• Title/Summary/Keyword: Hybrid target

Search Result 338, Processing Time 0.025 seconds

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

  • Park, Ilsu
    • Communications for Statistical Applications and Methods
    • /
    • v.10 no.1
    • /
    • pp.203-210
    • /
    • 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
    • Proceedings of the Korea Inteligent Information System Society Conference
    • /
    • 2007.05a
    • /
    • pp.329-337
    • /
    • 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.

  • PDF

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

  • Kwon, Se Jung;Sung, Changho;Song, Hae-Sang;Kim, Tag Gon
    • Journal of the Korea Society for Simulation
    • /
    • v.22 no.1
    • /
    • pp.21-30
    • /
    • 2013
  • A hybrid system is a combination of sub systems which have different types of state and time: a typical example is a combination of discrete event and continuous systems. A HDEVS(Hybrid DEVS) formalism was proposed for modeling and analyzing a hybrid system. The HDEVS formalism allows modelers to construct a hierarchical and modular model based on the mathematical set theory. Because the HDEVS formalism was applied to the distributed and interoperated simulators, modelers should make several heterogenous models dividing a target system. Hence, this paper proposes an extended hybrid coupled model of HDEVS formalism and an integrated hybrid modeling methodology in contrast to the existing simulation framework on interoperable simulators. By applying the proposed modeling method, a target system can be translated to a hybrid model in a similar form as the target system. This paper also contains a simulation engine design for the proposed modeling methodlogy and a case study which simulates water tank control systems.

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
    • /
    • v.20 no.1
    • /
    • pp.81-99
    • /
    • 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.

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

  • Hwang, Dae-Geun;Hwang, Jae-Ho;Kim, Jae-Moung
    • The Journal of The Korea Institute of Intelligent Transport Systems
    • /
    • v.10 no.1
    • /
    • pp.49-59
    • /
    • 2011
  • Recently, ToA and TDoA estimation are favorable among all of estimation techniques because they have the best accuracy in estimating position. ToA and TDoA estimation are typical techniques based on time. So, it is important to have the time syncronization and offset between a target node and several reference nodes. If they don't have the time syncronization between a reference node and target node or have a time offset among reference nodes, the positioning error will increase due to the ranging error. The conventional positioning algorithm does not have a accurate device's position because ranging error is added the calc dation of the position. In this paper, we propose a hybrid method of ToA and TDoA ll increase due. We use MHP pulse that has orthogonal pulse instead of the existing pulse to transmit and receive pulses between a target node and reference nodes. We can estimate the target node's position by ToA and TDoA estimation to transmit and receive MHP pulses only once. When the proposed Hybrid method iteratively calculate the distance, we can select the ranging technique to have more accurate position. The simulation results confirm the enhancement of the Hybrid method.

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

  • 강문식;이상용
    • Journal of Information Technology Applications and Management
    • /
    • v.9 no.2
    • /
    • pp.1-16
    • /
    • 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.

  • PDF

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

  • Kim, Heungseob;Cho, Yongnam
    • Journal of the Korea Institute of Military Science and Technology
    • /
    • v.20 no.4
    • /
    • pp.566-578
    • /
    • 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.

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

  • 이상이;류충상;김승현;김은수
    • Journal of the Korean Institute of Telematics and Electronics A
    • /
    • v.31A no.3
    • /
    • pp.1-9
    • /
    • 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.

  • PDF

Mixed Agreement with a Hybrid Pronoun in Latvian

  • Hahm, Hyun-Jong
    • Language and Information
    • /
    • v.14 no.2
    • /
    • pp.85-101
    • /
    • 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.

  • PDF