• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.30.1-30.1
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• 2020

The ASKAP-EMU survey is a deep wide-field radio continuum survey designed to cover the entire southern sky and a significant fraction of the northern sky up to +30°. Here, we report a discovery of a radio relic in the merging cluster SPT-CL J2023-5535 at z=0.23 from the ASKAP-EMU pilot 300 square degree survey (800-1088 MHz). The deep high-resolution data reveal a ~2 Mpc-scale radio halo elongated in the east-west direction, coincident with the intracluster gas. The radio relic is located at the western edge of this radio halo stretched ~0.5 Mpc in the north-south orientation. The integrated spectral index of the radio relic within the narrow bandwidth is α1088MHz800MHz = -0.76 ± 0.06. Our weak-lensing analysis shows that the system is massive (M200 = 1.04 ± 0.36 × 1015M⊙) and composed of at least three subclusters. We suggest a scenario, wherein the radio features arise from the collision between the eastern and middle subclusters. Furthermore, the direct link between the local AGN and the relic along with the discontinuities in X-ray observation hint us that we are looking at the site of re-acceleration.

• Smart Media Journal
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• v.12 no.5
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• pp.28-35
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• 2023

In this study, we propose a new light-weight model RoutingConvNet with fewer parameters to improve the applicability and practicality of speech emotion recognition. To reduce the number of learnable parameters, the proposed model connects bidirectional MFCCs on a channel-by-channel basis to learn long-term emotion dependence and extract contextual features. A light-weight deep CNN is constructed for low-level feature extraction, and self-attention is used to obtain information about channel and spatial signals in speech signals. In addition, we apply dynamic routing to improve the accuracy and construct a model that is robust to feature variations. The proposed model shows parameter reduction and accuracy improvement in the overall experiments of speech emotion datasets (EMO-DB, RAVDESS, and IEMOCAP), achieving 87.86%, 83.44%, and 66.06% accuracy respectively with about 156,000 parameters. In this study, we proposed a metric to calculate the trade-off between the number of parameters and accuracy for performance evaluation against light-weight.

• Advances in concrete construction
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• v.14 no.3
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• pp.185-194
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• 2022

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R² metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.377-383
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• 2023

Purpose: There is increasing attention to the application of transcranial direct current stimulation (tDCS) for enhancing cognitive functions in subjects to aging, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Despite varying treatment outcomes in tDCS which depend on the amount of current reaching the brain, there is no general information on the impacts of anatomical features associated with AD on tDCS-induced electric field. Objective: The objective of this study is to examine how AD-related anatomical variation affects the tDCS-induced electric field using computational modeling. Methods: We collected 180 magnetic resonance images (MRI) of AD patients and healthy controls from a publicly available database (Alzheimer's Disease Neuroimaging Initiative; ADNI), and MRIs were divided into female-AD, male-AD, female-normal, and male-normal groups. For each group, segmented brain volumes (cerebrospinal fluid, gray matter, ventricle, rostral middle frontal (RMF), and hippocampus/amygdala complex) using MRI were measured, and tDCS-induced electric fields were simulated, targeting RMF. Results: For segmented brain volumes, significant sex differences were observed in the gray matter and RMF, and considerable disease differences were found in cerebrospinal fluid, ventricle, and hippocampus/amygdala complex. There were no differences in the tDCS-induced electric field among AD and normal groups; however, higher peak values of electric field were observed in the female group than the male group. Conclusions: Our findings demonstrated the presence of sex and disease differences in segmented brain volumes; however, this pattern differed in tDCS-induced electric field, resulting in significant sex differences only. Further studies, we will adjust the brain stimulation conditions to target the deep brain and examine the effects, because of significant differences in the ventricles and deep brain regions between AD and normal groups.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.34-41
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• 2023

In recent years, as the range of human activities has increased, the introduction of alien species has become frequent. Among them, raccoons have been designated as harmful animals since 2020. Raccoons are similar in size and shape to raccoon dogs, so they generally need to be distinguished in capturing them. To solve this problem, we use VGG19, ResNet152V2, InceptionV3, InceptionResNet and NASNet, which are CNN deep learning models specialized for image classification. The parameters to be used for learning are pre-trained with a large amount of data, ImageNet. In order to classify the raccoon and raccoon dog datasets as outward features of animals, the image was converted to grayscale and brightness was normalized. Augmentation methods were applied using left and right inversion, rotation, scaling, and shift to create sufficient data for transfer learning. The FCL consists of 1 layer for the non-augmented dataset while 4 layers for the augmented dataset. Comparing the accuracy of various augmented datasets, the performance increased as more augmentation methods were applied.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.1-7
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• 2024

