• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Korean Journal of Heritage: History & Science
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• v.44 no.1
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• pp.196-221
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• 2011

The Royal Private Shrines or the Samyo(私廟), were dedicated to members of Choseon's royal family who could not be enshrined at the (official) Royal Ancestral Shrine, the Jongmyo(宗廟). The Samyo were constructed at the national level and were systematically managed as such. Because these private Shrines were dedicated to those who couldn't belong to the Jongmyo but were still very important, such as the ruling king's biological father or mother. The details of all royal constructions were included in the State Event Manuals, and with them, the two-dimensional layouts of the Samyo also. From the remaining "Hyunsa-gung Private Tomb Construction Layout Record(顯思宮別廟營建都監儀軌)" of 1824, which is the construction record of Gyeongwoo-gung Shrine(景祐宮) dedicated to Subin, the mother of King Sunjo(純祖), it became possible to investigate the so far unknown "The Painting of Gyeongwoo-gung Shrine", in terms of the year produced, materials used and other situational contexts. The investigation revealed that the "The Painting of Gyeongwoo-gung Shrine" is actually the "Hyunsa-gung Private Tomb Layout" produced by the Royal Construction Bureau. The bureau painted this to build Hyunsa-gung Private Shrine in a separately prepared site outside the court in 1824, according to the royal verdict to close down and move the temporary shrine inside the courtyard dedicated to Subin who had passed away in 1822. As the Construction Bureau must have also produced the Gyeongwoo-gung Shrine Layout, the painter(s) of this layout should exist among the official artists listed in the State Event Manual, but sadly, as their paintings have not survived to this day, we cannot compare their painting styles. The biggest stylistic character of the Painting of Gyeongwoo-gung Shrine is its perfect diagonal composition method and detailed and neat portrayalof the many palace buildings, just as seen in Donggwoldo(東闕圖, Painting of a panoramic view for Changdeokgung and Changgyeonggung Palaces). A well-perceiving architectural painting employs a specific point of view chosen to fit the purpose of the painting, or it can opt to the multi-viewpoint. Korean traditional architectural paintings in early ages utilized the diagonal composition method, the bird-eye viewpoint, or the multi-viewpoint. By the 18th century, detailed but also artistic architectural paintings utilizing the diagonal method are observed. In the early 19th century, the peak of such techniques is exhibited in Donggwoldo(Painting of a panoramic view for Changdeokgung and Changgyeonggung Palaces). From the perfect diagonal composition method employed and the details of the palace buildings numbering almost two hundreds, we can determine that the Painting of Gyeongwoo-gung Shrine also belongs to the same category of the highly technical architectural paintings as Donggwoldo(Painting of a panoramic view for Changdeokgung and Changgyeonggung Palaces). We can also confirm this hypothesis by comparing the painting techniques employed in these two paintings in detailthe way trees and houses are depicted, and the way ground texture is expressed, etc. The unique characteristic of the Painting of Gyeongwoo-gung Shrine is, however, that the area surrounding the central shrine building(正堂), the most important area of the shrine, is drawn using not the diagonal method but the bird-eye viewpoint with the buildings lying flat on both the left and right sides, just as seen in the "Buildings Below the Central Shrine(正堂以下諸處)" in the State Event Manual's Painting Method section. The same viewpoint method is discovered in some other concurrent paintings of common residential buildings, so it is not certain that this particular viewpoint had been a distinctive feature for shrine paintings in general. On the other hand, when the diagonalmethod pointing to the left direction is chosen, the top-left and bottom-right sections of the painting become inevitably empty. This has been the case for the Painting of Gyeongwoo-gung Shrine, but in contrast, Donggwoldo shows perfect screen composition with these empty margins filled up with different types of trees and other objects. Such difference is consistent with the different situational contexts of these two paintings: the Painting of Gyeongwoo-gung Shrine is a simple single-sheet painting, while Donggwoldo is a perfected work of painting book given an official title. Therefore, if Donggwoldo was produced to fulfill the role of depiction and documentation as well as the aesthetic purpose, contrastingly, the Painting of Gyeongwoo-gung Shrine only served the purpose of copying the circumstances of the architecture and projecting them onto the painting.

