• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.383-392
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• 2021

It has been known that oyster mushrooms cultivated in facilities with thermal insulation have been strongly affected by inner environments. Forced air-circulation fans exert much direct influence on disturbing air inside the facility so the matter is of particular interest. This study is carried out to investigate the measured levels of air uniformity in a cultivation facility for oyster mushroom in the various cases that reversibly controlled air-circulation fans which drove the flow in the upward and reverse direction by turn and unidirectional fans by which the wind blew upwards only were operated from July 1 to 10. The actual survey for the selection of ongoing operation cases presented that farmers, even though there were some discrepancies, have made use of fans in a way that it paused for 5-30min after running for 5-15min by turn. The level of air uniformity in the case of adopting reversible fans revealed a slight difference of 1.4-1.8℃ (Temp.) and 7.8-8.7% (R.H.) under the condition of not using a cooler during the investigation period. By contrast, unidirectional fans showed a noticeable difference of 3.2-3.7℃ and 14.0-15.4%, which meant that air uniformity driven by reversible fans much more increased compared to that for unidirectional fans. Among the twenty operational applications considered for reversible fans, the circumstance that the wind blew upwards for 10-15min and ceased for 5-10min and blew again in the reverse direction for 10-15min in succession gave minor improvements at the level of air uniformity, but at present there was somewhat difficult to make decision on which cases were optimally best. It seems necessary that the effects of reversible fans on air uniformity as well as qualities of oyster mushrooms have to be appraised in the cultivation period and the flow visualization needs to be done to ascertain the performance of air mixture.

• Progress in Medical Physics
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• v.15 no.1
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• pp.1-8
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• 2004

Patient dose verification is one of the most Important responsibilities of the physician in the treatment delivery of radiation therapy. For the task, it is necessary to use an accurate dosimeter that can verify the patient dose profile, and it is also necessary to determine the physical characteristics of beams used in intensity modulated radiation therapy (IMRT) The Beam Intensity Scanner (BInS) System is presented for the dosimetric verification of the two dimensional photon beam. The BInS has a scintillator, made of phosphor Terbium-doped Gadolinium Oxysulphide (Gd$_2$O$_2$S:Tb), to produce fluorescence from the irradiation of photon and electron beams. These fluoroscopic signals are collected and digitized by a digital video camera (DVC) and then processed by custom made software to express the relative dose profile in a 3 dimensional (3D) plot. As an application of the BInS, measurements related to IWRT are made and presented in this work. Using a static multileaf collimator (SMLC) technique, the intensity modulated beam (IMB) is delivered via a sequence of static portals made by controlled leaves. Thus, when static subfields are generated by a sequence of abutting portals, the penumbras and scattered photons of the delivered beams overlap in abutting field regions and this results in the creation of “hot spots”. Using the BInS, inter-step “hot spots” inherent in SMLC are measured and an empirical method to remove them is proposed. Another major MLC technique in IMRT, the dynamic multileaf collimator (DMLC) technique, has different characteristics from SMLC due to a different leaf operation mechanism during the irradiation of photon and electron beams. By using the BInS, the actual delivered doses by SMLC and DMLC techniques are measured and compared. Even if the planned dose to a target volume is equal in our experimental setting, the actual delivered dose by DMLC technique is measured to be larger by 14.8% than that by SMLC, and this is due to scattered photons and contaminant electrons at d$_{max}$.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Management & Information Systems Review
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• v.31 no.3
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• pp.223-248
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• 2012

This study analyses support policy and structural change of textile industry in Daegu Kyungbuk region, and suggests major issues for textile industry's innovation. In Daegu Kyungbuk, it was 1999 that a policy, so called Milano Project, in order to promote a textile industry was devised. In 2004, the Regional Industrial Promotion Plan was devised. The plan was born from a view point of establishing a regional innovation system and of promoting the innovative clusters under a knowledge based economy. After then, the Regional Industry Promotion Project or Regional Strategic Industry Promotion Project became a core of regional textile industrial policy. Research results indicated that the first stage Milano project (1999-2003) showed both positive and negative effects. There were no long-term development plan, clear vision and strategy. But, core industrial infrastructure for differentiated product development, such as New product Development Support Center and Dyeing Design Practical Application Center, was constructed. The second stage Daegu Textile Industry Promotion Plan (2004-2008) displayed a significant technological performance and new product sales with the assistance of Kyungbuk province. Also, textile industry revealed positive fruits such as financial structure, productivity, and profitability as a result of strong restructuring. In industrial structure, there was a important change from clothe textile material to industry textile material. Most of textile companies did not showed high capability in CEO's technology innovation intention, entrepreneurship, R&D and human resource competency in compare with other industry. We suggested that Daegu Kyungbuk has to select and concentrate on the high-tech textile material and living textile for sustainable development and competitiveness. We also proposed a confidence and cooperation based innovation network and company oriented innovation cluster.

• 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.