• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.819-824
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• 2021

As technologies related to image processing such as autonomous driving and CCTV develop, fog removal algorithms using a single image are being studied to improve the problem of image distortion. As a method of predicting fog density, there is a method of estimating the depth of an image by generating a depth map, and various fog features may be used as training data of the depth map. In addition, it is essential to implement a hardware capable of processing high-definition images in real time in order to apply the fog removal algorithm to actual technologies. In this paper, we implement NLCV (Normalize Local Coefficient of Variation), a feature of fog based on coefficient of variation, in hardware. The proposed hardware is an FPGA implementation of Xilinx's xczu7ev-2ffvc1156 as a target device. As a result of synthesis through the Vivado program, it has a maximum operating frequency of 479.616MHz and shows that real-time processing is possible in 4K UHD environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.17-22
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• 2024

This paper introduces the implementation of a secondhand clothing trading system equipped with virtual fitting functionality based on deep learning. The proposed system provides users with the ability to visually try on secondhand clothing items online and assess their fit. To achieve this, it utilizes the Convolutional Neural Network (CNN) algorithm to create virtual representations of users considering their body shape and the design of the clothing. This enables buyers to pre-assess the fit of clothing items online before actually wearing them, thereby aiding in their purchase decisions. Additionally, sellers can present accurate clothing sizes and fits through the system, enhancing customer satisfaction. This paper delves into the CNN model's training process, system implementation, user feedback, and validates the effectiveness of the proposed system through experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.12
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• pp.1970-1982
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• 1993

This paper describes an efficient array algorithm for parallel computation of vector-radix two-dimensional (2-D) fast discrete cosine transform (VR-FCT), and its VLSI implementation. By mapping the 2-D VR-FCT onto a 2-D array of processing elements (PEs), the butterfly structure of the VR-FCT can be efficiently importanted with high concurrency and local communication geometry. The proposed array algorithm features architectural modularity, regularity and locality, so that it is very suitable for VLSI realization. Also, no transposition memory is required, which is invitable in the conventional row-column decomposition approach. It has the time complexity of O(N+Nnzp-log2N) for (N*N) 2-D DCT, where Nnzd is the number of non-zero digits in canonic-signed digit(CSD) code, By adopting the CSD arithmetic in circuit desine, the number of addition is reduced by about 30%, as compared to the 2`s complement arithmetic. The computational accuracy analysis for finite wordlength processing is presented. From simulation result, it is estimated that (8*8) 2-D DCT (with Nnzp=4) can be computed in about 0.88 sec at 50 MHz clock frequency, resulting in the throughput rate of about 72 Mega pixels per second.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.139-146
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• 2004

A 2D/3D complex optical system and its vision inspection algerian is proposed and implemented as a single probe system for high speed, precise vision inspection of the solder pastes. One pass un length labeling algorithm is proposed instead of the conventional two pass labeling algorithm for fast extraction of the 2D shape of the solder paste image from the recent line-scan camera as well as the conventional area-scan camera, and the optical probe path generation is also proposed for the efficient 2D/3D inspection. The Moire interferometry-based phase shift algerian and its optical system implementation is introduced, instead of the conventional laser slit-beam method, for the high precision 3D vision inspection. All of the time-critical algorithms are MMX SIMD parallel-coded for further speedup. The proposed system is implemented for simultaneous 2D/3D inspection of 10mm${\times}$10mm FOV with resolutions of 10 ${\mu}{\textrm}{m}$ for both x, y axis and 1 ${\mu}{\textrm}{m}$ for z axis. Experiments conducted on several nBs show that the 2D/3D inspection of an FOV, excluding an image capturing, results in high speed of about 0.011sec/0.01sec, respectively, after image capturing, with $\pm$1${\mu}{\textrm}{m}$ height accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.354-359
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• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.11
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• pp.14-23
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• 2002

