• Journal of Information Processing Systems
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• 제11권1호
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• pp.39-56
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• 2015

Continuous multi-interval prediction (CMIP) is used to continuously predict the trend of a data stream based on various intervals simultaneously. The continuous integrated hierarchical temporal memory (CIHTM) network performs well in CMIP. However, it is not suitable for CMIP in real-time mode, especially when the number of prediction intervals is increased. In this paper, we propose a real-time integrated hierarchical temporal memory (RIHTM) network by introducing a new type of node, which is called a Zeta1FirstSpecializedQueueNode (ZFSQNode), for the real-time continuous multi-interval prediction (RCMIP) of data streams. The ZFSQNode is constructed by using a specialized circular queue (sQUEUE) together with the modules of original hierarchical temporal memory (HTM) nodes. By using a simple structure and the easy operation characteristics of the sQUEUE, entire prediction operations are integrated in the ZFSQNode. In particular, we employed only one ZFSQNode in each level of the RIHTM network during the prediction stage to generate different intervals of prediction results. The RIHTM network efficiently reduces the response time. Our performance evaluation showed that the RIHTM was satisfied to continuously predict the trend of data streams with multi-intervals in the real-time mode.

• Journal of the Korean Data and Information Science Society
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• 제11권1호
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• pp.127-137
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• 2000

In this paper, we consider hierarchical Bayesian analysis for P(Y < X) using Gibbs sampler, where X and Y are independent normal distributions with unknown means and variances, respectively. Also numerical study using real data is provided.

• Journal of the Korean Data and Information Science Society
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• 제15권4호
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• pp.1033-1043
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• 2004

We consider the simultaneous Bayesian estimation for the normal means based on different noninformative type hyperpriors in hierarchical model. We provide numerical example using the famous baseball data in Efron and Morris (1975) for illustration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권11호
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• pp.4189-4202
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• 2014

In this paper, we propose a priority-based unequal error protection scheme of data partitioned H.264/AVC video with hierarchical quadrature amplitude modulation. In order to map data with higher priority onto the most significant bits of QAM constellation points, a priority sorting method categorizes different data partitions according to the unequal importance factor of encoded video data in one group of pictures by evaluated the average distortion. Then we propose a hierarchical quadrature amplitude modulation arrangement with adaptive constellation distances, which takes into account the unequal importance of encoded video data and the channel status. Simulation results show that the proposed scheme improves the received video quality by about 2 dB in PSNR comparing with the state-of-the-art unequal error protection scheme, and outperforms EEP scheme by up to 5 dB when the average channel SNR is low.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2000년도 추계학술대회논문집
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• pp.25.2-31
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• 2000

Recently, Fuzzy theory has been applied in evaluation problem. Fuzzy evaluation based on Fuzzy theory can accommodate fuzziness of judgement with people through introducing Fuzzy measure. Representative Fuzzy evaluation is Fuzzy Integral using Fuzzy measure. Definite methodology using Fuzzy Integral HFI(Hierarchical Fuzzy Integrals), HFEA(Hierarchical Fuzzy Evaluation Algorithm), HFP(Hierarchical Fuzzy Process), etc. In this paper, we deal with problem identifying evaluation value using Fuzzy Relation Equation at these Fuzzy evaluation. We verify relation between Input data and Output data through @-operation and apply this to HFP. And that we verify evaluation value which objects of evaluation are able to possess.

• 한국군사과학기술학회지
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• 제13권1호
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• pp.41-49
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• 2010

In this paper, we propose a hierarchical RAM simulation model framework which are used to analyze the RAM specifications on the concept refinement phase. The hierarchical RAM simulation model framework consists of RAM simulation models, class library and each model's input and output data lists. The hierarchical RAM simulation models are co-operated with 3 kinds of model - type I, II, III. Type I, II models are used to analyze the target operational availability and Type III is used to establish the initial RAM specifications. Each model's input and output data lists are defined by considering each model's purpose of RAM analysis. The class library is arranged with each model's classes for implementing the hierarchical simulation models. The proposed framework may be applied for executing the RAM activities effectively.

