• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.252-260
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• 2016

The performance of human detection system is affected by camera location and view angle. In 2D image acquired from such camera settings, humans are displayed in different sizes. Detecting all the humans with diverse sizes poses a difficulty in realizing a real-time system. However, if the size of a human in an image can be predicted, the processing time of human detection would be greatly reduced. In this paper, we propose a method that estimates human size by constructing an indoor scene in 3D space. Since the human has constant size everywhere in 3D space, it is possible to estimate accurate human size in 2D image by projecting 3D human into the image space. Experimental results validate that a human size can be predicted from the proposed method and that machine-learning based detection methods can yield the reduction of the processing time.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.48-55
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• 2009

Prior research works in indoor-outdoor classification have been conducted based on a simple combination of low-level features. However, since there are many challenging problems due to the extreme variability of the scene contents, most methods proposed recently tend to combine the low-level features with high-level information such as the presence of trees and sky. To extract these regions from videos, we need to conduct additional tasks, which may yield the increasing number of feature dimensions or computational burden. Therefore, an efficient indoor-outdoor scene classification method is proposed in this paper. First, the video is divided into the five same-sized blocks. Then we define and use the edge and color orientation histogram (ECOH) descriptors to represent each sub-block efficiently. Finally, all ECOH values are simply concatenated to generated the feature vector. To justify the efficiency and robustness of the proposed method, a diverse database of over 1200 videos is evaluated. Moreover, we improve the classification performance by using different weight values determined through the learning process.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.1-7
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• 2021

In the latest trend of machine translation research, the model is pretrained through a large mono lingual corpus and then finetuned with a parallel corpus. Although many studies tend to increase the amount of data used in the pretraining stage, it is hard to say that the amount of data must be increased to improve machine translation performance. In this study, through an experiment based on the mBART model using parallel corpus filtering, we propose that high quality data can yield better machine translation performance, even utilizing smaller amount of data. We propose that it is important to consider the quality of data rather than the amount of data, and it can be used as a guideline for building a training corpus.

• Korean Journal of Labor Studies
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• v.23 no.1
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• pp.221-253
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• 2017

This study aims at analyzing the management strategy and performance of companies that have been pursuing Generational Diversity. The management strategies were examined in terms of production, organizational structure and skill development. Performance was then evaluated using Balanced Scorecard (BSC). We selected four Japanese companies that practice Generational Diversity between the younger(age less then 34) and older generation(age older then 65). Our findings suggest the following. The common management strategies of the four companies include 1) creating generation-diverse teams 2) ensuring flexible work arrangements and 3) providing skill training programs. These strategies have yield positive outcomes such as sales increase, cost reduction (financial perspective) and expansion of the market share (customer perspective). Non-financial performance includes improvement of product and service quality (internal business perspective) and skill improvement of both the young and the old workers (learning and growth perspective). This study provides practical implications to domestic companies for their successful management of generational diversity in workplace.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.99-114
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• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4420-4438
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• 2021

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.

• Journal of Korean Society of Rural Planning
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• v.28 no.1
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• Smart Media Journal
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• v.12 no.9
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• pp.28-37
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• 2023

The main factor affecting the decline in rice yield is damage caused by localized heavy rains or typhoons. The method of analyzing the rice lodging area is difficult to obtain objective results based on visual inspection and judgment based on field surveys visiting the affected area. it requires a lot of time and money. In this paper, we propose the method of estimation and diagnosis for rice lodging areas using a Vision Transformer-based Segformer for RGB images, which are captured by unmanned aerial vehicles. The proposed method estimates the lodging, normal, and background area using the Segformer model, and the lodging rate is diagnosed through the rice field inspection criteria in the seed industry Act. The diagnosis result can be used to find the distribution of the rice lodging areas, to show the trend of lodging, and to use the quality management of certified seed in government. The proposed method of rice lodging area estimation shows 98.33% of mean accuracy and 96.79% of mIoU.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Journal of Korean Academy of Nursing
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• v.4 no.2
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• pp.17-31
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• 1974

