• 대한원격탐사학회지
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• 제39권1호
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• pp.77-86
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• 2023

Ship detection is widely used in areas such as maritime security, maritime traffic, fisheries management, illegal fishing, and border control, and ship detection is important for rapid response and damage minimization as ship accident rates increase due to recent increases in international maritime traffic. Currently, according to a number of global and national regulations, ships must be equipped with automatic identification system (AIS), which provide information such as the location and speed of the ship periodically at regular intervals. However, most small vessels (less than 300 tons) are not obligated to install the transponder and may not be transmitted intentionally or accidentally. There is even a case of misuse of the ship'slocation information. Therefore, in this study, ship detection was performed using high-resolution optical satellite images that can periodically remotely detect a wide range and detectsmallships. However, optical images can cause false-alarm due to noise on the surface of the sea, such as waves, or factors indicating ship-like brightness, such as clouds and wakes. So, it is important to remove these factors to improve the accuracy of ship detection. In this study, false alarm wasreduced, and the accuracy ofship detection wasimproved by removing wake.As a ship detection method, ship detection was performed using machine learning-based random forest (RF), and convolutional neural network (CNN) techniquesthat have been widely used in object detection fieldsrecently, and ship detection results by the model were compared and analyzed. In addition, in this study, the results of RF and CNN were combined to improve the phenomenon of ship disconnection and the phenomenon of small detection. The ship detection results of thisstudy are significant in that they improved the limitations of each model while maintaining accuracy. In addition, if satellite images with improved spatial resolution are utilized in the future, it is expected that ship and wake simultaneous detection with higher accuracy will be performed.

• Journal of Microbiology and Biotechnology
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• 제18권11호
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• pp.1858-1861
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• 2008

A PCR-enzyme-linked immunosorbent assay (PCR-ELISA) was developed for the rapid and sensitive detection of L. monocytogenes. PCR primers generating a 132-bp amplicon and a capture probe able to hybridize to the PCR amplicon were designed based on the L. monocytogenes-specific hly gene encoding listeriolysin. The detection limit of PCR-ELISA for L. monocytogenes was determined to be as low as 10 cells per PCR reaction, and this level of detection was achieved within 5 h. These results indicate that the PCR-ELISA provides a valuable tool for the rapid and sensitive detection of L. monocytogenes for the ready-to-eat food industry.

• 대한원격탐사학회지
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• 제38권5_3호
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• pp.835-850
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• 2022

전 세계적으로 모든 해양 환경에서 발견되는 미세플라스틱이 환경 문제로 대두되면서 해양 미세플라스틱을 모니터링 하기 위한 연구가 국내외적으로 활발히 수행되고 있다. 최근 국외에서는 대규모의 실시간 관측이 가능한 원격탐사 기술을 해양 플라스틱 모니터링에 적용하기 위한 활발한 연구가 진행되고 있다. 그러나 국내에서 해양 미세플라스틱 원격탐사 관련 연구는 매우 미비한 실정이며 중대형 해양 플라스틱 원격탐사 연구만 일부 수행되고 있다. 본 논문에서는 국내와 국외에서 수행된 해양 플라스틱 원격탐사와 관련된 대표적인 연구사례를 통해 국내외 연구 동향을 파악하고, 해양 미세플라스틱 모니터링 시 원격탐사 기술의 적용 가능성에 대해 고찰하여 앞으로 국내에서의 연구 방향성에 대해 제안하고자 한다.

• Smart Structures and Systems
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• 제29권1호
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• pp.63-75
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• 2022

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

• Journal of Microbiology and Biotechnology
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• 제25권11호
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• pp.1773-1781
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• 2015

Environmental security is one of the major concerns for the safety of living organisms from a number of harmful pollutants in the atmosphere. Different initiatives, legislative actions, as well as scientific and social concerns have been discussed and adopted to control and regulate the threats of environmental pollution, but it still remains a worldwide challenge. Therefore, there is a need for developing certain sensitive, rapid, and selective techniques that can detect and screen the pollutants for effective bioremediation processes. In this perspective, isolated enzymes or biological systems producing enzymes, as whole cells or in immobilized state, can be used as a source for detection, quantification, and degradation or transformation of pollutants to non-polluting compounds to restore the ecological balance. Biosensors are ideal for the detection and measurement of environmental pollution in a reliable, specific, and sensitive way. In this review, the current status of different types of microbial biosensors and mechanisms of detection of various environmental toxicants are discussed.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.163-168
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• 2008

