• Journal of Software Engineering Society
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• v.24 no.1
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• pp.27-33
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• 2011

The importance of the low-power management has increased due to the recent advances of the embedded systems. However, it is noted that low-power concerns are detrimental to the readability and the maintainability of the codes for the core concerns. In this study, in order to reduce occurring leakage power during run-time, we present a power management while considering the run-time leakage power of devices. the power management codes is separated from the core concern codes by applying aspect-oriented programming. Finally, we analyze the theoretical model of our proposed scheme and present experimental results.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1000-1010
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• 2019

Image semantic segmentation and dehazing are key tasks in the computer vision. In recent years, researches in both tasks have achieved substantial improvements in performance with the development of Convolutional Neural Network (CNN). However, most of the previous works for semantic segmentation assume the images are captured in clear weather and show degraded performance under hazy images with low contrast and faded color. Meanwhile, dehazing aims to recover clear image given observed hazy image, which is an ill-posed problem and can be alleviated with additional information about the image. In this work, we propose a deep multi-task network for simultaneous semantic segmentation and dehazing. The proposed network takes single haze image as input and predicts dense semantic segmentation map and clear image. The visual information getting refined during the dehazing process can help the recognition task of semantic segmentation. On the other hand, semantic features obtained during the semantic segmentation process can provide cues for color priors for objects, which can help dehazing process. Experimental results demonstrate the effectiveness of the proposed multi-task approach, showing improved performance compared to the separate networks.

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.455-460
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• 2001

For some applications, the computational structure changes dynamically during the program execution. When this happens, static partitioning and allocation of tasks are not enough to achieve high performance in parallel computers. In this paper, we propose a dynamic load balancing algorithm efficiently distributes the computation with dynamically growing tree structure to processors. We present an implementation technique for the algorithm on mesh architectures, and analyze its complexity. We also demonstrate through experiments how our algorithm provides good quality solutions.

• The Journal of Society for e-Business Studies
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• v.12 no.4
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• pp.145-163
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• 2007

Service-Oriented Architecture is a style of information systems that enables the creation of applications that are built by combining loosely coupled and interoperable services. A service is an implementation of business functionality with proper granularity and invoked with well-defined interface. In service modeling, when the granularity of a service is finer, the reusability and flexibility of the service is lower. For solving this problem concerns with the service granularity, it is critical to identify and define coarse-grained services from the domain analysis model. In this paper, we define the process to identify services from the Use Case model elicited from domain analysis. A task tree is derived from Use Cases and their descriptions, and Use Cases are reconstructed by the composition and decomposition of the task tree. Reconstructed Use Cases are defined and specified as services. Because our method is based on the widely used UML Use Case models, it can be helpful to minimize time and cost for developing services in various platforms and domains.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.1
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• pp.53-60
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• 2014

IEEE 802.15.4 is one of the most widely adopted physical layer standards in the area of LR-WPAN(Low-Rate Wireless Personal Area Network). Numerous previous researches have contributed to deep insights on energy efficiency, transmission throughput, and reliability that IEEE 802.15.4 delivers to the LR-WPAN. As a research that is orthogonal and complementary to previous researches, we explore the implementation and practical performance evaluation of IEEE 802.15.4 MAC software. We implement the MAC software from the perspective of the networking stack, exploring the issues raised when the MAC software serves as a functional component in a complete networking stack consisting of MAC, network as well as well as application support layers. The performance is evaluated on a realistic experimental software environment integrated with operating system, networking stack, and applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.474-476
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• 2021

Glioblastoma is the most common brain malignancies arising from glial cells. Early diagnosis and treatment plan establishment are important, and cancer is diagnosed mainly through T1CE imaging through injection of a contrast agent. However, the risk of injection of gadolinium-based contrast agents is increasing recently. Region segmentation that marks cancer regions in medical images plays a key role in CAD systems, and deep neural network models for synthesizing new images are also being studied. In this study, we propose a model that simultaneously learns the generation of T1CE images and segmentation of cancer regions. The performance of the proposed model is evaluated using similarity measurements including mean square error and peak signal-to-noise ratio, and shows average result values of 21 and 39 dB.

