Earticle

본 연구는 지역주민공동체 조직을 건설하기 위하여 지역교환시스템으로서의 레츠(LETS: Local Exchange & Trading System)를 용인지역을 중심으로 제안하였다. 현대식 물물교환 시스템인 레츠는 지역사회의식을 고취시키고 지역주민들이 지역공동체 형성을 위한 활동의 장으로 활용되고 있는 세계적인 지역공동체조직 활동이다. 본 연구는 정부 주도의 지역개발에만 익숙해져있는 지역주민들에게 주민 스스로가 목소리를 내어 지역경제를 살리고, 지역의 욕구를 해결해 나갈 수 있다는 작은 사고의 전환을 꾀하고자 하였다. 즉, 주민 스스로의 자생적인 운동을 통해 지역공동체 조직을 건설하는 방안으로서 레츠를 제안한 것이다. 이를 위해 문헌연구를 기반으로 레츠가 지역주민과 지역조직 건설의 매개역할을 할 수 있을 유용한 지역운동시스템인지를 분석하고, 지역주민 역량을 강화하기 위한 지역공동체 조직화방안을 조직건설의 원칙 및 목적, 표적 선정 및 조직원 모집방안, 조직원 및 잠재적 조직원들을 위한 의식화 방안, 리더십개발, 연합방안 등 5가지로 제시하였다. 본 연구는 주민의 복지욕구를 충족시키면서 주민자치의 자생적인 조직운동을 건설하고자 하는 지역공동체 조직화를 활성화시켜, 사회복지실천을 이론과 접목하고자 하였다는 데에 그 의의를 찾을 수 있을 것이다.

This research is to construct a local resident community proposing LETS as　Local Exchange and Trading System centered at the Yong-in district. A modern way of bartering, LETS, will inspire local residency awareness. Also, it is an community organization activity that local residents use to form local residence. This research is to make the residents, who are familiar with government lead community development, to sense that they are able to resolve the needs of the local community as well as reviving their economy. In a sense, the LETS is a proposal as a way to make a social community through resident's own self-governing activities. In order for this to happen, I have presented five ways to analyze whether LETS will be a local community system that will be a firm medium between community members and construction of community organization. Those five ways are 1. The purpose and principle of constructing a community which can foster members of the community, 2. Target making and member recruitment method, 3. Making the members and potential members aware of the program LETS, 4. Fostering leadership 5. Ways to unite.This research can find its meaning from the fact that it vitalized local community which tried to build an autonomical organization and tried to graft to the theory of social work practice.

지역빈도해석은 대상 지점과 수문학적 동질성을 만족하는 주변 지점을 하나의 지역으로 보고 빈도해석을 수행하는 방법이다. 따라서 동질한 지역의 구분은 지역빈도해석에 있어서 가장 중요한 가정이라고 할 수 있다. 이에 본 연구에서는 인공신경망 기법중 하나인 자기조직화지도(self-organizing map, SOM) 기법을 활용하여 강우 지역빈도해석을 위한 동질 강수 지역을 구분하였다. 지역구분 인자로는 지형 정보와 시 단위 강우 자료를 활용하였다. 최적 SOM 지도 구성을 위해 정량적 오차와 위상관계 오차를 활용하였다. 그 결과 $7{\times}6$ 배열의 42개의 노드를 갖는 모형을 선정하였고 최종적으로 강우 지역빈도해석을 위해 6개의 군집으로 구분하였다. 동질성 검토 결과 6개의 군집 모두 동질한 지역으로 나타났으며 기존의 유사하게 구분된 지역들과 이질성 척도를 비교하였을 때 좀 더 안정적인 지역 구분결과를 나타내는 것을 확인하였다.

The regional frequency analysis is the method which uses not only sample of target station but also sample of neighborhood stations in which are classified as hydrological homogeneous regions. Consequently, identification of homogeneous regions is a very important process in regional frequency analysis. In this study, homogeneous regions for regional frequency analysis of precipitation were identified by the self-organizing map (SOM) which is one of the artificial neural network. Geographical information and hourly rainfall data set were used in order to perform the SOM. Quantization error and topographic error were computed for identifying the optimal SOM map. As a result, the SOM model organized by $7{\times}6$ array with 42 nodes was selected and the selected stations were classified into 6 clusters for rainfall regional frequency analysis. According to results of the heterogeneity measure, all 6 clusters were identified as homogeneous regions and showed more homogeneous regions compared with the result of previous study.

