• Journal of Environmental Impact Assessment
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• v.28 no.5
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• pp.440-456
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• 2019

The controversy of the risk perception related to particulate matters becomes significant. Therefore, in order to understand the nature of the particulate matters, we gathered articles and comments in on-line community related to bicycling which is affected by exposure of the particulate matters. As a result, firstly, the government - led particulate matter policy was strengthened and segmented every period, butthe risk perception related to particulate matters in the bicycle community has become active and serious. Second, as a result of analyzing the perception change of outdoor activities related to particulate matters, bicycle users in community showed a tendency of outdoor activity depending on the degree of particulate matters ratherthan the weather. In addition, the level of the risk perception related to particulate matters has been moved from fears of serious threat in daily life and health, combined with the disregard of domestic particulate matter levels or mask performance. Ultimately, these risk perception related to particulate matters have led some of the bicycling that were mainly enjoyed outdoors to the indoor space. However, in comparison with outdoor bicycling enjoyed by various factors such as scenery, people, and weather, the monotonous indoor bicycling was converted into another type of indoor exercise such as fitness and yoga. In summary, it was derived from mistrust of excessive information or policy provided by the government or local governments. It is considered that environmental policy should be implemented after discussion of risk communication that can reduce the gap between public anxiety and concern so as to cope with the risk perception related to particulate matters. Therefore,this study should be provided as an academic basis for the effective communication direction when decision makers establish the policy related to particulate matters.

• Membrane Journal
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• v.31 no.2
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• pp.101-119
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• 2021

The demand for clean water is virtually present in all modern human societies even as our society has developed increasingly more advanced and sophisticated technologies to improve human life. However, as global climate change begins to show more dramatic effects in many regions in the world, the demand for a cheap, effective way to treat wastewater or to remove harmful bacteria, microbes, viruses, and other solvents detrimental to human health has continued to remain present and remains as important as ever. Well-established synthetic membranes composed of polyaniline (PANI), polyvinylidene fluoride (PVDF), and others have been extensively studied to gather information regarding the characteristics and performance of the membrane, but recent studies have shown that making these synthetic membranes conductive to electrical current by doping the membrane with another material or incorporating conductive materials onto the surface of the membrane, such as allotropes of carbon, have shown to increase the performance of these membranes by allowing the adjustability of pore size, improving antifouling and making the antibacterial property better. In this review, modern electrically conductive membranes are compared to conventional membranes and their performance improvements under electric fields are discussed, as well as their potential in water filtration and wastewater treatment applications.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.31-38
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• 2022

An electrocardiogram(ECG) is a recoding of electrical signals of the heart's cyclic activity and an important body information for diagnosing myocardial rhythm. Large amount of information are generated continuously and a significant period of cumulative signal is required for the purpose of diagnosing a specific disease. Therefore, research on compression including clinically acceptable lossy technique has been developed to reduce the amount of information significantly. Recently, wearable smart heart monitoring devices that can transmit electrocardiogram(ECG) are being developed. The use of electrocardiogram, an important personal information for healthcare service, is rapidly increasing. However, devices generally have limited capability and power consumption for user convenience, and it is often difficult to apply the existing compression method directly. It is essential to develop techniques that can process and transmit a large volume of signals in limited resources. A method for compressing and transmitting the ECG signals efficiently by using the cumulative average (template) of the unit waveform is proposed in the paper. The ECG is coded lovelessly using template matching. It is analyzed that the proposed method is superior to the existing compression methods at high compression ratio, and its complexity is not relatively high. And it is also possible to apply compression methods to template matching values.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.211-227
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• 2022

