• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.19 no.4
• /
• pp.461-469
• /
• 2008

This paper proposes a hybrid symbol offset estimation algorithm for MIMO(Multiple Input Multiple Output) OFDM system. As MIMO OFDM systems are multiple transmitter and receiver antenna systems, apart from SISO(Single Input Single Output) system, it is possible to use several combining techniques which are used in multiple receive antenna system. In this paper, we propose hybrid symbol offset estimation algorithms using combining techniques in multiple receive antenna systems, simulate and show the performances in MIMO system environments. The proposed equal gain combining correlation algorithm has better performance 1.8 times in searching the ideal symbol offset rather than the conventional early symbol offset algorithm in severe ISI channel.

• Korean Management Science Review
• /
• v.14 no.1
• /
• pp.77-85
• /
• 1997

This paper proposes an approach for individual selection in genetic algorithms to improve problem solving efficiency and effectiveness. To investigate the utility of combining simulated annealing with genetic algorithm, two experiment are conducted that compare both the conventional genetic algorithm and suggested approach. Result indicated that suggested approach significantly reduced the required time to find optimal solution in moderate-sized problems under the conditions studied. It is also found that quality of the solutions generated by suggested approach in large- sized problems is greatly improved.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.8
• /
• pp.1265-1271
• /
• 2017

In this paper, the speech parameter fusion method is studied to improve the performance of the conventional emotion recognition system. For this purpose, the combination of the parameters that show the best performance by combining the cepstrum parameters and the various pitch parameters used in the conventional emotion recognition system are selected. Various pitch parameters were generated using numerical and statistical methods using pitch of speech. Performance evaluation was performed on the emotion recognition system using Gaussian mixture model(GMM) to select the pitch parameters that showed the best performance in combination with cepstrum parameters. As a parameter selection method, sequential feature selection method was used. In the experiment to distinguish the four emotions of normal, joy, sadness and angry, fifteen of the total 56 pitch parameters were selected and showed the best recognition performance when fused with cepstrum and delta cepstrum coefficients. This is a 48.9% reduction in the error of emotion recognition system using only pitch parameters.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.23
• /
• pp.10539-10542
• /
• 2015

A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

• Journal of Information Processing Systems
• /
• v.13 no.5
• /
• pp.1071-1088
• /
• 2017

For dual-hop multiple-input multiple-output (MIMO) decode-and-forward relaying systems, we propose a selective relaying scheme that uses orthogonal space-time block code (OSTBC) and transmit antenna selection with maximal-ratio combining (TAS/MRC) or vice versa at the first and second hops, respectively. The aim is to achieve an asymptotically identical performance to the dual-hop relaying system with only TAS/MRC, while requiring lower feedback overhead. In particular, we give the selection criteria based on the antenna configurations and the average channel powers for the first and second hops, assuming Rayleigh fading channels. Also, the numerical results are shown for the outage performance comparison between the dual-hop DF relaying systems with the proposed scheme, only TAS/MRC, and only OSTBC.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.11 no.7
• /
• pp.1186-1193
• /
• 2000

In this paper, the system performance with the selection diversity, which is applied to the FH/MFSK system in Nakagami fading channel, is analyzed. The deletion probability is derived from the received signal to noise ratio(SNR) after selection combining and the parameters such as the number of users(M), SNR, Nakagami fading figure(m), and the number of diversity branches(D) is used for the performance analysis of the FH/MFSK system. Assuming that m set 1, it is observed that the bit error rate(BER) is 1.0$\times$$10^{-3}$ and 1.0$\times$$10^{-4}$ at D =1(no diversity) and D=2, respectively, and then is decreased by 10 times. Assuming that m set 2, it is also shown that the BER has a constant value although D is increased. In the case of D=2, the system capacity is more 75% and 20% than that considering no diversity at SNR=15 dB and 25 dB, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.2
• /
• pp.287-293
• /
• 2013

Cognitive radio has been recently proposed to dynamically access unused-spectrum. The CR users can share the same frequency band with the primary user without interference to each other. Usually each CR user needs to determine spectrum availability by itself depending only on its local observations. But uncertainty communication environment effects can be mitigated so that the detection probability is improved in a heavily shadowed environment. Soft detection is a primary user detection method of cooperative cognitive radio networks. In our research, we will improve system detection probability by using optimal cooperative node selection algorithm. New algorithm can find optimal number of cooperative sensing nodes for cooperative soft detection by using maximum ratio combining (MRC) method. Through analysis, proposed cooperative node selection algorithm can select optimal node for cooperative sensing according to the system requirement and improve the system detection probability.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.9 no.2
• /
• pp.187-194
• /
• 2014

In this paper, we analyze the outage probability of macro diversity combining in cellular networks in consideration of aggregate interference from other mobile stations (MSs). Different from existing works analyzing the outage probability of macro diversity combining, we focus on a diversity gain attained by selecting a base station (BS) subject to relatively low aggregate interference. In our model, MSs are randomly located according to a Poisson point process. The outage probability is analyzed by approximating the multivariate distribution of aggregate interferences on multiple BSs by a multivariate lognormal distribution.

• Journal of Korean Library and Information Science Society
• /
• v.44 no.1
• /
• pp.27-47
• /
• 2013

This study investigated the classification performance of a kNN classifier using the feature selection methods based on document frequency(DF) and collection frequency(CF). The results of the experiments, which used HKIB-20000 data, were as follows. First, the feature selection methods that used high-frequency terms and removed low-frequency terms by the CF criterion achieved better classification performance than those using the DF criterion. Second, neither DF nor CF methods performed well when low-frequency terms were selected first in the feature selection process. Last, combining CF and DF criteria did not result in better classification performance than using the single feature selection criterion of DF or CF.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.1
• /
• pp.105-115
• /
• 2016

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.