Two implementation methods for SOVA (Soft Output Viterbi Algorithm)of Turbo decoder are applied and verfied. The first method is the combination of a trace back (TB) logic for the survivor state and a double trace back logic for the weight value in two-step SOVA. This architecure of two-setp SOVA decoder allows important savings in area and high-speed processing compared with that of one-step SOVA decoding using register exchange (RE) or trace-back (TB) method. Second method is adjusting the reliability value with a scaling factor between 0.25 and 0.33 in order to compensate for the distortion for a rate 1/3 and 8-state SOVA decoder with a 256-bit frame size. The proposed schemes contributed to higher SNR performance by 2dB at the BER 10E-4 than that of SOVA decoder without a scaling factor. In order to verify the suggested schemes, the SOVA decoder is testd using Xillinx XCV 1000E FPGA, which runs at 33.6MHz of the maximum speed with 845 latencies and it features 175K gates in the case of 256-bit frame size.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.179-185
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• 2019

This paper proposes a block-type classification based image binarization for the implementation of the low-power feature extraction algorithm. The proposed method can be implemented with threshold value re-use technique approach when the image divided into $64{\times}64$ macro blocks size and calculating the threshold value for each block type only once. The algorithm is validated based on quantitative results that only a threshold value change rate of up to 9% occurs within the same image/block type. Existing algorithms should compute the threshold value for 64 blocks when the macro block is divided by $64{\times}64$ on the basis of $512{\times}512$ images, but all suggestions can be made only once for best cases where the same block type is printed, and for the remaining 63 blocks, the adaptive threshold calculation can be reduced by only performing a block type classification process. The threshold calculation operation is performed five times when all block types occur, and only the block type separation process can be performed for the remaining 59 blocks, so 93% adaptive threshold calculation operation can be reduced.

• Journal of the Korean Geotechnical Society
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• v.22 no.9
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• pp.45-59
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• 2006

The successful performance of any numerical geotechnical simulation depends on the accuracy and efficiency of the numerical implementation of constitutive model used to simulate the stress-strain (constitutive) response of the soil. The corner stone of the numerical implementation of constitutive models is the numerical integration of the incremental form of soil-plasticity constitutive equations over a discrete sequence of time steps. In this paper a well known two-surface soil plasticity model is implemented using a generalized implicit return mapping algorithm to arbitrary convex yield surfaces referred to as the Closest-Point-Projection method (CPPM). The two-surface model describes the nonlinear behavior of coarse-grained materials by incorporating a bounding surface concept together with isotropic and kinematic hardening as well as fabric formulation to account for the effect of fabric formation on the unloading response. In the course of investigating the performance of the CPPM integration method, it is proven that the algorithm is an accurate, robust, and efficient integration technique useful in finite element contexts. It is also shown that the algorithm produces a consistent tangent operator $\frac{d\sigma}{d\varepsilon}$ during the iterative process with quadratic convergence rate of the global iteration process.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.31-37
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• 2024

In this study, we propose a machine vision-based pedestrian signal detection algorithm that operates efficiently even in computing resource-constrained environments. This algorithm demonstrates high efficiency within limited resources and is designed to minimize the impact of ambient lighting by sequentially applying HSV color space-based image processing, binarization, morphological operations, labeling, and other steps to address issues such as light glare. Particularly, this algorithm is structured in a relatively simple form to ensure smooth operation within embedded system environments, considering the limitations of computing resources. Consequently, it possesses a structure that operates reliably even in environments with low computing resources. Moreover, the proposed pedestrian signal system not only includes pedestrian signal detection capabilities but also incorporates IoT functionality, allowing wireless integration with a web server. This integration enables users to conveniently monitor and control the status of the signal system through the web server. Additionally, successful implementation has been achieved for effectively controlling 50W LED pedestrian signals. This proposed system aims to provide a rapid and efficient pedestrian signal detection and control system within resource-constrained environments, contemplating its potential applicability in real-world road scenarios. Anticipated contributions include fostering the establishment of safer and more intelligent traffic systems.

• The Journal of Information Systems
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• v.31 no.3
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.