• Journal of Information Processing Systems
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• 제16권3호
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• pp.541-556
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• 2020

Wireless networks have become integral to society as they provide mobility and scalability advantages. However, their disadvantage is that they cannot control the media, which makes them vulnerable to various types of attacks. One example of such attacks is the evil twin access point (AP) attack, in which an authorized AP is impersonated by mimicking its service set identifier (SSID) and media access control (MAC) address. Evil twin APs are a major source of deception in wireless networks, facilitating message forgery and eavesdropping. Hence, it is necessary to detect them rapidly. To this end, numerous methods using clock skew have been proposed for evil twin AP detection. However, clock skew is difficult to calculate precisely because wireless networks are vulnerable to noise. This paper proposes an evil twin AP detection method that uses a multiple-feature-based machine learning classification algorithm. The features used in the proposed method are clock skew, channel, received signal strength, and duration. The results of experiments conducted indicate that the proposed method has an evil twin AP detection accuracy of 100% using the random forest algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1122-1126
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• 1991

As microcomputers have become widespread and the high speed solenoid valves have been developed, digitally controlled hydraulic systems are used in many applications. This study deals with position control of hydraulic cylinder operated by two port 3-way high speed solenoid valve using a self-learning strategy. This was done by developing a control algorithm for the microcomputer which always automatically adjust the length of control pulse to the optimum value in accordance with the error regardless of changes in the operating condition and physical differences between components. Tests carried out in the laboratory indicate that a positional accuracy could be improved.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.127-130
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• 1990

The fuzzy logic controller which can be applied to various industrial processes is quite often dependent on the heuristics of the experienced operator. The operator's knowledge is often uncertain. Therefore an incorrect control rule on the basis of the operator's information is a cause of bad performance of the system. This paper proposes a new self-learning fuzzy control method by the fuzzy system identification using the data pairs of input and output and arbitrary initial relation matrix. The position control of a DC servo motor model is simulated to verify the effectiveness of the proposed algorithm.

• Advances in Computational Design
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• 제5권3호
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• pp.233-260
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• 2020

The main purpose to introduce model based controller in proposed control technique is to provide better and fast learning of the floating dynamics by means of fuzzy logic controller and also cancelling effect of nonlinear terms of the system. An iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle (AUV). The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.

• 한국정밀공학회지
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• 제20권8호
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• pp.86-93
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• 2003

The development of a fast, accurate, and inexpensive position-controlled pneumatic actuator that may be applied to a variety of practical positioning applications with various external loads is described in this paper. A novel modified pulse width modulation (MPWM) valve pulsing algorithm allows on/off solenoid valves to be used in place of costly servo valves. A comparison between the system response of standard PWM technique and that of the novel modified PWM technique shows that the control performance is significantly increased. A state feedback controller with position, velocity and acceleration feedback is successfully implemented as the continuous controller. Switching algorithm of control parameter using learning vector quantization neural network (LVQNN) is newly proposed, which estimates the external loads of the pneumatic actuator. The effectiveness of the proposed control algorithms are demonstrated through experiments with various loads.

• Journal of Advanced Marine Engineering and Technology
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• 제21권4호
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• pp.404-413
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• 1997

Direct-drive robots are suitable to position and force control with high accuracy, but it is difficult to design a controller which gives satisfactory perfonnance because of the system's nonlinearity and link-interactions. This paper is concerned with the force control of direct-drive robots. The pro¬posed algorithm consists of feedback controllers and a neural network. Mter the completion of learning, the outputs of feedback controllers are nearly equal to zero, and the neural network con¬troller plays an important role in the control system. Therefore, the optimum adjustment of parameters of feedback controllers is unnecessary. In other words, the proposed algorithm does not need any knowledge of the controlled system in advance. The effectiveness of the proposed algo¬rithm is demonstrated by the experiment on the force control of a parallelogram link-type direct¬drive robot.

• 한국정보처리학회논문지
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• 제4권8호
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• pp.2070-2079
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• 1997

본 논문에서는 호 수락 제어 문제를 해결하기 위해 퍼지 논리 제어기의 장점과 신경망의 학습 능력을 이용한 ATM 망의 호 수락 제어 시스템을 제안하였다. ATM 망의 새로운 호는 현재 서비스 중인 호의 서비스 품질(QoS : quality of service)이 영향을 받지 않을 경우 망에 접속이 된다. 신경망 호 수락 제어 시스템은 입/출력 패턴의 학습으로 예측성 잇게 호 수락/거절을 하는 시스템이다. 본 논문의 퍼지 신경망 호 수락 제어 시스템에서는 학습 속도 개선을 위해 학습율과 모맨텀 상수에 퍼지 추론을 적용하였다. 이 시스템은 시뮬레이션을 통해 기존의 신경망 방법과 퍼지 신경망 방법에서의 학습 횟수 측정으로 제안 알고리즘의 우수성을 검증하였다. 시뮬레이션 결과 퍼지 학습 규칙에 근거한 퍼지 신경망 CAC(call admission control) 방식이 종래의 신경망 이론에 근거한 CAC 방식보다 학습 속도면에서 약 5배의 속도 향상이 있었다.

• 한국소프트웨어감정평가학회 논문지
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• 제16권2호
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• pp.127-133
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• 2020

인공지능의 활용성이 다양해지면서 소형 휴대용기기에 알고리즘을 탑재하려는 요구가 증가하고 있다. 또한 임베디드 시스템이 고성능화하면서 운영체제는 물론 고속연산 및 머신러닝의 알고리즘 구현이 가능해 지고 있다. 그러나 반복연산과 방대한 학습데이터를 처리하는 머신러닝알고리즘의 특성으로 네트워크 연결에 의한 클라우드 환경에 의존하고 있다. 임베디드 시스템에서의 독자적인 운영을 위해서는 저 전력화 및 최적화 알고리즘에 의한 빠른 실행이 요구된다. 본 연구에서는 스마트 제어를 목적으로 임베디드 시스템에 에너지 측정용 센서를 연결하고, 실시간 측정 및 모니터링 시스템으로 측정정보를 데이터베이스로 저장하는 장치를 구현하였다. 연속적으로 측정되어 저장된 데이터는 학습 알고리즘에 적용하여, 최적화 전력제어에 활용가능하며, 에너지 측정에 요구되는 다양한 센서의 인터페이스가 가능한 시스템을 구성하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.469-472
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• 2022

최근 지능형 교통 시스템의 발전에 따라 딥러닝을 기술을 적용한 다양한 기술들이 활용되고 있다. 도로를 주행하는 불법 차량 및 범죄 차량 단속을 위해서는 차량 종류를 정확히 판별할 수 있는 차종 분류 시스템이 필요하다. 본 연구는 YOLO(You Only Look Once)를 이용하여 이동식 차량 단속 시스템에 최적화된 차종 분류 시스템을 제안한다. 제안 시스템은 차량을 승용차, 경·소·중형 승합차, 대형 승합차, 화물차, 이륜차, 특수차, 건설기계, 7가지 클래스로 구분하여 탐지하기 위해 단일 단계 방식의 객체 탐지 알고리즘 YOLOv5를 사용한다. 인공지능 기술개발을 위하여 한국과학기술연구원에서 구축한 약 5천 장의 국내 차량 이미지 데이터를 학습 데이터로 사용하였다. 한 대의 카메라로 정면과 측면 각도를 모두 인식할 수 있는 차종 분류 알고리즘을 적용한 지정차로제 단속 시스템을 제안하고자 한다.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.