• Journal of Energy Engineering
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• v.28 no.2
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• pp.18-35
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• 2019

The Korea District Heating Corporation operates a gas engine generator with a capacity of $4500m^3 /day$ of biogas generated from the sewage treatment plant of the Nanji Water Recycling Center and 1,500 kW. However, the actual operation experience of the biogas power plant is insufficient, and due to lack of accumulated technology and know-how, frequent breakdown and stoppage of the gas engine causes a lot of economic loss. Therefore, it is necessary to prepare technical fundamental measures for stable operation of the power plant In this study, a series of process problems of the gas engine plant using the biogas generated in the sewage treatment plant of the Nanji Water Recovery Center were identified and the optimization of the actual operation was made by minimizing the problems in each step. In order to purify the gas, which is the main cause of the failure stop, the conditions for establishing the quality standard of the adsorption capacity of the activated carbon were established through the analysis of the components and the adsorption test for the active carbon being used at present. In addition, the system was applied to actual operation by applying standards for replacement cycle of activated carbon to minimize impurities, strengthening measurement period of hydrogen sulfide, localization of activated carbon, and strengthening and improving the operation standards of the plant. As a result, the operating performance of gas engine # 1 was increased by 530% and the operation of the second engine was increased by 250%. In addition, improvement of vent line equipment has reduced work process and increased normal operation time and operation rate. In terms of economic efficiency, it also showed a sales increase of KRW 77,000 / year. By applying the strengthening and improvement measures of operating standards, it is possible to reduce the stoppage of the biogas plant, increase the utilization rate, It is judged to be an operational plan.

• The Journal of Engineering Geology
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• v.29 no.2
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• pp.85-97
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• 2019

It can be observed that steep slopes ($65^{\circ}$ to $80^{\circ}$) consist of rock masses were kept stable for a long time. In rock-mass slopes with similar ground condition, steeper slopes than 1 : 0.5 ($63^{\circ}$) may be applied if the discontinuities of rock-mass slope are distributed in a direction favorable to the stability of the slope. In making a decision the angle of the slope, if the preliminary rock mass conditions applicable to steep slope are quantitatively setup, they may be used as guidance in design practice. In this study, the above rock mass was defined as a good continuum rock mass and the quantitative setup criterion range was proposed using RMR, SMR and GSI classifications for the purpose of providing engineering standard for good continuum rock mass conditions. The methods of study are as follows. The stable slope at steep slopes ($65^{\circ}$ to $80^{\circ}$) for each rock type was selected as the study area, and RMR, SMR and GSI were classified to reflect the face mapping results. The results were reviewed by applying the calculated shear strength to the stable analysis of the current state of rock mass slope using the Hoek-Brown failure criterion. It is intended to verify the validity of the preliminary criterion as a rock mass condition that remains stable on a steep slope. Based on the analysis and review by the above research method, it was analyzed that a good continuum rock mass slope can be set to Basic RMR ${\geq}50$ (45 in sedimentary rock), GSI and SMR ${\geq}45$. The safety factor of the LEM is between Fs = 14.08 and 67.50 (average 32.9), and the displacement of the FEM is 0.13 to 0.64 mm (average 0.27 mm). This can be seen as a result of quantitative representation and verification of the stability of a good continuum rock mass slope that has been maintained stable for a long period of time with steep slopes ($65^{\circ}$ to $80^{\circ}$). The setup guideline for a good continuum rock mass slope will be able to establish a more detailed setup standard when the data are accumulated, and it is also a further study project. If stable even on steep slopes of 1 : 0.1 to 0.3, the upper limit of steep slopes is 1 : 0.3 with reference to the overseas design standards and report, thus giving the benefit of ensuring economic and eco-friendlyness. Also, the development of excavation technology and plantation technology and various eco-friendly slope design techniques will help overcome psychological anxiety and rapid weathering and relaxation due to steep slope construction.

• Korean Journal of Environment and Ecology
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• v.33 no.3
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• pp.354-365
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• 2019

This study measured and analyzed the weather characteristics and the air-ion characteristics of walking space by land use type in Chungju, Chungcheongbuk Province during the heat wave. We used the land registration map to classify the type of land use in walking areas in the studied into the production and green area, the residential area, and the commercial area. We then selected 44 measurement points in about 4.1 km. They included 12 walking space points in the green area, 14 in the residential area, and 18 in the commercial area. Moreover, we calculated the ion index by analyzing the impact of weather factors such as temperature, relative humidity, solar radiation, and net radiation in the walking space on the anion generation and cation generation by land use type during the heat wave. Comparison of air ion characteristics in walking space by type of land use during the heat wave showed that the average cation generation was in the order of commercial area ($700.73cations/cm^3$) > residential area ($600.76cations/cm^3$) > green area ($589.73cations/cm^3$). The average anion generation was in the order of green area ($663.95anions/cm^3$) > residential area ($628.48anions/cm^3$) > commercial area ($527.48anions/cm^3$). The average ion index was in the order of green area (1.13) > residential area (1.04) > commercial area (0.75). This study checked the weather characteristics, cation generation, and anion generation in walking space according to the land use type during the heat wave and checked the difference of ion indexes in the walking space according to the land use type. However, there were limitations in the lack of accurate comparison according to the land use due to the moving measurement and the insufficient quantitative comparison according to the change of road width. Therefore, we recommend further studies that consider the road characteristics.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.179-200
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• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.

