• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.1
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• pp.138-143
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• 2008

A study was conducted to evaluate nutritional value for twenty-one pork muscles. Ten market-weight crossbred pigs (five gilts and five barrows) were used for evaluating proximate chemical composition, cholesterol, total iron, calorie and fatty acid contents. As preliminary analysis revealed no noticeable sex effect, pooled data from both sexes were used for the final analysis. M. rectus femoris had the highest moisture content, while m. latissimus dorsi was lowest in moisture content (p<0.05). Protein content was highest for m. longissimus dorsi and lowest for m. supraspinatus (p<0.05). The tensor fasciae and latissimus dorsi muscles contained the highest intramuscular fat (p<0.05), while rectus femoris, adductor and vastus lateralis were lowest in intramuscular fat content. When simple correlations between chemical values were computed for the pooled dataset from all muscles, intramuscular fat had significant (p<0.05) negative linear relationships with moisture (r = -0.85) and protein (r = -0.51) contents. Calorie levels were not significantly affected by fat content, while rectus femoris and latissimus dorsi muscles showed lowest and highest calorie contents, respectively (p<0.05). Polyunsaturated fatty acid content was highest (p<0.05) for both m. adductor and m. rectus femoris, while it was lowest for m. longissimus dorsi. Collectively, the current study identified a large amount of variation in nutritional characteristics between pork muscles, and the data can be used for the development of muscle-specific strategies to improve eating quality of meats and meat products.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.555-564
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• 2021

Thalamus is known to play an important role in the regulation of nerve function. Thalamus, located in the center of the brain, is involved in sleep, arousal, and emotional regulation, and has been reported to be associated with multiple sclerosis, essential tremors, and neurodegenerative diseases such as Parkinson's disease. In addition, it has been reported that iron deposits in the thalamus can cause depressive symptoms with age. Although there are discrepancies between studies, it can be deduced that the thalamus region has a clear effect on neurological disorders due to a strong relationship between the thalamus and neurological functions such as emotional control and processing. Through tractography analysis, the connectivity between the detailed areas of each subcortical region was investigated in the form of a matrix, showing strong connectivity and weak interhemispheric connectivity. In the 59> group, the WM connectivity of thalamus was found to be weaker than those of the two groups. Comparisons between the two groups showed that the young groups (10-39 and 40-59) had higher connection intensity than the 59> group and that statistically significant differences in 3 connection pathways were found in each hemisphere. A decrease in thalamus-related connection strength in aging has shown that it can affect emotional and neurological disorders such as anxiety and depression, and network measurements can help assess cognitive impairment across clinical conditions.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Korea Water Resources Association
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• v.47 no.6
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• pp.537-546
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• 2014

Surface image velocimetry was introduced as an efficient and sage alternative to conventional river flow measurement methods during floods. The conventional surface image velocimetry uses a pair of images to estimate velocity fields using cross-correlation analysis. This method is appropriate to analyzing images taken with a short time interval. It, however, has some drawbacks; it takes a while to analyze images for the verage velocity of long time intervals and is prone to include errors or uncertainties due to flow characteristics and/or image taking conditions. Methods using spatio-temporal images, called STIV, were developed to overcome the drawbacks of conventional surface image velocimetry. The grayscale-gradient tensor method, one of various STIVs, has shown to be effectively reducing the analysis time and is fairly insusceptible to any measurement noise. It, unfortunately, can only be applied to the main flow direction. This means that it can not measure any two-dimensional flow field, e.g. flow in the vicinity of river structures and flow around river bends. The present study aimed to develop a new method of analyzing spatio-temporal images in two-dimension using cross-correlation analysis. Unlike the conventional STIV, the developed method can be used to measure two-dimensional flow substantially. The method also has very high spatial resolution and reduces the analysis time. A verification test using artificial images with lid-driven cavity flow showed that the maximum error of the method is less than 10 % and the average error is less than 5 %. This means that the developed scheme seems to be fairly accurate, even for two-dimensional flow.

• Investigative Magnetic Resonance Imaging
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• v.15 no.2
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• pp.110-122
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• 2011

Purpose : The purpose of this study is to establish the method generating human brain anatomical connectivity from Korean children and evaluating the network topological properties using small-world network analysis. Materials and Methods : Using diffusion tensor images (DTI) and parcellation maps of structural MRIs acquired from twelve healthy Korean children, we generated a brain structural connectivity matrix for individual. We applied one sample t-test to the connectivity maps to derive a representative anatomical connectivity for the group. By spatially normalizing the white matter bundles of participants into a template standard space, we obtained the anatomical brain network model. Network properties including clustering coefficient, characteristic path length, and global/local efficiency were also calculated. Results : We found that the structural connectivity of Korean children group preserves the small-world properties. The anatomical connectivity map obtained in this study showed that children group had higher intra-hemispheric connectivity than inter-hemispheric connectivity. We also observed that the neural connectivity of the group is high between brain stem and motorsensory areas. Conclusion : We suggested a method to examine the anatomical brain network of Korean children group. The proposed method can be used to evaluate the efficiency of anatomical brain networks in people with disease.