• 한국멀티미디어학회논문지
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• 제24권4호
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• pp.550-557
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• 2021

Image steganalysis is a technique for detecting images with steganographic algorithms applied, called stego images. With state-of-the-art CNN-based steganalysis methods, we can detect stego images with high accuracy, but it is not possible to know which steganographic algorithm is used. Identifying stego images is essential for extracting embedded data. In this paper, as the first step for extracting data from stego images, we propose a hierarchical CNN structure for senary classification of steganographic algorithms. The hierarchical CNN structure consists of multiple CNN networks which are trained to classify each steganographic algorithm and performs binary or ternary classification. Thus, it classifies multiple steganogrphic algorithms hierarchically and stepwise, rather than classifying them at the same time. In experiments of comparing with several conventional methods, including those of classifying multiple steganographic algorithms at the same time, it is verified that using the hierarchical CNN structure can greatly improve the classification accuracy.

• 디지털콘텐츠학회 논문지
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• 제16권1호
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• pp.33-41
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• 2015

비트맵 인덱스는 낮은 카디널리티를 갖는 컬럼에 대한 OLAP 질의의 수행 속도에 있어서 매우 우수한 성능을 보이고 있기 때문에, 데이터 웨어하우스에서 많이 사용하고 있는 인덱스 기법 중에 하나이다. 일반적으로 데이터 웨어하우스에 기반을 둔 많은 응용 프로그램들은 컬럼 값들이 계층 구조를 형성하는 경우가 많이 있다. 만일, 컬럼 값들이 계층적으로 표현될 수 있는 경우 일반적인 비트맵 인덱스 보다 계층적 비트맵 인덱스를 이용하는 것이 질의 처리 수행 속도에 있어서 더 높은 성능을 보인다고 알려지고 있다. 그러나 계층적 비트맵 인덱스의 경우 사용하는 계층 구조의 크기가 큰 경우 저장 공간 오버헤드가 발생할 수 있다는 문제점을 가지고 있다. 그러므로 본 논문에서는 저장 공간이 제약된 환경에서 컬럼 값들이 거대 계층 구조를 형성하고 있을 때, 질의 워크로드에 기반하여 계층적 비트맵 인덱스를 효과적으로 생성하기 위한 방법을 제안한다. 특히, 본 논문에서는 주어진 계층 구조를 두 개의 배타적 역영으로 나누는 Cut 선택 방법 제안함으로써, 계층적 비트맵 인덱스의 저장 공간 오버헤드 문제를 해결한다.

• Journal of the Korean Data and Information Science Society
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• 제27권4호
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• pp.1083-1090
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• 2016

In clinical studies, different types of outcomes (e.g. repeated measures data and time-to-event data) for the same subject tend to be observed, and these data can be correlated. For example, a response variable of interest can be measured repeatedly over time on the same subject and at the same time, an event time representing a terminating event is also obtained. Joint modelling using a shared random effect is useful for analyzing these data. Inferences based on marginal likelihood may involve the evaluation of analytically intractable integrations over the random-effect distributions. In this paper we propose a joint HGLM approach for analyzing such outcomes using the HGLM (hierarchical generalized linear model) method based on h-likelihood (i.e. hierarchical likelihood), which avoids these integration itself. The proposed method has been demonstrated using various numerical studies.

• 전기전자학회논문지
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• 제18권2호
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• pp.248-254
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• 2014

계층 회의는 회의에 참가한 단말기 간 교환되는 데이터가 계층 구조로 전달되기 때문에 정보 지연이 발생되게 된다. 본 논문에서, 단말기 사이의 평균 경로 거리를 최소화하고 단말기의 컴퓨터 자원을 고려하여 비점진적 계층 회의를 구성하는 새로운 방법을 제안하고자 한다. 참가를 원하는 단말기들을 현재 구성중인 계층 회의에 포함 시키고자 할 때, 제안한 알고리즘은 회의 내에서 컴퓨터 자원을 고려하여 참가한 단말기들을 가운데 연결 가능한 단말기들을 선택한다. 그 다음, 참가를 원하는 단말기들과 선택 단말기들 간의 거리를 계산하고 거리가 최소가 되는 단말기 쌍을 선택한 뒤, 이 단말기 쌍을 연결하여 계층 회의를 확장한다. 이러한 방법은 모든 단말기 들이 회의에 포함될 때까지 반복된다. 제안한 방법을 이용하여 모의실험 한 결과 비점진적 계층 회의 방법이 단말기 간의 평균 경로 거리 관점에서 점진적 계층회의 방법보다 24% 효율적으로 구성될 수 있음을 알 수 있다.