• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.1-16
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• 2024

Due to its complexity and high diagnosis and treatment costs, heart attack (HA) is the top cause of death globally. Heart failure's widespread effect and high morbidity and death rates make accurate and fast prognosis and diagnosis crucial. Due to the complexity of medical data, early and accurate prediction of HA is difficult. Healthcare providers must evaluate data quickly and accurately to intervene. This novel hybrid approach predicts HA using Long Short-Term Memory (LSTM) networks, Deep belief networks (DBNs) with attention mechanism, and robust data mining to fill this essential gap. HA is predicted using Kaggle, PhysioNet, and UCI datasets. Wearable sensor data, ECG signals, and demographic and clinical data provide a solid analytical base. To maintain consistency, ECG signals are normalized and segmented after thorough cleaning to remove missing values and noise. Feature extraction employs complex approaches like Principal Component Analysis (PCA) and Autoencoders to pick time-domain (MNN, SDNN, RMSSD, PNN50) and frequency-domain (PSD at VLF, LF, HF bands) characteristics. The hybrid model architecture uses LSTM networks for sequence learning and DBNs for feature representation and selection to create a robust and comprehensive prediction model. Accuracy, precision, recall, F1-score, and ROC-AUC are measured after cross-entropy loss and SGD optimization. The LSTM-DBN model outperforms predictive methods in accuracy, sensitivity, and specificity. The findings show that several data sources and powerful algorithms can improve heart attack predictions. The proposed architecture performed well on many datasets, with an accuracy rate of 96.00%, sensitivity of 98%, AUC of 0.98, and F1-score of 0.97. High performance proves this system's dependability. Moreover, the proposed approach is outperformed compared to state-of-the-art systems.

• Journal of Korea Water Resources Association
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• v.38 no.5 s.154
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• pp.345-354
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• 2005

Time Domain Reflectometry (TDR) with multiplex system has been installed to configure the spatial and temporal characteristics of soil moisture at the Bumreunsa hillslope of Sulmachun Watershed. An intensive surveying was performed to build a refined digital elevation model (DEM) and flow determination algorithms with inverse surveying have been applied to establish an efficient soil moisture monitoring system. Soil moisture data were collected through intensive monitoring during 380 hrs in November of 2003. Soil moisture data shows corresponding variation characteristics of soil moisture on the upper, middle and lower parts of the hillslope which were classified from terrain analysis. Measured soil moisture data have been discussed on the context of physical process of hydrological modeling.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.147-156
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• 2023

Efforts to employ smart home sensors to monitor the indoor activities of elderly single residents have been made to assess the feasibility of a safe and healthy lifestyle. However, the bathroom remains an area of blind spot. In this study, we have developed and evaluated a new edge computer device that can automatically detect water usage activities in the bathroom and record the activity log on a cloud server. Three kinds of sound as flushing, showering, and washing using wash basin generated during water usage were recorded and cut into 1-second scenes. These sound clips were then converted into a 2-dimensional image using MEL-spectrogram. Sound data augmentation techniques were adopted to obtain better learning effect from smaller number of data sets. These techniques, some of which are applied in time domain and others in frequency domain, increased the number of training data set by 30 times. A deep learning model, called CRNN, combining Convolutional Neural Network and Recurrent Neural Network was employed. The edge device was implemented using Raspberry Pi 4 and was equipped with a condenser microphone and amplifier to run the pre-trained model in real-time. The detected activities were recorded as text-based activity logs on a Firebase server. Performance was evaluated in two bathrooms for the three water usage activities, resulting in an accuracy of 96.1% and 88.2%, and F1 Score of 96.1% and 87.8%, respectively. Most of the classification errors were observed in the water sound from washing. In conclusion, this system demonstrates the potential for use in recording the activities as a lifelog of elderly single residents to a cloud server over the long-term.

