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In this paper, we implemented a Wi-Fi based cluster system using raspberry pi for multidisciplinary education. The cluster implementation on the desktop was more difficult to maintain the complexity, big size, high price, power consumption as the number of nodes increased. In this paper, we implemented a cluster using Raspberry Pi, which is developed for educational purposes, to reduce the cost of connecting nodes. In addition, the complexity of system construction is reduced by replacing the connection between each node with Wi-Fi. Also, the inconvenience of configuration due to node increase was reduced. It is expected that the implementation of the cluster will be a good alternative in the educational environment where distributed processing and parallel processing are performed in the embedded environment. Also, it is confirmed that it can be applied to the multidisciplinary education.

In this paper, we propose a layer structure of a pest image classifier model using CNN (Convolutional Neural Network) and background removal image processing algorithm for improving classification accuracy in order to build a smart monitoring system for pine wilt pest control. In this study, we have constructed and trained a CNN classifier model by collecting image data of pine wilt pest mediators, and experimented to verify the classification accuracy of the model and the effect of the proposed classification algorithm. Experimental results showed that the proposed method successfully detected and preprocessed the region of the object accurately for all the test images, resulting in showing classification accuracy of about 98.91%. This study shows that the layer structure of the proposed CNN classifier model classified the targeted pest image effectively in various environments. In the field test using the Smart Trap for capturing the pine wilt pest mediators, the proposed classification algorithm is effective in the real environment, showing a classification accuracy of 88.25%, which is improved by about 8.12% according to whether the image cropping preprocessing is performed. Ultimately, we will proceed with procedures to apply the techniques and verify the functionality to field tests on various sites.

With the rapid development of artificial intelligence technology, the patenting activities related to the fields of AI is increasing worldwide. In particular, a share of patent filed in China has exploded in recent years and overtakes the numbers in the US. In the present study, we focus our attention on the patenting activity of China and the US. We analyzed 6,281 and 13,664 patent applications in the US and China respectively between 2008 and 2018, and belonging to the “G06F(Electric Digital Data Processing)”, “G06N(Computer Systems Based on Specific Computational Models)”, “H04L(Transmission of Digital Information)” and nine more relevant technological classes, as indicated by the International Patent Classification(IPC). Our analysis contributes to: first, the understanding of patent application trends from foreign countries filed in the US and China, 2) patent application status by applicants category such as companies, universities and individuals, 3) the development direction and forecasting vacant technology of AI according to main IPC code. Through the analysis of this paper, we can suggest some implications for patent research related to artificial intelligence in Korea. Plus, by analyzing the most recent patent data, we can provide important information for future artificial intelligence technology research.

In this paper, we propose deep convolutional neural network(CNN) with bottleneck structure which improves the performance of earthquake classification. In order to address all possible forms of earthquakes including micro-earthquakes and artificial-earthquakes as well as large earthquakes, we need a representation and classifier that can effectively discriminate seismic waveforms in adverse conditions. In particular, to robustly classify seismic waveforms even in low snr, a deep CNN with 1x1 convolution bottleneck structure is proposed in raw seismic waveforms. The representative experimental results show that the proposed method is effective for noisy seismic waveforms and outperforms the previous state-of-the art methods on domestic earthquake database.

In this paper, we propose a new batter evaluation model that reflects the skill of the opponent pitcher in Korean professional baseball. The model consists of evaluation factors such as Run Value, Contribution Score and Ball Consumption considering the pitcher grade. These evaluation factors are calculated as different data. In order to include the evaluation factors having different characteristics into one model, each evaluation factor is weighted and added. The genetic algorithms were used to calculate the weights, and the data were based on the 2016 records of Korea Professional Baseball and the salary data of the players of 2017. As a result of calculation of the weight, the weight of the Run Value was high and the weight of the Contribution Score was very low. This means that when calculating the annual salary, it reflects much of the expected score according to the batting result of the batter. On the other hand, the contribution score indicating the degree to which the batting result contributed to the victory of the team according to the state of the economy is not reflected in the salary or point system.

