• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.9-12
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• 2001

Optical aligning process to archive the maximum optical coupling is crucial in many optical device manufacturing line such as laser diode module. Due to the three-dimensional nature of housing module and the aligning process for laser diode coupler, large amount of the manufacturing time, typically ranging from tens of minutes to an hour has to be devoted to the aligning process alone. In this thesis, we propose a new optical aligning process that employee a two-pass algorithm: coarse-to-fine search. Coarse search is a kind of blind search that finds the candidate region where the maximum optical coupling might mostly occur, followed by a fine searching that finds the maximum within the candidate region. The algorithm has been tested on 50 samples of cam-type laser diode modules, and the experimental results are analyzed in terms of aligning time and coupling efficiency.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.1-13
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• 2014

This study examines the relationship between firms' external search strategy and their innovation performance. In addition to revisiting the relationship between open search strategy and product innovation, we further extend the impact of use of external knowledge sources to process and organizational innovation. Using the 2010 Korean Innovation Survey (KIS) of manufacturing firms, we report that on average, venture firms search more widely (external search breadth) and deeply (external search depth) across a variety of external search channels than non-venture firms. We then further explore the impact of venture and non-venture firms' use of external search strategies on innovation performance. We find that both searching widely and deeply increase the likelihood of non-venture firm's successes in product, process and organizational innovation. Similar results can be found for the venture firm's success in organizational innovation. However, only searching deeply increases the likelihood of venture firms' success in product and process innovation.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.4
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• pp.745-750
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• 2009

The goal of this study is to examine on reasoning and search for construction of diagnosis ontology as a knowledge base of diagnosis expert system in oriental medicine. Expert system is a field of artificial intelligence. It is a system to acquire information with diverse reasoning methods after putting expert's knowledge in computer systematically. A typical model of expert system consists of knowledge base and reasoning & explanatory structure offering conclusion with the knowledge. To apply ontology as knowledge base to expert system practically, consideration on reasoning and search should be together. Therefore, this study compared and examined reasoning, search with diagnosis process in oriental medicine. Reasoning is divided into Rule-based reasoning and Case-based reasoning. The former is divided into Forward chaining and Backward chaining. Because of characteristics of diagnosis, sometimes Forward chaining or backward chaining are required. Therefore, there are a lot of cases that Hybrid chaining is effective. Case-based reasoning is a method to settle a problem in the present by comparing with the past cases. Therefore, it is suitable to diagnosis fields with abundant cases. Search is sorted into Breadth-first search, Depth-first search and Best-first search, which have respectively merits and demerits. To construct diagnosis ontology to be applied to practical expert system, reasoning and search to reflect diagnosis process and characteristics should be considered.

• Journal of Information Management
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• v.26 no.1
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• pp.80-98
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• 1995

Search strategies is a means to make an efficient search. Among several studies of search strategy, Bates' $\ulcorner$Search Tactics$\lrcorner$ has been often cited and introduced, but their studies have been superficial. In this paper, the contents of Bates' $\ulcorner$Search Tactics$\lrcorner$ are examined in detail, and we try to represent the actual online search process based upon Bates' $\ulcorner$Search Tactics$\lrcorner$. The following three kinds of survey are made in order to clarify which and how tactics are used: (1) Interview with 7 searchers who belong to different types of organizations, and use different kinds of databases (2) Analysis of some online search records. (3) Analysis of written applications for a newspaper database during one year. In conclusion, Bates' tactics falls into categories and new several tactics which often used are added. The following five factors affect online search activities, search strategy, and search tactics. (1) the difficulty of search requests (2) the kinds of databases (3) the charging policy for each online search (4) the presence of the user during the search (5) the searcher' online experience In the limited condition, typical tactics are suggested, but in order to generalize the pattern of using tactics, further investigation is necessary.

