• Research in Plant Disease
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• v.14 no.2
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• pp.138-141
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• 2008

Cucumber is high valued cash crop, for it is grown during the winter season in plastic house. Recently, virus disease spread widely in cucumber growing area and cause severe income loss. Therefore, occurrence of virus disease on cucumber were surveyed from 2004 to 2006 in Sangju and Gunwi area, Gyeongbuk province. The rate of plastic house which has infected plants was $55.0{\sim}88.6%$. Infection rate was the highest at Sangju in 2006 than others and ranged from 15 to 90.0% per plastic house. The 217 samples showing virus symptom were analyzed by RT-PCR using appropriate detection primer. Zucchini yellow mosaic virus(ZYMV) has the highest infection rate(detected over 85%) and followed by Cucumber green mottle mosaic virus(CGMMV). But Watermelon mosaic virus-2(WMV-2) was not detected in our survey. Therefore, we conclude that ZYMV is major pathogene of virus disease on cucumber. ZYMV induced chlorosis and severe mosaic on the leaves and distortion on the surface of fruits.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11B
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• pp.655-662
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• 2007

Monitoring systems used at present to operate HVAC(Heating, Ventilation and Air Conditioning) optimally do not have a function that enables to detect faults properly when there are faults of such as operating plants or performance falling, so they are unable to manage faults rapidly and operate optimally. In this paper, we have developed a classified rule-based fault detection system which can be inclusively used in HVAC system of a building by installation of sensor which is composed of HVAC system and required low costs compare to the model based fault detection system which can be used only in a special building or system. In order to experiment this algorithm, it was applied to HVAC system which is installed inside EC(Environment Chamber), verified its own practical effect, and confirmed its own applicability to the related field in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1069-1076
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• 2017

Recently $Pok\acute{e}mon$ GO implements an online game with location-based real time augmented reality on mobile. The correct play of this game should be based on collecting the $Pok\acute{e}mon$ that appears as the user moves around by foot, but as the popularity increases, it appears an abuse to play easily. Many people have used an application that provides a mock location service such as Fake GPS, and these applications can be judged to be cheating in online games because they can play games in the house without moving. Detection of such cheating from a client point of view (mobile device) can consume a large amount of resources, which can reduce the speed of the game. It is difficult for developers to apply detection methods that negatively affect game usage and user's satisfaction. Therefore, in this paper, we propose a method to detect users abusing mock location service in online game by route analysis using GPS location record from the server point of view.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.244-254
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• 2009

This paper describes a remote system to monitor the circadian behavioral patterns of elders who live alone. The proposed system was designed and implemented to provide more conveniently and reliably the required functionalities of a remote monitoring system for elders based on the development of first phase prototype[2]. The developed system is composed of an in-house sensing system and a server system. The in-house sensing system is a set of wireless sensor nodes which have pyroelectric infrared (PIR) sensor to detect a motion of elder. Each sensing node sends its detection signal to a home gateway via wireless link. The home gateway stores the received signals into a remote database. The server system is composed of a database server and a web server, which provides web-based monitoring system to caregivers (friends, family and social workers) for more cost effective intelligent care service. The improved second phase system can provide 'automatic diagnosis', 'going out detection', and enhanced user interface functionalities. We have evaluated the first and second phase monitoring systems from real field experiments of 3/4 months continuous operation with installation of 9/15 elders' houses, respectively. The experimental results show the promising possibilities to estimate the behavioral patterns and the current status of elder even though the simplicity of sensing capability.

• Smart Structures and Systems
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• v.14 no.5
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• Child Health Nursing Research
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• v.18 no.3
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• pp.95-100
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• 2012

Purpose: The purpose of this study was to facilitate home visits to assess the current rate of child abuse in order to provide an agenda for the early detection and prevention of child abuse and neglect in Korea. Methods: For this retrospective descriptive research, 20 public health centers were selected, 1,991 families were visited and 2,680 children were assessed. Results: We found 415 cases (15.5%) of potential abuse and 7 cases (0.3%) of actual abuse. The greatest risk group was to children age 4 to 6 years. According to the HOME Inventory, there were 17 infants (5.8%) presenting a potential risk for child abuse and neglect. Conclusion: Visitation screening is highly recommended for prevention in the high-risk preschool age group.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.319-331
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• 2022

The latest global COVID-19 pandemic has made the use of facial masks an important aspect of our lives. People are advised to cover their faces in public spaces to discourage illness from spreading. Using these face masks posed a significant concern about the exactness of the face identification method used to search and unlock telephones at the school/office. Many companies have already built the requisite data in-house to incorporate such a scheme, using face recognition as an authentication. Unfortunately, veiled faces hinder the detection and acknowledgment of these facial identity schemes and seek to invalidate the internal data collection. Biometric systems that use the face as authentication cause problems with detection or recognition (face or persons). In this research, a novel model has been developed to detect and recognize faces and persons for authentication using scale invariant features (SIFT) for the whole segmented face with an efficient local binary texture features (DLBP) in region of eyes in the masked face. The Fuzzy C means is utilized to segment the image. These mixed features are trained significantly in a convolution neural network (CNN) model. The main advantage of this model is that can detect and recognizing faces by assigning weights to the selected features aimed to grant or provoke permissions with high accuracy.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• Korean Journal of Environmental Agriculture
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• v.1 no.1
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• pp.1-13
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• 1982

Composite soil samples from 236 sites representing paddy field, up-land, orchard and plastic film house were examined for organochlorine residues by GLC-ECD. Detection frequencies and residual levels of most persistent organochlorine residues in the soil samples were found to depend on the cropping practices. Highest organochlorine residues were found in orchard soils and followed, in decreasing order, plastic film house, up-land and paddy field soils. ${\alpha}-Endosulfan$, dieldrin, p,p'-DDD and p,p'-DDT were responsible for the observed high organochlorine residues in the orchard soils. ${\alpha}-BHC$ and ${\gamma}-BHC$ were detected in all 236 soil samples. The mean residue levels of both BHC isomers were, however, remained fairly low. Residues of PCNB and ${\alpha}-endosulfan$ in native soils are reported, for the first time, in present work. PCNB was present in up-land plastic film house soils while ${\alpha}-endosulfan$ was found in all agricultural soils studied. High levels of p,p'-DDT and dieldrin were discussed in relation to crops cultivated, amount and duration of the pesticides usage. Need for continued observations on the persistent residue of pesticides in soils, already banned for general use, is emphasized.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1115-1122
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• 2015

In recent years, the use of game bots by illegal programs has been expanded from individual to group scale; this brings about serious problems in online game industry. The gold farmers group creates an in-game social community so-called "guild" to obtain a large amount of game money and manage game bots efficiently. Although game developers detect game bots by detection algorithms, the algorithms can detect only part of the gold farmers group. In this paper, we propose a detection method for the gold farmers group on a basis of normal and bot guilds characteristic analysis. In order to differentiate normal and bots guild, we analyze transaction patterns for individuals, auction house and chatting. With the analyzed results, we can detect game bot guilds. We demonstrate the feasibility of the proposed methods with real datasets from one of the popular online games named AION in Korea.