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In this paper, we propose a new program for university student’s IT leadership renovation. Automation and Artificial Intelligence(AI) are changing the nature of work. According to MGI report, they will change the skills needed in the workforce. Leadership is one of the skills needed more of in the future at workplace including IT industry. Leadership could be defined as having 5 level leadership. According to the 5 level leadership, a program was formulated to renovate university student’s self-leadership, teamwork, team leadership and finally global leadership. We named the program as Stepping-Stone Platform for Student’s IT Leadership Renovation(SSP-SLR). Each level of four stepping stones consists of one semester. Student can raise their leadership skills by the program step by step. Various instruction learning methodologies are adopted to the program, including Flipped learning which has been proved effective in many ways in the field of education recently.

We describes V2X technology, a connectivity-based recognition technology that is attracting attention as a key technology for implementing autonomous driving technology, and autonomous communication modules that implement ADAS technology, a sensor-based recognition technology. It also explains the trends in V2X technology standardization centered on IEEE 802.11p, which is a WAVE technology standard based on Wi-Fi/DSRC. Finally, we will discuss the market growth trend of V2X communication modules in the United States, the leading V2X technology module, and the development of technology development trends of major domestic and international companies that are leading the global technology market related to V2X communication modules. V2X and ADAS technologies will be the biggest influence on automotive purchasing decisions. In recent years, V2I mandates have been promoted beyond V2V, mainly in developed countries such as the United States. The related industry needs to focus on the development of information transmission network technology that can support high frequency high efficiency(transmission rate) and sophisticated positioning accuracy beyond conventional vehicle communication.

We categorize partnership types between network operators and a global video streaming or over-the-top service provider, Netflix from 2011 to the first quarter 2018. The options are based on the integration of over-the-top (OTT), Netflix with pay TV and telecommunication operators in the form of carrier billing, access to over-the-top (OTT) via devices or the development of their tariff plans. Options of the Type 3, ‘cooperation’ or the Type 4, ‘agreement’ entails a kind of the technical involvement between two partners and commercial agreement. The types of partnership are evolving from one to others. Some partnerships have characteristics of more than one type. The majority of technical or service integration cooperation of Type 3 entail bundling and marketing promotion of Type 2 and Type 1. Similarly, the ‘agreement’ of Type 4, co-branded or white-label service initiative entail tariff or device user interface (UI) integration of the ‘cooperation’ of Type 3 and joint marketing initiatives of Type 1.

The popularization of smartphones allows people to instantly get the information that they want, through mobile apps, including financial services, movies and public transportation reservations, games and shopping. Furthermore, leisure activities through overseas travel are increasing due to the improvement of the living standards of people. These increases in overseas travel and the use of mobile apps lead to the increased use of mobile travel apps when booking tour packages. Although there are many studies on the usability assessment and user interface improvement for mobile apps, studies on the usability assessment for mobile travel apps are limited. We tested the usability for mobile travel apps to present some suggestions for improvements so that anyone can purchase travel packages easily and simply. We selected the top three mobile travel apps on Rankey.com that have different UIs. The heuristic method was used to evaluate usability. We present a better UI to allow users to use the app functions more easily by analyzing user interaction, ease of learning, readability, efficiency, and satisfaction for the entire use.

In this paper, we proposed a compact printed monopole antenna with an inverted L-shaped slot for dual-band operations. Two operating frequency bands are achieved with the use of an inverted L-shaped slot etched on the radiating strip for bandwidth enhancement and a defected ground structure for return loss improvement in the higher frequency band. The measured results show that the proposed antenna has impedance bandwidths (S11< -10 dB) of 270 MHz (1.81–2.08 GHz) and 340 MHz (2.36–2.70 GHz), covering the required bandwidths for PCS (1850.5–1989.5 MHz), CDMA 2000 (1850–1990 MHz), TD-SCDMA (1880–2025 MHz) and 2.4 GHz WLAN (2400–2484 MHz). The measured return loss of the proposed antenna has a good value of approximately 27.2 dB at 2.4 GHz WLAN. The antenna’s peak gains also have a high value of 1.92 dBi at 2 GHz and 2.12 dBi at 2.45 GHz. The proposed antenna shows omnidirectional radiation patterns over the entire frequency range of interest.

