• Korean Journal of Optics and Photonics
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• v.20 no.5
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• pp.272-275
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• 2009

In order to enhance the background noise filtering performance of the white light interferometry(WLI), we demonstrate the noise filtering performance of preprocessing of the measured fringe signals. The WLI was realized through a mirau interferometer which was equipped with a green LED. When measuring large-height and rough surface objects, the illumination optics are considered the numerical aperture(NA) and the depth of focus(DOF). In this case, the limited NA of the illumination optics has a considerable impact on the interference fringe. Therefore, we propose a preprocessing method that uses the intensity difference between the measured intensity and the moving average intensity. The performance is demonstrated by measuring an array of metal solder balls fabricated on printed circuit board(PCB). The proposed method reduces the noise pixels by 15 percent.

• Journal of the Korean GEO-environmental Society
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• v.6 no.2
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• pp.51-60
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• 2005

The disposal problem of waste lime which is a residual product of lime industry have caused a lots of arguments in the past few years. Further more, waste lime contains a high moisture content which causes the disposal of waste lime is a great difficulty. The purpose of this study is to investigate for the effective dewatering solutions by placing various prefabricated vertical drains. The moisture content and degree of consolidation, pore water pressure, changes of settlement, bearing capacity with various vertical drains in waste lime were analyzed. The laboratory test results indicate that PBD is 2 times higher than circular drain in coefficient of consolidation. Based on the laboratory test results, settlement, pore water pressure, and dewatering measurements are shown in similar tendency. It is considered that PBD can drain primitive pore water much efficiently. The picture of SEM shows that circular drain filter has a serious clogging problem in comparison with PBD. In conclusion, PBD holds a superiority in waste lime's ground improvement and dewatering pore water pressure from the waste lime sludge. Also, circular drain is desired for some modification in its filtering system.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.1
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• pp.13-20
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• 2017

In this paper, we evaluate the basic test results of the 32 Gbps observational equipment introduced as the back-end system for the wideband VLBI (Very Long Baseline Interferometry) observation of KVN (Korean VLBI Network). Radio astronomers want to make a large radio telescope that has excellent performance in order to observe the superfine structure of a celestial body, but a lot of money is needed. Therefore, in order to increase the sensitivity, the performance improvement of the receiving system and the method of observing the wide frequency bandwidth are introduced. To do this, we adopted a wideband sampling method for converting analog signals to digital with ultra-fast speeds and a wideband sampler for performing digital filtering in order to observe a wide observational frequency bandwidth. The wideband sampler (OCTAD-K) supports up to 16 Gsps-2bits sampling and supports a variety of observational bandwidth using digital filtering techniques. In particular, it is designed to support KVN's 4-frequency simultaneous observation system and VERA(VLBI Exploration of Radio Astrometry)'s 2-beam observation system. It can also support polKVN(Korean VLBI Network), KaVA(KVN and VERA Array), 32Gbps Direct Sampler, Digital Filter, Widebandarization observations and supports the standard VDIF(VLBI Data Interchange Format) format of observed data. In this paper, the performance of the system and the problem solving are described in detail after performing the factory inspection and field test before the system is introduced.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.383-394
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• 2018

In this paper, we investigate image and video compression techniques based on deep learning which are actively studied recently. The deep learning based image compression technique inputs an image to be compressed in the deep neural network and extracts the latent vector recurrently or all at once and encodes it. In order to increase the image compression efficiency, the neural network is learned so that the encoded latent vector can be expressed with fewer bits while the quality of the reconstructed image is enhanced. These techniques can produce images of superior quality, especially at low bit rates compared to conventional image compression techniques. On the other hand, deep learning based video compression technology takes an approach to improve performance of the coding tools employed for existing video codecs rather than directly input and process the video to be compressed. The deep neural network technologies introduced in this paper replace the in-loop filter of the latest video codec or are used as an additional post-processing filter to improve the compression efficiency by improving the quality of the reconstructed image. Likewise, deep neural network techniques applied to intra prediction and encoding are used together with the existing intra prediction tool to improve the compression efficiency by increasing the prediction accuracy or adding a new intra coding process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.339-345
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• 2016

In general, mosaic blocks are used to hide some specified areas, such as human faces and disgusting objects, in an input image when images are uploaded on a web-site or blog. This paper proposes a new algorithm for robustly detecting grid mosaic areas in an image based on the edge projection. The proposed algorithm first extracts the Canny edges from an input image. The algorithm then detects the candidate mosaic blocks based on horizontal and vertical edge projection. Subsequently, the algorithm obtains real mosaic areas from the candidate areas by eliminating the non-mosaic candidate regions through geometric features, such as size and compactness. The experimental results showed that the suggested algorithm detects mosaic areas in images more accurately than other existing methods. The suggested mosaic detection approach is expected to be utilized usefully in a variety of multimedia-related real application areas.

