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The need for non-face-to-face online health care has emerged through the era of “untact”. However, there is a lack of standardization work and research cases on the exercise effect of immersive content. In this study, the possibility of the exercise effect of VR e-sports among e-sports cases were presented through a visual algorithm analysis. In addition, the evaluation criteria were established. The research method compares and analyzes e-sports cases and VR e-sports cases by applying existing evaluation research cases. It also sets up a new evaluation standard. As for the analysis result, the device immersion method and interaction range were set through an algorithm analysis; FOV and frame immersion were set through typification; the user recognition method and interaction method were set through the visual diagram. Then, each derived result value was quantified and a new evaluation criterion was proposed.
Sang-Guk Lim; Se-Hoon Jung; Jun-Ho Huh. Visual Algorithm of VR E-Sports for Online Health Care. Healthcare 2021, 9, 824 .
AMA StyleSang-Guk Lim, Se-Hoon Jung, Jun-Ho Huh. Visual Algorithm of VR E-Sports for Online Health Care. Healthcare. 2021; 9 (7):824.
Chicago/Turabian StyleSang-Guk Lim; Se-Hoon Jung; Jun-Ho Huh. 2021. "Visual Algorithm of VR E-Sports for Online Health Care." Healthcare 9, no. 7: 824.
The trash disposal system, using standard trash bags, has been adopted by the government of the Republic of Korea (ROK) for more than two decades. This has caused a sanitary problem, as well as some secondary pollution. It is possible to solve this problem by deploying more manpower, but considering the manpower and maintenance costs that impose a heavy burden on the local governments who are experiencing tight financial situations, it would not be feasible. Thus, an Internet of Things (IoT)-based Smart Trash Separation Bin model that can reduce the cost of trash separation work has been proposed in this paper. The three efficient designs that respectively use a sensor, image processing, or spectroscope technology are presented. These IoT-based designs can bring significant merit to reducing the manpower costs, as well as the administrative cost involved.
Jun-Ho Huh; Jae-Hyeon Choi; Kyungryong Seo. Smart Trash Bin Model Design and Future for Smart City. Applied Sciences 2021, 11, 4810 .
AMA StyleJun-Ho Huh, Jae-Hyeon Choi, Kyungryong Seo. Smart Trash Bin Model Design and Future for Smart City. Applied Sciences. 2021; 11 (11):4810.
Chicago/Turabian StyleJun-Ho Huh; Jae-Hyeon Choi; Kyungryong Seo. 2021. "Smart Trash Bin Model Design and Future for Smart City." Applied Sciences 11, no. 11: 4810.
The emergence of deep learning model GAN (Generative Adversarial Networks) is an important turning point in generative modeling. GAN is more powerful in feature and expression learning compared to machine learning-based generative model algorithms. Nowadays, it is also used to generate non-image data, such as voice and natural language. Typical technologies include BERT (Bidirectional Encoder Representations from Transformers), GPT-3 (Generative Pretrained Transformer-3), and MuseNet. GAN differs from the machine learning-based generative model and the objective function. Training is conducted by two networks: generator and discriminator. The generator converts random noise into a true-to-life image, whereas the discriminator distinguishes whether the input image is real or synthetic. As the training continues, the generator learns more sophisticated synthesis techniques, and the discriminator grows into a more accurate differentiator. GAN has problems, such as mode collapse, training instability, and lack of evaluation matrix, and many researchers have tried to solve these problems. For example, solutions such as one-sided label smoothing, instance normalization, and minibatch discrimination have been proposed. The field of application has also expanded. This paper provides an overview of GAN and application solutions for computer vision and artificial intelligence healthcare field researchers. The structure and principle of operation of GAN, the core models of GAN proposed to date, and the theory of GAN were analyzed. Application examples of GAN such as image classification and regression, image synthesis and inpainting, image-to-image translation, super-resolution and point registration were then presented. The discussion tackled GAN’s problems and solutions, and the future research direction was finally proposed.
Sung-Wook Park; Jae-Sub Ko; Jun-Ho Huh; Jong-Chan Kim. Review on Generative Adversarial Networks: Focusing on Computer Vision and Its Applications. Electronics 2021, 10, 1216 .
