• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.293-298
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• 2006

In this paper, a sensor fusion based robot navigation method for the autonomous control of a miniature human interaction robot is presented. The method of navigation blends the optimality of the Fuzzy Neural Network(FNN) based control algorithm with the capabilities in expressing knowledge and learning of the networked Intelligent Robotic Space(IRS). States of robot and IR space, for examples, the distance between the mobile robot and obstacles and the velocity of mobile robot, are used as the inputs of fuzzy logic controller. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a sensor fusion technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process. Preliminary experiment and results are shown to demonstrate the merit of the introduced navigation control algorithm.

• ETRI Journal
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• 제45권5호
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• pp.811-821
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• 2023

We propose an adaptive unmanned aerial vehicle (UAV)-assisted object recognition algorithm for urban surveillance scenarios. For UAV-assisted surveillance, UAVs are equipped with learning-based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self-adaptive control strategy to maximize the time-averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real-world data demonstrate that the proposed algorithm achieves the desired performance improvements.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권2호
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• pp.127-134
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• 2004

In this paper, human motor behaving model based sensory motor coordination(SMC) algorithm is implemented on robotic grasping task. Compare to conventional SMC models which connect sensor to motor directly, the proposed method used biologically inspired human behaving system in conjunction with SMC algorithm for fast grasping force control of robot arm. To characterize various grasping objects, pressure sensors on hand gripper were used. Measured sensory data are simultaneously transferred to perceptual mechanism(PM) and long term memory(LTM), and then the sensory information is forwarded to the fastest channel among several information-processing flows in human motor system. In this model, two motor learning routes are proposed. One of the route uses PM and the other uses short term memory(STM) and LTM structure. Through motor learning procedure, successful information is transferred from STM to LTM. Also, LTM data are used for next moor plan as reference information. STM is designed to single layered perception neural network to generate fast motor plan and receive required data which comes from LTM. Experimental results showed that proposed method can control of the grasping force adaptable to various shapes and types of greasing objects, and also it showed quicker grasping-behavior lumining time compare to simple feedback system.

• 한국지능시스템학회논문지
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• 제2권1호
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• pp.17-32
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• 1992

로보트 매니퓰레이터의 신경 제어기 구성에 널리 사용하는 다층 신경회로망은 로보트의 불확실한 동적 파라메터 변화에 대한 강건한 학습 적응능력, 그리고 병렬 처리를 통한 실시간 제어등의 장점들을 갖고있다. 그러나 대표적인 학습방법인 오차 역전파(error back propagation) 알고리즘은 그 학습 속도가 느리다는 문제점을 갖는다. 본 논문에서는 불확실하고 애매한 정보를 언어적인 방법에 의해 효율적으로 처리할 수 있는 퍼지 논리 (fuzzy logic)를 도입하여 로보트 매니퓰레이터 신경 제어기의 학습 속도를 개선하기위한 한 방법을 제안한다. 제안된 제어기의 효용성은 PUMA 560 로보트의 모의 실험을 통해 입증된다.

• Nuclear Engineering and Technology
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• 제55권10호
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• pp.3907-3912
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• 2023

Radiation portal monitors (RPMs) installed at airports and harbors to prevent illicit trafficking of radioactive materials generally use large plastic scintillators. However, their energy resolution is poor and radionuclide identification is nearly unfeasible. In this study, to improve isotope identification, a RPM system based on a multi-array plastic scintillator and convolutional neural network (CNN) was evaluated by measuring the spectra of radioactive sources. A multi-array plastic scintillator comprising an assembly of 14 hexagonal scintillators was fabricated within an area of 50 × 100 cm². The energy spectra of ¹³⁷Cs, ⁶⁰Co, ²²⁶Ra, and ⁴K (KCl) were measured at speeds of 10-30 km/h, respectively, and an energy-weighted algorithm was applied. For the CNN, 700 and 300 spectral images were used as training and testing images, respectively. Compared to the conventional plastic scintillator, the multi-arrayed detector showed a high collection probability of the optical photons generated inside. A Compton maximum peak was observed for four moving radiation sources, and the CNN-based classification results showed that at least 70% was discriminated. Under the speed condition, the spectral fluctuations were higher than those under dwelling condition. However, the machine learning results demonstrated that a considerably high level of nuclide discrimination was possible under source movement conditions.

