• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.273-288
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• 2010

In this paper, we propose a cooperative multi-relay scheme for a secondary system to achieve spectrum access along with a primary system. In the primary network, a primary transmitter (PT) transmits the primary signal to a primary receiver (PR). In the secondary network, N secondary transmitter-receiver pairs (ST-SR) selected by a centralized control unit (CCU) are ready to assist the primary network. In particular, in the first time slot, PT broadcasts the primary signal to PR, which is also received by STs and SRs. At STs, the primary signal is regenerated and linearly combined with the secondary signal by assigning fractions of the available power to the primary and secondary signals respectively. The combined signal is then broadcasted by STs in a predetermined order. In order to achieve diversity gain, STs, SRs and PT will combine received replicas of the primary signal, using selection combining technique (SC). We derive the exact outage probability for the primary network as well as the secondary network. The simulation results are presented to verify the theoretical analyses.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2042-2048
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• 2014

In medium voltage electrical distribution networks, reforming the loss reduction is important, and in line with this, the issue of system engineering and use of proper equipment Expansion of distribution systems results in higher system losses and poor voltage regulation. Therefore, an efficient and effective distribution system has become more important. So, proper selection of conductors in the distribution system is crucial as it determines the current density and the resistance of the line. Evaluation of aging conductors for losses and costs imposed in addition to the careful planning of technical and economic networks can be identified in the network design. In this paper the use of imperialist competitive algorithm; genetic algorithm; is proposed to optimal branch conductor selection and reconstruction in radial distribution systems planning. The objective is to minimize the overall cost of annual energy losses and depreciation on the cost of conductors to improve productivity given the maximum current carrying capacity and acceptable voltage levels. Simulations are carried out on 69-bus radial distribution network using genetic algorithm approaches to show the accuracy as well as the efficiency of the proposed solution technique.

• ETRI Journal
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• v.34 no.4
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• pp.602-612
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• 2012

It is becoming increasingly difficult to identify promising technologies due to the influx of new technologies and the high level of complexity involved in many of these technologies. Identifying promising information and communications technology (ICT)-based converging technologies holds the key to finding new sources of economic growth and forward momentum. The goal of this study is to identify cutting-edge ICT-based converging technologies by examining the latest trends in the US patent market. Analyzing the US patent market, the most competitive of such markets in the world, can yield certain clues about which of the ICT-based converging technologies may be the next revolutionary technologies. For a classification of these technologies, this study follows the International Patent Classification system. As for ICT, there are 58 related fields at the subclass level and 831 fields at the main-group level. For emerging and converging technologies, there are 75 at the main-group level. From these technologies, a final selection for cutting-edge ICT-based converging technologies is made using a composite index reflecting the converging coefficient, emerging coefficient, and technology impact index.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.633-635
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• 2016

Convolutional Networks (ConvNets) are powerful models that learn hierarchies of visual features, which could also be used to obtain image representations for transfer learning. The basic pipeline for transfer learning is to first train a ConvNet on a large dataset (source task) and then use feed-forward units activation of the trained ConvNet as image representation for smaller datasets (target task). Our key contribution is to demonstrate superior performance of multiple ConvNet layer features over single ConvNet layer features. Combining multiple ConvNet layer features will result in more complex feature space with some features being repetitive. This requires some form of feature selection. We use AdaBoost with single stumps to implicitly select only distinct features that are useful towards classification from concatenated ConvNet features. Experimental results show that using multiple ConvNet layer activation features instead of single ConvNet layer features consistently will produce superior performance. Improvements becomes significant as we increase the distance between source task and the target task.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.180-190
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• 2011

Efficient energy consumption is a critical factor for deployment and operation of wireless sensor networks (WSNs). To achieve energy efficiency there have been several hierarchical routing protocols that organize sensors into clusters where one sensor is a cluster-head to forward messages received from its cluster-member sensors to the base station of the WSN. In this paper, we propose a self-organized clustering method for cluster-head selection and cluster based routing for a WSN. To select cluster-heads and organize clustermembers for each cluster, every sensor uses only local information and simple decision mechanisms which are aimed at configuring a self-organized system. By these self-organized interactions among sensors and selforganized selection of cluster-heads, the suggested method can form clusters for a WSN and decide routing paths energy efficiently. We compare our clustering method with a clustering method that is a well known routing protocol for the WSNs. In our computational experiments, we show that the energy consumptions and the lifetimes of our method are better than those of the compared method. The experiments also shows that the suggested method demonstrate properly some self-organized properties such as robustness and adaptability against uncertainty for WSN's.

