• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.57-79
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• 2016

It is a rising trend that the number of users using one of the social media channels, the Social Network Service, so called the SNS, is getting increased. As per to this social trend, more companies have interest in this networking platform and start to invest their funds in it. It has received much attention as a tool spreading and expanding the message that a company wants to deliver to its customers and has been recognized as an important channel in terms of the relationship marketing with them. The environment of media that is radically changing these days makes possible for companies to approach their customers in various ways. Particularly, the social network service, which has been developed rapidly, provides the environment that customers can freely talk about products. For companies, it also works as a channel that gives customized information to customers. To succeed in the online environment, companies need to not only build the relationship between companies and customers but focus on the relationship between customers as well. In response to the online environment with the continuous development of technology, companies have tirelessly made the novel marketing strategy. Especially, as the one-to-one marketing to customers become available, it is more important for companies to maintain the relationship marketing with their customers. Among many SNS, Facebook, which many companies use as a communication channel, provides a fan page service for each company that supports its business. Facebook fan page is the platform that the event, information and announcement can be shared with customers using texts, videos, and pictures. Companies open their own fan pages in order to inform their companies and businesses. Such page functions as the websites of companies and has a characteristic of their brand communities such as blogs as well. As Facebook has become the major communication medium with customers, companies recognize its importance as the effective marketing channel, but they still need to investigate their business performances by using Facebook. Although there are infinite potentials in Facebook fan page that even has a function as a community between users, which other platforms do not, it is incomplete to regard companies' Facebook fan pages as communities and analyze them. In this study, it explores the relationship among customers through the network of the Facebook fan page users. The previous studies on a company's Facebook fan page were focused on finding out the effective operational direction by analyzing the use state of the company. However, in this study, it draws out the structural variable of the network, which customer committment can be measured by applying the social network analysis methodology and investigates the influence of the structural characteristics of network on the business performance of companies in an empirical way. Through each company's Facebook fan page, the network of users who engaged in the communication with each company is exploited and it is the one-mode undirected binary network that respectively regards users and the relationship of them in terms of their marketing activities as the node and link. In this network, it draws out the structural variable of network that can explain the customer commitment, who pressed "like," made comments and shared the Facebook marketing message, of each company by calculating density, global clustering coefficient, mean geodesic distance, diameter. By exploiting companies' historical performance such as net income and Tobin's Q indicator as the result variables, this study investigates influence on companies' business performances. For this purpose, it collects the network data on the subjects of 54 companies among KOSPI-listed companies, which have posted more than 100 articles on their Facebook fan pages during the data collection period. Then it draws out the network indicator of each company. The indicator related to companies' performances is calculated, based on the posted value on DART website of the Financial Supervisory Service. From the academic perspective, this study suggests a new approach through the social network analysis methodology to researchers who attempt to study the business-purpose utilization of the social media channel. From the practical perspective, this study proposes the more substantive marketing performance measurements to companies performing marketing activities through the social media and it is expected that it will bring a foundation of establishing smart business strategies by using the network indicators.

• Journal of Korean Home Economics Education Association
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• v.30 no.2
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• pp.77-102
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• 2018

The purpose of this study was to contribute to Home Economics(HE) teachers' culture by figuring out acknowledging characteristics of cultures of HE teachers and impeding factors on development of HE education. For this intensive interview were used. Intensive interviews were made with 14 HE teachers who completed coursework for master's or doctor's program of graduate school and belong to HE Teachers' Study Associations of each region or Korean Home Economics Education Association and analyzed by subject analysis method. The results of the study are as follows. First, HE teachers establish the philosophy of HE education, and practice education to provide profit to adolescents, their families, as well as society through HE class with their belief that HE is a practical and critical subject to benefit individual adolescents, families, and society. Second, HE teachers form culture to make an effort to continue to improve their expertises by attending graduate school, joining HE teachers' associations to enhance teaching methods, evaluation methods, and work ability or disclosing their own class. Third, HE teachers settle culture to conduct classes focusing on practical issues by converting the paradigm of HE education to that of practical critique. They also see that the system of three actions(technical action, communicative action, and emancipative action) should be applied in circulating ways to improve quality and value of life. Forth, for impeding factors of development of HE education, there are educational system and social recognition. However, with HE teachers' efforts, HE education settles well, as it reflects demands from students and society, finds students' talents, and actualizes its own goals. HE teachers believe that student will recognize that HE education is necessary for happiness of individuals and families. As a way to develop Home Economics teacher culture, Home Economics teachers should have the opportunity to develop more Home Economics teachers by participating in and working in research sessions in each area. It also called for a control tower to enable and lead collaborative networks between local Home Economics curriculum research committees. The Korean Home Economics Education Association should play a central role in the academic research community of each region and be able to help Home Economics teachers by moving more quickly and systematically to cope with the upcoming changes in education. Finally, participants said that in order to prepare a basic framework for the change in Home Economics education, practical critical Home Economics teacher training are needed. To this end, students can understand the essence of Home Economics education and establish their identity by taking a deeper Home Economics education curriculum philosophy for Home Economics teacher training.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.4
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• pp.415-423
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• 2010