A global path planning method using self-organizing feature map which is a method among a number of neural network is presented. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector On the other hand, the modified method in this research uses a predetermined initial weight vectors of 1-dimensional string and 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

본 연구는 기존의 도로기능분류 정의와 방법론을 벗어나 교통특성에 따른 도로분류 방법론인 도로특성분류를 기초로 분석을 수행하였다. 도로특성분류에 대한 일련의 과정 중에서 다양한 교통특성을 반영하는 설명변수를 기초로 요인점수를 산출하고, 동질한 도로구간을 그룹핑하는 군집화 분석과정과 적정 군집수 도출에 따른 군집결과비교에 본 연구는 초점을 맞추었다. 도로분류를 위해 병합적 계층 군집분석인 Ward법, 비계층적 군집분석인 K-means법, 자율신경 회로망을 이용한 K-SOM을 사용하여 비교분석하였다. 각 군집기법에 대한 결과를 토대로 비교분석한 결과, 군집 수 5 이하에서는 K-means법, 군집 수 14 이상에서는 Kohonen selforganizing maps가 가장 우수한 것으로 나타났으며, 군집수 5~9사이에서는 Ward법과 Kmeans법의 군집 성능이 불규칙한 패턴을 보임에 따라 세밀한 결과분석을 통해 우수성을 결정하는 것이 바람직할 것으로 분석되었다. 본 연구결과는 다양한 교통특성을 고려한 도로구간의 군집 속성을 분석하고 예측하는 분류화 작업에 중요한 기초적인 자료로 사용될 것으로 기대된다.

This paper is described clustering analysis of traffic characteristics-based highway classification in order to deviate from methodologies of existing highway functional classification. This research focuses on comparing the clustering techniques performance based on the total within-group errors and deriving the optimal number of cluster. This research analyzed statistical clustering method (Hierarchical Ward's minimum-variance method, Nonhierarchical K-means method) and Kohonen self-organizing maps clustering method for highway characteristic classification. The outcomes of cluster techniques compared for the number of samples and traffic characteristics from subsets derived by the optimal number of cluster. As a comprehensive result, the k-means method is superior result to other methods less than 12. For a cluster of more than 20, Kohonen self-organizing maps is the best result in the cluster method. The main contribution of this research is expected to use important the basic road attribution information that produced the highway characteristic classification.

산업재산권중 하나인 상표에 대한 효율적인 이미지 검색은 상표도용 및 이로 인한 분쟁을 방지할 수 있다. 이를 위해서 효율적인 내용기반 유사이미지 검색이 하나의 방안이 될 수 있다. 본 논문은 상표이미지로부터 회색조 히스토그램(gray histogram) 분석을 통하여 가시적인 자질을 추출하여 Self Organizing Map(SOM) 알고리즘을 적용한 내용기반 유사이미지 검색시스템을 이용하는 방법을 제안하였다. 또한 내용기반 유사이미지 검색시스템의 정량적인 성능평가 방안을 제시하여 본 연구에서 개발한 이미지 검색 시스템의 객관적인 성능평가를 수행하였다.

It will be possible to prevent the infringement of the trademarks and the insueing disputes regarding the originality of the trademarks by using an efficient content-based trademark image retrieval system. In this paper, we describe a content-based image retrieval system using the Self Organizing Map(SOM) algorithm. The SOM algorithm utilizes the visual features, which were derived from the gray histogram representation of the images. In addition, we made the objective effectiveness evaluation possible by coming up with a quantitative measure to gauge the effectiveness of the content-based image retrieval system.

웹 서비스는 개방형 서비스로 정보 공유가 주요 목적이다. 하지만 상대적으로 웹 서비스에 대한 공격 및 해킹 사고 또한 급증하고 있다. 현재 웹 해킹 등의 공격을 탐지하기 위해서는 웹 로그의 분석을 통해 우선적으로 수행 가능하며 중요한 역할을 수행하고 있다. 실제 웹 로그 분석을 통해 웹 서비스의 취약점을 분석하고 보완하는 사례가 늘어나고 있다. 이에 본 연구에서는 대용량의 웹 로그 정보에 대해 SOM 알고리즘을 적용하여 웹 DoS 공격 등과 같은 웹 서비스에서의 이상 탐지를 수행하였다. 구체적으로 대용량 웹 로그 정보에 대해 SOM 기반으로 BMU(Best Matching Unit)의 발생 빈도를 조사해 발생 빈도가 가장 높은 유닛을 이상(Abnormal) 유닛으로 판단하여 입력된 웹 로그 데이터에 대한 DoS 공격 탐지 성능을 향상시킬 수 있었다.

