Earticle

최근 소프트웨어 활용교육 중심에서 프로그래밍을 비롯한 컴퓨터과학을 기반으로 한 사고력 향상 중심의 컴퓨터 교육이 강조되고 있다. 시각화 자료를 이용하거나 활동 중심의 언플러그드 활동 중심의 알고리즘 교육을 기반으로 초등 알고리즘 교육에 대한 여러 연구가 이루어지고 있다. 그러나 아직 초등학생의 특성을 살린 학습자료나 수업 방법이 부족하여 실제 학교 현장에 도입하기에 여러 가지 어려움이 있다. 이에 본 연구에서 관련 선행 연구를 분석하여, 초등학생들의 발달단계에 적합한 활동중심의 검색 알고리즘 수업을 설계하였다. 본 연구에서 개발한 수업 설계를 통하여 학생들의 사고력을 향상시킬 수 있는 알고리즘 관련 수업이 더욱 확대되기를 기대한다.

Currently computer education has been emphasized improving thinking skill instead of practical education of applied software. There are various studies that are to teach algorithm with visualization learning materials or activitives-centered unplugged class. However, algorithm classes for elementary school have various difficulties. One of the reasons is insufficient learning materials and teaching methods. Therefore we designed a activities-centered algorithm class for elementary school students. We hope this study will contribute to the study of algorithm classes for improving kids’ thinking skill.

현대는 소프트웨어 융합의 시대이다. 4차 산업혁명이라고 불리우는 이러한 변화가 우리 삶의 거의 모든 분야에 영향을 미침에 따라 초․중등 교육의 내용도 이러한 변화를 수용해야 한다는 요구를 반영하여 2015 개정 교육과정에서 소프트웨어 교육이 강화되었다. 따라서, 본 연구는 초등 소프트웨어 교육을 위하여 플립러닝을 위한 STEAM 기반 초등 알고리즘 학습용 모바일 웹앱을 활용한 교육이 초등학생의 문제해결과정에 미치는 영향을 분석하여 그 효과성을 검증하는 데 목적이 있다. 이를 위해 본 연구에서는 새로운 교육과정을 분석하고, 초등학생의 플립러닝에 기여할 수 있도록 모바일 기기를 교육에 응용하여 IT 융합시대에 적합한 STEAM 기반 모바일 콘텐츠를 개발하였다. 그리고 개발한 모바일 자료를 초등학교 5학년 대상으로 모바일 기기를 활용하여 플립러닝 수업을 진행한 실험집단에 적용하였고, 활동지를 활용하여 강의식 수업을 진행한 통제집단과 문제해결과정을 비교할 수 있는 통계적 t-검증을 실시하였다. 그 결과 실험집단과 통제집단 간의 문제해결과정에 통계적으로 유의미한 차이가 있음이 검증되었다. 따라서, 본 연구결과를 토대로 STEAM 기반 모바일 학습자료 활용 교육이 초등학생의 문제해결력 향상에 효과적임을 확인하였다.

Software integration becomes very important in these days. Since the 4th industrial revolution has begun and influences its heavy effects on our daily life, software education has been introduced in the 2015 national revised curriculum. Therefore, the purpose of this study is to verify the effects of a mobile web application for the elementary algorithm class based on STEAM on the problem solving process of elementary school students. To do so, in this study we analyzed the new elementary school curriculum, constructed an algorithm learning class based on STEAM and developed a mobile web application for flipped learning to improve their problem solving ability. Further, an experimental group and a controlled group are selected respectively from the 5th grade elementary school students. Then, a new flipped learning class using our mobile materials was applied to the experimental group while a traditional lecture class using the activity papers was applied to the controlled group. Finally the paired samples t-tests were carried out. As a result, we found that there was a statistically significant difference in problem solving process between the two groups. Based on our experimental research and the results of statistical analysis, the mobile web application class based on STEAM turned out to be effective in improving the problem solving ability of elementary school students.

