• Journal of Korean Library and Information Science Society
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• v.35 no.1
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• pp.303-317
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• 2004

The purpose of this study was to develop a framework for predicting searching performance through an understanding of how cognitive ability relates to searching process and outcome. Specifically, this study examined the relationship between spatial visualization, logical reasoning, integrative reasoning, and information searching process and outcome. Information searching process was assessed by seven search process indicators: (1) search command selection: (2) combination of search commands; (3) application of Boolean logic: (4) application of truncation; (5) use of limit search function; (6) number of search statements; and (7) number of search errors made. Searching outcome was assessed by the number of correct answers to search questions. Subjects first took three standardized cognitive tests that measured cognitive abilities, and performed online catalog searching in response to seven information search questions. The searches were logged using Lotus ScreenCam, and reviewed for the analysis. Factor analysis was used to find underlying structures of the seven search process variables. Multiple regression analysis was applied to examine the predictive power of three cognitive variables on three extracted factors, and search outcome. Results of the data analysis showed that individual differences in logical reasoning could predict information searching process and outcome.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.266-286
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• 2022

Public key encryption with keyword search (PEKS) allows a user to make search on ciphertexts without disclosing the information of encrypted messages and keywords. In practice, cryptographic operations often occur on insecure devices or mobile devices. But, these devices face the risk of being lost or stolen. Therefore, the secret keys stored on these devices are likely to be exposed. To handle the key exposure problem in PEKS, the notion of key-updatable PEKS (KU-PEKS) was proposed recently. In KU-PEKS, the users' keys can be updated as the system runs. Nevertheless, the existing KU-PEKS framework has some weaknesses. Firstly, it can't update the keyword ciphertexts on the storage server without leaking keyword information. Secondly, it needs to send the search tokens to the storage server by secure channels. Thirdly, it does not consider the search token security. In this work, a new PEKS framework named key-updatable and ciphertext-sharable PEKS (KU-CS-PEKS) is devised. This novel framework effectively overcomes the weaknesses in KU-PEKS and has the ciphertext sharing function which is not supported by KU-PEKS. The security notions for KU-CS-PEKS are formally defined and then a concrete KU-CS-PEKS scheme is proposed. The security proofs demonstrate that the KU-CS-PEKS scheme guarantees both the keyword ciphertext privacy and the search token privacy. The experimental results and comparisons bear out that the proposed scheme is practicable.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.461-466
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• 2006

In this paper, an algorithm with a new Armijo-type line search is proposed that ensure global convergence of a correlative Polak-Ribiere conjugate method for the unconstrained minimization of non-convex differentiable function.

• Journal of the Korean Society for Library and Information Science
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• v.54 no.2
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• pp.399-417
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• 2020

In this study, we tried to analyze the relative importance of features for various functions provided by Discovery. We identified a total of 36 assessment items in five categories including contents, search function, serendipity, interactivity, and ease of use based on literature review. In order to objectively evaluate the relative importance of each evaluation element, an AHP technique was adopted. As a result, 'Easy of use' received the highest weighted value among the five categories, followed by 'contents', 'search function', 'interactivity', and 'serendipity'. In addition, among all the 36 assessment items, 'Quality of data for central index' had higher weighted value. These findings can be used as basic data to adopt a discovery tool for libraries.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.3
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• pp.11-23
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• 1993

A basic problem of sequencing a set of independent tasks at a single facility with the objective of minimizing total tardiness is considered. A variation of beam search, called knowledge-based beam search, has been studied which uses domain knowledge to reduce the problem size with an evaluation function to generate nodes probable to include the optimal solution. Its performance behavior is compared with some existing algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.379-382
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• 1996

Evolution Strategy is used as an effective search algorithm in optimization problems and Sliding Mode Control is well known as a robust control algorithm. In this paper, we propose a Sliding Mode Control Method for robot manipulator using Evolution Strategy. Evolution Strategy is used to estimate Sliding Mode Control Parameters such as sliding surface gradient, continuous function boundary layer, unknown plant parameters and switching gain. Experimental results show the proposed control scheme has accurate and robust performances with effective search ability.

• East Asian mathematical journal
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• v.35 no.5
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• pp.597-612
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• 2019

In this study, we propose a new logarithmic barrier approach to solve linear programming problem using the projective method of Karmarkar. We are interested in computation of the direction by Newton's method and of the step-size using minorant functions instead of line search methods in order to reduce the computation cost. Our new approach is even more beneficial than classical line search methods. We reinforce our purpose by many interesting numerical simulations proved the effectiveness of the algorithm developed in this work.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.331-338
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• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.4
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• pp.76-85
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• 1998

Recently, Genetic Algorithm(GA) is widely adopted into a search procedure for structural optimization, which is a stochastic direct search strategy that mimics the process of genetic evolution. This methods consist of three genetics operations maned selection, crossover and mutation. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA, being zero-order method, is very simple. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher probability of converge to global optimum compared to traditional techniques which take one-point search method. In this study, a method of finding the optimum values of suspension parameters is proposed by using the GA. And vehicle is modelled as planar vehicle having 5 degree-of-freedom. The generalized coordinates are vertical motion of passenger seat, sprung mass and front and rear unsprung mass and rotate(pitch) motion of sprung mass. For rapid converge and precluding local optimum, share function which distribute chromosomes over design bound is introduced. Elitist survival model, remainder stochastic sampling without replacement method, multi-point crossover method are adopted. In the sight of the improvement of ride comfort, good result can be obtained in 5-D.O.F. vehicle model by using GA.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.234-240
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• 1999

This paper presents a new approach for large-scale generator maintenance scheduling optimizations. The generator preventive maintenance scheduling problems are typical discrete dynamic n-dimensional vector optimization ones with several inequality constraints. The considered objective function to be minimized a subset of{{{{ { R}^{n } }}}} space is the variance (i.g., second-order momentum) of operating reserve margin to levelize risk or reliability during a year. By its nature of the objective function, the optimal solution can only be obtained by enumerating all combinatorial states of each variable, a task which leads to computational explosion in real-world maintenance scheduling problems. This paper proposes a new priority search mechanism based on each generator's discrete sensitivity value which was analytically developed in this study. Unlike the conventional capacity-based priority search, it can prevent the local optimal trap to some extents since it changes dynamically the search tree in each iteration. The proposed method have been applied to two test systems (i.g., one is a sample system with 10 generators and the other is a real-world lage scale power system with 280 generators), and the results anre compared with those of the conventional capacith-based search method and combinatorial optimization method to show the efficiency and effectiveness of the algorithm.