• Journal of Information Processing Systems
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• v.17 no.2
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.3
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• pp.215-228
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• 2003

National Geography Institute(NGI, National mapping agency) has been producing national basemap in automated process since middle of 1980's toward the systematic and efficient management of national land. In 1995, Korean government initiated a full-scale implementation of the National Geographic Information System(NGIS) Development Plan. Under the NGIS Development Plan, NGI began to produce digital maps in the scales of 1:1,000, 1:5,000, 1:25,000. However, digital maps of 1:250,000 scale, which are currently used for national land planning, were not included in NCIS Development Plan. Also, the existing laws and specifications related to digital maps of 1:250,000 scale are not clearly defined. It is fully appreciated that data dictionary will be a key element for users and generators of digital maps to rectify the existing problems in digital maps as well as to maximize the application of digital maps. There(ore this study proposed a feature classification system, which defines features that should be represented in digital map of 1:250,000 scale, and data dictionary as well.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.457-466
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• 2002

In this paper, we designed and implemented a web-based dictionary of computing which keeps the data up-to-date. This dictionary shows the English information based on the FOLDOC (Free On-Line Dictionary Of Computing) dictionary file at the beginning of searching, and then one or more users can translate the information into Korean. This function is the new one only this dictionary has. Also, we can easily find any words we want to took up, even if we don't know the spelling completely, because the dictionary has various searching interfaces (searching for the words starting with inputted characters, searching for the words including inputted characters in the description, etc.) using a SQL Server DBMS and SQL. The performance test for CPU load factor shows that the server can support at least 1780 users at the same time.

• Journal of Platform Technology
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• v.10 no.3
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• pp.69-78
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• 2022

Job offer and job search data related to employment are in the form of highly-unstructured texts that occur in real-time, NCS duty, learning modules, and job dictionaries. Job announcements and training information have a high data value amid changes in industrial technology, such as the Fourth Industrial Evolution. This study developed a job data dictionary by defining relevant data to intuitively understand and harness information on job offers and job searches. This study also designed, constructed, and evaluated a data map based on ontology to enable linking and inferring data about public announcement-job-training. Through this, it was found that the inference function centered on work ability enables QoS support that can satisfy users by minimizing mismatch between consumers and optimizing the data dictionary.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.113-124
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• 2015

Unlike structured data which are gathered and saved in a predefined structure, unstructured text data which are mostly written in natural language have larger applications recently due to the emergence of web 2.0. Text mining is one of the most important big data analysis techniques that extracts meaningful information in the text because it has not only increased in the amount of text data but also human being's emotion is expressed directly. In this study, we used R program, an open source software for statistical analysis, and studied algorithm implementation to conduct analyses (such as Frequency Analysis, Cluster Analysis, Word Cloud, Social Network Analysis). Especially, to focus on our research scope, we used keyword extract method based on a Data Dictionary. By applying in real cases, we could find that R is very useful as a statistical analysis software working on variety of OS and with other languages interface.

• MALSORI
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• no.65
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• pp.93-103
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• 2008

In this paper, we propose a method for optimizing a multiple pronunciation dictionary used for modeling pronunciation variations of non-native speech. The proposed method removes some confusable pronunciation variants in the dictionary, resulting in a reduced dictionary size and less decoding time for automatic speech recognition (ASR). To this end, a confusability measure is first defined based on the Levenshtein distance between two different pronunciation variants. Then, the number of phonemes for each pronunciation variant is incorporated into the confusability measure to compensate for ASR errors due to words of a shorter length. We investigate the effect of the proposed method on ASR performance, where Korean is selected as the target language and Korean utterances spoken by Chinese native speakers are considered as non-native speech. It is shown from the experiments that an ASR system using the multiple pronunciation dictionary optimized by the proposed method can provide a relative average word error rate reduction of 6.25%, with 11.67% less ASR decoding time, as compared with that using a multiple pronunciation dictionary without the optimization.

