KJC Korea
Journal Central

Before smartphones, English learners used to organize words in their notebooks. As smartphones became more popular over time, more people started to study by storing words in word book apps than notebooks. Users will be able to study words anytime, anywhere via their smartphones. These word-field apps are a way for users to enter data themselves. However, this paper designed and implemented an app that automatically enters words using vision system and letter recognition. The app recognizes areas painted with highlighter first, then recognizes words within the highlighter area and organizes them into tables. It also helps to understand how certain words are used in contexts in certain English bodies with simple actions. Because the data of words can have multiple words in a particular body, the database schema was designed in a one-to-many relationship between the images taken with the camera and the words contained in that body. The differentiated advantages of this app are as follows: First, it increases the time efficiency of studying. Second, it improves learners' vocabulary by learning based on context. These advantages are described in detail in this paper through practical research materials and techniques. The app's platform used Apple Inc.'s iOS. Character recognition, a key technology in the app, utilizes OpenCV's library and Apple Inc.'s CoreML.

New technologies such as big data analysis and artificial intelligence are evolving based on convergence with information technology and are affecting all industries. To cultivate problem-solving skills in line with this trend, universities are providing programming education as a basic liberal art course. However, students who take programming courses as liberal arts are having a lot of difficulties in connecting the problem-solving process with actual programming. Even at the stage of putting together basic programming elements and applying them to problem-solving, they tend to feel that they can't do anything or give up. To solve this problem, this paper proposes a method of applying a goal-based scenario to programming education. The goal-based scenario is one of the case-based learning approaches such as problem-based learning and action learning. Based on the scenarios, students can clearly understand the context/situation in which particular knowledge is used and learn to apply the earned knowledge to solving specific problems. Therefore, programming learning based on these scenarios can be used as a tool that can be applied to solving and analyzing similar problems, away from syntax and result-oriented education. In this study, we analyze the existing Python liberal arts programming education and present the effects and considerations of the method of applying the goal-based scenario.

With the recent development of the Internet, big changes have occurred in all areas of daily life. Because the Internet brings many opportunities beyond time and space constraints. This strength stands out especially in the commercial market. If the Internet is connected, transactions can be made regardless of the border. However, as the frequency of use increases, the problem is also increasing. In this paper, the cross-border consumer issues were presented due to the development of e-commerce. Today, the problem of cross-border e-commerce consumers is increasing in proportion to the increase in Internet use. Cross-border e-commerce has the advantage of broadening consumers' choices, but the structural nature of Internet transactions makes it difficult to remedy consumer damage. To solve this problem, we looked into legal and institutional responses that are being implemented in major countries, including Korea. But mostly about business-to-business transactions, failing to embrace the issue of e-commerce between consumers and businesses. In addition, the legal system for e-commerce is in the process of overhauling, but its effect is insufficient. To solve these consumer problems, we propose the need to seek legal and policy aspects to find out what form of 'inter-border e-commerce' is taking place in the practice of trading and alternative solutions to resolve consumer disputes and remedy damages.

This paper proposes an artificial intelligence doodle robot platform that can easily customize products without visiting offline stores in the pandemic situation caused by COVID-19. The doodle robot platform consists of an Android application, a Flask server, and a Dobot robot arm. When a user takes a picture in an Android application through the proposed platform, artificial intelligence reconstructs the picture into a doodle drawing, and then a robot arm with a pen module starts to draw it on paper. The YOLO algorithm detects objects in the photo sent by a user and places the same sized doodle data as the object where the object is located, and reconstructs the picture into a doodle drawing. In the process of reconstruction, the completeness of doodle images was enhanced by excluding objects of a certain size or smaller, allowing only those that are not small in size to be converted into doodle images to create abstract and neat doodle images. As a result, the user-entered photos could be reproduced naturally in doodle drawings. Through the platform proposed by this paper, we expect users to be able to create customized products that contain their own value at home without any special skills or tools.