• Economic and Environmental Geology
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• v.50 no.1
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• pp.61-71
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• 2017

The effects of joint density and size distribution on the hydrogeologic characteristics of jointed rock masses are addressed through numerical experiments based on the 2-D DFN (discrete fracture network) fluid flow analysis. Using two joint sets, a total of 51 2-D joint network system were generated with various joint density and size distribution. Twelve fluid flow directions were chosen every $30^{\circ}$ starting at $0^{\circ}$, and total of 612 $20m{\times}20m$ DFN blocks were prepared to calculate the directional block conductivity. Also, the theoretical block conductivity, principal conductivity tensor and average block conductivity for each generated joint network system were determined. The directional block conductivity and chance for the equivalent continuum behavior of the 2-D DFN system were found to increase with the increase of joint density or size distribution. However, the anisotropy of block hydraulic conductivity increases with the increase of density discrepancy between the joint sets, and the chance for the equivalent continuum behavior were found to decrease. The smaller the intersection angle of the two joint sets, the more the equivalent continuum behavior were affected by the change of joint density and size distribution. Even though the intersection angle is small enough that it is difficult to have equivalent continuum behavior, the chance for anisotropic equivalent continuum behavior increases as joint density or size distribution increases.

• Economic and Environmental Geology
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• v.49 no.1
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• pp.31-41
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• 2016

A program code was developed to calculate block hydraulic conductivity of the 2-D DFN(discrete fracture network) system based on equivalent pipe network, and implemented to examine the effect of joint orientation distribution on the hydraulic characteristics of fractured rock masses through numerical experiments. A rock block of size $32m{\times}32m$ was used to generate the DFN systems using two joint sets with fixed input parameters of joint frequency and gamma distributed joint size, and various normal distributed joint trend. DFN blocks of size $20m{\times}20m$ were selected from center of the $32m{\times}32m$ blocks to avoid boundary effect. Twelve fluid flow directions were chosen every $30^{\circ}$ starting at $0^{\circ}$. The directional block conductivity including the theoretical block conductivity, principal conductivity tensor and average block conductivity were estimated for generated 180 2-D DFN blocks. The effect of joint orientation distribution on block hydraulic conductivity and chance for the equivalent continuum behavior of the 2-D DFN system were found to increase with the decrease of mean intersection angle of the two joint sets. The effect of variability of joint orientation on block hydraulic conductivity could not be ignored for the DFN having low intersection angle between two joint sets.

• Journal of the Korean Society of Safety
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• v.11 no.4
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• pp.143-150
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• 1996

This is an explicit-Implicit, finite element analysis for linear as well as nonlinear hygrothermal stress problems. Additional features, such as moisture diffusion equation, crack element and virtual crack extension(VCE ) method for evaluating J-integral are implemented in this program. The Linear Elastic Fracture Mechanics(LEFM) Theory is employed to estimate the crack driving force under the transient condition for and existing crack. Pores in materials are assumed to be saturated with moisture in the liquid form at the room temperature, which may vaporize as the temperature increases. The vaporization effects on the crack driving force are also studied. The Ideal gas equation is employed to estimate the thermodynamic pressure due to vaporization at each time step after solving basic nodal values. A set of field equations governing the time dependent response of porous media are derived from balance laws based on the mixture theory Darcy's law Is assumed for the fluid flow through the porous media. Perzyna's viscoplastic model incorporating the Von-Mises yield criterion are implemented. The Green-Naghdi stress rate is used for the invariant of stress tensor under superposed rigid body motion. Isotropic elements are used for the spatial discretization and an iterative scheme based on the full newton-Raphson method is used for solving the nonlinear governing equations.

• Korean Journal of Biological Psychiatry
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• v.26 no.1
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• pp.8-13
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• 2019

Objectives Disrupted integrities of the fornix and stria terminalis have been suggested in schizophrenia. However, very few studies have focused on the fornix and stria terminalis comparing first-episode schizophrenia (FESZ), chronic schizophrenia (CS), and healthy controls (HCs) with the application of diffusion-tensor imaging (DTI) technique. The objective of this study is to compare the connectivity of the fornix and stria terminalis among FESZ, CS, and HCs. Methods We included the 44 FESZ patients, 39 CS patients and 20 HCs in this study. Voxel-wise statistical analysis of the fractional anisotropy (FA) data was performed using Tract-Based Spatial Statistics to analyze the connectivity of fornix and stria terminalis. In addition, the Scale for the Assessment of Positive Symptoms (SAPS) and the Scale for the Assessment of Negative Symptoms (SANS) were used to evaluate clinical symptom severities. Results There were no significant differences between the FESZ, CS, and HCs in age, sex, education years. The SAPS and SANS scores of the schizophrenia groups showed no significant differences. FA values of the right fornix cres/stria terminalis in the CS group were significantly lower than those in FESZ and HCs. There were no significant differences of FA values of the right fornix cres/stria terminalis between the FESZ and the HCs. Pearson correlation analyses revealed that significant correlation between FA values of the right fornix cres/stria terminalies of the the FESZ group and positive, negative symptom scales, and FA values of the right fornix cres/stria terminalis of the CS group and negative symptom scales. Conclusions This study shows that FA values of the fornix and stria terminalis in the CS were lower than in the FESZ and the HCs. These results suggest that the fornix and stria terminalis can play a role in pathophysiology of schizophrenia. Thus current study can broaden our understanding of the pathophysiology of schizophrenia.