• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.19-26
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• 2023

Humans have adapted and evolved to natural light. However, as humans stay in indoor longer in modern times, the problem of biorhythm disturbance has been induced. To solve this problem, research is being conducted on lighting that reproduces the correlated color temperature(CCT) of natural light that varies from sunrise to sunset. In order to reproduce the CCT of natural light, multiple LED light sources with different CCTs are used to produce lighting, and then a control index DB is constructed by measuring and collecting the light characteristics of the combination of input currents for each light source in hundreds to thousands of steps, and then using it to control the lighting through the light characteristic matching method. The problem with this control method is that the more detailed the steps of the combination of input currents, the more time and economic costs are incurred. In this paper, an LED lighting control method that applies interpolation and combination calculation based on the minimum spectral power distribution information for each light source is proposed to reproduce the CCT of natural light. First, five minimum SPD information for each channel was measured and collected for the LED lighting, which consisted of light source channels with different CCTs and implemented input current control function of a 256-steps for each channel. Interpolation calculation was performed to generate SPD of 256 steps for each channel for the minimum SPD information, and SPD for all control combinations of LED lighting was generated through combination calculation of SPD for each channel. Illuminance and CCT were calculated through the generated SPD, a control index DB was constructed, and the CCT of natural light was reproduced through a matching technique. In the performance evaluation, the CCT for natural light was provided within the range of an average error rate of 0.18% while meeting the recommended indoor illumination standard.

• Journal of Nutrition and Health
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• v.57 no.1
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• pp.153-169
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• 2024

Purpose: The main goal of this study was to identify the impact of coronavirus disease 2019 (COVID-19) on grocery purchases (i.e., fresh and processed foods by grain, vegetable, fruit, seafood, and meat categories) in Korea. To understand the specific impact of COVID-19, the study period was divided into 3 segments: PRE-COVID-19, INTER-COVID-19, and POST-COVID-19. Methods: We used the microdata of household income and expenditure from Statistics Korea (KOSTAT), representing households across the country. The data comprised monthly grocery expenditure data from January 2019 to September 2022. First, we compared the PRE-COVID-19 period to INTER-COVID-19 and then INTER-COVID-19 to POST-COVID-19 and used multiple regression analysis. The covariates used were the gender and age of the head of the household, the household's monthly income, the number of family members, the price index, and the month (dummy variable). Results: The expenditures on all grocery categories except fresh fruit increased from PRE-COVID-19 to INTER-COVID-19. From INTER-COVID-19 to POST-COVID-19, almost all grocery category spending declined, with processed meat being the only exception. Most purchases of protein sources, increased during INTER-COVID-19 compared to PRE-COVID-19, while ham/sausage/bacon for meat protein, fish cakes and canned seafood for seafood protein, and soy milk for plant-based protein did not decrease during POST-COVID-19 compared to INTER-COVID-19. Conclusion: These results show an overall increase in in-home grocery expenditure during COVID-19 due to an increase in eating at home, followed by a decrease in this expenditure in the POST-COVID-19 period. Among the trends, the protein and highly processed convenience food categories did not see a decline in spending during the POST-COVID-19 period, which is a reflection of the preferences of consumers in the post-COVID-19 period.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.355-380
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• 2004

I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Korean Journal of Soil Science and Fertilizer
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• v.2 no.1
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• pp.53-68
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• 1969

The nutriophysiological response of rice plant to root environment was investigated with eye observation of root development and rhizosphere in situation. The results may be summarized as follows: 1) The quick decomposition of organic matter, added in low yield soil, caused that the origainal organic matter content was reached very quickly, in spite of it low value. In high yield soil the reverse was seen. 2) In low yield soil root development, root activity and T/R value were very low, whereas addition of organic matter lowered them still wore. This might be contributed to gas bubbles around the root by the decomposition of organic matter. 3) Varietal difference in the response to root environment was clear. Suwon 82 was more susceptible to growth-inhibitine conditions on low-yield soil than Norin 25. 4) Potassium uptake was mostly hindered by organic matter, while some factors in soil hindered mostly posphorus uptake. When the organic matter was added to such soil, the effect of them resulted in multiple interaction. 5) The root activity showed a correlation coeffieient of 0.839, 0.834 and 0.948 at 1% level with the number of root, yield of aerial part and root yield, respectively. At 5% level the root-activity showed correlation-coefficient of 0.751, 0.670 and 0.769 with the uptake of the aerial part of respectively. N, P and K and a correlation-coefficient of 0.729, 0.742 and 0.815 with the uptake of the root of respectively N.P. and K. So especially for K-uptake a high correlation with the root-activity was found. 6) The nitrogen content of the roots in low-yield soil was higher than in high-yield soil, while the content in the upper part showed the reverse. It may suggest ammonium toxicity in the root. In low-yield soil Potassium and Phosphorus content was low in both the root and aerial part, and in the latter particularly in the culm and leaf sheath. 7) The content of reducing sugar, non-recuding sugar, starh and eugar, total carbohydrates in the aerial part of plants in low yield soil was higher than in high yield soil. The content of them, especially of reducing sugar in the roots was lower. It may be caused by abnormal metabolic consumption of sugar in the root. 8) Sulfur content was very high in the aerial part, especially in leaf blade of plants on low yield soil and $P_2O_5/S$ value of the leaf blade was one fifth of that in high yield soil. It suggests a possible toxic effect of sulfate ion on photophosphorization. 9) The high value of $Fe/P_2O_5$ of the aerial part of plants in low yield soil suggests the possible formation of solid $Fe/PO_4$ as a mechanical hindrance for the translocation of nutrients. 10) Translocation of nutrients in the plant was very poor and most nutrients were accumulated in the root in low yield soil. That might contributed to the lack of energy sources and mechanical hindrance. 11) The amount of roots in high yield soil, was greater than that in low yield soil. The in high-yield soil was deep, distribution of the roots whereas in the low-yield soil the root-distribution was mainly in the top-layer. Without application of Nitrogen fertilizer the roots were mainly distributed in the upper 7cm. of topsoil. With 120 kg N/ha. root were more concentrated in the layer between 7cm. and 14cm. depth. The amount of roots increased with the amount of fertilizer applied.