• Journal of IKEEE
• /
• v.28 no.2
• /
• pp.221-227
• /
• 2024

This paper presents a smartphone-attachable vascular compliance monitoring module. The proposed sensor module measures photoplethysmogram (PPG) and reconstructs an accelerated PPG waveform. The feature points are extracted from the accelerated PPG waves, and vascular compliance is estimated using these extracted features. The module is powered via the smartphone's USB terminal and transmits the acquired waveforms along with vascular compliance values through Bluetooth. The transmitted waveforms and vascular compliance value are displayed through the smartphone application. This work proposes an assessment method for consistency of PPG instrumentation, and it was implemented in a processor of sensor module. The proposed sensor module can be easily attached to smartphone that does not support PPG instrumentation, providing simple measurment and numerical analysis of vascular compliance. To verify the performance of the implemented sensor module, we acquired vascular compliance and pulse pressure data from 29 subjects. Pulse pressure, which serves as a representative indicator of vascular compliance, was obtained using a commercial blood pressure monitor. The analysis results showed that the Pearson coefficient between vascular compliance and pulse pressure was 0.778, confirming a relatively high correlation between two metrics.

• Smart Media Journal
• /
• v.12 no.11
• /
• pp.57-66
• /
• 2023

In this paper, we present a study on an AI-based system utilizing the CCTV system within city buses to predict the intentions of boarding and alighting passengers, with the aim of preventing accidents. The proposed system employs the YOLOv7 Pose model to detect passengers, while utilizing an LSTM model to predict intentions of tracked passengers. The system can be installed on the bus's CCTV terminals, allowing for real-time visual confirmation of passengers' intentions throughout driving. It also provides alerts to the driver, mitigating potential accidents during passenger transitions. Test results show accuracy rates of 0.81 for analyzing boarding intentions and 0.79 for predicting alighting intentions onboard. To ensure real-time performance, we verified that a minimum of 5 frames per second analysis is achievable in a GPU environment. his algorithm enhance the safety of passenger transitions during bus operations. In the future, with improved hardware specifications and abundant data collection, the system's expansion into various safety-related metrics is promising. This algorithm is anticipated to play a pivotal role in ensuring safety when autonomous driving becomes commercialized. Additionally, its applicability could extend to other modes of public transportation, such as subways and all forms of mass transit, contributing to the overall safety of public transportation systems.

• Phonetics and Speech Sciences
• /
• v.16 no.1
• /
• pp.67-76
• /
• 2024

Recent advances in text-to-speech (TTS) technology have significantly improved the quality of synthesized speech, reaching a level where it can closely imitate natural human speech. Especially, TTS models offering various voice characteristics and personalized speech, are widely utilized in fields such as artificial intelligence (AI) tutors, advertising, and video dubbing. Accordingly, in this paper, we propose a one-shot multi-speaker TTS system that can ensure acoustic diversity and synthesize personalized voice by generating speech using unseen target speakers' utterances. The proposed model integrates a speaker encoder into a TTS model consisting of the FastSpeech2 acoustic model and the HiFi-GAN vocoder. The speaker encoder, based on the pre-trained RawNet3, extracts speaker-specific voice features. Furthermore, the proposed approach not only includes an English one-shot multi-speaker TTS but also introduces a Korean one-shot multi-speaker TTS. We evaluate naturalness and speaker similarity of the generated speech using objective and subjective metrics. In the subjective evaluation, the proposed Korean one-shot multi-speaker TTS obtained naturalness mean opinion score (NMOS) of 3.36 and similarity MOS (SMOS) of 3.16. The objective evaluation of the proposed English and Korean one-shot multi-speaker TTS showed a prediction MOS (P-MOS) of 2.54 and 3.74, respectively. These results indicate that the performance of our proposed model is improved over the baseline models in terms of both naturalness and speaker similarity.

• Korean Journal of Radiology
• /
• v.21 no.7
• /
• pp.869-879
• /
• 2020

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

• Journal of Korea Water Resources Association
• /
• v.57 no.3
• /
• pp.209-223
• /
• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.167-183
• /
• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.

• The KIPS Transactions:PartC
• /
• v.10C no.5
• /
• pp.625-634
• /
• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.

• Journal of Global Scholars of Marketing Science
• /
• v.20 no.1
• /
• pp.80-88
• /
• 2010

