• 한국수학교육학회지시리즈A:수학교육
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• 제46권4호
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• pp.371-388
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• 2007

This is a case study trying to understand from the view of affordance which certain three middle school students perceive an activation of previous knowledge in the course of problem solving when they solve algebra word problems with a previous knowledge. The results of this study showed that at first, every subjects perceived the text as affordance which explaining superficial similarities, that is, a working(painting)situation rather than problem structure and then activated the related solution knowledge on the ground of the experience of previous problem solving which is similar to current situation. The subject's applying process for solving knowledge could be arranged largely into two types. The first type is a numeral information connected with the described problem situation or a symbolic representation of mathematical meaning which are the transformed solution applied process with a suitable solution formula to the current problem. This process achieved by constructing a virtual mental model that indicating mathematical situation about the problem when the solver read the problem integrating symbolized information from the described text. The second type is a case that those subjects symbolizing a formal mathematical concept which is not connected with the problem situation about the described numeral information from the applied problem or the text of mathematical meaning, which process is the case to perceive superficial phrases or words that described from the problem as affordance and then applied previously used algorithmatical formula as it was. In conclusion, on the ground of the results of this case study, it is guessed that many students put only algorithmatical knowledge in their memories through previous experiences of problem solving, and the memories are connected with the particular phrases described from the problems. And it is also recognizable when the reflection process which is the last step of problem solving carried out in the process of understanding the problem and making a plan showed the most successful in problem solving.

• 한국항해항만학회지
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• 제29권2호
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• pp.141-146
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• 2005

For a long time, genetic algorithms have been recognized as a new method to solve difficult and complex problems and the performance of genetic algorithms depends on genetic operators, especially crossover operator. Various problems like the traveling salesman problem, the transportation problem or the job shop problem, in logistics engineering can be modeled as a sequencing problem This paper proposes modified genetic crossover operators to be used at various sequencing problems and uses the traveling salesman problem to be applied to a real world problem like the delivery problem and the vehicle routing problem as a benchmark problem Because the proposed operators use parental information as well as network information, they could show better efficiency in performance and computation time than conventional operators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.5129-5152
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• 2016

Multi-view super-resolution (MVSR) aims to estimate a high-resolution (HR) image from a set of low-resolution (LR) images that are captured from different viewpoints (typically by different cameras). MVSR is usually applied in camera array imaging. Given that MVSR is an ill-posed problem and is typically computationally costly, we super-resolve multi-view LR images of the original scene via image fusion (IF) and blind deblurring (BD). First, we reformulate the MVSR problem into two easier problems: an IF problem and a BD problem. We further solve the IF problem on the premise of calculating the depth map of the desired image ahead, and then solve the BD problem, in which the optimization problems with respect to the desired image and with respect to the unknown blur are efficiently addressed by the alternating direction method of multipliers (ADMM). Our approach bridges the gap between MVSR and BD, taking advantages of existing BD methods to address MVSR. Thus, this approach is appropriate for camera array imaging because the blur kernel is typically unknown in practice. Corresponding experimental results using real and synthetic images demonstrate the effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1441-1456
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• 2015

The bidimensional empirical mode decomposition (BEMD) algorithm with high adaptability is more suitable to process multiple image fusion than traditional image fusion. However, the advantages of this algorithm are limited by the end effects problem, multiscale integration problem and number difference of intrinsic mode functions in multiple images decomposition. This study proposes the multiscale self-coordination BEMD algorithm to solve this problem. This algorithm outside extending the feather information with the support vector machine which has a high degree of generalization, then it also overcomes the BEMD end effects problem with conventional mirror extension methods of data processing,. The coordination of the extreme value point of the source image helps solve the problem of multiscale information fusion. Results show that the proposed method is better than the wavelet and NSCT method in retaining the characteristics of the source image information and the details of the mutation information inherited from the source image and in significantly improving the signal-to-noise ratio.

• 한국컴퓨터정보학회논문지
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• 제17권8호
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• pp.181-188
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• 2012

본 논문에서는 정보영재의 창의적 문제해결력 향상을 위한 STEAM 기반 쓰기 활용 전략을 제안한다. 창의적 문제해결(creative problem solving)을 위해서는 다양한 요소의 복합적이며 역동적인 상호작용이 필요하며, 이러한 상호작용을 유도하고 실세계의 복잡한 문제를 해결할 수 있는 능력을 함양하기 위해서는 융합교육을 통해 다양한 학문을 아우르는 학습 경험을 제공해야 한다. 또한, 다양한 교과에서 이미 교육적 효과가 검증된 쓰기는 비판적 사고를 유도하고 문제해결의 시작이라 할 수 있는 문제인식을 도와 창의적 문제해결에 긍정적 영향을 줄 뿐만 아니라, 문제 해결과정과의 유사성을 바탕으로 문제해결력 향상을 위한 효과적인 도구로 쓰일 수 있다. 학습자들은 일상생활에서 흔히 사용하는 자판기, 휴대전화 같은 첨단기술 제품을 사용한 경험을 쓰고 분석하는 과정을 통해 알고리즘을 찾고 실생활에 쓰이는 여러 학문의 원리를 자연스럽게 학습하면서 다양한 사고의 융합과 상호작용을 경험하고 창의적 문제해결력을 함양할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.2003-2021
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• 2020