Web-services have originally been devised to share information as open services. In connection with it, hacking incidents have surged. Currently, Web-log analysis plays a crucial clue role in detecting Web-hacking. A growing number of cases are really related to perceiving and improving the weakness of Web-services based on Web-log analysis. Such as this, Web-log analysis plays a central role in finding out problems that Web has. Hence, Our research thesis suggests Web-DoS-hacking detective technique In the process of detecting such problems through SOM algorithm, the emergence frequency of BMU(Best Matching Unit) was studied, assuming the unit with the highest emergence frequency, as abnormal, and the problem- detection technique was recommended through the comparison of what's called BMU as input data.

이 연구는 경기도 소재 엘리트 유소년 선수의 체격 및 체력자료를 바탕으로 인공지능 기법 중에 하나인 자기구성지 도를 학습한 후, 체격 및 체력자료가 엘리트 유소년 선수의 실제 나이를 예측 할 수 있는지를 알아보는데 목적을 두었다. 이 연구의 목적을 달성하기 위하여 경기스포츠과학센터에서 실시한 체력측정에 임한 엘리트 선수 4,432명의 자료를 수집 하였으며 수집된 전체 자료 중에서 결측값이 존재하는 사례를 제거하였으며, 최종적으로 선정된 자료의 수는 1,889명의 엘리트 유소년 선수의 체력 자료였다. 예측 자료의 도출을 위하여 Kohonen(1989)의 연구에서 제안된 인공지능 기법 중에 하나인 자기구성지도를 이용하였다. 유소년 선수들의 체격 및 체력 자료를 바탕으로 실제나이와 추정나이의 예측 정확성을 알아본 결과, 동일 연령±5는 약 87.3%, 동일 연령±4세는 약 85.0%, 동일 연령±3세는 약 81.2%, 동일 연령 ±2세는 약 75.2%, 동일 연령±1세는 약 51.8%, 동일 연령은 약 25.5%의 예측 정확성이 나타났다. 향후 연구를 통하여 경기력의 특성을 자기구성지도에 나타내게 학습시켜 보다 많은 자료의 특성이 고려될 수 있도록 해야 할 것으로 사료된다.

The purpose of current study was to identify if physique and physical fitness data could predict the actual age of an elite youth athletes using the Self-Organizing Map among the artificial intelligent techniques into the field of sport science. In order to approach the purpose of this study, 4,432 elite athletes participated in the physical fitness test, and 1,889 elite youth athletes were finally selected. To derive the predict data, the proposed the study of Kohonen(1989) was used in Self-Organizing Map. Based on physique and physical fitness data of youth athletes, the results of the predicted accuracy of actual age and estimated age were: Predictive accuracy was around 87.3% for the same age±5, 85.0% for the same age±4, 81.2% for the same age±3, 75.2% for the same age±2, 51.8% for the same age±1, 25.5% for the same age. It is required that the following researches would be considered more data characteristics in order to using Self-Organizing Map to learn the characteristics of performance.

In this paper, we present a graphics processing unit (GPU)-based matching technique for the purpose of fast feature matching between different images. The scale invariant feature transform algorithm developed by Lowe for various feature matching applications, such as stereo vision and object recognition, is computationally intensive. To address this problem, we propose a matching technique optimized for GPUs to perform computations in less time. We optimize GPUs for fast computation of keypoints to make our system quick and efficient. The proposed method uses a self-organizing map feature matching technique to perform efficient matching between the different images. The experiments are performed on various image sets to examine the performance of the system under varying conditions, such as image rotation, scaling, and blurring. The experimental results show that the proposed algorithm outperforms the existing feature matching methods, resulting in fast feature matching due to the optimization of the GPU.

Since mobile traffic has been growing recently, the deployment of indoor small cells has become an attractive solution to enhance coverage. However, the increasing density of cells makes inter-cell interference more considerable. In this paper, we propose a max-throughput Interference Avoidance (MTIA) centralized algorithm to improve the system’s throughput. Based on signaling and reports, a central controller connected to each base station can properly turn off base stations that may induce a relatively strong interference, and thus increase SINR. We implemented the MTIA algorithm in an LTE TDD network simulation and showed that MTIA effectively reduces inter-cell interference and improves the system’s throughput.

