This paper presents research focusing on visualization and pattern recognition based on computer science. Although deep neural networks demonstrate satisfactory performance regarding image and voice recognition, as well as pattern analysis and intrusion detection, they exhibit inferior performance towards adversarial examples. Noise introduction, to some degree, to the original data could lead adversarial examples to be misclassified by deep neural networks, even though they can still be deemed as normal by humans. In this paper, a robust diversity adversarial training method against adversarial attacks was demonstrated. In this approach, the target model is more robust to unknown adversarial examples, as it trains various adversarial samples. During the experiment, Tensorflow was employed as our deep learning framework, while MNIST and Fashion-MNIST were used as experimental datasets. Results revealed that the diversity training method has lowered the attack success rate by an average of 27.2 and 24.3% for various adversarial examples, while maintaining the 98.7 and 91.5% accuracy rates regarding the original data of MNIST and Fashion-MNIST.

The growing use of collaborative robots (cobots) in industrial environments raises concerns about worker safety; flexible and adaptive systems are required to predict and prevent real-time cobot-worker collisions. This study proposes a digital-twin-based, cobot-worker collision risk-prediction system that utilizes the Unity game-development engine, robot operating system (ROS), and ultra-wideband (UWB) communication. The proposed system incorporates three modules: 1) real-time cobot-worker data capture, 2) digital-twin-based dynamic collision-zone management, and 3) collision-risk prediction. The core of the system is a data-driven cobot-worker digital twin that reflects the movements of the cobot and worker using ROS and UWB communication. This dynamic approach addresses the limitations of fixed safety-zone configurations by considering the cobot trajectory and the worker path projection in real time within the digital-twin modules. The system demonstrated low latency in data transmission from ROS to Unity (average 13 ms) and from UWB to Unity (average 140 ms), through implementation and evaluation. It also accurately predicted the minimum distance between the worker and cobot, accounting for varying worker speeds and future prediction times. This research offers a promising solution for enhancing worker safety in collaborative robotic environments by providing a real-time, digital-twin-based collision risk prediction system with dynamic collision zone management.

This paper proposes a pseudo-haptic interface that depicts the virtual weights of dumbbells in a virtual gymnasium. When a user performs a dumbbell biceps curl, he/she fixes the elbow joint as a standard joint and lifts the dumbbell, with its movement trajectory represented as a circular arc. The trajectories and velocity of dumbbell bicep curls differ depending on human physiological characteristics. Therefore, the proposed system provides an adaptable exercise area and force visualization of virtual dumbbells using a velocity-based pseudo-haptic interface and computer vision-based tracking method. The system recognizes the position and rotation of joints related to a dumbbell biceps curl with the implementation of density-based spatial clustering of applications with noise (a clustering algorithm) and resizes the radius and angle of an integrated force circular gauge. Furthermore, when a user lifts a dumbbell, the system recognizes, using linear regression, the current position and lifting force of the virtual dumbbell and visualizes the current lifting force with a guided movement trajectory to match the lifting force. Experimental results show that the proposed pseudo-haptic interface increased weight perception and usability by up to 30% compared to conventional methods (p < 0.05).

사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.

This paper presents an integrated AI-driven framework that unifies semantic segmentation and deep reinforcement learning (DRL) to enable tactical pathfinding in battlefield environments characterized by complex terrain and adversarial threats. High-resolution aerial imagery captured by reconnaissance drones is processed with a lightweight yet accurate segmentation model to classify terrain into eleven operational categories, such as roads, forests, rice paddies, and restricted zones. The resulting semantic map is transformed into a cost-weighted potential field, where each terrain type is assigned a traversal weight reflecting its impact on mobility and mission risk. A Deep Q-Network (DQN) agent is then trained to optimize its navigation policy by selecting appropriate repulsive potential coefficients that minimize environment-weighted travel time while adaptively avoiding impassable regions and predicted enemy zones. Through continuous interaction with the environment, the agent adaptively adjusts its repulsive potential coefficient to ensure both efficiency and safety. Experimental results demonstrate that the proposed framework outperforms conventional pathfinding approaches in terms of path efficiency and threat avoidance, offering a practical and scalable solution for real-time decision-making in autonomous military mobility.