• Journal of Digital Convergence
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• v.20 no.3
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• pp.325-331
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• 2022

As IoT devices are used in various ways in an edge network environment, multiple studies are being conducted that utilizes the information collected from IoT devices in various applications. However, it is not easy to apply accurate IoT data immediately as IoT data collected according to network environment (interference, interference, etc.) are frequently missed or error occurs. In order to minimize mistakes in IoT data collected in an edge network environment, this paper proposes a management technique that ensures the reliability of IoT data by randomly generating signature values of IoT data and allocating only Security Information (SI) values to IoT data in bit form. The proposed technique binds IoT data into a blockchain by applying multiple hash chains to asymmetrically link and process data collected from IoT devices. In this case, the blockchainized IoT data uses a probability function to which a weight is applied according to a correlation index based on deep learning. In addition, the proposed technique can expand and operate grouped IoT data into an n-layer structure to lower the integrity and processing cost of IoT data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.128-130
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• 2021

This study aims to implement a Regression Analysis system for predicting the appropriate production of flatfish. Due to Korea's signing of FTAs with countries around the world and accelerating market opening, Korean flatfish farming businesses are experiencing many difficulties due to the specificity and uncertainty of the environment. In addition, there is a need for a solution to problems such as sluggish consumption and price drop due to the recent surge in imported seafood such as salmon and yellowtail and changes in people's dietary habits. in this study, Using the python module, xlwings, it was used to obtain for the production amount of flatfish and to predict the amount of flatfish to be produced later. was used to predict the amount of flatfish to be produced in the future. Therefore, based on the analysis results of this prediction of flatfish production, the flatfish aquaculture industry will be able to come up with a plan to achieve an appropriate production volume and control supply and demand, which will reduce unnecessary economic loss and promote new value creation based on data. In addition, through the data approach attempted in this study, various analysis techniques such as artificial neural networks and multiple regression analysis can be used in future research in various fields, which will become the foundation of basic data that can effectively analyze and utilize big data in various industries.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.122-124
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• 2022

Handling Passenger Traffic is the main challenge for designing an elevator group-control algorithm. Advanced control systems such as Hyundai's Destination Selection System(DSS) lets passengers select the destination by pressing on a selecting screen, and the systems have shown great efficiency. However, the algorithm cannot be applied to the general elevator control system due to the expensive cost of the technology. Often many elevator systems use Nearest Car(NC) algorithms based on the SCAN algorithm, which results in time efficiency problems. In this paper, we designed an elevator group-control algorithm for specific buildings that have approximate timetable data for most of the passengers in the building. In that way, it is possible to predict the destination and the location of passenger calls. The algorithm consists of two parts; the waiting function and the assignment function. They evaluate elevators' actions with respect to the calls and the overall situation. 10 different timetables are created in reference to a real timetable following midday traffic and interfloor traffic. The specific coefficients in the function are set by going through the genetic algorithm process that represents the best algorithm. As result, the average waiting time has shortened by a noticeable amount and the efficiency was close to the known DSS result. Finally, we analyzed the algorithm by evaluating the meaning of each coefficient result from the genetic algorithm.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.165-184
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• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Journal of Sericultural and Entomological Science
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• v.9
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• pp.21-25
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• 1969

