• 한국정보과학회논문지:데이타베이스
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• 제34권6호
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• pp.473-482
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• 2007

네트워크 기반의 침입탐지시스템에서는 수집된 패킷데이타의 분석을 통해 침입인지 정상행위 인지를 판단하여 경보를 발생 시키며 이런 경보데이타의 양은 기하급수적으로 증가하고 있다. 보안관리자는 이러한 대량의 경보데이타들을 분석하고 통합 관리하여 네트워크 보안레벨을 진단하거나 시간에 따른 적절한 대응을 하는데 유용하게 사용하여야 한다. 그러나 오경보의 비율이 너무 높아 경보 데이터들간의 상관관계 분석이나 고수준의 의미 분석에 어려움이 많으므로 분석결과에 대한 신뢰성이나 분석의 효율성이 낮아지는 문제점을 가진다. 이 논문에서는 데이타 마이닝의 분류 기법을 적용하여 오경보율을 최소화하는 방법을 제안한다. 결정트리기반의 분류 기법을 오경보 분류 모델로 적용하여 오경보들 중 실제는 공격이 아님에도 불구하고 공격이라 판단된 오경보를 정상으로 분류할 수 있는 경보 데이타 분류 모델을 설계하고 구현한다. 구현된 경보데이타 분류 모델은 오경보율을 최소화하므로 경보데이타의 분석 및 통합을 통해 경보메시지의 축약 및 침입탐지시스템의 탐지율을 높이는데 활용될 수 있다.

• 전자공학회논문지CI
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• 제44권3호
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• pp.74-80
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• 2007

전자와 컴퓨터 기술의 발전은 무선 센서 네트워크 증대의 토대를 마련하였다. 이에 따라, 센서 네트워크상의 충돌 방지와 인증 기술의 필요성이 증대되어 지고 있다. 센서 네트워크의 충돌 방지를 위해 개발될 알고리즘은 무선 센서 네트워크 플랫폼 상에 쉽게 적용될 수 있으며 또한 동시에 분산 연산, 분산 저장, 데이터 강인성, 센싱된 데이터를 자동 분류할 수 있어야한다. 그리고 무선 센서 네트워크에서 보안을 유지하기 위하여 여러 센서 간에 안전하게 채널을 확립할 수 있어야한다. 본 논문 우리는 센서의 인증과 충돌 방지를 위하여 유비쿼터스 센서 네트워크 채널 확립 알고리즘을 제안하였다. 본 논문에서는 두 가지 다른 형태의 구조를 제안하였으며, 각 구조에서는 센서 노드 사이에서 채널을 확립하기위하여 웨이블렛 필터를 사용한 알고리즘과 센서의 충돌 방지를 위하여 BIBD(Balanced Incomplete Block Design) 코드를 사용하였다. 결과적으로, BIBD와 웨이블렛 필터 기반으로 제안된 알고리즘은 이상적인 환경에서 98% 충돌 검출율을 가졌다.

• 전자공학회논문지CI
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• 제43권4호
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• pp.80-87
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• 2006

최근 인터넷 응용 프로그램과 관련 기술의 발전에 따라 디지털 멀티미디어 콘텐츠의 보급과 사용이 쉬워지고 있다. 디지털 신호는 복제가 용이하고 복제된 신호는 원신호와 동일한 품질을 갖는다. 이러한 문제점을 해결하고 저작권 보호를 위해 멀티 미디어 핑거프린트가 연구되어지고 있다. 핑거프린팅 기법은 암호학적인 기법들을 이용하여 디지털 데이타를 불법적으로 재배포한 사용자를 찾아냄으로써 디지털 데이타의 저작권을 보호한다. 핑거프린팅 기법은 대칭적이나 비대칭적인 기법과 달리 사용자만이 핑거프린트가 삽입된 데이타를 알 수 있고 데이타가 재배포되기 전에는 사용자의 익명성이 보장되는 기법이다. 본 논문에서는 신경회로망에 의한 공모된 멀티미디어 핑거프린트의 검출 알고리즘을 제안한다. 제안된 알고리즘은 불법공모방지 코드 생성과 에러정정을 위한 신경회로망으로 구성되어 있다. BIBD(Balance Incomplete Block Design) 기반의 불법공모방지 코드는 평균화 선형 공모공격에 대해 100% 공모코드 검출이 이루어졌으며, 에러비트 정정을 위해 (n,k)코드를 사용한 홉필드 신경회로망은 2비트 이내의 에러비트를 정정할 수 있음을 확인하였다.