AMA StyleSung-Wook Park, Jae-Sub Ko, Jun-Ho Huh, Jong-Chan Kim. Review on Generative Adversarial Networks: Focusing on Computer Vision and Its Applications. Electronics. 2021; 10 (10):1216.
Chicago/Turabian StyleSung-Wook Park; Jae-Sub Ko; Jun-Ho Huh; Jong-Chan Kim. 2021. "Review on Generative Adversarial Networks: Focusing on Computer Vision and Its Applications." Electronics 10, no. 10: 1216.
Foreign Direct Investment (FDI) is an important resource that helps accelerate the development of the country’s economy, add substantial funding to growth and facilitate technology transfer. Republic of Korea (ROK) is one of the world’s developed countries with dynamic economy, advanced science and technology. In recent years, the Korean government has continuously formulated tax policies, policies to support the business economy and import policies to support foreign businesses in Korea. The Pangyo Valley Creative Economy Valley is being groomed as a global startup hub in Asia. Small and medium enterprises (SMEs) in foreign countries are increasingly interested and eager to seek investment opportunities in the Korean market. Nonetheless, for these companies, language barriers and cultural and institutional differences make it more difficult and time-consuming to learn about the Korean market (such as investment trends, laws, visa policies, taxes and business establishment issues in Korea, etc.). In this study, we explored the process of searching information and seeking investment opportunities and built a business consulting and support application in the first stages of starting a business in ROK to increase effectiveness and save time, which is also an innovative business practice in Use-case ROK. We designed our Virtual Assistant system that can crawl and analyze data on foreign investments in ROK from open data resource websites (data.co.kr) and used analytic and aggregation techniques to explore trends in investments of foreign enterprises. We also researched the process of searching information and seeking investment opportunities for SMEs when investing in ROK, government support policies, laws and taxes as well as a number of other related issues. We built datasets and used Natural Language Processing (NLP) together with Natural Language Understanding (NLU) algorithms to build chatbot applications. Friendly framework for new developers to add and build up the dataset of AI Assistant is built by providing input intent data function, input Entity data function, input utterance data function as well as training and test function. In addition, we built a web-app connected to the server to visualize all the results of research so that SMEs owners can easily use and look for information on investments. Based on the research results, we can make recommendations to SMEs in keeping with the changing investment trends in ROK.
Hong-Danh Thai; Jun-Ho Huh. Building an Operational Solution Assistant System for Foreign SMEs in ROK. Applied Sciences 2021, 11, 4510 .
AMA StyleHong-Danh Thai, Jun-Ho Huh. Building an Operational Solution Assistant System for Foreign SMEs in ROK. Applied Sciences. 2021; 11 (10):4510.
Chicago/Turabian StyleHong-Danh Thai; Jun-Ho Huh. 2021. "Building an Operational Solution Assistant System for Foreign SMEs in ROK." Applied Sciences 11, no. 10: 4510.
Recently, the interest in the plant factory-based crop production technologies is rising following the application of the smart farm technology to the agricultural arena. A lettuce production system platform is proposed in this study considering the effects of indoor environmental conditions and artificial light sources. The spectral characteristics of a visible ray according to growth performances were analyzed first to develop a control algorithm that can stimulate the plant’s growth for the proposal. Secondly, an imaging system was designed to analyze the plant’s growth characteristics based on the images and set up the system configuration. Lastly, a crop production system was proposed by using an experimental crop production group for environmental control and monitoring.
Ki-Youn Kim; Jun-Ho Huh; Han-Jong Ko. Research on Crop Growing Factory: Focusing on Lighting and Environmental Control with Technological Proposal. Energies 2021, 14, 2624 .
AMA StyleKi-Youn Kim, Jun-Ho Huh, Han-Jong Ko. Research on Crop Growing Factory: Focusing on Lighting and Environmental Control with Technological Proposal. Energies. 2021; 14 (9):2624.
Chicago/Turabian StyleKi-Youn Kim; Jun-Ho Huh; Han-Jong Ko. 2021. "Research on Crop Growing Factory: Focusing on Lighting and Environmental Control with Technological Proposal." Energies 14, no. 9: 2624.
