• Journal of the Korean Society for Precision Engineering
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• v.31 no.2
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• pp.149-156
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• 2014

Flexible tactile sensors can provide valuable feedback to intelligent robots about the environment. This is especially important when the robots, e.g., service robots, are sharing the workspace with human. This paper presents a flexible tactile sensor that was manufactured by direct writing technique, which is one of 3D printing method with multi-walled carbon nano-tubes. The signal processing system consists of two parts: analog circuits to amplify and filter the sensor output and digital signal processing algorithms to reduce undesired noise. Finally, experimental setup is implemented and evaluated to identify the characteristics of the flexible tactile sensor system. This paper showed that this type of sensors can detect the initiation and termination of contacts with appropriate signal processing.

• Journal of Computing Science and Engineering
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• v.10 no.2
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• pp.39-50
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• 2016

In this paper, we present a comparative study of spatial domain features for writer identification and verification with different ink width conditions. The existing methods give high error rates, when comparing two handwritten images with different pen types. To the best of our knowledge, we are the first to design the feature with different ink width conditions. To address this problem, contour based features were extracted using a chain code method. To improve accuracy at higher levels, we considered histograms of chain code and variance in bins of histogram of chain code as features to discriminate handwriting samples. The system was trained and tested for 1,000 writers with two samples using different writing instruments. The feature performance is tested on our newly created dataset of 4,000 samples. The experimental results show that the histogram of chain code feature is good compared to other methods with false acceptance rate of 11.67%, false rejection rate of 36.70%, average error rates of 24.18%, and average verification accuracy of 75.89% on our new dataset. We also studied the effect of amount of text and dataset size on verification accuracy.

• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.625-634
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• 2002

Cryptography is the methods of making and using secret writing that is necessary to keep messages private between two parties. Cryptography is compute-intensive algorithm and needs cpu resource excessively. To solve these problems, there exists hardware approach that implements cryptographic algorithm with hardware chip. In this paper, we presents the design and implementation of cryptographic hardware and compares its performance with software cryptographic algorithms. The experimental result shows that the hardware approach causes high I/O overheads when it transmits data between cryptographic board and host cpu. Hence, low complexity cryptographic algorithms such as DES does not improve the performance. But high complexity cryptographic algorithms such as Triple DES improve the performance with a high rate, roughly from two times to Sour times.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.729-736
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• 2014

Integrated multimedia education in English language education shifts instructor-orientated education to be more learner-orientated. The goal of this research is to analyze the effects of multimedia such as software, power point, flash animation and video in English language education. Experimental research with student subjects and multimedia in English education were used for this study and divided into the following categories: instructor-focused analysis, student-focused analysis, and response-based analysis. Teacher-focused analysis is comprised of prediction analysis and backward induction methods. This study aims to analyze whether multimedia tools achieves its intended effects, and to describe what sort of effects are achieved by the tools. This research intends to confirm the effectiveness and helpfulness of multimedia tools in school classrooms.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.11
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• pp.103-110
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• 1990

This paper studies on an on-line recognition of handwritten Hangeul characters using the fuzzy inference. To solve the ambiguity due to the variations of writing style by writes, these handwri-tten characters are recognized by means of the fuzzy inference on the production rule which is generated with every relative position information between strokes. In order to reduce the processing time, a subgroup which is previously classified with the number of strokes of reference characters is selected according to the number of strokes of input character, and the tolerance limit of distances between input character and reference characters of a subgroup is introduced to reduce the reference characters which is applied to the fuzzy inference. Experimental results for handwritten Hanguel charters 3990 by 10 writers show the recognition rate of $99.5{\%}$and the average processing time of 0.4sec/character.

• Journal of Digital Contents Society
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• v.16 no.5
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• pp.707-715
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• 2015

With the development of computer technology, the multimedia mediums that allow for animated videos, conversational illustrations are increasingly receiving attention for materials for educational purposes. Accordingly, there is a need to research whether multimedia resources and material is more effective compared to traditional educational material and resources. This study aims to compare traditional English reading and writing learning methods with learning methods using educational multimedia mediums such as illustrations or animation. Students were divided into a experimental group and a control group, and during 6 months the groups were exposed to different educational resources and on the basis of student evaluation feedback and grades, a new approach to English education is offered.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.151-156
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• 2018

Because of its large differences in writing style, context-independency and high recognition accuracy requirement, free handwritten digital identification is still a very difficult problem. Analyzing the characteristic of handwritten digits, this paper proposes a new handwritten digital identification method based on combining structural features. Given a handwritten digit, a variety of structural features of the digit including end points, bifurcation points, horizontal lines and so on are identified automatically and robustly by a proposed extended structural features identification algorithm and a decision tree based on those structural features are constructed to support automatic recognition of the handwritten digit. Experimental result demonstrates that the proposed method is superior to other general methods in recognition rate and robustness.

• ETRI Journal
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• v.44 no.3
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• pp.413-425
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• 2022

The study of literary texts is one of the earliest disciplines practiced around the globe. Poetry is artistic writing in which words are carefully chosen and arranged for their meaning, sound, and rhythm. Poetry usually has a broad and profound sense that makes it difficult to be interpreted even by humans. The essence of poetry is Rasa, which signifies mood or emotion. In this paper, we propose a poetry classification-based approach to automatically extract similar poems from a repository. Specifically, we perform a novel Rasa-based classification of Hindi poetry. For the task, we primarily used lexical features in a bag-of-words model trained using the support vector machine classifier. In the model, we employed Hindi WordNet, Latent Semantic Indexing, and Word2Vec-based neural word embedding. To extract the rich feature vectors, we prepared a repository containing 37 717 poems collected from various sources. We evaluated the performance of the system on a manually constructed dataset containing 945 Hindi poems. Experimental results demonstrated that the proposed model attained satisfactory performance.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1057-1070
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• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.374-388
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• 2022

Database systems usually have many parameters that must be configured by database administrators and users. RocksDB achieves fast data writing performance using a log-structured merged tree. This database has many parameters associated with write and space amplifications. Write amplification degrades the database performance, and space amplification leads to an increased storage space owing to the storage of unwanted data. Previously, it was proven that significant performance improvements can be achieved by tuning the database parameters. However, tuning the multiple parameters of a database is a laborious task owing to the large number of potential configuration combinations. To address this problem, we selected the important parameters that affect the performance of RocksDB using random forest. We then analyzed the effects of the selected parameters on write and space amplifications using analysis of variance. We used a genetic algorithm to obtain optimized values of the major parameters. The experimental results indicate an insignificant reduction (-5.64%) in the execution time when using these optimized values; however, write amplification, space amplification, and data processing rates improved considerably by 20.65%, 54.50%, and 89.68%, respectively, as compared to the performance when using the default settings.