• 품질경영학회지
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• 제44권4호
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• pp.799-811
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• 2016

Purpose: The conjoint analyst in marketing are interested in detecting whether there exist synergy or antagonistic effects between two attributes. In the cases where attributes have two or three levels, we research on the design of survey questionnaire to estimate all the main effect and as many two factor interaction effects as possible. Methods: We consider the balanced incomplete block (BIB) mixed level factorial design $2^f{\times}3^g$ or fractional factorial design. To reduce the number of questions in a questionnaire, we propose the balanced incomplete block mixed level design with minimum aberration which is generated by implementing proc factex in SAS. Also, we propose using two or three level BIB factorial design instead of mixed level designs by transforming three level attributes into two attributes of two levels and two level attribute into three level attribute by using dummy level technique. Results: We propose three methods for designing survey questionnaire where the block and design generators are found with practical number of questions in a questionnaire. By analyzing all the respondents survey data generated by the simulation study, we find the proper model and do the concepts optimization. Conclusion: The proposed methods of designing survey questionnaires seem to perform well in the sense that the proper model, and then the optimal concept is found in a case study where all the respondents survey data are generated by the simulation study.

• 전자공학회논문지CI
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• 제43권5호
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• pp.68-75
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• 2006

멀티미디어 핑거프린팅은 각각의 유저에게 배포되어지는 디지털 콘텐츠마다 고유한 정보를 가지게 만듦으로써 불법적으로 콘텐츠를 배포하는 사용자로부터 멀티미디어 콘텐츠를 보호한다. 또한, 핑거프린팅 기법은 대칭적이나 비대칭적인 기법과 달리 사용자만이 핑거프리트가 삽입된 데이터를 알 수 있고 데이터가 재배포되기 전에는 사용자의 익명성이 보장되는 기법이다. 본 논문에서는 공모자 검출과 에러 신호의 정정을 위하여 LDPC(Low Density Parity Check) 알고리즘을 이용한 멀티미디어 핑거프린트의 검출 알고리즘을 제안한다. 제안된 알고리즘은 LDPC 블록, 홉필드 망, 그리고 불법공모방지코드 생성 알고리즘으로 구성되어 있다. BIBD(Balanced Incomplete Block Design) 기반의 불법공모방지코드는 평균화 선형 공모공격(평균, AND, OR)에 대해 100% 공모코드 검출이 이루어졌으며, LDPC 블럭은 AWGN 0dB까지 에러비트를 정정할 수 있음을 확인하였다.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2015년도 제49회 하계 정기학술대회 초록집
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• pp.64-64
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• 2015