The character dialogue created by AI is unnatural when compared with human-made dialogue, and it can not reveal the character’s personality properly in spite of remarkable development of AI. The purpose of this paper is to classify characters through the linguistic style and to investigate the relation of the specific linguistic style with the personality. We analyzed the dialogues of 92 characters selected from total 60 movies categorized four movie genres, such as romantic comedy, action, comedy and horror/thriller, using Linguistic Inquiry and Word Count (LIWC), a text analysis software. As a result, we confirmed that there is a unique language style according to genre. Especially, we could find that the emotional tone than analytical thinking are two important features to classify. They were analyzed as very important features for classification as the precision and recall is over 78% for romantic comedy and action. However, the precision and recall were 66% and 50% for comedy and horror/thriller. Their impact on classification was less than romantic comedy and action genre. The characters of romantic comedy deal with the affection between men and women using a very high value of emotional tone than analytical thinking. The characters of action genre who need rational judgment to perform mission have much greater analytical thinking than emotional tone. Additionally, in the case of comedy and horror/thriller, we analyzed that they have many kinds of characters and that characters often change their personalities in the story.

In this paper, we propose a Rights Management Information(RMI) expression systems for individual sites are integrated and the performance evaluation is performed to find out an efficient comparing and updating method of RMI through various image feature point search techniques. In addition, we proposed a weighted scoring model for both public domain sites and posts in order to use the most latest RMI based on reliable data. To solve problem that most public domain sites are exposed to copyright infringement by providing inconsistent RMI(Rights Management Information) expression system and non-up-to-date RMI information. The weighted scoring model proposed in this paper makes it possible to use the latest RMI for duplicated images that have been verified through the performance evaluation experiments of SIFT and CNN techniques and to improve the accuracy when applied to search engines. In addition, there is an advantage in providing users with accurate original public domain images and their RMI from the search engine even when some modified public domain images are searched by users.

With continued technology innovation in the fields of computer graphics (CG) and virtual reality (VR), digital animated avatars (or virtual humans) are evolving into ones that are more interactive at a suitable location such as museum, airport, and shopping mall. Specially, the form of the avatar (or the virtual human) realistically need to be expressed in a way that matches the users’ physical space. In recent many researches, the form of virtual human has been expressed as mixed-reality human (MRH)-the virtual human combines with the physicality as the real part. In this paper, we propose to carry out a study comparing various MRH on co-presence and emotional response in two-typed virtual humans depending on how many actual parts are included: (1) (Level 1) small parts in the virtual human combined virtual components (e.g., the head only) and (2) (Level 2) large parts in the virtual human with the physicality as the real part such as head, arms, and upper body). We report on the implemented results of our virtual humans and experimental results on co-presence and emotional response.

In this paper, we propose the augmented reality(AR) based driving navigation based on robust lane detection method to dynamic environment changes. The proposed technique uses the detected lane position as a marker which is a key element for enhancing driving information. We propose Symmetrical Local Threshold(SLT) algorithm which is able to robustly detect lane to dynamic illumination environment change such as shadows. In addition, by using Morphology operation and Connected Component Analysis(CCA) algorithm, it is possible to minimize noises in the image, and Region Of Interest(ROI) is defined through region division using a straight line passing through several vanishing points We also propose the augmented reality aided visualization method for Interchange(IC) and driving navigation using reference point detection based on the detected lane coordinates inside and outside the ROI. Validation experiments were carried out to assess the accuracy and robustness of the proposed system in vairous environment changes. The average accuracy of the proposed system in daytime, nighttime, rainy day, and cloudy day is 79.3% on 4600 images. The results of the proposed system for AR based IC and driving navigation were also presented. We are hopeful that the proposed research will open a new discussion on AR based driving navigation platforms, and thus, that such efforts will enrich the autonomous vehicle services in the near future

In this paper, we propose a efficient feature point extraction method that can solve the problem of performance degradation by introducing a preprocessing process when extracting feature points by utilizing the characteristics of 360-degree realistic media. 360-degree realistic media is composed of images produced by two or more cameras and this image combining process is accomplished by extracting feature points at the edges of each image and combining them into one image if they cover the same area. In this production process, however, the stitching process where images are combined into one piece can lead to the distortion of non-seamlessness. Since the realistic media of 4K-class image has higher resolution than that of a general image, the feature point extraction and matching process takes much more time than general media cases.

One of critical issues in dealing with 360-degree realistic contents is the performance degradation in searching and recognition process since they support up to 4K UHD quality and have all image angles including the front, back, left, right, top, and bottom parts of a screen. To solve this problem, in this paper, we propose an efficient search and comparison method for 360-degree realistic contents. The proposed method first corrects the distortion at the less distorted regions such as front, left and right parts of the image excluding severely distorted regions such as upper and lower parts, and then it extracts feature points at the corrected region and selects the representative images through sequence classification. When the query image is inputted, the search results are provided through feature points comparison. The experimental results of the proposed method shows that it can solve the problem of performance deterioration when 360-degree realistic contents are recognized comparing with traditional 2D contents.