The emergence of 5G technology, along with the rapid expansion of ultra-dense small cell (UDSC) deployments, has significantly increased the complexity of mobility management. Handover (HO) management is a fundamental function of wireless networks that ensures uninterrupted service continuity when a user equipment (UE) moves across different coverage areas or base stations. 5G UDSC environments require efficient handover management for maintaining stable connectivity and high-quality user experience. The effectiveness of handover management depends on the proper adjustment of Handover Control Parameters (HCPs), named as Time-to-Trigger (TTT) and Handover Margin (HOM), these two parameters collectively determine when and how a handover should occur. This study presents a systematic literature review (SLR) that examines handover optimization strategies within 5G Self-Organizing Networks (SON). This study follows the guidelines provided by Kitchenham and the PRISMA framework, with coverage of publications from 2015 to 2025. In this research HO is categorized into three main categories namely, traditional, AI/ML-based, and hybrid approaches. Traditional methods include rule-based, fuzzy logic based, velocity-based, and weighted based techniques that give predictable and efficient solutions, but these approaches are limited by static thresholds and reduced adaptability in dynamic network scenarios. AI/ML-based methods such as supervised learning, reinforcement learning, and deep learning introduce adaptive and predictive tuning of HCPs parameters that result in reductions of handover failures and ping-pong effects and also improve throughput, however, these methods require extensive datasets and computational resources. Hybrid approaches combine the stability of traditional techniques with the adaptability of AI/ML that results in outperforming of single method solutions but still face challenges such as higher complexity and lack of standardization. Most reviewed studies rely on simulation environments such as MATLAB and ns-3, with only a few validated through real-world experimentation or focused specifically on UDSC settings. Overall, this research leads towards the future direction on adaptive, context-aware, and scalable frameworks, emphasizing the need for online learning, hybrid AI integration, privacy-preserving optimization, and large-scale real-world testing to advance intelligent handover management in next-generation networks.

Smart meters for measuring electricity usage are expected in electricity usage management. Although the relevant power supplier stores the measured data, the data are worth sharing among power suppliers because the entire data of a city will be required to control the regional grid stability or demand?supply balance. Even though many techniques and methods of privacy-preserving data mining have been studied to share data while preserving data privacy, a study on sharing electricity usage data is still lacking. In this paper, we propose a sharing method of electricity usage while preserving data privacy using a self-organizing map.

In the preceding studies, an analysis of Saga Prefectural sightseeing information by a Self-Organizing Map (SOM) has been tried. And recent development on Information and Communication Technology (ICT) will help us to access any results via the mobile devices easily. This is why the mobile devices, e.g., smartphones and tablet computers, have an operating system installed, and we can improve their functions by downloading any applications on the Web. Then, in order to realize this basic idea, development of an application for the mobile devices is investigated through some computer simulations on the standard desktop PC in this paper. As a result, it is found that i) a developed feature map is useful to identify some candidate topics, ii) a touchscreen is suitable to show the feature map, and iii) arrangement of the feature map can be modified based on our interests. Then, it is concluded that the proposed idea seems to be applicable, even though further consideration is required to brush it up.

기존의 연구 결과에 따르면 민주주의 국가에서 집단행동은 집단행동의 ‘일반 논리’와 ‘행동 능력’을 바탕으로 설 명되어 왔다. 집단행동이론의 일반 논리는 다음 세 가지 중요한 통찰력을 시사해 준다. 첫째, 대규모 집단은 무임승차로 인해 집단행동을 하는 데 ‘내재적 어려움’을 가지고 있지만, ‘선택적 유인’이라는 특별 장치를 가지고 있으면 집단행동의 어려움을 극복할 수 있다. 둘째, 집단행동은 어떤 집단들에서 자주 일어나 고, 다른 집단들에서는 발생하지 않는 ‘불균등성’이 존재한다. 올슨(M. Olson)은 집단 행동의 일반 논리를 바탕으로 집단행동은 사회 내에서 비교적 안정되고 부유하고 내부 자들로 구성된 집단들에서 가장 자주 일어나고, 가난하고 불안정한 집단들에서는 거의 발생하지 않는다는 ‘집단행동의 불균등한 발생 편향성’을 주장하였다. 마지막으로, 부유하고 안정적인 집단들은 사회 전체의 이익을 고려할 유인을 가진다. 이러한 집단 들을 ‘망라이익집단’이라고 부른다. 망라이익집단을 통해 사회 전체를 더 생산적이고 부유하게 만들 수 있다. 본 논문의 목적은 집단행동의 ‘이론’과 ‘현장에서의 관찰’ 결과를 접목시키고자 하는 데 있다. 이러한 접목은 두 가지 방향에서 시도된다. 첫째, 송호근 교수의 최근 관찰 결과(2017a, 2017b)를 올슨의 집단행동이론과 집단행동의 불균등한 발생 편향성을 바탕으로 설명함으로써 이론과 실제를 접목시키고자 한다. 둘째, 현대차 노조의 독점 기능과 실리주의화를 통해 올슨의 ‘특수이익집단’의 행동 논리를 찾고, 포스코의 노경 협의회의 활동을 통해 ‘망라이익집단’의 특성을 찾아보고자 한다.

