• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.82-89
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• 2012

This paper presents the intelligent tracking algorithm for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. Fuzzy c-mean clustering and predicted impact point are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by fuzzy c-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. The filtering process in a series of the algorithm which estimates the target value recognize the nonlinear maneuvering target as linear one because the filter recognize only remained noise by extracting acceleration from the positional error. After filtering process, we get the estimates target by compensating extracted acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. To maximize the effectiveness of the proposed system, we construct the multiple model structure. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.132-139
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• 2015

Visual SLAM(Simultaneous Localization And Mapping) has been used widely to estimate a mobile robot's location. Visual SLAM estimates relative motions with static visual features over image sequence. Because visual SLAM methods assume generally static features in the environment, we cannot obtain precise results in dynamic situation including many moving objects: cars and human beings. This paper presents a stereo vision based SLAM method in dynamic environment. First, we extract disparity map with stereo vision and compute optical flow. We then compute disparity change that is the estimated flow field between stereo views. After examining the disparity change value, we detect ROIs(Region Of Interest) in disparity space to determine dynamic scene objects. In indoor environment, many structural planes like walls may be determined as false dynamic elements. To solve this problem, we segment the scene into planar structure. More specifically, disparity values by the stereo vision are projected to X-Z plane and we employ Hough transform to determine planes. In final step, we remove ROIs nearby the walls and discriminate static scene elements in indoor environment. The experimental results show that the proposed method can obtain stable performance in dynamic environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.44-49
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• 2016

This paper proposed the method to improve performance of a pixel, which has low accuracy, by applying image segmentation methods based on color and multi-threshold of brightness. Stereo matching is the process to find the corresponding point on the right image with the point on the left image. For this process, distance(depth) information in stereo images is calculated. However, in the case of a region which has textureless, stereo matching has low accuracy and bad pixels occur on the disparity map. In the proposed method, the relationship between adjacent pixels is considered for compensating bad pixels. Generally, the object has similar color and brightness. Therefore, by considering the relationship between regions based on segmented regions by means of color and multi-threshold of brightness respectively, the region which is considered as parts of same object is re-segmented. According to relationship information of segmented sets of pixels, bad pixels in the disparity map are compensated efficiently. By applying the proposed method, the results show a decrease of nearly 28% in the number of bad pixels of the image applied the method which is established.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.1
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• pp.47-57
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• 2016

In this paper, we measured human body signals in order to verify a active harness system that we developed for gait and balance training. The experimental procedure was validated by tests with 20 healthy male subjects. They conducted motions of Activities of Daily Living(ADL)(Normal Walking, Stand-to-Sit, Sit-to-Stand, Stair Walking Up, and Stair Walking Down) according to body weight support rates (0%, 30%, 50% of subjects' body weight). The effectiveness of the active harness system is verified by using the results of foot pressure distribution. In normal walking, the decrease of fore-foot pressure, lateral soleus muscle and biceps femoris muscle were remarkable. The result of stand-to-sit results motion indicated that the rear-foot pressure and tibialis anterior muscle activities exceptionally decreased according to body weight support. The stair walking down show the marked drop of fore-foot pressure and rectus femoris muscle activities. The sit-to-stand and stair walking up activities were inadequate about the effect of body weight support because the velocity of body weight support system was slower than male's activity.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.2
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• pp.89-96
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• 2009

As increasing the number of digital control devices installed on aircrafts and their transmission speed, various digital data buses have been introduced to provide reliable and high-speed characteristics. These characteristics of avionics data bus are highly related on the fault-tolerant performance which can make minimize jitter and loss during data transfer. In this paper, we concerned about a new traffic shaping scheme for increasing the reliability of Avionics Full Duplex Switched Ethernet (AFDX) systems based on ARINC 664 standard. We note that the conventional AFDX with a single regulator per virtual link system may produce aggregated traffics as the number of virtual links increasing. The aggregated traffic results in large jitters among frames. To remedy for the jitter and loss of data, we propose a dual regulator scheme for the AFDX system. The purpose of the additional regulator is to additionally regulate aggregated traffics from a number of per virtual link regulators. Using NS-2 simulator, we show that the proposed scheme provides a better performance than the single regulator one. It is worthwhile note that the proposed AFDX with Dual Regulator scheme can be employed to not only aircraft networks but other QoS sensitive networks for robot and industrial control systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.343-350
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• 2022

