• 제목/요약/키워드: Hybrid target

검색결과 338건 처리시간 0.025초

Bayesian Estimation of State-Space Model Using the Hybrid Monte Carlo within Gibbs Sampler

  • Park, Ilsu
    • Communications for Statistical Applications and Methods
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    • 제10권1호
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    • pp.203-210
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    • 2003
  • In a standard Metropolis-type Monte Carlo simulation, the proposal distribution cannot be easily adapted to "local dynamics" of the target distribution. To overcome some of these difficulties, Duane et al. (1987) introduced the method of hybrid Monte Carlo(HMC) which combines the basic idea of molecular dynamics and the Metropolis acceptance-rejection rule to produce Monte Carlo samples from a given target distribution. In this paper, using the HMC within Gibbs sampler, an asymptotical estimate of the smoothing mean and a general solution to state space modeling in Bayesian framework is obtaineds obtained.

Two-Step Filtering Datamining Method Integrating Case-Based Reasoning and Rule Induction

  • Park, Yoon-Joo;Chol, En-Mi;Park, Soo-Hyun
    • 한국지능정보시스템학회:학술대회논문집
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    • 한국지능정보시스템학회 2007년도 한국지능정보시스템학회
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    • pp.329-337
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    • 2007
  • Case-based reasoning (CBR) methods are applied to various target problems on the supposition that previous cases are sufficiently similar to current target problems, and the results of previous similar cases support the same result consistently. However, these assumptions are not applicable for some target cases. There are some target cases that have no sufficiently similar cases, or if they have, the results of these previous cases are inconsistent. That is, the appropriateness of CBR is different for each target case, even though they are problems in the same domain. Thus, applying CBR to whole datasets in a domain is not reasonable. This paper presents a new hybrid datamining technique called two-step filtering CBR and Rule Induction (TSFCR), which dynamically selects either CBR or RI for each target case, taking into consideration similarities and consistencies of previous cases. We apply this method to three medical diagnosis datasets and one credit analysis dataset in order to demonstrate that TSFCR outperforms the genuine CBR and RI.

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HDEVS 형식론에 기반한 통합 하이브리드 모델링 방법론 및 시뮬레이션 엔진 설계 (Integrated Hybrid Modeling Methodology and Simulation Engine Design Based on HDEVS Formalism)

  • 권세중;성창호;송해상;김탁곤
    • 한국시뮬레이션학회논문지
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    • 제22권1호
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    • pp.21-30
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    • 2013
  • 하이브리드 시스템은 서로 다른 상태와 시간을 가지는 부 시스템의 조합으로 이루어진다. 대표적인 예가 이산 사건 시스템과 연속 시간 시스템의 조합이다. HDEVS 형식론은 이러한 하이브리드 시스템을 모델링하고 분석하기 위해 제안되었는데, 이러한 형식론을 통해 모델러는 수학적인 형식론에 기초한 계층적이고 모듈성이 있는 모델을 설계할 수 있었다. 그러나 HDEVS 형식론이 주로 분산된 연동 시뮬레이션에 적용되었기 때문에 모델러는 하이브리드 시스템을 연동에 참여할 시뮬레이터에 맞게 서로 다른 모델들로 구분하여 재구성해야 했다. 따라서 모델은 시스템을 그대로 표현하기보다 나누어진 모델들의 연동 구조로 표현되었다. 본 논문은 이러한 문제를 해결하고 통합된 하이브리드 모델을 만들 수 있는 모델링 방법론과 그에 대한 시뮬레이션 방법론을 제안한다. 기존에 연동형 구조에 적용되었던 것과 달리, 하이브리드 시스템은 그 시스템 본래의 형태 그대로 통합된 모델로 모델링 될 수 있다. 또한 이 논문은 제안하는 모델링 방법론에 따르는 시뮬레이션 엔진 구조에 대해서 논하고 물탱크 조절 예제를 통한 간단한 사례 연구도 포함한다.

