• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.505-508
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• 2013

스마트폰 응용 프로그램의 메모리 관리는 응용 프로그램의 속도와 안정성 측면에서 중요하게 다루어진다. 응용 프로그램에서 다루는 요소 중 이미지는 메모리 사용량의 많은 부분을 차지하며, 메모리의 여유 공간 내에서 이미지가 사용될 수 있도록 관리되어야 한다. 그러나 이미지의 해상도가 커지거나 다루게 되는 이미지의 개수가 늘어날 수록 이미지 객체의 관리의 어려움도 늘어나게 된다. 이미지 객체가 메모리 공간이 부족한 시점에서 메모리에 적재될 경우 응용 프로그램은 성능이 저하되거나 강제 종료될 수 있어 응용 프로그램의 사용성과 안정성이 낮아지게 된다. 본 논문에서는 안드로이드의 응용 프로그램에서 사용되는 이미지가 메모리의 많은 공간을 차지할 때, 안정적인 응용 프로그램 구동 환경을 제공해주는 동적 메모리 관리 기법을 적용하여 OOM(Out of Memory) 오류가 발생하는 문제를 해결하고자 한다.

• Journal of KIISE
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• v.45 no.2
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• pp.113-125
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• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.8 s.227
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• pp.1099-1108
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• 2004

In this paper, a study on the development of expert system for Al matrix composite with shape memory alloy fiber is performed to evaluate termomechanical behavior and mechanical properties. Expert system is very useful computer-based analysis system designed to make analysis technique and knowledge conveniently available to a lot of fabricable condition. In the developed system, it is possible to predict termomechanical behavior and mechanical properties for other composite with shape memory alloy fiber. The smartness of the shape memory alloy is given due to the shape memory effect of the TiNi fiber which generates compressive residual stress in the matrix material when heated after being prestrained. For finite element analysis, an analytical model is assumed two dimensional axisymmetric model compared of one fiber and the matrix. To evaluate the strength of composite using FEM, the concept of smart composite was simulated on computer Thus, in this paper, the FEA was carried out at two critical temperature conditions; room temperature and high temperature(363k). The finite element analysis result was compared with the test result for the analysis validity.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.9
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• pp.434-441
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• 2001

The concept of network memory was introduced for the efficient exploitation of main memory in a cluster. Network memory can be used to speed up applications that frequently access large amount of disk data. In this paper, we present a memory a management algorithm that does not require prior knowledge of access patterns and that is practical to implement under the web server cluster, In addition, our scheme has a good user response time for various access distributions of web documents. Through a detailed simulation, we evaluate the performance of our memory managment algorithms.

• Sleep Medicine and Psychophysiology
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• v.4 no.1
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• pp.15-28
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• 1997

Longterm memory is encoded in the neuronal connectivities of the brain. The most successful models of human memory in their operations are models of distributed and self-organized associative memory, which are founded in the principle of simulaneous convergence in network formation. Memory is not perceived as the qualities inherent in physical objects or events, but as a set of relations previously established in a neural net by simultaneousy occuring experiences. When it is easy to find correlations with existing neural networks through analysis of network structures, memory is automatically encoded in cerebral cortex. However, in the emergence of informations which are complicated to classify and correlated with existing networks, and conflictual with other networks, those informations are sent to the subcortex including hippocampus. Memory is stored in the form of templates distributed across several different cortical regions. The hippocampus provides detailed maps for the conjoint binding and calling up of widely distributed informations. Knowledge about the distribution of correlated networks can transform the existing networks into new one. Then, hippocampus consolidats new formed network. Amygdala may enable the emotions to influence the information processing and memory as well as providing the visceral informations to them. Cortico-striatal-pallido-thalamo-cortical loop also play an important role in memory function with analysis of language and concept. In case of difficulty in processing in spite of parallel process of informations, frontal lobe organizes theses complicated informations of network analysis through temporal processing. With understanding of brain mechanism of memory and information processing, the brain mechanism of mental phenomena including psychopathology can be better explained in terms of neurobiology and meuropsychology.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.315-320
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• 1997

Rule minimization technique adapted from rough set theory was applied to remove redundant knowledge which is not necessary to make a knowledge base. New algorithm to diagnose fault using Improved Fuzzy Cognitive Maps(I-FCMs), and Fuzzy Associative Memory(FAM) is proposed. I-FCM[22] is superior to gathering knowledge from many experts and descries dynamic behaviors of systems very well. I-FCM is not only a knowledge base, but also a inference engine. FAM has learning capability like neural network[12]. Rule minimization and composition of I-FCM and FAM make it possible to construct compact knowledge base and breaks the border between inference engine and knowledge base.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.747-749
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• 1998

