• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5545-5550
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• 2014

Background: Human biological materials from cancer patients are linked directly with public health issues in medical science research as foundational resources so securing "human biological material" is truly important in bio-industry. However, because South Korea's national R and D project lacks a proper managing system for establishing a national standard for the outputs of certain processes, high-value added human biological material produced by the national R and D project could be lost or neglected. As a result, it is necessary to develop a managing process, which can be started by establishing operating guidelines to handle the output of human biological materials. Materials and Methods: The current law and regulations related to submitting research outcome resources was reviewed, and the process of data 'acquisition' and data 'distribution' from the point of view of big data and health 2.0 was examined in order to arrive at a method for switching paradigms to better utilize human biological materials. Results: For the deposit of biological research resources, the original process was modified and a standard process with relative forms was developed. With deposit forms, research information, researchers, and deposit type are submitted. The checklist's 26 items are provided for publishing. This is a checklist of items that should be addressed in deposit reports. Lastly, XML-based deposit procedure forms were designed and developed to collect data in a structured form, to help researchers distribute their data in an electronic way. Conclusions: Through guidelines included with the plan for profit sharing between depositor and user it is possible to manage the material effectively and safely, so high-quality human biological material can be supplied and utilized by researchers from universities, industry and institutes. Furthermore, this will improve national competitiveness by leading to development in the national bio-science industry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.469-478
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• 2019

Car-sharing services have been settled on as a new type of public transportation owing to their enhanced convenience, expanded awareness of practical consumption patterns, the inspiration for environmental conscientiousness, and the diffusion of smart phones following the economic crisis. With development of the market, many people have started using such services. However, security is still an issue. Damage is expected since IDs and passwords are required for log-in when renting and controlling the vehicles. The protocol suggested in this study uses bio-information, providing an optimized service, and convenient (but strong) authentication with various service-provider clouds registering car big data about users through brokers. If using the techniques suggested here, it is feasible to reduce the exposure of the bio-information, and to receive service from multiple service-provider clouds through one particular broker. In addition, the proposed protocol reduces public key operations and session key storage by 20% on mobile devices, compared to existing car-sharing platforms, and because it provides convenient, but strong, authentication (and therefore constitutes a secure channel), it is possible to proceed with secure communications. It is anticipated that the techniques suggested in this study will enhance secure communications and user convenience in the future car-sharing-service cloud environment.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.3
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• pp.197-212
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• 2022

This study was conducted to empirically analyze the effects and interactions among beneficial bacteria, commensal bacteria, and acne bacteria, which are factors in the skin microbiomes, on detrimental bacteria by 289 people, who are 20 to 49 years old among Koreans. As a result of multiple regression models using bio big data of skin microbiomes, when the difference in skin microbiomes according to the sex and age of the subjects was controlled, the beneficial bacteria showed a negative (-) effect on the detrimental bacteria, while the commensal and acne bacteria showed a positive (+) effect. Particularly, the negative (-) effect of beneficial bacteria on detrimental bacteria was different through interaction with acne bacteria according to the level of commensal bacteria. These results demonstrate that the activation of beneficial bacteria inhibits detrimental bacteria, and the effect of skin microbiomes on detrimental bacteria is balanced with skin microbiomes through interaction with independent influence. Therefore, it is suggested that when studying skin microbiomes products to help the proliferation of beneficial bacteria and to create a skin environment that inhibits detrimental bacteria in the personalized cosmetics manufacturing industry, it is necessary to consider the independent effects and interactions among skin microbiome factors together.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.85-92
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• 2021

Ambient Air Vaporizer (AAV) is an essential facility in the process of generating natural gas that uses air in the atmosphere as a medium for heat exchange to vaporize liquid natural gas into gas-state gas. AAV is more economical and eco-friendly in that it uses less energy compared to the previously used Submerged vaporizer (SMV) and Open-rack vaporizer (ORV). However, AAV is not often applied to actual processes because it is heavily affected by external environments such as atmospheric temperature and humidity. With insufficient operational experience and facility operations that rely on the intuition of the operator, the actual operation of AAV is very inefficient. To address these challenges, this paper proposes an artificial intelligence-based model that can intelligent AAV operations based on operational big data. The proposed artificial intelligence model is used deep neural networks, and the superiority of the artificial intelligence model is verified through multiple regression analysis and comparison. In this paper, the proposed model simulates based on data collected from real-world processes and compared to existing data, showing a 48.8% decrease in power usage compared to previous data. The techniques proposed in this paper can be used to improve the energy efficiency of the current natural gas generation process, and can be applied to other processes in the future.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.932-933
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• 2014

최근에는 의료 빅데이터 분야에서 의료기기, 의료전문가로부터 생성 또는 감지되는 사운드 생체신호(심장박동, 호흡, 맥박, 진맥) 데이터의 특징을 디지털 데이터로 추출하여 패턴 데이터로 변환한 후, 이를 빅데이터 분석 플랫폼 기반으로 분석하여 진료, 처방, 예방 등에 유용한 정보를 생성하는 모델 구축 연구가 활성화되고 있다. 본 논문에서는 사운드 생체신호 특징을 디지털 데이터로 추출하여 (주)리아컴즈 NeoQubit 빅데이터 플렛폼을 기반으로 패턴 데이터를 분석하고 예측할 수 있는 모델을 제시한다.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.19-24
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• 2019

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.2
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• pp.24-29
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• 2020

In South Korea, agricultural mechanization has been carried out in paddy field, but not in the upland field during recent decades. Among crops such as root vegetables, leafy vegetables from upland field, corn is used as forage for livestock as well as food for men. The corn harvester needs to be developed to replace men's labor in rural area to follow the recent needs in the farm industry. The corn harvester is comprised of three parts such as cutting part, feeding part and pick-up part. The feeding part is so long for cut corns to be delivered from the cutting part to the pick-up part. Structurally, the load from the long moment arm is likely to be big. Thus, the setup to measure the stress on the duct of the feeding part was configured with the data acquisition system. The strain gages were attached on several points that seem to be loaded a lot comparatively. The stress was measured and the measured stresses were divided by the yield stress to get the safety factor. And then, we made sure the safety factors were above 1 on the all points. In conclusion, the feeding part of the corn harvester which convey the cut corn from the cutting part from the pick-up part can be regarded to be made safe structurally.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.389-396
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• 2020

In this paper, a cultivation traceability system to implement smart agriculture developed and implemented, and in particular, devised a system that manages the cultivation traceability of grains that are difficult to grow in smart farms. Mobile and web programs based on smart devices are designed, and the collected information is stored in a DB server and can be used as big data. In addition, real-time location information and agricultural activity information can be matched using an electronic map(Vworld) based on GIS/LBS applying GPS of a mobile device. By designing the cultivation traceability information DB required in the field, the farmhouse, farmers, and cultivation information were developed to make it easy for managers to use, and implemented mobile and web programs in the field. The system is expected to raise the quality and safety management capabilities to the next level in response to variables such as labor saving effect and climate change.