• 대한후두음성언어의학회지
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• 제7권1호
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• pp.43-49
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• 1996

In this study, remote articulation training system which connects the hearing disabled trainee and the speech therapist via B-ISDN is introduced. The hearing disabled does not have the hearing feedback of his own pronuciation, and the chance of watching his speech organs movement trajectory will offer him the self-training of articulation. So the system has two purposes of self articulation training and trainer's on-line checking in remote place. We estimate the vocal tract articultory movements from the speech signal using inverse modelling and display the movement trajectoy on the sideview of human face graphically. The trajectories of trainees articulation is displayed along with the reference trajectories, so the trainee can control his articulating to make the two trajectories overlapped. For on-line communication and ckecking training record the system has the function of video conferencing and tranferring articulatory data.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2001년도 학술발표회 발표논문집
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• pp.59-64
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• 2001

The objectives of the thesis are to propose a pattern classification method for remote sensing data using artificial neural network. First, we apply the error back propagation algorithm to classify the remote sensing data. In this case, the classification performance depends on a training data set. Using the training data set and the error back propagation algorithm, a layered neural network is trained such that the training pattern are classified with a specified accuracy. After training the neural network, some pixels are deleted from the original training data set if they are incorrectly classified and a new training data set is built up. Once training is complete, a testing data set is classified by using the trained neural network. The classification results of Landsat TM data show that this approach produces excellent results which are more realistic and noiseless compared with a conventional Bayesian method.

• Archives of Plastic Surgery
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• 제44권5호
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• pp.384-389
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• 2017

Background The aim of this study was to investigate the effects of remote ischemic conditioning on ischemia-reperfusion injury in rat muscle flaps histopathologically and biochemically. Methods Thirty albino rats were divided into 5 groups. No procedure was performed in the rats in group 1, and only blood samples were taken. A gracilis muscle flap was elevated in all the other groups. Microclamps were applied to the vascular pedicle for 4 hours in order to achieve tissue ischemia. In group 2, no additional procedure was performed. In groups 3, 4, and 5, the right hind limb was used and 3 cycles of ischemia-reperfusion for 5 minutes each (total, 30 minutes) was applied with a latex tourniquet (remote ischemic conditioning). In group 3, this procedure was performed before flap elevation (remote ischemic preconditoning). In group 4, the procedure was performed 4 hours after flap ischemia (remote ischemic postconditioning). In group 5, the procedure was performed after the flap was elevated, during the muscle flap ischemia episode (remote ischemic perconditioning). Results The histopathological damage score in all remote conditioning ischemia groups was lower than in the ischemic-reperfusion group. The lowest histopathological damage score was observed in group 5 (remote ischemic perconditioning). Conclusions The nitric oxide levels were higher in the blood samples obtained from the remote ischemic perconditioning group. This study showed the effectiveness of remote ischemic conditioning procedures and compared their usefulness for preventing ischemiareperfusion injury in muscle flaps.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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• pp.114-117
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• 1996

In this study, remote articulation training system which connects the hearing disabled trainee and the speech therapist via B-ISDN is introduced. The hearing disabled does not have the hearing feedback of his own pronunciation, and the chance of watching his speech organs' movement trajectory will offer him the self-training of articulation. So the system has two purposes of self articulation training and trainer's on-line checking in remote place. We estimate the vocal tract articulatory movements from the speech signal using inverse modelling and display the movement trajectory on the sideview of human face graphically. The trajectories of trainees' articulation is displayed along with the reference trajectories, so the trainee can control his articulating to make the two trajectories overlapped. For on-line communication and ckecking training record, the system has the function of video conferencing and transferring articulatory data.

• 대한원격탐사학회지
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• 제24권5호
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• pp.517-524
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• 2008

In order to classify an satellite imagery into geospatial features of interest, the supervised classification needs to be trained to distinguish these features through training sampling. However, even though an imagery is classified, different results of classification could be generated according to operator's experience and expertise in training process. Users who practically exploit an classification result to their applications need the research accomplishment for the consistent result as well as the accuracy improvement. The experiment includes the classification results for training process used VITD polygons as a prior probability and training parameter, instead of manual sampling. As results, classification accuracy using VITD polygons as prior probabilities shows the highest results in several methods. The training using unsupervised classification with VITD have produced similar classification results as manual training and/or with prior probability.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.795-808
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• 2020

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.

• 대한원격탐사학회지
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• 제18권3호
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• pp.155-162
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• 2002

Neural networks have been focused on as a robust classifier for the remotely sensed imagery due to its statistical independency and teaming ability. Also the artificial neural networks have been reported to be more tolerant to noise and missing data. However, unlike the conventional statistical classifiers which use the statistical parameters for the classification, a neural network classifier uses individual training sample in teaming stage. The training performance of a neural network is know to be very sensitive to the discrepancy of the number of the training samples of each class. In this paper, the effect of the population discrepancy of training samples of each class was analyzed with three layered feed forward network. And a method for reducing the effect was proposed and experimented with Landsat TM image. The results showed that the effect of the training sample size discrepancy should be carefully considered for faster and more accurate training of the network. Also, it was found that the proposed method which makes teaming rate as a function of the number of training samples in each class resulted in faster and more accurate training of the network.

• 해양환경안전학회지
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• 제28권6호
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• pp.1002-1012
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• 2022

4차 산업이 발전함에 따라 해상운송 분야에서도 자율운항선박의 연구가 진행되고 있다. 현재 2, 3단계의 자율운항선박이 운항을 하고 있으며 육상에서 원격조종의 장비로 감시하며 상황에 따라 운항에 개입하는 육상원격제어사가 이미 활용되고 있다. 하지만 이들의 교육과정이 국제적으로 정립되지 않아 부적격한 육상원격제어사에 의한 사고 위험성이 높아지고 있다. 본 논문에서는 육상원격제어사에 필요한 교육을 기존의 해기사 교육 중 육상원격제어사에게 필요한 교육과 원격제어환경에서 필요한 교육으로 구성하였고 효과적인 교육의 활용을 위해 비기술적 역량교육을 포함하였다. 이러한 교육과정은 신속하게 활용될 수 있으며 역량평가를 통한 해사안전에 부합하는 신규 육상원격제어사를 배출할 수 있다. 그리고 기존의 선원들도 육상원격제어사로 전직할 수 있는 교육을 제공할 수 있다.

• 한국항공운항학회지
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• 제21권1호
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• pp.74-79
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• 2013

Early remotely-piloted aircraft system (RPAS) development focused on simple reconnaissance to obtain information by visual observation for military demands. Currently, various types of remotely-piloted aircraft (RPA) is being developed worldwide for applications in many different areas. As RPA avionics are combined with CNS/ATM technologies, RPA capabilities will be enhanced and the market is expected to grow rapidly. ICAO has been held the Air Navigation Commission on 14 October 2011 to discuss revising Procedures for air navigation services (PANS) and guidance material related to RPA and their associated systems. It progressed that Annex 2 and 7 will be revised and came into effect from 2012. However most of incorporate revisions are not clear yet. Because the revision articles recommend follow requirements of the state(s). Considering operations of RPA in controlled airspace and between adjacent states, the qualification and training of RPA pilot will be one of the main issues for RPA operation. In this paper, we will take a look at international and domestic trends of regulation framework in ICAO and RPA advanced country in chapter 2.1 and suggest about consideration of remote pilot qualification and training for establishing regulations of remote pilot license.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.637-639
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• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.