• Journal of Korean Institute of Industrial Engineers
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• v.20 no.1
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• pp.133-146
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• 1994

Conditional constraints are frequently used to represent relations. To use these conditional constraints, it is necessary to develop an appropriate logic in which these conditional constraints can be represented and manipulated. Nevertheless, there has been little research that addresses interval-based conditional constraints. The proposed approach addresses the use of conditional constraints involving intervals in constraint networks. Two algorithms are presented: (1) a propagation algorithm for an interval-based conditional constraint, which is similar to one for an exact-value conditional constraint; (2) a propagation algorithm for interval-based conditional constraints which satisfy some conditions. The former can be applied to any conditional constraint. However, with the former algorithm, conditional constraints are usually categorized into the cases that they cannot be propagated. After investigating several methods in which most conditional constraints can be propagated, we propose the latter algorithm under certain condition that usually results in smaller resulting design space comparing to the former.

• Journal of Information Technology Services
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• v.6 no.2
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• pp.125-143
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• 2007

IT Services market is growing rapidly in the business industry and SOA-based Web Services have been introduced as an effective vehicle for the integration of enterprise-wide applications within organizations. The number of publicly available Web Services is ever increasing recently in a variety of areas, and as the number of public Web Services increases, there will be many Web Services with the same functionality. These services, however, will vary in their QoS properties, such as price, response time and availability, and it is very important to choose a right service while satisfying given QoS constraints. This paper addresses the issue of selecting composite Web Services which involves conditional branches in business processes. It is essential to have any conditional branches satisfy the global QoS constraints at service selection phase, since the branches are chosen to execute at run-time dynamically. We proposed service selection procedures for basic structure of conditional branches and explained them by examples. Experiments were conducted to analyze the impact of the number of candidate services and service types on the time of finding service solutions.

• Language and Information
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• v.15 no.1
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• pp.13-29
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• 2011

Conditional sentences also can be formed by inversion of subject and auxiliary, but it happens only in a limited environment. This paper addresses grammatical constraints in conditional inversion and how they behave differently from the regular conditional clauses based on corpus investigations. Our corpus search reveals many different types of conditional inversion constructions, indicating the difficulties of deriving inverted conditionals from movement operations. In this paper, we provide a construction-based approach to the inverted conditional construction. The paper shows that the most optimal way of describing the general as well as idiosyncratic properties of the inverted conditional constructions is an account in the spirit of construction grammar in which a grammar is a repertory of constructions forming a network connected by links of inheritance.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.2
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• pp.196-204
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• 1996

This paper presents a new scheduling algorithm, which is the most improtant subtask in the high level synthesis. The proposed algorithm performs scheduling in consideration of resource sharing concept based on characteristics of conditionsla bransches in the intermediate data structure. CDFG (control data flow graph) generated by a VHDL analyzer. This algorithm constructs a conditon graph based on time frame of each operation using both the ASAP and the ALAP scheduling algorithm. The conditon priority is obtained from the condition graph constructed from each conditional brance. The determined condition priority implies the sequential order of transforming the CDFG with conditonal branches into the CDFG without conditional branches. To minimize resource cost, the CDFG with conditional branches are transformed into the CDFG without conditonal brancehs according to the condition priority. Considering the data dependency, the hardware constraints, and the data execution time constraints, each operation in the transformed CDFG is assigned ot control steps. Such assigning of unscheduled operations into contorl steps implies the performance of the scheduling in the consecutive movement of operations. The effectiveness of this algorithm is hsown by the experiment for the benchmark circuits.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.162-176
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• 2007

Machine Learning (ML) has become very popular within Data Mining (KDD) and Artificial Intelligence (AI) research and their applications. In the ML and KDD contexts, two main approaches can be used for inducing a Bayesian Network (BN) from data, namely, Conditional Independence (CI) and the Heuristic Search (HS). When a BN is induced for classification purposes (Bayesian Classifier - BC), it is possible to impose some specific constraints aiming at increasing the computational efficiency. In this paper a new CI based approach to induce BCs from data is proposed and two algorithms are presented. Such approach is based on the Markov Blanket concept in order to impose some constraints and optimize the traditional PC learning algorithm. Experiments performed with the ALARM, as well as other six UCI and three artificial domains revealed that the proposed approach tends to execute fewer comparison tests than the traditional PC. The experiments also show that the proposed algorithms produce competitive classification rates when compared with both, PC and Naive Bayes.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.193-207
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• 2001

When designing a test set, we need to consider constraints on items that are deemed important by item developers or test specialists. The constraints are essentially on the components of the test domain or abilities relevant to a given test set. And so if the test domain could be represented in a more refined form, test construction would be made in a more efficient way. We assume that relationships among task abilities are representable by a causal model and that the item response theory (IRT) is not fully available for them. In such a case we can not apply traditional item selection methods that are based on the IRT. In this paper, we use entropy as an uncertainty measure for making inferences on task abilities and developed an optimal item selection algorithm which reduces most the entropy of task abilities when items are selected from an item pool.

• ETRI Journal
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• v.43 no.3
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• pp.538-548
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• 2021

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.7
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• pp.104-114
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• 1995

In this paper, an intermediate representation HSFG(Hanyang Sequential Flow GRaph) and a new scheduling algorithm for the control-dominated ASIC design is presented. The HSFG represents control flow, data dependency and such constraints as resource constraints and timing constraints. The scheduling algorithm minimizes the total operating time by reducing the number of the constraints as maximal as possible, searching a few paths among all the paths produced by conditional branches. The constraints are substitute by subgraphs, and then the number of subgraphs (that is the number kof the constraints) is minimized by using the inclusion and overlap relation among subgraphs. The proposed algorithm has achieved the better results than the previous ones on the benchmark data.

• Journal of the Korean Statistical Society
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• v.13 no.2
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• pp.87-106
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• 1984

It ${X_t}$ is a sequence of independent identically distributed normal random variables, then the conditional probability density of $X_1, X_2, \cdots, X_n$ given the first p+1 sample autocovariances converges to the maximum entropy probability density satisfying the corresponding covariance constraints as the length of the sample sequence tends to infinity. This establishes that the maximum entropy probability density and the associated Gaussian autoregressive process arise naturally as the answers of conditional limit problems.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.275-284
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• 2023

Gene expression data can be utilized in various studies, including the prediction of disease prognosis. However, there are challenges associated with collecting enough data due to cost constraints. In this paper, we propose a gene expression data generation model based on Conditional Variational Autoencoder. Our results demonstrate that the proposed model generates synthetic data with superior quality compared to two other state-of-the-art models for gene expression data generation, namely the Wasserstein Generative Adversarial Network with Gradient Penalty based model and the structured data generation models CTGAN and TVAE.