• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.57-62
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• 2014

This paper proposes the improving the equalization performance using the adaptive modulus concept to the MCMA blind equalizer in order to the reduction of intersymbol interference which occurs in the band limited and time dispersive communication channel. In MCMA blind algorithm, it is possible to reducing the amplitude and phase rotation of intersymbol interference without training sequence, the fixed constant modulus of transmission signal is used. But in proposed algorithm, the modulus are adaptively varies according to the equalizer output signal, then the improved equalization performance were obtained by the computer simulation. For this, the recovered signal constellation that is the output of the equalizer, the convergence performance by MSE, MD (maximum distortion) and residual isi characteristic learning curve were used. The propose algorithm has fairly good performance compared to the traditional MCMA algorithm in the same adaptive equalization algorithm.

• Journal of KIISE
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• v.45 no.1
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• pp.94-98
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• 2018

We propose Wide Inception ResNet (WIR Net) an optimized neural network architecture as a real-time semantic segmentation method for autonomous driving. The neural network architecture consists of an encoder that extracts features by applying a residual connection and inception module, and a decoder that increases the resolution by using transposed convolution and a low layer feature map. We also improved the performance by applying an ELU activation function and optimized the neural network by reducing the number of layers and increasing the number of filters. The performance evaluations used an NVIDIA Geforce GTX 1080 and TX1 boards to assess the class and category IoU for cityscapes data in the driving environment. The experimental results show that the accuracy of class IoU 53.4, category IoU 81.8 and the execution speed of $640{\times}360$, $720{\times}480$ resolution image processing 17.8fps and 13.0fps on TX1 board.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.568-573
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• 2019

In the diagnosis of pulmonary diseases, auscultation technique is simpler than the other methods, and lung sounds can be used for predicting the types of pulmonary diseases as well as identifying patients with pulmonary diseases. Therefore, in this paper, we identify patients with pulmonary diseases and classify lung sounds according to their sound characteristics using various convolutional neural networks, and compare the classification performance of each neural network method. First, lung sounds over affected areas of the chest with pulmonary diseases are collected by using a single-channel lung sound recording device, and spectral features are extracted from the collected sounds in time domain and applied to each neural network. As classification methods, we use general, parallel, and residual convolutional neural network, and compare lung sound classification performance of each neural network through experiments.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.979-993
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• 2021

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). In this study, we considered the problem that ASC faces in real-world applications that the model used should have low-complexity. We compared several models that apply light-weight techniques. First, a base CNN model was proposed using log mel-spectrogram, deltas, and delta-deltas features. Second, depthwise separable convolution, linear bottleneck inverted residual block was applied to the convolutional layer, and Quantization was applied to the models to develop a low-complexity model. The model considering low-complexity was similar or slightly inferior to the performance of the base model, but the model size was significantly reduced from 503 KB to 42.76 KB.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1604-1611
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• 2020

Light field image captured by a microlens array-based camera has many limitations in practical use due to its low spatial resolution and angular resolution. High spatial resolution images can be easily acquired with a single image super-resolution technique that has been studied a lot recently. But there is a problem in that high angular resolution images are distorted in the process of using disparity information inherent among images, and thus it is difficult to obtain a high-quality angular resolution image. In this paper, we propose light field angular super-resolution that extracts an initial feature map using an dilated convolutional neural network in order to effectively extract the view difference information inherent among images and generates target image using a residual neural network. The proposed network showed superior performance in PSNR and subjective image quality compared to existing angular super-resolution networks.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.47-53
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• 2021

In this paper, we propose a quadtree-based optimization technique that enables fast Super-resolution(SR) computation by efficiently classifying and dividing physics-based simulation data required to calculate SR. The proposed method reduces the time required for quadtree computation by downscaling the smoke simulation data used as input data. By binarizing the density of the smoke in this process, a quadtree is constructed while mitigating the problem of numerical loss of density in the downscaling process. The data used for training is the COCO 2017 Dataset, and the artificial neural network uses a VGG19-based network. In order to prevent data loss when passing through the convolutional layer, similar to the residual method, the output value of the previous layer is added and learned. In the case of smoke, the proposed method achieved a speed improvement of about 15 to 18 times compared to the previous approach.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.95-100
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• 2020

This paper related with the performance evaluation of mSE-MMA (modified Signed Error-Multi Modulus Algorithm) adaptive equalization algorithm which is possible to reduce the distortion that is occurs in nonlinear communication channel like as additive noise, intersymbol interference and fading. The SE-MMA algorithm are emerged in order to reducing the computational load compared to the presently MMA algorithm, it has the degraded equalization performance by this. In order to improve the performance degradation of SE-MMA, the mSE-MMA controls the step size according to the existence of arbitrary radius circle of equalizer output is centered at transmitted symbol point. The performance of proposed mSE-MMA algorithm were compared to present SE-MMA using the same channel and noise environment by computer simulation. For this, the recoverd signal constellation which is the output of equalizer, residual isi and MD (Maximum Distortion), MSE learning curve which is represents the convergence performance and SER which is represents the roburstness of noise were used as performance index. As a result of simulation, the mSE-MMA has more superior to the SE-MMA in every performance index, and was confirmed that mSE-MMA has roburstness to the noise in the SER performance than SE-MMA especially.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.103-109
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• 2014

This paper related with the CR-MMA which is possible to improving the equalization performance by applying the concept of constellation reduction in the MMA adaptive equalization alogorithm in order to reduce the intersymbol interference that is occurred in the nonlinear communication channel. In the updating process of MMA adaptive equalizer, the error signal is being obtained by using the equalizer output, and the performance will be degraded by the increase the error signal in the high order QAM constellation. But by using the constellation reduction, the high order QAM signal will be changed to the 4-QAM signal constellation and then the error signal will be obtained. By doing so, the error signal will be minimized and it is possible to improve the equalization performance in the high order QAM transmitted signal. The Computer simulation was performed in order to compare the performance of the proposed CR-MMA algorithm and original MMA algorithm in the same communication channel and noise environment. For this, the recoverd signal constellation which is the output of equalizer, residual isi and MD (Maximum Distortion) learning curve which is represents the convergence performance and SER which is represents the roburstness of noise were used. As a result of simulation, the CR-MMA has more superior to the MMA. And it was confirmed that the CR-MMA has roburstness to the noise in the SER performance.