Various formulae for estimation of leaf production in mulberry trees were investigated and obtained. Four varieties of mulberry trees were used as the materials, and four characters. namely branch length (X, 1). branch diameter (X, 2). leaf number per branch (X, 3), and leaf area per branch (X, 4). were studies. The formulae to eatimate the leaf yield of mulberry trees are as follows: 1. Y$_1$v$_1$＝-115.760＋0.068X$_1$＋165.756X$_2$ Y$_1$v$_2$＝-221.500＋1.768X$_1$＋38.152X$_2$ Y$_1$v$_3$＝-253.826-0.116X$_1$＋289.507X$_2$ Y$_1$v$_4$＝ -157.559＋1.063X$_1$＋106.088X$_2$ where Y$_1$v$_1$, Y$_1$v$_2$, Y$_1$v$_3$, Y$_1$v$_4$, are showed the estimated yield of the each variety, namely Gaeryang souban, Ilchirye, Nosang. and Suwon Sang No. 4, respectively. X$_1$ and X$_2$ denote the measured values of branch length and branch diameter, respectively. 2. Y$\sub$7/v$_1$＝-118.478-0.665X$_1$＋184.445X$_2$＋2.346X$_3$ Y$\sub$7/v$_2$＝-217.432＋2.062X$_1$＋35.668X$_2$-1.058X$_3$ Y$\sub$7/v$_3$＝-206. 249-0.739X$_1$＋268.08X$_2$＋2.770X$_3$ Y$\sub$7/v$_4$＝-153.383＋0.009X$_1$＋2.024X$_2$＋0.171X$_3$where Y$\sub$7/v$_1$, Y$\sub$7/v$_2$, Y$\sub$7/v$_3$, Y$\sub$7/v$_4$, are the estimated yield of the each variety, namely Gaeryang. Souban, Ilichirye, Nosang, and Suwon Sang No. 4, respectively. X$_1$, X$_2$, X$_3$, denote the measured values of each character. branch length, branch diameter and leaf number per branch, respectively. 3. Y$\sub$11/v$_1$＝82. 567-1.283X$_1$＋15.501X$_2$＋0.640X$_3$＋3.511X$_4$ Y$\sub$11/v$_2$＝136.411＋0.311X$_1$＋1.921X$_2$-0. 217X$_3$＋0.214X$_4$ Y$\sub$11/v$_3$＝150.2Z7-0.139X$_1$＋11.788X$_2$＋0.143X$_3$＋0.381X$_4$ Y$\sub$11/v$_4$＝160.850＋0.323X$_1$＋66.076X$_2$-0.794X$_3$＋2..614X$_4$ where Y$\sub$11/v$_1$, Y$\sub$11/v$_2$, Y$\sub$11/v$_3$, Y$\sub$11/v$_4$, are the estimated yield values of four varieties, and X$_1$, X$_2$, X$_3$, X$_4$ denote the measured values of four characters. namely branch length, branch diameter. leaf number per branch and leaf area per branch. respectively. The estimation method of mulberry leaf yield by measurement of some characters, branch length. branch diameter. leaf number per branch and leaf area per branch. could be the better method to determine the leaf yield of mulberry trees without destroying the leaves and without weighting the leaves of mulberry trees than the other methods.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of agricultural medicine and community health
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• v.38 no.4
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• pp.217-228
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• 2013

Objective: This study was performed to identify associations between the practice of health behaviors and awareness of metabolic syndrome (MetS) among adults aged 19 to 64 years in the Gwangju-Jeonnam area. Methods: This study utilized data from the 2010 Community Health Survey (CHS). Health behaviors considered in this study were smoking, alcohol drinking, physical activity, low-salt diet, and perception of stress. The index for the health behaviors was calculated as the sum of the practice of each health behavior (range: 0-5). The analysis was weighted with a complex sampling design, and the chi-square test and multiple logistic regression analysis were used to identify the association between the practice of health behaviors and awareness of MetS. Results: A total of 19.8% of the population were aware of MetS. The perception of MetS was statistically significantly associated with healthy behaviors, including nonsmoking (aOR = 1.33, 95% CI = 1.14-1.56), non-high-risk drinking (aOR = 1.54, 95% CI = 1.27-1.88), engagement in physical activity (aOR = 1.48, 95% CI = 1.28-1.72), and a low-salt diet (aOR = 1.30, 95% CI = 1.13-1.51). The ORs of the perception of MetS were significantly higher in patients with a health behavior index of 2 to 3 (aOR = 1.64, 95% CI = 1.01-2.66) and in those with an index of ${\geq}4$ (aOR = 2.47, 95% CI = 1.51-4.04) than in those with an index of 0. Among all health behaviors, physical activity had the highest OR for the perception of MetS (aOR = 1.50, 95% CI = 1.29-1.74). Conclusions: This study revealed associations between health behaviors, especially physical activity, and awareness of MetS. Therefore, integrated health promotion programs may be needed to enhance awareness of MetS and to effectively prevent MetS and non-communicable diseases.