• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.359-365
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• 2023

In order to satisfy the requirements of dynamic X-ray imaging with high frame rate and low image lag, minimizing parasitic capacitance in photodiode and overlapped electrodes in pixels is critically required. This study presents duoPIX^TM dynamic X-ray imaging sensor composing of readout thin film transistor, reset thin film transistor and photodiode in a pixel. Furthermore, dynamic X-ray detector using duoPIX^TM imaging sensor was manufactured and evaluated its X-ray imaging performances such as frame rate, sensitivity, noise, MTF and image lag. duoPIX^TM dynamic X-ray detector has 150 × 150 mm² imaging area, 73 um pixel pitch, 2048 × 2048 matrix resolution(4.2M pixels) and maximum 50 frames per second. By means of comparison with conventional dynamic X-ray detector, duoPIX^TM dynamic X-ray detector showed overall better performances than conventional dynamic X-ray detector as shown in the previous study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.240-257
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• 2022

Autonomous vehicles use various local sensors such as camera, radar, and lidar to perceive the surrounding environment. However, it is difficult to predict the movement of vulnerable road users using only local sensors that are subject to limits in cognitive range. This is true especially when these users are blocked from view by obstacles. Hence, this paper developed an algorithm for collision avoidance with VRU using V2X information. The main purpose of this collision avoidance system is to overcome the limitations of the local sensors. The algorithm first evaluates the risk of collision, based on the current driving condition and the V2X information of the VRU. Subsequently, the algorithm takes one of four evasive actions; steering, braking, steering after braking, and braking after steering. A simulation was performed under various conditions. The results of the simulation confirmed that the algorithm could significantly improve the performance of the collision avoidance system while securing vehicle stability during evasive maneuvers.

• The Journal of Korean Society for Radiation Therapy
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• v.21 no.2
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• pp.83-88
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• 2009

Purpose: Cone beam computed tomography (CBCT) using an on board imager (OBI) can check the movement and setup error in patient position and target volume by comparing with the image of computer simulation treatment in real.time during patient treatment. Thus, this study purposed to check the change and movement of patient position and target volume using CBCT in IMRT and calculate difference from the treatment plan, and then to correct the position using an automated match system and to test the accuracy of position correction using an electronic portal imaging device (EPID) and examine the usefulness of CBCT in IMRT and the accuracy of the automatic match system. Materials and Methods: The subjects of this study were 3 head and neck patients and 1 pelvis patient sampled from IMRT patients treated in our hospital. In order to investigate the movement of treatment position and resultant displacement of irradiated volume, we took CBCT using OBI mounted on the linear accelerator. Before each IMRT treatment, we took CBCT and checked difference from the treatment plan by coordinate by comparing it with the image of CT simulation. Then, we made correction through the automatic match system of 3D/3D match to match the treatment plan, and verified and evaluated using electronic portal imaging device. Results: When CBCT was compared with the image of CT simulation before treatment, the average difference by coordinate in the head and neck was 0.99 mm vertically, 1.14 mm longitudinally, 4.91 mm laterally, and 1.07o in the rotational direction, showing somewhat insignificant differences by part. In testing after correction, when the image from the electronic portal imaging device was compared with DRR image, it was found that correction had been made accurately with error less than 0.5 mm. Conclusion: By comparing a CBCT image before treatment with a 3D image reconstructed into a volume instead of a 2D image for the patient's setup error and change in the position of the organs and the target, we could measure and correct the change of position and target volume and treat more accurately, and could calculate and compare the errors. The results of this study show that CBCT was useful to deliver accurate treatment according to the treatment plan and to increase the reproducibility of repeated treatment, and satisfactory results were obtained. Accuracy enhanced through CBCT is highly required in IMRT, in which the shape of the target volume is complex and the change of dose distribution is radical. In addition, further research is required on the criteria for match focus by treatment site and treatment purpose.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.