• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.67-74
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• 2024

In this paper, we present a study aimed at analyzing how different rainfall measurement methods affect the performance of reservoir water level predictions. This work is particularly timely given the increasing emphasis on climate change and the sustainable management of water resources. To this end, we have employed rainfall data from ASOS, AWS, and Thiessen Network-based measures provided by the KMA Weather Data Service to train our neural network models for reservoir yield predictions. Our analysis, which encompasses 34 reservoirs in Jeollabuk-do Province, examines how each method contributes to enhancing prediction accuracy. The results reveal that models using rainfall data based on the Thiessen Network's area rainfall ratio yield the highest accuracy. This can be attributed to the method's accounting for precise distances between observation stations, offering a more accurate reflection of the actual rainfall across different regions. These findings underscore the importance of precise regional rainfall data in predicting reservoir yields. Additionally, the paper underscores the significance of meticulous rainfall measurement and data analysis, and discusses the prediction model's potential applications in agriculture, urban planning, and flood management.

• Information Systems Review
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• v.18 no.3
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• pp.1-29
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• 2016

The radical changes in the business environment have motivated firms to exert serious efforts in managing sustainable development. This study identified the effects of sustainable development on the perceived performance of firms from the viewpoint of the balanced scorecard. Independent variables include economic efforts (of efficiency and ethic of accounting and fairness), environmental efforts (management and energy control), and social efforts (consumer protection and contribution for local community). The result from the analysis of data collected in this research suggests that sustainable development efforts are the critical sources of the incorporated performance of firms. The consumer protection efforts of the local community determine the competitiveness of a firm in managing social responsibility and creating value and social activities. Efforts to reconsider efficiency determine the competitiveness of a firm, becoming the critical factors that determine sustainable performance. Energy control facilitates value creation for the environment through cooperation and harmonization with nature, resulting in sustainable business performances through the vitalization of practical establishments and operations. Sustainable management needs to meet international standards, cooperation, and harmony. These standards are based on the economic, environmental, and social efforts that enable firms to adopt sustainable management efforts that are suitable for their own systems.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.106-116
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• 2024

Purpose: This paper conducts IoT and CCTV-based safety monitoring to analyze accidents and potential risks occurring at construction sites, and detect and analyze risks such as falls and collisions or abnormalities and to establish a system for early warning using devices like a walkie-talkie and chatbot service. Method: A safety management service model is presented through smart construction technology case studies at the construction site and review a relevant literature analysis. Result: According to 'Construction Accident Statistics,' in 2021, there were 26,888 casualties in the construction industry, accounting for 26.3% of all reported accidents. Fatalities in construction-related accidents amounted to 417 individuals, representing 50.5% of all industrial accident-related deaths. This study suggests implementing AI chatbot services for construction site safety management utilizing IoT-based health monitoring technologies in smart construction practices. Construction sites where stakeholders such as workers participate were demonstrated by implementing an artificial intelligence chatbot system by selecting major risk areas within the workplace, such as scaffolding processes, openings, and access to hazardous machinery. Conclusion: The possibility of commercialization was confirmed by receiving more than 90 points in the satisfaction survey of participating workers regarding the empirical results of the artificial intelligence chatbot service at construction sites.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.841-853
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• 2024

This paper proposes an optimization method for the GF(2)[x] multiplication operation in HQC on AVX2. HQC is a candidate in NIST PQC standardization round 4 and is a binary code-based key exchange algorithm. The multiplication operation is one of the most time-complex operations in HQC, accounting for about 30% of the total clock cycles in the AVX2 environment. For the optimization, we used Karatsuba and Toom-Cook algorithms. Both algorithms are based on divide-and-conquer methods, which require multiplications of smaller order within them. We propose a method to optimize polynomial multiplication in HQC by finding the most efficient combination of Karatsuba and Toom-Cook algorithms, and compare the performance of the proposed method based on the implementation submitted to the PQC standardization. The results of the comparison demonstrate a performance improvement of 4.5%, 2.5%, and 30.3% over the GF(2)[x] multiplications of original hqc-128, -192, and -256. When applied to key generation, encapsulation, and decapsulation, the performance improvement over the original HQC is 2.2%, 2.4%, and 2.3% for hqc-128, 1.6%, 4.2%, and 2.6% for hqc-192, and 13.3%, 14.7%, and 13.3% for hqc-256, respectively.

