나방은 성페로몬에 대한 통신시스템이 잘 발달되어 있다. 동종의 암컷이 방출하는 성페로몬을 원거리에서 감지하여 암컷을 정확히 찾아가 교미할 수 있도록 하기 위해서, 수컷 나방은 고도로 발달된 성페로몬 감지 시스템을 갖고 있다. 이러한 시스템을 이용해서 수컷 나방은 페로몬 냄새 기둥(plume)을 따라 바람을 거슬러 비행하면서 간헐적으로 감지되는 페로몬 냄새가닥(odor filaments)을 추적하는 고정행동양식(stereotypic behavior)을 보인다. 일반적으로 여러 성분으로 구성되는 나방의 암컷 성페로몬은 그 조성이 종특이적(species-specific)이며, 비슷한 성분을 공 유하는 유사종들이 방출하는 성페로몬과 동종의 암컷이 방출하는 성페로몬을 정확히 구분하기 위해서 수컷 나방은 촉각에 여러 종류의 고도로 특 화된 페로몬 감각세포들을 갖고 있어서, 이들이 페로몬을 감지할 때 나오는 신경 신호들을 종합해서 동종의 페로몬을 인식하여 행동반응이 일어 나게 된다. 수컷 나방은 보통 동종의 페로몬 성분뿐만 아니라 유사종이 사용하는 페로몬 성분들을 특이적으로 감지하는 길항적(antagonistic) 냄 새감각세포들도 갖고 있어서 페로몬 식별력을 강화한다. 본 종설에서는 지금까지 보고된 수컷 나방의 페로몬 감지 시스템과 이와 연관된 수컷의 감각기 및 행동반응에 대한 연구 결과들을 정리하고, 이를 종합하여 앞으로의 연구 방향을 제시하고자 한다.

PURPOSES : Road surface conditions are vital to traffic safety, management, and operation. To ensure traffic operation and safety during periods of snow and ice during the winter, each local government allocates considerable resources for monitoring that rely on field-oriented manual work. Therefore, a smart monitoring and management system for autonomous snow removal that can rapidly respond to unexpected abrupt heavy snow and black ice in winter must be developed. This study addresses a smart technology for automatically monitoring and detecting road surface conditions in an experimental environment using convolutional neural networks based on a CCTV camera and infrared (IR) sensor data. METHODS : The proposed approach comprises three steps: obtaining CCTV videos and IR sensor data, processing the dataset acquired to apply deep learning based on convolutional neural networks, and training the learning model and validating it. The first step involves a large dataset comprising 12,626 images extracted from the acquired CCTV videos and the synchronized surface temperature data from the IR sensor. In the second step, image frames are extracted from the videos, and only foreground target images are extracted during preprocessing. Hence, only the area (each image measuring 500 × 500) of the asphalt road surface corresponding to the road surface is applied to construct an ideal dataset. In addition, the IR thermometer sensor data stored in the logger are used to calculate the road surface temperatures corresponding to the image acquisition time. The images are classified into three categories, i.e., normal, snow, and black-ice, to construct a training dataset. Under normal conditions, the images include dry and wet road conditions. In the final step, the learning process is conducted using the acquired dataset for deep learning and verification. The dataset contains 10,100 (80%) data points for deep learning and 2,526 (20%) points for verification. RESULTS : To evaluate the proposed approach, the loss, accuracy, and confusion matrix of the addressed model are calculated. The model loss refers to the loss caused by the estimated error of the model, where 0.0479 and 0.0401 are indicated in the learning and verification stages, respectively. Meanwhile, the accuracies are 97.82% and 98.00%, respectively. Based on various tests that involve adjusting the learning parameters, an optimized model is derived by generalizing the characteristics of the input image, and errors such as overfitting are resolved. This experiment shows that this approach can be used for snow and black-ice detections on roads. CONCLUSIONS : The approach introduced herein is feasible in road environments, such as actual tunnel entrances. It does not necessitate expensive imported equipment, as general CCTV cameras can be applied to general roads, and low-cost IR temperature sensors can be used to provide efficiency and high accuracy in road sections such as national roads and highways. It is envisaged that the developed system will be applied to in situ conditions on roads.

