• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.1090-1093
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• 2002

Location management schemes dealing with location information of moving terminals play an important role in the personal communications systems (PCS). Since the location management involves heavy signaling traffics to update the location information, reducing the location update cost becomes a critical research issue. This paper proposes a location management scheme which reduces the location update cost by employing the hierarchical structure in PCS environment. The paper also develops analytical models to evaluate the performance of the proposed scheme. The results obtained from the performance evaluation shows that the proposed scheme outperforms the conventional schemes in terms of the location update rates. Also, the difference in the performance becomes larger as the size of the location area (LA) becomes smaller and as the residual time of the mobile user becomes smaller.

• ETRI Journal
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• 제28권6호
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• pp.815-818
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• 2006

In the global Internet, a constraint-based routing algorithm performs the function of selecting a routing path while satisfying some given constraints rather than selecting the shortest path based on physical topology. It is necessary for constraint-based routing to disseminate and update link state information. The triggering policy of link state updates significantly affects the volume of update traffic and the quality of services (QoS). In this letter, we propose an adaptive triggering policy based on link-usage statistics in order to reduce the volume of link state update traffic without deterioration of QoS. Also, we evaluate the performance of the proposed policy via simulations.

• 한국정보통신학회논문지
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• 제24권9호
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• pp.1117-1122
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• 2020

모바일 기기의 성능 향상과 활용 분야가 점점 커지고 넓어지고 있다. 이러한 추세에 따라 모바일 기기에서 데이터베이스 엔진을 사용하는 응용 분야도 보편화되고 있다. 모바일 데이터베이스를 필요로 하는 응용은 모바일 서버용 데이터베이스, 에지 컴퓨팅, 포그 컴퓨팅 등이 있다. 그런데, 가장 대표적이고 널리 사용되는 모바일 데이터베이스는 SQLite3이다. 이 논문에서는 이 SQLite3의 파일 시스템 별 갱신 성능을 테스트하고 비교 평가하고자 한다. 모바일 환경에서 파일 시스템에 따른 갱신 성능은 제한된 H/W 환경에서 중요한 성능 요인으로 작용한다. 비교 파일 시스템은 가장 보편적으로 사용되는 FAT, Ext2, 그리고 NTFS로 선정하였다. 동일한 조건에서 각 파일 시스템들의 갱신 성능 및 특성을 테스트하기 위한 실험을 진행하였다. 실험 결과로부터 각 데이터베이스 갱신 패턴에 따른 파일 시스템 별 장단점을 분석할 수 있었다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.301-308
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• 1997

The learning speed of the neural networks, the most important factor in applying to real problems, greatly depends on the learning rate of the networks, Three approaches-empirical, deterministic, and stochastic ones-have been proposed to date. We proposed a new learning-rate selection algorithm using an evolutionary programming search scheme. Even though the performance of our method showed better than those of the other methods, it was found that taking much time for selecting evolutionary learning rates made the performance of our method degrade. This was caused by using static intervals (called static windows) in order to update learning rates. Out algorithm with static windows updated the learning rates showed good performance or didn't update the learning rates even though previously updated learning rates shoved bad performance. This paper introduce a window control scheme to avoid such problems. With the window control scheme, our algorithm try to update the learning ra es only when the learning performance is continuously bad during a specified interval. If previously selected learning rates show good performance, new algorithm will not update the learning rates. This diminish the updating time of learning rates greatly. As a result, our algorithm with the window control scheme show better performance than that with static windows. In this paper, we will describe the previous and new algorithm and experimental results.

• 한국멀티미디어학회논문지
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• 제24권6호
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• pp.789-795
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• 2021

In order to provide useful Internet of Things (IoT) services, the locations of IoT devices should be well managed. However, frequent location updates of lots of IoT devices result in signaling overhead in networks. To solve this problem, this paper utilizes the opportunistic fog node (OFN) which is opportunistically available according to the mobility to perform the location updates as a representative of IoT devices. Therefore, the location updates through OFN can reduce the signaling loads of networks. To show the performance of the proposed scheme, we develop an analytic model for the opportunistic location update offloading probability that the location update can be offloaded to OFN from the IoT device. Then, the extensive simulation results are given to validate the analytic model and to assess the performance of the proposed scheme in terms of the opportunistic location update offloading probability.