• Korean Journal of Heritage: History & Science
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• v.50 no.4
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• pp.148-165
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• 2017

Mural paintings drawn inside ancient tombs are very sensitive to changes in the environment such as temperature and humidity, especially the finish layer of the tomb murals differ in preservability depending on the material properties and humidity conditions. In this study, I examined the mural painting of Songsan-ri Tomb No.6, where the finish layer was made of earth, and identified the physical changes that can occur due to drying, depending on the material properties of the finish layer. I found out through particle size analysis that the finish layer of the mural painting in Songsan-ri Tomb No.6 is about 85.0wt% below silt, about 14.0wt% clay therein, mostly composed of silt and below clay. I also found out through physical property evaluation that surface change rate of samples showed the largest change at 15.5% in reproduced finish layer sample made up of bentonite, followed by 7.8% of reproduced finish layer sample made up of celadon soil, 6.3% of reproduced finish layer sample made up of loess, 6.2% of reproduced finish layer sample composed of white clay and the same order of change in appearance was confirmed in each sample consisted of soil. In addition, it showed the same trend of surface change rate, and the bentonite condition showed the largest change, in the measurement of shrinkage rate and expansion rate. The experiment shows that the finish layer composed of soil is affected by cohesion among particles according to the content of fine parts and the relationship between the agglomeration due to the content of the differentiated part and the stress due to the expansibility depending on the kind of the clay mineral etc. Therefore, it can be concluded that the physical damage occurred in the mural painting finish layer of the Songsan-ri Tomb No.6 is related to the factors such as the material characteristics of the soil and the highly humid environmental change inside the tomb.

• Korean Journal of Food Science and Technology
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• v.51 no.4
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• pp.316-323
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• 2019

It is difficult to analyze pymetrozine in citrus fruits using the hydromatrix method because of its low efficiency of purification and overlap of matrix and pymetrozine peaks. Liquid-liquid extraction can analyze pymetrozine in citrus fruits using dichloromethane. Since low pH interferes with the extraction of pymetrozine, the extracts of citrus fruits were maintained over pH 7.0 by adding borax buffer and 1 N NaOH in the improved method. According to the improved method, citrus fruits (such as lemon, lime, orange, tangerine, and grapefruit) were extracted and purified for HPLC-photo diode array analysis. The results of validation were as follows: $4.360{\mu}g/kg$ of limit of detection, $14.533{\mu}g/kg$ of limit of quantitation, and 0.007 mg/kg of method quantitative limit. Citrus fruits spiked with pymetrozine showed a recovery range from 71.8 to 83.7% and a coefficient of variation below 6%. Thus, the improved method can efficiently analyze pymetrozine in citrus fruits.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.3
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• pp.39-48
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• 2019

This research is based on the analysis of in-depth interviews of designers who participated in the design of the Yeonnam-dong section, which was completed in 2016. The case study site has received many domestic and foreign awards and is receiving very positive reviews from actual users. 53 concepts were derived from the open coding of the ground theory methodology. Thirty-four higher categories incorporated the concepts and 18 higher categories that reintegrated them. Later, the six categories of the ground theory were interpreted as the paradigm, and it was determined that the aspects of 'will of client' and 'work efficiency', 'site resources' and 'field manager's specialty' were the categories that had the greatest positive impact on the park construction. The key category of this park's construction was interpreted as "a park-construction model with active empathy and communication." The results of the study and are linked to the following research proposals. First, the need to improve the trust between the client and the landscape designer and the need to improve the customary administrative procedures; second, the importance of the input of landscape experts into the park construction process; third, the importance of all efforts to develop the design; fourth, the importance of on-site circular resources and landscape preservation; and fifth active social participation to increase the opportunity. This study, which seeks to grasp the facts that existed behind the park's construction, which received excellent internal and external evaluations, and has a qualitative, objective and structural interpretation of the social network related to the park's construction, in contrast to the conventional quantitative post-evaluation. It is expected that the administration and system improvements related to landscaping will be further improved through the continuation of in-depth post-evaluation studies.

