• The Journal of the Acoustical Society of Korea
• /
• v.15 no.1E
• /
• pp.5-19
• /
• 1996

This paper proposes a loose part monitoring system (LPMS) design with a signal processing method based on fuzzy logic. Considering fuzzy characteristics of metallic impact waveform due to not only interferences from various types of noises in an operating nuclear power plant but also complex wave propagation paths within a monitored mechanical structure, the proposed LPMS design incorporates the comprehensive relation among impact signal features in the fuzzy rule bases for the purposes of alarm discrimination and impact diagnosis improvement. The impact signal features for the fuzzy rule bases include the rising time, the falling time, and the peak voltage values of the impact signal envelopes. Fuzzy inference results based on the fuzzy membership values of these impact signal features determine the confidence level data for each signal feature. The total integrated confidence level data is used for alarm discrimination and impact diagnosis purposes. Through the perpormance test of the proposed LPMS with mock-up structures and instrumentation facility, test results show that the system is effective in diagnosis of the loose part impact event(i.e., the evaluation of possible impacted area and degree of impact magnitude) as well as in suppressing false alarm generation.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.3
• /
• pp.421-427
• /
• 2013

The dependence between wind speeds in multiple wind sites has a considerable impact on the reliability of power systems containing wind energy. This paper presents a new method to generate dependent wind speed time series (WSTS) based on copulas theory. The basic feature of the method lies in separating multivariate WSTS into dependence structure and univariate time series. The dependence structure is modeled through the use of copulas, which, unlike the cross-correlation matrix, give a complete description of the joint distribution. An autoregressive moving average (ARMA) model is applied to represent univariate time series of wind speed. The proposed model is illustrated using wind data from two sites in Canada. The IEEE Reliability Test System (IEEE-RTS) is used to examine the proposed model and the impact of wind speed dependence between different wind regimes on the generation system reliability. The results confirm that the wind speed dependence has a negative effect on the generation system reliability.

• Journal of Biomedical Engineering Research
• /
• v.45 no.2
• /
• pp.75-80
• /
• 2024

This research investigates the influence of chair tilt functionality on biometric signals and user comfort, addressing the ergonomic challenges posed by modern sedentary lifestyles. Through an experimental study involving eight male participants, the impact of chair tilt on electromyography (EMG), heart rate, metabolic rate, pressure distribution, and distance between the lumbar spine and the lumbar support part of the chair was measured across different seating postures. The study utilized chairs with both synchronous and non-synchronous tilt mechanisms to explore how adjustments in chair design affect user comfort and physiological responses during prolonged sitting. Key findings suggest that chair tilt functionality can significantly reduce muscle activity and energy expenditure, enhancing user comfort and potentially mitigating health risks associated with prolonged sedentary behavior. Notably, the study revealed a preference among participants for chairs that aligned the rotational center of the tilt with the hip joint, highlighting the importance of this ergonomic feature in enhancing user comfort. Additionally, the research proposes a novel methodology for assessing seating comfort through the analysis of both biometric and physical signals, providing valuable insights for the development of ergonomic chair designs focused on user health and comfort.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.2115-2127
• /
• 2021

Although significant progress has been made in synthesizing visually realistic face images by Generative Adversarial Networks (GANs), there still lacks effective approaches to provide fine-grained control over the generation process for semantic facial attribute editing. In this work, we propose a novel cross channel self-attention based generative adversarial network (CCA-GAN), which weights the importance of multiple channels of features and archives pixel-level feature alignment and conversion, to reduce the impact on irrelevant attributes while editing the target attributes. Evaluation results show that CCA-GAN outperforms state-of-the-art models on the CelebA dataset, reducing Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) by 15~28% and 25~100%, respectively. Furthermore, visualization of generated samples confirms the effect of disentanglement of the proposed model.

• Journal of Information Technology Services
• /
• v.17 no.2
• /
• pp.191-205
• /
• 2018

With the advancement of artificial intelligence (AI) techniques, many consumer products have adopted AI features for providing proactive and personalized services to customers. One of the most prominent products featuring AI techniques is a smart speaker. The fundamental of smart speaker is a portable wireless Internet connecting speaker which already have existed in a consumer market. By applying AI techniques, smart speakers can recognize human voices and communicate with them. In addition, they can control other connecting devices and provide offline services. The goal of this study is to identify the impact of AI techniques for customer rating to the products. We compared customer reviews of other portable speakers without AI features and those of a smart speaker. Amazon echo is used for a smart speaker and JBL Flip 4 Bluetooth Speaker and Ultimate Ears BOOM 2 Panther Limited Edition are used for the comparison. These products are in the same price range ($50~100) and selected as featured products in Amazon.com. All reviews for the products were collected and common words for all products and unique words of the smart speaker were identified. Information gain values were calculated to identify the influences of words to be rated as positive or negative. Positive and negative words in all the products or in Amazon echo were identified, too. Topic modeling was applied to the customer reviews on Amazon echo and the importance of each topic were measured by summating information gain values of each topic. This study provides a way of identifying customer responses on the AI feature and measuring the importance of the feature among diverse features of the products.

