• Management & Information Systems Review
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• v.36 no.1
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• pp.115-141
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• 2017

This study is to find the key factors of the Internet of Things for development in public sector. In previous studies, it is said that Internet of Things can work digital system without human operation and gives a lot of outputs(information) users. Generally, people are a subject of operating digital system in traditional way, while people are an object on the internet of things. In other words, it is possible to work digital system with only networking from things to things. After all, it is reported that these advantages of the Internet of Things make possible to reduce social costs significantly in public sector. However, despite the strengths of the Internet of Things, there is a specific user acceptance of the technology factor for the Internet of Things rarely. It means that developing of the Internet of Things only focuses on the final purpose. If the focus on development meet this purpose, the user is ignored for the specific reason that using a technique. As a result of this, many users gradually decrease the continuous using of the Internet of Things. Thus, in this study, we need to find what critical factors should reflect to the Internet of Things in public sector. To find this result, there is no choice to use Technology Acceptance Model(TAM). Many researchers have proved that Technology Acceptance Model is valid through the four process in model introduction, confirmation, expansion and refinement from 1986 to 2003. The results of this study showed that the result explanatory power of Internet of Things in public sector is the most important factor affecting only perceived social usefulness and ease of use. Finally, it can be seen that the user has a positive attitude toward use, which has a positive effect on the intention to use continuously. The implications of this study are summarized as follows: When the public Internet of Things service is provided, it means that the user can easily understand the result, and when the person and the object communicate the result to each other, they should be able to communicate with each other. This means that a lot of user effort is needed to understand the outcome of the public Internet of Things being provided.

• The Journal of Fisheries Business Administration
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• v.22 no.2
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• pp.75-99
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• 1991

A quantitative analysis was carried out to monitor the commercial yield level of walleye pollock Theragra chalcogramma in the east coast of Korea, based on available data on catch and fishing effort, catch per unit of effort including fish prices from 1911 to 1988, using a traditional yield model. The results from the quantitative assessment were based to estimate maximum economic yield (MEY) and optimal fishing effort (E-opt) at MEY. On the other hand, interaction aspects between danish seine fishery and trawl fishery mainly targeting walleye pollock in the east coast of Korea were studied to predict optimal situation in fishing effort level from economic point of view which gives the most benefits to the two fisheries. Total production of walleye pollock in 1911 when its catch record was begun for the first time was about 12, 000 metric tons(M/T), and then the catch trend maintained nearly at the level of 50, 000 M/T per annum, showing a decreasing trend until 1930. The highest production from historical data base on walleye pollock fishery statistics was from the years in 1939 and 1940, about 270, 000 M/T and 26, 000 M/T, respectively. No production of the fish species was recorded during the years from 1943 to 1947, and from 1949 to 1951. From 1952 onwards annual production was only available from the southern part of 38$^{\circ}$N in the east coast. During two decades from 1952 to 1970, the production had sustained about less than 30, 000 M/T every year. Annual production showed an increasing trend from 1971, reaching a maximum level of approximately 162, 000 M/T in 1981. Afterwards, it has deceased sharply year after year and amounted to 180, 000 M/T in 1988. The catch composition of walleye pollock for different fishery segments during 1970~1988 showed that more than 70% of the total catch was from danish seine fishery until 1977 but from 1978 onwards, the catch proportion did not differ from one another, accounting for the nearly same proportion. Catch per unit of effort (CPUE) for both danish seine fishery and trawl fishery maintained a decline tendency after 1977 when the values of CPUE were at level of 800 kg/haul for the former fishery and 1, 300 kg/haul for the latter fishery, respectively. CPUEs of gillnet fishery during 1980~1983 increased to about 3.5 times as high value as in the years, 1970~1979 and during 1987~1988 it decreased again to the level of the years, 1970~1978. The bottom longline fishery's CPUE wa at a very low level (20 kg/basket) through the whole study years, with exception of the value (60 kg/basket) in 1980. Fishing grounds of walleye pollock in the east coast of Korea showed a very limited distribution range. Danish seine fishery concentrated fishing around the coastal areas of Sokcho and Jumunjin during January~February and October~December. Distributions of fishing grounds of trawl fishery were the areas along the coastal regions in the central part of the east coast. Gillnet and bottom longline fisheries fished walleye pollock mainly in the areas of around Sokcho and Jumunjin during January~February and December. Relationship between CPUEs' values from danish seine fishery and trawl fishery was used to standardize fishing effort to apply to surplus production model for estimating maximum sustainable yield (MSY) and optimum fish effort (F-opt) at MSY. The results suggested a MSY of 114, 000 M/T with an estimated F-opt of 173, 000 hauls per year. Based on the estimates of MSY and F-opt, MEY was estimated to be about 94, 000 M/T with a range of 81, 000 to 103, 000 M/T and E-opt 100, 000 hauls per year with a range of 80, 000 to 120, 000 hauls. The estimated values of MEY and E-opt corresponded to 82% of MSY and 58% of F-opt, respectively. An optimal situation in the fishing effort level, which can envisage either simultaneously maximum yield or maximum benefit for both danish seine fishery and trawl fishery, was determined from relationship between revenue and cost of running the fleet : the optimal fishing effort of danish seine fishery was about 52, 000 hauls per year, corresponding to 50 danish seiners and 27, 000 hauls per year which is equal nearly to 36 trawlers, respectively. It was anticipated that the net income from sustainable yield estimated from the respective optimal fishing effort of the two fisheries will be about 3, 800 million won for danish seine fishery and 1, 000 million won for trawl fishery.

