• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2000년도 추계학술대회 논문집
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• pp.73-80
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• 2000

The paper is addressed to the topic of physiological response-specificity in disgust induced by visual stimulation. The purpose of this study was to evaluate reproducibility of physiological reactivity pattern during disgust elicited by the International Affective Pictures System (IAPS) in 2 experiments. Twenty-nine subjects participated in the first experiment with 3 visual stimulation sessions with disgust-eliciting slides (3 slides in each 1 min long session). In the second experiment disgust-eliciting slides from the IAPS were presented to 42 subjects in 2 sessions (one slide for 1 min). Spectral power of frontal EEG, skin conductance (SCL, SCR and NS.SCR), heart rate(HR), heart period variability(HPV) and respiration rate were recorded. Visual stimulation evoked 1:.n deceleration, higher power of high frequency component of HPV, increased SCL and NS.SCR frequency, frontal slow alpha blocking and moderate increase in fast beta power in most of the sessions in both experiments. However in the second experiment the EEG pattern associated with disgust showed inconsistent shifts in fast alpha and slow beta bands, but was marked by higher power of theta activity. Our data in both experiments emphasizes presence of disgust-specific profiles of autonomic and at the less extent EEG responses in visual stimulation context. Discussed are potential behavioral mechanisms leading to observed physiological manifestations in disgust elicited by visual stimulation. The results support the consideration that disgust is an withdrawal type negative valence emotion associated with relatively low autonomic arousal (low HR, low amplitude SCRs with relatively high NS.SCR frequency) and moderate EEG activation signs. Obtained data showed more consistent reproducibility of disgust-specific autonomic rather than EEG response patterns during visual stimulation design.

• 대한영양사협회학술지
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• 제28권2호
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• pp.99-113
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• 2022

This study compared the pattern of purchasing nutritional supplements and obtaining nutrition information and the Nutrition Quotient of adults who exercise regularly according to the frequency of muscle training (LM, muscle training less than two days/week; MM, 2~3 days/week; HM, more than three days/week). Compared to the other groups, significantly more men in the HM group answered that they had purchased supplements and would repurchase them. They presented "increasing muscle mass" as the purpose of buying them and "types and contents" as the major factor to consider before buying. The supplements that significantly more subjects in the HM group purchased than in the other groups were protein supplements, BCAA (Branched-chain amino acid), arginine, and caffeine supplements for men and protein supplements, BCAA and carnitine for women. Compared to the other groups, significantly more men and women in the HM group reported "trainer" as the major source of nutrition information, and significantly more men in the HM group reported "increasing muscle mass" as the 1st topic of nutrition education that they wanted to receive and were satisfied with the information that they obtained. Most of all, males and females in the HM group had significantly a higher Nutrition Quotient than in the other groups. The differences between males and females in variables regarding exercise, the patterns of using supplements and acquiring nutrition information, and Nutrition Quotient were also found. These results can provide basic data for developing educational materials emphasizing the wise consumption of nutrition information, including supplement intake.

• Journal of the Korean Data and Information Science Society
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• 제26권5호
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• pp.1061-1069
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• 2015

최근 교통 분야에서 발생하는 교통 빅데이터 (교통카드 데이터, ATMS 데이터 등)의 분석결과를 교통 정책에 활용하는 사례가 늘어나고 있는 추세이다. 또한 교통 데이터 분석 기법을 기존의 단순 빈도 분석 기법에서 다양한 데이터 마이닝 기법으로 확장하여 교통 데이터 속에 숨어있는 의미를 파악하려는 연구도 진행되고 있다. 본 연구에서는 교통카드 데이터에 대하여 토픽모델링 기법 중의 하나인 LDA (Latent Dirichlet Allocation) 기법을 적용하여 청주시 버스 승객들의 이동패턴을 분석한다. 이를 위해 교통카드 데이터의 하차 결측치를 추정하고, LDA 기법을 적용하여 이동패턴을 추출하였다. 또한 LDA 분석으로 도출된 값을 측정값으로 하여 다차원적 분석을 함으로써 청주시 버스 승객들의 이동패턴 특징을 파악할 수 있다. 분석 결과, 청주시의 경우 크게 1) 시외지역에서 터미널을 이용해 청주시에서 유입되는 패턴, 2) 주거지역에서 상업지역으로 이동하는 패턴, 3) 청주 인근 학교에서 상업 지역 (청주 중심가)로 이동하는 패턴을 발견할 수 있었다. 이동패턴은 도시 계획, 대중교통서비스 향상, 버스 노선 신설 등 다양한 교통정책의 수립에 활용될 수 있을 것으로 기대된다.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1369-1378
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• 2017

Pub/Sub (Publish/Subscribe) paradigm is a simple and easy to use model for interconnecting applications in a distributed environment. In general, subscribers register their interests in a topic or a pattern of events and then asynchronously receive events matching their interest, regardless of the events' publisher. In order to build a low latency lightweight pub/sub system for Internet of Things (IoT) services, we propose a GQSFPS (Group Quorum System-based Fog Pub/Sub) system that is a core component in the event-driven service oriented architecture framework for IoT services. The GQSFPS organizes multiple installed pub/sub brokers in the fog servers into a group quorum based P2P (peer-to-peer) topology for the efficient searching and the low latency accessing of events. Therefore, the events of IoT are cached on the basis of group quorum, and the delay-sensitive IoT applications of edge devices can effectively access the cached events from group quorum fog servers in low latency. The performance of the proposed GQSFPS is evaluated through an analytical model, and is compared to the GQPS (grid quorum-based pud/sub system).

