• Journal of Platform Technology
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• v.6 no.1
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• pp.3-8
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• 2018

This paper proposes a technique for recognizing online handwritten cursive data obtained by tracing a motion trajectory while a user is in the 3D space based on a convolution neural network (CNN) algorithm. There is a difficulty in recognizing the virtual character input by the user in the 3D space because it includes both the character stroke and the movement stroke. In this paper, we divide syllable into consonant and vowel units by using labeling technique in addition to the result of localizing letter stroke and movement stroke in the previous study. The coordinate information of the separated consonants and vowels are converted into image data, and Korean handwriting recognition was performed using a convolutional neural network. After learning the neural network using 1,680 syllables written by five hand writers, the accuracy is calculated by using the new hand writers who did not participate in the writing of training data. The accuracy of phoneme-based recognition is 98.9% based on convolutional neural network. The proposed method has the advantage of drastically reducing learning data compared to syllable-based learning.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.81-86
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• 2018

Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.21-27
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• 2019

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.91-98
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• 2023

Recently, according to development of artificial intelligence, a wide range of industry being automatic and optimized. Also we can find out some research of using supervised learning for deteceting defect of railway in domestic rail industry. However, there are structures other than rails on the track, and the fastener is a device that binds the rail to other structures, and periodic inspections are required to prevent safety accidents. In this paper, we present a method of reducing cost for labeling using semi-supervised and transfer model trained on rail fastener data. We use Resnet50 as the backbone network pretrained on ImageNet. At first we randomly take training data from unlabeled data and then labeled that data to train model. After predict unlabeled data by trained model, we adopted a method of adding the data with the highest probability for each class to the training data by a predetermined size. Futhermore, we also conducted some experiments to investigate the influence of the number of initially labeled data. As a result of the experiment, model reaches 92% accuracy which has a performance difference of around 5% compared to supervised learning. This is expected to improve the performance of the classifier by using relatively few labels without additional labeling processes through the proposed method.

• Journal of the Korean Society of Food Culture
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• v.12 no.5
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• pp.573-582
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• 1997

The purpose of this study is to establish the one serving size of Korean Processed Food. Defining the one serving size is very important for nutrition labeling and foodservice operation, because the one serving size is used to set up a proper portion by each foodservice operation. The basic data of 200 items were collected through three methods. Searching many cookbooks, exploring the commercial and noncommercial foodservices -6 industrial foodservices, 100 nationwide elementary school foodservice recipes analysis, and 3 hospital foodservice systems as the samples - moreover, experimental cooking and sensory evaluation by trained panels were conducted to assess quantity preference of selected food items. All data were rearranged through food type, that is, main dish, side dish, dessert and health food. One serving sizes of processed foods showed wide variety according to the different menus that include selected food items. Therefore, means and ranges of serving size by three research methods were presented item by item. The results obtained were: 1. The Korean Processed Foods were dried and sugar adding and soused foods, and many of them used the natual processing methods. 2. There were wide varieties in the classification of main dishes, but many of them were cereals, noodles, and sugar products. One serving size of noodles were around $50{\sim}100\;g$, cereals were $20{\sim}40\;g$, which means the one serving size can be differenciated by the food usage. 3. According to the Food classification of side dishes, many of them were as following; natural dried foods, processed fish products, salted or sugar added foods, seasoned foods and sugar products. Moreover the Types of cooking in side dishes were almost culinary vegetables, teas, health foods and condiments, and soused fish products. 4. About desserts, they were almost teas and sugars, and the Types of cooking were teas, health foods and seasonings. 5. We can conclude that almost Korean Processed foods used the drying and soused processing methods for long-time preservation, but it can make the higher content of any special elements, such as sodium or carbohydrates.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.445-456
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• 2017

