• Asia pacific journal of information systems
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• 제18권3호
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• 한국수자원학회논문집
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• 제48권7호
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• pp.553-566
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• 2015

ADCP는 하천의 3차원 유속과 수심 자료를 매우 효율적이고 빠르게 측정할 수 있으며, 그 자료의 공간 및 시간적 해상도는 기존의 전통적인 유속 측정 방법들과 비교하여 매우 정밀하다는 장점이 있다. 하지만 ADCP는 하상 부근과 센서 근처에서의 미계측 영역이 발생하고 이 미계측 영역의 유속을 얼마나 정확하게 산정하느냐에 따라 ADCP 유량 측정의 정확도에 영향을 미친다. 본 연구에서는 ADCP 유량 산정 시 범용적으로 활용되고 있는 1/6 멱법칙(power law)을 활용한 미계측 영역의 유량 측정 결과의 정확도를 분석하였다. 이를 위해 실규모 직선수로에서 ADCP를 고정시킨 상태에서 측정한 유속 자료를 1/6 멱법칙과 대수 법칙(log law)을 적용하여 외삽 한 유속분포와 유량 산정 결과를 ADV를 이용하여 정밀하게 측정한 결과와 비교하였다. 비교 결과 전체적으로 대수 법칙으로 외삽한 경우가 높은 정확도를 나타냈으며, 수표면 근처 미계측 영역에서는 1/6 멱법칙은 유량을 작게 산정하는 경향을 나타냈고, 하상 근처의 미계측 영역에서는 유량을 크게 산정하는 경향을 나타냈다. 이 결과는 기존 1/6 멱법칙을 활용한 하상 및 수표면 부근 미계측 영역 유량 추정 방법이 오차를 수반함을 의미한다. 따라서 ADCP 정지법 측정 방식을 사용할 경우, 대수 법칙이 1/6 멱법칙보다 정확한 상하부 미계측 유량 추정 결과를 보여주었으므로 대안으로 고려되어야 할 것이다. 또한 제방 근처 미계측 영역의 유량 측정 정확도를 높이기 위해서는 수심이 0.6 m 이상을 확보한 측선을 기준으로 유량을 산정할 경우 신뢰도 높은 유량 측정 결과를 보였다. 향후, ADCP 정지법 측정 방식에 비해 보다 많이 활용되고 있는 보트탑재 이동식 ADCP의 경우도 이와 같은 검증이 필요하다고 하겠다.

• 수산해양기술연구
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• 제36권3호
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• pp.175-185
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• 2000

