• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.37-46
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• 2023

This study aims to evaluate the usefulness of Deep Learning Image Reconstruction (TrueFidelity, TF), the image quality of existing Filtered Back Projection (FBP) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) were compared. Noise, CNR, and SSIM were measured by obtaining images with doses fixed at 17.29 mGy and altered to 10.37 mGy, 12.10 mGy, 13.83 mGy, and 15.56 mGy in reconstruction techniques of FBP, ASIR-V 50%, and TF-H. TF-H has superior image quality compared to FBP and ASIR-V when the reconstruction technique change is given at 17.29 mGy. When dose changes were made, Noise, CNR, and SSIM were significantly different when comparing 10.37 mGy TF-H and FBP (p<0.05), and no significant difference when comparing 10.37 mGy TF-H and ASIR-V 50% (p>0.05). TF-H has a dose-reduction effect of 30%, as the highest dose of 15.56 mGy ASIR-V has the same image quality as the lowest dose of 10.37 mGy TF-H. Thus, Deep Learning Reconstruction techniques (TF) were able to reduce dose compared to Iterative Reconstruction techniques (ASIR-V) and Filtered Back Projection (FBP). Therefore, it is considered to reduce the exposure dose of patients.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.1
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• pp.65-75
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• 2016

Purpose : By analyzing seroma volume changes in the patients who underwent Partial breast radiation therapy after breast conserving surgery, we try to contribute to the improvement of radiotherapy effect. Materials and Methods : Enrolled 20 patients who underwent partial breast radiation therapy by ViewRay MRIdian System were subject. After seeking for the size of the removed sample in the patients during surgery and obtained seroma volume changes on a weekly basis. On the Basis of acquired volume, it was compared with age, term from start of the first treatment after surgery, BMI (body mass index) and the extracted sample size during surgery. And using the ViewRay MRIdian RTP System, the figure was analyzed by PTV(=seroma volume + margin) to obtain a specific volume of the Partial breast radiation therapy. Results : The changes of seroma volume from MR simulation to the first treatment (a week) is 0~5% in 8, 5~10% in 3, 10 to 15% in 2, and 20% or more in 5 people. Two patients(A, B patient) among subjects showed the biggest change. The A patient's 100% of the prescribed dose volume is 213.08 cc, PTV is 181.93 cc, seroma volume is 15.3 cc in initial plan. However, while seroma volume decreased 65.36% to 5.3 cc, 100% of the prescribed dose volume was reduced to 3.4% to 102.43 cc and PTV also did 43.6% to 102.54 cc. In the case of the B patient, seroma volume decreased 42.57% from 20.2 cc to 11.6 cc. Because of that, 100% of the prescribed dose volume decreased 8.1% and PTV also did to 40%. Conclusion : As the period between the first therapy and surgery is shorter, the patient is elder and the size of sample is smaller than 100 cc, the change grow bigger. It is desirable to establish an adaptive plan according to each patient's changes of seroma volume through continuous observation. Because partial breast patients is more sensitive than WBRT patients about dose conformity in accordance with the volume change.

• Journal of KIISE:Databases
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• v.30 no.6
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• pp.629-640
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• 2003

A shared disk (SD) cluster couples multiple nodes for high performance transaction processing, and all the coupled nodes share a common database at the disk level. In the SD cluster, a transaction routing corresponds to select a node for an incoming transaction to be executed. An affinity-based routing can increase local buffer hit ratio of each node by clustering transactions referencing similar data to be executed on the same node. However, the affinity-based routing is very much non-adaptive to the changes in the system load, and thus a specific node will be overloaded if transactions in some class are congested. In this paper, we propose a dynamic transaction routing scheme that can achieve an optimal balance between affinity-based routing and dynamic load balancing of all the nodes in the SD cluster. The proposed scheme is novel in the sense that it can improve the system performance by increasing the local buffer hit ratio and reducing the buffer invalidation overhead.