• Journal of the Korean Society of Clothing and Textiles
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• v.16 no.3 s.43
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• pp.271-284
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• 1992

The purpose of this study is to clarify the change of important clothing evaluative criteria according to the clothing buying process and to bulid the flow chart of clothing buying process and clothing evaluative criteria. The study is carried out in two ways, they are, literature study and empirical survey. The results are as following; 1. the change of the important clothing evaluative criteria according to the clothing buying process is found out. 2. The clothing buying process is that problem recognition -1 decision of clothing wearing situation $\rightarrow$ search and evaluation of genaral Clothing information $\rightarrow$ decision of price limit $\rightarrow$ (search and evaluation of brand information 1 narrowing of brand range) $\rightarrow$ search and evaluation of store information $\rightarrow$ decision of store $\rightarrow$ search and evaluation of clothings in the store $\rightarrow$ narrowing of determinant clothing range $\rightarrow$ trial, trial evaluation and decision $\rightarrow$ buy-ing (or reject) $\rightarrow$ result evaluation. 3. The flow chart is built by the clothing buying process and the clothing evaluative criteria

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.437-444
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• 2012

In this paper, we propose a method for predicting a user's location based on their past movement patterns. There is no restriction on the length of past movement patterns when using this method to predict the current location. For this purpose, a modified search tree has been devised. The search tree is constructed in an effective manner while it additionally learns the movement patterns of a user one by one. In fact, the time complexity of the learning process for a movement pattern is linear. In this process, the search tree expands to take into consideration more details about the movement patterns when a pattern that conflicts with an existing trained pattern is found. In this manner, the search tree is trained to make an exact matching, as needed, for location prediction. In the experiments, the results showed that this method is highly accurate in comparison with more complex and sophisticated methods. Also, the accuracy deviation of users of this method is significantly lower than for any other methods. This means that this method is highly stable for the variations of behavioral patterns as compared to any other method. Finally, 1.47 locations were considered on average for making a prediction with this method. This shows that the prediction process is very efficient.

• International Journal of Contents
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• v.8 no.1
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• pp.100-107
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• 2012

While considerable research suggests that users' uncertainty gradually decreases, as they proceed through the information seeking process, others argue that it can arise at any stage of their information seeking process. Reflecting the latter view, this study examined user perceptions of uncertainty in the final stage of users' information seeking process, the stage of search results evaluation. Considering the significance of Web search engines for academic study, this study investigated the relevance decision stage of scholarly researchers in the field of science, who use Web search engines for their academic study. Based on the analysis of the users' uncertainty, this study provided implications to improve information systems and Web contents design.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.85-94
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• 2017

In this paper, I proposed a sound model generation and a most frequent model search algorithm for recognizing animal vocalization. The sound model generation algorithm generates a optimal set of models through repeating processes such as the training process, the Viterbi Search process, and the most frequent model search process while adjusting HMM(Hidden Markov Model) structure to improve global recognition rate. The most frequent model search algorithm searches the list of models produced by Viterbi Search Algorithm for the most frequent model and makes it be the final decision of recognition process. It is implemented using MFCC(Mel Frequency Cepstral Coefficient) for the sound feature, HMM for the model, and C# programming language. To evaluate the algorithm, a set of animal sounds for 27 species were prepared and the experiment showed that the sound model generation algorithm generates 27 HMM models with 97.29 percent of recognition rate.

• Journal of the Korean Society of Earth Science Education
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• v.3 no.1
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• pp.76-85
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• 2010

The purpose of this study is to examine the effect of higher grades in elementary the science process skill and scientific attitudes by multiple-intelligence. To verify research problem, the subject of this study were sixth-grade students selected from two classes of an elementary school located in U1san : the search group is composed of twenty-nine students who were participated in multiple-Intelligence situation, and the other is composed of thirty-two students(comparison group) who were participated in teacher map based learning situation. During six weeks, the multiple-Intelligence was executed in th search group while the teacher map based instruction in comparison group Post-test showed following results: First, the search group showed a significant improvement in the science process skill compared th the comparison group. Second, the search group did not showed a significant improvement in the scientific attitudes compared th the comparison group. In conclusion, multiple-Intelligence teaching model was more effective than the teacher map based teaching model on science process skill. However, since the study has a limit on an object of the study and the applied curriculum, the additional studies need to be conducted with an extended comparative group and curriculum.