The aim of this study was to investigate the difference in the orofacial muscles according to the sex, the age, and the length of the tongue in Korean healthy adults. The present study was enrolled in Three hundred healthy adults in Korea. All subjects measured the length of the tongue first, and then measured the strength, the endurance, the accuracy and the timing of the orofacial muscles using the Tongue Pressure Strength-100 (TPS-100). In order to select the subjects, a Korean version of mini mental state examination was performed to check the cognitive function and to measure the strength, the endurance, the accuracy, and the timing of the orofacial muscles. The result showed that there was no significant difference of gender except for anterior tongue elevation and cheek compression (p>.05), and there was a high negative correlation between the orofacial muscles and the age (p<.01). And there was a significant negative correlation between the tongue length and the tongue strength, the endurance, the accuracy, and the timing (p<.01). The current study provides to the literature on normal orofacial muscles strength, the endurance, the accuracy, the timing and indicates that the improvement on swallowing ability in persons with dysphagia by comparing patients with criteria.

Recently, A variety of vehicular network services are key technologies to estimate that vehicle could be located, and Intelligent Transportation Systems have a solution to this problem. In addition, Vehicular Ad hoc Networks emerge as ITS component that provides cooperative communication with vehicles and the necessary infrastructure to improve the flow of vehicles in cities. By the result, it has been a proliferation of challenges for authorities with regard to traffic management. Especially, according to consequence of congestion on traffic, accidents, and pollution, they are a still major cause, despite the development of well-made systems for traffic management and other technologies linked with vehicles. After all, it is necessary that a general system for accident management is developed. Such as, traffic congestion in most urban areas can be alleviated by the real-time planning of routes. Nevertheless, the designing of an efficient route planning algorithm to attain a globally optimal vehicle control is still a challenge that needs to be solved, particularly when the unique preferences of drivers are considered. The development of the vehicular network prediction algorithms using dynamic models, often required building a system model with complex equations. Accordingly, we propose the protocol of vehicular traffic that will receive a popular interesting in the vehicular network service in this paper. It has proven the usefulness of the system in order to apply toward an existing vehicular communication network. Among the experimented existing algorithm performance, we confirmed the effectiveness of all range from a single data, to verify the proposed algorithm.

A search engine maintaining whole Web consistency is very important to retrieve information correctly and efficiently. However, as the size of Web is rapidly growing and content is also dynamically changing, it is impossible for the search engine to achieve the goal by using limited resources such as hardware, network and computing time. In order to solve this problem, a focused web crawler has been introduced which can identify and visit the most promising links related to a specific topic and avoid downloading off-topic documents efficiently under limited resources. In this research, we propose a paragraph-based focused web crawler using semantic priority of link. The proposed system selects promising links from a downloaded web page by measuring similarity between a topic and link's data such as anchor text and a paragraph containing the link. In this paper, different from existing methods, we proposed a novel similarity function for calculating a link priority by using WordNet. And we introduced a method to visit high-priority link first. We conducted experiments to prove the performance of the proposed paragraph-based web focused crawler by using some topics. The experimental result showed the paragraph-based web focused crawler using semantic priority of link improves the term frequency of document retrieval.

There are many studies on depression in middle-aged people, but few studies on oral health and depression. Therefore, the purpose of this study is to investigate the relationship between oral health-related factors and depression in adults aged 40 to 60 years. In this study, cross-sectional analysis was used to investigate the relationship between demographic factors and depression in middle-aged adults and the relationship between depression and oral health factors in middle-aged adults. Key finding of the present study included. First, Middle-aged adults showed more depression when they were female, economically active, and less educated. Second, The time when depression was diagnosed is 41~60 years old (62.8%) applicable to the middle age and it was surveyed 58.5% did not take treatments after diagnosed as depressed. Third, In the relationship between oral health factors and depression in middle-aged adults, the prevalence of depression was high when they experienced toothache, chewing problems, and mental discomforts during the last year. Since oral health factors in middle-aged adults can affect depression, which can negatively affect quality of life, basic data can be developed to increase motivation and interest in oral health in middle-aged adults. It is expected to be utilized.

