PURPOSES : The objective of this study is to investigate the spatiotemporal factors influencing the waiting time of special transportation systems for disabled people in Seoul, Korea. METHODS : A parametric survival analysis was employed to identify the primary factors influencing longer waiting times, primarily because censoring data were included in the analysis. For the analysis, one-year historical data collected from the Seoul Call-taxi for the disabled in 2019 were used. Using normal probability plots and Anderson-Darling statistics, a log-normal model was estimated to fit the data. RESULTS : In terms of time, there was a time zone in which the service level in terms of waiting time, such as late-night and dawn, decreased depending on the time of day called by call taxi users with disabilities in Seoul. Moreover, the waiting time on weekends was shorter than on weekdays. Spatially, when a user requests a service, there are many nearby vehicles when the traffic conditions nearby are good, and the waiting time tends to decrease near the depot. CONCLUSIONS : The existing system needs to reflect the increase in vulnerable times and the improvement of the dispatch method, considering the difference in the primary purpose of travel on weekdays and weekends. Additionally, to resolve the phenomenon in which vehicles are concentrated in a specific area, waiting time can be improved using operational strategies that can uniformly distribute vehicles in space, such as spatial subdivision of depots, real-time monitoring, and relocation.

A telesounder is a device that can monitor the appearance of fish in the sea on land and store fish detection data. This study was conducted to monitor the appearance of fish resources in coastal or near seas by using LTE communication for data transmission of the telesounder. The purpose of this study was to develop a prototype telesounder that can monitor the appearance of fish groups in the waters about 50 km away from the coast and store fish detection data. In this study, the prototype telesounder including a fish finder, communication device and battery for stable operation at sea was developed. The stability of telesounder buoy, data transmission/reception and expected use time were investigated. The expected use time of the telesounder using LTE communication with a lithium battery (12 V, 120 Ah) was about 274 hours under the conditions of 10 minutes off and 10 minutes on, about 520 hours under the conditions of 30 minutes off and 10 minutes on, and about 142 hours under continuous conditions. As a result of the sea test, it was found that the telesounder can be used in the sea area moved about 34 km from the land and the telesounder buoy was evaluated to have secured basic stability (buoyancy balance, waterproof, antenna strength, etc.) for operation in a marine environment.

Recently, wideband acoustic technology has been introduced and started to be used in fisheries acoustic surveys in various waters worldwide. Wideband acoustic data provides high vertical resolution, high signal-to-noise ratio and continuous frequency characteristics over a wide frequency range for species identification. In this study, the main characteristics of wideband acoustic systems were elaborated, and a general methodology for wideband acoustic data analysis was presented using data collected in frequency modulation mode for the first time in Republic of Korea. In particular, this study described the data recording method using the mission planner of the wideband autonomous acoustic system, wideband acoustic data signal processing, calibration and the wideband frequency response graph. Since wideband acoustic systems are currently installed on many training and research vessels, it is expected that the results of this study can be used as basic knowledge for fisheries acoustic research using the state-of-the-art system.

Along with the current rapid development of technology, object classification is being researched, developed, and applied to security systems, autonomous driving, and other applications. A common technique is to use vision cameras to collect data of objects in the surrounding environment. Along with many other methods, LiDAR sensors are being used to collect data in space to detect and classify objects. By using the LiDAR sensors, some disadvantages of image sensors with the negative influence on the image quality by weather and light condition will be covered. In this study, a volumetric image descriptor in 3D shape is developed to handle 3D object data in the urban environment obtained from LiDAR sensors, and convert it into image data before using deep learning algorithms in the process of object classification. The study showed the potential possibility of the proposal and its further application.

The use of heat exchangers in various applications such as chemical, air conditioning systems, fuel processing, and power industries is increasing. In order to improve the performance of the heat exchanger, the problem of bonding quality of the copper tube, which is a major member, is emerging. However, since the copper tube is in the form of a pipe, it is difficult to identify internal defects with external factors. In this study, a thermal imaging camera was used to develop and verify an algorithm for detecting defects in the brazing part, and in the process, the brazing performance characteristics were analyzed according to the electrode position, and finally, a learning model was developed and performance evaluation was performed. It was confirmed that the method of supplying heat to the base material and melting the filler metal through the heat transfer effect is more effective than supplying heat input to the filler metal in the bonding process of copper tubes through high-frequency induction heating brazing. Thermal image data was used to develop a defect discrimination model, and 80% of training data and 20% of test data were selected, and a neural network-based single-layer copper tube brazing defect discrimination model was developed through k-Flod cross-validation., the prediction accuracy of 95.2% was confirmed as a result of the error matrix analysis.

이 연구는 전염병의 잠재적 확산 가능성이 높은 지역의 탐색을 목적으로, 코로나19 전후의 버스 네트워크 클러스터의 시공간적 변화를 분석한다. 분석방법으로는 Getis와 Ord의  통계를 공간 네트워크로 확장 및 적용한  통계 값을 사용하였다. 이 과정은 서울시 전체 버스 네트워크의 개별 흐름에 대해 각각 적용되기 때문에 대규모 연산을 위해 병렬컴퓨팅 방식을 적용한 슈퍼컴퓨터를 사용하였다. 연구 결과, 첫째, 코로나19 이후 버스 네트워크가 일부 흐름으로 집중된 경향을 보였다. 둘째, 코로나19이 후의 버스 흐름은 주거지, 농업지로의 이동은 증가하고 상업지역, 교통지역으로의 이동은 감소했음을 확인하였다. 셋째, 중심업무 지구 중 여의도 방면의 클러스터, 구로디지털단지역 방면의 클러스터와 달리, 강남일대는 코로나19 전후의 유의미한 변화가 나타나 지 않았다. 이 연구는 국내에서 처음으로 코로나19전후의 버스 네트워크 클러스터를 확인하고 변화 특징을 제시한다는 의미가 있다.