• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.2
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• pp.62-67
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• 2012

Purpose : At this time, the sentinel lymph node mapping using radioisotope and blue dye is preceded for breast cancer patient's sentinel lymph node biopsy. But all patients were applied the same protocol without consideration of physical specific character like the breast sizes and body mass indexes. The purpose of this study is search the optimized scan time in breast sentinel lymphangiography by observing how much the body mass index and breast size influence speed of lymphatic flow. Materials and Methods : The Object of this study was 100 breast cancer patients(Female, 100 persons, average age $50.34{\pm}10.26$ years old)at Severance hospital from October 2011 to December 2011. They were scanned breast sentinel lymphangiography before operation. This study was performed on Forte dual heads gamma camera (Philips Medical Systems, Nederland B.V.). All patients were intra-dermal injected $^{99m}Tc$-Phytate 18.5 MBq, 0.5 ml. For 80 patients, we have scanned without limitation of scan time until the lymphatic flow from the lymph node since injection. We measured how long the lymphatic flow time between departures from injects site and arrival to lymph node using stopwatch. After we calculated patient's Body mass Index and classified as 4 groups. And we measured patient's breast size and classified 3 groups. The modified breast lymphangiography that changing scan time according to comparison study's result was performed on 20 patients and was estimated. Results : The mean scan time as breast size was A group 2.48 minutes, B group 7.69 minutes, C group 10.43 minutes. The mean scan time as body mass index was under weight 1.35 minutes, normal weight 2.56 minutes, slightly over 5.62 minutes, over weighted 5.62 minutes. The success rate of modified breast lymphangiography was 85%. Conclusion : As the Body mass index became higher and breast size became bigger, the total scan time is increased. Based on the obtained information, we designed modified breast lymphangiography protocol. At the cases applying that protocol, most of sentinel lymph nodes were visualized as lymphatic pool. In conclusion, we found that the more success rate in modified protocol considering physical individuality than study carrying out in the same protocol.

• Magazine of the Korean Society of Agricultural Engineers
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• v.11 no.1
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• pp.1549-1560
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• 1969

When this experiment was treated with various factors of times and vacant intervals of intermittent irrigation in order to search for the effect on the growth of rice-plant and ti's amount of havestr, the following results were obtained during the period of this study. 1. Temperature was high, precipitution during nuturitive growing period, was suitable and Much rainfull, scanty sunlight during reproductive growing period and especially during decrease-sementation period, the cultivative situation of rice-plant of 1968 was almost similar to that of mean year. 2. It was found out that the quality of irrigated water used in the experioment was due to ti's neutural acidity. 3. The soil used in each experimental section was good for fertiligation and similar to the quality of general soil according to the result of soil analysis. 4. It was generally found out that the earlier times of intermittent irrigating and the longer vacant intervals of intermittent irrigation, the worse the growing condition of segmentation period was. 5. When vacant intervals of suspension of water supply were longer, the begining of being in ear of rice-plant ant the time tended to be late about one day. 6. In the view of the growth of maturity period and the amount of intermittent irrigation, it tended to be that the length of stalk of rice-plant was short when time of intermittent irrigation began earlier and the length of ear which came from any various section was not different. When times intermittent of irrigation began gradually early, the number of ears, grains and the weight of grains tended to decrease depending on times of that. All the growing of rice-plant and the amount of havesty tended to decrease, depending on which vacant intervals of intermittent irrigation were long. Finally, it was founedt out that from the point of view of the statistical analysis of weight of grains, it was more then 1% what highly significance of mutual action between times and vacant intermittent irrigation was researched.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.2
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• pp.115-125
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• 2022

The purpose of this study was to examine the effect of packaging on quality maintenance of paprika (Capsicum annuum L. cv. Nagano RZ) stored at three different temperatures. In Korea, paprika is stored and distributed under ambient conditions. To ensure the freshness maintenance, determining optimal storage temperature is necessary. Paprika were unpacked (control) or packed with polyamide film and stored at 5℃, 10℃ and 20℃ for 35 days. Quality characteristics such as weight loss and appearance were examined. Paprika packed with polyamide film showed less quality changes compared to unpacked paprika under all the storage temperatures. The commercial properties tended to decrease rapidly during storage at 20℃ regardless of packing. The degree of weight loss was significantly lower in packed paprika compared to unpacked paprika. It was found that soluble solids, pigments, hardness, etc. were complexly affected by storage temperature and film packaging. For paprika, the storage temperature of 5℃ or 10℃ was effective in maintaining freshness; paprika packed in polyamide film packing maintained greater freshness than unpacked paprika. Our results showed that, packaging is required to preserve the freshness and to improve the marketability of paprika in the domestic market. It seems that it is necessary to continuously search for an effective packaging method.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Korean Journal of Poultry Science
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• v.8 no.2
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• pp.55-68
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• 1981