Today, over seventy five percent of nursing in Korea provide a psychiatric experience in the basic curriculum. The psychiatric affiliation presents numerous major problems of adjustment to the student. The Importance of positive attitude toward the nursing care of psychiatric patients is recognized by the nursing profession. I have fined out the unfavorable attitude of non collegiate nursing students toward psychiatric nursing affiliation by previous research. This study was undertaken in response to a felt need to explore the use of several devices which might yield information about attitudes toward psychiatric nursing as a basis for future planning of the program offered at a selected hospital. This study is designed to meet the following objectives; (1) In order to find out the expressed attitudes of fifty·three collegiate nursing students toward their psychiatric affiliation. (2) To compare responses given by selected group of collegiate and non collegiate nursing students to same questionnaire (3) To determine the relationship between the attitudes of nursing students toward psychiatric nursing and the type of instructions where experience was obtained. A questionnaire, a Korean translation of the "Psychiatric Nursing Attitude Questionnaire" by Moldered Elizabeth fletcher, was administered to fifty-three collegiate nursing students who had completed a four-week psychiatric affiliation in a S hospital psychiatric ward during May 7, 1973 to Dec. 16, 1973. - The questionnaire of 100 statements was administered in the following way; (1) Part Ⅰ, Preconceptions, was, given in individual conferences with each subject, during the first few days of their affiliation, and again during the final week of affiliation. The responses to Part I were oral. (2) Part Ⅱ, Expectations, Part Ⅲ, Personal Relations, Part Ⅳ, Personal Feelings, and Part V, Attitudes and Activities of Patients were given to all of the subjects in a group meeting during the second week of the affiliation, and again, during the fourth week at the termination of the affiliation. Responses to Parts Ⅱ, Ⅲ, Ⅳ·, and V, were written. Each of the 100 statements of the questionnaire was considered to be either Positive or Negative. A favorable response was assigned the positive value of 1 and an unfavorable response was assigned the Negative value of O. The coefficient of correlation was computed between the two sets of scores for the fifty-three nursing students, The mean score, the standard deviation, and the differences in the means on each of the five parts of the questionnaire were computed and the relationships calculated by at-test. The results of the study were as follows; 1. There was no significant correlation between the two sets of the scores for the fifty-three nursing students during the four-week psychiatric affiliation. (r= 0.36) 2. There was no significant difference in the mean scores between the first and final tests for any of the questionnaire. 3. The Part Ⅰ, Preconceptions, data indicated collegiate nursing students have positive attitudes in preconceptions than non collegiate nursing students and preconceptions toward the psychiatric affiliation which affect their psychiatric nursing experience. 4. The Part Ⅱ, Expectations, data indicated more appropriate expectations of collegiate nursing students related to pre psychiatric affiliation orientation and sufficient theory learning than non-collegiate nursing students. 5. The Part Ⅲ, Personal relations, data indicated some students have negative attitudes in personal relations with normal people in respect to psychological security and social responsibilities. 6. The Part Ⅳ, Personal feelings, data indicated nursing students have psychological insecurity & inappropriateness. 7. The Part V, Attitudes and activities of patients, data indicated collegiate nursing students have more positive attitudes to the psychotic behavior of certain situations due to sufficient theory learning. 8. The data indicated collegiate·nursing students have more positive attitude than non-collegiate nursing students. 5. The Part Ⅲ, Personal relations, data indicated some students have negative attitudes in personal relations with normal people in respect to psychological security and social responsibilities. 6. The Part Ⅳ, Personal feelings, data indicated nursing students have psychological insecurity & inappropriateness. 7. The Part V, Attitudes and activities of patients, data indicated collegiate nursing students have more positive attitudes to the psychotic behavior of certain situations due to sufficient theory learning. 8. The data indicated collegiate·nursing students have more positive attitude than non-collegiate nursing students through psychiatric affiliation.