https://scholar.dgist.ac.kr/handle/20.500.11750/11770 2026-04-04T11:17:43Z https://scholar.dgist.ac.kr/handle/20.500.11750/60180 Title: Long Short-Term Memory Network-Based H∞ Synchronization Control and Anomaly Detection for Cyber-Physical Systems Author(s): Kwon, Hyoeun; Lee, Suwoong; Kwon, Wookyong; Lim, Yongseob; Jin, Yongsik Abstract: In the synchronization of cyber-physical systems (CPSs), modeling the nonlinear dynamics of physical plants is a challenging task. To address this challenge, we propose a novel H∞ controller design method that leverages a data-driven approach to robustly synchronize CPSs and ensure their stability. In the proposed approach, the input-output relationship of the physical system is learned using long short-term memory (LSTM) networks to approximate the unknown dynamics of CPSs. Furthermore, we exploit an effective control scheme for trained LSTM networks to effectively handle the nonlinearity of activation functions. To ensure stability and performance in the convergence of synchronization error, a controller design criterion is derived for the trained LSTM network in terms of linear matrix inequalities, and the controller gain is computed using convex optimization techniques. In addition, we present an anomaly detection algorithm using the proposed method, which can synchronize CPSs and detect abnormal signals without requiring any prior physical model information. Consequently, the stability of the synchronization control system can be ensured, enabling its application to anomaly detection. Finally, the effectiveness of the proposed method is validated through an experiment on a motor control system even in abnormal operating conditions. 2025-10-06T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60145 Title: 차량의 경로 추종 시스템 Author(s): 임용섭; 임성진 Abstract: 본 발명의 일 실시예에 따른 차량의 경로 추종 시스템은, 차량의 2차원 좌표 정보() 및 차량의 전방에 놓인 장애물의 2차원 좌표 정보()를 기반으로 목표 회전 값() 및 목표 횡방향 거동 값()을 산출하는 차량의 주행 경로를 설정하는 경로 지령 생성부; 차량에 장착되어 차량의 회전 정보를 센싱하는 센서부; 센서부로부터 전달된 회전 정보를 학습된 인공신경망 기반 추론 모델에 입력하여 차량의 휠의 종방향 힘()과 차량의 횡방향 속도()를 산출하는 자세 정보 추정부; 경로 지령 생성부로부터 목표 회전 값() 및 목표 횡방향 거동 값()을 전달받고 자세 정보 추정부로부터 차량의 휠의 종방향 힘()과 차량의 횡방향 속도()를 전달받아 모델 예측 제어기(MPC;Model Predict Control)를 통해 차량의 휠의 요구 조향각()을 산출하는 자세 제어부; 요구 조향각()을 기반으로 차량의 조향각을 제어하는 조향각 제어부;를 포함할 수 있다. https://scholar.dgist.ac.kr/handle/20.500.11750/59051 Title: AVOIDANCE PATH GENERATION METHOD ON BASIS OF MULTI-SENSOR CONVERGENCE USING CONTROL INFRASTRUCTURE, AND CONTROL DEVICE Author(s): 김제석; 권순; 김예온; 최경호; 임용섭 Abstract: An avoidance path generation method on the basis of multi-sensor convergence using a control infrastructure comprises the steps in which: a control device receives first sensing data relating to a region of interest from sensors of the control infrastructure; the control device receives second sensing data relating to a peripheral area from a moving object which moves on a set path; the control device generates convergence data by converging the first sensing data and the second sensing data; the control device determines whether or not there is a risk factor in an area to which the moving object is to move, by means of the convergence data; and, if there is a risk factor, the control device determines an avoidance path enabling the moving object to avoid the risk factor. https://scholar.dgist.ac.kr/handle/20.500.11750/58836 Title: DEEP LEARNING-BASED SYSTEM AND METHOD FOR AUGMENTING VARIOUS LOW-LIGHT IMAGE DATA, CAPABLE OF ADJUSTING BRIGHTNESS Author(s): 서정현; 임용섭 Abstract: The present invention relates to a technology for performing low-light image data augmentation by converting a bright image such as a daytime situation into a low-light image such as a nighttime situation, and a low-light image data augmentation system according to an embodiment includes: a training data configuration unit for configuring training data on the basis of a first image generated in a first environment classified on the basis of illuminance and a second image generated in a second environment in low illumination compared to the first environment; an input image conversion unit for converting the first image into the second image and the second image into the first image, by receiving the first image and the second image as inputs; and a calculation unit for calculating a total loss for training a model by comparing an input image with at least one of the converted first image or the converted second image, wherein the input image conversion unit can convert the first image in the first environment into the second image in the second environment on the basis of a brightness degree (b) and a weight (w), through the model trained using the total loss.