• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.328-342
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• 2022

The Internet of Things (IoT) is a technology that offers lucrative services in various industries to facilitate human communities. Important information on people and their surroundings has been gathered to ensure the availability of these services. This data is vulnerable to cybersecurity since it is sent over the internet and kept in third-party databases. Implementation of data encryption is an integral approach for IoT device designers to protect IoT data. For a variety of reasons, IoT device designers have been unable to discover appropriate encryption to use. The static support provided by research and concerned organizations to assist designers in picking appropriate encryption costs a significant amount of time and effort. IoTES is a web app that uses machine language to address a lack of support from researchers and organizations, as ML has been shown to improve data-driven human decision-making. IoTES still has some weaknesses, which are highlighted in this research. To improve the support, these shortcomings must be addressed. This study proposes the "IoTES with Security" model by adding support for the security level provided by the encryption algorithm to the traditional IoTES model. We evaluated our technique for encryption algorithms with available security levels and compared the accuracy of our model with traditional IoTES. Our model improves IoTES by helping users make security-oriented decisions while choosing the appropriate algorithm for their IoT data.

• Journal of Science and Technology Studies
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• v.18 no.2
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• pp.7-41
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• 2018

The projects of citizen science which is originated from citizen data collecting action driven by governmental institutes and science associations have been implemented with different form of collaboration with scientists. The themes of citizen science has extended from the ecology to astronomy, distributed computing, and particle physics. Citizen science could contribute to the advancement of science through cost-effective science research based on citizen volunteer data collecting. In addition, citizen science enhance the public understanding of science by increasing knowledge of citizen participants. The community-led citizen science projects could raise public awareness of environmental problems and promote the participation in environmental problem-solving. Citizen science projects based on local tacit knowledge can be of benefit to the local environmental policy decision making and implementation of policy. These social values of citizen science make many countries develop promoting policies of citizen science. The korean government also has introduced some citizen science projects. However there are some obstacles, such as low participation of citizen and scientists in projects which the government has to overcome in order to promote citizen science. It is important that scientists could recognize values of citizen science through the successful government driven citizen science projects and the evaluation tool of scientific career could be modified in order to promote scientist's participation. The project management should be well planned to intensify citizen participation. The government should prepare open data policy which could support a data reliability of the community-led monitoring projects. It is also desirable that a citizen science network could be made with the purpose of sharing best practices of citizen science.

• The Research Journal of the Costume Culture
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• v.26 no.2
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• pp.251-263
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• 2018

As mobile shopping has increased in the new media age, fashion consumers' decision making and product consumption processes have changed. The volume of consumer-driven information has expanded since media and social networking sites have enabled consumers to share information they obtain. The purpose of this study was to determine the factors affecting information searching strategies and information sharing about fashion products. An online survey collected data from 466 respondents, relating to the influence of product price level and consumer SNS commitment level on information search and information sharing. Experimental design of three product price level and two consumer SNS commitment level was used. Analysis of the data identified factors in fashion information searching as ongoing searching, prepurchase web portal information search, and prepurchase marketing information search. For low-price fashion products, prepurchase product-detail influenced intention to share information. For mid-priced products, ongoing search significantly affected intention to share information. Both ongoing search and prepurchase marketing information search showed significant effects for high-price products. Consumers who are more committed to SNS engaged in significantly more searching in all aspects of information search factors. Significant interaction effect was detected for consumer SNS commitment level and product price level. When consumers with low consumer SNS commitment search for information on lower-priced fashion products, they are less likely do a prepurchase web portal information search.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.4
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• pp.29-41
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• 2014

The Korean government has purchased land properties alongside any significant water bodies before setting up the buffers to secure water qualities. Since the annual budgets are limited, however, there has always been the issue of which land parcels ought to be given the priority. Therefore, this study aims to develop efficient mechanism for land acquisition priorities in stream corridors that would ultimately be vegetated for riparian buffer zones. The criteria of land acquisition priority were driven through literary review along with experts' advice. The relative weights of their value and priorities for each criterion were computed using the Analytical Hierarchy Process(AHP) method. Major findings of the study are as follows: 1. The decision-making structural model for land acquisition priority focuses mainly on the reduction of non-point source pollutants(NSPs). This fact is highly associated with natural and physical conditions and land use types of surrounding areas. The criteria were classified into two categories-NSPs runoff areas and potential NSPs runoff areas. 2. Land acquisition priority weights derived for NSPs runoff areas and potential NSPs runoff areas were 0.862 and 0.138, respectively. This implicates that much higher priority should be given to the land parcels with NSPs runoff areas. 3. Weights and priorities of sub-criteria suggested from this study include: proximity to the streams(0.460), land cover(0.189), soil permeability(0.117), topographical slope(0.096), proximity to the roads(0.058), land-use types(0.036), visibility to the streams(0.032), and the land price(0.012). This order of importance suggests, as one can expect, that it is better to purchase land parcels that are adjacent to the streams. 4. A standard scoring system including the criteria and weights for land acquisition priority was developed which would likely to allow expedited decision making and easy quantification for priority evaluation due to the utilization of measurable spatial data. Further studies focusing on both point and non-point pollutants and GIS-based spatial analysis and mapping of land acquisition priority are needed.