In this paper, we investigate the patient fall prevention system to prevent the patient from falling out of the bed unintentionally on the bed of the bed. Patients stay in bed for many hours of hospitalization. During the hospitalization period, patients have low controllability of the body, as compared with normal persons, and fall due to intentional movements, resulting in a fall of the patient, can be a fatal threat to the patient. Therefore, an efficient fall prevention system is required. In this paper, the distance map to the patient is generated by the distance measuring sensor on the bed of the patient, and the risk is determined by estimating the position of the patient based on the distance map. As a result, when the distance map of the dangerous area is 150 mm or more, it is determined to be dangerous, and good results are obtained.

Along with the development of IOT, the number of people using IOT devices has enormously increased and the IOT era has come. Especially, people using the IP cameras among Internet devices have been drastically increasing. It is because the IP cameras are well networked and comparatively cheap compared with CCTVs, and they can also be monitored and controlled in real time through PCs and smart phones for the purposes of general theft prevention and shop surveillance. However, due to the user’s serious lack of security awareness and the fact that anyone can easily hack only with simple hacking tools and hacking sites information, security crimes that exploit those have been increasing as well. Therefore, this paper describes how easily the IP cameras can be hacked in the era of IOT, what kind of security incidents occurred, and also suggests possible government measures and new technical solutions to those problems.

In this paper, we propose a system and an intuitive interface that can create an athletic scene among athletes. We allow you to enter motion as if you were playing a game, so that the user's action becomes the player's action. The user can take various actions in front of the motion sensor and control the object flying to him. When a user specifies an opponent to pass or attack, and takes appropriate action in front of the motion sensor, the movement trajectory of the object is automatically generated by the physical optimization technique in accordance with the motion. In this way, you can create scenes where multiple players play together in a virtual environment. The method of this paper will be very useful for rapid prototyping for cinematic trailers of based on athletics games or animations.

An Internet of Things(IoT) technology which provides intelligent services by combining context-awareness based intelligences, inter-communication is made of between things and things or between things and person through the network connected with intelligent things is spreading rapidly. Especially as this technology is converged into smart device, mobile, cloud, big data technologies, it is applied into various domains. Therefore, this is different from existing Web or Mobile Application. New types of IoT applications are emerging by adapting IoT into Web or mobile. Because IoT application is not only focused on software but also considering hardware or things aspect, there are limitations existing development process. Existing development processes don’t consider analysis and design techniques considering both hardware and things. We propose not only a meta-model for development process which can support IoT application’s development but also meta-models for main activities in this paper. Especially we define modeling elements by using UML’s extension mechanisms, provide development process, and suggest design techniques how to apply those elements into IoT application’s modeling phase. Because there are many types of IoT application‘s type, we propose an Android and Arduino-based on IoT application as a case study. We expect that proposed technique can be applied into many of various IoT application development and design with a form of flexible and extensible as well as main functionalities or elements are more concretely described. As a result, it brings IoT application’s flexibility and the effect of quality improvement.

This paper is part of research and development for stand-alone solar system without commercial power supply. It implements firmware of controller for operation of stand-alone solar system by applying IoT technology and also develops communication modules that allow multiple solar lamps to send and receive data through wireless network. The controller of the developed stand-alone solar system can effectively charge the power generated by the solar module, taking into account the battery's charge and discharge characteristics. It also has the advantage of attaching wireless communication modules to solar lamp posts to establish wireless communication networks without incurring communication costs. In addition, by establishing IoT gateway middleware platform for each installation site, it forms a foundation to operate multiple solar lamp posts into multiple clusters. And, it is expected that the data collected in each cluster will be used to enable configuration and control of operational information, thereby inducing convenience and efficiency of remote operation and management.

As the performance of computers increases, the use of deep learning, which has faced technical limitations in the past, is becoming more diverse. In many fields, deep learning has contributed to the creation of added value and used on the bases of more data as the application become more divers. The process for obtaining a better performance model will require a longer time than before, and therefore it will be necessary to find an optimal model that shows the best performance more quickly. In the artificial neural network modeling a tuning process that changes various elements of the neural network model is used to improve the model performance. Except Gride Search and Manual Search, which are widely used as tuning methods, most methodologies have been developed focusing on heuristic algorithms. The heuristic algorithm can get the results in a short time, but the results are likely to be the local optimal solution. Obtaining a global optimal solution eliminates the possibility of a local optimal solution. Although the Brute Force Method is commonly used to find the global optimal solution, it is not applicable because of an infinite number of hyper parameter combinations. In this paper, we use a statistical technique to reduce the number of possible cases, so that we can find the global optimal solution.

