Black ice, a thin and nearly invisible ice layer on roads and pavements, poses a significant danger to drivers and pedestrians during winter due to its transparency. We propose an efficient black ice detection system and technique utilizing Global Positioning System (GPS)-reflected signals. This system consists of a GPS antenna and receiver configured to measure the power of GPS L1 band signal strength. The GPS receiver system was designed to measure the signal power of the Right-Handed Circular Polarization (RHCP) and Left-Handed Circular Polarization (LHCP) from direct and reflected signals using two GPS antennas. Field experiments for GPS LHCP and RHCP reflection measurements were conducted at two distinct sites. We present a Normalized Polarized Reflection Index (NPRI) as a methodological approach for determining the presence of black ice on road surfaces. The field experiments at both sites successfully detected black ice on asphalt roads, indicated by NPRI values greater than 0.1 for elevation angles between 45o and 55o. Our findings demonstrate the potential of the proposed GPS-based system as a cost-effective and scalable solution for large-scale black ice detection, significantly enhancing road safety in cold climates. The scientific significance of this study lies in its novel application of GPS reflection signals for environmental monitoring, offering a new approach that can be integrated into existing GPS infrastructure to detect widespread black ice in real-time.

This study proposes a soil moisture retrieval method from ground reflection signals received by Global Positioning System (GPS) antenna modules consisting of an up-looking (UP) right-hand circular polarization (RHCP) and two down-looking (DW) RHCP and left-hand circular polarization (LHCP) signals. Field experiments at four different surface types (asphalt, grassland, dry soil, and moist soil) revealed that the DW RHCP and LHCP signals are affected by antenna height and multipath interference signals. The strength differences between the DW LHCP and UP RHCP signals were in good agreement with the DW LHCP signals. Methodologically, this study applied a spectrum analysis to the detrended surface-reflected signals for RHCP and LHCP. The study indicated that the down-looking antenna exhibited greater sensitivity to reflected GPS signals than the up-looking antenna. We demonstrated the feasibility of estimating soil moisture using GPS signals, by comparing LHCP signals received by the down-looking antenna with theoretical values. This study presents a novel method for estimating soil moisture in vegetated areas, leveraging the advantage of crosspolarization comparisons to achieve stronger signal strength than single-polarization reflection signals. With further research, including long-term observations and detailed analysis, the proposed method has the potential to enhance performance significantly.

PURPOSES : This study aims to improve pedestrian safety by analyzing the effectiveness of displaying the remaining time of red crosswalk signal that provide pedestrians with information regarding the time available for crossing METHODS : This study analyzes the effectiveness of newly installed displays for remaining time of the red pedestrian signal in the cities of Yeongcheon and Yeongju in Gyeongsangbuk-do. The data collection was divided into periods before and after the installation of the facilities. Pedestrian departure delays were measured at the introduction and subsequent stages of installation, and changes in pedestrian behavior were quantitatively analyzed. Statistical significance of the results was verified using a t-test. RESULTS : For the displays of remaining time of crosswalk red signals installed in Yeongju, the pedestrian departure loss time decreased from 3.36 seconds before installation to 1.85 seconds after installation. Additionally, compared to the pre-installation situation, the postinstallation situation showed a relatively lower standard deviation, indicating that the pedestrians exhibited a more consistent pattern when starting to cross. Similarly, for the displays of remaining time of crosswalk red signal installed in Yeongcheon, the pedestrian departure loss time decreased from 2.37 seconds before installation to 1.89 seconds after installation. As in Yeongju, the post-installation situation showed a relatively lower standard deviation, indicating that the pedestrians exhibited a more consistent pattern when starting to cross. CONCLUSIONS : In this study, we verified the effect of newly installed pedestrian countdown signals on improving pedestrian safety at two intersections in Yeongcheon and Yeongju, which were selected through a survey of traffic accident-prone areas in Gyeongsangbuk-do. Pedestrian countdown signals were quantitatively confirmed to have a positive impact on pedestrian safety. Based on the observations, it is anticipated that expanding the installation of these signals will have a positive effect on pedestrian safety, particularly in areas with frequent traffic accidents and the senior and children protection zones. Results of this study indicate that the proposed policy measures are expected to strengthen pedestrian safety and reduce traffic accidents.

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.

