• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.1-17
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• 2008

Data mining is defined as the process of discovering meaningful and useful pattern in large volumes of data. In particular, finding associations rules between items in a database of customer transactions has become an important thing. Some data structures and algorithms had been proposed for storing meaningful information compressed from an original database to find frequent itemsets since Apriori algorithm. Though existing method find all association rules, we must have a lot of process to analyze association rules because there are too many rules. In this paper, we propose a new data structure, called a Frequent Pattern Network (FPN), which represents items as vertices and 2-itemsets as edges of the network. In order to utilize FPN, We constitute FPN using item's frequency. And then we use a clustering method to group the vertices on the network into clusters so that the intracluster similarity is maximized and the intercluster similarity is minimized. We generate association rules based on clusters. Our experiments showed accuracy of clustering items on the network using confidence, correlation and edge weight similarity methods. And We generated association rules using clusters and compare traditional and our method. From the results, the confidence similarity had a strong influence than others on the frequent pattern network. And FPN had a flexibility to minimum support value.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.10
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• pp.839-843
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• 2011

ZnO nanostructures were developed on a Si (100) substrate from powder mixture of ZnO and 5 mol% Pd (ZP-5) as reactants by ${\times}$ sccm oxygen pressures(x= 0, 10, 20, 40). DTA (differential thermal analysis) result shows the Pd(5 mol%)+ZnO mixtured powder(PZ-5) is easily evaporated than pure ZnO powder. The PZ-5 mixtured powder was characterized by DTA to determine the thermal decomposition which was found to be at $800^{\circ}C$, $1,100^{\circ}C$. Weight loss(%) and ICP (inductively coupled plasma) analysis reveal that Zn vaporization is decreased by increased oxygen pressures from the PZ-5 at $1,100^{\circ}C$ for 30 mins. Needle-like ZnO nanostructures array developed from 10 sccm oxygen pressure, was well aligned vertically on the Si substrate at $1,100^{\circ}C$ for 30 mins. The lengths of the Needle-like ZnO nanostructures is about 2 ${\mu}m$ with diameters of about 65 nm. The developed ZnO nanostructures exhibited growth direction along [001] with defect-free high crystallinity. It is considered that Zn vaporization is responsible for the growth of Needle-like ZnO nanostructures by controlling the oxygen pressures. The photoluminescence spectra of ZnO nanostructures exhibited stronger 376.7 nm NBE (near band-edge emission) peak and 529.3 nm DLE (deep level energy) peak.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.353-359
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• 2021

The Army is constructing a training system using Miles equipment that applies the latest science and technology to carry out military training. The Miles training system is a system that uses Miles equipment to simulate the damage situation of combat personnel and equipment in the same way as an actual battlefield by conducting practiced maneuvers in the field. Through this, the training force can experience conditions similar to an actual battle. In particular, the training effects of the warriors participating in the training can be maximized by establishing an integrated system that utilizes cutting-edge science technologies, such as information communication and computer simulation. This study analyzed the effects of Miles training in the army using scientific techniques targeted at the mid-range Miles. In particular, the effect index for analyzing the training effect was derived from a literature survey and expert opinions. The weight of each effect index was calculated by applying the Swing method. The final training effect was calculated by combining the results of the survey from train-experienced people. The Miles training effect was 2.6 times more effective than previous training without using Miles, and the satisfaction rate with Miles training according to status was high through variance analysis, and the difference was statistically significant.

• Korean small business review
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• v.41 no.3
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• pp.151-172
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• 2019

The changes in the 4th industrial revolution era are not limited to specific sectors, but affect all sectors of industry. Thus all companies are required to respond effectively to changes. Some companies response by adopting cutting-edge ICT and some companies improve the organizational structure, or enhance the competence of individual employees. This study is based on the assumption that the responses to the change in the 4th industrial revolution era should not be uniform, and that the response strategies and priorities should vary according to the characteristics of the companies. The purpose of this study is to suggest both different response strategies and the priority of the responding factors(areas) to small and medium-sized enterprises. Data were collected through the semi-Delphi method. As a result of data analysis, the priorities of the medium-sized enterprises were as follows: introduction of IT-strengthening the competence of the individuals - establishing technology infrastructure-improving organizational structure - efficiency of work - improving organizational culture. While the priorities of the response factors(area) of the small-sized companies were as follows: strengthening the competence of the individuals - efficiency of work - introduction of IT - establishing technology infrastructure - improving organizational structure - improving organizational culture.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.57-76
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• 2017