A New Item Recommendation Procedure Using Preference Boundary

  • Kim, Hyea-Kyeong;Jang, Moon-Kyoung;Kim, Jae-Kyeong;Cho, Yoon-Ho
    • Asia pacific journal of information systems
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    • 제20권1호
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    • pp.81-99
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    • 2010
  • Lately, in consumers' markets the number of new items is rapidly increasing at an overwhelming rate while consumers have limited access to information about those new products in making a sensible, well-informed purchase. Therefore, item providers and customers need a system which recommends right items to right customers. Also, whenever new items are released, for instance, the recommender system specializing in new items can help item providers locate and identify potential customers. Currently, new items are being added to an existing system without being specially noted to consumers, making it difficult for consumers to identify and evaluate new products introduced in the markets. Most of previous approaches for recommender systems have to rely on the usage history of customers. For new items, this content-based (CB) approach is simply not available for the system to recommend those new items to potential consumers. Although collaborative filtering (CF) approach is not directly applicable to solve the new item problem, it would be a good idea to use the basic principle of CF which identifies similar customers, i,e. neighbors, and recommend items to those customers who have liked the similar items in the past. This research aims to suggest a hybrid recommendation procedure based on the preference boundary of target customer. We suggest the hybrid recommendation procedure using the preference boundary in the feature space for recommending new items only. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. Customers' preferences and characteristics of items including new items are represented in a feature space, and the scope or boundary of the target customer's preference is extended to those of neighbors'. The new item recommendation procedure consists of three steps. The first step is analyzing the profile of items, which are represented as k-dimensional feature values. The second step is to determine the representative point of the target customer's preference boundary, the centroid, based on a personal information set. To determine the centroid of preference boundary of a target customer, three algorithms are developed in this research: one is using the centroid of a target customer only (TC), the other is using centroid of a (dummy) big target customer that is composed of a target customer and his/her neighbors (BC), and another is using centroids of a target customer and his/her neighbors (NC). The third step is to determine the range of the preference boundary, the radius. The suggested algorithm Is using the average distance (AD) between the centroid and all purchased items. We test whether the CF-based approach to determine the centroid of the preference boundary improves the recommendation quality or not. For this purpose, we develop two hybrid algorithms, BC and NC, which use neighbors when deciding centroid of the preference boundary. To test the validity of hybrid algorithms, BC and NC, we developed CB-algorithm, TC, which uses target customers only. We measured effectiveness scores of suggested algorithms and compared them through a series of experiments with a set of real mobile image transaction data. We spilt the period between 1st June 2004 and 31st July and the period between 1st August and 31st August 2004 as a training set and a test set, respectively. The training set Is used to make the preference boundary, and the test set is used to evaluate the performance of the suggested hybrid recommendation procedure. The main aim of this research Is to compare the hybrid recommendation algorithm with the CB algorithm. To evaluate the performance of each algorithm, we compare the purchased new item list in test period with the recommended item list which is recommended by suggested algorithms. So we employ the evaluation metric to hit the ratio for evaluating our algorithms. The hit ratio is defined as the ratio of the hit set size to the recommended set size. The hit set size means the number of success of recommendations in our experiment, and the test set size means the number of purchased items during the test period. Experimental test result shows the hit ratio of BC and NC is bigger than that of TC. This means using neighbors Is more effective to recommend new items. That is hybrid algorithm using CF is more effective when recommending to consumers new items than the algorithm using only CB. The reason of the smaller hit ratio of BC than that of NC is that BC is defined as a dummy or virtual customer who purchased all items of target customers' and neighbors'. That is centroid of BC often shifts from that of TC, so it tends to reflect skewed characters of target customer. So the recommendation algorithm using NC shows the best hit ratio, because NC has sufficient information about target customers and their neighbors without damaging the information about the target customers.

UWB 시스템에서의 MHP 펄스를 이용한 ToA와 TDoA의 Hybrid 방식 (The Hybrid Method of ToA and TDoA Using MHP Pulse in UWB System)