• Horticultural Science & Technology
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• v.29 no.4
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• pp.317-325
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• 2011

This study was conducted to examine the effect of supplemental lighting on the growth and yield of hydroponically grown sweet pepper (Capsicum annuum L. cv. sprit) under low levels of natural light in winter. The plants were treated with natural light only (control), 3-hour supplemental lighting before sunrise, after sunrise and after sunset with high pressure sodium (HPS, 400W). As the result of these three treatments, the supplemental lighting promoted photosynthesis in the low light intensity condition and particularly photosynthesis was more active right after sun rise in the morning, 1.5-$3.0{\mu}molCO_2{\cdot}m^{-2}{\cdot}s^{-1}$ comparing to those of supplemental lighting after sunset, 0.5-$1.5{\mu}molCO_2{\cdot}m^{-2}{\cdot}s^{-1}$. Transpiration rate and stomatal conductance sharply increased with supplemental lighting after sunrise then they decreased again after turning the lights off. Stomatal size was observed $32.2{\mu}m^2$ after supplemental lighting, whereas the size of the natural light was almost closed at $7.7{\mu}m^2$. The average plant height of sweet papper cv. spirit was 185 cm before sunrise, 188 cm after sunrise and 208 cm after sunset with supplemental lighting for 3hours while the control was 171 cm. With supplemental lighting a better number of fruit set per plant was measured 4.3 before and after sunrise, 3.7 after sunset but 2.6 in the control. Interestingly, there were no significant differences in the sugar content ($^{\circ}Brix$) degree between treatment of supplemental lighting, whereas slight differences between seasons were seen. The marketable fruit yield of sweet pepper (cv. spirit) was $116.0kg{\cdot}ha$ with supplemental lighting, whereas the control (natural light only) was $75.8kg{\cdot}ha$. Despite of spending electricity and depreciation cost, the economic analysis showed net income with supplemental lighting after sunrise was 51% higher than control treatment in cv. spirit.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• Journal of agricultural medicine and community health
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• v.24 no.1
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• pp.49-63
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• 1999

The objective of the study was to examine and compare health behavior between rural area and urban area in Soonchun city. Data were collected through personal interviews from 25, April to 30, May in 1998. Questions were asked to the rural area residents(n=399) and urban area residents(n=149) about their health behaviors, including such as self-recognition of health status, health related behaviors(smoking, drinking, eating habit, and exercising), status of disease and prevention, and utilization of hospital. As we examine the demographic characteristics, rural area residents were more aged(p<0.001) than urban area residents. And the urban residents had higher education(p<0.01), higher income(p<0.01) and higher health care cost(p<0.01) than rural residents. There were difference in health status existed between rural and urban residents. Rural residents had poorer health status(p<0.01) than urban residents, and however urban residents had more anxiety about their health(p<0.01) than rural residents. Comparison of the health related behavior between rural and urban area residents, rural residents were more likely to smoke(p<0.05), less intake of milk(p<0.01), do not exercise(p<0.01), and less try to lose their weight(p<0.01) than urban residents. Rural resident used to suffer from chronic diseases than urban residents(p<0.01). Consideration of health care need for rural residents are required due to the results shown as above. Therefore, the health care center, where most of the rural residents depend on for their treatment and prevention of disease, should make inquiries about resident's health care need and evaluate the important information sources for construction of a health care information system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.3
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• pp.75-86
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• 2017