In this paper, we propose a method to detect spam tweets containing unhealthy information by using an n-gram dictionary under limited labeling. Spam tweets that contain unhealthy information have a tendency to use similar words and sentences. Based on this characteristic, we show that spam tweets can be effectively detected by applying a Naive Bayesian classifier using n-gram dictionaries which are constructed from spam tweets and normal tweets. On the other hand, constructing an initial training set requires very high cost because a large amount of data flows in real time in a twitter. Therefore, there is a need for a spam detection method that can be applied in an environment where the initial training set is very small or non exist. To solve the problem, we propose a method to generate pseudo-labels by utilizing twitter's retweet function and use them for the configuration of the initial training set and the n-gram dictionary update. The results from various experiments using 1.3 million korean tweets collected from December 1, 2016 to December 7, 2016 prove that the proposed method has superior performance than the compared spam detection methods.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.18 no.1
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• pp.62-71
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• 2008

The use of chemical products to enhance and improve life is a widespread practice worldwide. But alongside the benefits of these products, there is also the potential for adverse effects to people or the environment. As a result, a number of countries or organizations have developed laws or regulations over the years that require information to be prepared and transmitted to those using chemicals, through labels or Material Safety Data Sheets (MSDS). While these existing laws or regulations are similar in many respects, their differences are significant enough to result in different labels or MSDS for the same product in different countries. Given the reality of the extensive global trade in chemicals, and the need to develop national programs to ensure their safe use, transport, and disposal, it was recognized that a Globally harmonization system of classification and labeling of chemicals(GHS) would provide the foundation for such programs. This study offered complementary details of GHS classification criteria adopted in Korea by analyzing the differences in chemical classification system between UN and Korea Ministry of Labor. Also it is proposed that mutual agreement of information DB used is required by comparing classification results of chemicals in Korea, Japan, and EU. We offered the lists of information sources useful for chemical classification.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.322-332
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• 2013

This study was done to investigate emotional awareness, emotional expressiveness, ambivalence over emotional expressiveness, and college student adjustment, to analyze the factor affecting college student adjustment. The subjects were 159 freshman nursing students. Data were collected through structured questionnaires from May 20 to June 10, 2012. Data analysis was done using descriptive statistics, independent t-test, ANOVA, Pearson correlation coefficient, and multiple regression with SPSS WIN v 18.0. Positive correlation were found between college student adjustment and mood monitoring as well as mood labeling. On the other hand, ambivalence over positive emotional expressiveness and ambivalence over negative emotional expressiveness were significantly negative correlation with college student adjustment. In addition, mood monitoring, ambivalence over positive emotional expressiveness, mood labeling, and ambivalence over negative emotional expressiveness accounted for 31.8% of variance in college student adjustment. The results of this study suggest that programs for promoting emotional awareness and reducing ambivalence over emotional expressiveness are important for college adjustment in freshman nursing students.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.15-21
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• 2007

We propose a 3D modeling method for surface inspection of non-constant velocity moving object. 1'lie laser lines reflect tile surface curvature. We can acquire 3D surface information by analyzing projected laser lines on object. In this paper, we use multi-line laser to improve the single stripe method and high speed of single frame. Binarization and edge extraction of frame image were proposed for robust laser each line extraction. A new labeling method was used for laser line labeling. We acquired some feature points for image matching from the frame data and juxtaposed the frames data to obtain a 3D shape image. We verified the superiority of proposed method by applying it to inspect container's damages.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.718-723
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• 2011

In this paper, we propose a visual multi-touch air input device using vision cameras. The implemented device provides a barehanded interface which copes with the multi-touch operation. The proposed device is easy to apply to the real-time systems because of its low computational load and is cheaper than the existing methods using glove data or 3-dimensional data because any additional equipment is not required. To do this, first, we propose an image processing algorithm based on the HSV color model and the labeling from obtained images. Also, to improve the accuracy of the recognition of hand gestures, we propose a motion recognition algorithm based on the geometric feature points, the skeleton model, and the Kalman filter. Finally, the experiments show that the proposed device is applicable to remote controllers for video games, smart TVs and any computer applications.