Recently, consumer needs are diversifying as information technologies are evolving rapidly. A lot of IT devices such as smart phones and tablet PCs are launching following the trend of information technology. While IT devices focused on the technical advance and improvement a few years ago, the situation is changed now. There is no difference in functional aspects, so companies are trying to differentiate IT devices in terms of appearance design. Consumers also consider design as being a more important factor in the decision-making of smart phones. Smart phones have become a fashion items, revealing consumers' own characteristics and personality. As the design and appearance of the smartphone become important things, it is necessary to examine consumer values from the design and appearance of IT devices. Furthermore, it is crucial to clarify the mechanisms of consumers' design evaluation and develop the design evaluation model based on the mechanism. Since the influence of design gets continuously strong, various and many studies related to design were carried out. These studies can classify three main streams. The first stream focuses on the role of design from the perspective of marketing and communication. The second one is the studies to find out an effective and appealing design from the perspective of industrial design. The last one is to examine the consumer values created by a product design, which means consumers' perception or feeling when they look and feel it. These numerous studies somewhat have dealt with consumer values, but they do not include product attributes, or do not cover the whole process and mechanism from product attributes to consumer values. In this study, we try to develop the holistic design evaluation model based on consumer values based on three-step approach from product attributes, perceived attributes, to consumer values. Product attributes means the real and physical characteristics each smart phone has. They consist of bezel, length, width, thickness, weight and curvature. Perceived attributes are derived from consumers' perception on product attributes. We consider perceived size of device, perceived size of display, perceived thickness, perceived weight, perceived bezel (top - bottom / left - right side), perceived curvature of edge, perceived curvature of back side, gap of each part, perceived gloss and perceived screen ratio. They are factorized into six clusters named as 'Size,' 'Slimness,' 'No-Frame,' 'Roundness,' 'Screen Ratio,' and 'Looseness.' We conducted qualitative research to find out consumer values, which are categorized into two: look and feel values. We identified the values named as 'Silhouette,' 'Neatness,' 'Attractiveness,' 'Polishing,' 'Innovativeness,' 'Professionalism,' 'Intellectualness,' 'Individuality,' and 'Distinctiveness' in terms of look values. Also, we identifies 'Stability,' 'Comfortableness,' 'Grip,' 'Solidity,' 'Non-fragility,' and 'Smoothness' in terms of feel values. They are factorized into five key values: 'Sleek Value,' 'Professional Value,' 'Unique Value,' 'Comfortable Value,' and 'Solid Value.' Finally, we developed the holistic design evaluation model by analyzing each relationship from product attributes, perceived attributes, to consumer values. This study has several theoretical and practical contributions. First, we found consumer values in terms of design evaluation and implicit chain relationship from the objective and physical characteristics to the subjective and mental evaluation. That is, the model explains the mechanism of design evaluation in consumer minds. Second, we suggest a general design evaluation process from product attributes, perceived attributes to consumer values. It is an adaptable methodology not only smart phone but also other IT products. Practically, this model can support the decision-making when companies initiative new product development. It can help product designers focus on their capacities with limited resources. Moreover, if its model combined with machine learning collecting consumers' purchasing data, most preferred values, sales data, etc., it will be able to evolve intelligent design decision support system.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• 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.

• The Korean Journal of Pain
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• v.1 no.2
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• pp.214-222
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• 1988

With the arm in hyperabduction, we have carried out 525 procedures of supraclavicular brachial plexus block from Aug. 1976 to June 1980, whereas block with the arm in adduction has been customarily performed by other authors. The anesthetic procedure is as follows: 1) The patient lies in the dorsal recumbent position without a pillow under his head or shoulder. His arm is hyperabducted more than a 90 degree angle from his side, and his head is turned to the side opposite from that to be blocked. 2) An "X" is marked at a point 1 cm above the mid clavicle, immediately lateral to the edge of the anterior scalene muscle, and on the palpable portion of the subclavian artery. The area is aseptically prepared and draped. 3) A 22 gauge 3.5cm needle attached to a syringe filled with 2% lidocaine (7~8mg/kg of body weight) and epineprine(1 : 200,000) is inserted caudally toward the second portion of the artery where it crosses the first rib and parallel with the lateral border of the muscle until a paresthesia is obtained. 4) Paresthesia is usually elicited while inserting the needle tip about 1~2 em in depth. If so, the local anesthetic solution is injected after careful aspiration. 5) If no paresthesia is elicited, the needle is withdrawn and redirected in an attempt to elicit paresthesia. 6) If, after several attempts, no paresthesia is obtained, the local anesthetic solution is injected into the perivascular sheath after confirming that the artery is not punctured. 7) Immediately after starting surgery, Valium is injected for sedation by the intravenous route in almost all cases. The age distribution of the cases was from 11 to 80 years. Sex distribution was 476 males and 49 females (Table 1). Operative procedures consisted of 103 open reductions, 114 skin grafts combined with spinal anesthesia in 14, 87 debridements, 75 repairs, i.e. tendon (41), nerve(32), and artery (2), 58 corrections of abnormalities, 27 amputations above the elbow (5), below the elbow (3) and fingers (17), 20 primary closures, 18 incisions and curettages, 2 replantations of cut fingers. respectively (Table 2). Paresthesia was obtained in all cases. Onset of analgesia occured within 5 minutes, starting in the deltoid region in almost all cases. Complete anesthesia of the entire arm appeared within 10 minutes but was delayed 15 to 20 minutes in 5 cases and failed in one case. Thus, our success rate was nearly 100%. The duration of anesthesia after a single injection ranged from $3\frac{1}{2}$ to $4\frac{1}{2}$, hours in 94% of the cases. The operative time ranged from 0.5 to 4 hours in 92.4% of the cases(Table 3). Repeat blocks were carried out in 33 cases when operative times which were more than 4 hours in 22 cases and the others were completed within 4 hours (Table 4). Two patients of the 33 cases, who received microvasular surgery were injected twice with 2% lidocaine 20 ml for a total of $13\frac{1}{2}$ hours. The 157 patients who received surgery on the forearms or hands had pneumatic tourniquets (250 torrs) applied without tourniquet pain. There was no pneumothorax, hematoma or phrenic nerve paralysis in any of the unilateral and 27 bilateral blocks, but there was hoarseness in two, Horner's syndrome in 11 and shivering in 7 cases. No general seizures or other side effects were observed. By 20ml of 60% urcgratin study, we confirm ed the position of the needle tip to be in a safer position when the arm is in hyperabduction than when it is in adduction. And also that the humoral head caused some obstraction of the distal flow of the dye, indicating that less local anesthetic solution would be needed for satisfactory anesthesia. (Fig. 3,4).

