• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.109-115
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• 2022

The CNN (Convolution Neural Network) algorithm which combines a deep learning technique, and a computer vision technology, makes image classification feasible with the high-performance computing system. In this thesis, the CNN algorithm is applied to the classification problem, by using a typical deep learning framework of TensorFlow and machine learning techniques. The data set required for supervised learning is generated with the same type of bolts. some of which have undamaged threads, but others have damaged threads. The learning model with less quantity data showed good classification performance on detecting damage in a bolt image. Additionally, the model performance is reviewed by altering the quantity of convolution layers, or applying selectively the over and under fitting alleviation algorithm.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.313-320
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• 2023

The cumulative registration status of dogs is from 1.07 million in 2016 to 2.32 million in 2020. Animal registration is increasing by more than 10% every year, and accordingly, a name must be decided when registering a dog. We want to give a name that fits the characteristics of a dog's appearance, but there are many difficulties in naming it. This paper explains the development of a system for recognizing dog images and recommends dog names based on similar objects or food. This system extracts similarities with dogs' images through models that learn images of various objects and foods, and recommends dog names based on similarities. In addition, by recommending additional related words based on the image data of the result value, it was possible to provide users with various options, increase convenience, and increase interest and fun. Through this system, it is expected that users will be able to solve their concerns about naming their dogs, check names that suit their dogs comfortably, and give them various options through various recommended names to increase satisfaction.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1215-1218
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• 2021

한국을 방문하는 외국인들은 매년 증가하고 있고 방한 목적 중 식도락관광이 3위에 오를 만큼 세계에서 한국 음식은 위상이 높아지고 있다. 하지만, 한국에서의 알레르기 성분 표시는 법적 의무가 아니기 때문에 대부분의 한식당에서는 이를 표시하지 않고 있고 알레르기가 있는 외국인 관광객들은 한국 음식 섭취에 있어서 상당한 위험과 불편함을 부담하고 있다. 이에 본 논문에서는 머신러닝을 활용하여 사진 촬영만으로 쉽고 정확하게 알레르기 성분을 제공하고자 사물 이미지 데이터 전처리를 위한 HSV(Hue, Saturation, Value) 데이터 전처리 기법을 제안한다. 제안하는 기법은 이미지의 HSV의 평균 및 분산, 표준편차를 통해 불필요한 데이터를 제거한다. 성능평가에서는 비빔밥, 불고기, 제육볶음 등 사진 약 500장의 데이터 셋을 구성하여 HSV의 평균 및 분산을 통해 이미지를 제거하는 방식으로 구축한 데이터 셋을 TensorFlow를 통해 정확도와 학습시간을 측정한다. 측정결과, 제안하는 기법으로 구축한 데이터 셋은 최소 15%에서 최대 25% 높은 정확도와 최소 37.96%에서 최대 42.85% 높은 정도 낮은 학습시간을 보여주었다. 향후 HSV를 활용한 데이터 전처리 기법은 더 많은 데이터를 통해 더욱 구체적인 성능 분석이 필요하다. 또한, 실질적인 개발 및 구현을 통해 제안하는 데이터 전처리 기법의 더욱 현실적인 검증이 필요하다.

• Korean Journal of Radiology
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• v.24 no.6
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• pp.564-573
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• 2023

Objective: To investigate the feasibility of assessing the viscoelastic properties of the brain using magnetic resonance elastography (MRE) and a novel MRE transducer to determine the relationship between the viscoelastic properties and glymphatic function in neurologically normal individuals. Materials and Methods: This prospective study included 47 neurologically normal individuals aged 23-74 years (male-to-female ratio, 21:26). The MRE was acquired using a gravitational transducer based on a rotational eccentric mass as the driving system. The magnitude of the complex shear modulus |G^*| and the phase angle 𝛗 were measured in the centrum semiovale area. To evaluate glymphatic function, the Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS) method was utilized and the ALPS index was calculated. Univariable and multivariable (variables with P < 0.2 from the univariable analysis) linear regression analyses were performed for |G^*| and 𝛗 and included sex, age, normalized white matter hyperintensity (WMH) volume, brain parenchymal volume, and ALPS index as covariates. Results: In the univariable analysis for |G^*|, age (P = 0.005), brain parenchymal volume (P = 0.152), normalized WMH volume (P = 0.011), and ALPS index (P = 0.005) were identified as candidates with P < 0.2. In the multivariable analysis, only the ALPS index was independently associated with |G^*|, showing a positive relationship (β = 0.300, P = 0.029). For 𝛗, normalized WMH volume (P = 0.128) and ALPS index (P = 0.015) were identified as candidates for multivariable analysis, and only the ALPS index was independently associated with 𝛗 (β = 0.057, P = 0.039). Conclusion: Brain MRE using a gravitational transducer is feasible in neurologically normal individuals over a wide age range. The significant correlation between the viscoelastic properties of the brain and glymphatic function suggests that a more organized or preserved microenvironment of the brain parenchyma is associated with a more unimpeded glymphatic fluid flow.