Various supplementary authentication methods are used to supplement user authentication and authorization provided by existing password verification online1. In recent years, authentication and authorization methods using user attribute information have been studied and utilized in various services. User attribute information can be divided into static information and dynamic information. The existing methods focus on research to identify users using dynamic information or to generate challenge questions for user reauthentication. Static information such as a user's home address, school, company, etc. is associated with dynamic information such as location information. We propose a method to verify user attribute information by using the association between two attribute information. For this purpose, the static information of the user is verified by using the user 's location record which is dynamic information. The experiment of this paper collects the dynamic information of the actual user and extracts the static information to verify the user attributes. And we implemented the user attribute information authentication system using the proposal verification method and evaluated the utility based on applicability, convenience, and security.

This study aimed to analyze the research trend related on obesity in physical education in Korea through the keyword network analysis and to establish a basic database for effective design of prospective studies. To achieve it the study crawled co-occured keywords with ‘obesity’ and ‘physical education’ from RISS and analyzed the list from 1990 to 2018. They include 25 journal papers and 38 dissertations. The results are as follows. First, recent 30 years 63 papers published in Korea with ‘Obesity’ and ‘Physical Education’, and there were 144 related keywords. Second, analyzing journals which have ‘Obesity’ and ‘Physical Education’, co-occured keywords in 4 centrality were 24 keywords(student, Korea, prevention, effect, level, body, activation, actual condition, lesson, child, investigation, participation, book, cause, activity, normal, degree, nutrition, physical strength, weight, elementary, light, inquiry, health), and 37 keyword occurred in top 30. Lastly, by CONCOR analysis the result could be divided into 2 clusters. One consists of the object of obesity and its invervention, and the other consists of negative keywords of obesity and its preliminery dimenstion. Through the result, this study showed the research trend which involves the concept of obesity in physical education in Korea. Through the result, prospective obesity research in physical education in Korea would be promoted.

Recently, the advanced development of smart devices has increased the interest in health-care, and many people are paying more attentions to disease prevention than disease treatment. Among these prevention methods, the bare body movement has received much attention, and especially walking exercise is attracting much attention because it is enjoyable without any restrictions on place and time. Walking exercise is generally divided into two types: walking on the ground and climbing the stairs. Walking up the stairs consumes much more calories compared to walking on the ground. These walking exercises have the advantage that they can be easily performed by male and female without special equipments or economic considerations. However, there is a lack of applications and systems that accurately determine such walking and stair walking and measure momentum according to stair walking. In this paper, we designed and implemented a real-time walking status analysis system using smartwatch’s , pedometer, smartphone’s barometer and beacons.

In this paper, we study the multi-objectives vehicle and drone routing problem with time windows, MOVDRPTW for short, which is defined in an urban delivery network. We consider the dual modal delivery system consisting of drones and vehicles. Drones are used as a complement to the vehicle and operate in a point to point manner between the depot and the customer. Customers make various requests. They prefer to receive delivery services within the predetermined time range and some customers require fast delivery. The purpose of this paper is to investigate the effectiveness of the delivery strategy of using drones and vehicles together with a multi-objective measures. As experiment datasets, we use the instances generated based on actual courier delivery data. We propose a hybrid multi-objective evolutionary algorithm for solving MOVDRPTW. Our results confirm that the vehicle-drone mixed strategy has 30% cost advantage over vehicle only strategy.

The purpose of this study is to investigate the drinking culture and attitude difference by gender and grade of Korean college students. A questionnaire was conducted to analyze the characteristics and meaningful differences by two variables about drinking pattern, culture and related policies. The analysis showed that regardless of gender, far more students were against than in favor of prohibition on drinking within campus, whose legislation recently failed. Meanwhile, there was a significant difference by grade in victimization experience regarding drinking within the campus, and by gender in frequency, beverage type, volume, onset of drinking within school. There was no statistically significant gender-based difference in response to experience of damage caused by drinking, but the research results of the related papers show that the female students are more dangerous and need special attention and care. Universities should implement a professional counseling program that is customized according to gender or level of education, and managed by group. Moreover, health authorities must seek a way to lead this move, such as providing incentives to the universities, which efficiently deal with students’ drinking problem. And the prohibition on drinking within campus, whose legislation recently failed should be reconsidered.