• Journal of Korean Society of Forest Science
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• v.78 no.3
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• pp.314-322
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• 1989

For the improvement of nursery condition and the study of eco-physiological characteristics of Pinus koraiensis(2-2) seedlings, the primary productivity was investigated and the growth characteristics and their correlationship were analyzed from May to September in 1987. Seedlings used in this study were grown at the nursery of Experimental Forestry of Kyung Hee Univ. The installation of experimental plots were divided into control plot (100%), 63%, 37% and 19% relative light intensity, and each relative light intensity plots were split into $15{\times}15/m^2$, $12{\times}12/m^2$, $9{\times}9/m^2$ and $6{\times}6/m^2$ plnting density by randomized black design method. To take into account the edge effect of plant population, material were selected from each plot by random sampling at 30 days interval. Each sampled material was divided into leaf and stem drived in a drying oven at the temperature of $85^{\circ}C$ until it had constant weight and weighed. and leaf area was surveyed. Growth analysis for RGR and NAR was done by Blackman method and correlation coefficient were investigated between RGR and NAR by analyzing the dry matter production and growth characteristics of the material, cultivated on the experimental nursery under the condition of different treatment the obtained result were as follows : 1. The increasing rate of dry matter was similar at early stage of growth, but not at late stage. 2. Leaf area growth was the maximum value at 63% relative light intensity and the minimum at 19% RLI 3. The value of RGR was the highest on June under $6{\times}6/m^2$ planting density in 63% relative light intensity, the lowest on September under $12{\times}12/m^2$ of 19% relative light intensity. 4. The change NAR decreased in early stage of growth, while it increased on August and September, The value of RGR on June under $6{\times}6/m^2$ planting density in 100 light intensity was the highest, and on September under $12{\times}12/m^2$ planting density in 19% relative light intensity was the lowest. 5. Regression and correlation between RGR and NAR showed significantly positive.

• Journal of Sericultural and Entomological Science
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• v.47 no.2
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• pp.68-77
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• 2005

A characteristic of subtropical zones region MK-T2 compares with an gaeryangppong, and the 9-10 schedule the times when a leaf blooms to are fast, and ratio that a branch edge by the colds becomes lean showed 5.7%, and a growth of the new branch which went out delivers 67.2 cm, mulberry loaves of the new branch which went out, and 18.6, a form of a leaf is the 1.10 that length of a leaf grew more a bit than width of a leaf up. Thickness of a leaf is $228.2{\mu}m$, and an area is more similar than gaeryangppong as $225.6cm^2$. in plant taxonomy, the hair whom the style exists short with 0.7 mm, and go to the pistil head inside so as to be rare is distributed, and belong to Dolichostylae Pubescentes. The new branch cutting which executed without remedy processes was independent of a thickness of a case branch, and the form and 100% root was said, and an gaeryangppong compared with the fact that 10% root went out of 15 mm ideal, and was excellent very, and looked, a root went out a root the soil and water, all showed a characteristic to go out at central of a branch bases at 45% ratio. Length was 24.6 mm, and were water rate 78.8%, and mulberry of MK-T2 was carrying together sweetness and acidity to pH 4.7 while, besides, arrival was 19.21 Brix%. A larva period and pupa ratio, cocoon thickness ratio are almost similar to gaeryangppong, or weight of one cocoon, cocoon thickness, 20,002 cocoon quantity shows some results to drop, and be soft of a leaf, and feed value certifications are comparatively top-ranking. As a result of having analyzed amino acid of the 3rd day of 5th silkworm larva which bred to MK-T2, a collation absorbing an gaeryangppong went, and looked, but compared with a collation in case of tests to eat MK-T2, and looked, and the lie collations were not detected a difference at Leu, but MK-T2 tests were detected mutual almost similar amino acid creation. medical efficacy of the 3rd day of 5th silkworm larva ethanol extract which bred to MK-T2 and black results, histologic a case did not appear at HE dyeing about the kidney organization which extracted form the rats which ate a silkworm ethanol extract and dyeing all chemical organization immunity, and one step protein revelation became lower with almost unidentified levels.