• Korean Journal of Heritage: History & Science
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• v.44 no.1
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• pp.72-105
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• 2011

This study explores the value of the Dicentra spectabilis community as a nature reserve in provincial forests at San 1-2, Daea-ri, Dongsang-myeon, Wanju-gun, Jellabuk-do, also known as Gamakgol, while defining the appropriateness of its living environment and eventually providing basic information to protect this area. For these reasons, we investigated 'morphological and biological features of Dicentra spectabilis' and the 'present situation and problems of designing a herbaceous nature reserve in Korea.' Furthermore, we researched and analyzed the solar, soil and vegetation condition here through a field study in order to comprehend its nature reserve value. The result is as follows. According to the analytic result for information on the domestic wild Dicentra spectabilis community, it is evenly spread throughout mountainous areas, and there is one particularly outstanding in size in Wanju Gamakgol. Upon the findings from literature and the field study about its dispersion, Gamakgol has been discovered as an ideal district for Dicentra spectabilis since it meets all the conditions this plant requires to grow vigorously, such as a quasi-high altitude and rich precipitation during its period of active growth duration in May. Dicentra spectabilis grows in rocky soil ranging from 300~375m above sea level, 344.5m on average, towards the north, northwest and dominantly in the northeast. The mean inclination degree is $19.5^{\circ}$. Also, upon findings from analyzing solar conditions, the average light intensity during its growth duration, from Apr. to Aug., is 30,810lux on average and it tends to increase, as it gets closer to the end. This plant requires around 14,000~18,000lux while growing, but once bloomed, fruits develop regardless of the degree of brightness. The soil pH has shown a slight difference between the topsoil, at 5.2~6.1, and subsoil, at 5.2~6.2. Its mean pH is 5.54 for topsoil and 5.58 for subsoil. These results are very typical for Dicentra spectabilis to grow in, and other comparative areas also present similar conditions. Given the facts, the character of the soil in Gamakgol has been evaluated to have high stability. Analysis of its vegetation environment shows a wide variation of taxa numbering from 13 to 52 depending on area. The total number of taxa is 126 and they are a homogenous group while showing a variety of species as well. The Dicentra spectabilis community in the Daea-ri Arboretum is an herbaceous community consisting of dominantly Dicentra spectabilis, Cardamine leucantha, Boehmeria tricuspi and Impatiens textori while having many differential species such as Impatiens textori, Pueraria thunbergiana, Rubus crataegifolius vs Staphylea bumalda, Securinega suffruticosa, and Actinidia polygama. It suggests that it is a typical subcolony divided by topographic features and soil humidity. Considering the above results on a comprehensive level, this area is an excellent habitat for wild Dicentra spectabilis providing beautiful viewing enjoyment. Additionally, it is the largest wild colony of Dicentra spectabilis in Korea whose climate, topography, soil conditions and vegetation environment can secure sustainability as a wild habitat of Dicentra spectabilis. Therefore, We have determined that the Gamakgol community should be re-examined as natural asset owing to its established habitat conditions and sustainability.

• The Korean Journal of Air & Space Law and Policy
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• v.33 no.2
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• pp.35-59
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• 2018

