• Korean Journal of Community Nutrition
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• v.21 no.1
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• pp.75-83
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• 2016

Objectives: The purpose of this study was to analyze correlation thresholds and assessment for salty taste and high-salt dietary behaviors by age. Methods: A total of 524 subjects including 100 each of elementary school students, middle school students, college students, and elderly as well as 124 adults were surveyed for detection and recognition thresholds, salty taste assessments, and high-salt dietary behaviors. Results: Elementary students had a lower detection threshold (p<0.05) and recognition threshold (p<0.01) than did the other groups. Salty taste assessments were lowest among elementary students, followed by middle school students, while college students, adults, and elderly had higher assessment score (p<0.001). Elementary students had significantly lower scores for high-salt dietary behavior than did middle school students, college students, adults and elderly (p<0.001). Middle school students had higher scores for high-salt dietary behavior than did elementary school students and elderly (p<0.001) but no meaningful difference was found in dietary behavior scores between college students, adults, and elderly. There were positive correlations between high-salt dietary behavior and detection thresholds (p<0.001), recognition thresholds (p<0.001), and salty taste assessment (p<0.001). High-salt dietary behavior was more positively correlated with salty taste assessment than detection and recognition thresholds for salty taste. Conclusions: This study suggested that salty taste assessments were positively associated with scores for the detection and recognition thresholds and high-salt dietary behavior.

• Korean Journal of Adult Nursing
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• v.13 no.3
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• pp.441-450
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• 2001

The purpose of this study was to identify the effects of cancer prevention and early detection education on cancer-related knowledge, attitudes, and preventive health behavior of middle-aged women in Korea. The research design was a nonequivalent control group pretest-posttest design. The subjects of this study were 38 middle-aged women from a church in Taegu. An Experimental group of 19 and a control group of 19 women were studied. The study was conducted from September 21, 2000 to October 27, 2000. The cancer prevention and early detection education had been provided to the experimental group for 2 weeks. The contents of the education program for the third most prevalent cancer of Korean women were : 'the risk factors of cancer', 'the early symptoms of cancer', 'the diagnostic test for cancer detection', and 'the cancer prevention methods'. The instruments used for this study were modified, cancer-related knowledge, and attitude, preventive health behavior tools of Suh et al.(1998). Data were analyzed using descriptive statistics, $\chi^2$-test, t-test, ANCOVA with SPSS WIN 9.0/PC. The results were as follows : 1) Hypothesis 1 that the women who get cancer prevention and early detection education will have higher scores of the cancer-related knowledge than the women do not get cancer prevention and early detection education was accepted(F=4.732, p=.037). 2) Hypothesis 2 that the women who get cancer prevention and early detection education will have higher scores of cancer-related attitudes than the women do not get cancer prevention and early detection education was rejected(F=.118, p=.733). 3) Hypothesis 3 that the women who get cancer prevention and early detection education will have higher scores of cancer-related preventive health behavior than the women who do not get cancer prevention and early detection education was rejected(F=2.250, p=.143). On the basis of the above findings, the following recommendations are suggested : 1) It is necessary to identify the variables affected on cancer-related knowledge, attitudes and preventive health behavior. 2) It is necessary to develop a well organized cancer prevention and early detection education program to change cancer-related attitude and preventive health behavior.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1453-1463
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• 2021

Feeding is the most important behavior that represents the health and welfare of weanling pigs. The early detection of feed refusal is crucial for the control of disease in the initial stages and the detection of empty feeders for adding feed in a timely manner. This paper proposes a real-time technique for the detection and recognition of small pigs using a deep-leaning-based method. The proposed model focuses on detecting pigs on a feeder in a feeding position. Conventional methods detect pigs and then classify them into different behavior gestures. In contrast, in the proposed method, these two tasks are combined into a single process to detect only feeding behavior to increase the speed of detection. Considering the significant differences between pig behaviors at different sizes, adaptive adjustments are introduced into a you-only-look-once (YOLO) model, including an angle optimization strategy between the head and body for detecting a head in a feeder. According to experimental results, this method can detect the feeding behavior of pigs and screen non-feeding positions with 95.66%, 94.22%, and 96.56% average precision (AP) at an intersection over union (IoU) threshold of 0.5 for YOLOv3, YOLOv4, and an additional layer and with the proposed activation function, respectively. Drinking behavior was detected with 86.86%, 89.16%, and 86.41% AP at a 0.5 IoU threshold for YOLOv3, YOLOv4, and the proposed activation function, respectively. In terms of detection and classification, the results of our study demonstrate that the proposed method yields higher precision and recall compared to conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2781-2800
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• 2016

Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.