• Tunnel and Underground Space
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• v.13 no.1
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• pp.52-63
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• 2003

Discontinuities such as faults, fractures and joints in rock mass play the dominant role in the mechanical and hydraulic properties of the rock mass. The key factors that influence on the flow of groundwater are hydraulic and geometric characteristics of discontinuities and their connectivity. In this study, a program that analyzes groundwater flow in the 3D discontinuity network was developed on the assumption that the discontinuity characteristics such as density, trace length, orientation and aperture have particular distribution functions. This program generates discontinuities in a three-dimensional space and analyzes their connectivity and groundwater flow. Due to the limited computing capacity In this study, REV was not exactly determined, but it was inferred to be greater than 25$\times$25$\times$25 ㎥. By calculating the extent of aperture that influences on the groundwater flow, it was found that the discontinuities with the aperture smaller than 30% of the mean aperture had little influence on the groundwater flow. In addition, there was little difference in the equivalent hydraulic conductivity for the the two cases when considering and not considering the boundary effect. It was because the groundwater flow was mostly influenced by the discontinuities with large aperture. Among the parameters considered in this study, the length, aperture, and orientation of discontinuities had the greatest influence on the equivalent hydraulic conductivity of rock mass in their order. In case of existence of a fault in rock mass, elements of the equivalent hydraulic conductivity tensor parallel to the fault fairly increased in their magnitude but those perpendicular to the fault were increased in a very small amount at the first stage and then converged.

• 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.

• 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 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.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.142-142
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• 2017

본 연구에서는 파프리카를 자동 수확하기 위한 시스템 개발의 일환으로 파프리카 재배환경에서 획득한 영상 내에 존재하는 파프리카 영역과 비 파프리카 영역의 RGB 정보를 입력으로 하는 인공신경망을 설계하고 학습을 수행하고자 하였다. 학습된 신경망을 이용하여 영상 내 파프리카 영역과 비 파프리카 영역의 구분이 가능 할 것으로 사료된다. 심층 신경망을 설계하기 위하여 MS Visual studio 2015의 C++, MFC와 Python 및 TensorFlow를 사용하였다. 먼저, 심층 신경망은 입력층과 출력층, 그리고 은닉층 8개를 가지는 형태로 입력 뉴런 3개, 출력 뉴런 4개, 각 은닉층의 뉴런은 5개로 설계하였다. 일반적으로 심층 신경망에서는 은닉층이 깊을수록 적은 입력으로 좋은 학습 결과를 기대 할 수 있지만 소요되는 시간이 길고 오버 피팅이 일어날 가능성이 높아진다. 따라서 본 연구에서는 소요시간을 줄이기 위하여 Xavier 초기화를 사용하였으며, 오버 피팅을 줄이기 위하여 ReLU 함수를 활성화 함수로 사용하였다. 파프리카 재배환경에서 획득한 영상에서 파프리카 영역과 비 파프리카 영역의 RGB 정보를 추출하여 학습의 입력으로 하고 기대 출력으로 붉은색 파프리카의 경우 [0 0 1], 노란색 파프리카의 경우 [0 1 0], 비 파프리카 영역의 경우 [1 0 0]으로 하는 형태로 3538개의 학습 셋을 만들었다. 학습 후 학습 결과를 평가하기 위하여 30개의 테스트 셋을 사용하였다. 학습 셋을 이용하여 학습을 수행하기 위해 학습률을 변경하면서 학습 결과를 확인하였다. 학습률을 0.01 이상으로 설정한 경우 학습이 이루어지지 않았다. 이는 학습률에 의해 결정되는 가중치의 변화량이 너무 커서 비용 함수의 결과가 0에 수렴하지 않고 발산하는 경향에 의한 것으로 사료된다. 학습률을 0.005, 0.001로 설정 한 경우 학습에 성공하였다. 학습률 0.005의 경우 학습 횟수 3146회, 소요시간 20.48초, 학습 정확도 99.77%, 테스트 정확도 100%였으며, 학습률 0.001의 경우 학습 횟수 38931회, 소요시간 181.39초, 학습 정확도 99.95%, 테스트 정확도 100%였다. 학습률이 작을수록 더욱 정확한 학습이 가능하지만 소요되는 시간이 크고 국부 최소점에 빠질 확률이 높았다. 학습률이 큰 경우 학습 소요 시간이 줄어드는 반면 학습 과정에서 비용이 발산하여 학습이 이루어지지 않는 경우가 많음을 확인 하였다.