• ETRI Journal
• /
• v.34 no.3
• /
• pp.450-453
• /
• 2012

IEEE 802.11n standards introduced a mixed-mode format frame structure to achieve higher throughput with multiple antennas while providing backward compatibility with legacy systems. Although multi-input multi-output channel estimation was possible only with high-throughput long training fields (HT-LTFs), the proposed scheme utilizes a legacy LTF as well as HT-LTFs in a decision feedback manner to improve the accuracy of the estimates. It was verified through theoretical analysis and simulations that the proposed scheme effectively enhances the mean square error performance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.3
• /
• pp.423-431
• /
• 1997

A new decision tree generator MEC is proposed in this paper, which uses the difference of multi-base entropy as a consistent criterion for discretization and selection of attributes. To evaluate the performance of the proposed generator, it is compared to other generators which use criteria based on entropy and adopt different discretization styles. As an experimental result, it is shown that the proposed generator produces the most efficient classifiers, which have the least number of leaves at the same error rate, regardless of whether attribute values constituting the training set are discrete or continuous.

• Journal of Industrial Convergence
• /
• v.20 no.10
• /
• pp.39-49
• /
• 2022

The purpose of this study was to systematic review about randomized controlled trials the characteristics and effect of non-pharmacological intervention on depressive symptom in elderly with mild cognitive impairment. We searched studies published from January 2011 to July 2021 in 3 databases. A total 1,455 studies were found and included 11 studies in final analysis. Methodological quality was assessment with the Cochrane's RoB(risk of bias) tool. Geriatric Depression Scale(GDS) was the most used as the assessment tool for identifying the depressive symptom. Intervention were yoga, psychosocial intervention, cognitive training, health education, multi-component intervention, game training, aerobic/pulmonary physiotherapy, art therapy, music reminiscence activity, memory specificity training, cognitive stimulation therapy and SWTW(sleep well, think well) program. Among the intervention programs, yoga, multi-component intervention and game training were effective in improving depressive symptom. This study provided a clinical evidence for planning and implementing intervention on depressive symptom in elderly with mild cognitive impairment.

• Journal of Multimedia Information System
• /
• v.8 no.4
• /
• pp.203-210
• /
• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.9
• /
• pp.4025-4028
• /
• 2014

Background: Relationships between smoking and bladder neoplasms, one of the common malignancies, are well-known. Different smoking-related malignancies may occur together. In this study, we evaluated the stage and grade of bladder neoplasms in patients also featuring lung or larynx cancer. Materials and Methods: From January 2006 to February 2012, patients who underwent surgery for bladder neoplasms in our clinic were screened retrospectively. In the evaluation, 5 patients had larynx cancer and 20 patients have lung cancer in addition, all having been smoking for a long time. The bladder tumor stage and grade were investigated in these 25 cases. Results: Mean age of patients was 66.8 (49-78). In the evaulation, all of 5 patients who had larnyx cancer also had high grade urothelial cancer. One had T2 urothelial, and 3 T1 urothelial cancer. In the same way, all of the 20 patients with lung cancer also have high grade urothelial cancer, three T2, and 13 T1. Bladder cancer stage and grade were determined to be significantly increased in patients with concomitant bladder and lung or larynx cancer. Conclusions: In the patients who have smoking releated second malignancy, bladder cancer prognosis appears more aggressive. We now need a larger series and multi-center studies for understanding relevant pathophysiology.

• Nuclear Engineering and Technology
• /
• v.54 no.8
• /
• pp.3027-3033
• /
• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• Structural Engineering and Mechanics
• /
• v.90 no.2
• /
• pp.189-208
• /
• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.33 no.2
• /
• pp.141-148
• /
• 1997

This paper is concerned with the vibration control of multi-layer structure for ultra-tall buildings and main tower of large bridge etc. We have modeled the multi-layer structure with the distributed mass system as the lumped mass system of two-degree-of-freedom structure and made experimental equipment. The LQ optimal control theory is applied to the design of the control system. The designed control system is simulated by computer. As a result, the LQ regulator showed good vibration control performance with impact excitation.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.4
• /
• pp.57-65
• /
• 1997

The purpose of this paper is to propose the HMM (hidden markov model) based on multi-observation sequence for the isolated word recognition. The proosed model generates the codebook of MSVQ by dividing each word into several sections followed by dividing training data into several sections. Then, we are to obtain the sequential value of multi-observation per each section by weighting the vectors of distance form lower values to higher ones. Thereafter, this the sequential with high probability value while in recognition. 146 DDD area names are selected as the vocabularies for the target recognition, and 10LPC cepstrum coefficients are used as the feature parameters. Besides the speech recognition experiments by way of the proposed model, for the comparison with it, the experiments by DP, MSVQ, and genral HMM are made with the same data under the same condition. The experiment results have shown that HMM based on multi-observation sequence proposed in this paper is proved superior to any other methods such as the ones using DP, MSVQ and general HMM models in recognition rate and time.

• Journal of Korea Multimedia Society
• /
• v.21 no.12
• /
• pp.1467-1472
• /
• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.