Wireless sensor systems are primarily used for monitoring natural environments or industrial automation. The physical environment where these systems are installed is often unstable, making it difficult to replenish sensor energy immediately. Complex and harsh conditions can impact the network's structure, affecting monitoring performance. Wireless sensor systems consist of hundreds of sensors that collect data from hazardous environments and transmit information to a central system. However, due to the system's physical structure, information delays or losses may occur. This paper proposes a distance-based tree structure to address these issues in wireless sensor systems, and experimental results confirm its superior performance.

PURPOSES : Even when autonomous vehicles are commercialized, a situation in which autonomous vehicles and regular drivers are mixed will persist for a considerable period of time until the percentage of autonomous vehicles on the road reaches 100%. To prepare for various situations that may occur in mixed traffic, this study aimed to understand the changes in traffic flow according to the percentage of autonomous vehicles in unsignalized intersections. METHODS : We collected road information and constructed a network using the VISSIM traffic simulation program. We then configured various scenarios according to the percentage of autonomous vehicles and traffic volume to understand the changes in the traffic flow in the mixed traffic by scenario. RESULTS : The results of the analysis showed that in all scenarios, the traffic flow on major roads changed negatively with the mix of autonomous vehicles; however, the increase or decrease was small. By contrast, the traffic flow on minor roads changed positively with a mix of autonomous vehicles. CONCLUSIONS : This study is significant because it proactively examines and designs traffic flow changes in congested traffic that may occur when autonomous vehicles are introduced.

New motor development requires high-speed load testing using dynamo equipment to calculate the efficiency of the motor. Abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft or looseness of the fixation, which may lead to safety accidents. In this study, three single-axis vibration sensors for X, Y, and Z axes were attached on the surface of the test motor to measure the vibration value of vibration. Analog data collected from these sensors was used in classification models for anomaly detection. Since the classification accuracy was around only 93%, commonly used hyperparameter optimization techniques such as Grid search, Random search, and Bayesian Optimization were applied to increase accuracy. In addition, Response Surface Method based on Design of Experiment was also used for hyperparameter optimization. However, it was found that there were limits to improving accuracy with these methods. The reason is that the sampling data from an analog signal does not reflect the patterns hidden in the signal. Therefore, in order to find pattern information of the sampling data, we obtained descriptive statistics such as mean, variance, skewness, kurtosis, and percentiles of the analog data, and applied them to the classification models. Classification models using descriptive statistics showed excellent performance improvement. The developed model can be used as a monitoring system that detects abnormal conditions of the motor test.

PURPOSES : This study develops a model that can estimate travel speed of each movement flow using deep-learning-based probe vehicles at urban intersections. METHODS : Current technologies cannot determine average travel speeds for all vehicles passing through a specific real-world area under obseravation. A virtual simulation environment was established to collect information on all vehicles. A model estimate turning speeds was developed by deep learning using probe vehicles sampled during information processing time. The speed estimation model was divided into straight and left-turn models, developed as fully-offset, non-offset, and integrated models. RESULTS : For fully-offset models, speed estimation for both straight and left-turn models achieved MAPE within 10%. For non-offset models, straight models using data drawn from four or more probe vehicles achieved a MAPE of less than 15%. The MAPE for left turns was approximately 20%. CONCLUSIONS : Using probe-vehicle data(PVD), a deep learning model was developed to estimate speeds each movement flow. This, confirmed the viability of real-time signal control information processing using a small number of probe vehicles.

PURPOSES : This paper proposes an artificial neural network (ANN)-based real-time traffic signal time design model using real-time field data available at intersections equipped with smart intersections. The proposed model generates suitable traffic signal timings for the next cycle, which are assumed to be near the optimal values based on a set of counted directional real-time traffic volumes. METHODS : A training dataset of optimal traffic signal timing data was prepared through the CORSIM Optimal Signal Timing program developed for this study to find the best signal timings, minimizing intersection control delays estimated with CORSIM and a heuristic searching method. The proposed traffic signal timing design model was developed using a training dataset and an ANN learning process. To determine the difference between the traditional pre-time model primarily used in practice and the proposed model, a comparison test was conducted with historical data obtained for a month at a specific intersection in Uiwang, Korea. RESULTS : The test results revealed that the proposed method could reduce control delays for most of the day compared to the existing methods, excluding the peak hour periods when control delays were similar. This is because existing methods focus only on peak times in practice. CONCLUSIONS : The results indicate that the proposed method enhances the performance of traffic signal systems because it rapidly provides alternatives for all-day cycle periods. This would also reduce the management cost (repeated field data collection) required to increase the performance to that level. A robust traffic-signal timing design model (e.g., ANN) is required to handle various combinations of directional demands.