• Journal of Korea Water Resources Association
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• v.46 no.4
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• pp.401-412
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• 2013

Water footprint of a product and service is the volume of freshwater used to produce the product, measured in the life cycle or over the full supply chain. Since water footprint assessment helps us to understand how human activities and products relate to water scarcity and pollution, it can contribute to seek a sustainable way of water use in the consumption perspective. For the introduction of WFP scheme, it is indispensable to construct water inventory/accounting for the assessment, but there is no database in Korea to cover all industry sectors. Therefore, the aim of the study is to develop water footprint inventory within a nation at 403 industrial sectors using Input-Output Analysis. Water uses in the agricultural sector account for 79% of total water, and industrial sector have higher indirect water at most sectors, which is accounting for 82%. Most of the crop water is consumptive and direct water except rice. The greatest water use in the agricultural sectors is in rice paddy followed by aquaculture and fruit production, but the greatest water use intensity was not in the rice. The greatest water use intensity was 103,263 $m^3$/million KRW for other inedible crop production, which was attributed to the low economic value of the product with great water consumption in the cultivation. The next was timber tract followed by iron ores, raw timber, aquaculture, water supply and miscellaneous cereals like corn and other edible crops in terms of total water use intensity. In holistic view, water management considering indirect water in the industrial sector, i.e. supply chain management in the whole life cycle, is important to increase water use efficiency, since more than 56% of total water was indirect water by humanity. It is expected that the water use intensity data can be used for a water inventory to estimate water footprint of a product for the introduction of water footprint scheme in Korea.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.4
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• pp.36-48
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• 2018

This study surveyed 599 elementary schools in Seoul to provide measures for the quantitative expansion and sustainable operation of environmentally-friendly school garden. Of all schools, 161 schools had formed and were operating school gardens. The total area of school gardens was $166,901m^2$ and the mean area was $131.2m^2$ in elementary, junior high and high schools in Seoul. Meanwhile, the total area of school gardens was $65,493m^2$ and the mean area was $363m^2$ in 161 schools that participated in the survey, indicating $1.15m^2$ per student. Of these schools, 11.8% were operating gardens themselves, while 50.3% were operating gardens that had been newly renovated or environmentally improved by institutional support projects after initially managing gardens themselves. According to the locations of school gardens, mixed-type gardening (a combination of school gardening and container vegetable gardening) accounted for 34.8%, followed by school gardening at 32.9%, container vegetable gardening at 29.2%, and suburb community gardening at 3.1%. Those in charge of garden operations were teachers at 51.6%, comprising the largest percentage. Facilities built when forming the garden included storage facilities for small-scale greenhouses and farming equipment at 26.1%, accounting for the largest percentage. No additional facilities constructed accounted for 21.7%. The greatest difficulty in operating gardens was garden management at 34.2%. The most needed elements for the sustainable operation of gardens were improvement in physical environment and the need for hiring a paid garden, each accounting for 32%. The most important purpose for school gardening was creating educational environments (81.6%). The major source for gaining information on garden management was consultation from acquaintances (67.8%). Schools that utilize plant waste from gardens as natural fertilizers accounted for 45.8% of all schools. Responses to the impact of operating school gardens for educational purpose were positive in all schools as 'very effective' in 63.2% and 'effective' in 36.8%. This study was meaningful in that it intended to identify the current status of the operation of school gardens in elementary schools in Seoul, support the formation of school gardens appropriate for each school with sustainable operation measures, implement a high-quality education program, develop teaching materials, expand job training opportunities for teachers in charge, devise measures to support specialized instructors, and propose the need for a garden management organization.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.

• Journal of the Korean Society of School Health
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• v.11 no.2
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• pp.297-307
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• 1998

