• Journal of Korean Society on Water Environment
• /
• v.36 no.3
• /
• pp.194-205
• /
• 2020

The characteristics and behavior of dissolved organic matter (DOM) were determined by analyzing the molecular weight fractions and fluorescence properties of water samples in the downstream Nakdong River. Biogeochemical water quality parameters and fluorescent dissolved organic matter (FDOM) were analyzed at five sampling points in the downstream area of the Nakdong River January-August 2019. The molecular weight fractions of the DOM were separated by the Liquid Chromatography-Organic Carbon Detection (LC-OCD). The DOM predominantly comprised humic substances, followed by the building blocks, low molecular weight neutrals and biopolymers. The hydrophobic (aromatic) and hydrophilic properties were shown as coexisting, as most of the SUVA_254nm values were under four. The FDOM was characterized as humic-like (FDOM_H) with allochthonous origin and protein-like (FDOM_P) with autochthonous origin; the FDOM_H with autochthonous origin was also identified. The FDOM_H relies on the aromaticity of the allochthonous organic matter and increases during summer. The FDOM_H and FDOM_P, which depend on the biodegradable dissolved organic matter from phytoplankton, were highly fluorescent in winter. The allochthonous organic matter was the dominant factor contributing to the behavior of the DOM, externally introduced to the river by rainfall. The FDOM only minimally contributed to the behavior of the DOM. It can be explained as the seasonal characteristics of the DOM, varied by the source of the organic matter.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.23 no.2
• /
• pp.181-192
• /
• 2013

There have been various techniques to detect and control document leakage; however, most techniques concentrate on document leakage by outsiders. There are rare techniques to detect and monitor document leakage by insiders. In this study, we observe user's document reading behavior to detect and control document leakage by insiders. We make each user's document reading patterns from attributes gathered by a logger program running on Microsoft Word, and then we apply the proposed system to help determine whether a current user who is reading a document matches the true user. We expect that our system based on document reading behavior can effectively prevent document leakage.

• KIISE Transactions on Computing Practices
• /
• v.22 no.3
• /
• pp.139-144
• /
• 2016

There exist many threats in cyber space, however current anti-virus software and other existing solutions do not effectively respond to malware that has become more complex and sophisticated. It was shown experimentally that it is possible for the proposed approach to provide an automatic execution environment for the detection of malicious behavior of active malware, comparing the virtual-machine environment with the real-machine environment based on user interaction. Moreover, the results show that it is possible to provide a dynamic analysis environment in order to analyze the intelligent malware effectively, through the comparison of malicious behavior activity in an automatic binary execution environment based on real-machines and the malicious behavior activity in a virtual-machine environment.

• Journal of Korea Multimedia Society
• /
• v.11 no.11
• /
• pp.1491-1500
• /
• 2008

This paper proposes an intelligent surveillance system to recognize suspicious patterns of the human behavior by using the Hidden Markov Model. First, the method finds foot area of the human by motion detection algorithm from image sequence of the surveillance camera. Then, these foot locus form observation series of features to learn the HMM. The feature that is position of the human foot is changed to each code that corresponds to a specific label among 16 local partitions of image region. Therefore, specific moving patterns formed by the foot locus are the series of the label numbers. The Baum-Welch algorithm of the HMM learns each suspicious and specific pattern to classify the human behaviors. To recognize the inputted human behavior pattern in a test image, the probabilistic comparison between the learned pattern of the HMM and foot series to be tested decides the categorization of the test pattern. The experimental results show that the method can be applied to detect a suspicious person prowling in corridor.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.12
• /
• pp.5133-5138
• /
• 2016

Background: Breast cancer is one of the most common cancers and a major public health problem in developing countries. However, early detection and treatment may be achieved by breast self-examination (BSE). Despite the importance of BSE in reducing the incidence of breast cancer and esultant deaths, the disease continues to be the most common cause of cancer death among women in Iran.This study aimed to determine the effects of self-care education on performance of BSE among women referring to health centers in our country. Materials and Methods: This quasi-experimental interventional study with pretest/posttest control group design was conducted on 168 women referred to health centers. The data were collected using a validated researcher-made questionnaire including demographic variables and trans-theoretical model constructs as well as a checklist assessing BSE behavior. The instruments were administered to groups with and without self-care education before, a week after, and 10 weeks after the intervention. Then, the data were entered into the SPSS statistical software (version 19) and analyzed using independent sample t-tests, paired sample t-test, repeated measures ANOVA, Chi-square, and Friedman tests (p<0.05). Results: The results showed an increase in the intervention group's mean scores of trans-theoretical model constructs (stages of change, self-efficacy, decisional balance, and processes of change) and BSE behavior compared to the control group (p<0.001). Conclusion: The study confirmed the effectiveness of aneducational intervention based ona trans-theoretical model in performing BSE. Therefore, designing educational interventions based on this model is recommended to improve women's health and reduce deaths due to breast cancer.