In this paper, a shared model of systematic urban facility construction planning system is proposed. As the scope of construction on urban facilities has expanded, the construction process may occur simultaneously in the same location and short period of time by the different public agencies. This kind of construction duplications may cause the inefficiency of construction time and cost. In order to improve this, we studied the sharing method of the construction information which are produced by the different construction agencies. And also studied the method of find out the duplicate constructions occurring in the same place and short period of time. And Then we suggest a system that can rearrange the order of urban facility construction.

The purpose of this study is to develop and validate a model for diagnosing core competencies at the higher education level. Based on literature reviews, a potential model for core competencies at university was suggested. A tool for validating the model was composed of 24 items, which were delivered to 226 professors and administrative staffs, 730 students, and 134 graduates & external industrial experts. Five constructs (core competencies) were extracted from the data collected among professors and administrative staff responding to the importance of the items. The results of importance and performance surveys on core competencies with students were respectively 3.28 to 3.66 and 2.68 to 3.28 (4-point Likert scale). Statistical differences between importance level and performance level were found in all the sub-categories of core competencies. Borich priority determination formula and Locus for Focus Model were used for the determination of the priority of needs. Importance survey among graduates and external experts showed that the mean of each items ranged from 2.80 to 3.76 (4-point Likert scale). The overall results of the analyses suggest that the final model is appropriate for measuring the core competencies.

The insurance system is indispensable to our society. However in recent years, there have been a lot of insurance fraud crimes, such as the abuse of these valuable insurance system and the cheating the insurance proceeds. These insurance frauds make the insurance companies harder to manage, and as a result, the insurance premiums have risen, which has caused a lot of damaging good policyholders. However, the damage caused by insurance fraud has been continuously increasing due to the punishment of cotton stick. Therefore, after the long discussion, the 'The Act for Prevention of Insurance Fraud', which is a special law recently has been enacted. However, within two years of the enactment of this law, which was enacted after much anticipation and long waiting, there is already debate about its effectiveness. The reason for this is that even though the law was enacted and enforced, insurance fraud continues to increase and even punishment for these crimes is not strengthened, and now it is time to look for specific problems and resolutions for these crimes see. So in this paper the author dealt with the problems of the law, first, related regulation of insurance payment, second, right to terminate insurance contract and return of insurance proceeds, third, regulation on notification of investigations, fourth, regulations on the adequacy of hospitalization. Of course, since this law has just been enacted, there are many other problems besides these problems, but I tried to present a fresh resolution based on the problems that have been mainly discussed since the legislative period.

The intelligent robot industry is a complex which encompasses all fields of science and technology, and its marketability and industrial impact are remarkable. Major countries in the world have been strengthening their policies to foster the intelligent robot industry, but discussions on liability issues and legal actions that are accompanied by the related big or small accidents are still insufficient. In this study, therefore, the patent law by artificial intelligence robots and the legislation for relevant legal actions at the criminal law level are presented. Patent law legislation by artificial intelligence robots should comply with the followings. First, the electronic human being other than humans ought to be given legal personality, which is the subject of patent infringement. Even if artificial intelligence has legal personality, legal responsibility will be varied depending on the judgment of whether the accident has occurred due to the malfunction of the artificial intelligence itself or due to the human intervention with malicious intention. Second, artificial intelligence as a subject of actors and responsibility should be distinguished strictly; in other words, the injunction is the responsibility of the intelligent robot itself, but the financial repayment is the responsibility of the owner. In the criminal law legislation, regulations for legal punishment of intelligent robot manufacturing companies and manufacturers should be prepared promptly in case of legal violation, by amending the scope of application of Article 47 (Penal Provisions) of the Intelligent Robots Development and Distribution Promotion Act. In this way, joint penal provisions, which can clearly distinguish the responsibilities of the related parties, should be established to contribute to the development of the fourth industrial revolution

The purpose of this study is to provide basic data that mother 's smart phone overdependence prevention education are needed by confirming the relationship between mother' s smartphone overdependence and parent role intelligence. The research subjects were 220 mothers of 0-2 years old who are attending 15 day care centers in Cheong-ju, Chung-buk. The research tool used smartphone overdependence scale, and parent role intelligence scale. For the analysis of the study, frequency analysis, Pearson 's Ratio correlation and regression analysis were performed. The results showed that mother 's smartphone overdependence was negatively correlated with parental role intelligence and negatively correlated with developmental stimulation, parenting, support, and management. The multiple regression analysis showed that there was a significant negative effect on the smartphone overdependency, parent role intelligence, and sub factors. This suggests that smartphone overdependence prevention education for mother should be needed for nurturing of desirable infants.