Control of plasma processing methodologies can only occur by obtaining a thorough understanding of the physical and chemical properties of plasmas. However, all plasma processes are currently used in the industry with an incomplete understanding of the coupled chemical and physical properties of the plasma involved. Thus, they are often 'non-predictive' and hence it is not possible to alter the manufacturing process without the risk of considerable product loss. Only a more comprehensive understanding of such processes will allow models of such plasmas to be constructed that in turn can be used to design the next generation of plasma reactors. Developing such models and gaining a detailed understanding of the physical and chemical mechanisms within plasma systems is intricately linked to our knowledge of the key interactions within the plasma and thus the status of the database for characterizing electron, ion and photon interactions with those atomic and molecular species within the plasma and knowledge of both the cross-sections and reaction rates for such collisions, both in the gaseous phase and on the surfaces of the plasma reactor. The compilation of databases required for understanding most plasmas remains inadequate. The spectroscopic database required for monitoring both technological and fusion plasmas and thence deriving fundamental quantities such as chemical composition, neutral, electron and ion temperatures is incomplete with several gaps in our knowledge of many molecular spectra, particularly for radicals and excited (vibrational and electronic) species. However, the compilation of fundamental atomic and molecular data required for such plasma databases is rarely a coherent, planned research program, instead it is a parasitic process. The plasma community is a rapacious user of atomic and molecular data but is increasingly faced with a deficit of data necessary to both interpret observations and build models that can be used to develop the next-generation plasma tools that will continue the scientific and technological progress of the late 20th and early 21st century. It is therefore necessary to both compile and curate the A&M data we do have and thence identify missing data needed by the plasma community (and other user communities). Such data may then be acquired using a mixture of benchmarking experiments and theoretical formalisms. However, equally important is the need for the scientific/technological community to recognize the need to support the value of such databases and the underlying fundamental A&M that populates them. This must be conveyed to funders who are currently attracted to more apparent high-profile projects.

• 천문학회지
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• 제36권2호
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• pp.67-72
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• 2003

The basic objective of helioseismology is to determine the structure and the dynamics of the Sun by analysing the frequency spectrum of the solar oscillations. Accurate frequency measurements provide information that enables us to probe the solar interior structure and the dynamics. Therefore the frequency of the solar oscillation is the most fundamental and important information to be extracted from the solar oscillation observation. This is why many efforts have been put into the development of accurate data analysis techniques, as well as observational efforts. To test one's data analysis method, a realistic artificial data set is essential because the newly suggested method is calibrated with a set of artificial data with predetermined parameters. Therefore, unless test data sets reflect the real solar oscillation data correctly, such a calibration is likely incomplete and a unwanted systematic bias may result in. Unfortunately, however, commonly used artificial data generation algorithms insufficiently accommodate physical properties of the stochastic excitation mechanism. One of reason for this is that it is computaionally very expensive to solve the governing equation directly. In this paper we discuss the nature of solar oscillation excitation and suggest an efficient algorithm to generate the artificial solar oscillation data. We also briefly discuss how the results of this work can be applied in the future studies.

• 한국산업융합학회 논문집
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• 제27권1호
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• pp.151-160
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• 2024

Artificial Neural Networks that enabled Artificial Intelligence are being used in many fields. However, the application to mechanical structures has several problems and research is incomplete. One of the problems is that it is difficult to secure a large amount of data necessary for learning Artificial Neural Networks. In particular, it is important to detect and recognize external forces and forces for safety working and accident prevention of mechanical structures. This study examined the possibility by applying the Current Neural Network of Artificial Neural Networks to detect and recognize the load on the machine. Tens of thousands of data are required for general learning of Recurrent Neural Networks, and to secure large amounts of data, this paper derives load data from ANSYS structural analysis results and applies a stacked auto-encoder technique to secure the amount of data that can be learned. The usefulness of Stacked Auto-Encoder data was examined by comparing Stacked Auto-Encoder data and ANSYS data. In addition, in order to improve the accuracy of detection and recognition of load data with a Recurrent Neural Network, the optimal conditions are proposed by investigating the effects of related functions.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.63-72
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• 2021

Mobile crowd-sensing (MCS) is a promising sensing paradigm that leverages mobile users with smart devices to perform large-scale sensing tasks in order to provide services to specific applications in various domains. However, MCS sensing tasks may not always be successfully completed or timely completed for various reasons, such as accidentally leaving the tasks incomplete by the users, asynchronous transmission, or connection errors. This results in missing sensing data at specific locations and times, which can degrade the performance of the applications and lead to serious casualties. Therefore, in this paper, we propose a missing data inference approach, called missing data approximation with probabilistic tensor factorization (MDI-PTF), to approximate the missing values as closely as possible to the actual values while taking asynchronous data transmission time and different sensing locations of the mobile users into account. The proposed method first normalizes the data to limit the range of the possible values. Next, a probabilistic model of tensor factorization is formulated, and finally, the data are approximated using the gradient descent method. The performance of the proposed algorithm is verified by conducting simulations under various situations using different datasets.