One of the typical characteristics of smart media is that it is interactive, which shows a different form and property from the existing mass media, and also changes the way users use it. This change requires a different system from the existing method in the advertising process strategic model for effective interactive advertising in smart media. We attempted to derive an effective smart media interactive advertising process strategic model by understanding the interactive advertising process in smart media that is expected to continue to grow as the main market of the advertising market and identifying the constituent factors. To this end, We analyzed the preminiary research results of interactive advertising and organized expert panels for the Delphi method and reflected their opinions and evaluations. The components and factors of interactive advertising were found and the effective interactive advertising process strategy model in smart media was derived from this study.

Adaptive recommendation systems have been developed with big data processing as a system that provides services tailored to users based on user information and usage patterns. Deep learning can be used in these adaptive recommendation systems to handle big data, providing more efficient user-friendly recommendation services. In this paper, we propose a system that uses deep learning to categorize and recommend tourism types to suit the user's personality. The system was divided into three layers according to its core role to increase efficiency and facilitate maintenance. Each layer consists of the Service Provisioning Layer that real users encounter, the Recommendation Service Layer, which provides recommended services based on user information entered, and the Adaptive Definition Layer, which learns the types of tourism suitable for personality types. The proposed system is highly scalable because it provides services using deep learning, and the adaptive recommendation system connects the user's personality type and tourism type to deliver the data to the user in a flexible manner.

Many SMEs are interested in entering the dementia industry as the national dementia responsibility system begins in earnest. However, it is ambiguous about which technology and field to enter, and accordingly, it is impossible to make a quick judgment and misses the time to enter the market. Therefore, the purpose of this study is to provide a simple but clear information to many SMEs in this situation, which products and related technologies will be able to help product development and market entry in the dementia prevention and diagnosis technology market more easily. In this regard, I would like to suggest the direction through hierarchical analysis (AHP) through conducting with a group of experts who can make professional judgments about the development of dementia medical technology, including the four-year university senior welfare department, nurses, directors and directors of long-term care institutions, medical device workers and experts' opinions on what sectors SMEs can most effectively apply to product development to enter the dementia market.

The fifth generation (5G) mobile communication has an impact on the human life over the whole world, nowadays, through the artificial intelligence (AI) and the internet of things (IoT). The low latency of the 5G new radio (NR) access is implemented by the state-of-the art technologies, such as non-orthogonal multiple access (NOMA). This paper investigates a practical issue that in NOMA, for the practical channel models, such as fading channel environments, the successive interference cancellation (SIC) should be performed on the stronger channel users with low power allocation. Only if the SIC is performed on the user with the stronger channel gain, NOMA performs better than orthogonal multiple access (OMA). Otherwise, NOMA performs worse than OMA. Such the superiority requirement can be easily implemented for the channel being static or slow varying, compared to the block interval time. However, most mobile channels experience fading. And symbol by symbol channel estimations and in turn each symbol time, selections of the SIC-performing user look infeasible in the practical environments. Then practically the block of symbols uses the single channel estimation, which is obtained by the training sequence at the head of the block. In this case, not all the symbol times the SIC is performed on the stronger channel user. Sometimes, we do perform the SIC on the weaker channel user; such cases, NOMA performs worse than OMA. Thus, we can say that by what percent NOMA is better than OMA. This paper calculates analytically the percentage by which NOMA performs better than OMA in the practical mobile communication systems. We show analytically that the percentage for NOMA being better than OMA is only the function of the ratio of the stronger channel gain variance to weaker. In result, not always, but almost time, NOMA could perform better than OMA.

The OECD surveys the questionnaire on the background of ICT every three years since 2003. In this study, we compare and analyze the changes in ICT accessibility and ICT usability of Korean students in 2015 and 2018 using the ICT background data of OECD PISA. ICT accessibility refers to the degree of access to ICT equipment at school or at home. There are 10 items of accessibility surveys at schools used by PISA, and 11 items at home. ICT usability refers to how much ICT equipment is used at or outside of school for learning or non-learning purposes. There are ten items for surveying usability at school, and twenty-four items for surveying usability outside of school. In the analysis of this study, the arithmetic mean of the items is used. As a result, Korean students' accessibility at school improved from 25.48% in 2015 to 40.40% in 2018, and from the lowest group in 2015 to below-average group in 2018. In terms of students' usability at school, we analyzed the percentage of students who use the ICT equipment 'almost every day' and the students who use it 'everyday'. Both 2015 and 2018 are among the lowest. Accessibility at home is higher in 2018 than in 2015, and belongs to the average group of OECD countries in 2018. ICT Usability for learning purposes outside of school is the lowest group in both 2015 and 2018. ICT Usability outside of school for non-learning purposes is in the average group of OECD countries. As a result of this study, we can see that Korea's digital literacy education is weak compared to other OECD countries. Prior to education about the Fourth Industrial Revolution, investment in digital literacy education is needed.