In real estate, there are various variables for the forecasting of future land prices, in addition to the macro and micro perspectives used in the current research. Examples of such variables are the economic growth rate, unemployment rate, regional development and important locations, and transportation. Therefore, in this paper, data on real estate and national price fluctuation rates were used to predict the ways in which future land prices will fluctuate, and macro and micro perspective variables were actively utilized in order to conduct land analysis based on Big Data analysis. We sought to understand what kinds of variables directly affect the fluctuation of the land, and to use this for future land price analysis. In addition to the two variables mentioned above, the factor of the landscape was also confirmed to be closely related to the real estate market. Therefore, in order to check the correlation between the landscape and the real estate market, we will examine the factors which change the land price in the landscape district, and then discuss how the landscape and real estate can interact. As a result, re-explaining the previous contents, the future land price is predicted by actively utilizing macro and micro variables in real estate land price prediction. Through this method, we want to increase the accuracy of the real estate market, which is difficult to predict, and we hope that it will be useful in the real estate market in the future.
Sang-Hyang Lee; Jae-Hwan Kim; Jun-Ho Huh. Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach. Symmetry 2021, 13, 616 .
AMA StyleSang-Hyang Lee, Jae-Hwan Kim, Jun-Ho Huh. Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach. Symmetry. 2021; 13 (4):616.
Chicago/Turabian StyleSang-Hyang Lee; Jae-Hwan Kim; Jun-Ho Huh. 2021. "Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach." Symmetry 13, no. 4: 616.
Currently, most of the transportation systems require changes to intelligent transportation systems, but most of them focus on efficient transportation rather than on improvement in human life. Sometimes, traffic systems are designed for economic value, and safety-related issues are neglected. A traffic information system that reflects various kinds of environmental information related to people’s safety must be able to reflect not only the existing economic goals but also a safe traffic environment. The traffic environment can be thought of as safety and direct information such as rainfall, including information on specific days when many people are scheduled to be gathered for certain events nearby. Intelligent transportation systems using this information can provide safety-related information for traveling to a specific area or for business trips. In addition, traffic congestion is a social problem and is directly related to a comfort life for individuals. Therefore, addressing various social and environmental factors could make human life more stable and reduce stress as a result. To do that, we need to estimate the impact on traffic based on environmental Big Data. The data can generally be divided into structured data and unstructured data. In inference, structured data analysis is relatively easy due to the precise meaning of the data. Nonetheless, it can be very difficult to predict environmentally sensitive data, such as traffic volume in intelligent transportation systems. To cope with this problem, there are a few systems for handling unstructured data to find out specific events that affect the traffic volume and improve its reliability. This paper shows that it is possible to estimate the exact volume of traffic using correlation analysis with various predicted data. Thus, we may apply this technique to the existing intelligent transportation system to predict the exact volume of traffic with environmentally sensitive data including various unstructured data.
Yonghoon Kim; Jun-Ho Huh; Mokdong Chung. Traffic Inference System Using Correlation Analysis with Various Predicted Big Data. Electronics 2021, 10, 354 .
AMA StyleYonghoon Kim, Jun-Ho Huh, Mokdong Chung. Traffic Inference System Using Correlation Analysis with Various Predicted Big Data. Electronics. 2021; 10 (3):354.
Chicago/Turabian StyleYonghoon Kim; Jun-Ho Huh; Mokdong Chung. 2021. "Traffic Inference System Using Correlation Analysis with Various Predicted Big Data." Electronics 10, no. 3: 354.
The necessity of offshore wind power plants has emerged due to the issues involving low-frequency sound or noise-related health problems, socio-economic or environmental problems. It is not easy to determine the optimal location for the wind power plants: First, as we can see from the overseas cases, the safety must be considered first followed by economic and environmental problems. Since these plants should not become an obstacle for traffic emergencies, they should not be constructed in the vicinity of a civil or military airport and at the same time, the existence of any nearby fish farms should be checked as well as the plants might largely affect their economy. Thus, this study attempts to provide the most efficient and preferable service by identifying and informatizing the issues related to the changes in the navigation routes of vessels or the condition of individual fish farms based on the big data accumulated over 30 years and realistic simulations. In conclusion, this study aims to find the optimal location for an offshore floating wind power plant.