• Journal of Biomedical Engineering Research
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• v.33 no.3
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• pp.148-154
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• 2012

Hemorrhagic shock is a primary cause of deaths resulting from injury in the world. Although many studies have tried to diagnose accurately hemorrhagic shock in the early stage, such attempts were not successful due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in acute hemorrhagic shock using a random forest (RF) model. Heart rate (HR), mean arterial pressure (MAP), respiration rate (RR), lactate concentration (LC), and peripheral perfusion (PP) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed 5-fold cross validation for RF variable selection, and forward stepwise variable selection for the LR model to examine which variables were important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 0.83, 0.95, 0.88, and 0.96, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.97, 0.95, 0.96, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4738-4753
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• 2018

Efficient rendezvous node selection and routing algorithm (RNSRA) for wireless sensor networks with mobile sink that visits rendezvous node to gather data from sensor nodes is proposed. In order to plan an optimal moving tour for mobile sink and avoid energy hole problem, we develop the RNSRA to find optimal rendezvous nodes (RN) for the mobile sink to visit. The RNSRA can select the set of RNs to act as store points for the mobile sink, and search for the optimal multi-hop path between source nodes and rendezvous node, so that the rendezvous node could gather information from sensor nodes periodically. Fitness function with several factors is calculated to find suitable RNs from sensor nodes, and the artificial bee colony optimization algorithm (ABC) is used to optimize the selection of optimal multi-hop path, in order to forward data to the nearest RN. Therefore the energy consumption of sensor nodes is minimized and balanced. Our method is validated by extensive simulations and illustrates the novel capability for maintaining the network robustness against sink moving problem, the results show that the RNSRA could reduce energy consumption by 6% and increase network lifetime by 5% as comparing with several existing algorithms.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.967-975
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• 2010

Since the least squares estimation is not appropriate when multicollinearity exists among the regressors of the linear regression model, the principal components regression is used to deal with the multicollinearity problem. This article suggests a new procedure for the selection of suitable principal components. The procedure is based on the condition index instead of the eigenvalue. The principal components corresponding to the indices are removed from the model if any condition indices are larger than the upper limit of the cutoff value. On the other hand, the corresponding principal components are included if any condition indices are smaller than the lower limit. The forward inclusion method is employed to select proper principal components if any condition indices are between the upper limit and the lower limit. The limits are obtained from the linear model which is constructed on the basis of the conjoint analysis. The procedure is evaluated by Monte Carlo simulation in terms of the mean square error of estimator. The simulation results indicate that the proposed procedure is superior to the existing methods.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3029-3035
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• 2013

Background: Most developing countries have been unable to implement well-organized health care systems, especially comprehensive Pap smear screening-based programs. One of the reasons for this is regional differences in medical services, and a low-cost portable cervical screening system is necessary. To improve regional discrepancies in cervical screening systems, we investigated the usefulness and acceptability of cervical selfsampling by liquid-based cytology (LBC) for 290 volunteers in the Lao PDR. Materials and Methods: Following health education with comprehensive documents, cervical self-sampling kits by LBC were distributed in three provincial, district, and village areas to a total of 290 volunteers, who were asked to take cytology samples by themselves. Subsequently, the acceptability of self-sampling was evaluated using a questionnaire. Results: The documents were well understood in all three regions. Regarding the acceptability of self-sampling, the selections for subsequent screening were 62% self-sampling, 36% gynecologist-sampling, 1% either method, and 1% other methods. The acceptability rates were higher in the district and the village than in the province. For the relationship between acceptability and pregnancy, the self-sampling selection rate was higher in the pregnancy-experienced group (75%) than in the pregnancy-inexperienced group (60%). For the relationship between selection of self-sampling and experience of screening, the self-sampling selection rate was higher in the screening-inexperienced group (62%) than in the screening-experienced group (52%). Conclusions: Our data show that this new way forward, involving a combination of self-sampling and LBC, is highly acceptable regardless of age, educational background, and residence in rural areas in a developing country.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.249-259
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• 2020

This study presents a recently obtained social media data set based upon the case study of Electric Vehicles (EV) and looks to implement a sentiment analysis (SA) in order to gain insights. This study uses two methods in order to fully analyze the public's sentiment on EVs. First, we implement a SA tool in which we used to extract the sentiment of comments. Next we labeled the data with these sentiments obtained and classified them. While performing classification we found the problem of dimensionality and also explored the use of feature selection (FS) models in order to reduce the data set's dimensionality. We found that the use of three FS models (Chi Squared, Information Gain and ReliefF) showed the most promising results when used alongside a logistic and support vector machines classification algorithm. the contributions of this paper are in providing an real-world example of social media text analytics which can be adopted in many other areas of research and business. Moving forward researchers can use the methodological approach in this paper to further refine and improve their own case uses in text analytics.