• Journal of Distribution Science
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• v.11 no.10
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• pp.27-36
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• 2013

Purpose - This paper empirically studies the effects of advanced design innovation strategy on business performance, to investigate manufacturing industries that can develop design-driven-innovation strategies. Many researchers now recognize the importance of design in a CEO's decision-making process. To analyze these effects, this study deduces the definition of advanced design strategy by reviewing existing studies. The advanced design is a strategy that is applied to improve business performance instead of the appearance of a product for increasing its sales. In terms of business processes, the advanced design strategy is defined as the incorporation of business activities prior to the development of the product, to offer new experiences and values to users, from those designs. Research design/data/methodology - This paper establishes a model for empirical analysis. In this study, we derived factors of the characteristics of advanced design based on previous studies. We tried to investigate whether advanced design innovation strategy and entrepreneur's characteristics could have any impact on business performance. At the same time, we tried to find out the moderating effect of entrepreneurs' characteristics. The advanced design is made up of three elements: precedence, integration, and immersion of design activities. These three elements are independent variables for the model. The dependent variables are: increased rate of sales, R & D performance, and public image of the company. Specifically, this study establishes a CEO's characteristics as a moderating variable between the independent and dependent variables. Results - We proved that the level of entrepreneurs' characteristics has a moderating effect on the business performance. The findings of this study offer the following theoretical implications. The precedence of design activities positively affects the increased rate of sales by offering new experiences to users and creating new values. The integration of design activities also has a positive effect on the R&D performance. In addition, the immersion of design activities positively influences all the elements comprising business performance. The analysis of moderating variables elucidates that CEO's characteristics have a moderating role between precedence, integration, and immersion of design activities, and business performance. Conclusions - The practical implications of the study are as follows. This study contributes to the progression of advance design theories by conducting an empirical study on the advanced design concept. More importantly, the empirical study on the CEO group seeking exploratory innovation supports Verganti's "design-driven innovation" concept, according to which design can make innovation successful by offering useful values to users, as evident in the case of many innovative companies, such as Nintendo and Apple. Future studies need to investigate the reliability of practical examples, including the various activities of business. We suppose that there may be real differences between the results of this study and the applicative situation in the presence of a CEO group.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.91-105
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• 1999

Spatial data integration using multiple geo-based data sets has been regarded as one of the primary GIS application issues. As for this issue, several integration schemes have been developed as the perspectives of mathematical geology or geo-mathematics. However, research-based approaches for statistical/quantitative assessments between integrated layer and input layers are not fully considered yet. Related to this niche point, in this study, spatial data integration using multiple geoscientific data sets by known integration algorithms was primarily performed. For spatial integration by using raster-based GIS functionality, geological, geochemical, geophysical data sets, DEM-driven data sets and remotely sensed imagery data sets from the Ogdong area were utilized for geological thematic mapping related by mineral potential mapping. In addition, statistical/quantitative information extraction with respective to relationships among used data sets and/or between each data set and integrated layer was carried out, with the scope of multiple data fusion and schematic statistical assessment methodology. As for the spatial integration scheme, certainty factor (CF) estimation and principal component analysis (PCA) were applied. However, this study was not aimed at direct comparison of both methodologies; whereas, for the statistical/quantitative assessment between integrated layer and input layers, some statistical methodologies based on contingency table were focused. Especially, for the bias reduction, jackknife technique was also applied in PCA-based spatial integration. Through the statistic analyses with respect to the integration information in this case study, new information for relationships of integrated layer and input layers was extracted. In addition, influence effects of input data sets with respect to integrated layer were assessed. This kind of approach provides a decision-making information in the viewpoint of GIS and is also exploratory data analysis in conjunction with GIS and geoscientific application, especially handing spatial integration or data fusion with complex variable data sets.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.377-392
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• 2015

In today's data-driven society, we've been hearing a great deal about the power of Big Data over the last couple of years. At the same time, it has become the most important issue that the problems is caused by the data collection, management and utilization. Moreover, Big Data has a wide applications ranging from situation awareness, decision-making to the area to enable for the foreseeable future with man-made and analysis of data. It is necessary to process data into meaningful information given that the huge amount of structured and unstructured data being created in the private and the public sector, even in disaster management. This data should be public and private sector at the same time for the appropriate linkage analysis for effective disaster management. In this paper, we conducted a literature review and case study efficient Big Data to derive the revitalization of national disaster management. The study obtained data on the role and responsibility of the public sector and the private sector to leverage Big Data for promotion of national disaster management plan. Both public and private sectors should promote common development challenges related to the openness and sharing of Big Data, technology and expansion of infrastructure, legal and institutional maintenance. The implications of the finding were discussed.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Journal of Distribution Science
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• v.14 no.11
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• pp.75-82
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• 2016

Purpose - In order for actively pursuing medium and long term policies of Gangwon region to be effectively and efficiently driven, efficacious and practical development strategies are needed. In terms of regional revitalization in most regions that are dependent on the primary industry like Gangwon-do Province, the maintaining of local community becomes difficult and there are limitations on the support from the central government and local governments. Therefore, local communities need to implement measures not only to be financially independent but also maintain and activate themselves. And community business can be adopted to be a proper strategy to cope with this change. This study drew importance of a community business model appropriate for Gangwon-do region to figure out success factors. Research design, data, and methodology - This study aimed to come up with importance of community business model for Gangwon-do region by using AHP Method. AHP Method, which was developed by Professor Saaty in 1970', is a methodology to simplify complex problems for a rational decision making. A survey targeting related public officials and expert group was carried out and a total of 30 questionnaires were collected for the analysis. Results - Analysis model used in this study was to prioritize community business models of Gangwon-do region. The second hierarchy was divided according to local restoration type, local resource utilization type, environment improvement type, and life support type. The third hierarchy consisted of 5 items such as network, the middle structure, program, government support, and human resources to measure each importance. As a result, in the second hierarchy, local resource utilization type had the highest importance. In the third hierarchy, the middle structure had the highest importance, followed by government support, program, network, and human resources. Collectively, the results suggested that important critical factors of community business model of Gangwon-do region was the importance of local resource utilization model and the middle structure. Conclusions - Not only should projects that are already operating in the region but next community business projects that are planning in the Gangwon-do region should be practically operated in view of the importance and the models derived from this study.