• Bulletin of the Korean Mathematical Society
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• v.59 no.1
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• pp.73-82
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• 2022

Let K be a field of characteristic zero. We first show that images of the linear derivations and the linear 𝓔-derivations of the polynomial algebra K[x] = K[x₁, x₂, …, x_n] are ideals if the products of any power of eigenvalues of the matrices according to the linear derivations and the linear 𝓔-derivations are not unity. In addition, we prove that the images of D and 𝛿 are Mathieu-Zhao spaces of the polynomial algebra K[x] if D = ∑ⁿ_i=1 (a_ix_i + b_i)∂_i and 𝛿 = I - 𝜙, 𝜙(x_i) = λ_ix_i + 𝜇_i for a_i, b_i, λ_i, 𝜇_i ∈ K for 1 ≤ i ≤ n. Finally, we prove that the image of an affine 𝓔-derivation of the polynomial algebra K[x₁, x₂] is a Mathieu-Zhao space of the polynomial algebra K[x₁, x₂]. Hence we give an affirmative answer to the LFED Conjecture for the affine 𝓔-derivations of the polynomial algebra K[x₁, x₂].

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.73-81
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• 2004

To extract the creditable features in fingerprint image, many people use the thinning algorithm that has a very important position in the preprocessing. In this paper, we propose the robust parallel thinning algorithm that can preserve the connectivity of the binarized fingerprint image, make the thinnest skeleton with 1-pixel width and get near to the medial axis extremely. The proposed thinning method repeats three sub-iterations. The first sub-iteration takes off only the outer boundary pixel by using the interior points. To extract the one side skeletons, the second sub-iteration finds the skeletons with 2-pixel width. The third sub-iteration prunes the needless pixels with 2-pixel width existing in the obtained skeletons and then the proposed thinning algorithm has the robustness against the rotation and noise and can make the balanced medial axis. To evaluate the performance of the proposed thinning algorithm we compare with and analyze the previous algorithms.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.173-185
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• 2009

This paper proposes two new image contrast enhancement methods, RSWHE (Recursively Separated and Weighted Histogram Equalization) and RSWHS (Recursively Separated and Weighted Histogram Specification). RSWHE is a histogram equalization method based on histogram decomposition and weighting, whereas RSWHS is a histogram specification method based on histogram decomposition and weighting. The two proposed methods work as follows: 1) decompose an input histogram based on the image's mean brightness, 2) compute the probability for the area corresponding to each sub-histogram, 3) modify the sub-histogram by weighting it with the computed probability value, 4) lastly, perform histogram equalization (in the case of RSWHE) or histogram specification (in the case of RSWHS) on the modified sub-histograms independently. Experimental results show that RSWHE and RSWHS outperform other methods in terms of contrast enhancement and mean brightness preservation as well.

• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.78-82
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• 2021

The Ga₂O₃ single crystal was grown through a floating zone method, and its structural and optical properties were instigated. It has a monoclinic crystal structure with a (100) crystal orientation and an optical band gap energy of 4.6 eV. It showed an average transmittance of 70% in the visible region. At room temperature, its photoluminescent spectrum showed three different peaks: the ultraviolet at 360 nm, the blue-green at 500 nm, and the red peaks at 700 nm. Especially, at liquid nitrogen temperature, the ultraviolet peak was optically active while the others were quenched.

• The Journal of Korean Academic Society of Nursing Education
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• v.21 no.1
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• pp.5-15
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• 2015

Purpose: This study, on the basis of a structural model that includes nurse image, image determinants, and self-esteem of Cambodian nursing students influenced by the Korean nursing education system, demonstrates distinctions between senior and junior according to experience in clinical practice. Methods: Data were collected via a questionnaire from 194 nursing students in Cambodia and subsequently analyzed using the STATA IC 12 program. Results: First, image determinants and their sub-factors-subjective, institutional, and media-effect both the nurse image and self-esteem of nursing students. Second, the study confirms that nurse image has no significant effect on self-esteem. Also, the individual factor, a sub-factor of nurse image, possesses a weak relationship with nurse image. Third, the structural model mediating between senior and junior reveals differences resulting from experiences derived from clinical practice. Conclusion: The study has significance in that Cambodian nursing students, who are rarely studied in terms of nurse image, have been systemically analyzed via a structural model incorporating image determinants and self-esteem.

• Journal of the Chungcheong Mathematical Society
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• v.37 no.1
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• pp.41-55
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• 2024

The nonconvex and nonsmooth optimization problem has been widely applicable in image processing and machine learning. In this paper, we propose an extension of the proximal iteratively reweighted ℓ₁ algorithm for nonconvex and nonsmooth minmization problem. We assume the co-coerciveness of a term of objective function instead of Lipschitz gradient condition, which is generalized property of Lipschitz continuity. We prove the global convergence of the proposed algorithm. Numerical results show that the proposed algorithm converges faster than original proximal iteratively reweighed algorithm and existing algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.208-217
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• 2001

Model-based color image halftoning method using dot-pattern database is proposed for low-resolution color image printing. Dot-pattern database used in the proposed method is based on Blue-Noise Mask. The database consists of dot-patterns constructed by circular dot-overlap model according to each color value. In halftoning procedure, input color value is reproduced as the dot-pattern selected to minimize the difference between the color values of the original image and those of the printed image. Also, the contrast sensitivity function as a human visual model is used to improve the perceived quality of the printed image in dot-pattern selection. Thus, the proposed method can substantially reproduce the color values of the pixels in original image and obtain better image quality. In the experiment, the proposed method has less ΔΕ/Sub ab/ between the original image in monitor and the printed one than that of ED and BNM halftoning. This result approves that the proposed method reproduces better image quality.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.765-778
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• 2016

In watermarking schemes, the discrete wavelet transform (DWT) is broadly used because its frequency component separation is very useful. Moreover, LU decomposition has little influence on the visual quality of the watermark. Hence, in this paper, a novel blind watermark algorithm is presented based on LU transform and DWT for the copyright protection of digital images. In this algorithm, the color host image is first performed with DWT. Then, the horizontal and vertical diagonal high frequency components are extracted from the wavelet domain, and the sub-images are divided into $4{\times}4$ non-overlapping image blocks. Next, each sub-block is performed with LU decomposition. Finally, the color image watermark is transformed by Arnold permutation, and then it is inserted into the upper triangular matrix. The experimental results imply that this algorithm has good features of invisibility and it is robust against different attacks to a certain degree, such as contrast adjustment, JPEG compression, salt and pepper noise, cropping, and Gaussian noise.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.224-230
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• 2013

Lane boundary detection plays a key role in the driver assistance system. This study proposes a robust method for detecting lane boundary in severe environment. First, a horizontal line detects form the original image using improved Vertical Mean Distribution Method (iVMD) and the sub-region image which is under the horizontal line, is determined. Second, we extract the lane marking from the sub-region image using Canny edge detector. Finally, K-means clustering algorithm classifi left and right lane cluster under variant illumination, cracked road, complex lane marking and passing traffic. Experimental results show that the proposed method satisfie the real-time and efficient requirement of the intelligent transportation system.