• Journal of Auto-vehicle Safety Association
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• v.13 no.2
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• pp.35-41
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• 2021

In this study, improved path tracking control algorithm based on pure pursuit algorithm is newly proposed by using improved lane detection algorithm through real time post-processing with interpolation methodology. Since the original pure pursuit works well only at speeds below 20 km/h, the look-ahead distance is implemented as a sigmoid function to work well at an average speed of 45 km/h to improve tracking performance. In addition, a smoothing filter was added to reduce the steering angle vibration of the original algorithm, and the stability of the steering angle was improved. The post-processing algorithm presented has implemented more robust lane recognition system using real-time pre/post processing method with deep learning and estimated interpolation. Real time processing is more cost-effective than the method using lots of computing resources and building abundant datasets for improving the performance of deep learning networks. Therefore, this paper also presents improved lane detection performance by using the final results with naive computer vision codes and pre/post processing. Firstly, the pre-processing was newly designed for real-time processing and robust recognition performance of augmentation. Secondly, the post-processing was designed to detect lanes by receiving the segmentation results based on the estimated interpolation in consideration of the properties of the continuous lanes. Consequently, experimental results by utilizing driving guidance line information from processing parts show that the improved lane detection algorithm is effective to minimize the lateral offset error in the diverse maneuvering roads.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.5
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• pp.544-555
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• 2002

The aim of this study is to develop the $^1H$-MRS data postprocessing software for both single-voxel and multi-voxel technique, which plays and important role as a diagnostic tool in clinical field. This software is based on graphical user interface(GUI) under windows operating system of personal computer(PC). In case of single-voxel MRS, both of raw data in time-domain and spectrum data in frequency-domain are simultaneously displayed in a screen. Several functions such as DC correction, zero filling, line broadening, Lorentz-Gauss filtering and phase correction, etc. are included to increase the quality of spectrum data. In case of multi-voxel analysis, spectroscopic image reconstructed by 3-D FFT was displayed as a spectral grid and overlapped over previously obtained T1- or T2-weighted image for the spectra to be spatially registered with the image. The analysis of MRS peaks were performed by obtaining the ratio of peak area. In single-voxel method, statistically processed peak-area ratios of MRS data obtained from normal human brain are presented. Using multi-voxel method, MR spectroscopic image and metabolite image acquired from brain tumor are demonstrated.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1092-1099
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• 2022

Research on exhaust aftertreatment devices to reduce air pollutants and greenhouse gas emissions is being actively conducted. However, in the case of the particulate matters/nitrogen oxides (PM/NOx) simultaneous reduction device for ships, the problem of back pressure on the diesel engine and replacement of the filter carrier is occurring. In this study, for the optimal design of the integrated device that can simultaneously reduce PM/NOx, an appropriate standard was presented by studying the flow inside the device and change in back pressure through the inlet/outlet pressure. Ansys Fluent was used to apply porous media conditions to a diesel particulate filter (DPF) and selective catalytic reduction (SCR) by setting porosity to 30%, 40%, 50%, 60%, and 70%. In addition, the ef ect on back pressure was analyzed by applying the inlet velocity according to the engine load to 7.4 m/s, 10.3 m/s, 13.1 m/s, and 26.2 m/s as boundary conditions. As a result of a computational fluid dynamics analysis, the rate of change for back pressure by changing the inlet velocity was greater than when inlet temperature was changed, and the maximum rate of change was 27.4 mbar. This was evaluated as a suitable device for ships of 1800kW because the back pressure in all boundary conditions did not exceed the classification standard of 68mbar.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1183-1190
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• 2017

With the development of network and IT technology, people are searching and purchasing items they want, not bounded by places. Therefore, there are various studies on how to solve the scalability problem due to the rapidly increasing data in the recommendation system. In this paper, we propose an item-based collaborative filtering method using Tag weight and a recommendation technique using MapReduce method, which is a distributed parallel processing method. In order to improve speed and efficiency, the proposed method classifies items into categories in the preprocessing and groups according to the number of nodes. In each distributed node, data is processed by going through Map-Reduce step 4 times. In order to recommend better items to users, item tag weight is used in the similarity calculation. The experiment result indicated that the proposed method has been more enhanced the appropriacy compared to item-based method, and run efficiently on the large amounts of data.