The purpose of this study is to detect future signals and changes in nuclear-related research to apply safeguards by design to new nuclear facilities or to determine nuclear fuel cycle-related research and development (R&D) activities. First, a total of 2,029 scientific articles published between 2015 and 2022 in the journal of “Nuclear Engineering and Technology” by the Korean Nuclear Society were collected. The authors of the scientific article used their expertise and knowledge to select keywords that can properly represent the article. Therefore, in this study, the keywords of each scientific article were analyzed using the technique of text mining. We then calculated the “word frequency” and “term frequency-inverse document (TF-IDF)” values of the keywords. Consequently, significant words such as “reactor,” “nuclear,” and “fuel” were extracted, which were represented as word clouds. Furthermore, keywords extracted through text mining were quantitatively classified into weak or strong signals using a keyword emergence map (KEM). The KEM is a tool that can explore future signals because essential keywords have a high frequency of appearance, and newer keywords are more important than older keywords. The KEM results showed no keywords in the strong-signal area in the field of nuclear academia. However, keywords such as “deep learning,” “earthquake,” “zircaloy,” and “CFD” were confirmed to be distributed in the weak signal area. A weak signal indicates the most probable topic that could become a strong signal in the near future. The weak signal methodology can be applied to predict future nuclear scientific trends in the rapidly changing world. Based on the results of the study, changes in the subject of nuclear-related scientific articles over the past eight years and future signals were interpreted. The results confirmed that this method can be applied to safeguards measures of new nuclear facilities in the design stage and can be used to detect R&D activities related to the nuclear fuel cycle in advance.

저층 침적 위험·유해물질(Hazardous and Noxious Substances)은 해저에 침적되는 위험·유해물질로 직접 및 광학 탐지 기법의 적용 이 어렵기 때문에 수중에서 효과적인 음향 탐지 기법 적용이 요구된다. 본 연구에서는 저층 침적 위험·유해물질인 클로로폼(Chloroform)을 이용한 후방산란신호 측정 실험을 통해 저층 침적 위험·유해물질 음향 탐지 가능성을 확인하였다. 제작된 아크릴 수조 내에 지점토를 이 용하여 웅덩이를 만든 후 Pan&Tilt를 이용하여 수평입사각을 90°에서 50°까지 0.5° 간격으로 변화시켜가며 클로로폼 유무에 따른 후방산란 신호 측정이 수행되었다. 송수신기를 단상태로 주파수 200 kHz, 신호길이 25 인 정현파 신호를 이용하여 송수신하였으며, 클로로폼 유 무에 따른 후방산란신호를 측정하였다. 클로로폼이 침적된 경우 수평입사각 약 80°이하에서 물과 클로로폼 경계면에서의 후방산란신호 수신준위가 작아지는 것이 확인되었다. 물과 클로로폼 경계면에서의 후방산란신호 측정된 결과를 통해 저층 침적 위험·유해물질 음향 탐 지 가능성을 확인하였다.

The integrity of the disposal repository structure must be guaranteed for few hundreds to few hundred thousand years until toxicity of radioactive waste is surely degraded. Acoustic emission (AE) method is widely utilized to evaluate the integrity of the structure because it can detect crack wave signals of the structures. It is well known that the cracking AE energy is proportional to the volume of the structure (Fractal theory). However, it is hard to destroy whole structures for obtaining AE energy. Therefore, the scaled specimens are prepared to obtain the relationship between volume of the structure and AE energy. The specimens are prepared with same of Wolsong Low and Intermediate Level Radioactive Waste Disposal Center (WLDC) silo concrete recipe. Their diameters are from 50 mm to 150 mm in each 10 mm and their heights are twice of the diameter. One set of 50 mm to 150 mm specimens (11 specimens in one set) are made in single mixers to maintain uniformity. Surface of the specimens are flatten with cement milk to prevent from applying load with eccentricity. The uniaxial compression test is performed by controlling displacement as 0.1 mm/min. The fractal constant is obtained using least square function from volume-cumulative AE energy relationship.

Low and intermediate radioactive wastes in South Korea have been disposed in Wolsong Low and Intermediate Level Radioactive Waste Disposal Center (WLDC), Gyeongju. This repository structure is planned to be operated few hundred years while toxicity of the waste is sufficiently decayed. The structural integrity of the repository is required to protect the waste in safe. The integrity of the structure is commonly estimated using acoustic emission (AE) method. The integrity of the structure using AE is obtained by following process: 1) Estimation of maximum acoustic crack energy of the structure, 2) Acoustic signal measurement and filtering, and 3) Measurement of simultaneous acoustic cracking energy. The damage of the structure can be obtained from cumulative cracking energy from the structure divided by the predicted maximum cracking energy of the structure. Estimation of maximum cracking energy is gained by the specimens whose components are identical to the repository structure. The cracking energy of the different specimens are obtained during uniaxial compressive test and volume of the specimen is calculated. Then, the fractal coefficient for the structure is obtained and the maximum crack energy of the target structure can be calculated. The specimens whose diameters vary from 50 mm to 150 mm and heights are twice of the diameter are made with same recipe of WLDC silo concrete. The uniaxial compression test is conducted with loading rate of 0.1 mm·min−1. The fractal coefficient is obtained by least square method from the volume-cumulative energy relationship.