  • 황대근;황재호;김재명
    • 한국ITS학회 논문지
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    • 제10권1호
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    • pp.49-59
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    • 2011
  • 현재 목표기기(target node)의 위치를 측정하는 기술 중에 가장 정확도가 높다고 평가되는 방법은 ToA(Time of Arrival) 거리 측정(Ranging) 기술과 TDoA(Time Difference of Arrival) 거리 측정 기술을 이용한 위치 측정 방법이다. ToA와 TDoA는 시간을 기반으로 하는 거리 측정 기술이기 때문에 여러 개의 참조기기(Reference node)와 목표기기 사이의 시간 동기화와 오프셋이 중요시 된다. 참조기기와 목표기기 사이의 시간 동기화가 정확하게 이루어지지 않거나 참조기기 간 시간 오프셋이 발생할 경우 정확한 시점에서 신호를 검출할 수 없게 되어 거리오차가 발생하게 되고, 이러한 거리오차를 일반적인 위치 측정 알고리즘에 적용하게 되면 목표 기기의 정확한 위치를 측정할 수 없다. 따라서 본 논문에서는 참조기기와 목표기기 사이에 시간 동기화가 맞지 않을 경우와 참조기기와 참조기기 사이의 시간 오프셋이 발생할 경우에 위치 측정의 오차를 줄이는 ToA와 TDoA의 Hybrid 방식을 제안한다. 각각의 펄스가 직교성을 갖는 특징을 지닌 MHP(Modified Hermite Polynomial) 펄스를 이용하여 참조기기들이 각기 다른 MHP 펄스를 송수신하도록 하고 이를 통해 한 번의 MHP 펄스 송수신만으로 TDoA와 ToA 두 가지 방법을 모두 이용하여 각각의 거리를 측정하고 위치 계산을 할 수 있도록 한다. Hybrid 방식은 TDoA와 ToA 방법을 이용한 거리 측정을 반복적인 계산을 통해 실제 거리 오차가 적은 방법을 선택하여 목표기기의 위치를 좀 더 정확하게 측정할 수 있음을 시뮬레이션을 통해 보였다.

데이터 마이닝을 위한 경쟁학습모텔과 BP알고리즘을 결합한 하이브리드형 신경망 (A Neural Network Combining a Competition Learning Model and BP ALgorithm for Data Mining)

  • 강문식;이상용
    • Journal of Information Technology Applications and Management
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    • 제9권2호
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    • pp.1-16
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    • 2002
  • Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

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공격편대군-표적 최적 할당을 위한 수리모형 및 병렬 하이브리드 유전자 알고리즘 (New Mathematical Model and Parallel Hybrid Genetic Algorithm for the Optimal Assignment of Strike packages to Targets)

  • 김흥섭;조용남
    • 한국군사과학기술학회지
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    • 제20권4호
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    • pp.566-578
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    • 2017
  • For optimizing the operation plan when strike packages attack multiple targets, this article suggests a new mathematical model and a parallel hybrid genetic algorithm (PHGA) as a solution methodology. In the model, a package can assault multiple targets on a sortie and permitted the use of mixed munitions for a target. Furthermore, because the survival probability of a package depends on a flight route, it is formulated as a mixed integer programming which is synthesized the models for vehicle routing and weapon-target assignment. The hybrid strategy of the solution method (PHGA) is also implemented by the separation of functions of a GA and an exact solution method using ILOG CPLEX. The GA searches the flight routes of packages, and CPLEX assigns the munitions of a package to the targets on its way. The parallelism enhances the likelihood seeking the optimal solution via the collaboration among the HGAs.

광 BJTC와 신경회로망을 이용한 광-신경망 다중 표적 추적 시스템 (Optoneural Multitarget Tracking System Based on Optical BJTC and Neural Networks)

  • 이상이;류충상;김승현;김은수
    • 전자공학회논문지A
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    • 제31A권3호
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    • pp.1-9
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    • 1994
  • In this paper as a new approach for real-time multitarget tracking, a hybrid OptoNeural multitarget tracking system based on optical BJTC and neural networks data association algorithm is suggested. In the proposed hybrid tracking system, an optical BJTC is introduced as a preprocessor to reduce the massive input target data into a few correlation peak signals and then the neural networks data association algorithm is used for the massively parallel data association between measurement signals and targets in real-time. Finally, new hybrid type OptoNeural target tracking system is constructed and then some experimental results on multitarget tracking is included. The real-time implementation method of the proposed hybrid system is also discussed.

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Mixed Agreement with a Hybrid Pronoun in Latvian

  • Hahm, Hyun-Jong
    • 한국언어정보학회지:언어와정보
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    • 제14권2호
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    • pp.85-101
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    • 2010
  • This paper discusses mixed agreement triggered by hybrid pronouns. Hybrid pronouns considered in this paper show number discrepancy in that they are plural in form but singular in meaning. When predicates agree with these hybrid pronouns, the puzzle of number agreement arises: finite verbs show syntactic agreement, while predicate adjectives show semantic agreement. This is explained by three factors in grammar of agreement, the feature specification of agreement controllers, the types of agreement targets, and the Agreement Marking Principle that mediates the relation of two poles of agreement, controllers and targets.

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