Calcium and phosphorus are not only indispensable for the bone formation and body fluids equilibrium but also are major components of egg shell. It is nutritionally important, therefore, to investigate the metabolism of calcium and phosphorus and to search for optimum requirement of calcium and phosphorus and the availability of various sources of calcium an4 phosphorus by poultry. An attempt was made to review the nutrition of calcium and phosphorus in poultry diets. 1, Calcium and phosphorus have great interrelationship with vitamin D in their metabolisms. 2. Most of the plant-origin phosphorus are existing in phytic form and it leads to low availability when used in poultry rations, although calcium and phosphorus present in animal-origin or mineral supplements are highly available in general. 3. Calcium and phosphorus requirement from existing information indicated that 1.0% calcium and 0.7% phosphorus for broiler and egg-type chicks, and 3.5% calcium and 0.4% phosphorus for laying hen. 4. It has been recommended that calcium and phosphorus level should be increased when the feed intake was decreased or when the egg Production rate was higher or when the hens are old. 5. Mono-, ci-, tri-, calcium phosphate, calcium carbonate, bone meal, limestone and oyster shell u the most readily available among various sources of calcium phosphorus supplements. Soft rock phosphate, deflourinated phosphate and gypsum are somewhat inferior to the previous ones in bioavailability. 6. The effect of particle size of calcium supplements on egg shell quality and egg production rate is not yet clearly defined but recent works showed that oyster shell is more available when it was coarse and limestone is more available when it was fine in panicle. size. 7. Present data indicated that mixed feeding of oyster shell and limestone is superior to the single feeding of each on laying performance. 8. Significant interaction between phosphorus and sodium was observed, that is, excessive sodium decreased egg production in layer and body weight growth in broiler in the low phosphorus diets but increased them in the high phosphorus diets.

• Health Policy and Management
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• v.12 no.2
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• pp.23-42
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• 2002

The purpose of this study was to analyze the results of 133 studies related to patient compliance published between 1980 and 2001 and to assess the effectiveness of intervention on compliance by using meta-analysis. We collected the existing literatures by using web and manual search 'patient compliance', 'sick role behavior', 'major clinical disease', and 'intervention' as key words and by reviewing content of journals related to medicine, nursing and public health. The compliance interventions were classified by theoretical focus into educational, behavioral, and affective categories within which specific intervention strategies were further distinguished. The compliance indicators broadly represent five classes of compliance-related assessments: (1) health outcomes (eg, blood pressure and hospitalization), (2) direct indicators (eg, urine and blood tracers and weight change), (3) indirect indicators (eg, pill count and refill records), (4) subjective report (eg, patients' or others' reports), (5) utilization (appointment making and keeping, use of preventive services). Quantitative meta-analysis was performed by MetaKorea program which was developed for meta-analysis in Korea. Among the 133 articles, 10 studies were selected through the qualitative meta-analysis process, and then only 6 studies were selected for the quantitative meta-analysis finally. The interventions produced significant effects for all the compliance indicators with the magnitude of common effect size (4.1192) than the non-intervention group in a random effect model. The largest effects were each study for patient of hypertension using health outcome such as blood pressure (0.4679) and diabetes mellitus using direct indicator such as glucose level in blood and urine (0.7753). These results suggest that strategic interventions showed clear advantage for improvement of patient compliance compared with non-intervention group.

• Applied Biological Chemistry
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• v.50 no.2
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• pp.95-100
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• 2007

The cellulase gene of Bacillus licheniformis K11 which has plant growth-promoting activity by auxin and antagonistic ability by siderophore was cloned in pUC18 using PCR employing heterologous primers. The 1.6kb PCR fragment contained the full sequence of the cellulase gene, denoted celW which has been reported to encode a 499 amino acid protein. Similarity search in protein data base revealed that the cellulase from B. licheniformis K11 was more than 97% identical in amino acid sequence to those of various Bacillus spp. The cellulase protein from B. licheniformis K11, overproduced in E. coli DH5${\alpha}$ by the lac promoter on the vector, had apparent molecular weight of 55 kDa upon CMC-SDS-PAGE analysis. The protein not only had enzymatic activity toward carboxymethyl-cellulose (CMC), but also was able to degrade insoluble cellulose, such as Avicel and filter paper (Whatman$^{\circledR}$ No. 1). In addition, the cellulase could degrade a fungal cell wall of Phytophthora capsici. Consequently B. licheniformis K11 was able to suppress the peperblight causing P. capsici by its cellulase. Biochemical analysis showed that the enzyme had a maximum activity at 60$^{\circ}C$ and pH 6.0. Also, the enzyme activity was activated by Co$^{2+}$ of Mn$^{2+}$ but inhibited by Fe$^{3+}$ or Hg$^{2+}$. Moreover, enzyme activity was not inhibited by SDS or sodium azide.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.381-396
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• 2003

Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time-series databases. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by resolving the performance bottleneck. First, we analyze the disk access and CPU processing times required during the index searching and post processing steps through preliminary experiments. Based on their results, we show that the post processing step is the main performance bottleneck in subsequence matching, and them claim that its optimization is a crucial issue overlooked in previous approaches. In order to resolve the performance bottleneck, we propose a simple but quite effective method that processes the post processing step in the optimal way. By rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancy of disk accesses and CPU processing occurred in the post processing step. We formally prove that our method is optimal and also does not incur any false dismissal. We show the effectiveness of our method by extensive experiments. The results show that our method achieves significant speed-up in the post processing step 3.91 to 9.42 times when using a data set of real-world stock sequences and 4.97 to 5.61 times when using data sets of a large volume of synthetic sequences. Also, the results show that our method reduces the weight of the post processing step in entire subsequence matching from about 90% to less than 70%. This implies that our method successfully resolves th performance bottleneck in subsequence matching. As a result, our method provides excellent performance in entire subsequence matching. The experimental results reveal that it is 3.05 to 5.60 times faster when using a data set of real-world stock sequences and 3.68 to 4.21 times faster when using data sets of a large volume of synthetic sequences compared with the previous one.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.197-217
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• 2018

Music is one of the most creative act that can express human sentiment with sound. Also, since music invoke people's sentiment to get empathized with it easily, it can either encourage or discourage people's sentiment with music what they are listening. Thus, sentiment is the primary factor when it comes to searching or recommending music to people. Regard to the music recommendation system, there are still lack of recommendation systems that are based on customer sentiment. An algorithm's that were used in previous music recommendation systems are mostly user based, for example, user's play history and playlists etc. Based on play history or playlists between multiple users, distance between music were calculated refer to basic information such as genre, singer, beat etc. It can filter out similar music to the users as a recommendation system. However those methodology have limitations like filter bubble. For example, if user listen to rock music only, it would be hard to get hip-hop or R&B music which have similar sentiment as a recommendation. In this study, we have focused on sentiment of music itself, and finally developed methodology of defining new index for music recommendation system. Concretely, we are proposing "SWEMS" index and using this index, we also extracted "Sentiment Pattern" for each music which was used for this research. Using this "SWEMS" index and "Sentiment Pattern", we expect that it can be used for a variety of purposes not only the music recommendation system but also as an algorithm which used for buildup predicting model etc. In this study, we had to develop the music recommendation system based on emotional adjectives which people generally feel when they listening to music. For that reason, it was necessary to collect a large amount of emotional adjectives as we can. Emotional adjectives were collected via previous study which is related to them. Also more emotional adjectives has collected via social metrics and qualitative interview. Finally, we could collect 134 individual adjectives. Through several steps, the collected adjectives were selected as the final 60 adjectives. Based on the final adjectives, music survey has taken as each item to evaluated the sentiment of a song. Surveys were taken by expert panels who like to listen to music. During the survey, all survey questions were based on emotional adjectives, no other information were collected. The music which evaluated from the previous step is divided into popular and unpopular songs, and the most relevant variables were derived from the popularity of music. The derived variables were reclassified through factor analysis and assigned a weight to the adjectives which belongs to the factor. We define the extracted factors as "SWEMS" index, which describes sentiment score of music in numeric value. In this study, we attempted to apply Case Based Reasoning method to implement an algorithm. Compare to other methodology, we used Case Based Reasoning because it shows similar problem solving method as what human do. Using "SWEMS" index of each music, an algorithm will be implemented based on the Euclidean distance to recommend a song similar to the emotion value which given by the factor for each music. Also, using "SWEMS" index, we can also draw "Sentiment Pattern" for each song. In this study, we found that the song which gives a similar emotion shows similar "Sentiment Pattern" each other. Through "Sentiment Pattern", we could also suggest a new group of music, which is different from the previous format of genre. This research would help people to quantify qualitative data. Also the algorithms can be used to quantify the content itself, which would help users to search the similar content more quickly.