This paper introduces a novel cable-driven rodent ankle exoskeleton system designed for in-vivo research on the restoration and enhancement of sensorimotor abilities. The system features a lightweight, actuator-decoupled exoskeleton for shaping motion and providing kinesthetic feedback, along with a vision system and feedback-controlled treadmill for gait analysis. Experiments conducted under anesthesia and in awake conditions demonstrated effective control with minimal interference to natural gait. Dynamic time warping distance and Pearson correlation coefficients were calculated between joint angles from natural gait and those from rats wearing both passive and active exoskeleton component. The knee joint showed a low DTW distance and high correlation regardless of conditions, while all three joint displayed a greater maximum value from natural gait when the active component was engaged. These results provide valuable insights into the physiological impacts of wearable robotics in animal models, advancing sensorimotor rehabilitation technologies.

Occupancy detection is crucial for efficiently managing energy in building. However, purchasing occupancy sensors incurs additional cost, and their accuracy is typically low. This paper proposes AI-based occupancy prediction model using Channel State Information (CSI) from WiFi APs. Two models have been implemented based on logistic regression and deep neural network. By synergistically combining their respective advantages, its accuracy can be improved. This enables high-accuracy occupancy prediction using existing WiFi APs without deploying new sensors. Experimental results show that proposed model achieves a high accuracy exceeding 94%, which is an acceptable rate for real-world applications.

For the real-time object detection and tracking, CNN-based YOLO(You Only Look Once) is widely used. However, employing YOLO in a multi-streaming environment poses a challenge due to the increasing demand for computing resources as the number of streams increases. This paper proposes a system that minimizes FPS degradation without necessitating additional computing resources when using YOLO in a multi-streaming environment. Experimental results show that the proposed system can handle multi-streaming with 8 CCTV cameras without encountering FPS degradation, while maintaining delays at an acceptable level.

Recently, the amount of power generation from new and renewable energy sources is increasing compared to electricity demand, which is harming the stability of the system. To solve these problems, a microgrid-type power source configuration is being selected instead of expanding power infrastructure, and this paper proposes a microgrid operation strategy through optimization-based ESS scheduling. To verify the effectiveness of the proposed method, the algorithm was applied and analyzed by simulating a virtual microgrid, and energy savings of 7.1% were verified.