In this study, black garlic was produced by treated high temperature(110, 120 and 130℃) and treat time(6, 12, 24 and 48hr.) conditions. Then, the physicochemical activity as total polyphenol, total flavonoid, reduction sugar, and nitrite scavenging activity content of it extract were compared fresh garlic extract. Total polyphenol, total flavonoid, reduction sugar and nitrite scavenging activity content have a top position within increasing treated temperature and time. Highest total polyphenol content was 9.97㎎/g at 130℃ and 12hr. about 4.4times higher then fresh garlic. Highest total flavonoid content was 2.78㎎/g at 130℃ and 12hr. about 36times higher then fresh garlic. Highest reduction sugar content was 39.53㎎/g at 110℃ and 24hr. about 4times higher then fresh garlic. As a result of investigating the statistical significance of the variables of treatment temperature and treatment time, it was found that treatment temperature had more influence than treatment time. As the treatment temperature and treatment time increased, the active ingredient decreased. The decrease in active ingredients was suspected of being converted to poly aromatic hydrocarbons. As aresult of examining the content of benzo[a]pyrene, it was not detected.

Now, the world, including Korea, is facing severe economic downturn and limited economic activity, both nationally and personally, due to the Corona 19 Pandemic crisis. As the environment of social communication changes as the society moves toward an untact society, many changes are expected throughout society. At the center of the change is in-depth research on telemedicine and telemedicine monitoring. However, there have been many problems with telemedicine, where there are many side effects caused by the exposure of personal information during implementation, and misdiagnosis due to carelessness in patient measurement and error in the remote medical system. These various problems have led to legal difficulties in telemedicine between patients and medical staff. However, as the situation of Corona 19 continues, telemedicine monitoring for elderly people with chronic diseases in the medical blind spot is drawing attention. Therefore, in this study, non-face-to-face health care services, especially for chronic diseases of the elderly patients with chronic diseases, were studied with parsona techniques through experiments to see if telemedicine monitoring was useful for health care. As a result, patients with chronic diseases expressed satisfaction with their high interest and participation in telemedicine, but it was difficult to proceed smoothly due to poor communication. Therefore, telemedicine will require a variety of high-quality devices for measurement and will be able to operate safely only when a system is established to ensure that there is no misdiagnosis by bringing the patient's health information accurately.

In the 4th Industrial Revolution and Defense Reform 2.0, changes in military education and training are essential. The military sector, which is based on advanced science, is changing rapidly, and military education and training systems must evolve as well. The current Virtual Reality (VR) technology is already applied in many education and training fields, and its effects have been confirmed through various studies. VR technology is widely used in military fields of advanced countries such as the United States of America (USA), and has been applied as a platform for efficient mission execution through virtual combat training and combat support. The purpose of this study is to analyze the factors influencing the use of virtual reality in the defense education and training system so that it can be used for future research and development. It also contributes to the introduction of VR in military education and training by deriving priorities for the projects proposed by the Department of Defense and researching the use of these priorities in terms of defense budget and efficiency. In the first study, we adopted presence, interaction, learning flow, VR motion sickness, and diversity among the characteristics of virtual reality to identify factors that influence the introduction and continuous utilization of virtual reality in military education and training. As a theoretical background, the operational definition was made to fit this study using the UTAUT model. The respondents were 300 participants involved in the use of military VR and training, where 256 of them were selected. As a research methodology, Smart PLS 3.0, a PLS-SEM suitable for exploratory research, was used.