• The Journal of the Acoustical Society of Korea
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• v.16 no.4
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• pp.11-20
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• 1997

The passive noise control techniques used until now cancel the noise in terms of the characteristics of materials, which increase the mass and the dimension and have a limit that is effective only to attenuate the high frequency components of the noise. But the active noise control techniques developed in recent years have merits that they decrease the mass and the dimension and are effective to attenuating the low frequency noises. In this paper, the real-time active noise control system attenuating the engine booming noise in a car using the digital signal processing(DSP) techniques in terms of the principle of active noise control. The multiple-error filtered-x LMS(Least-Mean Square) algorithm is used as the adaptive algorithm for active noise control and is implemented using the DSP processor Motorola DSP56001 as a controller. According to the result that the experiments are performed for the engine as the RPM changes in a car, the noise attenuating performances are achieved in an overall car interior and is verified to be 20 dB higher for pure-tone and globally, 15 dB.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.233-242
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• 2009

The security of medical information need continued research about steady and flexible security model because of privacy of patient's as well as directly relation in the patient's life. In particular, u-healthcare environment is need flexible and detailed access control by variety changes of context. Control model analyzed relation of resource and authority, and analyzed authority about all accessible resource from access point using K2BASE. The context-based access control model can change flexibly authority change and role, and can obtain resource of authority granted and meaningly connected resource. As a result, this thesis can apply flexible and adaptive access control model at u-healthcare domain which context change various.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1495-1504
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• 2013

Renewable energy integration and increased system complexities make system operator maintain supply and demand balance harder than before. To keep the grid frequency in a stable range, an appropriate spinning reserve margin should be procured with consideration of ever-changing system situation, such as demand, wind power output and generator failure. This paper propose a novel concept of dynamic reserve, which arrange different spinning reserve margin depending on time. To investigate the effectiveness of the proposed dynamic reserve, we developed a new short-term reliability criterion that estimates the probability of a spinning reserve shortage events, thus indicating grid frequency stability. Uncertainties of demand forecast error, wind generation forecast error and generator failure have been modeled in probabilistic terms, and the proposed spinning reserve has been applied to generation scheduling. This approach has been tested on the modified IEEE 118-bus system with a wind farm. The results show that the required spinning reserve margin changes depending on the system situation of demand, wind generation and generator failure. Moreover the proposed approach could be utilized even in case of system configuration change, such as wind generation extension.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.297-302
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• 2012

In this paper, we present a object detection and tracking method based on image context analysis. It is robust from the image variations such as complicated background, dynamic movement of the object. Image context analysis is carried out using the hybrid network of k-means and RBF. The proposed object detection employs context-driven adaptive Bayesian framework to relive the effect due to uneven object images. The proposed method used feature vector generator using 2D Haar wavelet transform and the Bayesian discriminant method in order to enhance the speed of learning. The system took less time to learn, and learning in a wide variety of data showed consistent results. After we developed the proposed method was applied to real-world environment. As a result, in the case of the object to detect pass outside expected area or other changes in the uncertain reaction showed that stable. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.9-18
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• 2015

In recent years, the number of applications embedded in the various devices such as a smart phone is getting larger. Due to the frequent changes of states in the execution environment, various malfunctions may occur. In order to handle the issue, this paper suggests an approach to detecting method-level failures in the legacy software systems. We can determine if the software executes the abnormal behavior based on the behavior model. However, when we apply the context-sensitive behavior model to the method-level, several problems happen such as false alarms and monitoring overhead. To tackle those issues, we propose CIBFD (Context-Insensitive Behavior Model-based Failure Detection) method. Through the case studies, we compare CIBFD method with the existing method. In addition, we analyze the effectiveness of the method for each application domains.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.77-82
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• 2018

This paper is concerned with the performance analysis of a modified stop-and-go generalized multi-modulus algorithm (MSAG-GMMA) adaptive blind equalization algorithm with variable step size. The proposed algorithm multiplies the fixed step size by the error signal of the decision-oriented algorithm in the equalization coefficient update equation, and changes the step size according to the error size. Also, the MSAG-GMMA having a fixed step size is operated so as to maintain a fast convergence speed from a certain threshold to a steady state by determining the error signal size of the decision-directed algorithm, and when the MSAG-GMMA to work To evaluate the performance of the proposed algorithm, we use the ensemble ISI, ensemble-averaged MSE, and equalized constellation obtained from the output of the equalizer as the performance index. Simulation results show that the proposed algorithm has faster convergence speeds than MMA, GMMA, and MSAG-GMMA and has a small residual error in steady state.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.276-283
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• 2018

Climate changes consistently cause coastal accidents such as coastal flooding, so the studies on monitoring the marine environments are progressing to prevent and reduce the damage from coastal accidents. In this paper, we propose a new method to predict the sea level which can be applied to coastal monitoring systems to observe the variation of sea level and warn about the dangers. Existing sea level models are very complicated and need a lot of tidal data, so they are not proper for real-time prediction systems. On the other hand, the proposed algorithm is very simple but precise in short period such as one or two hours since we use the measured data from the sensor. The proposed method uses Kalman filter algorithm for harmonic analysis and double exponential smoothing for additional error correction. It is shown by experimental results that the proposed method is simple but predicts the sea level accurately.