• Smart Media Journal
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• v.6 no.3
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• pp.95-102
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• 2017

A large volume of research has been devoted to the development of security tools for protecting the Smart Grid systems, however the most of them have not taken the Availability, Integrity, Confidentiality (AIC) security triad model, not like CIA triad model in traditional Information Technology (IT) systems, into account the security measures for the electricity control systems. Thus, this study would propose a novel security framework, an abnormal behavior detection system, to maximize the availability of the control systems by considering a unique set of characteristics of the systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.978-985
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• 2003

The implementation of abnormal behavior detection IDS is more difficult than the implementation of misuse behavior detection IDS because usage patterns are various. Therefore, most of commercial IDS is misuse behavior detection IDS. However, misuse behavior detection IDS cannot detect system intrusion in case of modified intrusion patterns occurs. In this paper, we apply data mining so as to detect intrusion with only audit data related in intrusion among many audit data. The agent in the distributed IDS can collect log data as well as monitoring target system. False positive should be minimized in order to make detection accuracy high, that is, core of intrusion detection system. So We apply data mining algorithm for prediction of modified intrusion pattern in the level of audit data learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3151-3168
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• 2015

Automatic detection of estrus in cows is important in cattle management. This paper proposes a method of estrus detection by automatically checking cattle mounting. We use a side-view video camera and apply computer vision techniques to detect mounting behavior. In particular, we extract motion information to select a potential mount-up and mount-down motion and then verify the true mounting behavior by considering the direction, magnitude, and history of the mount motion. From experimental results using video data obtained from a Korean native cattle farm, we believe that the proposed method based on the abrupt change of a mounting cow's height and motion history information can be utilized for detecting mounting behavior automatically, even in the case of fence occlusion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2191-2203
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• 2011

In this paper, we propose a fast and robust algorithm for fighting behavior detection based on Motion Vectors (MV), in order to solve the problem of low speed and weak robustness in traditional fighting behavior detection. Firstly, we analyze the characteristics of fighting scenes and activities, and then use motion estimation algorithm based on block-matching to calculate MV of motion regions. Secondly, we extract features from magnitudes and directions of MV, and normalize these features by using Joint Gaussian Membership Function, and then fuse these features by using weighted arithmetic average method. Finally, we present the conception of Average Maximum Violence Index (AMVI) to judge the fighting behavior in surveillance scenes. Experiments show that the new algorithm achieves high speed and strong robustness for fighting behavior detection in surveillance scenes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4553-4562
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• 2010

With developing networks, information security is going to be important and therefore lots of intrusion detection system has been developed. Intrusion detection system has abilities to detect abnormal behavior and unknown intrusions also it can detect intrusions by using patterns studied from various penetration methods. Various algorithms are studying now such as the statistical method for detecting abnormal behavior, extracting abnormal behavior, and developing patterns that can be expected. Etc. This study using clustering of data mining and association rule analyzes detecting areas based on two models and helps design detection system which detecting abnormal behavior, unknown attack, misuse attack in a large network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.965-983
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• 2014

Concurrent behaviors of multithreaded programs cannot be described effectively by automata-based models. Thus, concurrent program intrusion attempts cannot be detected. To address this problem, we proposed the process algebra-based detection model for multithreaded programs (PADMP). We generate process expressions by static binary code analysis. We then add concurrency operators to process expressions and propose a model construction algorithm based on process algebra. We also present a definition of process equivalence and behavior detection rules. Experiments demonstrate that the proposed method can accurately detect errors in multithreaded programs and has linear space-time complexity. The proposed method provides effective support for concurrent behavior modeling and detection.