Earticle

As the demand for health screening increases, there is a need for efficient design of screening items. We build machine learning models for health screening and recommend screening items to provide personalized health care service. When offline, a synthetic data set is generated based on guidelines and clinical results from institutions, and a machine learning model for each screening item is generated. When online, the recommendation server provides a recommendation list of screening items in real time using the customer's health condition and machine learning models. As a result of the performance analysis, the accuracy of the learning model was close to 100%, and server response time was less than 1 second to serve 1,000 users simultaneously. This paper provides an adaptive and automatic recommendation in response to changes in the new screening environment.

We see an energy fucused smart city evolution of the UK along with the project of “Smart London Plan (SLP).” A theoretical logic of business model innovation has been discussed and a research framework of evolving energy focused smart city is formulated. The starting point is the silo system. In the second stage, the private investment in smart meters establishes a basement for next stages. As results, the UK’s smart energy sector has evolved from smart meter installation through smart grid to new business models such as water-energy nexus and microgrid. Before smart meter installation of the government, the electricity system was centralized. However, after consumer engagement plan has been set to make them understand benefits that they can secure through smart meters, the customer behavior has been changed. The data analytics firm enables greater understanding of consumer behavior and it helps energy industry to be smart via controlling, securing and using that data to improve the energy system. In the third stage, distribution network operators (DNOs)’ access to smart meter data has been allowed and the segmentation starts. In the fourth stage, with collaboration of Ofwat and Ofgem, it is possible to eliminate unnecessary duplication of works and reduce interest conflict between water and electricity. In the fifth stage, smart meter and grid has been integrated as an “adaptive” system and a transition from DNO to DSO is accomplished for the integrated operation. Microgrid is a prototype for an “adaptive” smart grid. Previous steps enable London to accomplish a platform leadership to support the increasing electrification of the heating and transport sector and smart home.

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

We conducted a comprehensive review on the previous research on r-Learning. By reviewing 843 previous studies about r-Learning published from 2004 to 2015, this study investigated 1) the trend of research on r-Learning over time, 2) the characteristics of targeted students in r-Learning, 3) the educational activities implemented for r-Learning, and 4) the types of educational robots used for r-Learning. The study found that the research on r-Learning has rapidly and steadily increased and the types of educational activities and educational robots has been diversified. Relying on the findings of this review, this study suggests 1) ensuring growth in both the quality and the quantity of research on r-Learning, 2) broadening the target student population of r-Learning beyond the age-limited boundaries, 3) enhancing educational activities of r-Learning, and 4) recognizing the necessity for systematic and clear concepts of types of educational robots.

The shaking-off the dust from balustrade of higher stories may cause the higher risk of falling accident rate. Main purpose of this study was to quantify an effect of balustrade heights and blanket types on possibility of falling accident relative to one’s motor controllability during shaking-off the dust of the blanket from balcony. Female participants, who consisted of total 10 under condition of balustrade height of 3 kinds (90 cm, 110 cm, 130 cm) and blanket types (0.4 kg, 1.6 kg, 3 kg, 200*230 cm), performed repetitively the task of shaking-off the dust of the blanket. Vertical position and velocity of center of mass due to increase of blanket weigh in case of balustrade height of 90 cm was increased, but vertical ground reaction force was decreased swiftly. That is, the higher balustrade height was, the less distance difference between center of mass and center of pressure was.

We have not identified on what gender difference during gait at a pace of one’s preferred velocity effects on the function of bilateral lower limb. This study was undertaken to investigate a difference of gait strategy by gender during gait at a one’s preferred velocity of participants of adult male and female (n=20). Cinematographic data for motion analysis, ground reaction force (GRF) variables, and muscle volume of lower limb were analyzed. Significant difference of variables on movement of center of mass whole body, joint angle and moment of lower limb, and ground reaction force were tested by 2-way ANOVA analysis (P<0.05). Male group showed more muscle volume than female, and both showed more volume in dominant leg than non-dominant. Main effect by bilateral leg during gait showed higher difference in right than left leg in change of vertical position of center of mass (maximal, minimal). Main effect by gender in vertical change of position and velocity of center of mass showed higher difference in male than female (maximal, minimal). Hip joint showed more flexed and extended angle in male than female, and also dorsiflexion of ankle and flexion moment of knee and hip joint showed higher in male than female group. Therefore, this result was assumed that dominant showed furthermore more contribution for propulsive function than non-dominant leg. Gender difference showed in strategy controlling of biomechanical characteristics, and perhaps influenced by muscle volume.

