• International Journal of Contents
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• v.8 no.2
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• pp.1-6
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• 2012

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.

• Journal of Information Science Theory and Practice
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• v.3 no.1
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• pp.24-39
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• 2015

Pre-retrieval query formulation is an important step for identifying local text reuse. Local reuse with high obfuscation, paraphrasing, and translation poses a challenge of finding the reused text in a document. In this paper, three pre-retrieval query formulation strategies for heuristic retrieval in case of low obfuscated, high obfuscated, and translated text are studied. The strategies used are (a) Query formulation using proper nouns; (b) Query formulation using unique words (Hapax); and (c) Query formulation using most frequent words. Whereas in case of low and high obfuscation and simulated paraphrasing, keywords with Hapax proved to be slightly more efficient, initial results indicate that the simple strategy of query formulation using proper nouns gives promising results and may prove better in reducing the size of the corpus for post processing, for identifying local text reuse in case of obfuscated and translated text reuse.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1312-1320
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• 2004

Recently, even if a lot of visual feature representations have been studied and systems have been built, there is a limit to existing content-based image retrieval mechanism in its availability. One of the limits is the gap between a user's high-level concepts and a system's low-level features. And human beings' subjectivity in perceiving similarity is excluded. Therefore, correct visual information delivery and a method that can retrieve the data efficiently are required. Relevance feedback can increase the efficiency of image retrieval because it responds of a user's information needs in multimedia retrieval. This paper proposes an efficient CBIR introducing positive and negative relevance feedback with threshold value and pre-fetching to improve the performance of conventional relevance feedback mechanisms. With this Proposed feedback strategy, we implement an image retrieval system that improves the conventional retrieval system.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.5 no.1
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• pp.85-105
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• 1981

Information is an essential factor leading the rapid progress which is one of the distinguished characteristics in modem society. As more information is required and as more is supplied by individuals, governmental units, businesses, and educational institutions, the greater will be the requirement for efficient methods of communication. One possibility for improving the information dissemination process is to use computers. The capabilities of such machine are beginning to be used in the process of Information storage, retrieval and dissemination. An important problems, that must be carefully examined is whether one technique for information retrieval is better for worse than another. This paper examines problem of how to evaluate an information retrieval system. One specific approach is a cost accounting model for use in studying how to minimize the cost of operating a mechanized retrieval system. Through the use of cost analysis, the model provides a method for comparative evaluation between systems. The general cost accounting model of the literature retrieval system being designed by this study are given below. 1. The total cost accounting model of the literature retrieval system. The total cost of the literature retrieval system = (the cost per unit of user time X the amount of user time) + ( the cost per unit of system time X the amount of system time) 2. System cost accounting model system cost = (the pre-search system cost per unit of time X time) + (the search system cost per unit of time X time) + (the post search system cost per unit of time X time) 1) Pre-search system cost per unit of time = cost of channel per unit time + cost of central processing unit per unit time + cost of storage per unit time 2) Search system cost per unit of time = comparison cost + document representation cost. 3) Post-search system cost per unit of time. = cost of channel per unit time + cost of central processing unit per unit time + cost of storage per unit time 3. User cost accounting model Total user cost = [pre-search user cost per unit of time X (time + additional time) ] + [search user cost per unit of time X (time + additional time) ] + [post-search user cost per unit of time X (time + additional time) ].

• Speech Sciences
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• v.7 no.1
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• pp.65-75
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• 2000

The present study attempted to determine the effect of olfactory stimulation on word retrieval deficit in Korean aphasics. Eleven nonfluent and 9 fluent aphasic patients served as subjects. The 20 subjects' age ranged from 14 to 65 with the mean of 43.0 (SD: 17.6). A neurologist examined them and diagnosed that they had no visual, auditory, and olfactory impairment. The study consisted of 2 experiments: Experiment I included visual stimulation while experiment II included visual and olfactory stimulation. The subjects were presented with 19 pictures for them to name in the experiment I, whereas they were exposed to the 19 pictures along with the corresponding olfactory stimulus in the experiment II. The 19 items included soy sauce, chilly pepper, Kimchi, chewing gum, cigarette (smoked), soybean paste, lemon, banana, alcohol (wine), apple, fish, vinegar, cuttlefish (dried), milk, rose, sesame oil, melon, coffee, and perfume. The results were as follows: First, olfactory stimulation tended to improve aphasics' word retrieval deficit although the improvement was not statistically significant. Second, the nonfluent aphasics seemed to take more advantage from olfactory stimulation compared to the fluent aphasics. Third, olfactory stimulation (olfactory + visual) did not produce a different naming performance compared to visual stimulation in the pre- and post-test when the pre- and post-test was composed of naming tasks through auditory + visual stimulation. Fourth, the fluent aphasics performed better with unpleasant olfactory stimulus while the nonfluent aphasics performed better with pleasant olfactory stimulus.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.532-539
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• 2003