• Journal of Internet Computing and Services
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• v.1 no.2
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• pp.69-88
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• 2000

One of the most important factors on the performance of the distributed systems is the effective distribution of the software components, There have been a lot of researches on partitioning and allocating the task-based system, while the studies on the allocating the objects of the object-oriented system into the distributed object environments are very little relatively. In this paper. we defines the graph model for partitioning the existing C++ application and allocating the C++ objects into CORBA-base distributed system, In addition, we propose a distributed object allocation algorithm based on this graph model. The performance of distributed systems is determined by the concurrency between objects, the load balance among the processors and the communication cost on the networks. To search for the solutions optimizing the above three factors simultaneously, the object allocation algorithm of this paper is based on the Niched Pareto Genetic Algorithm (NPGA). We performed the experiment on the typical C++ application and CORBA system to prove the effectiveness of our graph model and our object allocation algorithm.

• Journal of Korea Multimedia Society
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• v.5 no.3
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• pp.323-330
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• 2002

Recently, the existing embedded real-time operating systems(RTOSs) are being developed in terms of various modified versions in every application fields. Major characteristics and difference of these OSs lie in their distinct development of mechanisms which can be used in various environment and task-scheduling function which can control time-limited contingencies. In this paper, we design and implement round/robin scheduling algorithm based on time-sharing with equal-priority for multiple tasks which are provided preemptive and priority task allocation function in $\mu{C/OS-II}$ version 2.03. We propose the most important event-ready list structure in $\mu{C/OS-II}$; kernel, and provide the running result for multiple tasks with equal priority for the proposed structure.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.9
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• pp.385-394
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• 2007

In this paper, we present a workflow system which manages efficiently operations in workflow form. The workflow of this system is made up of concepts of meta-services for increasing reusability. Thus users can execute the workflow by calling their services. Also, GUI workflow editor is developed with the workflow system which provides users with convenience. Furthermore the workflow model is divided into 3-layers such as service, flow, and task layer for reusability and scalability of workflow. Therefore, this middleware can use grid resources effectively and offer convenience to users.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.397-400
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• 2003

본 연구에서는 KM-Net(Hidden Markov Network)을 다양한 태스크에의 적용과 화자의 특성을 효과적으로 나타내기 위해 HM-Net 음성인식 시스템에 MLLR(Maximum Likelihood Linear Regression) 적응방법을 도입하였으며, HM-Net 학습 알고리즘을 개량하여 회귀클래스 생성방법을 제안한다. 제안방법은 PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) 알고리즘의 문맥방향 상태분할에 의한 상태레벨 공유를 이용한 방법으로 새로운 화자로부터 문맥정보와 적응화 데이터의 발성 양에 의존하여 결정된 많은 적응 파라미터들을(평균, 분산) 자유롭게 제어할 수 있게 된다. 제안방법의 유효성을 확인하기 위해 국어공학센터(KLE) 452 음성 데이터와 항공편 예약관련 연속음성을 대상으로 인식실험을 수행한 결과, 전체적으로 음소인식의 경우 평균 34-37％, 단어인식의 경우 평균 9％, 연속음성인식의 경우 평균 7-8％의 인식성능 향상을 각각 보였다. 또한 적응화 데이터의 양에 따른 인식성능 비교에서, 제안방법을 적용한 인식 시스템이 적응 데이터의 양이 적은 경우에도 향상된 인식률을 보였으며. 잡음을 부가한 음성에 대한 적응화 실험에서도 향상된 인식성능을 보여 MLLR 적응방법의 특성을 만족하였다. 따라서 MLLR 적응방법을 도입한 HM-Net 음성인식 시스템에 제안한 회귀클래스 생성방법이 유효함을 확인한 수 있었다.