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Various landslide disasters due to abnormal climate are increasing all over the world, and the inflow of debris flow is causing great damage to human life and social infrastructure. As such, in order to preemptively respond to debris flow, it is necessary to conduct a vulnerability assessment of the hazardous area based on the vulnerability curve. Therefore, in this study, the hazard intensity (depth, velocity, and impact pressure of debris flow) was analysed by performing back analysis using the DAN3D numerical model for 27 debris flow disaster areas in Korea from 2011 to 2020. And the vulnerability curve for the building was developed through the relationship between the degree of damage to the building and the impact pressure obtained through the case analysis of debris flow disasters.

This study aims to develop a potential system for real-time detection of debris flow motion using a deep convolutional neural network (CNN) and image processing techniques. A system consisting of a pre-trained CNN model, NVIDIA Jetson Nano, and a camera was used to identify debris flow movement. The pre-trained CNN model was trained on an image dataset derived from 12 debris flow videos obtained from small flume tests, large flume tests, and several debris flow events. The application results of the proposed system on the flume test in the laboratory reached an F1 score of 72.6 to 100%. The real-time processing speed of the CNN model achieved from 2 to 21 frames per second (FPS) on the Jetson Nano. Both the accuracy and the processing speed of CNN model depend on the size of the video input and the input size of the model CNN. The CNN model of 320 × 320 pixels with a resolution of 800 × 480 pixels gives accuracy (F1 = 99.2%) and processing speed (FPS = 20) considered the optimal model when running the Jetson Nano device; thus, it can be applied for early detection and warning systems.

This paper presents the control strategy of autonomous base station placement for localization of the GNSS interference source. The proposed algorithm deals with the optimization of the base station trajectory for target motion analysis based on bearing only tracking problem. The control strategy of the proposed algorithm is designed to maximize a cost function which is generally a functional of the Fisher information matrix. Compared to the optimal control methods, the proposed algorithm is easy to be designed and implemented, and constraints of multiple base stations’ trajectories can be effectively included. In addition, the proposed algorithm also considered target’s dynamics that is both uncertain and random, and there are multiple base stations for observing the target. In order to verify the performance of the proposed algorithm, simulation was performed with dynamic target case in the 2D scenario. It is assumed that the base stations’ networks have non-fully connected topology. According to the simulation results, it was confirmed that the proposed algorithm presents a flexible control strategy of autonomous multiple base stations’ placement for bearing only target tracking system.

Power and energy consumption are the two most important factors for successful operation; they also play important roles in performance identification. The measurement of power and energy consumption is a common test in the development process of a competition mobile robot. If the power of a competition mobile robot is not sufficient, the running time in the competition will be too long and winning the competition will not be possible. Thus, the power and energy consumption are basic and important measurement parameters for a competition mobile robot. In this paper, five types of hand-made competition mobile robots are successfully developed and their performances are measured. From the measurements, their powers and energy consumptions are evaluated and analyzed, respectively. The test results show large differences in the powers and energy consumptions of the five models, even though the same motors were used. The design and construction of the competition mobile robot are the key parameters that cause these huge differences. It is possible to develop the measurement techniques for power and energy consumption, quickly and precisely, to determine how to modify a competition mobile robot rapidly and efficiently to a condition optimal for a mobile robot competition.