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources.Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google’s PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high.Kleinberg’s algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg’s algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages.As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web (WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework (RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a ‘refers to’ property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web.Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg’s algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg’s, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community (TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it.In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predi-cate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC (Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

본 논문은 기 촬영된 수업 동영상을 분석 결과와 동기화하여 재생시키고 확인할 수 있는 소프트웨어 구현에관한 것이다. 이를 위하여 본 논문에서는 동영상 재생 구간과 분석 내용이 동기화 될 수 있도록 하는 동영상 인덱싱 방법, 자료구조, 그에 따른 선별적 재생 방안을 제시한다. 사용자는 제안하는 방식을 이용하여 동영상 재생내용을 구간별로 확인할 수 있고 역으로 분석 결과를 입력으로 원하는 지점의 동영상 구간을 검색할 수 있다. 본 논문에서는 수업 분석을 도입, 전개, 정리의 시간 흐름 단계로 구분하여 진행하였고, 단계별 재생에 맞게 일정 간격으로 분석 필터를 적용하였다. 구현된 수업 분석 소프트웨어를 사용하여 몇 가지 수업 사례를 적용한 결과, 성공적으로 필요한 수업 분석을 진행할 수 있었고 동기화 성능 실험에서도 그 효율성을 확인할 수 있었다.

This paper describes about a software implementation for class analysis and quantization based on our video synchronization method. We proposed a new indexing method, synchronization strategies, and data structure for our analyzer implementation. We implemented a class video analyzer using intelligent multimedia technologies which can play class video selectively. Our proposed method analyzes class videos depending on the time schedule composed of introduction, development and summary stages. We apply our analysis filters to the class videos in the predefined regular intervals. We experimented on the synchronization performance of our proposed method and software. In the experimental, we could demonstrate the effectiveness and practicality of our class analyzing method within the margin of error.

본 연구는 학습자 특성(성별 및 학교 급)에 따른 온라인 수업 유형 선호도를 파악하고자 하는데 그 목적이 있다. 이를 위하여 전국 17개 지역의 초ㆍ중ㆍ고등학교 학생 4,803명을 대상으로 설문조사를 실시하였다. 이후, 유효데이터 인 4,524명 학생들의 성별 및 학교급을 기반한 온라인 수업 유형 선호도 패턴을 확인하기 위해 Apriori 알고리즘을 이용한 연관규칙 분석을 실시하였다. 연구결과 초등 7개, 중등 4개, 고등 5개 등 총 16개의 규칙을 도출하였으며, 학교 급과 무관하게 여학생들은 메이커활동 중심 수업을, 초⋅중 남학생은 가상체험중심 수업을 공통적으로 선호하였다. 보 다 구체적으로, 초등학교 남학생은 SW중심수업을, 여학생은 메이커활동 중심 수업을 선호하였으며, 중학생의 경우 남 여 모두 가상체험중심 수업을 선호하였다. 반면 고등학생은 교과별 강의중심에 대한 선호도가 높았다. 이러한 연구결과 는 학습의 주체자인 학생이 가진 온라인 수업의 요구를 설명하는 실증적 근거로서 제시될 수 있다. 또한, 본 연구는 향후 온라인 수업의 다각화를 위한 개선방향을 제시, 탐색하는 기초자료로 활용될 수 있을 것으로 기대한다. 이상의 연구결과를 바탕으로 추후 연구에서는 다양한 온라인 수업 활동 및 모델 설계, 온라인 수업을 지원하는 플랫폼 개발, 여학생의 이공계 진로동기 형성과정에 대한 심층적 분석이 계속되어야 할 것이다.