한국 연근해에서 조업하고 있는 어선을 효율적으로 관리할 수 있는 어선관제시스템의 구축을 위한 기초 연구로서 제주도 성산포항을 거점으로 하여 조업중인 대형선망어선단의 어로과정의 ARPA 영상을 디지털신호로 변환시켜 분석하고 VTMS를 이용하여 모의실험을 행한 결과를 요약하면 다음과 같다. (1) 대형선망어선단의 어로과정을 분석한 결과 투망소요시간은 16분, 양망소요시간은 35분이었고, 앞잡이 배가 끌어 주는 로프의 길이는 200m, 투망시 선회경은 340.8m, 선회속도는 약 6kts로써 조업 과정을 명확하게 파악할 수 있었다. (2) 실선실험에서 구한 투$.$양망과정에 유향$.$유속을 NE, 2kts와 SW, 2kts로 가상하여 시뮬레이션한 결과, 각각 SW, NE 방향으로 편위됨을 알 수가 있었다. 이와 같이 어장환경정보 또는 어업 정보나 조선정보를 관제시스템에 가미함으로써 실제조업과 같은 상황을 예측할 수 있었으며, 클로즈업시킨 화면을 통해 투 양망중 예상되는 상황과 문제점을 검토할 수 있었다. (3) 시뮬레이션에서 사용한 VTMS의 레이더 관제범위는 16mile이었고, 관제범위를 넘었더라도 타관제선으로의 이관이 가능하였다. 또한, 관제선과 집단선단들과의 거리와 방위를 측정하고 분석하면 관제선의 위치선정이 용이함을 알 수 있었다. (4) 조업선들이 어황정보와 안전항행정보를 제공받아 안전하고 효율적인 조업을 행할 수 있는 어선관제 시스템(FVTMS)의 예측모델을 제시하였다. 이와 같이 VTMS용 관제시스템을 이용하여 선단조업어선의 어로과정에 대한 시뮬레이션 한 결과, 근접조업에 따른 잦은 경보와 추적 상실 등 몇가지 기능상의 문제점이 발견되었으므로 어선관제시스템(FVTMS)에 적합한 프로그램이 시급히 개발되어야 할 것으로 사료된다. 대한 추종 성능이 현용 어로시스템에 비하여 매우 우수하기 때문에 해상에서 어로작업시 과부하에 대한 어구의 손상 방지 및 조업 효율의 향상에 크게 기여할 것으로 판단된다.Exp.2), 실험 수온 27$^{\circ}C$에서, Exp. 1에서와 동일한 3개의 수리학적 부하량에서 산소 전달률을 측정한 결과, Exp. 1에서와 같이 수리학적 부하량과 매질의 깊이의 증가에 따라 산소 전달률이 증가하였으며, 매질의 깊이가 가장 깊은 36 cm에 대해, 수리학적 부하량이 2 $m^3$/$m^2$/min 일때, 2 kg 02 kg $O_2$/kW-hr의 가장 높은 표준에어레이션효율을 나타내었다. 위의 두 실험 결과에 따라 packed column 에어레이터에서 발포스티로폼 입자를 산소전달 매질로 이용하여 산소 전달률을 증가시킬 수 있다는 것을 확인할 수 있었다.i, Cu, Y, Nb, La, Nd, Pb, Th in excess of 10 ppm. Relatively high amount of most trace elements were detected in the Hwangto. The major and minor chemical compositions of the Hwangto were different depending on the types of host rocks. However, their difference was in the similar range compared with the compositions of host rocks. electron acceptor triggers sensory transduction processes in B. japonicum.t the Christian rejection

• 한국영양학회:학술대회논문집
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• 한국영양학회 1995년도 추계학술대회 초록
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• pp.11-34
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• 1995

Growth hormone (GH) plays a key role in regulating postnatal growth and can stimulate growth of animals by acting directly on specific receptors on the plasma membrane of tissues or indirectly through stimulating insulin-like growth factor (IGF)-I synthesis and secretion by the liver and other tissues. IGF-I and IGF-Ⅱ are polypeptides with structural similarity with proinsulin that stimulate cell proliferation by endocrine, paracrine and autocrine mechanisms. The initial event in the metabolic action of IGFs on target cells appears to be their binding to specific receptors on the plasma membrane. Current evidence indicates that the mitogenic actions of both IGFs are mediated primarily by binding to the type I IGF receptors, and that IGF action is also mediated by interactions with IGF-binding proteins (IGFBPs). Six distinct IGFBPs have been identified that are characterized by cell-specific interaction, transcriptional and post-translational regulation by many different effectors, and the ability to either potentiate or inhibit IGF actions. Nutritional deficiencies can have their devastating consequence during growth. Although IGF-I is the major mediator of GH's action on somatic growth, nutritional status of an organism is a critical regulator of IGF-I and IGFBPs. Various nutrient deficiencies result in decreased serum IGF-I levels and altered IGFBP levels, but the blood levels of GH are generally unchanged or elevated in malnutrition. Effects of protein, energy, vitamin C and D, and zinc on serum IGF and IGFBP levels and tissue mRNA levels were reviewed in the text. Multiple factors are involved in the regulation of intestinal epithelial cell growth and differentiation. Among these factors the nutritional status of individuals is the most important. The intestinal epithelium is an important site for mitogenic action of the IGFs in vivo, with exogenous IGF-I stimulating mucosal hyperplasia. Therefore, the IGF system appears to provide and important mechanism linking nutrition and the proliferation of intestinal epithelial cells. In order to study the detailed mechanisms by which intestinal mucosa is regulated, we have utilized IEC-6 cells, an intestinal epithelial cell line and Caco-2 cells, a human colon adenocarcinoma cell line. Like intestinal crypt cells analyzed in vivo or freshly isolated intestinal epithelial cells, IEC-6 cells and Caco-2 cells possess abundant quatities of both type Ⅰ and type Ⅱ IGF receptors. Exogenous IGFs stimulate, whereas addition of IGFBP-2 inhibits IEC-6 cell proliferation. To investigate whether endogenously secreted IGFBP-2 inhibit proliferation, IEC-6 cells were transfected with a full-length rat IGFBP-2 cDNA anti-sense expression construct. IEC-6 cells transfected with anti-sense IGFBP-2 protein in medium. These cells grew at a rate faster than the control cells indicating that endogenous IGFBP-2 inhibits proliferation of IEC-6 cells, probably by sequestering IGFs. IEC-6 cells express many characteristics of enterocyte, but do not undergo differentiation. On the other hand, Caco-2 cells undergo a spontaneous enterocyte differentiation. On the other hand, Caco-2 cells undergo a spontaneous enterocyte differentiation after reaching confluency. We have demonstrated that Caco-2 cells produce IGF-Ⅱ, IGFBP-2, IGFBP-3, and an as yet unidentified 31,000 Mr IGFBP, and that both mRNA and peptide secretion of IGFBP-2 and IGFBP-3 increased, but IGFBP-4 mRNA and protein secretion decreased after the cells reached confluency. These changes occurred in parallel to and were coincident with differentiation of the cells, as measured by expression of sucrase-isomaltase. In addition, Caco-2 cell clones forced to overexpress IGFBP-4 by transfection with a rat IGFBP-4 cDNA construct exhibited a significantly slower growth rate under serum-free conditions and had increased expression of sucrase-isomaltase compared with vector control cells. These results indicate that IGFBP-4 inhibits proliferation and stimulates differentiation of Caco-2 cells, probably by inhibiting the mitogenic actions of IGFs.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권7호
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• pp.271-278
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• 2021

