• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.481-486
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• 2023

Anomaly detection holds paramount significance across diverse fields, encompassing fraud detection, risk mitigation, and sensor evaluation tests. Its pertinence extends notably to the military, particularly within the Warrior Platform, a comprehensive combat equipment system with wearable sensors. Hence, we propose a data-compression-based anomaly detection approach tailored to unlabeled time series and sequence data. This method entailed the construction of two distinctive features, typicality and atypicality, to discern anomalies effectively. The typicality of a test sequence was determined by evaluating the compression efficacy achieved through the pattern dictionary. This dictionary was established based on the frequency of all patterns identified in a training sequence generated for each sensor within Warrior Platform. The resulting typicality served as an anomaly score, facilitating the identification of anomalous data using a predetermined threshold. To improve the performance of the pattern dictionary method, we leveraged atypicality to discern sequences that could undergo compression independently without relying on the pattern dictionary. Consequently, our refined approach integrated both typicality and atypicality, augmenting the effectiveness of the pattern dictionary method. Our proposed method exhibited heightened capability in detecting a spectrum of unpredictable anomalies, fortifying the stability of wearable sensors prevalent in military equipment, including the Army TIGER 4.0 system.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.812-820
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• 1999

In two-dimensional dictionary matching problem, we are given a two-dimensional text T and a dictionary D={P\ulcorner, ...., P\ulcorner} as a set of two-dimensional patterns. We seek the locations of all the dictionary patterns that appear in T. We present a new two-dimensional pattern matching algorithm that can handle just a single pattern, and then show how to extend it into two-dimensional dictionary matching algorithm. The suggested algorithm is practical in the sense that it can deal that it uses a small extra space proportional to the size of the dictionary, and that it is quite simple to be implemented without depending on complicated data structures.

• Korean Journal of Cognitive Science
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• v.3 no.2
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• pp.311-328
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• 1992

The level and accuracy of English sentence that is generated by a man or machine translator are determined by the content of the verb dictionary and effective generation algorithm.The conventional English critiques is not adequate for foreigners because they do not have the verb dictionary including verb pattern or the important grammatical constraints. In this paper,Ipropose a structure of verb dictionary and an English sentence critique based on extended verb pattern that is useful to check and correct mistakes of English sentences generated by machine translator.

• Journal of Auto-vehicle Safety Association
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• v.8 no.1
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• pp.19-25
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• 2016

As new car technology is developing, temporal interaction is needed in automotive. Rhythmic pattern is one of the practical examples of temporal interaction in vehicle. To recognize rhythmic pattern and its input medium, dictionary learning is applicable algorithm. In this paper, performance and memory requirement of the learning algorithm is tested and is sufficiently good for use this acoustic sound.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5278-5283
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• 2011

One approach for very high speed korean morphological analysis is to use pre-built morphological results in dictionary. It pays the high cost to build this morphological pattern dictionary manually, besides the dictionary may contain errors. This paper proposes a method to generate morphological patterns automatically using Korean morphological synthesis. The experiment shows that we automatically generate 86% morphological patterns for analyzing Korean sentences. It takes 52.68 seconds for the morphological system using the patterns to analyze 403MB Korean corpus on 2.8GHz Window system.

• Korean Journal of Cognitive Science
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• v.16 no.2
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• pp.123-140
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• 2005

The purpose of this paper is to examine the frequency and usage of sentence patterns appearing in electronic dictionaries used in Korean language education in order to design an automatic sentence patterns checking. First, the concept of sentence patterns is defined and it is classified into sentence structure patterns and sentencial expression patterns. Sentence structure patterns and sentencial expression patterns are analyzed how they are expressed in the Korean Learner's Corpus. learner's Corpus is built into the Standard Corpus, which all Korean Learners must learn, and the Errors Corpus made by learners. From these research, we will find out how frequently the Sentential Patterns are being used in the Standard Corpus which has been made of Korean Texts and how the Sentential Pattern are being used in the Errors Corpus which were constructed from Korean learner's writings. Finally, having described the Sentential Patterns on the Sentential Electric Dictionary, we determine the optimum speed in the search for the Sentential Pattern.

• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.10-19
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• 2013

This Paper proposed the development of new magnetic sensor and recognition system to expendite pattern recognition of a handwriting character. Received character graphics should be performed the session and balancing and no extraction of end points, bend points and juntions separately. The Artifical intelligence algorithm is adapted to structure snalysis and recognition process by individual basic letter dictionary except for the handwriing character graphic dictionaryimproving error of recognition algorithm and enomous dictionary for generalization. In this Paper, recognition rate of the received character are compared with pre registered character at letter dictionary for performance test of magnetic ball sensor. As a result of unicode conversion and eomparison, the artificial intelligence study have recognition rate more than 95% at initial recognition rate of 70%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.142-151
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• 2023

Sentiment analysis is a method used to comprehend feelings, opinions, and attitudes in text, and it is essential for evaluating consumer feedback and social media posts. However, creating sentiment dictionaries, which are necessary for this analysis, is complex and time-consuming because people express their emotions differently depending on the context and domain. In this study, we propose a new method for simplifying this procedure. We utilize syntax analysis of the Korean language to identify and extract sentiment words based on the Reason-Sentiment Pattern, which distinguishes between words expressing feelings and words explaining why those feelings are expressed, making it applicable in various contexts and domains. We also define sentiment words as those with clear polarity, even when used independently and exclude words whose polarity varies with context and domain. This approach enables the extraction of explicit sentiment expressions, enhancing the accuracy of sentiment analysis at the attribute level. Our methodology, validated using Korean cosmetics review datasets from Korean online shopping malls, demonstrates how a sentiment dictionary focused solely on clear polarity words can provide valuable insights for product planners. Understanding the polarity and reasons behind specific attributes enables improvement of product weaknesses and emphasis on strengths. This approach not only reduces dependency on extensive sentiment dictionaries but also offers high accuracy and applicability across various domains.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1208-1221
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• 2006

In this paper, we implement an AAC(Augmentative and Alternative Communication) system based on image dictionary in order that speech defectives can easily communicate their opinion to others by using images. Normally, those who have a speech defect use only a few limited words to express their intentions and it is an effective way to use images in their communication because they have a difficulty in speaking. Therefore we make verbal images of verb and adjective that play an important role in expressing the speaker's intention, define pattern of semantic relation between the verbal images and noun images, and construct the image dictionary. In this AAC system, when a user clicks a verbal image, the system generates a sentence by selecting noun images which are component parts of corresponding pattern based on semantic relation with the verbal image. We evaluate the implemented system by how efficiently children of speech defect can express their intention and the result shows more than 70% of success rate in communication.

• Journal of Information Processing Systems
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• v.5 no.3
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.