• 한국멀티미디어학회논문지
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• 제21권4호
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• pp.441-449
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• 2018

Deep learning has been used for various processing centered on image recognition. One core algorithms of the deep learning, convolutional neural network is an deep neural network that specialized in image recognition. In this paper, we use a convolutional neural network to classify forest insects and propose an optimization method. Experiments were carried out by combining two weight initialization and six parameter update methods. As a result, the Xavier-SGD method showed the highest performance with an accuracy of 82.53% in the 12 different combinations of experiments. Through this, the latest learning algorithms, which complement the disadvantages of the previous parameter update method, we conclude that it can not lead to higher performance than existing methods in all application environments.

• International Journal of Internet, Broadcasting and Communication
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• 제8권3호
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• pp.12-24
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• 2016

Recently, as the price per bit is decreasing at a fast rate, flash memory is considered to be used as primary storage of large-scale database systems. Although flash memory shows off its high speeds of page reads, however, it has a problem of noticeable performance degradation in the presence of increasing update workloads. When updates are requested for pages with random page IDs, in particular, the shortcoming of flash tends to impair significantly the overall performance of a flash-based database system. Therefore, it is important to have a way to efficiently update the B+-tree, when it is stored in flash storage. This is because most of updates in the B+-tree arise at leaf nodes, whose page IDs are in random. In this light, we propose a new flash B+-tree that stores up-to-date versions of leaf nodes in sibling-leaf blocks (SLBs), while updating them. The use of SLBs improves the update performance of B-trees and provides the mechanism for fast key range searches. To verify the performance advantages of the proposed flash B+-tree, we developed a mathematical performance evaluation model that is suited for assessing B-tree operations. The performance comparisons from it show that the proposed flash B+-tree provides faster range searches and reduces more than 50% of update costs.

• 한국컴퓨터정보학회논문지
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• 제17권6호
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• pp.75-81
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• 2012

본 논문에서는 QoS 라우팅 알고리즘의 성능을 유지하면서 LSU(Link State Update) 메시지의 수를 제어할 수 있는 QoS 라우팅 링크 상태 갱신 알고리즘을 제안하였다. 기존에 제시된 대부분의 LSU 알고리즘은 QoS 라우팅의 성능을 향상시키는데 중점을 두고 있기 때문에 LSU 메시지의 수가 늘어나더라도 제어할 수 있는 메커니즘을 가지고 있지 않다. 특히 트래픽 통계에 근거한 적응형 알고리즘의 경우 더욱더 그러하며 트래픽이 과도하거나 변화가 심할 경우 이와 비례해서 LSU 메시지 수도 증가하여 과도한 LSU 메시지가 성능을 오히려 좋지 않게 한다. 제시하는 알고리즘은 QoS 라우팅 성능과 상충관계에 있는 과도한 LSU 메시지의 수를 제어하기 위해 요구 대역폭이 가용대역폭에 미치는 영향에 따라 LSU 메시지의 중요도를 구분하고 중요도와 단위시간 당 업데이트 비율 ${\gamma}$에 따라 LSU 메시지의 전송 여부를 결정하여 LSU 메시지 수를 제어한다. 성능 평가를 위해 기존에 제시된 다양한 LSU 알고리즘과 본 논문에서 제안하는 알고리즘을 MCI 네트워크상에서 라우팅 Blocking 확률과 링크 당 평균 LSU 메시지의 개수 등을 성능 평가 항목으로 하여 시뮬레이션을 수행하였고 제안하는 알고리즘의 우수성을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.633-638
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• 1988

In this paper, a zero-velocity update technique to improve navigation accuracy of a SDINS(Strapdown Inertial Navigation System) has been studied. An indirect feedback Kalman filter which includes SDINS error equations based on a quaternion between body-fixed frame and local level navigation frame is employed for processing zero-velocity updates in an on-board navigation filter. Simulation results for land-mobile vehicle show that the zerovelocity update technique make a significant contribution to improving SDINS performance without any external aids.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권4호
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• pp.1442-1462
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• 2014

Packet classification is a key technology of the Internet for routers to classify the arriving packets into different flows according to the predefined rulesets. Previous packet classification algorithms have mainly focused on search speed and memory usage, while overlooking update performance. In this paper, we propose PreCuts, which can drastically improve the update speed. According to the characteristics of IP field, we implement three heuristics to build a 3-layer decision tree. In the first layer, we group the rules with the same highest byte of source and destination IP addresses. For the second layer, we cluster the rules which share the same IP prefix length. Finally, we use the heuristic of information entropy-based bit partition to choose some specific bits of IP prefix to split the ruleset into subsets. The heuristics of PreCuts will not introduce rule duplication and incremental update will not reduce the time and space performance. Using ClassBench, it is shown that compared with BRPS and EffiCuts, the proposed algorithm not only improves the time and space performance, but also greatly increases the update speed.