• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.2
• /
• pp.542-558
• /
• 2016

Vehicular Ad Hoc Networks (VANETs) are self-configuring networks where the nodes are vehicles equipped with wireless communication technologies. In such networks, limitation of signal coverage and fast topology changes impose difficulties to the proper functioning of the routing protocols. Traditional Mobile Ad Hoc Networks (MANET) routing protocols lose their performance, when communicating between vehicles, compromising information exchange. Obviously, most applications critically rely on routing protocols. Thus, in this work, we propose a methodology for investigating the performance of well-established protocols for MANETs in the VANET arena and, at the same time, we introduce a routing protocol, called Genetic Network Protocol (G-NET). It is based in part on Dynamic Source Routing Protocol (DSR) and on the use of Genetic Algorithms (GAs) for maintenance and route optimization. As G-NET update routes periodically, this work investigates its performance compared to DSR and Ad Hoc on demand Distance Vector (AODV). For more realistic simulation of vehicle movement in urban environments, an analysis was performed by using the VanetMobiSim mobility generator and the Network Simulator (NS-3). Experiments were conducted with different number of vehicles and the results show that, despite the increased routing overhead with respect to DSR, G-NET is better than AODV and provides comparable data delivery rate to the other protocols in the analyzed scenarios.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.10C
• /
• pp.929-936
• /
• 2009

In this paper, we consider designing a multi-input multi-output (MIMO) overlay system for fixed MIMO wireless link, where a frequency flat narrowband channel is shared by multiple transmitter and receiver pairs. Assuming the perfect knowledge of the second-order statistics of the received legacy signals and the composite channels from the overlay transmitter to the legacy receivers, the jointly optimal linear precoder and decoder matrices of the MIMO overlay system is derived to minimize the total mean squared error (MSE) of the data symbol vector, subject to total average transmission power and zero interference induced to legacy MIMO systems already existing in the frequency band of interest. Furthermore, the necessary and sufficient condition for the existence of the optimal solution is also derived.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.6
• /
• pp.755-760
• /
• 2010

In general, Lagrangian method for discrete optimization is a kind of technique to easily manage constraints. It is traditionally used for finding upper bounds in the branch-and-bound method. In this paper, we propose a new Lagrangian search method for the 0-1 knapsack problem with multiple constraints. A novel feature of the proposed method different from existing Lagrangian approaches is that it can find high-quality lower bounds, i.e., feasible solutions, efficiently based on a new property of Lagrangian vector. We show the performance improvement of the proposed Lagrangian method over existing ones through experiments on well-known large scale benchmark data.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.23 no.11
• /
• pp.754-761
• /
• 2011

The present study numerically investigates two-dimensional flow and heat transfer in the multiple confined impinging slot jet. Numerical simulations are performed for the different Reynolds numbers(Re=100 and 200) in the range of nozzles from 1 to 9 and height ratios(H/D) from 2 to 5, where H/D is the ratio of the channel height to the slot width. The vector plots of velocity profile, stagnation and averaged Nusselt number distributions are presented in this paper. The dependency of thermal fields on the Reynolds number, nozzle number and height ratio can be clarified by observing the Nusselt number as heat transfer characteristic at the stagnation point and impingement surface. The Nusselt number at the stagnation point of the central slot shows unsteadiness at H/D=3 and Re=200. The value of Nusselt number at the stagnation point of the central slot decreases with higher Reynolds number and number of nozzle although overall area averaged Nusselt number increases. Hence careful selection of geometrical parameters and number of nozzle are necessary for optimization of the heat transfer performance of multiple slot impinging jet.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2014.07a
• /
• pp.1-4
• /
• 2014

인텔 Many Integrated Core (MIC) 아키텍쳐는 61개의 코어가 하나의 칩에 결합되어 있다. Xeon Phi 로 명명된 인텔 MIC는 인텔 E5 Xeon CPU 보다 2배의 single precision GFLOPs 성능을 제공한다. 인텔 MIC 는 수치연산에 최적화 되어 있는 아키텍쳐이다. 우리는 Xeon Phi 7120P를 가지고 벤치마킹을 하였고 클락스피드 1.238GHz, 61Core 이고 한 개의 코어당 4쓰레드를 사용하며 이론상 최고 성능은 Peak Double Precision(GFLOP)는 약 2-TFlops 이다. 이에 우리는 인텔 X86 아키텍쳐에서 openMP 와 인텔 MKL(Math kernel library) 라이브러리를 이용한 병렬프로그램을 작성하여 쓰레드 수를 증가 시키면서 인텔 Xeon Phi 와 E5 Xeon CPU에서 single precision 성능을 벤치마킹 하여, Xeon Phi 와 Xeon E5 의 이론적인 성능을 비교해 보고자 한다. 또한 openMP와 인텔 MKL라이브러리를 사용한 병렬환경에서 CPU의 성능 지표인 클락스피드와 코어수 외에 Vector unit size 의 크기가 성능에 어떤 영향을 미치는지 살펴보았다.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.39 no.9
• /
• pp.37-44
• /
• 2002

This paper proposes efficient digital hardware design method by using fixed weight of pre-trained neural network. For this, arithmetic operations of PEs(Processing Elements) are represented with matrix-vector multiplication. The relationship of fixed weight and input data present bit-level array structure architecture which is consisted operation node. To minimize the operation node, this paper proposes node elimination method and setting common node depend on bit pattern of weight. The result of FPGA simulation shows the efficiency on hardware cost and operation speed with full precision. And proposed design method makes possibility that many PEs are implemented to on-chip.

• Journal of Information Processing Systems
• /
• v.8 no.4
• /
• pp.621-652
• /
• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• Journal of the Korean Society of Mechanical Technology
• /
• v.20 no.6
• /
• pp.809-817
• /
• 2018

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.3
• /
• pp.285-291
• /
• 2022

The number of people enrolling in universities is rising due to the simplicity of applying and the benefit of earning a bachelor's degree. However, the on-time graduation rate has declined since plenty of students fail to complete their courses and take longer to get their diplomas. Even though there are various reasons leading to the aforementioned problem, it is crucial to emphasize the cause originating from the management and care of learners. In fact, understanding students' difficult situations and offering timely Number of Test data and advice would help prevent college dropouts or graduate delays. In this study, we present a machine learning-based method for early detection at-risk students, using data obtained from graduates of the Faculty of Information Technology, Dainam University, Vietnam. We experiment with several fundamental machine learning methods before implementing the parameter optimization techniques. In comparison to the other strategies, Random Forest and Grid Search (RF&GS) and Random Forest and Random Search (RF&RS) provided more accurate predictions for identifying at-risk students.

• Journal of Korean Association for Spatial Structures
• /
• v.23 no.3
• /
• pp.87-94
• /
• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.