• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.2
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• pp.99-103
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• 2011

Purpose: Head movement during brain perfusion SPECT (Diamox) scan is a one of important issues which decreases image quality. It also causes repeated scans. This study was designed to evaluate variable factors causing scan failures. Materials and Methods: 676 patients (359 men, 317 women, age average $54.5{\pm}18.4$) for brain perfusion SPECT (Diamox) scan from March, 2010 to Feb. 2011 were used as a subject. Age data and the kind of disease(Moyamoya disease (MMD), None moyamoya disease (NMMD), Cerebral infarction (CI)), test performance outcome (success,failure) were collected. The head movement factors(gender, disease, age, head fixation device) were evaluated by chi-square test and logistic regression analysis Results: The result showed that men had higher scan failure rate than women. Seniors in seventies(men 3.4%, women 1.5%) showed the most highest failure rate. Using head fixation device increased scan success rate up to 94.4~97.7%. The scan success rate is dependent upon gender, head fixation device by chi-square test(${\chi}^2$=3.8 (df=1, p<0.05), ${\chi}^2$=10.4 (df=1, p<0.001)) Gender, disease(CI), head fixation device showed very effective result in logistic regression analysis.(Wald=3.3 (p<0.07), Wald=3.7 (p<0.05), Wald=9.3 (p<0.05) Conclusion: It is demonstrated that gender, disease, using head fixation device is statistically very useful factors. Especially, head fixation device is a main key minimizing repeated scan.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.63-82
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• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.

• Journal of Mushroom
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• v.14 no.2
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• pp.59-63
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• 2016

A new commercial strain of oyster mushroom (was developed by hyphal anastomosis, and was improved byhybridization between a monokaryotic strain derived from Pleurotus ostreatus ASI 0635 (Gonji 7ho) and a dikaryotic strain derived from P. ostreatus ASI 0666 (Mongdol). The optimum temperatures for mycelial growth and fruiting body development were $25{\sim}30^{\circ}C$ and $12{\sim}18^{\circ}C$, respectively. When PDA (potato dextrose agar medium) and MCM (mushroom complete medium) were compared, mycelial growth was faster in MCM. Similar results were observed with the control strain P. ostreatus ASI 2504 (Suhan 1ho). Analysis of the genetic characteristics of the new cultivar ('Gosol') showed a different DNA profile from that of the control ASI 2504 strain, when RAPD (raurpDNA) primers URP1, 2, 3, and 7 were used. Fruiting body production per bottle was approximately116 g based on a production performance test. In addition, yields from a farm field trial were stably achieved in an inadequate production enviro. The color of the pileus was blackish gray, and the stipe was long and thick. Therefore, we expect that this new strain will satisfy consumer demand for high quality mushrooms.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• Cartoon and Animation Studies
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• s.33
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• pp.597-616
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• 2013

Character industry is not only knowledge-based industry chiefly with copyrights but also motive power for creative economy to take a role functionally over the fields of industries because it has industrial characteristic as complement product to promote sale value in manufacturing industry and service industry and increase profit on sales. Since 2003, the national policy related to character has aimed to maximize effect among connected industries, extend its business abroad, enforce copyrights through the improvement of marketing system, develop industrial infrastructure through raising quality of character products. With the result of this policy, the successful cases of connected contents have been crystallized and domestic character industry has stepped up methodically since 2007. It is needed to reset the scales of character industry and industrial stats because there are more know-how of self industry promotion and more related characters through strategy of market departmentalization starting with cartoon, animation, games, novels, movies and musicals. Especially, The Korea government set our target for 'Global Top Five Character Power' since 2009 and has started to carry out to find global star characters, support to establish network among connected industries, diversify promotion channels, and develop licensing business. Particularly, since 2013, There have been prospered the indoor character theme park with time management just like character experimental marketing or Kids cafes using characters, the demand market of digital character focusing on SNS emoticon, and the performance market for character musical consistently. Moreover, The domestic and foreign illegal black markets on off-line have been enlarged, so we need another policy alternative. To prepare for the era of exploding character demand market and diversifying platform, it is needed to set up a solid strategy that is required the elements of policy changes in character industry to vitalize character industry and support new character design and connected contents. the following shows that the elements of policy changes related to the existing policy, the current position of market. Nowadays, the elements of policy changes in domestic character industry are that variety of consumers in the digital character market according to platform diversification, Convergence contents of character goods for the Korean waves, legalization of the illegal black contents market, and controling the tendency of consumers in departmentalized market. This can help find the policy issue entirely deferent with the existing character powers like US, Japan or Europe. In its final analysis, the alternatives are the promotion of models with contract copyrights of domestic and foreign connected contents, the diversification of profit models of platform economy, the additive development of target market related to enlarging the Korean waves, and the strategy of character market for the age-specific tendency according to developing character demand market.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.87-95
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• 2010

Portable multi-media products which can service the highest audio-quality by using lossless audio codec has been released and the international lossless codecs, MPEG-4 audio lossless coding(ALS) and MPEG-4 scalable lossless coding(SLS), were standardized by MPEG in 2006. The simple profile of MPEG-4 ALS, it supports up to stereo, was defined by MPEG in 2009. The lossless audio codec should have low-complexity in stereo to be widely used in portable multi-media products. But the previous researches of MPEG-4 ALS have focused on an improvement of compression ratio, a complexity reduction in multi-channels coding, and a selection of linear prediction coefficients(LPCs) order. In this paper, the complexity and compression ratio of MPEG-4 ALS encoder is analyzed in simple profile of MPEG-4 ALS, the method to reduce a complexity of MPEG-4 ALS encoder is proposed. Based on an analysis of complexity of MPEG-4 ALS encoder, the complexity of short-term prediction filter of MPEG-4 ALS encoder is reduced by using the low-complexity filter that is proposed in previous research to reduce the complexity of MPEG-4 ALS decoder. Also, we propose a joint coding decision method, it reduces the complexity and keeps the compression ratio of MPEG-4 ALS encoder. In proposed method, the operation of joint coding is decided based on the relation between cross-correlation of residual and compression ratio of joint coding. The performance of MPEG-4 ALS encoder that has the method and low-complexity filter is evaluated by using the MPEG-4 ALS conformance test file and normal music files. The complexity of MPEG-4 ALS encoder is reduced by about 24% by comparing with MPEG-4 ALS reference encoder, while the compression ratio by the proposed method is comparable to MPEG-4 ALS reference encoder.