The general logic of collective action offers three insightful implications. Firstly, large groups face more difficulties for collective action than small groups because of the free-rider problem, and thus large groups need special arrangements, or selective incentives, to organize collective action. Secondly, the well-established, well-off and insider groups are better positioned to overcome the collective action problem than the poorer and more insecure groups: there is an unequal bias in organizing the collective action. Finally, an encompassing group has an incentive to make the society as a whole more efficient and prosperous. The purpose of this paper is to apply the theory of collective action pioneered by M. Olson to the field observations by Professor Song Ho-keun regarding labor practices in Hyundai Motor Company and POSCO. That is, the present paper tries to interpret the recent field observations of Professor Song Ho-keun in light of the theory of collective action. The present paper tries to combine theory and practice in two directions: First, the present paper attempts to explain not only the recent observations of Song (2017a, 2017b) based on Olson’s collective action theory but also the inegalitarian bias in the occurrence of collective action. Second, it aims to find the behavioral logic of special interest group through labor union’s monopoly function and selfish pragmatism in the labor union of Hyundai Motor Company and to understand the characteristics of an encompassing interest group through the activities of Labor Management Council of POSCO. This paper identifies Hyundai Motor Company’s union as a monopolistic function, or ‘special interest group’, through the politics of exchange between the labor union and the company, while POSCO’s Labor-Management Council is identified as ‘encompassing interest group’ in Olson’s sense. This behavioral nature of labor union in terms of the politics of exchange is worthy of further study.

Labor education is the educational inheritance of the wisdom of traditional Chinese labor thinking, the reiteration of Marx's idea of “labor educating people”, and the promotion of educational practice in the new era forging the “Chinese Dream”. Agricultural Labor Games is a perfect fusion of labor education and physical education in practical education, opening up a “new track” of labor education in colleges and universities. In the practice of labor education in colleges and universities, sorting out the logic of the college Agricultural Labor Games: first of all, the theoretical logic, is the intermingling of labor education and physical education nurturing attributes; historical logic, is the transmutation from the labor tool to the sports apparatus; practical logic, is the optimization of physical education course ideology and implementation of labor education, to meet the new needs of students on the new demand for sports competitions. Under the guidance of the party and state documents on labor education, actively responding to General Secretary Xi's call for education to promote the “spirit of labor”, we put forward the development strategy of Agricultural Labor Games: increase the declaration and establishment of labor education practice projects in colleges and universities; actively explore the structure of the Agricultural Labor Games for collaborative education; make full use of the advantages of social media dissemination, and increase the mainstream media push; increase the strength of the mainstream media push; and make full use of the advantages of social media dissemination, and increase the strength of the mainstream media push. Increase the mainstream media's push; sound financial investment, strengthen security and other strategies.

Online events are becoming more and more popular thanks to their flexibility and convenience, especially in the Covid-19 pandemic. The process of organizing an online event is a high-volume, labor-intensive process. Therefore, this study focuses on applying robotic process automation (RPA) technology to automate a number of processes and improve the efficiency of the online event process. RPA is a form of business process automation technology created to mimic human actions and perform repetitive tasks to increase work efficiency based on metaphorical software robots or on artificial intelligence. Currently, RPA is being used in many different fields such as finance, banking, accounting, and many more areas. This study proposes an application model of RPA in email marketing automation, automatically collecting and replying to comments. On Facebook. Testing with 20 emails and 12 comments shows that the bots work stably and correctly. Future works include testing with various email delivery systems and testing with a larger number of emails and comments.