Pore network models are useful tools to investigate soil pore geometry. These models provide quantitative information of pore geometry from 3D images. This study presents a pore network model to quantify pore structure and hydraulic characteristics. The objectives of this work were to apply the pore network model to characterize pore structure from large images to quantify pore structure, calculate water retention and hydraulic conductivity properties from a three dimensional soil image, and to combine measured hydraulic properties from experiments with calculated hydraulic properties from image. Soil samples were taken from a site located at the Baltimore science center, which is located inside of the city. Undisturbed columns were taken from the site and scanned with a computer tomographer at resolutions of 22 ${\mu}m$. Pore networks were extracted by medial-axis transformation and were used to measure pore geometry from one of the scanned samples. Water retention and unsaturated hydraulic conductivity values were calculated from the soil image. Properties of soil bulk density, water retention and unsaturated hydraulic conductivity were measured from three replicates of scanned soil samples. 3D image analysis provided accurate detailed pore properties such as individual pore volumes, pore length, and tortuosity of all pores. These data made possible to calculate accurate estimations of water retention and hydraulic conductivity. Combination of the calculated and measured hydraulic properties gave more accurate information on pore sizes over wider range than measured or calculated data alone. We could conclude that the hydraulic property computed from soil images and laboratory measurements can describe a full structure of intra- and inter-aggregate pores in soil.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.173-178
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• 2010

A privately held, third generation family owned company, Boom Technologies, Inc. (BTI), a provider of products and services to the electric utility, telecommunications and contractor markets, continues to make progress in exporting. Although export sales only equaled 5% of total revenue in 2008, BTI has an entire export division. Their export division's Managing Director reveals the trial and errors of a privately held company and their quest for success overseas. From its inception, BTI has always believed its greatest asset is its employees. When export sales struggled due to lack of strategy and direction, BTI hired a Managing Director for its export division. With leadership and guidance from BTI's president and from the Managing Director, they utilized the department's skills and knowledge. Structural changes were made to expand their market presence abroad and increase export sales. As a result, export sales increased four-fold, area managers in new countries were added and distribution networks were successfully cultivated. At times, revenue generation was difficult to determine due to the structure of the company. Therefore, in 1996, the export division was restructured as a limited liability company. This allowed the company to improve the tracking of revenue and expenses. Originally, 80% of BTI's export sales came from two countries; therefore, the initial approach to selling overseas was not reaching their anticipated goals of expanding their foreign market presence. However, changes were made and now the company manages the details of selling to over 80 countries. There were three major export expansion challenges noted by the Managing Director: 1. Product and Shipping - The major obstacle for BTI was product assembly. Originally, the majority of the product was assembled in the United States, which increased shipping and packaging costs. With so many parts specified in the order, many times the order would arrive with parts missing. The missing parts could equate to tens of thousands of dollars. Shipping these missing parts separately in another shipment also cost tens of thousands of dollar, plus a delivery delay time of six to eight weeks; all of which came out of the BTI's pockets. 2. Product Adaptation - Safety and product standards varied widely for each of the 80 countries to which BTI exported. Weights, special licenses, product specification requirements, measurement systems, and truck stability can all differ from country to country and can serve as a type of barrier to entry, making it difficult to adapt products accordingly. Technical and safety standards are barriers that serve as a type of protection for the local industry and can stand in the way of successfully pursuing foreign markets. 3. Marketing Challenges - The importance of distribution creates many challenges for BTI as they attempt to determine how each country prefers to operate with regard to their distribution systems. Some countries have competition from a small competitor that only produces one competing product; whereas BTI manufactures over 100 products. Marketing material is another concern for BTI as they attempt to push marketing costs to the distributors. Adapting the marketing material can be costly in terms of translation and cultural differences. In addition, the size of paper in the United States differs from those in some countries, causing many problems when attempting to copy the same layout and With distribution being one of several challenges for BTI, the company claims their distribution network is one of their competitive advantages, as the location and names of their distributors are not revealed. In addition, BTI rotates two offerings yearly: training to their distributors one year and then the next is a distributor's meeting. With a focus on product and shipping, product adaptation, and marketing challenges, the intricacies of selling overseas takes time and patience. Another competitive advantage noted is BTI's cradle to grave strategy, where they follow the product from sale to its final resting place, whether the truck is leased or purchased new or used. They also offer service and maintenance plans with a detailed cost analysis provided to the company prior to purchasing or leasing the product. Expanding abroad will always create challenges for a company. As the Managing Director stated, "If you don't have patience (in the export business), you better do something else." Knowing how to adapt quickly provides BTI with the skills necessary to adjust to the changing needs of each country and its own unique challenges, allowing them to remain competitive.