There has been considerable research examining the relationship between stockholders equity and various marketing strategies. These include studies linking stock price performance to advertising, customer service metrics, new product introductions, research and development, celebrity endorsers, brand perception, brand extensions, brand evaluation, company name changes, and sports sponsorships. Another facet of marketing investments which has received heightened scrutiny for its purported influence on stockholder equity is television advertisement embedded within specific sporting events such as the Super Bowl. Research indicates that firms which advertise in Super Bowls experience stock price gains. Given this reported relationship between advertising investment and increased shareholder value, for both general and special events, it is surprising that relatively little research attention has been paid to investigating the relationship between advertising in the Olympic Games and its subsequent impact on stockholder equity. While attention has been directed at examining the effectiveness of sponsoring the Olympic Games, much less focus has been placed on the financial soundness of advertising during the telecasts of these Games. Notable exceptions to this include Peters (2008), Pfanner (2008), Saini (2008), and Keller Fay Group (2009). This paper presents a study of Olympic advertisers who ran TV ads on NBC in the American telecasts of the 2000, 2004, and 2008 Summer Olympic Games. Five hypothesis were tested: H1: The stock prices of firms which advertised on American telecasts of the 2008, 2004 and 2000 Olympics (referred to as O-Stocks), will outperform the S&P 500 during this same period of time (i.e., the Monday before the Games through to the Friday after the Games). H2: O-Stocks will outperform the S&P 500 during the medium term, that is, for the period of the Monday before the Games through to the end of each Olympic calendar year (December 31st of 2000, 2004, and 2008 respectively). H3: O-Stocks will outperform the S&P 500 in the longer term, that is, for the period of the Monday before the Games through to the midpoint of the following years (June 30th of 2001, 2005, and 2009 respectively). H4: There will be no difference in the performance of these O-Stocks vs. the S&P 500 in the Non-Olympic time control periods (i.e. three months earlier for each of the Olympic years). H5: The annual revenue of firms which advertised on American telecasts of the 2008, 2004 and 2000 Olympics will be higher for those years than the revenue for those same firms in the years preceding those three Olympics respectively. In this study, we recorded stock prices of those companies that advertised during the Olympics for the last three Summer Olympic Games (i.e. Beijing in 2008, Athens in 2004, and Sydney in 2000). We identified these advertisers using Google searches as well as with the help of the television network (i.e., NBC) that hosted the Games. NBC held the American broadcast rights to all three Olympic Games studied. We used Internet sources to verify the parent companies of the brands that were advertised each year. Stock prices of these parent companies were found using Yahoo! Finance. Only companies that were publicly held and traded were used in the study. We identified changes in Olympic advertisers' stock prices over the four-week period that included the Monday before through the Friday after the Games. In total, there were 117 advertisers of the Games on telecasts which were broadcast in the U.S. for 2008, 2004, and 2000 Olympics. Figure 1 provides a breakdown of those advertisers, by industry sector. Results indicate the stock of the firms that advertised (O-Stocks) out-performed the S&P 500 during the period of interest and under-performed the S&P 500 during the earlier control periods. These same O-Stocks also outperformed the S&P 500 from the start of these Games through to the end of each Olympic year, and for six months beyond that. Price pressure linkage, signaling theory, high involvement viewers, and corporate activation strategies are believed to contribute to these positive results. Implications for advertisers and researchers are discussed, as are study limitations and future research directions.

• Journal of KIISE:Information Networking
• /
• v.29 no.6
• /
• pp.595-612
• /
• 2002

One of the most important issues in guaranteeing the high degree of QoS on mobile computing is how to reduce hand-off drops caused by lack of available bandwidth in a new cell. Each cell can request bandwidth reservation to its adjacent cells for hand-off calls. This reserved bandwidth can be used only for hand-offs, not for new calls. It is also important to determine how much of bandwidth should be reserved for hand-off calls because reserving too much would increase the probability of a new call being blocked. Therefore, it is essential to develop a new mechanism to provide QoS guarantee on a mobile computing environment by reserving an appropriate amount of bandwidth and call admission control. In this paper. bandwidth reservation and call admission control mechanisms are proposed to guarantee a consistent QoS for multimedia traffics on a mobile computing environment. For an appropriate bandwidth reservation, we propose an adaptive bandwidth reservation mechanism based on an MPP and a 2-tier cell structure. The former is used to predict a next move of the client while the latter to apply our mechanism only to the client with a high hand-off probability. We also propose a call admission control that performs call admission test only on PNC(Predicted Next Cell) of a client and its current cell. In order to minimize a waste of bandwidth caused by an erroneous prediction of client's location, we utilize a common pool and QoS adaptation scheme. In order evaluate the performance of our call admission control mechanism, we measure the metrics such as the blocking probability of new calls, dropping probability of hand-off calls, and bandwidth utilization. The simulation results show that the performance of our mechanism is superior to that of the existing mechanisms such as NR-CAT2, FR-CAT2, and AR-CAT2.

• Journal of the Society of Disaster Information
• /
• v.19 no.4
• /
• pp.976-983
• /
• 2023

Purpose: The dynamic behavior of a bridge structure under seismic loading depends on many uncertainties, such as the nature of the seismic waves and the material and geometric properties. However, not all uncertainties have a significant impact on the dynamic behavior of a bridge structure. Since probabilistic seismic performance evaluation considering even low-impact uncertainties is computationally expensive, the uncertainties should be identified by considering their impact on the dynamic behavior of the bridge. Therefore, in this study, a global sensitivity analysis was performed to identify the main parameters affecting the dynamic behavior of bridges with I-curved girders. Method: Considering the uncertainty of the earthquake and the material and geometric uncertainty of the curved bridge, a finite element analysis was performed, and a surrogate model was developed based on the analysis results. The surrogate model was evaluated using performance metrics such as coefficient of determination, and finally, a global sensitivity analysis based on the surrogate model was performed. Result: The uncertainty factors that have the greatest influence on the stress response of the I-curved girder under seismic loading are the peak ground acceleration (PGA), the height of the bridge (h), and the yield stress of the steel (fy). The main effect sensitivity indices of PGA, h, and fy were found to be 0.7096, 0.0839, and 0.0352, respectively, and the total sensitivity indices were found to be 0.9459, 0.1297, and 0.0678, respectively. Conclusion: The stress response of the I-shaped curved girder is dominated by the uncertainty of the input motions and is strongly influenced by the interaction effect between each uncertainty factor. Therefore, additional sensitivity analysis of the uncertainty of the input motions, such as the number of input motions and the intensity measure(IM), and a global sensitivity analysis considering the structural uncertainty, such as the number and curvature of the curved girders, are required.