It is worth verifying the effectiveness of data integration between data with different features. This study investigated whether the data integration affects the accuracy of deep neural network (DNN), and which integration method shows the best improvement. This study used two different public datasets. One public dataset was taken in an actual farm in India. And another was taken in a laboratory environment in Korea. Leaf images were selected from two different public datasets to have five classes which includes normal and four different types of plant diseases. DNN used pre-trained VGG16 as a feature extractor and multi-layer perceptron as a classifier. Data were integrated into three different ways to be used for the training process. DNN was trained in a supervised manner via the integrated data. The trained DNN was evaluated by using a test dataset taken in an actual farm. DNN shows the best accuracy for the test dataset when DNN was first trained by images taken in the laboratory environment and then trained by images taken in the actual farm. The results show that data integration between plant images taken in a different environment helps improve the performance of deep neural networks. And the results also confirmed that independent use of plant images taken in different environments during the training process is more effective in improving the performance of DNN.

• Journal of Chest Surgery
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• v.41 no.5
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• pp.598-604
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• 2008

Background: The improvements in endoscopic equipment and surgical robots has encouraged the performance of minimally invasive cardiac operations. Yet only a few Korean studies have compared this procedure with the sternotomy approach. Material and Method: Between December 2005 and July 2007, 48 patients (group A) underwent minimally invasive cardiac surgery with AESOP through a small right thoracotomy. During the same period, 50 patients (group B) underwent conventional surgery. We compared the operative time, the operative results, the post-operative pain and the recovery of both groups. Result: There was no hospital mortality and there were no significant differences in the incidence of operative complications between the two groups. The operative $(292.7{\pm}61.7\;and\;264.0{\pm}47.9min$, respectively; p=0.01) and CPB times ($128.4{\pm}37.6\;and\;101.7{\pm}32.5min$, respectively; <0.01) were longer for group A, whereas there was no difference between the aortic cross clamp times ($82.1{\pm}35.0\;and\;87.8{\pm}113.5min$, respectively; p=0.74) and ventilator times ($18.0{\pm}18.4\;and\;19.7{\pm}9.7$ hr, respectively; p=0.57) between the groups. The stay on the ICU $(53.2{\pm}40.2\;and\;72.8{\pm}42.1hr$, respectively; p=0.02) and the hospitalization time ($9.7{\pm}7.2\;and\;14.8{\pm}11.9days$, respectively; p=0.01) were shorter for group A. The Patients in group B had more transfusions, but the difference was not significant. For the overall operative intervals, which ranged from one to four weeks, the pair score was significantly lower for the patients of group A than for the patients of group B. In terms of the postoperative activities, which were measured by the Duke Activity Scale questionnaire, the functional status score was clearly higher for group A compared to group B. The analysis showed no difference in the severity of either post-repair of mitral ($0.7{\pm}1.0\;and\;0.9{\pm}0.9$, respectively; p=0.60) and tricuspid regurgitation ($1.0{\pm}0.9\;and\;1.1{\pm}1.0$, respectively; p=0.89). In both groups, there were no valve related complications, except for one patient with paravalvular leakage in each group. Conclusion: These results show that compared with the median sternotomy patients, the patients who underwent minimally invasive surgery enjoyed significant postoperative advantages such as less pain, a more rapid return to full activity, improved cosmetics and a reduced hospital stay. The minimally invasive surgery can be done with similar clinical safety compared to the conventional surgery that's done through a median sternotomy.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.