This study aims to investigate the online class preference depending on students' gender and school level. To achieve this aim, the study conducted a survey on 4,803 elementary, middle, and high school students in 17 regions nationwide. The valid data of 4,524 were then analyzed using the Apriori algorithm to discern the associated patterns of the online class preference corresponding to their gender and school level. As a result, a total of 16 rules, including 7 from elementary school students, 4 from middle school students, and 5 from high school students were derived. To be specific, elementary school male students preferred software-based classes whereas elementary female students preferred maker-based classes. In the case of middle school, both male and female students preferred virtual experience-based classes. On the other hand, high school students had a higher preference for subject-specific lecture-based classes. The study findings can serve as empirical evidence for explaining the needs of online classes perceived by K-12 students. In addition, this study can be used as basic research to present and suggest areas of improvement for diversifying online classes. Future studies can further conduct in-depth analysis on the development of various online class activities and models, the design of online class platforms, and the female students’ career motivation in the field of science and technology.

Based on the the characteristics of maximal function, the algorithm for solving Min- max problem was researched in this paper. A new differential approximation function for the nondifferentiability of the objection function was also constructed. At same time, the property of the new differential approximation function is discussed and the processes of proves show that using the new approximation function to solve the nonlinear unconstrained min-max problem is feasible and effective. The preliminary numerical example shows that the algorithm is effective, and that have a large of convergence characteristics.

In the traditional model of fault diagnosis, neural network classification requires high demand for the number and completeness of samples with a problem cannot be overcome- -"the curse of dimensionality". While the actual bearing failure is a typical case of small sample with few samples and the number of different types of samples is asymmetrical and even not complete. And the pattern classification effects of the support rector machine in case of small sample are better. Therefore, according to the above comparative analysis, combined with the character of small samples of actual bearing failure mode, this paper selects to build classification model based on the support vector machines, and after researching, the model proved to be feasible.

본 논문은 전술 통신망에서 QoS 향상을 위한 클래스 기반의 FQ-CoDel(Flow queue-Control Delay) 알고리 즘을 제안한다. 전술 통신망에서 다양한 응용체계 서비스를 제공하게 되면서 병목현상으로 인한 트래픽 손실 및 지연 문제가 대두되었고 따라서 효과적인 트래픽 처리 방안 연구에 대한 필요성이 제기되었다. 제안방안은 클래스 기반의 FQ-CoDel 알고리즘을 이용한 동적인 버퍼 관리 및 스케줄링 기술로써 서비스 특성 및 중요도에 따라 해당 큐에 패킷을 분류하고 주기적으로 각 패킷의 대기시간을 체크하여 기준 대기 시간을 초과하는 경우 해당 패킷을 폐기하여 큐에 저장 된 트래픽 양을 일정 수준으로 조절한다.

This paper proposes a class-based FQ-CoDel(Flow Queue-Control Delay) algorithm. A variety of application system services create bottlenecks in tactical communication network and the bottlenecks cause some problems such as traffic loss and delay. Therefore, more research on effective traffic processing is needed. The proposed class-based FQ-CoDel algorithm, suggests dynamic buffer management and scheduling, classifies specific packets in each queue according to service attribute and criticality and checks periodically latency of the packets in each queue. Also, it abandons the packets if some packets stay in queue above schedule time and manages the total amount of traffic stored in queue with certain level.

Terrain classification is still a challenging issue in image processing, especially with high resolution satellite images. The well-known obstacles include low accuracy in the detection of targets, especially for the case of man-made structures, such as buildings and roads. In this paper, we present an efficient approach to classify and detect building footprints, foliage, grass and road from high resolution grayscale satellite images. Our contribution is to build a strong classifier using AdaBoost based on a combination of co-occurrence and Haar-like features. We expect that the inclusion of Harr-like feature improves the classification performance of the man-made structures, since Haar-like feature is extracted from corner features and rectangle features. Also, the AdaBoost algorithm selects only critical features and generates an extremely efficient classifier. Experimental result indicates that the classification accuracy of AdaBoost classifier is much higher than that of the conventional classifier using back propagation algorithm. Also, the inclusion of Harr-like feature significantly improves the classification accuracy. The accuracy of the proposed method is 98.4% for the target detection and 92.8% for the classification on high resolution satellite images.

