• Speech Sciences
• /
• v.8 no.4
• /
• pp.87-98
• /
• 2001

Vocal nodule is one of the representative chronic diseases of vocal folds, and it can be cured by surgical removal or voice therapy. The aim of this study is to evaluate the effect of the accent method, one of the popular effective voice therapy, in the patients with vocal nodule. Authors executed the accent method in 17 patients with vocal nodule who visited the Voice & Speech Therapy Clinic, Pusan National University Hospital analysed the voice before and after treatment using the local findings, acoustic analysis and aerodynamic analysis MPT. The voice was analysed with MDVP of CSL and MPT was checked using stop watch. The parameters included Fo, Jitter, Shimmer and noise to harmonic ratio(NHR) as acoustic analysis. The results were obtained as follows. In the evaluation by the local findings, it was improved to 77% in the patients of vocal nodule. Jitter and Shimmer were shown to be improved significantly. In particular, it was shown to be improved significantly in patients with vocal nodule. As the result of this study, the improvement of aerodynamic aspect was more statistically significant than that of acoustic parameters. When I generalized the above mentioned results, we suggest that it is a useful voice therapy which can be helpful to the improvement of voice, applying the accent method to the vocal nodule patients, and there are currently many methods to be used in the voice therapy, but it is thought which the accent method is the good treatment as the alternatives of keeping the continuous medical treatment.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.9C
• /
• pp.863-870
• /
• 2009

Bilateral filter (BF) is a nonlinear filter for sharpness enhancement and noise removal. The BF performs the function by the two Gaussian filters, the domain filter and the range filter. To apply the BF to infrared (IR) small target detection, the standard deviation of the two Gaussian filters need to be changed adaptively between the background region and the target region. This paper presents a new BF with the adaptive standard deviation based on the analysis of the edge component of the local window, also having the variable filter size. This enables the BF to perform better and become more suitable in the field of small target detection Experimental results demonstrate that the proposed method is robust and efficient than the conventional methods.

• Journal of Digital Convergence
• /
• v.11 no.7
• /
• pp.157-163
• /
• 2013

In this paper, we propose a vision and depth information based real-time hand gesture interface method using finger joint estimation. For this, the areas of left and right hands are segmented after mapping of the visual image and depth information image, and labeling and boundary noise removal is performed. Then, the centroid point and rotation angle of each hand area are calculated. Afterwards, a circle is expanded at following pattern from a centroid point of the hand to detect joint points and end points of the finger by obtaining the midway points of the hand boundary crossing and the hand model is recognized. Experimental results that our method enabled fingertip distinction and recognized various hand gestures fast and accurately. As a result of the experiment on various hand poses with the hidden fingers using both hands, the accuracy showed over 90% and the performance indicated over 25 fps. The proposed method can be used as a without contacts input interface in HCI control, education, and game applications.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.4
• /
• pp.246-254
• /
• 2020

In this paper, we propose a learning based compressive sensing algorithm for the purpose of side scan sonar image denoising. The proposed method is based on Iterative Shrinkage and Thresholding Algorithm (ISTA) framework and incorporates a powerful strategy that reinforces the non-linearity of deep learning network for improved performance. The proposed method consists of three essential modules. The first module consists of a non-linear transform for input and initialization while the second module contains the ISTA block that maps the input features to sparse space and performs inverse transform. The third module is to transform from non-linear feature space to pixel space. Superiority in noise removal and memory efficiency of the proposed method is verified through various experiments.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.10 no.3
• /
• pp.221-228
• /
• 2016

In this paper, we introduce our simulation software for EEG signal accuracy improvement. Users can check and train own EEG signal accuracy using our simulation software. Subjects were shown emotional imagination condition with landscape photography and logical imagination condition with a mathematical problem to subject. We use that EEG signal data, and apply Independent Component Analysis algorithm for noise removal. So we can have beta waves(${\beta}$, 14-30Hz) data through Band Pass Filter. We extract feature using Root Mean Square algorithm and That features are classified through Support Vector Machine. The classification result is 78.21% before EEG signal accuracy improvement training. but after successive training, the result is 91.67%. So user can improve own EEG signal accuracy using our simulation software. And we are expecting efficient use of BCI system based EEG signal.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.34 no.5
• /
• pp.479-493
• /
• 2016