The purpose of this study is to investigation the effects of functional compression clothing on muscle function by comparing the iEMG response of muscle during exercise according to the wearing of taping applied functional clothing. Six men in their twenties in Chungcheongnam-do were selected for the study. Resistance exercise was performed by cross-distributing the conditions of wearing and not wearing functional clothing. Resistance exercises for iEMG measurements are biceps curl, wrist curl, reverse wrist curl, kickback and push-up. iEMG measurement muscles were the biceps brachii, triceps brachii, extensor carpi ulnaris, flexor carpi radialis. During biceps curl exercise, the iEMG of triceps brachii, biceps brachii wearing condition was lower than the non-wearing condition. During kickback exercise, the iEMG of triceps brachii, extensor carpi ulnaris wearing condition was lower than the non-wearing condition. During reverse wrist curl exercise, the iEMG of extensor carpi ulnaris wearing condition was lower than the non-wearing condition. During wrist curl exercise, the iEMG of flexsor biceps brachii, carpi radialis wearing condition was lower than the non-wearing condition. During push-up exercise, the iEMG of triceps flexsor biceps brachii, carpi radialis, brachii, biceps brachii non-wearing condition was lower than the wearing condition.

Artificial intelligence (AI) is software that learns large amounts of data and provides the desired results for certain patterns. In other words, learning a large amount of data is very important, and the role of memory in terms of computing systems is important. Massive data means wider bandwidth, and the design of the memory system that can provide it becomes even more important. Providing wide bandwidth in AI systems is also related to power consumption. AlphaGo, for example, consumes 170 kW of power using 1202 CPUs and 176 GPUs. Since more than 50% of the consumption of memory is usually used by system chips, a lot of investment is being made in memory technology for AI chips. MRAM, PRAM, ReRAM and Hybrid RAM are mainly studied. This study presents various memory technologies that are being studied in artificial intelligence chip design. Especially,MRAM and PRAM are commerciallized for the next generation memory. They have two significant advantages that are ultra low power consumption and nearly zero leakage power. This paper describes a comparative analysis of the four representative new memory technologies.

In the study, we show the six metropolitan cities, Incheon Metropolitan City had the highest annual average rate(3.2%) of the death toll, from 2011 to 2018, and the overall annual average rise rate was 2.6%. Also, the annual average rise rate of the number of cremators using E-Haneul funeral information system, Gwangju Metropolitan City had the highest rate(7.6%) and the overall annual average rise rate was 5.6%. The annual average rise rate of the cremation rate, Gwangju Metropolitan City had the highest rate(2.8%) and the overall annual average rise rate was 1.9%. Focusing on the actual state shown in the results of this study above, the policy measures for improving the efficiency of supply & demand policy of funeral facilities in six metropolitan cities in the whole nation could be suggested as follows. First, expanding the supply of cremation facilities in consideration of the installation period of cremation facilities. Second, given the increase in the number of cremators using E-Haneul Funeral Information System, it is necessary to expand the infrastructure for meeting the demand of using funeral facilities, such as enshrinement facilities of ash and natural burial sites. Third, in such metropolitan cities, it is difficult to resolve a conflict of location with local residents who recognize cremation facilities as unpleasant facilities. Therefore, it is necessary to devise and implement a plan for improving local residents awareness of cremation facilities and resolving the conflict of location in diversified ways. Fourth, it is predicated that Busan and Daegu Metropolitan City will face a lack of supply of cremation facilities in a short time due to the increased demand for them. Therefore, in order to meet the demand for cremation, it is necessary to construct more cremation furnaces in current cremation facilities or to jointly use and expand cremation facilities with their neighboring local government.