• 한국보건간호학회지
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• 제6권1호
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• pp.32-45
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• 1992

An important role of community health nurses in health centers is to solve community health problems found through data collection methodology which has been used to identify the health needs of the community, diagnose the health problems and to plan health programs suitable for the health problems. Also community health nurses must be prepared to know the community health needs and to participate in the planning process. Since 1956 when the health center law was established, community health nurses have really implemented only the services which the government has asked them to do. This has kept them busy enough. But these days as society is in rapid change, community health nurses should have the flexibility to deal with the social change and demands that are unique to their community each which has different health needs and demands. So community health nurses need to identify what community health problems exist in their particular communities. The purposes of this study were as follows. 1) To explore the suitability of the health programs which the government has asked the community health nurses to do for their own communities and if these programs are not suitable, to explore the reasons why. 2) To explore the degree to which the community health nurses have the ability to identify health problems in their own communities and activate the community diagnostic process. 3) To identify the degree that the community health nurses have the ability to implement plans related to community diagnosis. 4) To find out how much data related to community health problems, the community health nurses have and how they are utilizing it. 5) To measure the community health nurses self-confidence concerning diagnostic activities for community health. The study subjects were 454 Community Health Nurses working in Health Centers in Seoul, Korea. The period of data collection was 6 days(Nov. 9th 1991-Nov. 15th 1991). A questionnaire used for data collection was composed of three different items; general characteristics, community health diagnostic activities and self-confidence in performing diagnostic activities. The results of the study are as follows. First, over one third of the respondents replied that the government required activities for their communities are not appropriate. Of these activities the most frequent reply $(51.2\%)$ indicated that many of the activities in the community were inappropriate to the actual situation. Further, $25\%$ of the replies indicated that many activities were only administratively oriented and as such not appropriate. Second, $49.8\%$ of the respondents replied that they had done general assessments and had a general idea of the health problems of their community. Effective solutions to health problems could be found with an increase in health personnel and management ability according to $41.5\%$ of the respondents. Third, to the question as to whether they had ever independently implemented a plan towards solving community diagnosed problems, $52\%$ of nurses replied 'never', $40\%$ 'occasionally' but only $7.5\%$ replied that they did it frequently. Actually there was very little done even in the basic work of collecting the necessary data. Fourth, when asked how much of basic information they had collected that might be used in community diagnosis activity, of 26 items in 5 areas, there was hardly one for which complete data had been collected. Fifteen percent did have data on the geographical aspects of their area, housing distribution and types of housing, while $17.8\%$ knew the frequency with which the health center was used. Concerning community resources, even with a list of community resources, only $12.3\%$ had data on any of these resources, and this data was incomplete. Further, information about social work institutions, and facilities was also incomplete, only $14.2\%$ of the respondents had any data and even it was incomplete; that is, in general, the nurses did not have this information. Fifth, concerning the confidence of the community health nurse in their ability to carry out community diagnoses activities, $60\%$ replied that they were very or at least nominally confident, indicating that although they were not doing community diagnostic activities they felt they could do so, as they were carrying out home visits and program planning as part of their official duties. The following recommendations are made based on the results of this study. First; since the community health nurses have a high perception of the need for community diagnostic activities and. high confidence in their ability to carry out this activity and high percentage of respondents replied that with a little training they could do this even better it is recommended that community diagnostic activity training be included in the continuing education program for community health nurses. Second, in order for the Community Health Nurses to successfully solve the health problems of their respective community they reported to a need to increase the number of health personnel, improve the facilities and the system of managing their work. Considering this, it is recommended that ways be sought to remedy these deficits.