현재 사용되고 있는 Internet상의 트래픽은 어플리케이션의 종류에 따라 다양한 수준의 QoS 요구조건, 즉, Delay, Loss 그리고 Throughput성능에 대한 요구조건을 오청하고 있다. TCP protocol을 사용하는 ETP서비스나 E-Mail 등의 서비스는 Delay나 Loss 성능보다는 Throughput성능에 대한 요구가 높은 편이기 때문에 앞의 두 성능악화의 반대급부로서 Throughput성능을 보장받곤 한다. 반면에, 대부분 UDP protocol을 사용하는 real-time streaming 어플리케이션, 예를 들면, IP telephony, Video conferencing, 혹은 network games등의 어플리케이션은 여타의 것들에 비하여 Throughput 보다는 delay나 loss에 대한 성능을 상대적으로 높게 요구한다. 하지만 현재의 AQM들은 best-effort 서비스에 초점을 맞추고 있다. 즉, throughput 성능을 위하여 delay나 loss성능을 희생하고 있기 때문에 다양한 어플리케이션에 적합한 서비스를 제공하기 힘들다. 따라서 본 논문에서는 각 어플리케이션이 필요로 하는 QoS 성능을 고려하여 어플리케이션들을 세 가지 클래스로 분류한 뒤 적합한 QoS 요구조건을 고려한 새로운 AQM 알고리즘을 제안한 뒤, 시뮬레이션을 통하여 다른 AQM 알고리즘과 비교 분석하고 그에 대한 결론을 도출한다.

Traditional Internet applications such as FIP and E-mail are increasingly sharing bandwidth with newer, more demanding applications such as Web browsing, IP telephony, video conference and online games. These new applications require Quality of Service (QoS), in terms of delay, loss and throughput that are different from QoS requirements of traditional applications. Unfortunately, current Active Queue Management (AQM) approaches offer monolithic best-effort service to all Internet applications regardless of the current QoS requirements. This paper proposes and evaluates a new AQM technique, called MCDT that provides dynamic and separated buffer threshold for each Applications, those are FTP and e-mail on TCP traffic, streaming services on tagged UDP traffic, and the other services on untagged UDP traffic. Using a new QoS metric, our simulations demonstrate that MCDT yields higher QoS in terms of the delay variation and a packet loss than RED when there are heavy UDP traffics that include streaming applications and data applications. MCDT fits the current best-effort Internet environment without high complexity.

Using the recently developed ABS algorithm for solving linear Diophantine equations we introduce an algorithm for solving a system of m linear integer inequalities in n variables, m $\leq$ n, with full rank coefficient matrix. We apply this result to solve linear integer programming problems with m $\leq$ n inequalities.

동적통행배정모형을 이용해서 교통정보를 ㅈ공하기 위해서는 다양한 여행자의 경로선택행태를 고려하는 것이 필요하다. 여행자계층은 일반적으로 3가지 형태로 분류된다: 1) 버스나 지하철과 같은 대중교통의 고정된 경로(fixed route class)를 이용하는 그룹, 2) 자신이 인지하는 경로비용을(perceived route, unguided class) 판단하여 경로를 선택하는 그룹, 3) 정확한 경로선택에 대한 정보를(guided class)기반으로 경로를 선택하는 그룹. 본 연구에서는 이 3그룹의 여행자를 포함하는 동적통행배정모형의 해법을 제안한다. 제안된 해법에서는 링크의 교통량과 유출교통류를 진입교통류 단일변수로 축소하여 시간과 공간을 확장하기 않고 실재의 네트워크에서 최단경로를 도출하는 방법을 적용한다. 따라서 시간종속적인 통행비용함수에 진입교통유율, 교통량, 유출교통유율 3가지 변수를 고려해야 하는 시공간확장방법에 비해 네트워크의 규모와 수행시간에 있어 유리하다.

