• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.193-214
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• 2017

In this paper, we study the problem of network utility maximization in a CSMA based multi-hop wireless network. Existing work in this aspect typically adopted continuous time Markov model for performance modelling, which fails to consider the channel conflict impact in actual CSMA networks. To maximize the utility of a CSMA based wireless network with channel conflict, in this paper, we first model its weighted network capacity (i.e., network capacity weighted by link queue length) and then propose a distributed link scheduling algorithm, called CSMA based Maximal-Weight Scheduling (C-MWS), to maximize the weighted network capacity. We derive the upper and lower bounds of network utility based on C-MWS. The derived bounds can help us to tune the C-MWS parameters for C-MWS to work in a distributed wireless network. Simulation results show that the joint optimization based on C-MWS can achieve near-optimal network utility when appropriate algorithm parameters are chosen and also show that the derived utility upper bound is very tight.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2015-2028
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• 2016

In this paper, we propose a sum transmission rate maximization based cooperative spectrum sharing protocol with quality-of-service (QoS) support for both of the primary and secondary systems, which exploits the situation when the primary system experiences a weak channel. The secondary transmitter ST_b which provides the best performance for the primary and secondary systems is selected to forward the primary signal. Specifically, ST_b helps the primary system achieve the target rate by using a fraction of its power to forward the primary signal. As a reward, it can gain spectrum access by using the remaining power to transmit its own signal. We study the secondary user selection and optimal power allocation such that the sum transmission rate of primary and secondary systems is maximized, while the QoS of both primary and secondary systems can be guaranteed. Simulation results demonstrate the efficiency of the proposed spectrum sharing protocol and its benefit to both primary and secondary systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.1926-1945
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• 2014

In this paper, we propose a distributed signal subspace interference alignment algorithm for single beam K-user ($3K{\geq}$) MIMO interference channel based on sum rate maximization and game theory. A framework of game theory is provided to study relationship between interference signal subspace and altruistic-egoistic bayesian game cost function. We demonstrate that the asymptotic interference alignment under proposed scheme can be realized through a numerical algorithm using local channel state information at transmitters and receivers. Simulation results show that the proposed scheme can achieve the total degrees of freedom that is equivalent to the Cadambe-Jafar interference alignment algorithms with perfect channel state information. Furthermore, proposed scheme can effectively minimize leakage interference in desired signal subspace at each receiver and obtain a moderate average sum rate performance compared with several existing interference alignment schemes.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.364-373
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• 2016

Disassembly is one of the important activities in treating with the product at the End of Life time (EOL). Disassembly is defined as a systematic technique in dividing the products into its constituent elements, segments, sub-assemblies, and other groups. We concern with a Fuzzy Disassembly Line Balancing Problem (FDLBP) with multiple objectives in this article that it needs to allocation of disassembly tasks to the ordered group of disassembly Work Stations. Tasks-processing times are fuzzy numbers with triangular membership functions. Four objectives are acquired that include: (1) Minimization of number of disassembly work stations; (2) Minimization of sum of idle time periods from all work stations by ensuring from similar idle time at any work-station; (3) Maximization of preference in removal the hazardous parts at the shortest possible time; and (4) Maximization of preference in removal the high-demand parts before low-demand parts. This suggested model was initially solved by GAMS software and then using Genetic Algorithm (GA) in MATLAB software. This model has been utilized to balance automotive engine disassembly line in fuzzy environment. The fuzzy results derived from two software programs have been compared by ranking technique using mean and fuzzy dispersion with each other. The result of this comparison shows that genetic algorithm and solving it by MATLAB may be assumed as an efficient solution and effective algorithm to solve FDLBP in terms of quality of solution and determination of optimal sequence.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.800-811
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• 2018

This paper presents a regenerative braking control scheme for Switched Reluctance Machine (SRM) drive in Electric Vehicles (EVs). The main purpose is to maximize the recovered energy during battery charging by taking into account the nonlinear physical characteristics of the Switched Reluctance Machine. The proposed regenerative braking method employs the back-EMF in the generation process as a complicated position-dependent voltage source. The proposed maximum power recovery (MPR) operation of the regenerative braking is first based on the maximization of the extracted power from the machine and then the maximization of the power transferred to the battery. The maximum power extraction (MPE) from SRM is based on maximizing the energy conversion ratio by the calculation of the optimum PWM switching duty cycle, turn-on, and turn-off angles. By using the impedance matching theorem that allows the maximum power transfer (MPT) of the MPE, the proposed MPR is achieved. The parametric averaged value modeling of the machine phase currents in the chopping control mode is used for MPR realization. By following this model, a nonlinear equivalent input resistance is derived for the battery internal resistance matching. The effectiveness of the proposed regenerative braking method is demonstrated through simulation results and experimental implementation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.583-585
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• 2016

The recent advances in wireless networks area have led to new techniques, such as small cells or full-duplex (FD) transmission, have also been developed to further increase the network capacity. Particularly, full-duplex communication promises expected throughput gain by doubling the spectrum compared to half-duplex (HD) communication. Because this technique permits one set of frequencies to simultaneously transmit and receive signals. In this paper, we focus on the binary power control for the users and the base stations in full-duplex multiple cellulars wireless networks to obtain optimal sum-rate under the effect interference and noise. We investigate with a scenario in there one carrier is assigned to only one user in each cell and construct a model for this problem. In this work, we apply the binary power control by the its simplification in the implemented algorithm for both uplink and downlink simultaneously to maximize sum data rate of the system. At first, we realize the 2-cells case separately to check the optimal power allocation whether being binary. Then, we carry on with N-cells case in general through properties of binary power control.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.8
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• pp.556-561
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• 2011

In this study, the theoretical maximum power of a heat engine was investigated by sequential Carnot cycle model, for a low-grade heat source of about $100^{\circ}C$. In contrast to conventional approaches, the pattern search algorithm was employed to optimize the two design variables to maximize power. Variations of the maximum power and the optimum values of design variables were investigated for a wide range of UA(overall heat transfer conductance) change. The results show that maximizing heat source utilization does not always maximize power.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.202-208
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• 2015

In this study, we propose a new inference algorithm for a multiclass Gaussian process classification model using a variational EM framework and the Laplace approximation (LA) technique. This is performed in two steps, called expectation and maximization. First, in the expectation step (E-step), using Bayes' theorem and the LA technique, we derive the approximate posterior distribution of the latent function, indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. In the maximization step, we compute the maximum likelihood estimators for hyper-parameters of a covariance matrix necessary to define the prior distribution of the latent function by using the posterior distribution derived in the E-step. These steps iteratively repeat until a convergence condition is satisfied. Moreover, we conducted the experiments by using synthetic data and Iris data in order to verify the performance of the proposed algorithm. Experimental results reveal that the proposed algorithm shows good performance on these datasets.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.5
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• pp.237-243
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• 2014

In this paper, a calculation-efficient method for weight update in the algorithm based on maximization of the zero-error probability (MZEP) is proposed. This method is to utilize the current slope value in calculation of the next slope value, replacing the block processing that requires a summation operation in a sample time period. The simulation results shows that the proposed method yields the same performance as the original MZEP algorithm while significantly reducing the computational time and complexity with no need for a buffer for error samples. Also the proposed algorithm produces faster convergence speed than the algorithm that is based on the error-entropy minimization.