As we become an aging society, the number of elderly patients continues to increase. Pressure sores that can easily occur in patients with trauma cause serious socio-economic problems. In general, prevention of bedsores through predicting the patient's posture is being developed. Developed method usually use artificial intelligence techniques to estimate the patient’s posture by measured pressure images in the mattress. In this method, it has a problem the reduction of estimation accuracy when posture of patient is changed. Therefore, it is necessary to use the filter of pressure images in the position transition of patient. In this paper, we propose an algorithm to predict the patient's posture, and an algorithm to reduce the ambiguity that can occur in the patient's posture transition section. By obtaining stable data through this algorithm, learning/prediction stability of the neural network can be expected, and prediction performance is improved accordingly. Through experiments, the effectiveness of the algorithm was verified.

The aims of this paper is to develop a modular agricultural robot and its autonomous driving algorithm that can be used in field farming. Actually, it is difficult to develop a controller for autonomous agricultural robot that transforming their dynamic characteristics by installation of machine modules. So we develop for the model based control algorithm of rotary machine connected to agricultural robot. Autonomous control algorithm of agricultural robot consists of the path control, velocity control, orientation control. To verify the developed algorithm, we used to analytical techniques that have the advantage of reducing development time and risks. The model is formulated based on the multibody dynamics methods for high accuracy. Their model parameters get from the design parameter and real constructed data. Then we developed the co-simulation that is combined between the multibody dynamics model and control model using the ADAMS and Matlab simulink programs. Using the developed model, we carried out various dynamics simulation in the several rotation speed of blades.

In this paper, we introduce a target position reasoning system based on Bayesian network that selects destinations of robots on a map to explore compound disaster environments. Compound disaster accidents have hazardous conditions because of a low visibility and a high temperature. Before firefighters enter the environment, the robots notify information in advance, such as victim’s positions, number of victims, and status of debris of building. The problem of the previous system is that the system requires a target position to operate the robots and the firefighter need to learn how to use the robot. However, selecting the target position is not easy because of the information gap between eyewitness accounts and map coordinates. In addition, learning the technique how to use the robots needs a lot of time and money. The proposed system infers the target area using Bayesian network and selects proper x, y coordinates on the map based on image processing methods of the map. To verify the proposed system, we designed three example scenarios based on eyewetinees testimonies and compared time consumption between human and the system. In addition, we evaluate the system usability by 40 subjects.