The insect industry has been developed continuously. However, there was no development in specification control devices for edible insects. Therefore, we developed a smart breeding device for edible insects to solve the problems of insect breeding farms. The performance of the smart breeding device for Protaetia brevitarsis seulensis was evaluated. We tested and compared the breeding environment control performance between the smart breeding device and ordinary insect breeding farms in items of air temperature, relative humidity and the insect growth in the breeding room. The experimental result shows that the smart breeding device was hardly affected by the external environment, and the air temperature(25±1.3℃) and relative humidity(65±9.1%) have been kept very stable during the experimental period(May 19~Aug. 2), therefore it provided an optimal breeding environment for the growth of Protaetia brevitarsis seulensis. On the other hand, the breeding devices of ordinary insect breeding farms were easily affected by the external environment, and the range of changes in air temperature(24~31℃) and relative humidity(38~90%) was large and unstable, therefore these environmental changes were very adverse to the growth of Protaetia brevitarsis seulensis. When compared with the weight change of the Protaetia brevitarsis seulensis larva, the weight in the smart breeding device increased sharply in the early stages of breeding, and it was about 52.3% higher than that of ordinary insect breeding farms at 2 weeks after larval standing. Even after that, it was still high at 12.4~2.3% up to 11 weeks, therefore the weight was relatively heavy. As above results, the characteristics of the smart breeding device for Protaetia brevitarsis seulensis are as follows, 1. Provide the optimal environment for the breeding of the insect larva. 2. Reduce disease risk, enable early harvest, increase year-round production. 3. Efficiently improve the environment for the breeding of the insect larva. 4. Reduction of labor force of farmers by providing remote control function. 5. Contribute to improving farm household income. However, it costs some budget to introduce the smart breeding device in a small insect breeding farms. Therefore, in order for farmers to actively introduce it, a government policy support by central or local governments is needed.

This paper puts in-depth study on the investigation the status of online education of freshmen during the first semester of 2020 among four-year universities and two-year colleges. It also compares the perceptions of freshmen by universities to provide data in terms of identifying problems from online courses and to improve them. In this study, the classes ranked highest satisfaction from freshmen were real-time video classes and LMS-recorded lectures. Whereas, the classes with the lowest satisfaction level were with submitting assignments. Online education satisfaction reveals that the average of learner factors was 3.20 to 3.63, the instructor factors with 3.11 to 3.55, the system factor with 2.60 to 3.16 and contents factors with 3.23 to 3.57 which were higher in freshmen from 4-year universities than those who from 2-year colleges. The results of this study suggest that the average of perception for online education was 3.25, test and evaluation with 2.85, satisfaction with online courses with 3.02, and the need for improvement in online education was 3.43. The greatest advantage of online education is that there is no bound to physical class session. It allows students to attend the classes from any location and any time of their choice. However its disadvantage is that it is less effective in delivering educational contents compared to face-to-face classes. These results suggest that universities and colleges need to make great efforts to expand infrastructure for online learning, to implement a system providing content production and interaction similar to face-to-face classes, and to have fair and comprehensive evaluations.

Fuzzy set theory introduced by Zadeh has been very successful in dealing with vagueness and uncertainty in various fields. In the fuzzy set, each element of universe belongs to the fuzzy concept with a degree of membership in the unit interval [0, 1]. In order to overcome the problem of fuzzy sets expressing the degree of membership as only one real number, various extensions of fuzzy sets have been developed by many researchers: interval-valued fuzzy sets, intuitionistic fuzzy sets, vague sets, neutrosophic sets, hesitant fuzzy sets, Pythagorean fuzzy sets, orthopair fuzzy sets, etc. In interval-valued fuzzy sets proposed by Tursen, the degree of membership is expressed as a closed subinterval of [0, 1]. Intuitionistic fuzzy sets introduced by Atanassov allow us to represent the degree of membership as truth degree of membership and falsity degree of membership, and the sum of them is limited to 1. Gau et al. also explained vague sets that describe the degree of membership as subinterval. Bustince et al. has proved that these sets are mathematically equivalent to intuitionistic fuzzy sets. In neutrosophic sets proposed by Smarandache, the degree of membership is consisted of truth degree of membership, indeterminacy degree of membership, and falsity degree of membership, and then the indeterminacy is quantified explicitly. Torra introduced hesitant fuzzy sets in which the degree of membership is described by a set of possible values. In the Pythagorean fuzzy set proposed by Yager et al., to solve the problem that the sum of the truth degree of membership and the falsity is greater than one, each degree of membership is squared so that the sum of them is one or less. Orthpair fuzzy set proposed by Yager allow us to express the degree of membership as the q-th power of truth degree of membership and the q-th power of falsity degree of membership. The sum of them is bounded by one. These sets are called the q-rung orthopair fuzzy sets(q-ROFSs). If q = 1, q-ROFSs degenerates to an intuitionistic fuzzy sets and if q = 2, to a Pythagorean fuzzy sets. In this paper, we propose a method for calculating the reliability of fuzzy systems using q-ROFSs which are the generalization of the degree of membership expressed as intervals. Since this method uses the q-ROFS with generalized intervals, it is possible to calculate the reliability of systems more flexibly than the other approaches.

