클러스터링을 혼합하여 로드의 규칙을 생성한 후 퍼지 공간을 분할 및 가우시안 함수의 정정을 정의하는 방법으로 사용하고자 한다. PSO최적화 알고리즘을 이용하여 같은조건에서 최적화한 기존의 방법으로 모델링된 RBFNNs와 Min-Max와 FCM 클러스터링을 혼합하여 사용한 방법의 비교를 통하여 어떤 모델의 성능이 더욱 좋은지 비교하고자 한다.