• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.395-402
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• 2002

In the conventional public switched telephone network(PSTN), there are various types of modulation that can be used in ADSL to offer fast data communication, two of which are CAP(Carrierless Amplitude Phase) and DMT(Discrete MultiTone). As we consider the current situation, DMT is getting more predominant in the market than CAP. One of the reasons is that it gives high performance in spite of its high complexity Since DMT divides the full range of bandwidth into 256 sub-channels, it can be highly adaptive in the circumstances, where the problems of attenuation and noise caused by the propagation distance are very crucial. In this paper, a new bit loading algorithm for DMT modulation is proposed. The proposed algorithm can be efficiently implemented in a way that it requires less computation than the conventional modulation techniques. In contrast to the conventional algorithms which perform sorting processing, the proposed algorithm uses look-up tables to reduce the repetition of calculation. Consequently, it is shown that less processing time and lower complexity can be achieved.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.16 no.1
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• pp.147-157
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• 2013

Rice is a main crop and rice field is the most important farmland in Korea. This study was conducted to propose the methodology assessing impact and vulnerability on rice production by climate change at the regional and national level in Korea. We evaluated a vulnerability of rice paddy according to the outbreak of a main disease and pest of a rice plant. As results, Jeju-do, Gyeongsangnam-do, and Jeollanam-do were more vulnerable area than others. In contrast, the southern central region including Gyeonggi-do was less vulnerable than others. The vulnerable index was significantly higher in 2050s (0.5589) than in present (0.3500). This result showed that the vulnerable to the disease and pest enlarge in the future. The adaptive capacity highly contributed to the vulnerability assessment index. The daily maximum temperature of June and the daily average temperature from May to August also contributed the climate exposure index. The area of occurring sheath blight, rice leaf blast and striped rice borer was related to the system sensitivity index. The ability of water supply (readjustment area of arable land per paddy field area) and rice production technique (rice yield per hectare) were the highly contributed variables to the adaption capacity index.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.14 no.4
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• pp.265-268
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• 1981

The direction and spectra of underwater sound wave were a remarkable contrast to the sound wave in the air because of the difference of transmissive medium. The linear hydrophone array of passive system has so far been applied to find out the direction and spectra of underwater sound wave from the sources for many purposes. The conventional methods are generally classified into two systems such as, the system which varying frequency responses, other parameters and pattern of signal like an adaptive array controlled by internal feedback, and another system which obtaining maximum of S/N ratio by giving a appropriate delay and a weighting coefficient in the output of each hydrophone. The array device of passive system can easily change the amplitude and the phase of signal by separately controlled hydrophone. And here we introduce a method that the spectral analyzing and the direction finding can be simultaneously carried out using a linear array of hydrophones. By making a circular convolution of output of signal from each hydrophone with appropriate rectangular weighting coefficient on the array, a sharp response of single lobe directivity and the spectral analyzing by time averaging were simultaneously obtained. In tile computer simulation of the array system with the length of 250cm and the interhydrophone distance of l0cm the power levels of sound signals received from given array direction were 16dB higher than those from the other directions when processing with rectangular weightings, and 8dB higher when processing with rectangular sound signals and rectangular weightings.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.535-544
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• 2020

In synthetic aperture radar (SAR) based ship detection application, false alarms frequently occur due to various noises caused by the radar imaging process. Among them, radio frequency interference (RFI) and azimuth smearing produce substantial false alarms; the latter also yields longer length estimation of ships than the true length. These two noises are prominent at cross-polarization and relatively weak at co-polarization. However, in general, the cross-polarization data are suitable for ship detection, because the radar backscatter from background sea surface is much less in comparison with the co-polarization backscatter, i.e., higher ship-sea image contrast. In order to improve the ship detection accuracy further, the RFI and azimuth smearing need to be mitigated. In the present letter, Sentinel-1 VV- and VH-polarization intensity data are used to show a novel technique of removing these noises. In this method, median image intensities of noises and background sea surface are calculated to yield arithmetic tendency. A band-math formula is then designed to replace the intensities of noise pixels in VH-polarization with adjusted VV-polarization intensity pixels that are less affected by the noises. To verify the proposed method, the adaptive threshold method (ATM) with a sliding window was used for ship detection, and the results showed that the 74.39% of RFI false alarms are removed and 92.27% false alarms of azimuth smearing are removed.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1081-1085
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• 2010

In the wireless sensor networks, power consumption and interference among the nodes can be reduced by using the transmission power control. Because link quality is changed by spatial and temporal effect, link failures are frequently occurred. In order to adapt to link quality variation, existing transmission power control schemes broadcast beacon messages periodically to neighbor nodes and control the transmission power dynamically. However, it can effect on the time and energy overhead according to period of transmission power control. In this paper, the dynamic method of transmission power control by the link quality variation and variable period are proposed. When a link quality is unstable, the control duty cycle is reduced and the link quality is agilely maintained. In contrast, when link quality is stable, the control period is increased and control overhead is decreased.