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

Non-orthogonal multiple access (NOMA) has gained the significant attention in the fifth generation (5G) mobile communication, which enables the advanced smart convergence of the artificial intelligence (AI), the internet of things (IoT), and many of the state-of-the-art technologies. Recently, correlated superposition coding (SC) has been proposed in NOMA, to achieve the near-perfect successive interference cancellation (SIC) bit-error rate (BER) performance for the stronger channel users, and to mitigate the severe BER performance degradation for the weaker channel users. In the correlated SC NOMA scheme, the stronger channel user BER performance is even better than the perfect SIC BER performance, for some range of the power allocation factor. However, such excessively good BER performance is not good for the user-fairness, i.e., the more power to the weaker channel user and the less power to the stronger channel user, because the excessively good BER performance of the stronger channel user results in the worse BER performance of the weaker channel user. Therefore, in this paper, we propose the power splitting to establish the user-fairness between both users. First, we derive a closed-form expression for the power splitting factor. Then it is shown that in terms of BER performance, the user-fairness is established between the two users. In result, the power splitting scheme could be considered in correlated SC NOMA for the user-fairness.

The fourth industrial revolution (Industry 4.0) will generate great benefits and big challenges in equal measure. Industry 4.0 has an impact on context, technology and business as long as it transforms design, production systems and products themselves. Industry 4.0 has also created many opportunities and challenges for startups to improve their business efficiency. The reality has shown that the industry 4.0 has begun to strong impacts of various levels on many areas of Vietnam’s economy. In such context, it is necessary to implement sufficient and thorough research and evaluation of many issues of startups and innovation and to consider suitable solutions and effective ways to take advantages of opportunities that the Industry 4.0 brings to Vietnam. This paper proposes the solution for supporting and promoting to develope of innovative start-up enterprise in accordance with the conditions and circumstances of Vietnam in the context of Industry 4.0

This study multilaterally explores South Korean YouTuber Winter Library’s book-related contents uploaded on one’s channel. As science and technology have developed, now the ordinary people can produce their videos and share them online. YouTube has been the most popular video storage service, and many Koreans use and enjoy it as well. This study investigates book-related contents produced by YouTuber user Winter Library, often called BookTuber (Book plus YouTuber). More specifically, it analyzes Winter Library’s various contents by 1) Components of YouTube Channel 2) Contents of Components 3) Components that help to increase reading rates. This study is contributing as it focuses on book, which is not so common topic in YouTube. As a result, the study discovers unique aspects of YouTube contents, and also explores the book-related industry in South Korea. This YouTube channel could be functional for increasing reading rates, especially among adults.

It is difficult for non-experts to build machine learning (ML) models at the level that satisfies their needs. Deep learning models are even more challenging because it is unclear how to improve the model, and a trial-and-error approach is not feasible since training these models are time-consuming. To assist these novice users, we examined how interactive and explainable feedback while training a deep learning network can contribute to model performance and users’ satisfaction, focusing on the data preparation process. We conducted a user study with 31 participants without expertise, where they were asked to improve the accuracy of a deep learning model, varying feedback conditions. While no significant performance gain was observed, we identified potential barriers during the process and found that interactive and explainable feedback provide complementary benefits for improving users’ understanding of ML. We conclude with implications for designing an interface for building ML models for novice users.

In this paper, we proposed a soft switched synchronous boost converter, which can perform discharging the battery, is proposed. The proposed converter has low switching loss even at high frequency operation due to its soft switching characteristics. The converter operates in synchronous mode to minimize conduction loss because of changing the rectified diode to MOSFET with a low on resistance. In this reason, the efficiency of the converter can be greatly improved in high frequency. In this paper, the battery discharger with a switching frequency of 100 kHz, has been designed. The designed converter also simulated to prove the converter's characteristics of synchronous operation as well as soft switching operation. The simulation shows that the proposed converter always meets the soft switching conditions of turning on and off switching in the zero voltage and zero current states. Therefore, simulation results have confirmed that the proposed battery discharge had soft switching characteristics. The simulation results have confirmed that the proposed battery discharger had soft switching and synchronous operation characteristics.

When replacing hardware due to obsolescence, discontinuation, and expansion of software-equipped electronic equipment, software changes are required in the past, but if virtualization technology is applied, it can be applied without software changes. In this regard, we studied in order to apply virtualization technology in the development of naval multi-function radar signal processing, we studied hardware and OS independency for Docker and performance comparison between Docker and virtual machine. As a result, it was confirmed that hardware and OS independence exist when using Docker and that high-speed processing is possible compared to the virtual machine.

We constructed a cooling system for solar cells using convection phenomena and investigated its cooling performance. The cooling system didn’t need any driving power or water resources. The convection cooler manufactured with a right-triangle shape of an air duct was attached to the rear of the solar cell to confirm that cooling was performed using convection phenomena. When the ratio of duct width to attachment surface width was 3:7, and the ratio of entrance height and exit height of duct was 5:1, it showed the best cooling performance. Comparative experiments with solar cells without convection cooler showed that cooling effects from 16.5°C to 20.9°C occurred after 40 minutes exposed to the 1300W Xenon lamp condition.

