• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.948-956
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• 2014

Today, Intelligent Vehicle Detection System seeks to reduce the negative factors, such as accidents over to get the traffic information of existing system. This paper proposes detection algorithm for the illegal U-turn vehicles which can cause critical accident among violations of road traffic laws. We predicted that if calculated optical flow vectors were shown on the illegal U-turn path, they would be cause of the illegal U-turn vehicles. To reduce the high computational complexity, we use the algorithm of pyramid Lucas-Kanade. This algorithm only track the key-points likely corners. Because of the high computational complexity, we detect center lane first through the color information and progressive probabilistic hough transform and apply to the around of center lane. And then we select vectors on illegal U-turn path and calculate reliability to check whether vectors is cause of the illegal U-turn vehicles or not. Finally, In order to evaluate the algorithm, we calculate process time of the type of algorithm and prove that proposed algorithm is efficiently.

• Journal of Korean Society of Transportation
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• v.34 no.3
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• pp.278-290
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• 2016

Maximum traffic flow rate is an important performance measure of operational status in transport networks, and has been considered as a key parameter for transportation operation since a bottleneck in congestion decreases maximum traffic flow rate. Although previous studies for traffic flow analysis have been widely conducted, a detection method for changes in dynamic traffic flow has been still veiled. This paper explores the dynamic traffic flow detection that can be utilized for various traffic operational strategies. Turning point analysis (TPA), as a statistical method, is applied to detect the changes in traffic flow rate. In TPA, Bayesian approach is employed and vehicle arrival is assumed to follow Poisson distribution. To examine the performance of the TPA method, traffic flow data from Jayuro urban expressway were obtained and applied. We propose a novel methodology to detect turning points of dynamic traffic flow in real time using TPA. The results showed that the turning points identified in real-time detected the changes in traffic flow rate. We expect that the proposed methodology has wide application in traffic operation systems such as ramp-metering and variable lane control.

• Korean Journal Plant Pathology
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• v.3 no.2
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• pp.108-113
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• 1987

Rice black-streaked dwarf virus(RBSDV) was purified from infected maize leaves. Antiserum against RBSDV was prepared for virus detection by enzyme-linked immunosorbent assay(ELISA). In detection of RBSDV by ELISA, effective dilution range of antiserum extracted in RBSDV-containing host plants and insect vectors was from 320 to 2,560 times in rice plant, 320 to 5,120 in maize plant, and 160 to 2,560 times in insect vector, Laodelphax striatellus F, respectively. The percentage of viruliferous vector in overwintered nymphs of Laodelphax striatellus determined by ELISA were 3.0 in Milyang, 2.3 in Chilgok, and 3.7 in Sunsan area. Dead insect vector which could not be tested for vims infection by conventional rice seedling inoculation test could be tested by ELISA. One hundred plants of rice and maize were randomly sampled in the field and tested whether or not they were infected with RBSDV. In rice plants, 4 out of 98 plants turned out to be infected with RBSDV by ELISA. In maize plant, 3 out of 92 plants which were excepted 8 plants to be appeared symptom already were infected. As a result, ELISA could be detected even in case of symptomless plants at early stage of viral infection.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.801-810
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• 2018

Ship monitoring using satellite synthetic aperture radar (SAR) images consists of ship detection, ship discrimination, and ship classification. A large number of methods have been proposed to improve the detection and discrimination capabilities, while only a few studies exist for ship classification. Thus, many studies for the ship classification are needed to construct ship monitoring system having high performance. Note that constructing database (DB), which contains both SAR images and labels of various ships, is important for research on the ship classification. In the airborne SAR classification, many methods have been developed using moving and stationary target acquisition and recognition (MSTAR) DB. However, there has been no publicly available DB for research on the ship classification using satellite SAR images. Recently, Shanghai Key Laboratory has constructed OpenSARShip DB using both SAR images of various ships generated from Sentinel-1 satellite of European Space Agency (ESA) and automatic identification system (AIS) information. Thus, the applicability of OpenSARShip DB for ship classification should be investigated by using the concepts of airborne SAR classification which have shown high performances. In this study, ship classification using satellite SAR images are conducted by applying the concepts of airborne SAR classification to OpenSARShip DB, and then the applicability of OpenSARShip DB is investigated by analyzing the classification performances.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.31-39
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• 2014

Seasonal influenza epidemics cause 3 to 5 millions severe illness and 250,000 to 500,000 deaths worldwide each year. To prepare better controls on severe influenza epidemics, many studies have been proposed to achieve near real-time surveillance of the spread of influenza. Korea CDC publishes clinical data of influenza epidemics on a weekly basis typically with a 1-2-week reporting lag. To provide faster detection of epidemics, recently approaches using unofficial data such as news reports, social media, and search queries are suggested. Collection of such data is cheap in cost and is realized in near real-time. This research aims to develop regression models for early detecting the outbreak of the seasonal influenza epidemics in Korea with keyword query information provided from the Naver (Korean representative portal site) trend services for PC and mobile device. We selected 20 key words likely to have strong correlations with influenza-like illness (ILI) based on literature review and proposed a logistic regression model and a multiple regression model to predict the outbreak of ILI. With respect of model fitness, the multiple regression model shows better results than logistic regression model. Also we find that a mobile-based regression model is better than PC-based regression model in estimating ILI percentages.