According to the importance of exhibitions as a marketing promotion tool, exhibition organizers want to satisfy the visitor and exhibition for successful exhibition held. As the development of information technology is growing, valuable information can be extracted easily. Many companies in a variety of filed interest in this technology for acquisition customer’s personal profile information. For the same reason, many exhibition organizers focused on to applying the ubiquitous technologies in exhibition for promote the performance. To provide a booth recommendation and booth event recommendation to visitor have been already studied using the technologies. Booth location, one of the determinants to increasing the exhibition performance, also has a positive effect on maximizing the marketing performance and sales in exhibition. But there are no proper criteria and research about booth location for effective visitors’ visiting booths. To compensate the limitations of existing researches, this paper proposes effective booth configuration procedure based on SOM(Self Organizing Map). In this paper, we cluster the booths which are co-visited booths by visitors using SOM.

현재 교육 현장에서 문해력이란, 문자해독의 좁은 범주에서 벗어나 디지털 영역을 포함하는 다층적이고 포괄적인 의미로 사용되고 있다. 문해력이 주목받 은 이유는 팬데믹 이후 디지털 기반의 의사소통이 급격하게 늘면서 학습자들의 난독과 오독들이 학습을 저해하는 결정적인 요소로 인식되었기 때문이다. 이 논의를 실질적으로 진행하기 위해서는 대학의 ‘학습자’들이 ‘독자’로서 어떤 특 징을 가지고 있는지를 면밀하게 살필 필요가 있다. 이들은 네트워크가 탑재된 디지털 기기에 대한 경험이 풍부하고, 한정된 목적을 가진 입시 패턴의 읽기에 익숙하며 동시에 읽을 대상을 스스로 선택하거나 구성해 본 경험이 부족한 독 자들이다. 하지만 대학이 새로운 사고 방식과 다양한 방법론에 대한 가능성을 사유하는 읽기를 진행하는 학습 공간이라는 점을 고려할 때 이에 대한 실질적 인 해결 방안이 필요하다. 본고는 읽기 교육을 대학 교양과목 편성을 활용하는 방안과 기초 의사소통 교과목을 활용하는 방안으로 나누어 접근해 보고자 한 다. 우선, 지속적인 문제제기 차원에서 교양교육 편성을 활용하는 방안에는 읽 기 교육을 전문적으로 다루는 교과목의 신설, 읽기 교육에 관한 전공교과와 교 양교과의 협의체 구성, 다양한 교과와의 융복합 구성과 비교과 프로그램 운영 등의 방법이 있다. 다음으로 기초 의사소통 교과목을 활용하는 방안에는 공통 강의안에 읽기 교육을 가시적으로 배당, 읽기 교육을 위한 전문적인 읽기 자료 의 확충, 디지털 문해력에 관한 실질적 교육, 공통 강의안의 구성적 변주 등을 들 수 있다. 교양교육을 활용한 읽기 교육의 확장은 현재의 학습 독자들에게 스스로 자신 의 읽기가 가진 문제점을 인식하고 수정할 수 있는 방법론을 제시할 수 있다.

Literacy is currently used in the educational field in a multi-layered and comprehensive sense that extends beyond the narrow scope of text decoding ability to digital reading beyond understanding vocabulary and the basis for background knowledge. Literacy has drawn attention because digital-based communication has increased rapidly in the educational field since the pandemic, making learners’ desultory-reading and misreading a decisive factor hindering learning. In order to actually proceed with this discussion, it is necessary to closely examine the characteristics of the university’s “learners” as “readers.” They have abundant experience in digital devices equipped with networks, are familiar with reading entrance examination patterns with limited purposes, and at the same time lack experience in selecting or configuring their own objects to read. However, considering that a university is a learning space for reading, which leads to a new way of thinking, various methodologies, and the power to think about their possibilities, it is necessary to seek practical solutions. This paper attempts to approach university reading education by dividing it into a plan to utilize liberal arts curriculum and a plan to use basic communication courses. First of all, the ways to use liberal arts education to raise continuous problems include the establishment of a subject specializing in reading education, the formation of a consultative body between major and liberal arts courses on reading education, and the operation of comparative programs. Next, the ways to use basic communication subjects include visually distributing reading education to common lectures, expanding professional reading materials for reading education, practical education on digital literacy, and structural variations in common lectures. The expansion of reading education using liberal arts education can present a methodology for learning readers who are familiar with digital viewing to recognize and correct the problems of their own reading.