• Asia pacific journal of information systems
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• v.25 no.1
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• pp.83-97
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• 2015

Recently, the exhibition industry has developed rapidly with the development of information technologies. Most exhibitors in an exhibition plan and deploy many events that may provide advantages to visitors as a method of effective promotion. The growth and propagation of wireless technologies is a powerful marketing tool for exhibitors. However, exhibitors still rely on domain experts who are costly and time consuming because of the manual knowledge input procedure. Moreover, it is prone to biases and errors and not suitable for managing fast-growing and tremendous amounts of data that far exceed a human's ability to comprehend. To overcome these problems, data mining technology may be a great alternative, but it needs to be fit to each exhibition. This study uses data mining technology with the Predictive Model Markup Language (PMML) to suggest a system that supports intelligent services and that improves stakeholder satisfaction. This system provides advantages to the exhibitor, show organizer, and system designer, and is first enhanced by integrating data mining technologies through the knowledge of exhibition experts. Second, using the PMML, the system can automate the process of applying data mining models to solve real-time processing problems in the exhibition environment.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.374-381
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• 2023

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

• Asian Oncology Nursing
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• v.6 no.2
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• pp.104-110
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• 2006

Purpose: This study was to investigate the satisfaction for voluntary activity and the meaning of life in hospice volunteers. Method: Data were obtained by self-reported questionnaire from 102 volunteers and were analyzed using a t-test, ANOVA and Pearson's correlation. Result: The mean score of the satisfaction for hospice volunteer activity was $2.48{\pm}0.38$. Of the domains of the satisfaction, the experience domain had the highest mean score $(2.93{\pm}0.53)$, and the social exchange domain had the lowest mean score $(1.65{\pm}0.63)$. The mean score of the meaning of life was $3.20{\pm}0.33$. The score of satisfaction was significantly different by economic status, and volunteering time per week. The score of meaning of life was statically different by financial status. There was a positive correlation between satisfaction for voluntary activity and the meaning of life. Conclusion: Satisfaction for hospice volunteer activity was significantly related to their meaning of life. In order to increase the satisfaction of volunteers, it is important to consider their financial aspect and the volunteering time.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.5
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• pp.566-582
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• 2018

A moored barge alongside quay can be influenced by a nearby passing ship and its ship-generated waves. In this study, a time-domain numerical method based on a three-dimensional potential flow solver is developed to investigate the passing ship problem with a moored barge alongside quay. Potential flows around the passing ship and the moored barge alongside a quay is directly solved by using a classical finite element method. Total computational meshes including a passing ship, a moored barge and a quay is updated at each step with an efficient re-mesh algorithm. To validate the developed numerical method, a conventional ship wave problem and a passing ship problem on the open sea has been solved and the solutions are compared with the existing data. Then, a series of numerical computations were carried out to investigate the passing ship effect on a moored barge alongside quay. The characteristics of the passing ship effects are studied with varying the simulation parameters such as passing ship speed, separation distance, wall distances and waves. Focus is made on hydrodynamic forces due to the passing ship effect and its ship waves.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.310-312
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• 2004

The aim of this study was to analyze the characteristics of knee joint sound in frequency domain and classify the knee joint diseases. The spectral analysis of knee joint sounds was performed using LPC(Linear Predictive Coding) and Wigner-Ville distribution. Ten normal subjects and 5 patients with meniscal tearing were enrolled. Each subject was seated on a chair and underwent active knee flexion and extension for 60 seconds. Sampling frequency was 10kHz and electronic stethoscope and electro-goniometer were applied during the knee motion for data collection. The spectral analysis showed 3 peaks in both groups and the difference energy distribution in time-frequency domain. These results suggest that the diagnosis of knee joint pathology using the auscultation could be easier and more correct.

• Journal of Broadcast Engineering
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• v.16 no.6
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• pp.927-940
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• 2011

In this paper, we improve a time-domain channel estimation algorithm with multi-input multi-output (MIMO) systems for the next-generation digital television (DTV). The conventional algorithm use orthogonal codes for separating channels from the time-domain orthogonal frequency division multiplexing (OFDM) symbols. However. it has the disadvantage of reduced data-rate because of many pilots. The improved algorithm shows better performance than the conventional one even with reduced number of pilots. The improved algorithm is evaluated by computer simulations.

• Journal of Ocean Engineering and Technology
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• v.28 no.6
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• pp.508-516
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• 2014

A numerical method to investigate the non-linear motion characteristics of a TLP is established. A time domain simulation that includes the memory effect using the convolution integral is used to consider the transient effect of TLP motion. The hydrodynamic coefficients and wave force are calculated using a potential flow model based on the HOBEM(higher order boundary element method). The viscous drag force acting on the platform and tendons is also considered by using Morison’s drag. The results of the present numerical method are compared with experimental data. The focus is the nonlinear effect due to the viscous drag force on the TLP motion. The ringing, springing, and drift motion are due to the drag force based on Morison's formula.