Professor Bin Cheng said that outer space was res extra commercium, while the moon and the other celestial bodies were res nullius before the 1967 Outer Space Treaty(OST). However, Article 2 of the OST made the moon and other celestial bodies have the legal status as res extra commmercium, not appropriated by any country or private enterprises or individual person, but the resources there can be freely available, as those on the high seas. The non-appropriation principle was introduced to corpus juris spatialis internationalis. Whether or not the non-appropriation principle is binding for the non-parties of the OST, many scholars see this principle as an international customary law, even developing into jus cogens. Article 11(2) of the Moon Agreement(MA) reconfirms the nonappropriation principle of Article 2 of the OST, but it has much less effect than the OST because the MA binds only the 18 parties involved. The MA applies only to the moon and celestial bodies other than the Earth in the Solar System, the OST's application scope extends to the Galaxy because the OST has no such substantive enactment. As referred to in the 2015 CSLCA of USA or Luxembourg's Law of Space Resources, allowing individuals and enterprises run by other countries to commercially explore and utilize the space resources, the question may arise whether this violates the non-appropriation principle under Article 2 of the OST and Article 11 of the MA. In the case of the CSLCA, the law explicitly specifies that sovereignty, possessory rights, and judiciary rights to a specific celestial body cannot be claimed, let alone ownership. This author believes that this law respects the legal status of outer space and the celestial bodies as res extra commmercium. As long as any countries or private enterprises or individuals respect the non-appropriation principle of outer space and the celestial bodies, they could use, exploit it. Another question might be raised in the difference between res extra commercium on the high seas and res extra commercium in outer space and the celestial bodies. Collecting resources on the high seas and exploiting space resources should be interpreted differently. On the high seas, resources can be collected without any obstacles like fishing, whereas, in the case of the deep sea-bed area, the Common Heritage of Mankind principles under the UNCLOS should be operated by the International Seabed Authority as an international regime. The nature or form of the sea resources found on the high seas are thus different from that of space resources, which are fixed on the moon and the celestial bodies without water. Thus, if individuals or private enterprises collect these resources from outer space and the celestial bodies, they might secure a certain section and continue collecting or mining works without any limitation. If an American enterprise receives an approval from the U.S. government, secures the best location and collects resources on the moon, can other countries' enterprises access to this area? How large the exploiting place can be allotted on the moon? How long should such a exploiting activity be lasted? Under the current international space law, these matters might be handled according to the principle of "first come, first served." As a consequence, the international community should provide a guideline or a proposal for the settlement of any foreseeable disputes during the space activity to solve plausible space legal questions in the near future.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.6
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• pp.49-59
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• 2018

The purpose of this study was to analyze the importance and satisfaction of resources in the mouth of Nakdong River. A Pearson's chi-square test was performed in SPSS 24.0 for statistical analysis and the result of the study was summarized by three points. First, the results of importance analysis on resources in Nakdong estuary found that the importance of ecology resources was the highest with 27.1%, followed by landscape resources (18.5%), waterside leisure resources (6.5%), complex cultural resources (5.4%), and historic and cultural resources (3.3%). The probability values (p-value) of each group had shown significant differences depending on gender, age, and the location of the survey. For instance, women respondents reported a higher preference to ecology resources and complex cultural resources such as museums than men respondents as much as two times and three times, respectively. Meanwhile, men respondents showed a higher preference to waterside leisure resources in three times as much as women respondents. As for the analysis by age, the respondents in their 20s and 30s recorded a higher value than those in other age groups, and people in their 30s reported a higher preference to waterside leisure resources than those in different age groups by three times. Lastly, no significant differences were found in the preference analysis by occupation (p>.05). With regard to the results of satisfaction analysis, the average level of satisfaction on landscape resources was 6.01, and that of ecology resources and complex cultural resource were 5.65 and 5.15, respectively. Also, significant differences were found between landscape and ecology resources in the satisfaction analysis by age, landscape resources by age, ecology resources by region, and between landscape resources and ecology resources by occupation. The p-value of complex cultural resources was p=0.012, although the satisfaction level of landscape resources and ecology resources were reported to have no significant differences by age. As for the level of satisfaction in landscape resources, respondents in their 40s and 50s showed a high level of satisfaction. However, those in their 20s showed a relatively low level of satisfaction in the same category. The survey respondents living in Busan and South Gyeongsang Province and those living outside the regions revealed no significant differences in terms of satisfaction in landscape resources and complex cultural resources. However, the two same groups were found to show significant differences in the satisfaction analysis on ecology resources. In the satisfaction analysis of landscape resources and ecology resources by occupation, significant differences were found among college students, government employees, ordinary citizens, and expert groups. However, they showed no significant differences in the level of satisfaction to complex cultural resources. Third, the results of importance-satisfaction analysis on Nakdong estuary found that the average levels of satisfaction to landscape resources for each group of respondents who considered landscape, ecology, and cultural resources as important was 6.19, 6.08, and 5.67, respectively. Their levels of satisfaction on ecology resources were 5.95, 5.57, and 5.41 for each. Its correlation to the importance was insignificant. However, it was confirmed that the correlation to the level of satisfaction on complex cultural resources had a significant difference (p=0.025). In addition, the results of the analysis on 15 detailed items that was carried out with the aim to improving values and vitalizing resources in the mouth of Nakdong River found that respondents considered that the vitalization of eco-tourism (49.5%) and restoration of reed marsh (47.5%) were important. The results of detailed analysis revealed respondents' high awareness on the need of enhancing values on ecology resources. Also, improving infrastructure nearby the mouth, creating cycling routes, walkways, waterside leisure facilities, and others were considered as the requirements for the vitalization of Nakdong estuary.