• Fire Science and Engineering
• /
• v.33 no.5
• /
• pp.103-108
• /
• 2019

The purpose of this study is to suggest the development direction of fire safety through the analysis of previous researches in the field of fire fighting. To this end, 929 papers published in the Journal of Korean Institute of Fire Science and Engineering have been sorted by field of study and analyzed for frequency. Accordingly, it was confirmed that researches were conducted in the order of fire chemistry and physics, detection and extinguishing system, fire safety design and management, evacuation and human behavior. In contrast, very little research has been conducted on human behavior, fire safety education, or training. This result is discussed in the article, and suggestions for future research objectives are made.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.22 no.1
• /
• pp.14-21
• /
• 2002

The development of a new experimental method is required to easily observe the growth behavior of fatigue cracks. To satisfy the requirement, an image processing technique was introduced to fatigue testing. The length of surface fatigue crack could be successfully measured by the image processing system. At first, the image data of cracks were stored into the computer while the cyclic loading was interrupted. After testing, crack length was determined using an image processing software which was developed by authors. Various image processing techniques like a block matching method was applied tc the detection of surface fatigue cracks. By comparing the data measured by the image processing system with those by the manual measurement with a microscope, the effectiveness of the image processing system was established. If the proposed method is used to monitor and observe the crack growth behavior automatically, the time and efforts for fatigue test could be dramatically reduced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.395-397
• /
• 2019

Despite technical advance, human error is the main reason for maritime accidents. To ensure a safety of maritime transporting environment, technical and methodological improvement to react to various types of maritime accidents should be developed instead of ambiguously anticipating maritime accidents due to human errors. Survey, questionnaires, and interview have been routinely applied to understand objective human lookout pattern differences in various navigational situations. Although the descriptive methodology helps systematically categorizing different patterns of human behavior to avoid accidents, the subjective methods limit to objectively recognize physical behavior patterns during navigation. The purpose of the study is to develop an objective lookout pattern detection system using wearable sensors in the simulated navigation environment. In the simulated maritime navigation environment, each participant performed a given navigational situation by wearing the wearable sensors on the wrist, trunk, and head. Activity classification algorithm that was developed in the previous navigation activity classification research was applied. The physical lookout behavior patterns before and after situation-aware showed distinctive patterns, and the results are expected to reduce human errors of navigators.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.6
• /
• pp.171-181
• /
• 2011

Proposed in this paper is the intelligent video surveillance system to effectively detect multiple loitering objects even that disappear from the out of camera's field of view and later return to a target zone. After the background and foreground are segmented using Gaussian mixture model and shadows are removed, the objects returning to the target zone is recognized using the chromaticity histogram and the duration of loitering is preserved. For more accurate measurement of the loitering behavior, the camera calibration is also applied to map the image plane to the real-world ground. Hence, the loitering behavior can be detected by considering the time duration of the object's existence in the real-world space. The experiment was performed using loitering video and all of the loitering behaviors are accurately detected.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.9
• /
• pp.1224-1230
• /
• 2020

Since the spread of smart phones, interest in wearable devices has increased and diversified, and is closely related to the lives of users, and has been used as a method for providing personalized services. In this paper, we propose a method to detect the user's behavior by applying information from a 3-axis acceleration sensor and a 3-axis gyro sensor embedded in a smartphone to a convolutional neural network. Human behavior differs according to the size and range of motion, starting and ending time, including the duration of the signal data constituting the motion. Therefore, there is a performance problem for accuracy when applied to a convolutional neural network as it is. Therefore, we proposed a Time-Division Feature Fusion Convolutional Neural Network (TDFFCNN) that learns the characteristics of the sensor data segmented over time. The proposed method outperformed other classifiers such as SVM, IBk, convolutional neural network, and long-term memory circulatory neural network.