Sang-Hyang Lee; Jun-Ho Huh. Optimal Location Recommendation System for Offshore Floating Wind Power Plant Using Big Data Analysis. Lecture Notes in Electrical Engineering 2021, 583 -590.
AMA StyleSang-Hyang Lee, Jun-Ho Huh. Optimal Location Recommendation System for Offshore Floating Wind Power Plant Using Big Data Analysis. Lecture Notes in Electrical Engineering. 2021; ():583-590.
Chicago/Turabian StyleSang-Hyang Lee; Jun-Ho Huh. 2021. "Optimal Location Recommendation System for Offshore Floating Wind Power Plant Using Big Data Analysis." Lecture Notes in Electrical Engineering , no. : 583-590.
One of the major keywords in the current digital world is Artificial Intelligence (AI) which is playing a major part in all kinds of advanced service systems, offering more convenience, better efficiency/effectiveness by controlling system hardware intelligently in a way humans never experienced. AI is also playing an essential part in the healthcare or bioelectronics industry where enhanced service function and sophistication have become a critical factor in a keen completion. Thus, this paper focuses on its contributing factors to human society and provides an opportunity for the discussions on the relevant convergence technologies.
Seong-Kyu Kim; Jun-Ho Huh. Artificial Intelligence Based Electronic Healthcare Solution. Lecture Notes in Electrical Engineering 2021, 575 -581.
AMA StyleSeong-Kyu Kim, Jun-Ho Huh. Artificial Intelligence Based Electronic Healthcare Solution. Lecture Notes in Electrical Engineering. 2021; ():575-581.
Chicago/Turabian StyleSeong-Kyu Kim; Jun-Ho Huh. 2021. "Artificial Intelligence Based Electronic Healthcare Solution." Lecture Notes in Electrical Engineering , no. : 575-581.
This study set out to invent an Information and Communication Technologies (ICT)-based smart Acer mono sap collection electric device to make efficient use of the labor force by reducing inefficient activities of old manual work to record sap exudation and state information. Based on the assumption that environmental information would have close connections with Acer mono sap exudation to reinforce the competitive edge of production in forest products, the study analyzed correlations between Acer mono sap exudation and environmental information and predicted Acer mono exudation. A smart collection of electric devices would gather data about Acer mono sap exudation per hour on outdoor temperature, humidity, conductivity, and wind direction and velocity, and was installed in four areas in the Republic of Korea, including Sancheong, Gwangyang, Geoje, and Inje. Collected data were used to analyze correlations between environmental information and Acer mono sap exudation using four different algorithms, including linear regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), and random forest, to predict Acer mono sap exudation. Remarkable outcomes were obtained across all the algorithms except for linear regression, demonstrating close connections between environmental information and Acer mono sap exudation. The random forest model, which showed the most outstanding performance, was used to make a mobile app capable of providing predicted Acer mono sap exudation and collected environmental information.
Se-Hoon Jung; Jun-Yeong Kim; Jun Park; Jun-Ho Huh; Chun-Bo Sim. A Study on Acer Mono Sap Integration Management System Based on Energy Harvesting Electric Device and Sap Big Data Analysis Model. Electronics 2020, 9, 1979 .
AMA StyleSe-Hoon Jung, Jun-Yeong Kim, Jun Park, Jun-Ho Huh, Chun-Bo Sim. A Study on Acer Mono Sap Integration Management System Based on Energy Harvesting Electric Device and Sap Big Data Analysis Model. Electronics. 2020; 9 (11):1979.
Chicago/Turabian StyleSe-Hoon Jung; Jun-Yeong Kim; Jun Park; Jun-Ho Huh; Chun-Bo Sim. 2020. "A Study on Acer Mono Sap Integration Management System Based on Energy Harvesting Electric Device and Sap Big Data Analysis Model." Electronics 9, no. 11: 1979.