• Phonetics and Speech Sciences
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• v.8 no.2
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• pp.11-16
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• 2016

This study explores the phoneme distribution and syllable structure of entry words in the CMU English Pronouncing Dictionary to provide phoneticians and linguists with fundamental phonetic data on English word components. Entry words in the dictionary file were syllabified using an R script and examined to obtain the following results: First, English words preferred consonants to vowels in their word components. In addition, monophthongs occurred much more frequently than diphthongs. When all consonants were categorized by manner and place, the distribution indicated the frequency order of stops, fricatives, and nasals according to manner and that of alveolars, bilabials and velars according to place. These results were comparable to the results obtained from the Buckeye Corpus (Yang, 2012). Second, from the analysis of syllable structure, two-syllable words were most favored, followed by three- and one-syllable words. Of the words in the dictionary, 92.7% consisted of one, two or three syllables. This result may be related to human memory or decoding time. Third, the English words tended to exhibit discord between onset and coda consonants and between adjacent vowels. Dissimilarity between the last onset and the first coda was found in 93.3% of the syllables, while 91.6% of the adjacent vowels were different. From the results above, the author concludes that an analysis of the phonetic symbols in a dictionary may lead to a deeper understanding of English word structures and components.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.209-215
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• 2021

Data quality management is an important issue these days. Improve data quality by providing consistent metadata. This study presents algorithms that facilitate standard word dictionary management for consistent metadata management. Algorithms are presented to automate synonyms management of database metadata through web dictionary crawling. It also improves the accuracy of the data by resolving homonym distinction issues that may arise during the web dictionary crawling process. The algorithm proposed in this study increases the reliability of metadata data quality compared to the existing passive management. It can also reduce the time spent on registering and managing synonym data. Further research on the new data standardization partial automation model will need to be continued, with a detailed understanding of some of the automatable tasks in future data standardization activities.

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1145-1161
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• 2004

The purpose of this research is to establish a method for the construction of a multimedia electronic dictionary system by integrating the media data available from linguistic resources on the Internet. As the result of this study, existing text-oriented electronic dictionary systems can be developed into multimedia lexical systems with greater efficiency and effectiveness. A method is proposed to integrate the media data of linguistic resources on the Internet by a web browser. In the proposed method, a web browser carries out all the work related to integration of media data, and it does not need a dedicated server system. The system constructed by our web browser environment integrates text, image, and voice sources, and also can produce moving pictures. Each media is associated with the meaning of data so that the data integration and movement may be specified in the associations. SMIL documents are generated by analyzing the meaning of each data unit and they are executed in a web browser. The proposed system can be operated without a dedicated server system. And also, the system saves storage space by sharing the each media data distributed on the Internet, and makes it easier to update data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2851-2865
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• 2014

To improve the rate-distortion performance of distributed video compressive sensing (DVCS), the adaptive sparse basis and nonlocal similarity of video are proposed to jointly reconstruct the video signal in this paper. Due to the lack of motion information between frames and the appearance of some noises in the reference frames, the sparse dictionary, which is constructed using the examples directly extracted from the reference frames, has already not better obtained the sparse representation of the interpolated block. This paper proposes a method to construct the sparse dictionary. Firstly, the example-based data matrix is constructed by using the motion information between frames, and then the principle components analysis (PCA) is used to compute some significant principle components of data matrix. Finally, the sparse dictionary is constructed by these significant principle components. The merit of the proposed sparse dictionary is that it can not only adaptively change in terms of the spatial-temporal characteristics, but also has ability to suppress noises. Besides, considering that the sparse priors cannot preserve the edges and textures of video frames well, the nonlocal similarity regularization term has also been introduced into reconstruction model. Experimental results show that the proposed algorithm can improve the objective and subjective quality of video frame, and achieve the better rate-distortion performance of DVCS system at the cost of a certain computational complexity.