최근 자기공명영상 획득을 위한 시뮬레이션 도구가 개발되어 오랜 시간이 소요되는 임상 연구를 대체할 수 있게 되었다. 이에 본 연구에서는 MRiLab 시뮬레이션을 사용하여 부가인자인 에코 시간의 변화에 따라 경사에코 펄스 시퀀스가 적용된 뇌 T2 강조 영상을 획득하여 영상의 신호 및 노이즈의 변화를 정량적으로 평가하고 경향성을 파악하고자 한다. 이를 위해 실제 MRI 장비를 기반으로 새롭게 개발된 MRiLab simulation tool을 사용하여 모든 파라미터를 같게 고정한 후 TE만을 20~95 ms범위에서 5 ms 간격으로 각각 설정하여 경사에코 펄스 시퀀스가 적용된 뇌 T2 강조 영상을 획득하였다. 획득된 영상들의 신호 및 노이즈 특성 변화를 정량적으로 평가하기 위해 신호대잡음비 및 대조대잡음비를 측정하였다. 결과적으로, TE가 증가할수록 SNR은 감소하고 CNR은 증가하는 경향을 보였다. 이는 TE가 증가할수록 관심 영역으로 설정된 뇌척 수액 신호는 일정하게 유지되는 반면 노이즈는 증가하였으며, 백그라운드로 설정된 백질의 경우 신호가 감소함과 동시에 노이즈가 증가한 것이 원인으로 분석된다. 결론적으로, 진단에 용이한 경사에코 펄스 시퀀스가 적용된 뇌 T2 강조 영상을 획득하기 위해서는 그 목적에 따라 적합한 TE를 설정하는 것이 중요함을 확인하였다.

최근 ICT 산업의 기술혁신이 일어남에 따라 생체신호을 인식하고 이에 대해 대응을 하기 위한 웨어러블 센싱 장치에 대한 수요가 증가하고 있다. 이에 따라 본 연구에서는 단순한 함침과정을 통해 3차원 스페이서(3D spacer)직물 을 단일벽 탄소나노튜브(SWCNT)분산용액에 함침공정을 진행해 단일층(monolayer) 압전 저항형 압력 센서 (piezoresistive pressure sensor)를 개발하였다. 3D 스페이서 원단에 전기전도성을 부여하기 위해 시료를 SWCNT 분 산용액에 함침공정을 진행한 후 건조하는 과정을 거쳤다. 함침된 시료의 전기적 특성을 파악하기 위해 UTM (Universal Testing Machine)과 멀티미터를 이용해서 압력의 변화에 따른 저항의 변화를 측정하였다. 또한 센서의 전기적 특성의 변화를 관찰하기 위해 분산용액의 농도, 함침횟수, 시료의 두께를 다르게 해서 시료의 센서로서의 성능을 평가했다. 그 결과 wt0.1%의 SWCNT 분산용액에 함침공정을 2번 진행한 시료가 센서로서 가장 뛰어난 성능 을 나타냄을 알 수 있었다. 두께별로는 7mm 두께의 센서가 가장 높은 GF를 보이고 13mm 두께의 센서가 작동범위가 가장 넓음을 확인했다. 본 연구를 통해 3D spacer 원단으로 제작한 스마트 텍스타일 센서는 공정과정이 단순하면서도 센서로서 성능이 뛰어나다는 장점을 확인할 수 있었다.

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

자연재해 발생을 예방하기 위한 방재센서 기술이 중요하며 광섬유를 이용한 센서에 대한 관심이 높아지고 있다. 본 논문은 광섬유 센서 내장 탄소섬유시트로 보강된 RC보의 계측된 데이터로 결함 탐지 연구를 수행하였다. 미분의 국부적 변동 특성을 이용한 Method Ⅰ과 컨벌루션 방법을 이용한 Method Ⅱ를 비교, 분석하였다. 다른 차원의 데이터를 비교하기 위해서 무차원화 시켰으며, 분석 결과 Mehtod Ⅱ가 결함의 위치를 예리하게 잘 탐지하는 것으로 나타났다. Method Ⅱ인 컨벌루션에 사용 되는 필터 벡터를 잘 응용하면 더 좋은 효과를 기대할 수 있을 것으로 판단된다.

PURPOSES : In this study, analyze the characteristics of IOC indicator 'threshold' which is needed when evaluating the traffic signal operation status with ESPRESSO in various grade road traffic environment of Seoul metropolitan city and derive suggested value to use in field practice. METHODS : Using the computerized database program (Postgresql), we extracted data with regional characteristics (Arterial, Collector road) and temporal characteristics (peak hour, non-peak hour). Analysis of variance and Duncan's validation were performed using statistical analysis program (SPSS) to confirm whether the extracted data contains statistical significance. RESULTS: The analysis period of the main and secondary arterial roads was confirmed to be suitable from 14 days to 60 days. For the arterial, it is suggested to use 20 km/h as the critical speed for PM peak hour and weekly non peak hour. It is suggested to use 25 km/h as the critical speed for AM peak hour and night non peak hour. As for the collector road, it is suggested to use 20 km/h as the critical speed for PM peak hour and weekly non peak hour. It is suggested to use 30 km/h as the critical speed for AM peak hour and night non peak hour. CONCLUSIONS : It is meaningful from a methodological point of view that it is possible to make a reasonable comparative analysis on the signal intersection pre-post analysis when the signal operation DB is renewed by breaking the existing traffic signal operation evaluation method.

PURPOSES: The objective of this study is to establish the traffic volume-based warrants of right-turn lanes at unsignalized intersections and to introduce a risk probability methodology based on the warrants.METHODS : In this study, a risk probability of a potential rear-end collision is applied between a right-turn vehicle and the immediately following through vehicle. Using the shifted negative exponential model and the compound probability theorem, the risk probability can be expressed as the function of directional volumes and the percentage of right-turns for a two-lane and four-lane highway, respectively.RESULTS : Based on the risk probablity, guidelines for installing right-turn lanes on two-lane and four-lane highways were developed. The risk probability also showed rationality by comparing with right-turn same-direction conflicts observed in-situ.CONCLUSIONS : The results of our study define the total approaching volumes to encourage a right-turn lane as a function of operating speed, percentage of right-turn, and number of lanes.