This study aims to understand the determinants and effects of SNS photo sharing on festival satisfaction among teenage festivalgoers, to derive strategies that festival organizers could employ to maintain long-term relationships with their audience. In this paper, we followed the festival attribute and SNS photo sharing behavior approaches for developing measurement constructs, by investigating previous related studies. Statistical analyses revealed that festival attributes positively impact consumer perceptions of festival value, which in turn influences SNS photo sharing behavior and festival satisfaction. In addition, SNS photo sharing behavior has a significant mediation effect on the process. Moreover, the differing intensities of SNS usage among the festivalgoers resulted in consumers presenting significantly different SNS photo sharing behavior. Our findings offer insights for marketers in the festival business who are interested in gaining a better understanding of the behavior of teenage festivalgoers.

This study aimed to provide basic data for enhancing the structural relationship among learning motivation, learning confidence, critical thinking skill and problem-solving ability in junior high school students and factors influencing problem-solving ability, by closely examining them. To this end, it investigated the causality among variables, for 390 junior high school students in Gangwondo, based on the outcomes of a questionnaire survey conducted to verify the effectiveness of digital textbooks. Although learning motivation did not have a significant effect on critical thinking skill, learning confidence had a direct effect on it. In addition, learning motivation, learning confidence and critical thinking skill had direct effects on problem-solving ability. In order to enhance problem-solving ability, therefore, We may be necessary to make efforts to support learning capabilities and provide opportunities for them to experience rich learning and resources.

ACM's CSTA has drafted standards for computing curricula and recommended them to schools in the United States. The five core concepts of the US elementary school computing curriculum are computing systems, network and the Internet, data and analysis, algorithms and programming, and impacts of computing. In 2005, Korea prepared ICT education guidelines, including five fields, their subfields, and achievement criteria for each subfield. In the 2015 revised curriculum, software education was introduced and five achievement standards were set. The ACM CSTA has 18 achievement criteria up to K-2 and 21 achievement criteria up to K-5. If we compare the 39 achievement standards of the US to Korea, Korea's 2005 ICT education guidelines include 25 of these, and the 2015 revised curriculum includes 5 of them. In this study, we aim to study the CSTA achievement criteria that second graders should know and the achievement criteria that fifth graders should know. This is compared and analyzed with Korea's 2005 ICT Guidelines and 2015 Software Curriculum. In comparison with the number of achievement standards, the US elementary school's computing achievement standards are much higher than in Korea. Comparing with each standard, there are many areas that are not covered in Korean curriculum, and we can see that the 2015 curriculum has rather receded from 2005.

Recently the blockchain technology has been actively studied due to its great potentiality. The smart contract is a key mechanism of the blockchain system. Due to the short history of the smart contract, many issues have not been solved yet. One main issue is vulnerability and another main issue is cost optimization. While the vulnerability of smart contract has been actively studied, the cost optimization has been rarely studied. In this paper, we propose two cost optimization methods for smart contracts running on the blockchain system. Triggering a function in a smart contract program code may require costs and it is repeated continuously. So the minimization of costs required to trigger a function of smart contract while maintaining the performance equally is very important. The proposed two methods minimize the usage of expensive permanent variables deployed on the blockchain system. We apply the proposed two methods to three prevalent blockchain platforms: Ethereum, Klaytn and Tron. Evaluation experiments verify that the proposed scheme significantly reduces the costs of functions in the smart contract written with Solidity.

The interest and importance of the convergence services for healthcare expand more and more as the average life expectancy increases. Convergence of ICT and healthcare technology unfold efficient and quick health services. Recently, healthcare services provide to clients with apps over web. On-demand wireless data broadcast supports any number of clients to access their desired data items dynamically by responding the needs for data items from the clients. In this paper, we propose an on-demand system to broadcast FHIR bundles for efficient healthcare services. We use time-parameterized replacing policy for renewing the bundle items on the wireless broadcast channel. The policy lets the on-demand broadcasting dynamic by controlling the time duration for the bundles to reside over the wireless channel. With simulation studies using an implemented testbed, we evaluate the performances of the proposed system in access time and tuning time. For evaluation, we compare the time-parameterized replacing policy of the proposed system with regular-number replacing policy. The proposed time-parameterized replacing policy shows shorter access time than the regular-number replacing policy because the policy responds more actively and dynamically to the change of the needs of the clients for FHIR bundles.