• Journal of Broadcast Engineering
• /
• v.28 no.2
• /
• pp.238-241
• /
• 2023

Recently deep learning models are widely used for various tasks such as facial recognition and face editing. Their training process often involves a dataset with imbalanced age distribution. It is because some age groups (teenagers and middle age) are more socially active and tends to have more data compared to the less socially active age groups (children and elderly). This imbalanced age distribution may negatively impact the deep learning training process or the model performance when tested against those age groups with less data. To this end, we propose an age-controllable face synthesis technique using a feature decomposition to classify age from facial images which can be utilized to synthesize novel data to balance out the age distribution. We perform extensive qualitative and quantitative evaluation on our proposed technique using the FFHQ dataset and we show that our method has better performance than existing method.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.7
• /
• pp.2685-2691
• /
• 2015

To evaluate the clinical impact of preoperative serum CEA and CA19-9 on resectable gastric cancer (GC), a total of 1,075 consecutive cases with gastric adenocarcinoma were obtained retrospectively from January 2012 and December 2013 in a single tertiary hospital, and the relationships between serum CEA, CA19-9 and clinicopathologic features were investigated. Positive preoperative serum rates of CEA and CA19-9 were 22.4% and 12.3% respectively, levels significantly correlating with each other and depth of invasion, lymph node involvement, pTNM and stage. The CEA level also presented a remarkable association with lymphovascular invasion. Both CEA and CA19-9 positivity significantly and positively correlated with depth of invasion, nodal involvement, pTNM stage, lymphovascular invasion, tumor size and tumor location. Stratified analyses according to gender or tumor location showed preoperative CEA or CA19-9 had different associations with clinicopathologic features in different gender subgroups or location subgroups. Preoperative serum CA19-9 positivity may be more meaningful for tumor size rather than CEA. In conclusion, preoperative serum CEA and CA19-9 correlate with disease progression of GC, and may have applications in aiding more accurate estimation of tumor stage, decision of treatment choice and prognosis evaluation.

• Earthquakes and Structures
• /
• v.15 no.6
• /
• pp.701-713
• /
• 2018

Many bridges in Algeria were constructed without taking into account the seismic effect in the design. The implantation of a new regulation code RPOA-2008 requires a higher reinforcement ratio than with the seismic coefficient method, which is a common feature of the existing bridges. For better perception of the performance bridge piers and evaluation of the risk assessment of existing bridges, fragility analysis is an interesting tool to assess the vulnerability study of these structures. This paper presents a comparative performance of bridge piers designed with the seismic coefficient method and the new RPOA-2008. The performances of the designed bridge piers are assessed using thirty ground motion records and incremental dynamic analysis. Fragility curves for the bridge piers are plotted using probabilistic seismic demand model to perform the seismic vulnerability analysis. The impact of changing the reinforcement strength on the seismic behavior of the designed bridge piers is checked by fragility analysis. The fragility results reveal that the probability of damage with the RPOA-2008 is less and perform well comparing to the conventional design pier.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.12
• /
• pp.4476-4491
• /
• 2021

Video service providers tend to face user network problems in the process of transmitting video streams. They strive to provide user with superior video quality in a limited bitrate environment. It is necessary to accurately determine the target bitrate range of the video under different quality requirements. Recently, several schemes have been proposed to meet this requirement. However, they do not take the impact of visual influence into account. In this paper, we propose a new multi-category model to accurately predict the target bitrate range with target visual quality by machine learning. Firstly, a dataset is constructed to generate multi-category models by machine learning. The quality score ladders and the corresponding bitrate-interval categories are defined in the dataset. Secondly, several types of spatial-temporal features related to VMAF evaluation metrics and visual factors are extracted and processed statistically for classification. Finally, bitrate prediction models trained on the dataset by RandomForest classifier can be used to accurately predict the target bitrate of the input videos with target video quality. The classification prediction accuracy of the model reaches 0.705 and the encoded video which is compressed by the bitrate predicted by the model can achieve the target perceptual quality.

• Journal of information and communication convergence engineering
• /
• v.21 no.4
• /
• pp.346-350
• /
• 2023

Trajectory prediction is an essential element for driving autonomous vehicles, and various trajectory prediction models have emerged with the development of deep learning technology. Convolutional neural network (CNN) is the most commonly used neural network architecture for extracting the features of visual images, and the latest models exhibit high performances. This study was conducted to identify an efficient CNN backbone model among the components of deep learning models for trajectory prediction. We changed the existing CNN backbone network of multiple-trajectory prediction models used as feature extractors to various state-of-the-art CNN models. The experiment was conducted using nuScenes, which is a dataset used for the development of autonomous vehicles. The results of each model were compared using frequently used evaluation metrics for trajectory prediction. Analyzing the impact of the backbone can improve the performance of the trajectory prediction task. Investigating the influence of the backbone on multiple deep learning models can be a future challenge.