• The KIPS Transactions:PartD
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• v.18D no.4
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• pp.261-274
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• 2011

In recent competitive business environment each enterprise has to be agile and flexible. For these purposes run-time monitoring ofservices provided by an enterprise and early decision making through this becomes core competition of the enterprise. In addition, in order to process various innumerable events which are generated on enterprise systems techniques which make filtering of meaningful data are needed. However, the existing study related with this is nothing but discovering of service faults by monitoring depending upon API of BPEL engine or middleware, or is nothing but processing of simple events based on low-level events. Accordingly, there would be limitations to provide useful business information. In this paper, through situation detection an extended complex event model is presented, which is possible to provide more valuable and useful business information. Concretely, first of all an event processing architecture in an enterprise system is proposed, and event meta-model which is suitable to the proposed architecture is going to be defined. Based on the defined meta-model, It is presented that syntax and semantics of constructs in our event processing language including various and progressive event operators, complex event pattern, key, etc. In addition, an event context mechanism is proposed to analyze more delicate events. Finally, through application studies application possibility of this study would be shown and merits of this event model would be present through comparison with other event model.

• Smart Media Journal
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• v.11 no.10
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• pp.46-53
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• 2022

Today, due to the 4th industrial revolution and extensive R&D funding, domestic companies have begun to possess world-class industrial technologies and have grown into important assets. The national government has designated it as a "national core technology" in order to protect companies' critical industrial technologies. Particularly, technology leaks in the shipbuilding, display, and semiconductor industries can result in a significant loss of competitiveness not only at the company level but also at the national level. Every year, there are more insider leaks, ransomware attacks, and attempts to steal industrial technology through industrial spy. The stolen industrial technology is then traded covertly on the dark web. In this paper, we propose a system for detecting industrial technology leaks in the dark web environment. The proposed model first builds a database through dark web crawling using information collected from the OSINT environment. Afterwards, keywords for industrial technology leakage are extracted using the KeyBERT model, and signs of industrial technology leakage in the dark web environment are proposed as quantitative figures. Finally, based on the identified industrial technology leakage sites in the dark web environment, the possibility of secondary leakage is detected through the PageRank algorithm. The proposed method accepted for the collection of 27,317 unique dark web domains and the extraction of 15,028 nuclear energy-related keywords from 100 nuclear power patents. 12 dark web sites identified as a result of detecting secondary leaks based on the highest nuclear leak dark web sites.

• The Journal of Bigdata
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• v.8 no.1
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• pp.49-59
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• 2023

There are various items in the safety and health standards of the manufacturing industry, but they can be divided into work-related diseases and musculoskeletal diseases according to the standards for sickness and accident victims. Musculoskeletal diseases occur frequently in manufacturing and can lead to a decrease in labor productivity and a weakening of competitiveness in manufacturing. In this paper, to detect the musculoskeletal harmful factors of manufacturing workers, we defined the musculoskeletal load work factor analysis, harmful load working postures, and key points matching, and constructed data for Artificial Intelligence(AI) learning. To check the effectiveness of the suggested dataset, AI algorithms such as YOLO, Lite-HRNet, and EfficientNet were used to train and verify. Our experimental results the human detection accuracy is 99%, the key points matching accuracy of the detected person is @AP0.5 88%, and the accuracy of working postures evaluation by integrating the inferred matching positions is LEGS 72.2%, NECT 85.7%, TRUNK 81.9%, UPPERARM 79.8%, and LOWERARM 92.7%, and considered the necessity for research that can prevent deep learning-based musculoskeletal diseases.

• Journal of the Korean Electrochemical Society
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• v.11 no.2
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• pp.105-108
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• 2008

In the recent years, increasing interests are being focused on the rational functionalization of the CNTs by some creative methods. However, the considerable toxicity of CNT is still a controversialissue and limits its biological application. To improve the biocompatibility of CNT, in this work we prepared CNT-$TiO_2$ nanocomposites with CNT and organic titanium precursors. Our observations demonstratethat the modified interface could accelerate the heterogeneous electron transfer rates and thusenhance the relevant detection sensitivity, suggesting its potential application as the new strategy for the development of the biocompatible and multi-signal responsive biosensors for the early diagnosis of cancers.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.473-480
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• 2018

The effect of three iron-based adsorbents pre-depositing on ultrafiltration membrane for humic acid (HA) removal and membrane fouling was investigated. The result showed that pre-depositing adsorbents on membrane could not only reduce membrane fouling but also enhance HA removal. The flux was related to the adsorbent dosage and the optimal dosage for pre-deposition was $35.0g/m^2$. The dissolved organic carbon (DOC) removal of HA was 38.3%, 67.3% and 41.1% respectively when pre-deposited $35.0g/m^2$ $FeO_xH_y$, $MnFe_2O_4$ and $Fe_3O_4$ on membrane. Different adsorption effect of adsorbents on HA contributed to increasing of the flux at different level. Zeta potential of three adsorbents all decreased after adsorbed HA. The adsorption capacity of the three adsorbents was $FeO_xH_y$ > $MnFe_2O_4$ > $Fe_3O_4$. Atomic Force Microscopy (AFM) measurement showed the thickness of pre-deposition layers formed by different adsorbents was different. The scanning electron microscope (SEM) detection showed the morphology and compactness of pre-deposition layers formed by different adsorbents was different.