LiDAR(Light Detection and Ranging) data acquired from ALS(Airborne Laser Scanner) has been intensively utilized to reconstruct object models. Especially, researches for 3D modeling from LiDAR data have been performed to establish high quality spatial information such as precise 3D city models and true orthoimages efficiently. To reconstruct object models from irregularly distributed LiDAR point clouds, sensor calibration, noise removal, filtering to separate objects from ground surfaces are required as pre-processing. Classification and segmentation based on geometric homogeneity of the features, grouping and representation of the segmented surfaces, topological analysis of the surface patches for modeling, and accuracy assessment are accompanied by modeling procedure. While many modeling methods are based on the segmentation process, this paper proposed to extract key points directly for building modeling without segmentation. The method was applied to simulated and real data sets with various roof shapes. The results demonstrate feasibility of the proposed method through the accuracy analysis.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.2
• /
• pp.347-352
• /
• 2006

Narrowband interference can severely degrade the performance of GPS receivers. Detecting the presence of interference and then characterizing it can lead to its removal. Receivers can be reconfigured to focus on other signals or satellites that are less vulnerable to that interference at that moment. Using hardware reconfigurability of FPGA receivers and characterizing the effect of narrowband interference on the GNSS signal quality lead us to a new RFI mitigation technique in which the highest quality and less vulnerable signal can be chosen at each moment. In the previous work [1], the post processing capability of a software GPS receiver, has been used to detect and characterize the CW interference. This is achieved by passing the GPS signal and the interference through the correlator. Then, using the conventional definition of C/No as the squared mean of the correlator output divided by its variance, the actual C/No for each satellite is calculated. In this work, first the 'Exclusion zone' for each satellite signal has been defined and then by using some experiments the effects of different parameters like signal power, jamming power and the environmental noise power on the Exclusion zone have been analyzed. By monitoring the Doppler frequency of each satellite and using the actual C/No of each satellite using the traditional definition of C/No and actual data from a software GPS receiver, the decision to reconfigure the receiver to other signal can be made.

• Journal of the Institute of Convergence Signal Processing
• /
• v.17 no.2
• /
• pp.71-76
• /
• 2016

Digital image processing is being utilized in various fields including medical industry, satellite photos, and factory automation image recognition. However, this kind of image data produces heat by an external cause in the course of being processed, transmitted, and stored. Most typical noises added in the images are AWGN and salt and pepper. MF, CWMF, and AWMF are methods used to restore images damaged by AWGN and the existing methods are likely to damage detailed information such as an edge. Therefore, this paper suggests an algorithm applying weight of average filter, average filter depending on pixel, and spatial weight filter based on edge size of local mask in an AWGN environment, in a different way. Also, this paper compares functions of existing methods by using PSNR to prove excellence of the suggested algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.8
• /
• pp.1931-1940
• /
• 2014

In this paper, we propose a new hand shape recognition algorithm based on the geometric features using the convex-hull from the depth image acquired by Kinect system. Kinect is a camera providing a depth image and user's skeleton information and used for detecting hand region. In the proposed algorithm, hand region is detected in a depth image acquired by Kinect and convex-hull of the region is found. Boundary points caused by noise and unnecessary points for recognition are eliminated in the convex-hull that changes depending on hand shape. Hand shape is recognized by the sum of internal angle of a polygon that is matched with convex-hull reconstructed with selected boundary points. Through experiments, we confirm that proposed algorithm shows high recognition rate not only for five models but also those cases rotated.

• Nuclear Engineering and Technology
• /
• v.39 no.3
• /
• pp.193-206
• /
• 2007

The Korea Atomic Energy Research Institute has developed an advanced fast reactor concept, KALIMER-600, which satisfies the Generation IV reactor design goals of sustainability, economics, safety, and proliferation resistance. The concept enables an efficient utilization of uranium resources and a reduction of the radioactive waste. The core design has been developed with a strong emphasis on proliferation resistance by adopting a single enrichment fuel without blanket assemblies. In addition, a passive residual heat removal system, shortened intermediate heat-transport system piping and seismic isolation have been realized in the reactor system design as enhancements to its safety and economics. The inherent safety characteristics of the KALIMER-600 design have been confirmed by a safety analysis of its bounding events. Research on important thermal-hydraulic phenomena and sensing technologies were performed to support the design study. The integrity of the reactor head against creep fatigue was confirmed using a CFD method, and a model for density-wave instability in a helical-coiled steam generator was developed. Gas entrainment on an agitating pool surface was investigated and an experimental correlation on a critical entrainment condition was obtained. An experimental study on sodium-water reactions was also performed to validate the developed SELPSTA code, which predicts the data accurately. An acoustic leak detection method utilizing a neural network and signal processing units were developed and applied successfully for the detection of a signal up to a noise level of -20 dB. Waveguide sensor visualization technology is being developed to inspect the reactor internals and fuel subassemblies. These research and developmental efforts contribute significantly to enhance the safety, economics, and efficiency of the KALIMER-600 design concept.