• KOREAN POULTRY JOURNAL
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• v.23 no.2 s.256
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• pp.114-117
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• 1991

• KOREAN POULTRY JOURNAL
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• v.27 no.2 s.304
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• pp.134-137
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• 1995

• Nuclear Engineering and Technology
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• v.12 no.3
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• pp.214-221
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• 1980

• Journal of the Korean Society for information Management
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• v.23 no.1 s.59
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• pp.83-98
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• 2006

This study proposes a template-based method of automatic summarization of multiple news articles using the semantic categories of sentences. First, the semantic categories for core information to be included in a summary are identified from training set of documents and their summaries. Then, cue words for each slot of the template are selected for later classification of news sentences into relevant slots. When a news article is input, its event/accident category is identified, and key sentences are extracted from the news article and filled in the relevant slots. The template filled with simple sentences rather than original long sentences is used to generate a summary for an event/accident. In the user evaluation of the generated summaries, the results showed the 54.l% recall ratio and the 58.l% precision ratio in essential information extraction and 11.6% redundancy ratio.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.169-189
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• 2005

In order to derive a better classifier with a limited number of training examples, active teaming alternately repeats the querying stage fur category labeling and the subsequent learning stage fur rebuilding the calssifier with the newly expanded training set. To relieve the user from the burden of labeling, especially in an on-line environment, it is important to minimize the number of querying steps as well as the total number of query examples. We can derive a good classifier in a small number of querying steps by using only a small number of examples if we can select multiple of diverse, representative, and ambiguous examples to present to the user at each querying step. In this paper, we propose a cluster-based batch query selection method which can select diverse, representative, and highly ambiguous examples for efficient active learning. Experiments with various text data sets have shown that our method can derive a better classifier than other methods which only take into account the ambiguity as the criterion to select multiple query examples.

• Journal of the Korean Society for information Management
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• v.23 no.2
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• pp.61-79
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• 2006

To come up with the recommendations to improve the patent & trademark retrieval efficiency, 100,016 patent & trademark search requests by 17,559 unique users over a period of 193 days were analyzed. By analyzing 2,202 multi-query sessions, where one user issuing two or more queries consecutively, we discovered a number of retrieval efficiency improvements clues. The session analysis result also led to suggestions for new system features to help users reformulating queries. The patent & trademark retrieval users were found to be similar to the typical web users in certain aspects especially in issuing short queries. However, we also found that the patent & trademark retrieval users used Boolean operators more than the typical web search users. By analyzing the multi-query sessions, we found that the users had five intentions in reformulating queries such as paraphrasing, specialization, generalization, alternation, and interruption, which were also used by the web search engine users.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.529-536
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• 2017

The general ground control system has control and information display functions for the operation of a single unmanned aerial vehicle. Recently, the function of the single ground control system extends to the operation of multiple UAVs. As a result, operators have been exposed to more diverse tasks and are subject to task overload due to various factors during their mission. This study proposes an adaptive ground control system that reflects the operator's condition through the task overload measurement of multiple UAV operators. For this, the ground control software is developed to control multiple UAVs at the same time, and the simulator with six degree-of-freedom aircraft dynamics is constructed for realistic human-machine-interface experiments by the operators.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.4
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• pp.359-364
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• 2001

This paper presents case studies on the multi-species fisheries in Korean waters, Multi-species fisheries were divided into two types, that is, multi-species by a single fishery and single species by multiple fisheries. For the case of single species by multiple fisheries, the small yellow croaker stock caught mainly by the Korean pair trawl fishery and the Korean stow net fishery was selected. This approach uses both standardized fishing efforts for the two fisheries by a general linear model and some data for the economic analysis, and then estimates maximum sustainable yield (MSY), maximum economic yield (MEY) and fishing efforts for MSY and MEY, An analysis of interaction aspects between pair trawl and stow net fisheries was carried out to predict the optimal level of fishing effort from the economic point of view, which gives the largest benefits to the two fisheries.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.352-362
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• 2010

The simultaneous operation of multiple UAV's makes it possible to enhance the mission accomplishment efficiency. In order to achieve this, easily scalable control algorithms are required, and swarm intelligence having such characteristics as flexibility, robustness, decentralized control, and self-organization based on behavioral model comes into the spotlight as a practical substitute. Recently, evolutionary robotics is applied to the control of UAV's to overcome the weakness of difficulties in the logical design of behavioral rules. In this paper, a neural network controller evolved by evolutionary robotics is applied to the control of multiple UAV's which have the mission of searching limited area. Several numerical demonstrations show the proposed algorithm has superior results to those of behavior based neural network controller which is designed by intuition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2067-2073
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• 2001

Biometric systems are forms of technology that use unique human physical characteristics to automatically identify a person. They have sensors to pick up some physical characteristics, convert them into digital patterns, and compare them with patterns stored for individual identification. However, lip-print recognition has been less developed than recognition of other human physical attributes such as the fingerprint, voice patterns, retinal at blood vessel patterns, or the face. The lip print recognition by a CCD camera has the merit of being linked with other recognition systems such as the retinal/iris eye and the face. A new method using multi-resolution architecture is proposed to recognize a lip print from the pattern kernels. A set of pattern kernels is a function of some local lip print masks. This function converts the information from a lip print into digital data. Recognition in the multi-resolution system is more reliable than recognition in the single-resolution system. The multi-resolution architecture allows us to reduce the false recognition rate from 15% to 4.7%. This paper shows that a lip print is sufficiently used by the measurements of biometric systems.