With the rapid advancement of technologies, need for different research fields where this technology can be used is also increasing. One of the most researched topic in computer vision is object detection, which has widely been implemented in various fields which include healthcare, video surveillance and education. The main goal of object detection is to identify and categorize all the objects in a target environment. Specifically, methods of object detection consist of a variety of significant techniq ues, such as image processing and patterns recognition. Anomaly detection is a part of object detection, anomalies can be found various scenarios for example crowded places such as subway stations. An abnormal event can be assumed as a variation from the conventional scene. Since the abnormal event does not occur frequently, the distribution of normal and abnormal events is thoroughly imbalanced. In terms of public safety, abnormal events should be avoided and therefore immediate action need to be taken. When abnormal events occur in certain places, real time detection is required to prevent and protect the safety of the people. To solve the above problems, we propose a modified YOLOv5 object detection algorithm by implementing dilated convolutional layers which achieved 97% mAP50 compared to other five different models of YOLOv5. In addition to this, we also created a simple mobile application to avail the abnormal event detection on mobile phones.

This research addresses sound command based human tracking problems for autonomous cleaning mobile robot in a networked AI space. To solve the problem, the difference among the traveling times of the sound command to each of three microphones has been used to calculate the distance and orientation of the sound from the cleaning mobile robot, which carries the microphone array. The cross-correlation between two signals has been applied for detecting the time difference between two signals, which provides reliable and precise value of the time difference compared to the conventional methods. To generate the tracking direction to the sound command, fuzzy rules are applied and the results are used to control the cleaning mobile robot in a real-time. Finally the experiment results show that the proposed algorithm works well, even though the mobile robot knows little about the environment.

This paper develops the theory for a novel fault-tolerant, permanent magnet biased, 6-active-pole, homopolar magnetic bearing. The Lagrange Multiplier optimization with equality constraints is utilized to calculate the optimal distribution matrices for the failed bearing. some numerical examples of distribution matrices are provided to illustrate the new theory. Simulations show that very much the same dynamic responses (orbits or displacements) are maintained throughout failure events (up to any combination of 3 coils failed for the 6 pole magnetic bearing) while currents and fluxes change significantly. The overall load capacity of the bearing actuator is reduced as coils fail. The same magnetic forces are then preserved up to the load capacity of the failed bearing.

In this paper, we conduct a study on the design that can secure the safety of the EPLA system by performing safety activities based on the ISO 26262 standard for vehicle functional safety. In the case of a company developing a detailed system, it is responsible for verification through hardware design and safety analysis in the overall flow of safety activities, and safety analysis according to the ASIL safety level must be properly performed. At this time, there are cases where the safety goal quantitative metric value suggested by the ISO 26262 standard cannot be satisfied only by the hardware design of the basic function, so it is necessary to design and install the safety mechanism. Based on ISO 26262 safety activities, it is possible to derive an effective design plan through hardware safety analysis.