현재 사이버 공격이 더욱 지능화됨에 따라 기존의 침입 탐지 시스템(Intrusion Detection System)은 저장된 패턴에서 벗어난 지능형 공격을 탐지하기 어렵다. 이를 해결하려는 방법으로, 데이터 학습을 통해 지능형 공격의 패턴을 분석하는 딥러닝(Deep Learning) 기반의 침입 탐지 시스템 모델이 등장했다. 침입 탐지 시스템은 설치 위치에 따라 호스트 기반과 네트워크 기반으로 구분된다. 호스트 기반 침입 탐지 시스템은 네트워크 기반 침입 탐지 시스템과 달리 시스템 내부와 외부를 전체적으로 관찰해야 하는 단점이 있다. 하지만 네트워크 기반 침입 탐지 시스템에서 탐지할 수 없는 침입을 탐지할 수 있는 장점이 있다. 따라서, 본 연구에서는 호스트 기반의 침입 탐지 시스템에 관한 연구를 수행했다. 호스트 기반의 침입 탐지 시스템 모델의 성능을 평가하고 개선하기 위해서 2018년에 공개된 호스트 기반 LID-DS(Leipzig Intrusion Detection-Data Set)를 사용했다. 해당 데이터 세트를 통한 모델의 성능 평가에 있어서 각 데이터에 대한 유사성을 확인하여 정상 데이터인지 비정상 데이터인지 식별하기 위해 1차원 벡터 데이터를 3차원 이미지 데이터로 변환하여 재구성했다. 또한, 딥러닝 모델은 새로운 사이버 공격 방법이 발견될 때마다 학습을 다시 해야 한다는 단점이 있다. 즉, 데이터의 양이 많을수록 학습하는 시간이 오래 걸리기 때문에 효율적이지 못하다. 이를 해결하기 위해 본 논문에서는 적은 양의 데이터를 학습하여 우수한 성능을 보이는 Few-Shot Learning 기법을 사용하기 위해 Siamese-CNN(Siamese Convolutional Neural Network)을 제안한다. Siamese-CNN은 이미지로 변환한 각 사이버 공격의 샘플에 대한 유사성 점수에 의해 같은 유형의 공격인지 아닌지 판단한다. 정확성은 Few-Shot Learning 기법을 사용하여 정확성을 계산했으며, Siamese-CNN의 성능을 확인하기 위해 Vanilla-CNN(Vanilla Convolutional Neural Network)과 Siamese-CNN의 성능을 비교했다. Accuracy, Precision, Recall 및 F1-Score 지표를 측정한 결과, Vanilla-CNN 모델보다 본 연구에서 제안한 Siamese-CNN 모델의 Recall이 약 6% 증가한 것을 확인했다.