KOMPSAT-3 is expected to provide data with 80-cm spatial resolution, which can be used to detect environmental change and create thematic maps such as land-use and land-cover maps. However, to analyze environmental change, change-detection technologies that use multi-resolution and high-resolution satellite images simultaneously must be developed and linked to each other. This paper describes a GIS-based strategy and methodology for revealing global and local environmental change. In the pre-processing step, we performed geometric correction using satellite, auxiliary, and training data and created a new classification system. We also describe the available technology for connecting global and local change-detection analysis.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.260-262
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• 2006

One of future remote sensing techniques for transportation application is vehicle detection from the space, which could be the basis of measuring traffic volume and recognizing traffic condition in the future. This paper introduces an approach to vehicle detection using image object segmentation approach. The object-oriented image processing is particularly beneficial to high-resolution image classification of urban area, which suffers from noisy components in general. The project site was Dae-Jeon metropolitan area and a set of true color aerial images at 10cm resolution was used for the test. Authors investigated a variety of parameters such as scale, color, and shape and produced a customized solution for vehicle detection, which is based on a knowledge-based hierarchical model in the environment of eCognition. The highest tumbling block of the vehicle detection in the given data sets was to discriminate vehicles in dark color from new black asphalt pavement. Except for the cases, the overall accuracy was over 90%.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• 한국시뮬레이션학회논문지
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• 제24권4호
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• pp.23-28
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• 2015

탐지 분야는 전장에 영향을 미치는 요인들 중 중요한 부분이다. 기본적으로 레이더는 정해진 방향으로 탐지를 수행하기 위해 전파를 방출한다. 그러나 기존의 레이더들 대부분이 돌아온 전파를 이용해 신호처리 과정에 의해 표적을 확인할 때, 환경 감쇠 요소는 반영되지 않는다. 이러한 전자파를 이용하는 레이더는 환경적 조건에 의한 감쇠요인에 따라 탐지 결과가 달라질 수 있는 가능성을 가지고 있어, 실제 전장에서 작전상 문제가 발생할 수도 있다. 그렇기 때문에, 이 논문에서는 전파가 돌아올때, 기존의 레이더 방정식에 환경 감쇠 요인을 반영하여 더 정확한 표적을 확인하기 위한 시도를 해보고자 한다.

• 한국토양비료학회지
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• 제50권4호
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• pp.235-250
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• 2017

In this study, we monitored 4 volatile organic compounds (VOCs) such as chloroform, dichloromethane, 1,2-dichloroethane, and tetrachloromethane in groundwater samples to determine the detection frequency and their concentrations and evaluated the health risk level considering ingestion, inhalation, and skin contact. 75 groundwater wells were selected. 24 wells were from monitoring background groundwater quality level and 51 wells were from monitoring groundwater quality level in industrial or contamination source area. In the results, the detection frequency for chloroform, dichloromethane, 1,2-dichloroethane, and tetrachloromethane was 42.3%, 8.1%, 6.0%, and 3.4%, respectively. The average concentrations of VOCs were high in the order of chloroform ($1.7{\mu}g\;L^{-1}$), dichloromethane ($0.08{\mu}g\;L^{-1}$), tetrachloromethane ($0.05{\mu}g\;L^{-1}$), and 1,2-dichloroethane ($0.05{\mu}g\;L^{-1}$). Chloroform had the highest detection frequency and average detection concentration. In the contaminated groundwater, the detection frequency of VOCs was high in the order of chloroform, dichloromethane, 1,2-dchloroethane, and tetrachloromethane. The average concentrations for chloroform, dichloromethane, 1,2-dichloroethane, and tetrachloromethane were $2.23{\mu}g\;L^{-1}$, $0.08{\mu}g\;L^{-1}$, $0.07{\mu}g\;L^{-1}$, and $0.06{\mu}g\;L^{-1}$, respectively. All the 4 compounds were detected at industrial complex and storage tank area. The maximum concentration of chloroform, dichloromethane, and 1,2-dichloroethane was detected at industrial complex area. Especially, the maximum concentration of chloroform and dichloromethane was detected at a chemical factory area. In the uncontaminated groundwater, the detection frequency of VOCs was high in the order of chloroform, dichloromethane, and 1,2-dchloroethane and tetrachloromethane was not detected. The average concentrations for chloroform, dichloromethane, and 1,2-dichloroethane were $0.57{\mu}g\;L^{-1}$, $0.07{\mu}g\;L^{-1}$, and $0.03{\mu}g\;L^{-1}$, respectively. Although chloroform in the uncontaminated groundwater was detected the most, the concentration of chloroform was not exceeding water quality standards. By land use, the maximum detection frequency of 1,2-dichloroethane was found near a traffic area. For human risk assessment, the cancer risk for the 4 VOCs was $10^{-6}{\sim}10^{-9}$, while the non-cancer risk (HQ value) for the 4 VOCs is $10^{-2}{\sim}10^{-3}$.