• ETRI Journal
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• 제36권3호
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• pp.418-428
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• 2014

In social network services, such as Facebook, Google+, Twitter, and certain postings attract more people than others. In this paper, we propose a novel method for predicting the lifespan and retweet times of tweets, the latter being a proxy for measuring the popularity of a tweet. We extract information from retweet graphs, such as posting times; and social, local, and content features, so as to construct prediction knowledge bases. Tweets with a similar topic, retweet pattern, and properties are sequentially extracted from the knowledge base and then used to make a prediction. To evaluate the performance of our model, we collected tweets on Twitter from June 2012 to October 2012. We compared our model with conventional models according to the prediction goal. For the lifespan prediction of a tweet, our model can reduce the time tolerance of a tweet lifespan by about four hours, compared with conventional models. In terms of prediction of the retweet times, our model achieved a significantly outstanding precision of about 50%, which is much higher than two of the conventional models showing a precision of around 30% and 20%, respectively.

• 한국산업융합학회 논문집
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• 제14권3호
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• pp.105-111
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• 2011

Landmark Spots in 2D gel electrophoresis are used in many methods of 2DEG spot matching. Landmark Spots are obtained manually and it is a bottle neck in the entire protein analysis process. Automated landmark spots detection is a very crucial topic in processing a massive amount of 2DGE data. In this paper, Automated landmark spot detection is proposed using point pattern matching and graph theory. Neighbor spots are defined by a graph theory to use and only a centered spot and its neighbor spots are considered for spot matching. Normalized Hausdorff distance is introduced as a criterion for measuring degree of similarity. In the conclusion, the method proposed in this paper can get about 50% of the total spot pairs and the accuracy rate is almost 100%, which the requirements of landmark spots are fully satisfied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4568-4584
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• 2016

Steganography based on text generation has become a hot research topic in recent years. However, current text-generation methods which generate texts of normal style have either semantic or syntactic flaws. Note that texts of special genre, such as poem, have much simpler language model, less grammar rules, and lower demand for naturalness. Motivated by this observation, in this paper, we propose a text steganography that utilizes Markov chain model to generate Ci-poetry, a classic Chinese poem style. Since all Ci poems have fixed tone patterns, the generation process is to select proper words based on a chosen tone pattern. Markov chain model can obtain a state transfer matrix which simulates the language model of Ci-poetry by learning from a given corpus. To begin with an initial word, we can hide secret message when we use the state transfer matrix to choose a next word, and iterating until the end of the whole Ci poem. Extensive experiments are conducted and both machine and human evaluation results show that our method can generate Ci-poetry with higher naturalness than former researches and achieve competitive embedding rate.

• Structural Engineering and Mechanics
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• 제64권6권
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• pp.803-817
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• 2017

Structural health monitoring has increasingly been a focus within the civil engineering research community over the last few decades. With increasing application of sensor networks in large structures and infrastructure systems, effective use and development of robust algorithms to analyze large volumes of data and to extract the desired features has become a challenging problem. In this paper, we grasp some precautions and key points of the signal processing approach, wavelet, establish a relative reliable framework, and analyze three problems that require attention when applying wavelet based damage detection approach. The cases studies how to use optimal scales for extracting mode shapes and modal curvatures in a reinforced concrete beam and how to effectively identify damages using maximum curves of wavelet coefficient differences. Moreover, how to make a recognition based on the wavelet multi-resolution analysis, wavelet packet energy, and fuzzy sets is a meaningful topic that has been addressed in this work. The relative systematic work that compasses algorithms, structures and evaluation paves a way to a framework regarding effective structural health monitoring, orientation, decision and action.

• 한국정보통신학회논문지
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• 제10권9호
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• pp.1596-1601
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• 2006

고객관리가 기업의 성패를 좌우하는 중요한 화두로 떠오르면서 보다 쉽고 편리하게 고객의 다양한 패턴을 발견하고 예측하기 위 해 많은 기업들이 CRM과 eCRM을 빠르게 도입하고 있다. 과거엔 고객관리가 통계학자들이나 전문적인 통계패키지에 의해 관리되어 왔으나 정보통신 분야의 급격한 발달을 기반으로 통계적 과정을 자동화시킨 데이터마이닝 기법으로 점점 대체되고 있는 추세이다. 이러한 데이터마이닝이 대표적으로 이용되고 있는 분야가 CRM, eCRM이다. 본 논문에서는 A할인점의 고객 데이터와 2004년도 매출 데이터를 기반으로 유전자알고리즘을 이용한 데이터마이닝을 통해 2005년도 우수 고객을 예측하였고 실제 고객 데이터와의 비교를 통해 데이터 마이닝이 eCRM에 얼마나 효과적인지를 입증하였다.

• International Journal of Oral Biology
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• 제43권2호
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• pp.93-100
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• 2018

The investigation of the embryonic development of the cerebellum has a long history. The postnatal normal development of the cerebellum in rodents and other animals became a popular topic for morphological investigations nearly a century ago. However, surprisingly, only a few studies are available regarding the prenatal normal development of the rodent cerebellum, especially in guinea pigs. Cell proliferation is essential for the development of the nervous system. The assessment of cell proliferation can be achieved by using various methods. In this study, we investigated the cell proliferation of the cerebellar cortex in guinea pigs at different stages of pregnancy and in postnatal life. Fetuses were obtained by cesarean section at 50 or 60 days of gestation (dg). Immunohistochemistry was performed with proliferating cell nuclear antigen (PCNA) antibody in the cerebellum. Strong PCNA immunoreactivity was observed in the external granular layer (EGL), which is a neurogenic zone in the cerebellum. The proportion of PCNA-IR cells was greater at 1 week than at 60 dg in lobule I, but not lobule VIII. After 50 dg, the width of the EGL continued to decline until 1 week, due to the maturation of the EGL cells. These results demonstrate the pattern of PCNA immunoreactivity in the developing cerebellum of guinea pigs. This serves as a guideline to study abnormal cerebellum development.