• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.7-13
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• 2020

Meteorology and climate are closely related to human life. By using high-resolution weather data, services that are useful for real-life are available, and the need to produce high-resolution weather data is increasing. We propose a method for super-resolution temperature data using SRCNN. To evaluate the super-resolution temperature data, the temperature for a non-observation point is obtained by using the inverse distance weighting method, and the super-resolution temperature data using interpolation is compared with the super-resolution temperature data using SRCNN. We construct an SRCNN model suitable for super-resolution of temperature data and perform super-resolution of temperature data. As a result, the prediction performance of the super-resolution temperature data using SRCNN was about 10.8% higher than that using interpolation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.397-404
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• 2013

This paper propose the hardware architecture of face detection hardware system using the AdaBoost algorithm. The proposed structure of face detection hardware system is possible to work in 30frame per second and in real time. And the AdaBoost algorithm is adopted to learn and generate the characteristics of the face data by Matlab, and finally detected the face using this data. This paper describes the face detection hardware structure composed of image scaler, integral image extraction, face comparing, memory interface, data grouper and detected result display. The proposed circuit is so designed to process one point in one cycle that the prosed design can process full HD($1920{\times}1080$) image at 70MHz, which is approximate $2316087{\times}30$ cycle. Furthermore, This paper use the reducing the word length by Overflow to reduce memory size. and the proposed structure for face detection has been designed using Verilog HDL and modified in Mentor Graphics Modelsim. The proposed structure has been work on 45MHz operating frequency and use 74,757 LUT in FPGA Xilinx Virtex-5 XC5LX330.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.267-276
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• 2016

This study was conducted to explore the possibilities of educational use of social curation service, which is the third-generation social networking service called the "matters of interest type." To this end, the study selected nine college students, then have them use social curation service for about 50 days, and analyzed their responses by performing the focus group interview (FGI). As a result of the analysis, social curation service was found to have strong points, including collection of various types of information, classification and summary through creation of my own category, possibility of constant updating of matters of interest, and reduction of relative deprivation compared to existing social networking services. To solve these issues, constant promotion was needed. From the aspect of educational use, it was found that social curation service had possibilities for individuals and teams: For individuals, the service enabled voluntary search and use of information, configuration of my own data, and facilitation of self-directed learning. For teams, it enabled discussion and presentation activities through sharing.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.455-464
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• 2009

Based on the observation that Korean speakers of English have difficulties in producing English fricatives, a perception experiment was designed to investigate whether Korean speakers also have difficulties perceiving English labial consonants including fricatives. Forty Korean college students were asked to perform a multiple-choice identification test. The consonant perception test consisted of nonce words which contained English labial consonants [p, b, f, v] in 4 different prosodic locations: initial onset position, intervocalic position before stress, intervocalic position after stress, and final coda position. The general perception pattern was that the mean accuracy rates were higher in strong position like CV and VCVV than in weak position like VC and VVCV. The difficulties in perceiving the English targets resulted mainly from bidirectional manner confusion between stop and fricative across all prosodic locations. The other types of misidentification were due to place confusion as well as voicing confusion. Place confusion was generated mostly by the target [f] in all prosodic position due to acoustic properties. Voicing confusion was heavily influenced by prosodic position. The misperception of the participants was accounted for by phonetic properties and/or the participants' native language properties.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.2
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• pp.517-524
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• 2010

Recently, the size of online texts and textual information is increasing explosively, and the automated classification has a great potential for handling data such as news materials and images. Text classification system is based on supervised learning which needs laborous work by human expert. The main goal of this paper is to reduce the manual intervention, required for the task. The other goal is to increase accuracy to be high. Most of the documents have high complexity in contents and the high similarities in their described style. So, the classification results are not satisfactory. This paper shows the implementation of classification system based on ETOM+RPost algorithm and classification progress using SPAM data. In experiments, we verified our system with right-training documents and wrong-training documents. The experimental results show that our system has high accuracy and stability in all situation as 16% improvement in accuracy.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.297-304
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• 2010

Clustering method which based on sentence type or document genre is a technique used to improve translation quality of SMT(statistical machine translation) by domain-specific translation. But there is no previous research using sentence type and document genre information simultaneously. In this paper, we suggest an integrated clustering method that classifying sentence type by syntactic structure similarity and document genre by word similarity information. We interpolated domain-specific models from clusters with general models to improve translation quality of SMT system. Kernel function and cosine measures are applied to calculate structural similarity and word similarity. With these similarities, we used machine learning algorithms similar to K-means to clustering. In Japanese-English patent translation corpus, we got 2.5% point relative improvements of translation quality at optimal case.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.73-81
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• 2015

The paper proposes a prototype selection method and evaluates the generalization performance of standard algorithms and prototype based classification learning. The proposed prototype classifier defines multidimensional spheres with variable radii within class areas and generates a small set of training data. The nearest-neighbor classifier uses the new training set for predicting the class of test data. By decomposing bias and variance of the mean expected error value, we compare the generalization errors of k-nearest neighbor, Bayesian classifier, prototype selection using fixed radius and the proposed prototype selection method. In experiments, the bias-variance changing trends of the proposed prototype classifier are similar to those of nearest neighbor classifiers with all training data and the prototype selection rates are under 27.0% on average.

• Journal of IKEEE
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• v.20 no.4
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• pp.351-360
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• 2016

Recently, machine learning algorithms, especially deep learning-based algorithms, have been receiving attention due to its high classification performance. Among the algorithms, Convolutional Neural Network(CNN) is known to be efficient for image processing tasks used for Advanced Driver Assistance Systems(ADAS). However, it is difficult to achieve real-time processing for CNN in vehicle embedded software environment due to the repeated operations contained in each layer of CNN. In this paper, we propose a hardware accelerator which enhances the execution time of CNN by parallelizing the repeated operations such as convolution. Xilinx ZC706 evaluation board is used to verify the performance of the proposed accelerator. For $36{\times}36$ input images, the hardware execution time of CNN is 2.812ms in 100MHz clock frequency and shows that our hardware can be executed in real-time.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.33-40
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• 2005

Recently So far Many low-cost mobile machinery have been produced. Those are being used for study and business. But those are some weak Points which are small-capacity storage and quite low-speed system. If we use general database programs or key-searching algorithm, It could decrease in performance of system. To solve those Problems, we applied CBDS(Compact Binary Digital Search) trie to mobile environment. As a result we could accomplish our goal which are quick searching and low-capacity indexing. We compared with some Java classes such as TreeSet to evaluation. As a result, the velocity of searching was a little slow than B-tree based TreeSet. But the storage space have been decreased by 29 percent. So I think that it would be practical use.

• Journal of the Korea Convergence Society
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• v.11 no.2
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• pp.17-22
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• 2020

In this paper, we measured the user's EEG signal and classified the EEG signal using the Support Vector Machine algorithm and measured the accuracy of the signal. An experiment was conducted to measure the user's EEG signals by separating men and women, and a single channel EEG device was used for EEG signal measurements. The results of measuring users' EEG signals using EEG devices were analyzed using R. In addition, data in the study was predicted using a 80:20 ratio between training data and test data by applying a combination of specific vectors with the highest classifying performance of the SVM, and thus the predicted accuracy of 93.2% of the recognition rate. This paper suggested that the user's EEG signal could be recognized at about 93.2 percent, and that it can be performed only by simple linear classification of the SVM algorithm, which can be used variously for biometrics using EEG signals.