Weather is one of the main causes of aircraft accidents, and among the phenomena caused by weather, icing is a phenomenon in which an ice layer is formed when an object exposed to an atmosphere below a freezing temperature collides with supercooled water droplets. If this phenomenon occurs in the rotor blades, it causes defects such as severe vibration in the airframe and eventually leads to loss of control and an accident. Therefore, it is necessary to foresee the icing situation so that it can ascend and descend at an altitude without a freezing point. In this study, vibration data in normal and faulty conditions was acquired, data features were extracted, and vibration was predicted through deep learning-based algorithms such as CNN, LSTM, CNN-LSTM, Transformer, and TCN, and performance was compared to evaluate blade icing. A method for minimizing operating loss is suggested.

• Dementia and Neurocognitive Disorders
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• v.23 no.1
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• pp.1-10
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• 2024

Background and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer's disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer's disease dementia (ADD). Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma. Results: A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset. Conclusions: Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.

• Journal of Digital Policy
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• v.3 no.3
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• pp.9-14
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• 2024

In this paper, proposes a method using a multi block structure composed of residual blocks with adaptive weights to improve the quality of results in single image super resolution. In the process of generating super resolution images using deep learning, the most critical factor for enhancing quality is feature extraction and application. While extracting various features is essential for restoring fine details that have been lost due to low resolution, issues such as increased network depth and complexity pose challenges in practical implementation. Therefore, the feature extraction process was structured efficiently, and the application process was improved to enhance quality. To achieve this, a multi block structure was designed after the initial feature extraction, with nested residual blocks inside each block, where adaptive weights were applied. Additionally, for final high resolution reconstruction, a multi kernel image reconstruction process was employed, further improving the quality of the results. The performance of the proposed method was evaluated by calculating PSNR and SSIM values compared to the original image, and its superiority was demonstrated through comparisons with existing algorithms.

• Journal of Wind Energy
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• v.13 no.2
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• pp.42-52
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• 2022

In this paper, we propose a time-series generation methodology using a generative adversarial network (GAN) for long-term prediction of wind and sea conditions, which are information necessary for operations and maintenance (O&M) planning and optimal plans for offshore wind farms. It is a "Conditional TimeGAN" that is able to control time-series data with monthly conditions while maintaining a time dependency between time-series. For the generated time-series data, the similarity of the statistical distribution by direction was confirmed through wave and wind rose diagram visualization. It was also found that the statistical distribution and feature correlation between the real data and the generated time-series data was similar through PCA, t-SNE, and heat map visualization algorithms. The proposed time-series generation methodology can be applied to monthly or annual marine weather prediction including probabilistic correlations between various features (wind speed, wind direction, wave height, wave direction, wave period and their time-series characteristics). It is expected that it will be able to provide an optimal plan for the maintenance and optimization of offshore wind farms based on more accurate long-term predictions of sea and wind conditions by using the proposed model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.185-195
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• 2018

As machines have been automated in the field of industries in recent years, it is a paramount importance to manage and maintain the automation machines. When a fault occurs in sensors attached to the machine, the machine may malfunction and further, a huge damage will be caused in the process line. To prevent the situation, the fault of sensors should be monitored, diagnosed and classified in a proper way. In the paper, we propose a sensor fault detection scheme based on SVM and CNN to detect and classify typical sensor errors such as erratic, drift, hard-over, spike, and stuck faults. Time-domain statistical features are utilized for the learning and testing in the proposed scheme, and the genetic algorithm is utilized to select the subset of optimal features. To classify multiple sensor faults, a multi-layer SVM is utilized, and ensemble technique is used for CNN. As a result, the SVM that utilizes a subset of features selected by the genetic algorithm provides better performance than the SVM that utilizes all the features. However, the performance of CNN is superior to that of the SVM.