• Annual Conference of KIPS
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• 2009.11a
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• pp.127-128
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• 2009

위치기반 라우팅 프로토콜에서의 기본 가정인 이웃 노드들과의 주기적인 위치정보 교환은 노드의 이동성이 있는 경우, 그 성공률을 보장하기가 어렵다. GPSR[1]에서 제안하고 있는 1-홉 거리에 있는 노드들의 정보를 테이블로 관리하는 방식은 데이터 전달이 없는 지역에서도 주기적인 비콘의 교환으로 불필요한 에너지 소모가 있으며, 노드들의 위치가 변경되었을 때, 비콘 (beacon)을 수신한 시점의 위치정보와 데이터 전달시점의 위치정보의 불일치 가능성을 배제할 수 없다. 따라서, 본 논문에서는 노드의 이동성이 빈번한 환경의 위치기반 라우팅 프로토콜에서 데이터전달시점에 이웃 노드들의 정보를 바탕으로 다음 전송노드를 선택하는 방법을 제안한다.

• Annual Conference of KIPS
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• 2007.11a
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• pp.1069-1072
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• 2007

MANET에서의 전통적인 라우팅 프로토콜은 일반적으로 두 종류로 나눠지는데 proactive 라우팅 프로토콜과 on-demand 라우팅 프로토콜이다. 라우팅 정보의 proactive 전파와 reactive 발견 사이에는 필수적인 trade-off 가 존재한다. 모든 시나리오들을 충족하는 라우팅 프로토콜이 존재하지 않는 것은 분명하다. 그래서 최적의 라우팅 프로토콜을 발견하기 위한 연구가 계속되고 있으며 IETF의 MANET 워킹그룹은 여러 다른 프로토콜을 제안 중에 있고 그 중에 하나가 on-demand 라우팅 프로토콜의 AODV를 계승한 DYMO(Dynamic MANET On-demand)이다. DYMO는 경로 축적 메커니즘을 채택하였다. 경로 발견 과정에서 노드들은 라우팅 메시지를 포워딩하기 전에 그들 자신의 라우팅 정보도 패킷에 추가할 수 있다. 결국 소스와 목적지 사이의 모든 노드들의 라우팅 정보가 교환되는 것이다. 이 논문에서는 이 경로 축적 메커니즘의 사용 유무에 따라 다양한 속도와 트래픽 부하에서 DYMO의 성능이 어떻게 변하고 최적의 환경은 무엇인지 연구해보고자 한다.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.147-152
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• 2023

This paper is a study of a linear algebra course taught in a high school on-demand course. Compared to the regular course, flipped learning was added to the course, and applications to signal processing related problems were covered in consideration of students' career aspirations. Overall, the class was a mixture of traditional lectures and flipped learning. Flipped learning was implemented twice. The flipped class consisted of pre-class, in-class and post-class. To verify the effectiveness of the course, a survey was conducted and most of the evaluation items were above 4. The topics of the flipped learning were Markov chains and least squares problem, which are very important in the field of signal processing.

• Proceedings of the KSR Conference
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• 2001.10a
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• pp.111-116
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• 2001

This paper introduces how to adopt the X-12-ARIMA to decompose the railway passenger demand of the Korea National Railroad Especially, selecting on proper filters is focused. The trend filter is identical to the low pass filter in the signal Processing field, and so the seasonal filter is to band pass filter too. Some considerations, selecting a filter, are provided from the view-point of the spectrum analysis. The technique introduced in this paper will be adopted to the project that is to develope the forecasting system of Korea railway passenger demand which is a part of the high speed rail information system.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.191-202
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• 2021

In an Internet of Things (IoT)-configured system, each device executes on-chip software. Recent IoT devices require fast execution time of complex services, such as analyzing a large amount of data, while maintaining low-power computation. As service complexity increases, the service requires high-performance computing and more space for embedded space. However, the low performance of IoT edge devices and their small memory size can hinder the complex and diverse operations of IoT services. In this paper, we propose a remote on-demand software code execution unit using the cloudification of on-chip code memory to accelerate the program execution of an IoT edge device with a low-performance processor. We propose a simulation approach to distribute remote code executed on the server side and on the edge side according to the program's computational and communicational needs. Our on-demand remote code execution unit simulation platform, which includes an instruction set simulator based on 16-bit ARM Thumb instruction set architecture, successfully emulates the architectural behavior of on-chip flash memory, enabling embedded devices to accelerate and execute software using remote execution code in the IoT environment.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1758-1761
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• 1997

The problem of long range capacity expansion planing for chemical processing network under uncertain demand forecast secnarios is addressed. This optimization problem involves capactiy expansion timing and sizing of each chemical processing unit to maximize the expected net present value considering the deviation of net present values and the excess capacity over a given time horizon. A multiperiod mixed integer nonlinear programming optimization model that is both solution and modle robust for any realization of demand scenarios is developed using the two-stage stochastic programming algorithm. Two example problems are considered to illustrate the effectiveness of the model.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.363-370
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• 2010

Load forecasting has always been essential to the operation and planning of power systems in deregulated electricity markets. Various methods have been proposed for load forecasting, and the neural network is one of the most widely accepted and used techniques. However, to obtain more accurate results, more information is needed as input variables, resulting in huge computational costs in the learning process. In this paper, to reduce training time in multi-layer perceptron-based short-term load forecasting, a graphics processing unit (GPU)-based computing method is introduced. The proposed approach is tested using the Korea electricity market historical demand data set. Results show that GPU-based computing greatly reduces computational costs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8B
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• pp.730-738
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• 2004

In this paper, System performance parameters such as throughput, blocking probability and delay have been analyzed and expressed as a function of demanding traffic and service termination, probability, and we centers our discussion at particular downlink port of satellite switch which is capable of switching the individual spot beam and processing the information signals in the packet satellite communications with demand assigned multiple access technique. Delay versus throughput as a function of traffic parameters with several service termination probability can be derived via mathematical formulation and the relative differences of transmission delay is also compared.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.537-544
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• 2004

An effective methodology is reported for determining the optimal lot size of batch processing and storage networks which include uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, infernally consumed, transported to or from other sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sires while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of the global supply chain.