Through the process of chemical vapor deposition, Tungsten Hexafluoride (WF6) is widely used by the semiconductor industry to form tungsten films. Tungsten Hexafluoride (WF6) is produced through manufacturing processes such as pulverization, wet smelting, calcination and reduction of tungsten ores. The manufacturing process of Tungsten Hexafluoride (WF6) is required thorough quality control to improve productivity. In this paper, a real-time detection system for oxidation defects that occur in the manufacturing process of Tungsten Hexafluoride (WF6) is proposed. The proposed system is implemented by applying YOLOv5 based on Convolutional Neural Network (CNN); it is expected to enable more stable management than existing management, which relies on skilled workers. The implementation method of the proposed system and the results of performance comparison are presented to prove the feasibility of the method for improving the efficiency of the WF6 manufacturing process in this paper. The proposed system applying YOLOv5s, which is the most suitable material in the actual production environment, demonstrates high accuracy (mAP@0.5 99.4 %) and real-time detection speed (FPS 46).

As the demand for the monitoring of VOCs increases, various unpowered colorimetric sensors are being developed, but the performance evaluation method of the developed sensors has not been systematically established. In this study, the device, experimental process, and data calculation methods for the performance evaluation of the colorimetric sensors were proposed. An aluminum chamber (70W× 128 L × 40 mm H) was designed to expose the sensor to a constant concentration of VOCs. In addition, an experimental apparatus was devised to evaluate the effect of environmental factors (temperature and humidity) affecting the ability of the sensor to detect VOCs. To calculate the color change value of the sensor corresponding to the concentration of VOCs, the ‘peak wavelength method’ that analyzes the wavelength of the highest intensity for high-concentration VOCs and the ‘spectral centroid method’ using a weighted arithmetic average for low-concentration VOCs were used. As a result of evaluating the ability of the colorimetric sensor to detect VOCs, which was made of polydimethylsiloxane (PMDS) by the method proposed in this study, the wavelength change values (bandgap shift) of the sensor for 1,000 ppm of benzene, toluene, oxylene, and acetone were 0.898 nm, 2.304 nm, 5.775 nm, and 0.249 nm, respectively. The precision was calculated by repeatedly measuring the sensing ability of the sensor 5 times for each type of VOCs. The precision of the sensor responses to benzene, toluene, o-xylene, and acetone were 15.23%, 7.84%, 4.14%, and 30.00% RSD, respectively. The method proposed in this study can be used to evaluate the performance of various types of VOCs colorimetric sensors.

This study developed a scenario to understand the reaction rate and operational time according to RTI value of rate of rise detector in each type in case of fire mattress. In the results of analyzing the reaction rate and operational time of detector in each scenario, in case when installing a single detector, the elevated temperature per minute was raised to 8℃/min ~ 9℃/min. In case when installing two detectors, it was raised to 9℃/min ~ 10℃/min. In case when installing three detectors, it was raised to 10℃/min. The horizontal distance between detector and mattress was 1.8m~2.5m. Whenever the number of detectors was increased, the horizontal distance was decreased. The operational time of detector was within maximum 540 seconds and minimum 420 seconds. As the research tasks in the future, there should be the researches on the effects of reaction rate of detector on the evacuation in case of fire through the result value of RSET by setting up the latency until the detector operates, and the researches on the safety by understanding if the operational time of detector is suitable for the evaluation standard of performancecentered design.

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.

