• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.3
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• pp.63-72
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• 1999

Recent studies on the Constant Bit Rate and Variable Bit Rate transmissions have mainly focused on the frame by frame encoder rate control based on the quantization parameter. With the existing approaches it is difficult to guarantee a consistent video quality. Also, the rate control overhead is too high for the real-time video sources. In this paper, a channel rate allocation scheme based on the control period is proposed to transmit a real-time video, in which the control period is defined by a pre-specified number of frames or group of pictures. At each control period, video traffic information is collected to determine the channel rate at the next control period. The channel rate is allocated to satisfy various channel rate constraints such that the buffer occupancy at the decoder is maintained at a target level. If the allocated channel rate approaches the level at which the negotiated traffic descriptions may be violated, the encoder rate is decreased through adjusting quantization parameters in the MPEG encoder. In the experimental results, the video quality and the overflow and underflow probabilities at the buffer are compared at different control periods. Experiments show that the video quality and the utilization of network bandwidth resources can be optimized through the suitable selection of the control period.

• International Journal of Contents
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• v.9 no.4
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• pp.22-29
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• 2013

In this paper, we propose a integrated real-time monitoring system for recycling agriculture resourcing based on USN. We design and implement the monitoring system so that we can integrate the quality control of farmyard and liquid manures, barn environment monitoring, and positioning information control into a total management system performing recycling of excrement and manure. Selection of sensors and sensor-node construction and requirements, structure of wire/wireless communication networks, and design of monitoring program are also presented. As a result of operating our system, we can get over various drawbacks of conventional separated system and promote the proper circulation of excrement up to the farmyard. We confirm that these advanced effects arise from the effective management of the total system integrating quality control of farmyard/liquid manure, barn/farmhouse information, and vehicle moving monitoring information etc. Moreover, this monitoring system is able to exchange real-time information throughout communication networks so that we can construct a convenient information environment for agricultural community by converging IT technology with farm and stockbreeding industries. Finally we present some results of processing using our monitoring system. Sensing data and their graphs are processed in real-time, positioning information on the v-world map offers various moving paths of vehicles, and statistical analysis shows all the procedure from excrement occurrence to recycling and resourcing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2849-2865
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• 2012

In order to block mobile junk messages automatically, many studies on spam filters have applied machine learning algorithms. Most previous research focused only on the accuracy rate of spam filters from the view point of the algorithm used, not on individual user's preferences. In terms of individual taste, the spam filters implemented on a mobile device have the advantage over spam filters on a network node, because it deals with only incoming messages on the users' phone and generates no additional traffic during the filtering process. However, a spam filter on a mobile phone has to consider the consumption of resources, because energy, memory and computing ability are limited. Moreover, as time passes an increasing number of feature words are likely to exhaust mobile resources. In this paper we propose a spam filter model distributed between a users' computer and smart phone. We expect the model to follow personal decision boundaries and use the uniform resources of smart phones. An authorized user's computer takes on the more complex and time consuming jobs, such as feature selection and training, while the smart phone performs only the minimum amount of work for filtering and utilizes the results of the information calculated on the desktop. Our experiments show that the accuracy of our method is more than 95% with Na$\ddot{i}$ve Bayes and Support Vector Machine, and our model that uses uniform memory does not affect other applications that run on the smart phone.

• Journal of KIISE:Information Networking
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• v.35 no.2
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• pp.149-157
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• 2008

Since wireless sensor networks consist of nodes with the constrained battery, energy efficient MAC operation is one of the important issues. Low duty cycle operation is critical to conserve energy in wireless sensor network MAC protocol. Some paper proposed a new approach to low power listening, which employs a short preamble to further reduce energy consumption and to reduce latency. But short preamble suffers from unfair channel access problem since there was no consideration for contention between transmission nodes. Preamble counting proposes a solution to each of these problems by employing node selection information. Simulation results show that the preamble counting provides an improved energy efficiency and fairness of packet delivery.

• Computers and Concrete
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• v.12 no.2
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• pp.151-167
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• 2013

Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.

• Environmental Engineering Research
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• v.22 no.3
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.407-410
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• 1998

Configuration is a set of building block processes, a series of selection and combining parts or components which composes a whole thing. A whole thing could be such a configurable object as manufacturing product, network system, financial portfolio, system development plan, project team, etc. Configuration problem could happen during any phase of product life cycle: design, production, sales, installation, and maintenance. Configuration has long been one of cost and time consuming work, because only high salaried technical experts on product and components can do configuration. Rework for error adjustments of configurations at later process causes far much cost and time, so accurate configuration is required. Under the on-line electronic commerce environment, configuration problem solving becomes more important, because component-based sales should be done automatically on the merchant web site. Automated product search, order placement, order fulfillment and payment make that manual configuration is no longer feasible. Automated configuration means that all the constraints among components should be checked and confirmed by configuration engine automatically. In addition, technical constraints and customer preferences like price range and a specific function required should be considered. This paper gives an brief overview of configuration problems: characteristics, representation paradigms, and solving algorithms and introduce CRSP(Constraint and Rule Satisfaction Problem) method. CRSP method adopts both constraint and rule for configuration domain knowledge representation. A survey and analysis on web sites adopting configuration functions are provided. Future directions of configuration for EC is discussed in the three aspects: methodology itself, companies adopting configuration function, and electronic commerce industry.

• Journal of Advanced Navigation Technology
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• v.13 no.1
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• pp.97-103
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• 2009

In this paper, the structure and detailed specifications of the HDR-WPAN physical layer have been analyzed and the block module of transmitter and receiver have been also designed, and analyzed the performance as well. In the process of transmitter design, it concentrated on all possibility of modulation of QPSK, DQPSK and 16/32/64QAM-TCM, which could be available for mode selection due to the transmission rate. In addition to the receiver module, DQPSK and TCM decoding algorithm is mainly concerned. After designing the transceiver MODEM using VHDL, we have programmed on the platform board and verified the functions of the MODEM. Some experimental results showed that it can be considered a possibility of data communication without error over SNR 22dB.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.93-105
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• 2006

The domination of the Internet by TCP-based services has spawned many efforts to provide high network utilization with low loss and delay in a simple and scalable manner. Active queue management (AQM) algorithms attempt to achieve these goals by regulating queues at bottleneck links to provide useful feedback to TCP sources. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of nonintuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm, hybrid random early detection (HRED), that combines the more effective elements of recent algorithms with a random early detection (RED) core. HRED maps instantaneous queue length to a drop probability, automatically adjusting the slope and intercept of the mapping function to account for changes in traffic load and to keep queue length within the desired operating range. We demonstrate that straightforward selection of HRED parameters results in stable operation under steady load and rapid adaptation to changes in load. Simulation and implementation tests confirm this stability, and indicate that overall performances of HRED are substantially better than those of earlier AQM algorithms. Finally, HRED control parameters provide several intuitive approaches to trading between required memory, queue stability, and response time.