In the 4th Industrial Revolution, the competitiveness of the software industry is important, and as a solution to fundamentally secure the competitiveness of the software industry, education classes should be provided to educate high quality software personnel in educational institutions. Despite this social situation, software-related classes in universities are largely composed of competitive or individual learning structures. Cooperative learning is a learning model that can complement the problems of competitive and individual learning. Cooperative learning is more effective in improving academic achievement than individual or competitive learning. In addition, most learners have the advantage of having a more desirable self-image by having a successful experience. In this paper, we apply a pair check model, which is a type of cooperative learning, in operating system classes. In addition, the class procedure and instruction plan are designed to apply the pair check model. We analyze the test results to analyze the performance of the cooperative learning model.

In this paper, I proposed an effective technique for accurately predicting pixel values using edge components. Adjacent pixel values are similar to each other. That is, generally, similarity exists between adjacent pixels in an image. In the proposed algorithm, edge components are detected using the surrounding pixels in the first step, and pixel values are estimated using the edge components in the second step. Therefore, the prediction accuracy of the pixel value is improved and the prediction error is reduced. Pixel value prediction is a necessary technique for various applications such as image magnification and confidential data concealment. Experimental results show that the proposed method has higher prediction accuracy and fewer prediction error. Therefore, the proposed technique can be effectively used for applications such as image magnification and confidential data concealment.

With the recent development of the 4th Industrial Revolution, efforts have been made to carry out communication in a smart factory, farm, etc. at low cost and reliably. Methods for utilizing empty frequencies using TVWS(TV White Space) have been studied which can be used locally within 30 km. However, there have not been many studies on Path-Loss model considering Korean environment. In this paper, the Path-Loss model is divided into LOS (Line Of Sight) and NLOS (None LOS). In case of LOS model, we checked the difference between Free space model, Friis model, 2-ray model and Hata model with measured data. In the case of NLOS model, we checked the difference between Lee Model, ITU-R526-3 Model with measured data. In order to overcome the difference in Korean environment, we derived a model that can be applied in LOS and NLOS and prove its usefulness through performance evaluation through simulation.

The circumference of the body is not only an indicator in order to buy clothes in our life but an important factor which can increase the effectiveness healing properly after figuring out the shape of body in a hospital. There are several measurement tools and methods so as to know this, however, it spends a lot of time because of the method measured by hand for accurate identification, compared to the modern advanced societies. Also, the current equipments for automatic body scanning are not easy to use due to their big volume or high price generally. In this papers, OpenPose model which is a deep learning-based Skeleton Tracking is used in order to solve the problems previous methods have and for ease of application. It was researched to find joints and an approximation by applying the data of the deep camera via reference data of the measurement parts provided by the hospitals and to develop a program which is able to measure the circumference of the body lighter and easier by utilizing the elliptical circumference formula.

There are many problems that occur currently in agriculture industries. The problems such as unexpected of changing weather condition, lack of labor, dry soil were some of the reasons that may cause the growth of the plants. Condition of the weather in local area is inconsistent due to the global warming effect thus affecting the production of the crops. Furthermore, the loss of farm labor to urban manufacturing jobs is also the problem in this industry. Besides, the condition for the plant like air humidity, air temperature, air quality index, and soil moisture are not being recorded automatically which is more reason for the need of implementation system to monitor the data for future research and development of agriculture industry. As of this, we aim to provide a solution by developing IoT-based platform along with the irrigation for increasing crop quality and productivity in agriculture field. We aim to develop a smart garden system environment which the system is able to auto-monitoring the humidity and temperature of surroundings, air quality and soil moisture. The system also has the capability of automating the irrigation process by analyzing the moisture of soil and the climate condition (like raining). Besides, we aim to develop user-friendly system interface to monitor the data collected from the respective sensor. We adopt an open source hardware to implementation and evaluate this research.

With the development of information and communication technology, various data have appeared and are being distributed. The use of various data has contributed to the enrichment and convenience of our lives. Data in the public areas is also growing in volume and being actively used. Public data in the field of education are also used in various ways. As the distribution and use of public data has increased, advantages and disadvantages have started to emerge. Among the various disadvantages, the privacy problem is a representative one. In this study, we deal with the privacy issues of public data in education. First, we introduce the privacy issues of public data in the education field and suggest various solutions. The various solutions include the expansion of privacy education opportunities, the need for a new privacy protection model, the provision of a training opportunity for privacy protection for teachers and administrators, and the development of a real-time privacy infringement diagnosis tool.

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.