• Journal of Korean Academy of Fundamentals of Nursing
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• v.14 no.2
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• pp.189-197
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• 2007

Purpose: Breast cancer is a common cancer in women in Korea. Early detection of breast cancer is very important for the protection of a woman's health. The purpose of this quasi-experimental study was to determine the effect of BSE education on knowledge, self efficacy and performance in middle-aged women. Method: The participants were 33 women who agreed to participate in the study. They responded to a questionnaire that included items on knowledge, self-efficacy and performance of BSE. The experimental group was given a 90 minute-session including a lecture and practice with a BSE practice model and their own body. Their knowledge of the BSE was measured using Choi's tool and self-efficacy was measured using the scale by Champion and Scott. Results: Homogeneity for knowledge, self-efficacy and performance of BSE between the experimental and the control groups was confirmed. After 3 months of BSE education, knowledge, self-efficacy and frequency of BSE performance in the experimental group were significantly higher than for the women in the control group. Conclusion: A BSE education program would be helpful to enhance health behavior by early detection of breast cancer in middle-age women.

• Journal of Communications and Networks
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• v.15 no.2
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• pp.164-172
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• 2013

We consider the problem of secure and robust clustering for quantized target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose a new method for jointly activating the best group of candidate sensors that participate in data aggregation, detecting the malicious sensors and estimating the target position. Firstly, we select the appropriate group in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Secondly, we detect the malicious sensor nodes based on the information relevance of their measurements. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The selection of the candidate sensors group is based on multi-criteria function, which is computed by using the predicted target position provided by the QVF algorithm, while the malicious sensor nodes detection is based on Kullback-Leibler distance between the current target position distribution and the predicted sensor observation. The performance of the proposed method is validated by simulation results in target tracking for WSN.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.200-214
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• 2024

Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the "information cocoon" bottleneck, and reduce the occurrence of "group polarization" in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.4
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• pp.285-289
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• 2000

This study suggests a rebar detection technique for simultaneous detection of size and cover of embedded reinforcement in concrete. The structure of the probe made in this study is somewhat different from commercial ones. This probe has three sensing coils. Rebar size and cover depth can be evaluated by detecting and analyzing the signal from them. Amplitude and phase variation of each coil in the probe was investigated using an impedance analyzer and the loci of transfer functions of the coils were analyzed. The locus of transfer function from the sensing coil positioned inside excitation coil was simple as well known, but the others from the coils outside excitation coil were not so. Actual experiment on rebar detection was performed with our probe and an eddy current test system for various rebar sizes and depths. The signal shape according to variation of cover depths showed the same tendency with the transfer function loci acquired by impedance analyzer. The different variation pattern of signal enabled to evaluate rebar size and cover depth simultaneously.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.849-861
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• 2015

As the rise in popularity of online games, the users start exchanging rare items for real money. As RMT (Real Money Trade) is prevalent, GFG (Gold Farming Group) who abuse RMT shows up. GFG causes social problems such as identity theft, privacy leaks. Because they needs many bot characters to gather game items. In addition, GFG induce RMT that makes in-game problems such as a destroying game economy, account hacking. Therefore, It is very important work to collapse GFG at the perspective of social and in-game. In this paper, we proposed a fundamental method for detecting RMT buyers for the collapse of GFG at the perspective of buyer by Law of Demand and Supply. We found two type of RMT by analyzing actual game data and detected RMT buyers with high recall ratio of 98% by ruled-based detection.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.80-86
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• 2005

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is includes of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module processes the stator current, voltage, temperatures, vibration signal of the motor.

• Food Science and Industry
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• v.54 no.4
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• pp.293-300
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• 2021

Food safety accidents have been increasing by 2% over 5,000 cases every year since 2009. Most people know that the best method to prevent food safety accidents is a quick inspection, but there is a lack of inspection technology that can be used at the non-analytic level to food production and distribution sites. Among the recent on-site diagnostic technologies, the methods for testing gene-based food poisoning bacteria were introduced with the STA technology, which can range from sample to detection. If food safety information can be generated without forgery by directly inspecting food hazard factors by remote, unmanned, not human, pollution sources can be managed by predicting risks more accurately from current big-data and artificial intelligence technology. Since this information processing can be used on smartphones using the current cloud technology, it is judged that it can be used for food safety to small food businesses or catering services.

• Korean Journal of Veterinary Service
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• v.45 no.2
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• pp.101-110
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• 2022

This study was performed to investigate infectious gastrointestinal diseases in 115 Korean cats (83 indoors and 32 outdoors) with digestive signs such as diarrhea, anorexia or abdominal distention. Detection of infectious pathogens was analyzed using real-time PCR. As a result, 85 of 115 Korean cats were detected with feline corona virus (FCoV), feline parvo virus, Group A rotavirus, Clostridium perfringens (C. perfringens), Campylobacter coli (C. coli), Campylobacter jejuni, enterotoxigenic Escherichia coli, enteropathogenic Escherichia coli, Salmonella spp., Tritrichomonas foetus, Cyclospora cayetanensis, and Giardia lamblia. The most frequently detected pathogen was C. perfringens (52 cats, 61.2%), followed by FCoV (43 cats, 50.6%) and C. coli (16 cats, 18.8%). Also, single infection was the most common (43 cats), followed by double infection in 31 cats, triple infection in 7 cats, and quadruple infection in 4 cats. There was no significant relationship between pathogen detection and age, gender, living environment, weather, and diarrhea. However, there was a significant difference between the age group under 1 year and the age group 1~7 (P value<0.05). In this study, cats with suspected gastrointestinal infection were randomly evaluated, and other factors that could affect pathogen detection were insufficiently considered. For this reason, additional epidemiological investigations with a larger number of cats and sufficient consideration of the causes that may affect the results are needed. Nevertheless, it is thought that this study can also provide valuable information on gastrointestinal pathogens in Korean cats.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.7
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• pp.1030-1037
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• 2013

Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea). In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone) and accurately (over 94% accuracy), either as a standalone solution or to complement known methods.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.