Today's computer systems are widely integrated with CPU and GPU to achieve considerable performance, but energy consumption of such system directly affects operational cost, maintainability and environmental problem, which has been aroused wide concern by researchers, computer architects, and developers. To cope with energy problem, we propose a task-scheduling framework to reduce energy under performance constraint by rationally allocating the tasks across the CPU and GPU. The framework first collects the estimated energy consumption of programs and performance information. Next, we use above information to formalize the scheduling problem as the 0-1 knapsack problem. Then, we elaborate our experiment on typical platform to verify proposed scheduling framework. The experimental results show that our proposed algorithm saves 14.97% energy compared with that of the time-oriented policy and yields 37.23% performance improvement than that of energy-oriented scheme on average.

• 디지털산업정보학회논문지
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• 제15권3호
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• pp.69-78
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• 2019

The amount of data is increasing significantly as information and communication technology advances, mobile, cloud computing, the Internet of Things and social network services become commonplace. As the data grows exponentially, there is a growing demand for services that recommend the information that users want from large amounts of data. Collaborative filtering method is commonly used in information recommendation methods. One of the problems with collaborative filtering-based recommendation method is the cold start problem. In this paper, we propose a method to improve the cold start problem. That is, it solves the cold start problem by mapping the item evaluation data that does not exist to the initial user to the automatically generated data from the mobile app. We describe the main contents of the proposed method and explain the proposed method through the book recommendation scenario. We show the superiority of the proposed method through comparison with existing methods.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 추계학술발표대회
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• pp.287-289
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• 2013

Fast Handover for Proxy Mobile IPv6 (FPMIPv6), a protocol described in RFC 5949, is used to reduce handover latency and minimize packet loss problem occuring in the Proxy Mobile IPv6 (PMIPv6) protocol. However, during the study of implementing FPMIPv6, we found the Out-of-Order Packet (OoOP) problem that occurs in the experiment of FPMIPv6. Since the OoOP is an issue that affects significantly to QoS of the network, in this paper, we analyze the OoOP problem by using network model. The analysis conducts the cause of occurring OoOP problem due to there exist two paths for data transmitted from Correspondent Node (CN) to MN in FPMIPv6.

• International Journal of Computer Science & Network Security
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• 제23권2호
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• pp.55-64
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• 2023

The travelling salesman problem (TSP) is an important combinatorial optimization problem that is used in several engineering science branches and has drawn interest to several researchers and scientists. In this problem, a salesman from an arbitrary node, called the warehouse, starts moving and returns to the warehouse after visiting n clients, given that each client is visited only once. The objective in this problem is to find the route with the least cost to the salesman. In this study, a meta-based ant colony system algorithm (ACSA) is suggested to find solution to the TSP that does not use local pheromone update. This algorithm uses the global pheromone update and new heuristic information. Further, pheromone evaporation coefficients are used in search space of the problem as diversification. This modification allows the algorithm to escape local optimization points as much as possible. In addition, 3-opt local search is used as an intensification mechanism for more quality. The effectiveness of the suggested algorithm is assessed on a several standard problem instances. The results show the power of the suggested algorithm which could find quality solutions with a small gap, between obtained solution and optimal solution, of 1%. Additionally, the results in contrast with other algorithms show the appropriate quality of competitiveness of our proposed ACSA.

• 한국컴퓨터정보학회논문지
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• 제22권9호
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• pp.9-16
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• 2017

The course timetabling problem is a problem assigning a set of subjects to the given classrooms and different timeslots, while satisfying various hard constraints and soft constraints. This problem is defined as a constraint satisfaction optimization problem and is known as an NP-complete problem. Various methods has been proposed such as integer programming, constraint programming and local search methods to solve a variety of course timetabling problems. In this paper, we propose an iterative improvement search method to solve the problem based on constraint programming. First, an initial solution satisfying all the hard constraints is obtained by constraint programming, and then the solution is repeatedly improved using constraint programming again by adding new constraints to improve the quality of the soft constraints. Through experimental results, we confirmed that the proposed method can find far better solutions in a shorter time than the manual method.