본 논문에서는 음성과 자연언어의 통합처리를 위한 효과적인 병렬 계산 모델을 제안한다. 음소모델은 continuous HMM에 기반을 둔 문맥종속형 음소를 사용하며, 언어모델은 knowledge-based approach를 사용한다. 또한 계층구조의 지식베이스상에서 다수의 가설을 처리하기 위해 memory-based parsing기술을 사용하였다. 본 연구의 병렬 음성인식 알고리즘은 분산메모리 MIMD 구조의 다중 Transputer 시스템을 이용하여 구현되었다. 실험을 통하여 음성인식 과정에서 발생하는 speech-specific problem의 해를 제공하고 음성인식 시스템의 병렬화를 통하여 실시간 음성인식의 가능성을 보여준다.

• Journal of Distribution Science
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• v.18 no.2
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• pp.49-58
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• 2020

Purpose: This study focuses on the performance of the strategy of Indonesia's companies in facing the development of e-commerce business. The relationship between Knowledge Management (Organizational Memory, Knowledge Sharing, Knowledge Absorption, Knowledge Acceptance), Organizational Innovativeness, Competitive Advantage (Time, Quality, Cost, Flexibility) and E-Commerce (Humanistic Factors: Management, Competence, Organizational Structures) examined in this case study. Research design, data, and methodology: This study uses two types such us qualitative and quantitative. A survey approach were conducted to collect data from the Group of Companies (Director and Manager), Academician (Lecturer), Regulator (Head of Government Institution Division), and Master of Management (at least five years). Total of 114 samples was collected and processed for statistical analysis using Smart PLS. Results: This study provide the findings proved that Knowledge Management and Organization Innovativeness simultaneously have positive influence on Competitive Advantage, while Knowledge Management, Organization Innovativeness, and Competitive Advantage simultaneously have positive influence on E-commerce where Competitive Advantage positively influence to E-commerce. Conclusions: The implementation of strategies or steps in this study are expected to steer and motivate an organization to successfully implement a good knowledge management system to pass on knowledge from generation to generation in the company Organizational Innovativeness strategies to improve e-commerce performance.

• The Journal of Korean Association of Computer Education
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• v.15 no.3
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• pp.37-47
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• 2012

One of the major goals in high school Informatics is for students to develop creative problem-solving abilities based on knowledge on computer science. Thus, the contents of the textbooks should be accurate and appropriate. However, we discovered that the current Informatics textbooks contain the untrue and/or inappropriate descriptions of main memory and virtual memory. The textbooks describe that main memory is composed of RAM and ROM. The virtual memory is described as a technique in which a part of the secondary storage is utilized as main memory to execute an application of which size is larger than that of main memory. In this study, we attempted to uncover the root causes of the fallacies, and suggest the accurate explanations by comparing with renowned books adopted in most schools worldwide including USA. Our study reveals that it is inappropriate to include ROM in main memory from the memory hierarchy perspective. Virtual memory is a technique that provides convenience to programmers, through which an operating system loads the necessary portion of a program from secondary storage to main memory. As for the advantages of virtual memory in the current computer systems, the focus should be on providing the effective multitasking capability, rather than on executing a larger program than the size of main memory. We suggest that it is appropriate to exclude virtual memory in textbooks considering its complexity.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1598-1605
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• 2021

Recently, convolutional neural networks (CNNs) have been widely used with excellent performance in various computer vision fields, including super-resolution (SR). However, CNN is computationally intensive and requires a lot of memory, making it difficult to apply to limited hardware resources such as mobile or Internet of Things devices. To solve these limitations, network lightening studies have been actively conducted to reduce the depth or size of pre-trained deep CNN models while maintaining their performance as much as possible. This paper aims to lighten the SR CNN model, SRGAN, using the knowledge distillation among network lightening technologies; thus, it proposes four techniques with different methods of transferring the knowledge of the teacher network to the student network and presents experiments to compare and analyze the performance of each technique. In our experimental results, it was confirmed through quantitative and qualitative evaluation indicators that student networks with knowledge transfer performed better than those without knowledge transfer, and among the four knowledge transfer techniques, the technique of conducting adversarial learning after transferring knowledge from the teacher generator to the student generator showed the best performance.