• Journal of the Korean Geotechnical Society
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• v.35 no.5
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• pp.5-19
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• 2019

Considering the natural phenomenon in which steep slopes ($65^{\circ}{\sim}85^{\circ}$) consisting of rock mass remain stable for decades, slopes steeper than 1:0.5 (the standard of slope angle for blast rock) may be applied in geotechnical conditions which are similar to those above at the design and initial construction stages. In the process of analysing the stability of a good to fair continuum rock slope that can be designed as a steep slope, a general method of estimating rock mass strength properties from design practice perspective was required. Practical and genealized engineering methods of determining the properties of a rock mass are important for a good continuum rock slope that can be designed as a steep slope. The Genealized Hoek-Brown (H-B) failure criterion and GSI (Geological Strength Index), which were revised and supplemented by Hoek et al. (2002), were assessed as rock mass characterization systems fully taking into account the effects of discontinuities, and were widely utilized as a method for calculating equivalent Mohr-Coulomb shear strength (balancing the areas) according to stress changes. The concept of calculating equivalent M-C shear strength according to the change of confining stress range was proposed, and on a slope, the equivalent shear strength changes sensitively with changes in the maximum confining stress (${{\sigma}^{\prime}}_{3max}$ or normal stress), making it difficult to use it in practical design. In this study, the method of estimating the strength properties (an iso-angle division method) that can be applied universally within the maximum confining stress range for a good to fair continuum rock mass slope is proposed by applying the H-B failure criterion. In order to assess the validity and applicability of the proposed method of estimating the shear strength (A), the rock slope, which is a study object, was selected as the type of rock (igneous, metamorphic, sedimentary) on the steep slope near the existing working design site. It is compared and analyzed with the equivalent M-C shear strength (balancing the areas) proposed by Hoek. The equivalent M-C shear strength of the balancing the areas method and iso-angle division method was estimated using the RocLab program (geotechnical properties calculation software based on the H-B failure criterion (2002)) by using the basic data of the laboratory rock triaxial compression test at the existing working design site and the face mapping of discontinuities on the rock slope of study area. The calculated equivalent M-C shear strength of the balancing the areas method was interlinked to show very large or small cohesion and internal friction angles (generally, greater than $45^{\circ}$). The equivalent M-C shear strength of the iso-angle division is in-between the equivalent M-C shear properties of the balancing the areas, and the internal friction angles show a range of $30^{\circ}$ to $42^{\circ}$. We compared and analyzed the shear strength (A) of the iso-angle division method at the study area with the shear strength (B) of the existing working design site with similar or the same grade RMR each other. The application of the proposed iso-angle division method was indirectly evaluated through the results of the stability analysis (limit equilibrium analysis and finite element analysis) applied with these the strength properties. The difference between A and B of the shear strength is about 10%. LEM results (in wet condition) showed that Fs (A) = 14.08~58.22 (average 32.9) and Fs (B) = 18.39~60.04 (average 32.2), which were similar in accordance with the same rock types. As a result of FEM, displacement (A) = 0.13~0.65 mm (average 0.27 mm) and displacement (B) = 0.14~1.07 mm (average 0.37 mm). Using the GSI and Hoek-Brown failure criterion, the significant result could be identified in the application evaluation. Therefore, the strength properties of rock mass estimated by the iso-angle division method could be applied with practical shear strength.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.