Illegal gambling through online gambling sites has become a significant social problem. The development of Internet technology and the spread of smartphones have led to the proliferation of illegal gambling sites, so now illegal online gambling has become accessible to anyone. In order to mitigate its negative effect, the Korean government is trying to detect illegal gambling sites by using self-monitoring agents or reporting systems such as 'Nuricops.' However, it is difficult to detect all illegal sites due to limitations such as a lack of staffing. Accordingly, several scholars have proposed intelligent illegal gambling site detection techniques. Xu et al. (2019) found that fake or illegal websites generally have unique features in the HTML tag structure. It implies that the HTML tag structure can be important for detecting illegal sites. However, prior studies to improve the model's performance by utilizing the HTML tag structure in the illegal site detection model are rare. Against this background, our study aimed to improve the model's performance by utilizing the HTML tag structure and proposes Tag2Vec, a modified version of Doc2Vec, as a methodology to vectorize the HTML tag structure properly. To validate the proposed model, we perform the empirical analysis using a data set consisting of the list of harmful sites from 'The Cheat' and normal sites through Google search. As a result, it was confirmed that the Tag2Vec-based detection model proposed in this study showed better classification accuracy, recall, and F1_Score than the URL-based detection model-a comparative model. The proposed model of this study is expected to be effectively utilized to improve the health of our society through intelligent technology.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.541-546
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• 2021

Recently, there is a trend of developing various identification and prediction models for hypertension using clinical information based on artificial intelligence and machine learning around the world. However, most previous studies on identification or prediction models of hypertension lack the consideration of the ideas of non-invasive and cost-effective variables, race, region, and countries. Therefore, the objective of this study is to present hypertension prediction model that is easily understood using only general and simple sociodemographic variables. Data used in this study was based on the Korea National Health and Nutrition Examination Survey (2018). In men, the model using the naive Bayes with the wrapper-based feature subset selection method showed the highest predictive performance (ROC = 0.790, kappa = 0.396). In women, the model using the naive Bayes with correlation-based feature subset selection method showed the strongest predictive performance (ROC = 0.850, kappa = 0.495). We found that the predictive performance of hypertension based on only sociodemographic variables was higher in women than in men. We think that our models based on machine leaning may be readily used in the field of public health and epidemiology in the future because of the use of simple sociodemographic characteristics.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.575-576
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• 2020

본 논문은 근래에 식용곤충 식품에 대한 수요 및 국민적 관심이 증가하여 관련 산업이 급격히 성장하고 있는 가운데, 건강기능성 효과가 널리 알려진 흰점박이꽃무지 유충의 안정적인 생산량 확보를 위한 스마트 사육사를 제작하고 그 성능을 평가한 결과이다. 사육사는 L6m×W3m×H2.8m 크기로 제작하였으며, 안정적인 사육환경을 위하여 사육실과 공조실을 분리하여 설계하였다. 공시재료는 생후 15일이 경과된 흰점박이꽃 무지 유충 1령이며, 스마트 사육사 내 사육환경은 온도 25±2℃, 습도 65±5%로 제어하였다. 사육조사는 매주 1회, 유충의 체중, 길이, 두께를 측정하였으며, 스마트 사육사의 성능평가를 위해 일반 사육농가(전북 소재)와 비교·분석하였다. 사육 4주 후 조사 결과, 스마트 사육사에서 사육한 유충의 체중과 길이는 각각 평균 1.97g/마리와 3.75cm로, 일반농가의 1.58g/마리와 3.55cm에 비해 비교적 높은 것으로 나타났다. 하지만, 두께의 경우 2주 차까지 일반농가에서 대체로 높은 것으로 나타났으며, 이후 3~4주 차에서는 큰 차이를 보이지 않았다. 따라서 본 연구를 통해 개발한 흰점박이꽃무지 유충 스마트 사육사는 일반농가와 비교해 사육이 비교적 빠르고 생산량을 더 많이 확보할 수 있는 시스템으로 농가소득 증대에 유용할 것으로 판단되며, 장소 및 시간에 상관없이 생육환경 제어가 가능하여 개발된 시제품의 보급 확대가 필요하다.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.69-75
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• 2023

In recent years, there has been a growing interest in deep learning-based animal pose estimation, especially in the areas of animal behavior analysis and healthcare. However, existing animal pose estimation techniques do not perform well when body parts are occluded or not present. In particular, the occlusion of dog tail or ear might lead to a significant degradation of performance in pet behavior and emotion recognition. In this paper, to solve this intractable problem, we propose a simple yet novel framework for pet pose estimation where pet pose is predicted on an outpainted image where some body parts hidden outside the input image are reconstructed by the image inpainting network preceding the pose estimation network, and we performed a preliminary study to test the feasibility of the proposed approach. We assessed CE-GAN and BAT-Fill for image outpainting, and evaluated SimpleBaseline for pet pose estimation. Our experimental results show that pet pose estimation on outpainted images generated using BAT-Fill outperforms the existing methods of pose estimation on outpainting-less input image.

• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.63-76
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• 2018

The purpose of this study was to investigate the effect of contact type textile electrode structure on heart activity signal acquisition for smart healthcare. In this study, we devised six contact type textile electrodes whose electrode size and configuration were manipulated for measuring heart activity signals using computerized embroidery. We detected heart activity signals using a modified lead II and by attaching each textile electrode to the chest band in four healthy male subjects in a standing static posture. We measured the signals four times repeatedly for all types of electrodes. The heart activity signals were sampled at 1 kHz using a BIOPAC ECG100, and the detected original signals were filtered through a band-pass filter. To compare the performance of heart activity signal acquisition among the different structures of the textile electrodes, we conducted a qualitative analysis using signal waveform and size as parameters. In addition, we performed a quantitative analysis by calculating signal power ratio (SPR) of the heart activity signals obtained through each electrode. We analyzed differences in the performance of heart activity signal acquisition of the six electrodes by performing difference and post-hoc tests using nonparametric statistic methods on the calculated SPR. The results showed a significant difference both in terms of qualitative and quantitative aspects of heart activity signals among the tested contact type textile electrodes. Regarding the configurations of the contact type textile electrodes, the three-dimensionally inflated electrode (3DIE) was found to obtain better quality signals than the flat electrode. However, regarding the electrode size, no significant difference was found in performance of heart signal acquisition for the three electrode sizes. These results suggest that the configuration method (flat/3DIE), which is one of the two requirements of a contact type textile electrode structure for heart activity signal acquisition, has a critical effect on the performance of heart activity signal acquisition for wearable healthcare. Based on the results of this study, we plan to develop a smart clothing technology that can monitor high-quality heart activity without time and space constraints by implementing a clothing platform integrated with the textile electrode and developing a performance improvement plan.

• Journal of Life Science
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• v.27 no.8
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• pp.919-929
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• 2017

This study investigated the biological activities and chemical characteristics of Monascus-fermented Angelica gigas Nakai (A. gigas) with a view to the development of health foods. We also investigated the effect of the cultivation region of three A. gigas cultivars, namely Sancheong, Pyeongchang, and Jecheon. After fermentation, the content of decursin and decursinol angelate was increased compared to non-fermentation; the highest content of decursin and decursinol angelate was found in non-fermentation Pyeongchang A. gigas (PA) at 615.504, 326.742 ppm and fermented Pyeongchang A. gigas (FPA) at 792.610, 429.500 ppm, respectively. The highest content of phenolic compounds, flavonoids, and minerals was found in the FPA group, in which DPPH (${\alpha},{\alpha}^{\prime}-diphenyl-{\beta}-picrylhydrazyl$) radical scavenging activity and Fe/Cu reducing power were stronger in fermented than in non-fermented A. gigas. The FPA group in particular showed the highest activity. We measured nitric oxide (NO) production from lipopolysaccharide-induced RAW 264.7 cells and the inhibition of cancer cell proliferation. The inhibition of activity of NO production and cancer cell (MCF-7 and Hep3B) viability was significantly decreased in the FPA group. The results suggest that FPA may be highly useful as a health food. Overall, the study provides basic data for understanding the biological activities and chemical characteristics of A. gigas fermented by Monascus purpureus for the development of health foods.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.467-475
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• 2014

With the development of genomics, wearable device and IT/NT, a vast amount of bio-medical data are generated recently. Also, healthcare industries based on big-data are booming and big-data technology based on bio-medical data is rising rapidly as a core technology for improving the national health and aged society. A pathway is the biological deep knowledge that represents the relations of dynamics and interaction among proteins, genes and cells by a network. A pathway is wildly being used as an important part of a bio-medical big-data analysis. However, a pathway analysis requires a lot of time and effort because a pathway is very diverse and high volume. Also, multidimensional analysis systems for various pathways are nonexistent even now. In this paper, we proposed a pathway analysis system that collects user interest pathways from KEGG pathway database that supports the most widely used pathways, constructs a network based on a hierarchy structure of pathways and analyzes the relations of dynamics and interaction among pathways by clustering and selecting core pathways from the network. Finally, to verify the superiority of our pathway analysis system, we evaluate the performance of our system in various experiments.