The photovoltaic (PV) industry is an important part of the renewable energy industry. With the growing use of PV systems, interest in their operation and maintenance (O&M) is increasing. In this regard, analyses of power generation efficiency and inverter efficiency are very important. The first step in efficiency analysis is solar power estimation based on environment sensor data. In this study, solar power was estimated using a univariate linear regression model. The estimated solar power data were cross-validated with the actual solar power data obtained from the inverter. The results provide information on the power generation efficiency of the inverter. The linear estimation model developed in this study was validated using a single PV system. It is possible to apply the coefficients presented in this study to other PV systems, even though the nature and error rates of the collected data may vary depending on the inverter manufacturer. To apply the proposed model to PV systems with different power generation capacities, reconstructing the model according to the power generation capacity is necessary.
Chul-Young Park; Seok-Hoon Hong; Su-Chang Lim; Beob-Seong Song; Sung-Wook Park; Jun-Ho Huh; Jong-Chan Kim. Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation. Processes 2020, 8, 1225 .
AMA StyleChul-Young Park, Seok-Hoon Hong, Su-Chang Lim, Beob-Seong Song, Sung-Wook Park, Jun-Ho Huh, Jong-Chan Kim. Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation. Processes. 2020; 8 (10):1225.
Chicago/Turabian StyleChul-Young Park; Seok-Hoon Hong; Su-Chang Lim; Beob-Seong Song; Sung-Wook Park; Jun-Ho Huh; Jong-Chan Kim. 2020. "Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation." Processes 8, no. 10: 1225.
This paper discusses the worldwide trend of aging as the lifespan of humans increases. Nonetheless, most people do not write wills, which results in many legal problems after their death. There are many reasons for this including the problem of the validity of their heritage possibly not being legally certified. Wills can be divided into two categories, i.e., testimony and documents. A lawyer in the middle should notarize them, however, instead of providing these notarized services, we propose more transparent algorithms, blockchain shading, and smart country functions. Architectures are designed based on a neural network, the blockchain deep neural network (DNN), and deep neural network-based units are built with a necessary artificial neural network (ANN) base. A heritage inherited blockchain architecture is designed to communicate between nodes based on the minimum distance algorithm and multichannel protocol. In addition, neurons refer to the nerve cells that make up the nervous system of an organism, and artificial neurons are an abstraction of the functions of dendrite, soma, and axon that constitute the neurons of an organism. Similar to the neurons in organisms, artificial neural algorithms such as the depth-first search (DFS) algorithm are expressed in pseudocode. In addition, all blockchain nodes are equipped with verified nodes. A research model is proposed for an artificial network blockchain that is needed for this purpose. The experimental environment builds the server and network environments based on deep neural networks that require verification. Weights are also set for the required verification and performance. This paper verifies the blockchain algorithm equipped with this non-fiction preprocessor function. We also study the blockchain neuron engine that can safely construct a block node for a suicide blockchain. After empirical testing of the will system with artificial intelligence and blockchain, the values are close to 2 and 10 and the distribution is good. The blockchain node also tested 50 nodes more than 150 times, and we concluded that it was suitable for actual testing by completing a demonstration test with 4500 TPS.
Seong-Kyu Kim; Jun-Ho Huh. Neuron Blockchain Algorithm for Legal Problems in Inheritance of Legacy. Electronics 2020, 9, 1595 .
AMA StyleSeong-Kyu Kim, Jun-Ho Huh. Neuron Blockchain Algorithm for Legal Problems in Inheritance of Legacy. Electronics. 2020; 9 (10):1595.
Chicago/Turabian StyleSeong-Kyu Kim; Jun-Ho Huh. 2020. "Neuron Blockchain Algorithm for Legal Problems in Inheritance of Legacy." Electronics 9, no. 10: 1595.