This paper proposes a solution algorithm for solving a multi-class analytical DTA model. In the DTA model, three traveler classes are classified according to different assumptions of traveler's route choice behavior: including fixed route, Stochastic Dynamic User Optimum(SDUO), and Dynamic User Optimum(DUO). To solve this DTA model, variables of link flow and exit flow are represented solely by inflow. The resulting Linear Program(LP) subproblem in the inner iteration is solved as a typical time-dependent shortest route problem over a physical network. Accordingly, the required time-space network expansion in solving DTA models is no longer needed.

This paper suggests O(n) linear-time algorithm for car sequencing problem (CSP) that has been classified as NP-complete because of the polynomial-time algorithm to solve the solution has been unknown yet. This algorithm applies maximum options-equiped car type first production rule to decide the car sequencing of n meet the r:s constraint. This paper verifies thirteen experimental data with the six data are infeasible. For thirteen experimental data, the proposed algorithm can be get the solution for in all cases. And to conclude, This algorithm shows that the CSP is not NP-complete but the P-problem. Also, this algorithm proposes the solving method to the known infeasible cases. Therefore, the proposed algorithm will stand car industrial area in good stead when it comes to finding a car sequencing plan.

In this paper, we address a layout design problem for determining an optimal 4-class-based dedicated linear storage layout in a class of unit load storage systems. Assuming that space requirement for a class is the sum of the maximum inventory levels of products assigned to the class, and that one-way travel time is a linear function of storage index, we formulate a 4-class-based dedicated linear storage problem PTL[4] and provide an exact splitting algorithm with $O(n{\lceil}logn{\rceil})$. Our algorithms could be applied to more than a 4-class-based dedicated storage layout problem with slight modification in order to reduce computational execution time.

This paper proposes a multiplication algorithm for $F_{p^m}$, which can be efficiently applied to many pairs of characteristic p and extension degree m except for the case that 8p divides m(p-1). It uses a special class of type-<k, m> Gauss period normal bases. This algorithm has several advantages: it is easily parallelized; Frobenius mapping is easily carried out since its basis is a normal basis; its calculation cost is clearly given; and it is sufficiently practical and useful when parameters k and m are small.

In this paper, we propose a modified proximal point algorithm which consists of a resolvent operator technique step followed by a generalized projection onto a moving half-space for approximating a solution of a variational inclusion involving a maximal monotone mapping and a monotone, bounded and continuous operator in Banach spaces. The weak convergence of the iterative sequence generated by the algorithm is also proved.

In this paper, we readdress a layout design problem, PTL[3], for determining an optimal 3-class-based dedicated linear storage layout in a class of unit load storage systems. Based on some fundamental properties derived, we provide a converging exact algorithm with O(n[logn]), which is more efficient than that of Yang and Kim [8] and can be applied to PTL[K] with $K{\ge}4$ in order to reduce computational execution time. In addition, we prove that the necessary condition suggested by them is also a sufficient condition to PTL[3].

In this communication, we introduce an Ishikawa type iterative algorithm for finding the approximate solutions of a class of nonlinear set valued variational inclusion problems. We also establish a characterization of strong convergence of this iterative techniques.

Motivated by the field experimental designs in agriculture, the theory of block designs has been applied to several areas such as statistics, combinatorics, communication networks, distributed systems, cryptography, etc. An explicit formula and its fast computational algorithm for a class of symmetric balanced incomplete block designs are presented. Based on the formula and the careful investigation of the modulus multiplication table, the algorithm is developed. The computational costs of the algorithm is superior to those of the conventional ones.

We consider discrete-time factorial Markov Decision Processes (MDPs) in multiple decision-makers environment for infinite horizon average reward criterion with a general joint reward structure but a factorial joint state transition structure. We introduce the "localization" concept that a global MDP is localized for each agent such that each agent needs to consider a local MDP defined only with its own state and action spaces. Based on that, we present a gradient-ascent like iterative distributed algorithm that converges to a local optimal solution of the global MDP. The solution is an autonomous joint policy in that each agent's decision is based on only its local state.cal state.