Bat-Erdene Byambasuren

The Internet of Things (IoT) has been enforced in different industries, including the electrical sector and the broader industrial landscape. IoT offers immense potential for enhanced electrical system monitoring, control, and optimization by seamlessly connecting devices, sensors, and equipment to the Internet. This issue explores IoT's current realization and implementation in electrical and industrial sectors and its impact on them. Additionally, it delves into the crucial connection between IoT, relay protection, and automation, highlighting the benefits of this integration in terms of efficiency, reliability, and safety. Furthermore, this issue explores perspectives and adaptations of IoT in the energy and industry sectors. The realization of IoT in electrical systems and the industry sector holds significant promise. As IoT technology continues to evolve, it will enable the seamless integration of various components within electrical systems and industrial processes. This integration will lead to intelligent automation, streamlined workflows, and real-time decision-making, improving operational efficiency, productivity, and resource optimization. The convergence of IoT, electrical systems, and the industry sector will create an intelligent ecosystem that redefines traditional practices and establishes a foundation for sustainable growth and competitiveness.

This paper introduces a simple algorithm and design of shepherding multi-robot system. The article intends to show possibility of using multi-robot system for shepherding in Mongolia. Shepherding is an activity to guide or control a flocking behavior by one or more agents called as shepherds man. In general, a complex strategy is necessary for treating to a flock which has a lot of members. In the paper, two types of shepherding multi-robots are used to show their capability to herd the sheep. Furthermore, we designed a simple structure for shepherding multi-robot system and demonstrated an algorithm. Two types of multi-robot systems like corner mobile robots and sideline mobile robots are used for shepherding. Mobile robots are connected to each other and are controlled by the given algorithm. Mobile robots should be high mobility and be able to go through uneven road.

Precision of bone mineral density (BMD) measurements of spine, femur and forearm using dual energy x-ray absorptiometry (DEXA) is greatly influenced operator dependent factors. Therefore, automatic region of interest determination in DEXA is very important. This research shows new developed algorithm of automatic region of interest determination of the femur and spine. The automatic determination method can detect region of interest (ROI) of the femur and spine using image processing tools. Firstly, adaptive threshold and separation are carried out by the detection algorithm. Then, the points of ROI for femur and spine are detected. It was validated by femur and spine phantom and real patient images. Detection algorithm, validated program representation and, experiment results are presented in this paper.

Smart sensing and power line tracking is very important in a smart grid system. Illegal electricity usage can be detected by remote current measurement on overhead power lines using an inspection robot. There is a need for accurate detection methods of illegal electricity usage. Stable and correct power line tracking is a very prominent issue. In order to correctly track and make accurate measurements, the swing path of a power line should be previously fitted and predicted by a mathematical function using an inspection robot. After this, the remote inspection robot can follow the power line and measure the current. This paper presents a new power line tracking method using parabolic and circle fitting algorithms for illegal electricity detection. We demonstrate the effectiveness of the proposed tracking method by simulation and experimental results.

Power delivery systems that use power lines can communicate with other electrical systems, monitor the quality of electrical energy and provide an economical solution for automatic meter reading (AMR). Energy meters can be connected to a low‐voltage power system by power line carriers for all consumers. Illegal electricity usage in the power delivery system can be easily detected if AMR is installed in the system, which consists of a terminal smart meter (TSM) and a gateway smart meter (GSM). This study proposes a novel remote detection method for illegal electricity usage that uses both TSM and GSM, where the TSM and the GSM are installed on the power system of each consumer and on the node of the power system feeder, respectively. TSM and GSM simultaneously disconnect an electrical source for a very short period of time. After this, the proposed detection method sends a low‐voltage signal with a high frequency from the TSM to the GSM during the time of disconnection. Once the signal has passed, the proposed detection method detects illegal electricity usage by measuring the amount of time it was disconnected. This study presents the structure of the proposed detection method as well as the mathematical model, proof, simulation and experimental results.

A noise reduction filter for Doppler audio signals is one important part of Transcranial Doppler ultrasound system. Several studies have been carried out for low-pass filter with fixed value for Doppler audio signals; however, most of these studies lacked complex research works and few studies analyzed the relationship of both parts of spectrum signal processing and audio signal processing. To solve the above-mentioned concern, this research is focused on the algorithms of de-noising and enveloping of spectrum signal processing part and improved new real-time method, which reduces the noise using low-pass filter with a variable threshold value based on maximum frequency of spectrum signals. The noise of Doppler audio signals was reduced efficiently by using this algorithm.