• 한국통신학회논문지
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• 제24권9A호
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• pp.1329-1338
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• 1999

본 논문에서, LMS 알고리즘의 수렴 속도를 향상시키기 위한 효율적인 신호간섭 제어기법을 제안한다. 수신 데이터를 재활용하여 심볼 시간 주기에 계수들을 곱함으로써 적응되는 제안된 알고리즘의 수렴특성이 수렴 속도의 향상을 이론적으로 증명하기 위해 분석한다. 스텝-크기 매개변수 $\mu$가 증가됨에 따라 알고리즘의 수렴 속도가 제어된다. 또한, 스텝-크기 매개변수 $\mu$의 증가는 실험적으로 계산된 학습 곡선에서 분산을 감소시키는 효과를 갖는다. 고유치 확산을 증가시킴에 따라 적응 등화기의 수렴속도를 천천히 제어하고 평균 자승 에러의 안정-상태 값을 증가시키는 효과를 나타내며 데이터-재사용 LMS 기술이 수렴속도를 (B+1)배만큼 증가시켜 필터 알고리즘에서 신호간섭제어의 우수성을 입증한다.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.371-376
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• 2007

This paper presents an emotion recognition and its expression system of an intelligent robot like a home robot or a service robot. Emotion recognition method in the robot is used by a facial image. We use a motion and a position of many facial features. apply a tracking algorithm to recognize a moving user in the mobile robot and eliminate a skin color of a hand and a background without a facial region by using the facial region detecting algorithm in objecting user image. After normalizer operations are the image enlarge or reduction by distance of the detecting facial region and the image revolution transformation by an angel of a face, the mobile robot can object the facial image of a fixing size. And materialize a multi feature selection algorithm to enable robot to recognize an emotion of user. In this paper, used a multi layer perceptron of Artificial Neural Network(ANN) as a pattern recognition art, and a Back Propagation(BP) algorithm as a learning algorithm. Emotion of user that robot recognized is expressed as a graphic LCD. At this time, change two coordinates as the number of times of emotion expressed in ANN, and change a parameter of facial elements(eyes, eyebrows, mouth) as the change of two coordinates. By materializing the system, expressed the complex emotion of human as the avatar of LCD.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.505-508
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• 1997

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of induction motor. For the torque control method, an indirect vector control scheme with slip calculation is used because of its stable characteristics regardless of speed. Motor input current is regulated by a current controlled voltage source PWM inverter using space voltage vector technique. Also, the scheme of current control fuzzy controller is synchronous reference frame with decoupling term. DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzz. control algorithm. An IPM is used to simplify hardware design.

• Structural Engineering and Mechanics
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• 제75권2호
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• pp.175-191
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• 2020

In this study, multi-story structures are actively controlled using metaheuristic algorithms. The soil conditions such as dense, normal and soft soil are considered under near-fault ground motions consisting of two types of impulsive motions called directivity effect (fault normal component) and the flint step (fault parallel component). In the active tendon-controlled structure, Proportional-Integral-Derivative (PID) type controller optimized by the proposed algorithms was used to achieve a control signal and to produce a corresponding control force. As the novelty of the study, the parameters of PID controller were determined by different metaheuristic algorithms to find the best one for seismic structures. These algorithms are flower pollination algorithm (FPA), teaching learning based optimization (TLBO) and Jaya Algorithm (JA). Furthermore, since the influence of time delay on the structural responses is an important issue for active control systems, it should be considered in the optimization process and time domain analyses. The proposed method was applied for a 15-story structural model and the feasible results were found by limiting the maximum control force for the near-fault records defined in FEMA P-695. Finally, it was determined that the active control using metaheuristic algorithms optimally reduced the structural responses and can be applied for the buildings with the soil-structure interaction (SSI).

• 한국결정성장학회지
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• 제31권6호
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• pp.258-263
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• 2021

ZnO 바리스터는 다결정구조를 가지는 반도체 소자로 결정립과 입계의 미세구조 제어를 통해 비선형적인 전류/전압 특성을 가지기 때문에 서지(surge)전압으로부터 회로를 보호하는 역할을 한다. 이러한 ZnO 바리스터에서 원하는 전기적 물성을 얻기 위해서는 소결 공정에서 미세구조의 제어가 중요하다. 따라서 소결 공정에서 중요한 변수들과 소결체의 전기적 물성인 유전율로 구성된 데이터셋을 정의한 후 실험계획법 기반으로 데이터를 수집했다. 수집된 실험데이터셋을 기계학습 알고리즘에 학습하여 메타모델을 개발했고, 개발된 메타모델에 수치기반 최적화 알고리즘인 HMA(Hybrid Metaheuristic Algorithm)를 적용하여 최대 유전율을 가질 수 있는 공정조건을 도출했다. 이러한 메타모델 기반의 최적화를 다변수 시스템인 세라믹공정에 적용한다면 최소한의 실험만으로 최적 공정조건 탐색이 가능할 것으로 판단된다.