• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.625-629
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• 2001

A cyber shopping mall is a place where consumers acquire product information, and make purchase decisions in the cyber space. Even though it offers many advantages over traditional malls, there are still several limitations to do shopping in an existing cyber mall. One of these limitations is the absence of an efficient shopping aid to compare multiple items from multiple malls. Existing search systems usually support a keyword search with limited conditions. Consumers spend lots of their time to compare multiple alternatives from search results. In this paper, we propose an intelligent product search system. There are two main features in our system. The first one is a full support of comparison shopping with multiple perspectives based on commercial search engines. The second one is an enhancement to the shopping aid based on a new concept of Shopping AssistanT. Our system is implemented in Visual Basic and PERL, and experimental results show a satisfactory performance.

• ETRI Journal
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• v.39 no.6
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• pp.782-793
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• 2017

This paper considers the use of the Partial Transmit Sequence (PTS) technique to reduce the Peak-to-Average Power Ratio (PAPR) of an Orthogonal Frequency Division Multiplexing signal in wireless communication systems. Search complexity is very high in the traditional PTS scheme because it involves an extensive random search over all combinations of allowed phase vectors, and it increases exponentially with the number of phase vectors. In this paper, a suboptimal metaheuristic algorithm for phase optimization based on an improved harmony search (IHS) is applied to explore the optimal combination of phase vectors that provides improved performance compared with existing evolutionary algorithms such as the harmony search algorithm and firefly algorithm. IHS enhances the accuracy and convergence rate of the conventional algorithms with very few parameters to adjust. Simulation results show that an improved harmony search-based PTS algorithm can achieve a significant reduction in PAPR using a simple network structure compared with conventional algorithms.

• Journal of Ocean Engineering and Technology
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• v.15 no.4
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• pp.101-107
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• 2001

This paper describes a fault diagnosis method by a T-invariance of Petri Net (PN). First, a complicated fault system with some failure is modeled into a PN graphic expressions. Next, the PN model is analyzed by using the backward chaining of T-invariance to find out causes of the faults. In this step, an inter-node search technique which is suggested in this paper is applied for reducing searching area in the fault system. Also, a novel idea to compose incidence matrices which have different dimension each other in PN model is proposed. As the new knowledges which is discovered newly about faults can be added easily to conventional systems, the diagnosis system will be very flexible. Finally, the proposed method is applied to the automobile fault diagnosis system to confirm the validity of the method.

• The Korean Journal of Food & Health Convergence
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• v.10 no.2
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• pp.27-36
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• 2024

This study aims to investigate whether information awareness, search purpose, and search expectations influence information avoidance among university students. The data were collected by using a self-completion questionnaire with convenience sampling of students from one university. The collected data were analyzed by descriptive statistics, t-test, analysis of variance (ANOVA), Pearson's correlation coefficient, and multiple regression using R 4.2.3. The main results are as follows: First, both search purpose and search expectations exhibited a significant inverse correlation with all information avoidance dependent variables. Second, there was a significant difference in the mean of search expectations across majors, such that science majors had higher search expectations than humanities majors. Third, there were significant differences in the means of the information avoidance-system and information avoidance variables by major, such that both variables had lower means for the science than the humanities group. Fourth, among the independent variables, search expectation had a significant effect on information avoidance-personal: the higher the search expectation variable, the lower the information avoidance-personal variable. This study confirmed that information avoidance should not only consider the psychological, emotional, and affective aspects of information seekers, but also that information seekers' information search purpose and search expectations are predictors of information avoidance.

• Korean Journal of Biological Psychiatry
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• v.25 no.4
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• pp.89-100
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• 2018

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method that delivers 1-2 mA of current to the scalp. Several clinical studies have been conducted to confirm the therapeutic effect of major depressive disorder (MDD) patients with tDCS. Some studies have shown tDCS's antidepressant effect, while the others showed conflicting results in antidepressant effects. Our aim of this review is to understand the biological bases of tDCS's antidepressant effect and review the results of studies on tDCS's antidepressant effect. For the review and search process of MDD treatment using tDCS, the US National Library of Medicine search engine PubMed was used. In this review, we discuss the biological mechanism of tDCS's antidepressant effect and the existing published literature including meta-analysis, systematic review, control trial, open studies, and case reports of antidepressant effects and cognitive function improvement in patients with MDD are reviewed. We also discuss the appropriate tDCS protocol for MDD patients, factors predictive of response to tDCS treatment, the disadvantages of tDCS in MDD treatment, and side effects.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.237-243
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• 2016

Since realtime search keywords of portal sites are arranged in descending order by instant increasing rates of search numbers, they easily show issues increasing in interests for a short time. But they have the limits extracted different results by portal sites and not shown issues by a period. Thus, to find key issues from the whole realtime search keywords for certain period, and to show results of summarizing them and analyzing differences, is significant in providing the basis of understanding issues more practically and in maintaining consistency of them. This paper analyzes differences of week key issues extracted from week analysis of realtime search keywords provided by two typical portal sites. The results of experiments show that the portal group means of realtime search keywords by the independent t-test and the survival functions of realtime search keywords by the survival analysis are statistically significant differences.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.480-483
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• 2008

According to develop the web technology, the data providers are trying to offer the efficient service for customers. Specially it is necessary to improve efficiency of the search function to help user access easily useful information their want. KISTI has introduced and customized the FAST search engine to improve search performance of the national science and technology information portal service system. But the design work for hardware and software implementation of search engine is important above all. In this paper, we discuss about the design and custormizing skill of FAST engine for the KISTI S&T information search service.

• Journal of Construction Engineering and Project Management
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• v.5 no.4
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• pp.7-11
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• 2015

The luffing-tower crane (T/C) is a key facility used in the vertical and horizontal transportation of materials in a tall building construction. Locating the crane in an optimal position is an essential task in the initial stages of construction planning. This paper proposes a new optimization model to locate the luffing T/C in the optimal position to minimize the transportation time. A newly developed mathematical formula is suggested to calculate the transportation time of luffing T/C correctly. An optimization algorithm, the Harmony Search (HS) algorithm, was used and the results show that HS has high performance characteristics to solve the optimization problem in a short period of time. In a case study, the proposed model offered a better position for T/C than the previous heuristic approach.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.05a
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• pp.32-37
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• 2001

This paper describes a noble fault diagnosis method using inter node search technique in PN model. First, a complicated fault system is modeled as PN graphic expressions. Next, to find out sources for faults on which we focus, the PN model is analyzed using the backward chaining of T-invariance. In this step, the technique of inter node search is applied for reducing some range of sources in a fault. Also, colnposing method of incidence matrix in PN is proposed. Then, it makes the diagnosis system to very flelible system because new knowledges about the sources in a fault can be added easily to conventional systems. Finally, the proposed method is applied to the automobile trouble diagnosis system to confirm the validity of the method.

• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.