The purpose of this paper is to understand the prescription and management of medicines by school-nurses. A survey was mailed to 199 school-nurses in elementary and secondary schools in Pusan from February 10 to March 31, 1997. It was shown that 97.0% of the schools have visiting school-doctors and only 29.6% have visiting school-pharmacists. 36.7% of the respondents don't know the amount of this annual health-related budget. Concerning the annual budget of purchasing medicines, 50.4% of the elementary schools spend 210,000 won to 400,000 won and 45.0% of the secondary schools spend more than 610,000 won. 56.3% of the respondents said the budget was enough, but 5% said it was not. 70.9% of the schools purchase medicines twice a year. The average number of students visiting the nurse in a year are 1,892 in elementary schools, 1.6 times per student and 2,471 in secondary schools, 1.7 times per student, respectively. The annual average number of students who were prescribed medicine a year are 1,804 in elementary schools, 1.5 times per student, 2,372 in secondary schools, 1.7 times per student. The percentage of students who are prescribed internal medicines was 45.5% in elementary, schools and 61.3% in secondary schools, respectively. To the preralence sicknesses, the wound was the most common, accounting for 42.7% in elementary and 22.6% in secondary schools. Next was abdominal pain, indigestion, and headaches in elementary schools; and colds, indigestion, and abdominal pain in secondary schools, respectively. To the dirersity of medicines prescribed: internal medicines 29 for abdominal pain, 25 for indigestion, 8 for physiological pain, 13 for headaches, 30 for colds, and 10 for eye disease; external medicines 2 for skin disease, 10 for toothaches and 31 for other sicknesses. 42.7% of the respondents said the schools have enough medicines, but 7.6% said that schools need more. 50.8% of the respondents said they get information on medicines from TV advertisements or medicine-related books, 16.6% get information from visiting pharmacists. More experienced nurse-teachers are likely to get information from visiting pharmacists, but 37.5% of the respondents who have less then four year experience in school get information through other nurse-teachers before deciding to buy medicines. To the choice of medicines: 83.9% of the respondents said that they choose safe medicines with less side-effects. 40.7% responded that they write down the prescription history daily, but 6.1% said they do this only once in two or three months. To the confidence in prescriptions, 37.7% of the respondents said they are sure of the effectiveness of the medicines they prescribe. To what extent the nurse-teachers prescribe, 50.3% said they prescribe to the level of anagelics, and 21.1% prescribe to anti-histamines and antibiotics. 80.4% said that the details of illnesses and medicines to be prescribed in school should be regulated by a school health-care law. To the problems in prescription, 79.9% of the respondents worry about abuse by students who want prescriptions but have no serious illnesses, 57.8% worrg about the lack of information on medicines and dosage. And 55.8% said they can't tell the difference between medicines whose brands are different, but bare the same ingredients. The conclusion of this study is that a health education program is necessary to prevent the misuse or abuse by students and a continuing education program for school-nurses is needed to solve the problems related to the purchasing and prescription of medicines. The criteria of the prescription of medicines also should be regulated by a school health-care law or management acts.

• Asia pacific journal of information systems
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• v.17 no.3
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• pp.105-130
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• 2007

Recently, there is a substantial interest in implementing Business Process Management System(BPMS) among enterprises with the purpose of business process innovation. BPMS redesigns and coordinates business processes in terms of both automated steps and human involvement in order to maximize the value of both involved people and systems. The reason why BPMS is getting attention from top managers is that it has the possibility to optimize the business processes by cycling the process of modeling, execution, monitoring, evaluation, and redesigning work processes. Thus, it has created high expectations about not only productivity improvement but also business process innovation. However. having an innovative nature, which is used for process innovation, BPMS implementation has great potential to stir up employee resistance. The analysis and the discussion about the prevention of the resistance against IS(Information Systems) is important because IS change the way people work and also alter the power structure within the organization, in general. The purpose of this study is to investigate factors that have an impact on the effective adoption of BPMS at the enterprise level. To find out these factors, this study considers two characteristics of BPMS: First. BPMS shares some characteristics with other enterprise-wide IS such as ERP. Second, it has special BPMS-specific characteristics. Due to the lack of previous research on BPMS adoption, interviews were carried out with IT-consultants and CIOs who conducted BPMS projects previously to find out BPMS-specific features that would make BPMS unique when compared to other enterprise-wide IS. As a result, the monitoring function was chosen as the main BPMS-specific factor. Thus, this paper reviewed studies both on enterprise-wide IS adoptions, which applied Technology Acceptance Model (TAM) and secondly on computer based monitoring to find out factors that would influence the employees' perception on the monitoring function of BPMS. Based on the literature review, the study suggested three factors that would have an impact on the employee's perception of the monitoring function: fairness of enterprise evaluation system, fairness of the boss, and self-efficacy of their work. Three factors that would impact the enterprise-wide IS adoption were also set: the shared belief in the benefit of BPMS, training, and communication. Then, these factors were integrated with TAM. Structural equation modeling was used to test hypotheses, out factors that would impact the employees' perception on the monitoring function of BPMS. Based on the literature review the study suggested three factors that would have an impact on the employee's perception of the monitoring function: fairness of enterprise evaluation system, fairness of the boss, and self-efficacy of their work. Three factors that would impact the enterprise-wide IS adoption were also set: the shared belief in the benefit of BPMS, training, and communication. Then, these factors were integrated with TAM. Structural equation modeling was used to test hypotheses. The data analysis results showed that two among three monitoring function related factors - enterprise evaluation system and fairness of the boss - were significant. This implies that employees would worry less about the BPMS implementation as long as they perceive the monitoring results will be used fairly for their performance evaluation. However, employees' high self-efficacy on their job was not a significant factor in their perception of the usefulness of BPMS. This is related to cases that showed employees resisted against the information systems because they automated their works (Markus, 1983). One specific case was an electronic company, where the accounting department workers were requested to redefine their job because their working processes were automated due to BPMS implementation.