• Therapeutic Science for Rehabilitation
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• v.8 no.3
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• pp.7-29
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• 2019

Objective: The purpose of this study is to analyze comprehensive lifestyle assessment and other assessments which evaluate essential lifestyle factors, including physical activity, nutrition and activity participation. Methods: To analyze the comprehensive lifestyle assessment, from January 2001 to June 2019, a literature search was conducted using the CINANL, NDSL, PubMed, and RISS databases. The search terms were 'lifestyle assessment' OR 'lifestyle profile' OR 'lifestyle test'. In terms of other assessments of essential factors of lifestyle, from January 2010 to June 2019, articles were searched using similar databases. The search terms were 'physical activity assessment' OR 'physical activity participation profile', 'nutrition assessment', 'activity participation assessment' OR 'activity participation and lifestyle'. Results: A total of 4,165 articles were obtained, and finally 31 articles were selected according to the inclusion criteria. Among 31 articles, there were five with comprehensive lifestyle assessments, and all of them were self-report questionnaires. The most popular assessments were the Health Enhancement Lifestyle Profile (HELP) and the Health-Promoting Lifestyle Profile (HPLP), which were used in three articles (33%). In terms of assessment of physical activity, the most frequently used evaluation method was the self-report questionnaire, which was used in seven articles (58%) followed by objective assessments, which were used in four articles (33%). It was demonstrated that the Mini-Nutritional Assessment (MNA) was the most frequently used for nutrition assessment in the elderly. There were five types of assessment tool used for activity participation. Among them, meaningful activity participation assessment (MAPA) was the most frequently used tool. Conclusion: As a result of the systematic review, it was found that there are 21 assessments related to the evaluation of lifestyle in the elderly. Most assessments employed the self-report questionnaire method and mainly evaluated frequency and duration of participation in drinking, smoking, exercise, nutrition and social activities. Assessments of essential lifestyle factors were the self-report questionnaire method and the participation and frequency of activity. Therefore, by analyzing assessment tools, types of items and measurement methods of comprehensive lifestyle assessments and other assessment of essential lifestyle factors, this study provides the basic data on which to develop a standardized assessment tool that can evaluate the multifaceted lifestyle profile of the elderly.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.19-41
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• 2019

According to the rapidly increasing demand for text data analysis, research and investment in text mining are being actively conducted not only in academia but also in various industries. Text mining is generally conducted in two steps. In the first step, the text of the collected document is tokenized and structured to convert the original document into a computer-readable form. In the second step, tasks such as document classification, clustering, and topic modeling are conducted according to the purpose of analysis. Until recently, text mining-related studies have been focused on the application of the second steps, such as document classification, clustering, and topic modeling. However, with the discovery that the text structuring process substantially influences the quality of the analysis results, various embedding methods have actively been studied to improve the quality of analysis results by preserving the meaning of words and documents in the process of representing text data as vectors. Unlike structured data, which can be directly applied to a variety of operations and traditional analysis techniques, Unstructured text should be preceded by a structuring task that transforms the original document into a form that the computer can understand before analysis. It is called "Embedding" that arbitrary objects are mapped to a specific dimension space while maintaining algebraic properties for structuring the text data. Recently, attempts have been made to embed not only words but also sentences, paragraphs, and entire documents in various aspects. Particularly, with the demand for analysis of document embedding increases rapidly, many algorithms have been developed to support it. Among them, doc2Vec which extends word2Vec and embeds each document into one vector is most widely used. However, the traditional document embedding method represented by doc2Vec generates a vector for each document using the whole corpus included in the document. This causes a limit that the document vector is affected by not only core words but also miscellaneous words. Additionally, the traditional document embedding schemes usually map each document into a single corresponding vector. Therefore, it is difficult to represent a complex document with multiple subjects into a single vector accurately using the traditional approach. In this paper, we propose a new multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. This study targets documents that explicitly separate body content and keywords. In the case of a document without keywords, this method can be applied after extract keywords through various analysis methods. However, since this is not the core subject of the proposed method, we introduce the process of applying the proposed method to documents that predefine keywords in the text. The proposed method consists of (1) Parsing, (2) Word Embedding, (3) Keyword Vector Extraction, (4) Keyword Clustering, and (5) Multiple-Vector Generation. The specific process is as follows. all text in a document is tokenized and each token is represented as a vector having N-dimensional real value through word embedding. After that, to overcome the limitations of the traditional document embedding method that is affected by not only the core word but also the miscellaneous words, vectors corresponding to the keywords of each document are extracted and make up sets of keyword vector for each document. Next, clustering is conducted on a set of keywords for each document to identify multiple subjects included in the document. Finally, a Multi-vector is generated from vectors of keywords constituting each cluster. The experiments for 3.147 academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the proposed multi-vector based method, we ascertained that complex documents can be vectorized more accurately by eliminating the interference among subjects.