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

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.

Deimatic display 는 평상시에는 위장으로 자신을 보호하는 동물들이 포식자에게 위협당하였을 때에만 일시적으로 화려한 경고색을 보여줌으로써 포식자를 놀라게 하여 자신을 보호하는 방어전략이다. 본 연구에서는 3가지의 서로 다른 연구 방법을 이용하여 이 방어전략의 진화에 대해 연구하였으며, 연구의 모델로는 태극나방과 (Erebidae)의 나방을 이용하였다. 첫 번째로 deimatic display 전략이 어떤 종에서 진화하였으며, 이 전략이 동물의 몸 크기와 함께 진화하였는지를 계통수 분석을 이용한 방법으로 연구하였다. 연구 결과 deimatic display 는 몸 크기가 상대적으로 큰 곤충에서 선택적으로 진화하였다는 사실을 발견하였다. 두 번째로 deimatic display 전략이 왜 body size 가 큰 종에서 진화하였는지를 이론적으로 검증하기 위하여 수학적 모델링을 활용, deimatic display 와 몸 크기와의 이론적인 관계를 연구하였다. 이론적 모델링이 시사하는 바는 1) 몸 크기가 클수록 위장이 실패할 확률이 높으며, 2) 몸 크기가 클수록 deimatic display 가 포식자를 위협하는 효과가 클수록 이러한 진화적인 관계가 생성될 수 있다는 점을 발견하였다. 마지막으로 수학적 모델링에서 도출된 결과를 실험적으로 검증하기 위하여 로보틱스 기술을 활용, deimatic display 행동을 보이는 인공나방로봇을 제작하였다. 제작된 인공나방 로봇의 날개크기와 날개색채를 변화시켜가며 나방의 크기가 커질수록 deimatic display 전략이 포식자(조류)를 위협하는데 더 효과적인지를 테스트하였으며, 실험 결과 deimatic display 전략은 나방의 사이즈가 클수록 더 효과적이라는 사실을 입증하였다. 추가 연구 결과 이러한 큰 크기 – 숨겨진 화려한 색채의 관계는 태극나방과 뿐만이 아니라 메뚜기목, 대벌레목, 사마귀목, 그리고 누에나방과(나비목)에서도 발견됨을 확인하였다. 본 연구의 결과는 deimatic display 전략이 큰 크기와 공진화하였음을 보여주며, 이러한 크기-색채 공진화는 deimatic display 전략의 효용성과 밀접한 관련을 가지며 이루어졌다는 사실을 보여준다.

감성은 복잡하고 다양한 요인들에 의해 영향을 받기 때문에 다각적인 측면에서 고려되어야 한다. 본 연구에서는 심리 평가 척도의 하나인 각성(arousal) 지표와 다중 생체신호에서 추출된 생체지표 반응을 이용하여 중립 및 부정감성(슬픔, 공포, 놀람)의 분류하였다. 이를 위하여 감성에 따른 생체지표 반응의 차이를 확인하였고, 다중 신경망 알고리즘 기반의 감성 인식기를 적용하여 이들 감성이 얼마나 정확하게 분류되는가를 확인하였다. 총 146명의 실험 참가자(평균 연령 20.1±4.0, 남성 41%)를 대상으로 감성 유발 자극을 제시하고 동시에 생체신호(심전도, 혈류맥파, 피부전기활동)를 측정하였다. 또한 감성 유발 자극에 대한 심리 반응을 감성 평가 척도로 평가하였다. 측정된 생체신호에서 심박률(HR), NN 간격의 표준편차(SDNN), 혈류량(BVP), 맥파전달시간(PTT), 피부전도수준(SCL), 피부전도반응 (SCR)을 추출하였다. 결과 분석을 위하여 감성 자극에 대한 각성도와 안정 상태와 감성 상태의 생체지표 반응을 활용하였다. 또한 감성 분류를 위하여 다중 신경망 기반의 감성 인식기를 활용하였다. 그 결과, 감성에 따른 생체지표 반응의 차이를 확인하였고, 이들 감성의 분류 성능은 각성도와 모든 생체지표 특징들을 조합하였을 때 정확도가 가장 높음(86.9%)을 확인하였다. 본 연구는 심리 및 생체지표 추출과 기계학습 기술의 적용을 통하여 부정 감성을 분류할 수 있음을 제안하며, 이는 인간의 감성을 탐지하는 감성 인식 기술을 확립하는데 기여할 것으로 예상한다.