• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.115.1-115
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• 2001

According as many people use a computer newly, damage of computer virus and hacking is rapidly increasing by the crucial users. A computer virus is one of program on computer and has abilities of self reproduction and destruction like a virus of biology. And hacking is to rob a person´s data in a intruded computer and to delete data in a person´s computer from the outside. To block hacking that is intrusion of a person´s computer and the computer virus that destroys data, a study for intrusion-detection of system and virus detection using a biological immune system is in progress. In this paper, we make a mood of positive selection and negative selection of self-recognition process that is ability of ...

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.636-638
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• 2022

This paper is a study to apply CNN(Convolution Neural Network) to fundus images for identifying glaucoma. Fundus images are evaluated in the field of medical diagnosis detection, which are diagnosing of blood vessels and nerve tissues, retina damage, various cardiovascular diseases and dementia. For the experiment, using normal image set and glaucoma image set, two types of image set are classifed by using AlexNet. The result performs that glaucoma with abnormalities are activated and characterized in feature map.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.747-754
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• 2014

In spite of its usefulness for health monitoring of structures on steady external load, the statistical pattern recognition technology (SPRT), based on Mahalanobis distance theory (MDT), is not good enough for the health monitoring of structures on large variability external load like earthquake. Damage is usually determined by the difference between the average measured value of undamaged structure and the measure value of damaged one. So when external variability gets larger, the difference gets bigger along, which is thus easily mistaken for a damage. This paper aims to overcome the problem and develop an improved Mahalanobis distance theory (IMDT), that is, a SPRT with revised MDT in order to decrease external variability so that we will be able to continue to monitor the structure on uncertain external variability. This method is experimentally tested to see if it precisely evaluates the health of a cable-stayed bridge on each general random load and earthquake load. As a result, the IMDT is found to be valid in locating structural damage made by damaged cables by means of data from undamaged cables. So it is proved to be effectively applicable to the health monitoring of bridges on external load of variability.

• Smart Structures and Systems
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• v.6 no.4
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• pp.405-421
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• 2010

This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

• Journal of Environmental Health Sciences
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• v.21 no.4
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• pp.63-74
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• 1995

The objective of this study were to identify the state of re. cognition and attitude of residents in Seoul city about air environment and to identify factors affecting attitude toward air environment. Study object was residents in Seoul city sampled by multistage random proportional sampling. Sample size was 0.0067%(500 persons) of total residents in Seoul city. The results were divided into two parts: (1) descriptive results of recognition and attitude toward air environment, (2) results of factor analysis to classify categories of attitudes toward air environment and regression analysis to identify factors affecting attitude toward air environment. Most of resident in Seoul city recognized that air environment in Seoul city was highly polluted and was not satisfactory. Experience of damage of air pollution was reported in about 70% of residents in Seoul city. More than 60% of residents in Seoul city had concern about air environment. Attitude toward air environment were classified into four categories using factor analysis: Necessity of intervention of local government for air environment conservation, Participation of residents and enterprises for air environment conservation, Optimistic attitude about air pollution, Preference of economy. Factors affecting the above attitudes were knowledge about air pollution, knowledge about policies and institutions related air environment conservation, concern about air environment, educational level, subjective assessment of air environment, sex, marital status. In conclusion this study suggested providing information of air environment in Seoul city to the residents and to educating residents for making positive attitude about air environment conservation.

• Korean journal of food and cookery science
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• v.25 no.6
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• pp.712-724
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• 2009

The purpose of this study was to examine the degree of concern about endocrine disruptors, the degree of recognition about the risk of endocrine disruptors towards humans, the degree of worry about the risk of endocrine disruptors towards ones self or family, information-obtaining behavior regarding endocrine disruptors, the degree of knowledge and the degree of behavior to decrease exposure to endocrine disruptors, and the degree of an educational need for endocrine disruptors in the dietary life of elementary school students. The data were collected from 162 students in Seoul, Incheon, Daejeon, Daegu, Busan and Gwangju. Frequencies, Cronbach's alpha, t tests, analysis of variance, Duncan's multiple range tests and chi-square tests were conducted using SPSS V.14.0 for WINDOWS. The results of this study were as follows. The degree of concern about endocrine disruptors was not high. The respondents obtained most of their information regarding endocrine disruptors from TV/radio. Respondents had difficulty in acquiring and understanding the information. Both the degree of knowledge and the degree of behavior to decrease exposure were not high. The respondents showed a high degree of educational need for endocrine disruptors, the most important was methods to prevent damage from endocrine disruptors followed by risk of endocrine disruptors.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.629-634
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• 2021

With the recent increase in the use of disposable products, it is urgently necessary to reduce the use of disposable products and to increase the recycling rate as much as possible in order to prevent environmental damage. In this paper, we introduce , a smartphone application that provides recycling-related information and supports correct separation and discharge. This system automatically recognizes and automatically classifies the type of item, by applying an effective object recognition technique, when the camera points at the item to be discharged. It is more effective and convenient compared to other existing smartphone applications. This system is expected to contribute to environmental protection by increasing the recycling rate in daily life.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.505-507
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• 2021

Image recognition technology is a technology that recognizes an image object by using the generated feature descriptor and generates object feature points and feature descriptors that can compensate for the shape of the object to be recognized based on artificial intelligence technology, environmental changes around the object, and the deterioration of recognition ability by object rotation. The purpose of the present invention is to implement a power management framework required to increase profits and minimize damage to livestock farmers by preventing accidents that may occur due to the improvement of efficiency of the use of livestock house power and overloading of electricity by integrating and managing a power fire management device installed for analyzing a complex environment of power consumption and fire occurrence in a smart safety livestock house, and to develop and disseminate a safe and optimized intelligent smart safety livestock house.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.281-286
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• 2011

Station structures, one of important infrastructures, which have been being operated since the 1970s, are especially vulnerable to even the medium-level earthquake and they could be damaged by long-term internal or external vibrations such as ambient vibrations. Recently, much attention has been paid to real-time monitoring of the fatal defect or long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. In this study, a structural health monitoring methodology using acceleration responses is proposed to evaluate the health-state of the station structures and to detect initial damage-stage. A damage index is developed using the acceleration data and it is applied to outlier analysis, one of unsupervised learning based pattern recognition methods. A threshold value for the outlier analysis is determined based on confidence level of the probabilistic distribution of the acceleration data. The probabilistic distribution is selected according to the feature of the collected data.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1997.04a
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• pp.97-105
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• 1997

A methodology to identify a baseline modal model of a complicated 3-D structure using limited structural and modal information is experimentally examined. In the first part, a system's identification theory for the methodology to identify, baseline modal responses of the structure is outlined. Next, an algorithm is designed to build a generic finite element model of the baseline structure and to calibrate the model by using only a set of post-damage modal parameters. In the second part, the feasibility of the methodology is examined experimentally using a field-tested truss bridge far which only post-damaged modal responses were measured for a few vibration modes. For the complex 3-D bridge with many members, we analyzed to identify unknown stiffness parameters of the structure by using modal parameters of the initial two modes of vibration.