• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.59-69
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• 2005

Data mining is the method to find useful information for large amounts of data in database It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze Gyeongnam social indicator survey data by 2001 using association rule technique for environment information. Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We can use to environmental preservation and environmental improvement by association rule outputs

• Neurospine
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• v.15 no.4
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• pp.306-322
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• 2018

This review introduces the current technique of photoacoustic imaging as it is applied in imaging-guided surgery (IGS), which provides the surgeon with image visualization and analysis capabilities during surgery. Numerous imaging techniques have been developed to help surgeons perform complex operations more safely and quickly. Although surgeons typically use these kinds of images to visualize targets hidden by bone and other tissues, it is nonetheless more difficult to perform surgery with static reference images (e.g., computed tomography scans and magnetic resonance images) of internal structures. Photoacoustic imaging could enable real-time visualization of regions of interest during surgery. Several researchers have shown that photoacoustic imaging has potential for the noninvasive diagnosis of various types of tissues, including bone. Previous studies of the surgical application of photoacoustic imaging have focused on cancer surgery, but photoacoustic imaging has also recently attracted interest for spinal surgery, because it could be useful for avoiding pedicle breaches and for choosing an appropriate starting point before drilling or pedicle probe insertion. This review describes the current instruments and clinical applications of photoacoustic imaging. Its primary objective is to provide a comprehensive overview of photoacoustic IGS in spinal surgery.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.29-35
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• 2024

TCP is considered as one of the major candidate transport protocols even for constrained IoT networks..In our previous work, we investigated the congestion control mechanism of the uIP TCP. Since the uIP TCP sets the window size to one segment by default, managing the retransmission timer is the primary approach to congestion control. However, the original uIP TCP sets the retransmission timer based on the fixed RTO, it performs poorly when a radio duty cycling mechanism is enabled and the hidden terminal problem is severe. In our previous work, we proposed a TCP retransmission timer adjustment scheme for uIP TCP which adopts the notion of weak RTT estimation of CoCoA, exponential backoffs with variable limits, and dithering. Although our previous work showed that the proposed retransmission timer adjustment scheme can improve performance, we observe that the scheme often causes a node to set the retransmission timer for an excessively too long time period. In this work, we show that slightly modifying the dithering mechanism of the previous scheme is effective for improving TCP fairness.

• Structural Monitoring and Maintenance
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• v.5 no.3
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• pp.363-377
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• 2018

The proper functioning of critical points on transport infrastructure is decisive for the entire network. Tunnels and bridges certainly belong to the critical points of the surface transport network, both road and rail. Risk management should be a holistic and dynamic process throughout the entire life cycle. However, the level of risk is usually determined only during the design stage mainly due to the fact that it is a time-consuming and costly process. This paper presents a simplified quantitative risk analysis method that can be used any time during the decades of a tunnel's lifetime and can estimate the changing risks on a continuous basis and thus uncover hidden safety threats. The presented method is a decision support system for tunnel managers designed to preserve or even increase tunnel safety. The CAPITA method is a deterministic scenario-oriented risk analysis approach for assessment of mortality risks in road tunnels in case of the most dangerous situation - a fire. It is implemented through an advanced risk analysis CAPITA SW. Both, the method as well as the resulting software were developed by the authors' team. Unlike existing analyzes requiring specialized microsimulation tools for traffic flow, smoke propagation and evacuation modeling, the CAPITA contains comprehensive database with the results of thousands of simulations performed in advance for various combinations of variables. This approach significantly simplifies the overall complexity and thus enhances the usability of the resulting risk analysis. Additionally, it provides the decision makers with holistic view by providing not only on the expected risk but also on the risk's sensitivity to different variables. This allows the tunnel manager or another decision maker to estimate the primary change of risk whenever traffic conditions in the tunnel change and to see the dependencies to particular input variables.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.429-438
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• 1997

Mechanical valve is one of the most widely used implantable artificial organs of which the reliability is so important that its failure means the death of patient. Therefore early noninvasive detection is essentially required, though mechanical valve failure with thrombosis is the most common. The objective of this paper is to detect the thrombosis formation by spectral analysis and neural network. Using microphone and amplifier, we measured the sound from the mechanical valve which is attached to the pneumatic ventricular assist device. The sound was sampled by A/D converter(DaqBook 100) and the periodogram is the main algorithm for obtaining spectrum. We made the thrombosis models using pellethane and silicon and they are thrombosis model on the valvular disk, around the sewing ring and fibrous tissue growth across the orifice of valve. The performance of the measurment system was tested firstly using 1 KHz sinusoidal wave. The measurement system detected well 1KHz spectrum as expected. The spectrum of normal and 5 kinds of thrombotic valve were obtained and primary and secondary peak appeared in each spectrum waveform. We find that the secondary peak changes according to the thrombosis model. So to distinguish the secondary peak of normal and thrombotic valve quantatively, 3 layer back propagation neural network, which contains 7, 000 input node, 20 hidden layer and 1 output was employed The trained neural network can distinguish normal and valve with more than 90% probability. As a conclusion, the noninvasive monitoring of implanted mechanical valve is possible by analysing the acoustical spectrum using neural network algorithm and this method will be applied to the performance evaluation of other implantable artificial organs.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.616-625
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• 2021

Deep neural networks are an approximation method that approximates an arbitrary function to a linear model and then repeats additional approximation using a nonlinear active function. In this process, the method of evaluating the performance of approximation uses the loss function. Existing in-depth learning methods implement approximation that takes into account loss functions in the linear approximation process, but non-linear approximation phases that use active functions use non-linear transformation that is not related to reduction of loss functions of loss. This study proposes parametric activation functions that introduce scale parameters that can change the scale of activation functions and location parameters that can change the location of activation functions. By introducing parametric activation functions based on scale and location parameters, the performance of nonlinear approximation using activation functions can be improved. The scale and location parameters in each hidden layer can improve the performance of the deep neural network by determining parameters that minimize the loss function value through the learning process using the primary differential coefficient of the loss function for the parameters in the backpropagation. Through MNIST classification problems and XOR problems, parametric activation functions have been found to have superior performance over existing activation functions.