• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.699-706
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• 2021

Existing license plate recognition system is used as an optical character recognition method, but a method of using deep learning has been proposed in recent studies because it has problems with image quality and Korean misrecognition. This requires a lot of data collection, but the collection of license plates is not easy to collect due to the problem of the Personal Information Protection Act, and labeling work to designate the location of individual license plates is required, but it also requires a lot of time. Therefore, in this paper, to solve this problem, five types of license plates were created using a virtual Korean license plate generation program according to the notice of the Ministry of Land, Infrastructure and Transport. And the generated license plate is synthesized in the license plate part of collectable vehicle images to construct 10,147 learning data to be used in deep learning. The learning data classifies license plates, Korean, and numbers into individual classes and learn using YOLOv5. Since the proposed method recognizes letters and numbers individually, if the font does not change, it can be recognized even if the license plate standard changes or the number of characters increases. As a result of the experiment, an accuracy of 96.82% was obtained, and it can be applied not only to the learned license plate but also to new types of license plates such as new license plates and eco-friendly license plates.

• Informatization Policy
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• v.29 no.2
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• pp.66-90
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• 2022

The radical digital transformation and development of the contactless digital economy in the wake of the COVID-19 pandemic are increasing the need to solve various problems such as conflicts of interest among market participants and delays in related laws and regulations. This study investigates the concept and characteristics of the contactless digital economy and identifies the related regulatory issues and resolutions through literature review, news article analysis, and expert interviews. From the literature review, it is identified that the contactless digital economy has eight hyper-innovation characteristics: hyper-intelligence, hyper-connectivity, hyper-convergence, hyper-personalization, hyper-automation, hyper-precision, hyper-diversity, and hyper-trust. From news article analyses and expert interviews, this study identifies various regulatory issues, such as competition between incumbents and new entrants, the collision of constitutional rights, collision of social values, conflict between market participants, absence of laws and regulations, and existence of excessive market power, and then proposes a series of resolutions.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.221-228
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• 2022

With the application of artificial intelligence technology, robots can provide efficient services in real life. Unlike industrial manipulators that do simple repetitive work, this study presented design methods of 6 degree of freedom robot arm and intelligent object search and movement methods for use alone or in collaboration with no place restrictions in the service robot field and verified performance. Using a depth camera and deep learning in the ROS environment of the embedded board included in the robot arm, the robot arm detects objects and moves to the object area through inverse kinematics analysis. In addition, when contacting an object, it was possible to accurately hold and move the object through the analysis of the force sensor value. To verify the performance of the manufactured robot arm, experiments were conducted on accurate positioning of objects through deep learning and image processing, motor control, and object separation, and finally robot arm was tested to separate various cups commonly used in cafes to check whether they actually operate.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.534-544
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• 2022

Medical ultrasound system has been widely used to visualize the lesion for diagnostics in most medical service site including hospitals and clinics thanks to its advantages such as real time operation, ease of use, safety. Among many signal processing blocks of the system, one of the most important part that governs the image quality is the beamformer, and technologies for this part has been continuously developed in long time. The synthetic aperture imaging method, that is one of the major technologies of beamforming, was introduced to maximize utilizing the information delivered from the patient's body through the probe, and contributed to breakthrough of the image quality since it was introduced in around 1990's, and evolved continuously in decades. This paper reviews and surveys the process of development of this technology and expects future evolution.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.261-268
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• 2022

Recently, the number of elderly living alone suffering from loneliness and depression is also increasing significantly due to the rapid aging of the population and nuclear families. In this paper, we propose a smart aging system that increases life satisfaction by providing the elderly with the optimal service tailored to the elderly with the help of IT according to their residential environment and health status. It is possible to provide an advanced customized support system for the elderly by fully utilizing IoT, AI, and Metaverse techniques not only for the elderly who want to live an active life in society but also for the elderly who need care in a nursing hospital. The proposed system provides human satisfaction by providing social connection in real space and virtual space in accordance with the residential environment and health status to the elderly suffering from loneliness in hospital (hospital care) facilities and at home. This paper proposes a new path for future-oriented welfare policy for the elderly by providing a user-customized smart aging system by combining AI and Metaverse technology with a rapidly changing social environment.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Computers and Concrete
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• v.29 no.6
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• pp.375-391
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• 2022

This paper examined the impact of the cross-sectional structure on the structural results under different loading conditions of reinforced concrete (RC) members' management limited in Carbon Fiber Reinforced Polymers (CFRP). The mechanical properties of CFRC was investigated, then, totally 32 samples were examined. Test parameters included the cross-sectional shape as square, rectangular and circular with two various aspect rates and loading statues. The loading involved concentrated loading, eccentric loading with a ratio of 0.46 to 0.6 and pure bending. The results of the test revealed that the CFRP increased ductility and load during concentrated processing. A cross sectional shape from 23 to 44 percent was increased in load capacity and from 250 to 350 percent increase in axial deformation in rectangular and circular sections respectively, affecting greatly the accomplishment of load capacity and ductility of the concentrated members. Two Artificial Intelligence Models as Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) were used to estimating the tensile and flexural strength of specimen. On the basis of the performance from RMSE and RSQR, C-Shape CFRC was greater tensile and flexural strength than any other FRP composite design. Because of the mechanical anchorage into the matrix, C-shaped CFRCC was noted to have greater fiber-matrix interfacial adhesive strength. However, with the increase of the aspect ratio and fiber volume fraction, the compressive strength of CFRCC was reduced. This possibly was due to the fact that during the blending of each fiber, the volume of air input was increased. In addition, by adding silica fumed to composites, the tensile and flexural strength of CFRCC is greatly improved.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• Journal of dental hygiene science
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• v.22 no.4
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• pp.233-240
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• 2022

Background: In modern society, rapid changes in the medical environment have required medical staff to access various information and be competent in active and effective problem-solving through collegial interactions. In line with these changes, universities are aiming to connect education. This study aimed to provide basic data of connected-convergence education by survey the awareness and needs of college students in health-related fields. Methods: This study included 122 college students from the health field. A survey regarding "the awareness and need of connected-convergence education" was conducted and general characteristics of the participants were collected from June to July 2022. Results: The awareness of connected-convergence education was low at 19.7%, but the intention to participate was high at 74.6%. Subject requirements were 18.0% for medical psychology, 13.5% for communication and counseling, 13.5% for medical artificial intelligence technology convergence, and 10.4% for sports health management. In the group showing high satisfaction with the major curriculum, the demand for connected education was also high. For efficient operation, it was investigated that it was necessary to secure specialized training courses, recognition of liberal arts credits, the right to register for courses equal to those of major students, and secure dedicated classrooms. Conclusion: Although the awareness and experience of connected-convergence education among the participants were low, the intention to participate was high. As such a plan to revitalize the university curriculum was required. It is timely to discuss the nurturing of convergence-type talents and multidisciplinary thinking skills. It is meaningful to provide basic data necessary for connected-convergence education in health-related fields at university. Universities should strive to enhance job competency in the health field by providing connected-convergence education based on student demands.