According to various researches, one of main causes of flight accidents is human factors. It is important to collect and analyze event data in advance to minimize and prevent human errors by developing safety checklist. As a result, international aviation industry developed FOQA(Flight Operations Quality Assurance) for commercial airliners, and, upon its proven effectiveness, it is now highly recommended to adopt for all operations of aircraft. Therefore, this thesis studied fundamental Airport Risk factors identification Algorithm based on ICAO(International Civil Aviation Organization), SMS(Safety Management Systems) and Matrix. Airport Risk factors identification algorithm has been applied for methodology of this research and categorized by level of safety based on two factors: Probability and Severity. In addition, altitude, traffic volume, and actual Flight Operation history for various airports, runway, aircraft model with different time and weather condition were used as variable values. This study conducted regression analysis of ‘A’ organization’s flight event frequency and applied ICAO SMS Matrix to categorize the severity of the events according to international standards. Based on the analysis, derived from fundamental Airport Risk Identification Algorithm, which can be a proactive measure for safe operation. As a consequence, this study assessed the Airport safety level of each airport and drew a conclusion with countermeasures based on the problem found.

In earlier study, the DSDC (Duties Separation & Data Coloring) -MAC model that applied the separation of duty (SoD) and data-coloring security techniques as a unified policy was provided. For practical use of this model, the implementation system must be designed and the model corrected. To do this, this paper proposes to design an architecture for an implementation system based on the DSDC-MAC model and check the accuracy of this model. First of all, the design for the implementation system identifies the necessary functional modules based on the DSDC-MAC model and structure them into design the architecture. Subsequently, the operating process of how the module-to-module access interactions are made during execution was designed. As a case study, the design of the actual implementation system is shown by applying the proposed architectural model for the MAC model of the HRMS (Human Resources Management System). Next, to verify the accuracy of the model, the model is formalized using the Z language and examined through the Z/EVES tool. This enables the system implementation of the data security access model using the DSDC-MAC model. Furthermore the accuracy of the DSDC-MAC model structure was checked to confirm that the model is correct.

Nowaday, towards adopting big data processing system has increased and it is commonly seen in every aspect of life. For this, the problem of finding connected components in undirected graphs has been well studied, and it is an essential pre-processing step to many graph computations, and a fundamental task in graph analytics applications. Recently, it has been a main area of interest in the large graph processing. However, much of the research has focused on solving the problem using High Performance Computers. In large distributed systems, the MapReduce framework dominates the processing of big data, and has been used for finding connected components in big graphs although iterative processing is not directly supported in MapReduce. Current big data processing systems have developed into supporting iterative processing and providing additional features other than MapReduce. This research investigates how to enhance the performance of finding connected components algorithm for large graph in distributed processing system. It uses the approach to considering the graph degree property in choosing the component identifier, reviewing how this can affect the efficiency of the algorithm. In the design of our proposed algorithm features provided by current new processing systems such as moving the computation more toward the data partition in Spark framework model are integrated.

The number of foreign workers entering Korea is increasing every year. Accordingly, Korean language education and education on understanding Korean culture are provided at training institutions of government departments. Despite these efforts, it can be seen that foreign workers face many difficulties when they first encounter Korean culture. In this paper, we introduce a public app developed to solve the cultural difficulties of foreign workers. Among the popular objects in Korea, a study was conducted on objects that foreign workers feel somewhat unfamiliar to them. We developed an app that recognizes an object in real time on a smartphone and translates its usage into several languages. Language selection was based on the languages of five countries with a large portion of entry into Korea. 7,900 copies of training images were collected and labeled. A total of 20,000 iterations were trained with the Yolov3-tiny learning model using Darknet, an AI neural network. The developed app can recognize and translate real-time objects on a smartphone with an average accuracy of 91.3%. It is a system that translates and provides the description of the detected object into five languages at the touch of a button. It is expected that this app will be able to bridge cultural and linguistic differences in daily life of foreign workers.

