• Korean Journal of Environmental Biology
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• v.41 no.3
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• pp.247-255
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• 2023

The occurrence of sudden strike pest events in urban areas is increasing as global warming intensifies, consequently, re causing harmful impacts. Studies on these incidents are fewer in number and insufficient compared to research on other nuisances such as mosquitoes and flies. Therefore, we conducted a study on the development of a selective collection method, using a filter layer to establish a monitoring system for Ephemera orientalis (Ephemeroptera: Ephemeridae), a species frequently identified as a sudden strike pest. Three sampling points were selected along the Hangang River in Namyangju, where E. orientalis outbreaks occur. Prototypes, consisting of four layers and with a light source attached to attract insects, were installed at each sampling point. Sampling was performed every 30 minutes between 19:00 and 22:30 in the month of June. The filter interval of each layer was adjusted so that the collected mayflies were distributed into specific layers. To evaluate the collection efficiency in line with the materials and the filter intervals, the optimal collection efficiency was investigated by combining two types of layer materials (stainless and acrylic) and filter intervals (1-5 mm). The optimal conditions were as follows: The selective collection efficiency was found to be highest at 96.5% when the interval of the selective target filter was 2.0 mm and there was one upper filter.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.147-154
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• 2020

In this study, four types of composite added filtration (aluminum, nickel, copper, and zinc) were combined for each thickness to evaluate dose reduction and optimal images due to X-ray attenuation. To evaluate dose and image quality. X-ray generators, Dose Area Product(DAP) and ICY programs with RQR9 standard quality were used. In the image quality evaluation element (PSNR, RMSE, SSIM), only images with PSNR value of 30 dB or more were analyzed. As a result, the best combination in dose evaluation was 3 mmAl + 0.6 mmNi (0.16µGy㎡), and the best filter in image quality evaluation was 0.9 mmAl (PSNR 34.24dB, RMSE 79.52, SSIM 0.24). In this study, the dose aspect and the image quality aspect are mentioned, So it is considered that further studies on patient's exposure dose and optimal image will be needed in the future.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.189-196
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• 2010

In this paper, we proposed the adaptive local sigma filter reducing noises generated by an image sensor. The small noises generated by the image sensor are amplified by increased an analog gain and an exposure time of the image sensor together with information. And the goal of this work was the system design that is reduce the these amplified noises. Edge data are extracted by Flatness Index Map algorithm. We made the threshold adaptively changeable by the luminance average in this algorithm that extracts the edge data not in high luminance, but just low luminance. The Local Sigma Filter performed only about the edge pixel that were extracted by Flatness Index Map algorithm. To verify the performance of the designed filter, we made the Window test program. The hardware was designed with HDL language. We verified the hardware performance of Local Sigma Filter system using FPGA Demonstration board and HD image sensor, $1280{\times}720$ image size and 30 frames per second.

• Journal of Convergence Society for SMB
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• v.1 no.1
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• pp.39-44
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• 2011

Recently, due to people's incontinent use all over the world, fossil fuels such as coal, oil, and natural gas were nearly to be exhausted and also causes serious environment pollutions. Therefore, there is a strong need to develop solar, wind, hydro, biomass, geothermal to replace fossil fuels to prevent suffering from above problems. Wish advances in sensor technology, such data is collected as a kind of stream data which arrives in an online manner so that it is characterized as high- speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. Therefore, the traditional data processing techniques are not fit to deal with stream data. In this paper, we propose a kalman filter-based algorithm to process renewable stream data.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.2
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• pp.71-76
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• 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 Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.270-277
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• 2010

The BAMS filter is a kind of wavelet shrinkage filter based on the Bayes estimators with no simulation, therefore it can be used for a real time filter. The denoising efficiency of BAMS filter is seriously affected by the estimated noise variance in each wavelet band. To remove noise in signals in existing BAMS filter, the noise variance is estimated by using the quartile of the finest level of details in the wavelet decomposition, and with this variance, the noise of the level is removed. In this paper, to remove the image noise includingodified quartile of the level of detail is proposed. And by these techniques, the image noises of mid and high frequency bands are removed, and the results showed that the increased PSNR of ab the midband noise, the noise variance estimation method using the monotonic transform and the mout 2[dB] and the effectiveness in denosing of low noise deviation images.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.207-213
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• 2006

This paper presents the design and implementation of a video processor for the device of need TV-OUT function. The designed video processor satisfies the standard conditions of ITU-R(International Telecommunication Union-Radiocommunication) BT.470. Also, in order to apply various digital device, we concentrate upon hardware complexity. ITU-R BT.470 can be classified as NTSC, PAL or SECAM. NTSC and PAL use QAM(Quardarature Amplitude Modulation) to transmit color difference signals and SECAM uses FM(Frequency Modulation). FM must have antic-cloche filter but filter recommended by ITU-R BT.470 is not easy to design due to sharpness of the frequency response. So this paper proposes that the special quality of anti-cloche filter is transformed easy to design and the modulation method is modified to be identical with the result required at standard. The processor can control power consumption by output mode to apply portable digital devices. The proposed processor is experimentally demonstrated with ALTERA FPGA APEX20KE EP20K1000EBC652-3 device and SAMSUNG LCD-TV.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.1-7
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• 2021

In the latest trend of machine translation research, the model is pretrained through a large mono lingual corpus and then finetuned with a parallel corpus. Although many studies tend to increase the amount of data used in the pretraining stage, it is hard to say that the amount of data must be increased to improve machine translation performance. In this study, through an experiment based on the mBART model using parallel corpus filtering, we propose that high quality data can yield better machine translation performance, even utilizing smaller amount of data. We propose that it is important to consider the quality of data rather than the amount of data, and it can be used as a guideline for building a training corpus.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.44-49
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• 2022

Abstract Recently, with the development of IoT technology and communication media, various video equipment is being used in industrial fields. Image data acquired from cameras and sensors are easily affected by noise during transmission and reception, and noise removal is essential as it greatly affects system reliability. In this paper, we propose a weight filter algorithm based on the pixel value distribution pattern to preserve details in the process of restoring images damaged in AWGN. The proposed algorithm calculates weights according to the pixel value distribution pattern of the image and restores the image by applying a filtering mask. In order to analyze the noise removal performance of the proposed algorithm, it was simulated using enlarged image and PSNR compared to the existing method. The proposed algorithm preserves important characteristics of the image and shows the performance of efficiently removing noise compared to the existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.17-27
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• 2014

As the importance of Cooperative Engagement Capability and network-centric warfare has been dramatically increasing, it is necessary to develop distributed tracking systems. Under the development of distributed tracking systems, it requires tracking filters and data fusion theory for nonlinear systems. Therefore, in this paper, the problem of nonlinear track fusion, which is suitable for distributed networks, is formulated, four algorithms to solve the problem of nonlinear track fusion are introduced, and performance of introduced algorithms are analyzed. It is a main problem of nonlinear track fusion that cross-covarinaces among multiple platforms are unknown. Thus, in order to solve the problem, two techniques are introduced; a simplification technique and a approximation technique. The simplification technique that help to ignore cross-covariances includes two algorithms, i.e. the sample mean algorithm and the Millman formula algorithm, and the approximation technique to obtain approximated cross-covariances utilizes two approaches, by using analytical linearization and statistical linearization based on the sigma point approach. In simulations, BCS fusion is the most efficient scheme because it reduces RMSE by approximating cross-covariances with low complexity.