The objective of this study is to understand the relationship between type of advertising and brand attachment. We divide advertising frame into loss and gain frame and examine the effect on brand attachment, The effect of unique advertising on brand attachment and the effects of interaction effects on brand attachment were examined. The results showed that the effect of the loss and gain frame on brand attachment was statistically significant. It also showed that unique advertising had positive effect on brand attachment. Moreover, the interaction effects of the advertising frame and the uniqueness of advertising have been shown to affect brand attachment. That is, the more emphasis on gain in advertising frame and the more unique advertising, the more effective on brand attachment.

In this paper, we propose an effective software education donation model for the social care class. The types of software education for elementary, middle, and high school for the social care class are in the order of after school classes, club activities, creative experiences, and regular classes. In elementary school students, it is effective to precede visual programming education based on block coding and to conduct curriculum convergence with SW and HW at the beginning, and high school students are carrying out text programming education like Python. Software education for social care class The contribution activity model can be classified into five types such as geographically difficult area, multicultural family areas, orphanage, reformatory, and basic livelihood security recipient. In addition, the survey results show that the students' interest in software education and their satisfaction are all very high at 96%. Effective software education for the social care class In the donation model, the lecturers consist of responsible professors, lecturers, and assistant instructors. Software training for the social care class is effective on a year-by-year basis, so that students can feel authenticity and trust. Software education contents focus on visual programming and physical computing education in elementary or middle school, and text programming and physical computing education in high school. It is necessary to construct a software education donor matching system that helps efficient management of software education donations by efficiently matching schools (consumers: elementary, middle, high school) and software education donors(suppliers).

In this paper, we propose the effect of project-based robot education program on the interest toward robots and the computational thinking of elementary school students. Software education is being actively carried out around the world in order to cultivate software talents in accordance with the 4th industrial revolution. As a result, the importance of robots in education has increased, and education using robots has been actively introduced. However, the activities of simply assembling and repeating robots in schools were not effective in enhancing elementary school students' interest toward robots and computational thinking. Therefore, it is necessary to overcome traditional teaching-learning methods and to develop robot education. So, in this study, the robot education program that introduces project-based learning was developed for improvement of interest toward robots and computational thinking of elementary school students. In order to verify the developed education program, 114 elementary six grade students were selected as research subjects and the traditional teaching-learning method and project-based learning were applied to the experimental and control group. As a result, project-based learning was more effective for elementary school students' interest toward robot than traditional teaching-learning method. In computing thinking, the experimental group showed a significant improvement, but there was no statistically significant difference in the post-test.

This paper is a study on mathematical aspects that can be basic for understanding and applying the contents of machine learning. If you are familiar with mathematics in the field of computer science, you can create algorithms that can diversify researches and implement them faster, so you can implement many real-life ideas. There is no curriculum standard for mathematics in the field of machine learning, and there are many absolutely lacking mathematical contents that are taught in the curriculum presented at existing universities. Machine learning now includes speech recognition systems, search engines, automatic driving systems, process automation, object recognition, and more. Many applications that you want to implement combine a large amount of data with many variables into the components that the programmer generates. In this course, the mathematical areas required for computer engineer (CS) practitioners and computer engineering educators have become diverse and complex. It is important to analyze the mathematical content required by engineers and educators and the mathematics required in the field. This paper attempts to present an effective range design for the essential processes from the basic education content to the deepening education content for the development of many researches.

In this paper, the Software Education program for elementary school students in Multicultural families developed so that Multicultural students can adapt to national, school, peer, and teacher. The effects of Resilience, Communication skills and Interpersonal Relationshiop were investigated. The developed Software education program was designed in such a way that the target students became more intimate with each other and adapted to Korean culture, peers, and teachers through the analysis of the research subjects. Resilience test showed statistically significant difference in peer relationship and support, family support and interaction, teacher 's interest and support, self - identity, and cultural acceptance. Communication ability test showed statistically significant differences in interpretation ability, self presentation ability, goal setting ability, and message conversion ability. The interpersonal Relationship test showed statistically significant differences in people 's orientation toward people, facing people, and away from people. The results of this study indicate that the software education program for elementary school students in multicultural families has effects on adaptive flexibility, communication ability, and interpersonal ability. In the future, There is a need for continuous research with development and effectiveness of various software education programs for Multicultural students.