Environmental sound classification is an area that automatically classifies sounds in our surroundings. It can be applied to home automation, security, and surveillance. Recently, the deep learning approaches have been adopted as a classifier for increasing performance. In this method, a deep neural network is trained using many sound data, and after the learning is completed, the microphone pickup sound is applied for classification. However, during this stage, the ambient noise can be put into the microphone along with the sound to be identified. And the sound cannot be properly classified due to this disturbing noise. The recognition rate of the deep neural network decreases as the loudness of the disturbing sound increases, but the analysis about the noise effect on the classification have been limited. In this paper, we present the effect of the disturbing noise on the classification rate. For this purpose, UrbanSound8K, which is composed of 10 types of urban environmental sounds, is used for training and test data. And the VGG16-based CNN which shows good performance in image classification was adopted as a baseline model. For the disturbing noise, we use three types of sounds that are consist of daily noises (hairdryer, vacuum cleaner, faucet water, and hammer), voices(male, female, and synthesized sound), and music(cello, piano, and trumpet). In the experiment, these disturbing noises are mixed with the clean sounds so that the signal-to-noise ratio is in the range of -50dB to 50dB. Then the mixed sound was applied to a deep neural network to obtain a relative recognition rate when compared to the clean cases. The results show that the recognition rate is more than 90% compared to the clean sound cases when the SNR is between 10 and 15 dB, and 95% or more when the SNR is greater than 20 dB, regardless of the type of the disturbing sound.

Where should the realistic realism of virtual characters created by computer graphics begin? When producing animations of digital creators expressed in three-dimensional graphics, the workers involved in the visual effects of the movie agonize over how to visualize the unique nature of the characters. As a visual illusion, a sense of weight plays an important part in expressing the emotions and character of such an essential character. Despite the fact that several animation artists are involved in the production of such weight through collaboration at the animation production site, they tend to fail to recognize the production of weight as an important part of the production of digital fiction animations, and not to naturally coordinate the overall production in a unified and delicate manner. Because such a weight is indispensable for producing high-quality animations, it is necessary for all collaborative workers to be well aware of the success of animation business, and for watching the entire movie, a harmonious production is required, so skilled animators are needed. Weight can be analyzed around timing and spacing, and this paper will compare and analyze some comparative experiments and visuals based on the principles of animation to examine the visual illusion of weight, and it is expected to be helpful research when animation directors are looking for ways to emphasize weight in the Animation production.

e-Commerce recommendation service is an essential function and plays an important role in increasing sales since it is provided in connection with functions such as product search, order processing, and shopping cart. This recommendation service requires a high level of technology from developers developing e-commerce, so it is developed by a specific artificial intelligence engineer or applied by introducing an external recommendation solution. Integration of recommended services by external solutions or external development companies cannot satisfy the requirements of e-commerce services to be developed, and cannot provide rapid maintenance due to frequent data changes. Accordingly, research on a generalized development platform for generalizing and providing recommendation services suitable for a specific domain or developing a recommendation service is being actively conducted. Amazon Personalize service and Microsoft Azure Machine Learning service are generalized tools for developing recommended services by developers. However, these recommendation model development tools have a workload of defining essential data information for training data required to generate a recommendation model. In this paper, we derive a learning algorithm without defining data by using an association analysis algorithm between data for analyzing learning data. Also, based on the derived learning algorithm, we propose an Open API for developing and verifying a recommendation model. In the experiment, the learning algorithm is derived and the open API is verified by using the open transaction data of the e-commerce transaction. Through this, the suitability of the open architecture of the recommendation model development platform is verified.