The paper proposes a pre-processing method for efficient processing of XML queries in information retrieval systems with a large amount of XML documents. For the pre-processing, we use a signature-based approach. In the conventional (flat document-based) information retrieval systems, user queries consist of keywords and boolean operators, and thus signatures are structured in a flat manner. However, in XML-based information retrieval systems, the user queries have the form of path query. Therefore, the flat signature cannot be effective for XML documents. In the paper, we propose a structured signature for XML documents. Through experiments, we evaluate the performance of the proposed method.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.683-688
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• 2008

The emotional information processing is to simulate and recognize human sensibility, sensuality or emotion, to realize natural and harmonious human-machine interface. This paper proposes an emotion-based image retrieval method. In this method, user can choose a linguistic query among some emotional adjectives. Then the system shows some corresponding representative images that are pre-evaluated by experts. Again the user can select a representative one among the representative images to initiate traditional content-based image retrieval (CBIR). By this proposed method any CBIR can be easily expanded as emotion-based image retrieval. In CBIR of our system, we use several color and texture visual descriptors recommended by MPEG-7. We also propose a fuzzy similarity measure based on Choquet integral in the CBIR system. For the communication between system and user, a relevance feedback mechanism is used to represent human subjectivity in image retrieval. This can improve the performance of image retrieval, and also satisfy the user's individual preference.

• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.399-410
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• 2008

This paper suggests a new retrieval method using weighted vector sum to resolve a problem of traditional location-based retrieval method, nearest neighbor (NN) query, and NN query using direction. The proposed method filters out data with the radius, and then the remained retrieval area is filtered by a direction information compounded of a user's moving direction, a pre-fixed interesting direction, and a pre-fixed retrieval angle. The moving direction is computed from a vector or a weighted vector sum of several vectors using a weight to adopt several cases. The retrieval angle can be set from traditional $360^{\circ}$ to any degree you want. The retrieval data for this method can be a still and moving image recorded shooting location, and also several type of media like text, web, picture offering to customer with location of company or resort. The suggested method guarantees more accurate retrieval than traditional location-based retrieval methods because that the method selects data within the radius and then removes data of useless areas like passed areas or an area of different direction. Moreover, this method is more flexible and includes the direction based NN.

• Journal of Korean Library and Information Science Society
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• v.18
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• pp.245-268
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• 1991

Some conclusions derived from the study are as follows : 1) Most conventional information retrieval systems require users do precisely that specify the information they require, but user's information needs are not always precise. 2) Information need arise from when users are in an anomalous stats of knowledge about some problem, so user's information needs are not always precise. 3) Information retrieval systems must assist to users make themselves correct, complement, and specify their information need. When information need arise, systems should understand the state of knowledge and will be design to present and specify their ill-defined potential information need. 4) Existing information retrieval techniques need a tool to complement current its disadvantages and to enhance retrieval efficiency. Browsing searching will be a role such as a tool. 5) Browsing searching can understand user's state of knowledge and assist to specify not only pre-searching information need but also changed information need during searching progress.

• Korean Journal of Adult Nursing
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• v.24 no.4
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• pp.398-405
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• 2012

Purpose: The purpose of this study was to develop spaced retrieval training as a nursing intervention for patients having an mild alzheimer's disease and to determine the effects of the program on their memory and cognitive function across training sessions. Methods: A non-equivalent control group pre test-post test design was used in this study. Participants were recruited from a local community: 14 patients were allocated into experimental group and 12 patients were allocated into control group. The experimental group was asked to participate in spaced retrieval training over 4 weeks, with seven times a week and 1 hour a session based. The study was conducted from June 20, 2011 to July 17, 2011. Data was analyzed with descriptive statistics, $x^2$-test and t-test using the SPSS/WIN 19.0 program. Results: After spaced retrieval training, the experimental group showed significant increases in scores for memory (t=12.40, p<.001) and cognitive function (t=7.69, p<.001) in comparison to the control group. Conclusion: Spaced retrieval training was effective in increasing cognitive function and memory of patients having mild alzheimer's disease. Therefore spaced retrieval training could be benefit the mild alzheimer's disease.