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The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the operation of railway vehicles. To address this issue, this paper proposes a method for predicting abnormalities in railway wheels in advance and enhancing the learning and prediction performance of machine learning algorithms. Data were collected during the operation of Line 4 of the Busan Metro in South Korea by directly attaching sensors to the railway vehicles. Through the analysis of key factors in the collected data, factors that can be used for tire condition classification were derived. Additionally, through data distribution analysis and correlation analysis, factors for classifying tire conditions were identified. As a result, it was determined that the z-axis of acceleration has a significant impact, and machine learning techniques such as SVM (Linear Kernel, RBF Kernel) and Random Forest were utilized based on acceleration data to classify tire conditions into in-service and defective states. The SVM (Linear Kernel) yielded the highest recognition rate at 98.70%. [ABSTRACT FROM AUTHOR]

According to data from the Ministry of Employment and Labor in Korea, a significant portion of fatal accidents on construction sites occur due to collisions between construction workers and equipment, with many of these collisions being attributed to worker negligence. This study introduces a method for accurately localizing construction equipment and workers on-site, delineating areas prone to collisions as 'a danger area of a collision', and defining collision risk states. Utilizing advanced deep learning models which specialize in object detection, video footage obtained from strategically placed closed-circuit television (CCTV) cameras across the construction site is analyzed. The positions of each detected object are determined using transformation or homography matrices representing the conversion relationship between a sufficiently flat reference plane and image coordinates. Additionally, 'a danger area of a collision' is proposed for evaluating equipment collision risk based on the moving equipment's speed, and the validity of this area is verified. Through this, the paper presents a system designed to preemptively identify potential collision risks, particularly when workers are located within the 'danger area of a collision', thereby mitigating accident risks on construction sites. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic air-to-ground (A2G) channel model selection method based on machine learning (ML) using digital surface model (DSM) terrain data. In order to verify whether a communication network for a new non-terrestrial user service such as Urban Air Mobility (UAM) satisfies the required performance, it is necessary to perform a simulation reflecting the characteristics of the corresponding terrain environments as accurately as possible. For this simulation, A2G channel models corresponding to various terrain environments and a method of automatically classifying the terrain type of the simulation area must be provided. Many A2G channel models based on actual measurement results exist, but the practical automatic topography classification method still needs to be developed. This paper proposes the first practical automatic topography classification method using a two-step neural network-based classifier utilizing various geographic feature data as input. Since there is no open topography dataset to evaluate the accuracy of the proposed method, we built a new dataset for five topography classes that reflect the characteristics of Korea's topography, which is also a contribution of our study. The simulation results using the new data set show that the proposed ML-based method could increase the selection accuracy compared to the technique for direct classification by humans or the existing cross-correlation-based classification method. Since the proposed method utilizes the DSM data, open to the public, it can easily reflect the different terrain characteristics of each country. Therefore, the proposed method can be effectively used in the realistic performance evaluation of new non-terrestrial communication networks utilizing vast airspace such as UAM or 6G mobile communications. [ABSTRACT FROM AUTHOR]

Research on autonomous driving technology is actively underway to solve the facing problems in the agricultural field. Combine harvesters used in East Asian countries, including Korea, are tracked-type vehicles. The steering control system of the tracked vehicle has different characteristics from the wheeled vehicle used in the agricultural tractor. In this paper, a dual GPS antenna-based autonomous driving system and path tracking algorithm were developed for a robot combine harvester. An α-turn-type work path generation algorithm and a path tracking algorithm were developed. The developed system and algorithm were verified through experiments using actual combine harvesters. The experiment consisted of an experiment with harvesting work and an experiment without harvesting work. In the experiment without harvesting work, an error of 0.052 m occurred during working driving and 0.207 m during turning driving. In the experiment where the harvesting work was carried out, an error of 0.038 m occurred during work driving and 0.195 m during turning driving. As a result of comparing the non-work area and driving time to the results of manual driving, the self-driving experiment with harvesting work showed an efficiency of 76.7%. [ABSTRACT FROM AUTHOR]

Rectification of vehicle plate images helps to improve the accuracy of license-plate recognition (LPR). It is a perspective-transformation process to project images as if taken from the front geometrically. To obtain the projection matrix, we require the (x, y) coordinates of four corner positions of plates in images. In this paper, we consider the problem of unsupervised domain adaptation for corner detection in plate images. We trained a model with plate images of one country, the source domain, and applied a domain adaptation scheme so that the model is able to work well on the plates of a different country, the target domain. For this study, we created a dataset of 22,096 Korea plate images with corner labels, which are source domain, and 6762 Philippines, which are target domain. To address this problem, we propose a heatmap-based corner-detection model, which outperforms existing scalar-regression methods, and an image classifier for mixed image of source and target images for domain adaptation. The proposed approach achieves better accuracy, which is 19.1% improvement if compared with baseline discriminator-based domain adaptation scheme. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN's object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations—the first air delivery service by drones in Korea. [ABSTRACT FROM AUTHOR]