• Journal of Environmental Health Sciences
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• v.37 no.2
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• pp.102-112
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• 2011

Objectives: The purpose of this study is to analyze the effects of chronic exposure by welders to manganese (Mn) through an analysis of the degree of brain activity in different activities such as cognition and motor activities using the neuroimaging technique of functional magnetic resonance imaging (fMRI). The neurotoxic effect that Mn has on the brain was examined as well as changes in the neuro-network in motor areas, and the usefulness of fMRI was evaluated as a tool to determine changes in brain function from occupational exposure to Mn. Methods: A survey was carried out from July 2010 to October 2010 targeting by means of a questionnaire 160 workers from the shipbuilding and other manufacturing industries. Among them, 14 welders with more than ten years of job-related exposure to Mn were recruited on a voluntary basis as an exposure group, and 13 workers from other manufacturing industries with corresponding gender and age were recruited as a control group. A questionnaire survey, a blood test, and an fMRI test were carried out with the study group as target. Results: Of 27 fMRI targets, blood Mn concentration of the exposure group was significantly higher than that of the control group (p<0.001), and Pallidal Index (PI) of the welder group was also significantly higher than that of the control group (p<0.001). As a result of the survey, the score of the exposure group in self-awareness of abnormal nerve symptoms and abnormal musculoskeletal symptoms was higher than those of the control group, and there was a significant difference between the two groups (p<0.05, respectively). In the correlation between PI and the results of blood tests, the correlation coefficient with blood Mn concentration was 0.893, revealing a significant amount of correlation (p<0.001). As for brain activity area within the control group, the right and the left areas of the superior frontal cortex showed significant activity, and the right area of superior parietal cortex, the left area of occipital cortex and cerebellum showed significant activity. Unlike the control group, the exposure group showed significant activity selectively on the right area of premotor cortex, at the center of supplementary motor area, and on the left side of superior temporal cortex. In the comparison of brain activity areas between the two groups, the exposure group showed a significantly higher activation state than did the control group in such areas as the right and the left superior parietal cortex, superior temporal cortex, and cerebellum including superior frontal cortex and the right area of premotor cortex. However, in nowhere did the control group show a more activated area than did the exposure group. Conclusions: Chronic exposure to Mn increased brain activity during implementation of hand motor tasks. In an identical task, activation increased in the premotor cortex, superior temporal cortex, and supplementary motor area. It was also discovered that brain activity increase in the frontal area and occipital area was more pronounced in the exposure group than in the control group. This result suggests that chronic exposure to Mn in the work environment affects brain activation neuro-networks.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.4
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• pp.488-498
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• 2007

The purposes of this study were to: a) provide evidences concerning the effects of emotional leadership b) examine the impacts of emotional leadership on employee-related variables, 'job satisfaction', 'organizational commitment', 'organizational performance' and 'turnover intention', and c) identify a conceptual framework underlying emotional leadership. A survey was conducted from August 23 to November 3, 2005 to collect data from mid-level managers in food service company headquarters (N=219). Statistical analyses were completed using SPSS Win (12.0) for descriptive, reliability, factor and correlation analyses and AMOS (5.0) for confirmatory factor analysis and structural equation modeling. The main results of this study were as follows. First, the managers gave the highest point to their leaders in the emotional leadership competence 'organizational awareness : reading the currents, decision networks, and politics at the organizational level' and gave the lowest point in the emotional leadership competence 'influence: wielding effective tactics for persuasion'. Second, the means of job satisfaction was above the midpoint (3 points). Employees' job satisfaction with 'coworkers' was relatively high. However, the extents of satisfaction with 'payroll' 'promotion', and 'work environment' were relatively low. Third, the organizational commitment was above the midpoint (3 points). In the organizational commitment, 'loyalty' factor was higher than 'commitment' factor. Fourth, the means of organizational performance was above the midpoint. The highest organizational performance variable was 'internal efficiency; trying to reduce cost' and the lowest organizational performance variable was 'internal fairness ; equitable treatment and all are treated with respect with no regard to status and grade'. Fifth, most respondents intended on 'thinking of quitting ; towards turnover process'. Sixth, the test of hypothesis using structural equation modeling found that emotional leadership produced p[Isitive effects on job attitude and job performance. Emotional leadership enhanced job satisfaction and organizational commitment, and in turn, employees' attitude positive effects on organizational performance; emotional leadership also had a direct impact on organizational performance