The development of web technologies and the generalization of smartphones have dramatically increased the number of social media users using the Internet. As a result, companies are perceived social media as a major marketing tool and operate a variety of SNS channels. In particular, start-ups conducting businesses with limited resources, social media is being used as an effective marketing tool to meet many potential customers at a low cost. Among them, facebook is the most used channel in the world and plays an important promotional tool not only in overseas but also in marketing activities of domestic start-ups. The purpose of this study is to analyze the relationship between satisfaction and purchase intention according to four personal characteristics of users who use social media contents and to measure the mediating effect of satisfaction on the relationship between content type and purchase intention. To this end, we classified into three types based on the previous research, and social media content is provided to 200 fans of Minbak Danawa(Minda), one of representative start-ups related to accommodation, The questionnaires were conducted for 3 weeks, and a total of 145 copies were collected. All the collected questionnaires were used for statistical analysis through SPSS 18.0. The empirical results show that all three types of content, such as task-oriented, self-oriented, and interaction-oriented, have a significant effect on the satisfaction level. Among them, it is confirmed that the satisfaction level plays a mediating role on the relationship between task-oriented contents and purchase intention. And the user 's personal characteristics showed a partially moderate effect on the satisfaction according to the content type. Therefore, social media content provided by corporations has an important effect on consumer satisfaction and purchasing, in order for start-up to prevail in the market, it is necessary to have an operational strategy to communicate with customers continuously through systematic contents analysis and planning. The result of this study suggests effective ways to build a social media marketing strategy for start-ups and suggests ways to utilize contents considering the characteristics of internet users.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.87-95
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• 2016

This study examined the characteristics of the hourly demand of city gas in Korea and proposed multiple regression models to obtain precise estimates of the hourly demand of city gas. Forecasting the hourly demand of city gas with accuracy is essential in terms of safety and cost. If underestimated, the pipeline pressure needs to be increased sharply to meet the demand, when safety matters. In the opposite case, unnecessary inventory and operation costs are incurred. Data analysis showed that the hourly demand of city gas has a very high autocorrelation and that the 24-hour demand pattern of a day follows the previous 24-hour demand pattern of the same day. That is, there is a weekly cycle pattern. In addition, some conditions that temperature affects the hourly demand level were found. That is, the absolute value of the correlation coefficient between the hourly demand and temperature is about 0.853 on average, while the absolute value of the correlation coefficient on a specific day improves to 0.861 at worst and 0.965 at best. Based on this analysis, this paper proposes a multiple regression model incorporating the hourly demand ahead of 24 hours and the hourly demand ahead of 168 hours, and another multiple regression model with temperature as an additional independent variable. To show the performance of the proposed models, computational experiments were carried out using real data of the domestic city gas demand from 2009 to 2013. The test results showed that the first regression model exhibits a forecasting accuracy of MAPE (Mean Absolute Percentage Error) around 4.5% over the past five years from 2009 to 2013, while the second regression model exhibits 5.13% of MAPE for the same period.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.

• Information Systems Review
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• v.21 no.4
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• pp.99-123
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• 2019

The software industry is a high value-added industry in the knowledge information age, and its importance is growing as it not only plays a key role in knowledge creation and utilization, but also secures global competitiveness. Among various SW available in today's business environment, Open Source Software(OSS) is rapidly expanding its activity area by not only leading software development, but also integrating with new information technology. Therefore, the purpose of this research is to empirically examine and analyze the effect of factors on the switching behavior to OSS. To accomplish the study's purpose, we suggest the research model based on "Push-Pull-Mooring" framework. This study empirically examines the two categories of antecedents for switching behavior toward OSS. The survey was conducted to employees at various firms that already switched OSS. A total of 268 responses were collected and analyzed by using the structural equational modeling. The results of this study are as follows; first, continuous maintenance cost, vender dependency, functional indifference, and SW resource inefficiency are significantly related to switch to OSS. Second, network-oriented support, testability and strategic flexibility are significantly related to switch to OSS. Finally, the results show that willingness to secures SW competitiveness has a moderating effect on the relationships between push factors and pull factor with exception of improved knowledge, and switch to OSS. The results of this study will contribute to fields related to OSS both theoretically and practically.