• Progress in Medical Physics
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• v.29 no.4
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• pp.150-156
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• 2018

This study aims to develop an improved Feldkamp-Davis-Kress (FDK) reconstruction algorithm using anisotropic total variation (ATV) minimization to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The algorithm first applies a filter that integrates the Shepp-Logan filter into a cosine window function on all projections for impulse noise removal. A total variation objective function with anisotropic penalty is then minimized to enhance the difference between the real structure and noise using the steepest gradient descent optimization with adaptive step sizes. The preserving parameter to adjust the separation between the noise-free and noisy areas is determined by calculating the cumulative distribution function of the gradient magnitude of the filtered image obtained by the application of the filtering operation on each projection. With these minimized ATV projections, voxel-driven backprojection is finally performed to generate the reconstructed images. The performance of the proposed algorithm was evaluated with the catphan503 phantom dataset acquired with the use of a low-dose protocol. Qualitative and quantitative analyses showed that the proposed ATV minimization provides enhanced CBCT reconstruction images compared with those generated by the conventional FDK algorithm, with a higher contrast-to-noise ratio (CNR), lower root-mean-square-error, and higher correlation. The proposed algorithm not only leads to a potential imaging dose reduction in repeated CBCT scans via lower mA levels, but also elicits high CNR values by removing noisy corrupted areas and by avoiding the heavy penalization of striking features.

• Journal of Biomedical Engineering Research
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• v.42 no.3
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• pp.80-85
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• 2021

We developed a model to classify the absence of cervical cancer using deep learning from the cervical image to which the histogram equalization algorithm was applied, and to compare the performance of each model. A total of 4259 images were used for this study, of which 1852 images were normal and 2407 were abnormal. And this paper applied Image Sharpening(IS), Histogram Equalization(HE), and Contrast Limited Adaptive Histogram Equalization(CLAHE) to the original image. Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity index for Measuring image quality(SSIM) were used to assess the quality of images objectively. As a result of assessment, IS showed 81.75dB of PSNR and 0.96 of SSIM, showing the best image quality. CLAHE and HE showed the PSNR of 62.67dB and 62.60dB respectively, while SSIM of CLAHE was shown as 0.86, which is closer to 1 than HE of 0.75. Using ResNet-50 model with transfer learning, digital image-processed images are classified into normal and abnormal each. In conclusion, the classification accuracy of each model is as follows. 90.77% for IS, which shows the highest, 90.26% for CLAHE and 87.60% for HE. As this study shows, applying proper digital image processing which is for cervical images to Computer Aided Diagnosis(CAD) can help both screening and diagnosing.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1453-1463
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• 2021

Feeding is the most important behavior that represents the health and welfare of weanling pigs. The early detection of feed refusal is crucial for the control of disease in the initial stages and the detection of empty feeders for adding feed in a timely manner. This paper proposes a real-time technique for the detection and recognition of small pigs using a deep-leaning-based method. The proposed model focuses on detecting pigs on a feeder in a feeding position. Conventional methods detect pigs and then classify them into different behavior gestures. In contrast, in the proposed method, these two tasks are combined into a single process to detect only feeding behavior to increase the speed of detection. Considering the significant differences between pig behaviors at different sizes, adaptive adjustments are introduced into a you-only-look-once (YOLO) model, including an angle optimization strategy between the head and body for detecting a head in a feeder. According to experimental results, this method can detect the feeding behavior of pigs and screen non-feeding positions with 95.66%, 94.22%, and 96.56% average precision (AP) at an intersection over union (IoU) threshold of 0.5 for YOLOv3, YOLOv4, and an additional layer and with the proposed activation function, respectively. Drinking behavior was detected with 86.86%, 89.16%, and 86.41% AP at a 0.5 IoU threshold for YOLOv3, YOLOv4, and the proposed activation function, respectively. In terms of detection and classification, the results of our study demonstrate that the proposed method yields higher precision and recall compared to conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.61-71
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• 2009

Vehicle license plate is the only way to check the registrated information of a vehicle. Many works have been devoted to the vision system of recognizing the license plate, which has been widely used to control an illegal parking. However, it is difficult to correctly segment characters on the license plate since an illumination is affected by a weather change and a neighboring obstacles. This paper proposes a robust method of segmenting the character of the license plate on irregular illumination condition. The proposed method enhance the contrast of license plate images using the Chi-Square probability density function. For segmenting characters on the license plate, binary images with the high quality are gained by applying the adaptive threshold. Preprocessing and labeling algorithm are used to eliminate noises existing during the whole segmentation process. Finally, profiling method is applied to segment characters on license plate from binary images.