The Republic of Korea also suffered direct and indirect damages from the Fukushima nuclear accident in Japan and realized the significance of security due to the cyber-threat to the Republic of Korea Hydro and Nuclear Power Co., Ltd. With such matters in mind, this study sought to suggest a measure for improving security in the nuclear power plant. Based on overseas cyber-attack cases and attacking scenario on the control facility of the nuclear power plant, the study designed and proposed a nuclear power plant control network traffic analysis system that satisfies the security requirements and in-depth defense strategy. To enhance the security of the nuclear power plant, the study collected data such as internet provided to the control facilities, network traffic of intranet, and security equipment events and compared and verified them with machine learning analysis. After measuring the accuracy and time, the study proposed the most suitable analysis algorithm for the power plant in order to realize power plant security that facilitates real-time detection and response in the event of a cyber-attack. In this paper, we learned how to apply data for multiple servers and apply various security information as data in the security application using logs, and match with regard to application of character data such as file names. We improved by applying gender, and we converted to continuous data by resetting based on the risk of non-continuous data, and two optimization algorithms were applied to solve the problem of overfitting. Therefore, we think that there will be a contribution in the connection experiment of the data decision part and the optimization algorithm to learn the security data.
Sangdo Lee; Jun-Ho Huh; Yonghoon Kim. Python TensorFlow Big Data Analysis for the Security of Korean Nuclear Power Plants. Electronics 2020, 9, 1467 .
AMA StyleSangdo Lee, Jun-Ho Huh, Yonghoon Kim. Python TensorFlow Big Data Analysis for the Security of Korean Nuclear Power Plants. Electronics. 2020; 9 (9):1467.
Chicago/Turabian StyleSangdo Lee; Jun-Ho Huh; Yonghoon Kim. 2020. "Python TensorFlow Big Data Analysis for the Security of Korean Nuclear Power Plants." Electronics 9, no. 9: 1467.
Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13–14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%.
Se-Hoon Jung; Hansung Lee; Jun-Ho Huh. A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost. Entropy 2020, 22, 902 .
AMA StyleSe-Hoon Jung, Hansung Lee, Jun-Ho Huh. A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost. Entropy. 2020; 22 (8):902.
Chicago/Turabian StyleSe-Hoon Jung; Hansung Lee; Jun-Ho Huh. 2020. "A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost." Entropy 22, no. 8: 902.
Blockchain and artificial intelligence are the most important keywords in the Fourth Industrial Revolution. This study sought to apply these core technologies to future validated algorithms that make real estate transactions secure to come up with an encryption algorithm. In addition, the real estate transaction is being paid a large fee by the middlemen, the real estate agent. Furthermore and recently, P2P (peer-to-peer) real estate exchange is used a lot. However, these P2P real estate exchanges also have problems that have not been identified by each other between landlords and tenants. In particular, a research model was established to compare and verify the PBFT (practical Byzantine fault tolerance) algorithm of Hyperledger through the blockchain agreement process. Subsequently, a process for verifying the real estate contract was established. Through VM (virtual machine) research methodology for the verification of blockchain real estate contracts, ElGamal communication was provided to prove quantum cryptography. We also automated lightweight encryption test verification tools and blockchain smart contract VM (virtual machine) models using artificial intelligence. Verification was performed through a reservation server and a monitoring server using a test verification tool for network-based lightweight security IoT (Internet of things) GW (gateway). It presents important ECP (elastic curve program) and elastic curve Qu-Vanstone (ECQV) models among the main functions of the blockchain smart contract, and it is equipped with quantum-based encryption algorithm. In addition, the necessary UML (unified modeling language) source code and performance data were calculated according to the actual experimental environment, and the average value for blockchain for administrative or government authorized assets—4000 TPS (transaction per second) were tested. In the future, we want to use this technology for real estate transactions.
Jun-Ho Huh; Seong-Kyu Kim. Verification Plan Using Neural Algorithm Blockchain Smart Contract for Secure P2P Real Estate Transactions. Electronics 2020, 9, 1052 .
AMA StyleJun-Ho Huh, Seong-Kyu Kim. Verification Plan Using Neural Algorithm Blockchain Smart Contract for Secure P2P Real Estate Transactions. Electronics. 2020; 9 (6):1052.
Chicago/Turabian StyleJun-Ho Huh; Seong-Kyu Kim. 2020. "Verification Plan Using Neural Algorithm Blockchain Smart Contract for Secure P2P Real Estate Transactions." Electronics 9, no. 6: 1052.