The cadastral system provides land ownership information by registering and representing land boundaries on a map. The current cadastral system in Korea, however, focuses mainly on the management of 2D land-surface boundaries. It is not yet possible to provide efficient or reliable land administration, as this 2D system cannot support or manage land information on 3D properties (including architectures and civil infrastructures) for both above-ground and underground facilities. A geometrical model of the 3D parcel, therefore, is required for registration of 3D properties. This paper, considering the role of the cadastral system, proposes a framework for a 3D underground cadastral system that can register various types of 3D underground properties using indoor mapping for as-built Building Information Modeling (BIM). The implementation consists of four phases: (1) geometric modeling of a real underground infrastructure using terrestrial laser scanning data; (2) implementation of as-built BIM based on geometric modeling results; (3) accuracy assessment for created as-built BIM using reference points acquired by total station; and (4) creation of three types of 3D underground cadastral map to represent underground properties. The experimental results, based on indoor mapping for as-built BIM, show that the proposed framework for a 3D underground cadastral system is able to register the rights, responsibilities, and restrictions corresponding to the 3D underground properties. In this way, clearly identifying the underground physical situation enables more reliable and effective decision-making in all aspects of the national land administration system. [ABSTRACT FROM AUTHOR]

Coronavirus 2019 (COVID-19) has posed a serious threat to the lives and health of the majority of people worldwide. Since the early days of the outbreak, South Korea's government and citizens have made persistent efforts to provide effective prevention against further spread of the disease. In particular, the participation of individual citizens in complying with the necessary code of conduct to prevent spread of the infection, through measures such as social distancing and mask wearing, is as instrumental as the geographical tracking of the trajectory of the infected. In this paper, we propose an activity recognition method based on a wristband equipped with an IR array and inertial measurement unit (IMU) to detect individual compliance with codes of personal hygiene management, such as mask wearing, which are recommended to prevent the spread of infectious diseases. The results of activity recognition were comparatively analyzed by applying conventional machine learning algorithms and convolutional neural networks (CNNs) to the IMU time series and IR array thermal images collected from 25 subjects. When CNN and 24 × 32 thermal images were used, 97.8% accuracy was achieved (best performance), and when 6 × 8 low-resolution thermal images were used, similar performance with 97.1% accuracy was obtained. In the case of using IMU, the performance of activity recognition was lower than that obtained with the IR array, but an accuracy of 93% was achieved even in the case of applying machine learning algorithms, indicating that it is more suitable for wearable devices with low computational capability. [ABSTRACT FROM AUTHOR]

Vessel traffic volume and vessel traffic service (VTS) operator workloads are increasing with the expansion of global maritime trade, contributing to marine accidents by causing difficulties in providing timely services. Therefore, it is essential to have sufficient VTS operators considering the vessel traffic volume and near-miss cases. However, no quantitative method for determining the optimal number of workstations, which is necessary for calculating the VTS operator staffing level, has yet been proposed. This paper proposes a new, microscopic approach for calculating the number of workstations from vessel trajectories and voice recording communication data between VTS operators and navigators. The vessel trajectory data are preprocessed to interpolate different intervals. The proposed method consists of three modules: Information services, navigational assistance services, and traffic organization service. The developed model was applied to the Yeosu VTS in Korea. Another workstation should be added to the current workstation based on the proposed method. The results showed that even without annual statistical data, a reasonable VTS operator staffing level could be calculated. The proposed approach helps prevent vessel accidents by providing timely services even if the vessel traffic is congested if VTS operators are deployed to a sufficient number of workstations. [ABSTRACT FROM AUTHOR]

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology for calculating the necessary spectrum requirements of aeronautical mobile airport communication system (AeroMACS) to provide various airport communication services. To accurately calculate the spectrum requirement, it is necessary to evaluate the AeroMACS traffic demand of the peak time and statistical data on the packet traffic generated at the airport. Because there is no AeroMACS traffic model and real trace data, we have developed the AeroMACS traffic simulator based on the report of Single European Sky Air Traffic Management Research (SESAR). To calculate the spectrum requirements, the AeroMACS traffic simulator is combined with the methodology of ITU-R M.1768-1. The developed traffic simulator reflects AeroMACS traffic priorities and can generate the required traffic according to its location in the airport. We observed the spectrum requirement by changing the number of sectors and the spectral efficiency. To show the feasibility of our methodology, we applied it to the case of Incheon International Airport in Korea. The simulation results show that the average bandwidth of 0.94 MHz is required in the ground area and 8.59 MHz is required in the entire airport. [ABSTRACT FROM AUTHOR]

Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc. First, PCA is adopted to reduce the number of features of the dataset and simplify the learning model accordingly. ANN then learns the path loss structure from the dataset with reduced dimension, and Gaussian process learns the shadowing effect. Path loss data measured in a suburban area in Korea are employed. We observe that the proposed combined path loss and shadowing model is more accurate and flexible compared to the conventional linear path loss plus log-normal shadowing model. [ABSTRACT FROM AUTHOR]

An Extended Line Map (ELM)-based precise vehicle localization method is proposed in this paper, and is implemented using 3D Light Detection and Ranging (LIDAR). A binary occupancy grid map in which grids for road marking or vertical structures have a value of 1 and the rest have a value of 0 was created using the reflectivity and distance data of the 3D LIDAR. From the map, lines were detected using a Hough transform. After the detected lines were converted into the node and link forms, they were stored as a map. This map is called an extended line map, of which data size is extremely small (134 KB/km). The ELM-based localization is performed through correlation matching. The ELM is converted back into an occupancy grid map and matched to the map generated using the current 3D LIDAR. In this instance, a Fast Fourier Transform (FFT) was applied as the correlation matching method, and the matching time was approximately 78 ms (based on MATLAB). The experiment was carried out in the Gangnam area of Seoul, South Korea. The traveling distance was approximately 4.2 km, and the maximum traveling speed was approximately 80 km/h. As a result of localization, the root mean square (RMS) position errors for the lateral and longitudinal directions were 0.136 m and 0.223 m, respectively. [ABSTRACT FROM AUTHOR]