Artificial intelligence (AI) has been applied to most industries by enhancing automation and contributing greatly to efficient processes and high-quality production. This research analyzes the applications of AI-based automobile accident prevention systems. It deals with AI-based collision prevention systems that learn information from various sensors attached to cars and AI-based accident detection systems that automatically report accidents to the control center in the event of a collision. Based on the literature review, technological and institutional changes are taking place at the national levels, which recognize the effectiveness of the systems. In addition, start-ups at home and abroad as well as major car manufacturers are in the process of commercializing auto parts equipped with AI-based collision prevention technology.

자갈해빈에 대한 태풍의 영향력을 조사하기 위하여 태종대 감지 자갈해빈에서 2018년 10월에 내습한 태풍 ‘콩레이’와 2019년 7월의 태풍 ‘다나스’에 대하여 VRS-GPS, 드론 측량을 수행하였다. 감지해빈의 전반적인 퇴적물 분포를 파악하기 위해서 입도분석을 하였으며, 자갈해빈의 회복력을 확인하기 위해 주기적으로 감지해빈의 지형측량을 수행 하였다. 감지해빈의 자갈퇴적물은 서쪽에서 동쪽으로 갈수록 평균 −6.2Φ에서 −5.4Φ로 세립해지며, 해안선의 수직방향으로는 포말대(swash zone)에서 상대적으로 세립한 구형의 퇴적물(−4.5Φ)이, 범(berm)에서는 상대적으로 조립하고 편평한 퇴적물(−5Φ - −6Φ)이 나타난다. 감지 자갈해빈은 특징적으로 2열의 범을 갖는데, 해빈의 전방에 정상조건에서 형성되는 하부 범(lower berm)과 약 10 m 후방에 상부 범(upper berm)이 존재한다. 태풍 콩레이 내습 후 감지해빈은 육지쪽에 위치한 상부 범에서 약 1.4 m의 침식이 발생하여 상부 범이 사라졌고, 상부 범의 배후지에서는 평균 약 50 cm 침식되어 그 고도가 낮아졌으나, 하부 범에서의 침식은 관찰되지 않았다. 한편 상대적으로 위력이 약한 태풍 다나스의 경우, 내습 직후 감지해빈은 하부 범과 상부 범에서 침식이 발생하여 평균 80 cm 높이의 퇴적물이 침식되었으나, 반면 배후지에서는 50 cm 높이의 퇴적이 확인되었다. 하지만 내습 후 하부 범에서 빠른 속도로 퇴적이 발생하여 내습 약 3일 내에 소실되었던 하부 범이 생성되었다. 이러한 결과는 감지 자갈해빈이 태풍에 의한 지형변화가 일시적으로 발생하지만, 이후 정상조건에서 태풍 이전의 지형으로 매우 빠르게 회복됨을 시사한다. 따라서 자갈해빈의 경우 태풍침식에 대한 복원력이 매우 뛰어나다고 평가된다.

Water supply through the water supply system is an essential element for normal industrial and living, and the interruption of water supply due to leakage and breakage can cause major problems. Local leaks and breakdowns of pipelines that make up the water supply system are unavoidable problems caused by the aging of pipelines, which require water leak detection and prevention through monitoring the integrity of structures. In this study, smart bolts, which can be determined whether or not to be loosened, were proposed for bolts used for tightening water pipes, valves, etc. that make up the water supply system, and their applicability was verified through actual fabrication and experimentation.

The purpose of this study is to develop and apply an oil leak detector using a capacitive sensor to detect oil leak in hydraulic equipment. The developed oil leak detector consists of a sensor and a sensing circuit. The sensor is designed using the difference in the permittivity of air and oil to change the capacitance, and the sensing circuit is composed of a charge amplifier and rectifier circuit. The sensing device is made of a PCB module to output the DC analog signal. In this study, this oil detector was installed in a cyclic pressure tester for evaluating valve life and was applied to detect the leakage of the test valve. It can also be applied to detecting the oil leakage of various hydraulic types of equipment and reduce maintenance costs by preventing large leakage of hydraulic oil.