The purpose of this study is to increase interest in health as human life is extended in modern society. Hence, many people in hospitals produce much medical data (EMR, PACS, OCS, EHR, MRI, X-ray) after treatment. Medical data are stored as structured and unstructured data. However, many medical data are causing errors, omissions and mistakes in the process of reading. This behavior is very important in dealing with human life and sometimes leads to medical accidents due to physician errors. Therefore, this research is conducted through the CNN intelligent agent cloud architecture to verify errors in reading existing medical image data. To reduce the error rule when reading medical image data, a faster R-CNN intelligent agent cloud architecture is proposed. It shows the result of increasing errors of existing error reading by more than 1.4 times (140%). In particular, it is an algorithm that analyses data stored by actual existing medical data through Conv feature map using deep ConvNet and ROI Projection. The data were verified using about 120,000 databases. It uses data to examine human lungs. In addition, the experimental environment established an environment that can handle GPU’s high performance and NVIDIA SLI multi-OS and multiple Quadro GPUs were used. In this experiment, the verification data composition was verified and randomly extracted from about 120,000 medical records and the similarity compared to the original data were measured by comparing about 40% of the extracted images. Finally, we want to reduce and verify the error rate of medical data reading.
Seong-Kyu Kim; Jun-Ho Huh. Consistency of Medical Data Using Intelligent Neuron Faster R-CNN Algorithm for Smart Health Care Application. Healthcare 2020, 8, 185 .
AMA StyleSeong-Kyu Kim, Jun-Ho Huh. Consistency of Medical Data Using Intelligent Neuron Faster R-CNN Algorithm for Smart Health Care Application. Healthcare. 2020; 8 (2):185.
Chicago/Turabian StyleSeong-Kyu Kim; Jun-Ho Huh. 2020. "Consistency of Medical Data Using Intelligent Neuron Faster R-CNN Algorithm for Smart Health Care Application." Healthcare 8, no. 2: 185.
Currently, the use of biometric systems is increasing following the increase in the non-face-to-face security transactions in the Health Care sector, where smart devices are extensively used. Additionally, hospital patients or their guardians had to sign every medical/surgery consent form with a pen. Currently, hospitals are attempting to digitalize the form to avoid its loss or delay to the operating room. Thus, this study proposes a surgery consent signature authentication system for the mobile health care system. Along with the vein or the fingerprint recognition technology, the smart electronic signature recognition technology is regarded as a new type of security solution for Mobile Health Care, which is a compound of Health Care and technology, or a smart contents and display technology. Thus, this study proposes a surgery agreement signature authentication system for Mobile Health Care while using the techniques, such as database segment units comparison in the cloud, Bag of Word, etc. The proposed system was implemented with Java language and developed in a way the reference signature stored in advance in a cloud database to be compared with the signature currently entered. For the comparison, the segment matching, spatial pyramid matching, and boundary matching techniques were used in addition to the Dynamic Time Warping (DTW) algorithm. Additionally, the system has been made lighter than the existing experimental products, so that it is easier to embed the system into a smart phone, tablet, or others. The Test Bed experiment result showed that the system operated flexibly.
Jun-Ho Huh. Surgery Agreement Signature Authentication System for Mobile Health Care. Electronics 2020, 9, 890 .
AMA StyleJun-Ho Huh. Surgery Agreement Signature Authentication System for Mobile Health Care. Electronics. 2020; 9 (6):890.
Chicago/Turabian StyleJun-Ho Huh. 2020. "Surgery Agreement Signature Authentication System for Mobile Health Care." Electronics 9, no. 6: 890.
This paper presents an overview of the maximum power point tracking (MPPT) methods for photovoltaic (PV) systems used in the Micro Grids of PV systems. In the PV system, the output varies nonlinearly with temperature and radiation, and the point at which power is maximized appears accordingly. The MPPT of the PV system can improve output by about 25%, and it is very important to operate at this point at all times. Various methods of tracking the MPP of the PV system have been studied and proposed. In this paper, we discuss commonly used methods for the MPPT of PV systems, methods using artificial intelligence control, and mixed methods, and present the characteristics, advantages, and disadvantages of each method.
Jae-Sub Ko; Jun-Ho Huh; Jong-Chan Kim. Overview of Maximum Power Point Tracking Methods for PV System in Micro Grid. Electronics 2020, 9, 816 .