• Management & Information Systems Review
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• v.34 no.5
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• pp.191-212
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• 2015

Unlike the existing researches related to salespersons, this study intends to place the focus on salespersons' psychological characteristic as an element affecting their selling behavior. This is because employees' psychological characteristic is very likely to affect their devotion and commitment to relationship with customers and long-term production by a company. In particular, salespersons are likely to get a feeling of fatigue or loss, or make a cynical or cold response to customers because of frequent interaction with them, and to show emotional indifference in an attempt to keep their distance from customers. But the likelihood can vary depending on salespersons' own psychological characteristic; in particular, the occurrence of these phenomena is very likely to vary significantly depending on relationship belief in interpersonal relations. In the field of psychology, under way are researches related to personal psychological characteristics to improve the quality of interpersonal relations and to maximize personal performance and enhance situational adaptability during this process; it is a personal relationship belief that is recently mentioned as such a psychological characteristic. For salespersons having frequent interaction with customers, particularly, relationship belief can be a very important element in forming relations with customers. So this study aims at determining how salespersons' relationship belief affects negative emotion regulation strategies and prosocial behavior to customer. As a result, salespersons' relationship belief was found to have effects on their negative emotion regulation strategies and prosocial behavior to customer. Negative emotion regulation strategies was found to have effects on prosocial behavior. Salespersons with intimate relationship belief try to use active regulation, support-seeking regulation and salespersons with controlling relationship belief try to use avoidant/distractive regulation. Intimate relationship belief was found to have more prosocial behavior, controlling relationship belief was found to have less prosocial behavior to customer. salespersons' negative emotion regulation strategies was found to have effects on their prosocial behavior to customer. Active, support-seeking influence prosocial behavior to customer positively, avoidant/distractive regulation influence prosocial behavior to customer negatively.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.661-676
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• 2017

This study suggests a new method not only to produce the 1 km-resolution seasonal prediction but also to improve the seasonal prediction skill of temperature over South Korea. This method consists of four stages of experiments. The first stage, EXP1, is a low-resolution seasonal prediction of temperature obtained from Pusan National University Coupled General Circulation Model, and EXP2 is to produce 1 km-resolution seasonal prediction of temperature over South Korea by applying statistical downscaling to the results of EXP1. EXP3 is a seasonal prediction which considers the effect of temperature changes according to the altitude on the result of EXP2. Here, we use altitude information from ASTER GDEM, satellite observation. EXP4 is a bias corrected seasonal prediction using genetic algorithm in EXP3. EXP1 and EXP2 show poorer prediction skill than other experiments because the topographical characteristic of South Korea is not considered at all. Especially, the prediction skills of two experiments are lower at the high altitude observation site. On the other hand, EXP3 and EXP4 applying the high resolution elevation data based on remote sensing have higher prediction skill than other experiments by effectively reflecting the topographical characteristics such as temperature decrease as altitude increases. In addition, EXP4 reduced the systematic bias of seasonal prediction using genetic algorithm shows the superior performance for temporal variability such as temporal correlation, normalized standard deviation, hit rate and false alarm rate. It means that the method proposed in this study can produces high-resolution and high-quality seasonal prediction effectively.

• Journal of the Korean Applied Science and Technology
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• v.33 no.3
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• pp.560-567
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• 2016

Acid-activated clays (SUPER-DC, DC-A3, and P1) are used for the bleaching of cold-pressed rapeseed oil. In this study we tested the bleaching performance of cold-pressed rapeseed oil according to the different reaction time (20, 40, 60, 80 min) and temperature (40, 80, $120^{\circ}C$). Oil color (lightness, redness, yellowness), pigments (chlorophyll A and carotenoid content) and quality properties (fatty acid composition, tocopherols (${\alpha}$, ${\beta}$, ${\gamma}$, ${\delta}$), and plant sterols (${\beta}$-sitosterol, campesterol, stigmasterol) content) were analyzed. The results showed that bleaching of cold-pressed rapeseed oil with 2% acid-activated clays at $40^{\circ}C$ for 20 min, brightness (L) increased, but redness (a) and yellowness (b) decreased. Bleaching of cold-pressed rapeseed oil with 2% DC-SUPER at $40^{\circ}C$ removed chlorophyll A and carotenoids pigments significantly. In addition, about 50% of total tocopherol content in cold-pressed rapeseed oil was reduced by bleaching. Originally total tocopherol content was 46.62mg/100g in cold-pressed rapeseed oil. But after bleaching, total tocopherol content was 12.67mg/100g (20 min bleaching), 15.31mg/100g (40 min bleaching), and 13.56mg/100g (60 min bleaching). However plant sterols content in cold-pressed rapeseed oil remained unchanged by bleaching. Overall, acid-activated clays were useful for the bleaching of pigmented rapeseed oil.