• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.176-181
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• 2007

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.

• Annual Conference of KIPS
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• 2022.05a
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• pp.428-429
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• 2022

최근에 자율 주행 자동차에 관련한 관심이 증가하면서 다양한 연구들이 도출되고 있다. 특히, 자율 주행 자동차를 시뮬레이터에서 검증하는 방법은 실 환경과 비교할 때 상대적으로 안전한 성능 검증 방법으로 많이 활용되고 있다. 시뮬레이터의 핵심 기술은 실 환경과 가상 시뮬레이션 환경의 차이를 줄이는 데 있다. 본 논문에서는 Generative Adversarial Imitation Learning(GAIL)[1] 기반으로 자율 주행 자동차 시뮬레이터 내에서 다수의 가상 동적 객체들의 움직임을 제어하는 방법을 제안한다. GAIL은 생성기와 판별기로 구성된다. 생성기는 강화학습 정책 생성기와 전문가 정책 생성기를 포함한다. 판별기는 보상 학습기를 포함한다. GAIL 기반으로 가상 자동차 및 가상 보행자를 제어함으로써 동영상에서의 이동경로를 학습해서 표현할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2895-2921
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• 2018

Security has become one of the major concerns in mobile adhoc networks (MANETs). Data and voice communication amongst roaming battlefield entities (such as platoon of soldiers, inter-battlefield tanks and military aircrafts) served by MANETs throw several challenges. It requires complex securing strategy to address threats such as unauthorized network access, man in the middle attacks, denial of service etc., to provide highly reliable communication amongst the nodes. Intrusion Detection and Prevention System (IDPS) undoubtedly is a crucial ingredient to address these threats. IDPS in MANET is managed by Command Control Communication and Intelligence (C3I) system. It consists of networked computers in the tactical battle area that facilitates comprehensive situation awareness by the commanders for timely and optimum decision-making. Key issue in such IDPS mechanism is lack of Smart Learning Engine. We propose a novel behavioral based "Smart Multi-Instance Multi-Label Intrusion Detection and Prevention System (MIML-IDPS)" that follows a distributed and centralized architecture to support a Robust C3I System. This protocol is deployed in a virtually clustered non-uniform network topology with dynamic election of several virtual head nodes acting as a client Intrusion Detection agent connected to a centralized server IDPS located at Command and Control Center. Distributed virtual client nodes serve as the intelligent decision processing unit and centralized IDPS server act as a Smart MIML decision making unit. Simulation and experimental analysis shows the proposed protocol exhibits computational intelligence with counter attacks, efficient memory utilization, classification accuracy and decision convergence in securing C3I System in a Tactical Battlefield environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2497-2503
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• 2012

In this study an educational robot for programming education was designed and implemented. The robot in this study is composed of hardware containing a sensor, a processor, and a motor driver circuit, software to control the educational robot, machine parts to manufacture the robot structure, and a teaching material containing educational contents and the manufacturing manual. This robot is characterized by direct programming without a computer, which gives no spatial restrictions on robot education and enables dynamic program education beyond limitations of the existing static computer program education since students' programming results are found in the robot's movements. User-centered functional commands, which make it possible to control the robot with simple knowledge concerning hardware and basic commands, were used to enable even students who first accessed a robot or computer program to make access with ease.

• Journal of Educational Research in Mathematics
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• v.14 no.3
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• pp.327-345
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• 2004

This study attempts to discuss holistic information in order to identify the characteristics of interactions using a graphing calculator. The use of a graphing calculator was divided into three stages: Visual, Analytical, and Self-regulated. The last stage can be called the Self-regulated instrument stage, because this last stage, the use of the calculator, is generally characterized as students actively controlling their ongoing efforts through self-regulating. The accomplishments of the operation can be divided into three levels: Immature, Maturing, and finally, Mature level. First, the characteristics of the Leading Statements were investigated to figure out who has the main role in cooperative learning. This study can support the previous study, which showed that computers could help improve the self-esteem of low-level students. Second, the point of transformation is referred to as the Turning Point. Several functions were observed in the Turning Point: student, instrument, and teacher. Third, when the students convert-sations reach a lull in class and then resume due to certain primary factors without the teachers intervention, this is a case of what is referred to as Structuralization. And last, in this study, the graphing calculator can be used as an auxiliary stimulus to help students control their stress and their attitudes, which in turn can also improve students social interaction.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.45-56
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• 2005

The weak foundation of the computing environment caused information leakage and hacking to be uncontrollable, Therefore, dynamic control of security threats and real-time reaction to identical or similar types of accidents after intrusion are considered to be important, h one of the solutions to solve the problem, studies on intrusion detection systems are actively being conducted. To improve the anomaly IDS using system calls, this study focuses on neural networks learning using the soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern, That Is, by changing variable length sequential system call data into a fixed iength behavior pattern using the soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm. The backpropagation neural networks technique is applied for anomaly detection of system calls using Sendmail Data of UNM to demonstrate its performance.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.95-108
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• 2010

In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.321-331
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• 2003

Experimental software datasets describing software projects in terms of their complexity and development time have been the subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such approaches as neural networks, fuzzy, and fuzzy neural network models. In this study, we introduce the concept of the Rule-based fuzzy polynomial neural networks (RFPNN) as a hybrid modeling architecture and discuss its comprehensive design methodology. The development of the RFPNN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the RFPNN results from a synergistic usage of RFNN and PNN. RFNN contribute to the formation of the premise part of the rule-based structure of the RFPNN. The consequence part of the RFPNN is designed using PNN. We discuss two kinds of RFPNN architectures and propose a comprehensive learning algorithm. In particular, it is shown that this network exhibits a dynamic structure. The experimental results include well-known software data such as the NASA dataset concerning software cost estimation and the one describing software modules of the Medical Imaging System (MIS).

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1074-1081
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• 2007

This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.

• Journal of KIISE
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• v.42 no.11
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• pp.1423-1429
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• 2015

The Internet of Things (IoT) is a new concept associated with the future Internet, and it has recently become a popular concept to build a dynamic, global network infrastructure. However, the deployment of IoT creates difficulties in satisfying different Quality of Service (QoS) requirements and achieving rapid service composition and deployment. In this paper, we propose a new QoS control scheme for IoT systems. The Markov game model is applied in our proposed scheme to effectively allocate IoT resources while maximizing system performance. The results of our study are validated by running a simulation to prove that the proposed scheme can promptly evaluate current IoT situations and select the best action. Thus, our scheme approximates the optimum system performance.