AMA StyleJae-Sub Ko, Jun-Ho Huh, Jong-Chan Kim. Overview of Maximum Power Point Tracking Methods for PV System in Micro Grid. Electronics. 2020; 9 (5):816.
Chicago/Turabian StyleJae-Sub Ko; Jun-Ho Huh; Jong-Chan Kim. 2020. "Overview of Maximum Power Point Tracking Methods for PV System in Micro Grid." Electronics 9, no. 5: 816.
Carbon credits should reduce the environmental pollution and carbon emission of the Earth in the future. The market for carbon credits will become a critical issue from 2021, and carbon credits will be applied to systems where individuals can trade. In order for these carbon credits to be traded between individuals, however, a corresponding exchange of carbon credits is needed. Policies, strategies, and technologies are also necessary to measure the trading of carbon credits. This paper aims at making transactions more reliable by applying blockchain technology to measure carbon emission rights. It uses blockchain to verify carbon emissions rights among the UN-SDGs’ (United Nations Sustainable Development Goals’) 17 tasks. In addition, it introduces the necessary dApp. In fact, we can protect against carbon emissions anomalies by using big data and artificial intelligence in mobile cloud environments. Thus, this paper proposes a blockchain-based carbon emission rights verification system to learn proven data further by using the governance system analysis and blockchain mainnet engine to solve these problems.
Seong-Kyu Kim; Jun-Ho Huh. Blockchain of Carbon Trading for UN Sustainable Development Goals. Sustainability 2020, 12, 4021 .
AMA StyleSeong-Kyu Kim, Jun-Ho Huh. Blockchain of Carbon Trading for UN Sustainable Development Goals. Sustainability. 2020; 12 (10):4021.
Chicago/Turabian StyleSeong-Kyu Kim; Jun-Ho Huh. 2020. "Blockchain of Carbon Trading for UN Sustainable Development Goals." Sustainability 12, no. 10: 4021.
The social interest in outdoor advertising signs, which have been recognized as an important element affecting impressions of a town, has been gradually increasing. However, when these signs are fully scattered around commercial areas, an oppressive feeling may be experienced by people, which cannot be neglected. Thus, this paper attempts to identify the characteristics of such visual oppression in urban landscapes through factor analysis, aiming to control them in such a way that does not oppress people. In addition, comparisons between Japanese and foreign nationals (i.e., foreign students from the Republic of Korea and the People’s Republic of China) were carried out, in order to check for any differences in perception of oppressive feelings depending on nationality. At the same time, to check for the possibility of different levels of perception depending on nationality, 20 Japanese and 20 foreign nationals (including Koreans and Chinese) were selected as test subjects. We expect this study to provide useful research material when reviewing the possibility of creating desirable urban landscapes or establishing guidelines for outdoor advertisements. For the research, landscape pictures focusing on outdoor advertising signs (15 wall, 6 roof-top, and 6 projection advertising signs) were taken as a sample for analysis. They were then presented in a 3D cyberdome for factor analysis, focusing on an impression evaluation test in relation to oppressive feelings. In addition, among the data obtained from the sample analysis, “Proportion,” which was considered to be the most influential factor on the oppressive feeling, was studied. The correlation coefficient between these two populations was 0.918, revealing a high level of correlation; thus, all subjects were treated as a single population. The factor analysis revealed 50.87% for the first factor (Evaluation), 25.39% for the second factor (capacity), and 9.47% for the third factor (Emotion), suggesting a relationship wherein the first factor decreased when the oppressive feeling increased; while the oppressive feeling increased along with the second factor.
Young-Woo Lee; Jun-Ho Huh. Evaluation of Urban Landscape Outdoor Advertisement Signboards Using Virtual Reality. Land 2020, 9, 141 .
AMA StyleYoung-Woo Lee, Jun-Ho Huh. Evaluation of Urban Landscape Outdoor Advertisement Signboards Using Virtual Reality. Land. 2020; 9 (5):141.
Chicago/Turabian StyleYoung-Woo Lee; Jun-Ho Huh. 2020. "Evaluation of Urban Landscape Outdoor Advertisement Signboards Using Virtual Reality." Land 9, no. 5: 141.