A common problem with neural network learning is that it is too suitable for the specificity of learning. In this paper, various methods were compared to avoid overfitting: regularization, drop-out, different numbers of data and different types of neural networks. Comparative studies of the above-mentioned methods have been provided to evaluate the test accuracy. I found that the more data using method is better than the regularization and dropout methods. Moreover, we know that deep convolutional neural networks outperform multi-layer neural networks and simple convolution neural networks.

Recently, webtoons-based animations have been expanding in Korea, where the role of mobile devices is growing, and media videos such as "YouTube" and "Tik-Tok," which have shorter screening and production cycles than traditional feature films, dramas, and animations, are attracting attention and being enjoyed by public. In order to produce animation content that fits the trend of this shortening video and production cycle, efficiency must be increased not only in story but also in production. Production methods and production technologies need changes to increase efficiency. Animation using motion capture, which is highly productionefficient, is widely used in movies that shows realistic movements, but still has little use in producing cartoonstyle animations with exaggerated movements. We analyzed the production method of 2D animation and CG animation and applied the result to CG animation to increase the efficiency of production and production. The methods of production through such experiments are expected to help improve the efficiency of producing animation content that is suitable for the latest trends such as webtoon animation.

Designing of a hierarchical clustering algorithm is one of the numerous approaches to minimize the energy consumption of the Wireless Sensor Networks (WSNs). In this paper, a homogeneous and randomly deployed sensor nodes is considered. These sensors are energy constrained elements. The nominal selection of the Cluster Head (CH) which falls under the clustering part of the network protocol is studied and compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. CHs in this proposed process is the function of total remaining energy of each node as well as total average energy of the whole arrangement. The algorithm considers initial energy, optimum value of cluster heads to elect the next group of cluster heads for the network as well as residual energy. Total remaining energy of each node is compared to total average energy of the system and if the result is positive, these nodes are eligible to become CH in the very next round. Analysis and numerical simulations quantify the efficiency and Average Energy Ratio (AER) of the proposed system.

The ultimate goal of CSV activities is to increase consumer loyalty to the company, brand or organization concerned. Thus, for a professional sports club, CSV activities will ultimately aim to enhance the loyalty of spectators who are consumers of the club. Subjects of this study are the spectators of professional volleyball. We distributed 300 survey to people who were aware of the club's CSV activities among the home spectators of Hyundai Capital SkyWalkers. Data processing was performed using SPSS 23 and AMOS 18 for the analysis of confirmed factors, correlation analysis, reliability analysis, and structural equation model analysis. From these results, we were able to come up with the social problem resolution and long-term orientation among the CSV activities of professional volleyball clubs which affect the club identification. And the identification formed within the spectators of the clubs through CSV affects the club loyalty directly or through the medium of club emotion. Professional volleyball clubs need to identify the problems the community has in planning and proceeding with CSV activities and seek strategies to address them together, and approach them from a long-term oriented perspective.

This study proposes a method for estimating gender and age that is robust to various external environment changes by applying deep learning-based learning. To improve the accuracy of the proposed algorithm, an improved CNN network structure and learning method are described, and the performance of the algorithm is also evaluated. In this study, in order to improve the learning method based on CNN composed of 6 layers of hidden layers, a network using GoogLeNet's inception module was constructed. As a result of the experiment, the age estimation accuracy of 5,328 images for the performance test of the age estimation method is about 85%, and the gender estimation accuracy is about 98%. It is expected that real-time age recognition will be possible beyond feature extraction of face images if studies on the construction of a larger data set, pre-processing methods, and various network structures and activation functions have been made to classify the age classes that are further subdivided according to age.

We study to confirm the relationship between leadership trust and job satisfaction on the leadership style of managers recognized by ICT organization members. To this end, the research hypothesis established by using SPSS23.0 for the survey results of 230 ICT organization members was analyzed as follows. First, the hypothesis 1 person leadership type (individual consideration: H1-1, intellectual stimulation: H1-2, charisma: H1-3, conditional compensation: H1-4, exceptional reward: H1-5) had a significant effect on leader trust. The results were obtained. Second, the hypothesis 2 manager's leadership type (individual consideration: H21-1, intellectual stimulation: H2-2, charisma: H2-3, conditional compensation: 2-4, expected compensation: H2-5) is based on job satisfaction. As a result of verifying the impact relationship, all showed significant effect on job satisfaction. Third, it acted as a positive factor in the job satisfaction of the leader of the hypothesis 3-member organizational member. It was confirmed that the job satisfaction of organizational members started based on the trust of actual leaders. Fourth, the role of leadership trust was partially confirmed between leadership type and job satisfaction. Through this study, in order to improve job satisfaction of ICT organizational members, the importance of exerting competency on the leadership type of managers and trusting the leaders on the characteristics consistent with ICT organizational characteristics was emphasized.