• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1122-1131
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• 2019

This paper covers research on terrain construction for unmanned aerial vehicle simulators using spatial information that was distributed by public institutions. Aerial photography, DEM, vector maps and 3D model data were used in order to create a realistic terrain simulator. A data converting method was suggested while researching, so it was generated to automatically arrange and build city models (vWorld provided) and classification methods so that realistic images could be generated by 3D objects. For example: rivers, forests, roads, fields and so on, were arranged by aerial photographs, vector map (land cover map) and terrain construction based on the tile map used by DEM. In order to verify the terrain data of unmanned aircraft simulators produced by the proposed method, the location accuracy was verified by mounting onto Unreal Engine and checked location accuracy.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.528-537
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• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.242-249
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• 2022

Classical cryptography with complex computations has recently been utilized in the latest computing systems to create secret keys. However, systems can be breached by fast-measuring methods of the secret key; this approach does not offer adequate protection when depending on the computational complexity alone. The laws of physics for communication purposes are used in quantum computing, enabling new computing concepts to be introduced, particularly in cryptography and key distribution. This paper proposes a quantum computing lattice (CQL) mechanism that applies the BB84 protocol to generate a quantum key. The generated key and a one-time pad encryption method are used to encrypt the message. Then Babai's algorithm is applied to the ciphertext to find the closet vector problem within the lattice. As a result, quantum computing concepts are used with classical encryption methods to find the closet vector problem in a lattice, providing strength encryption to generate the key. The proposed approach is demonstrated a high calculation speed when using quantum computing.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.263-279
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• 2022

This paper presents an automatic inspection of defects in semiconductor images. We devise a statistical method to find defects on homogeneous background from the observation that it has a log-normal distribution. If computer aided design (CAD) data is available, we use it to construct a signed distance function (SDF) and change the pixel values so that the average of pixel values along the level curve of the SDF is zero, so that the image has a homogeneous background. In the absence of CAD data, we devise a hybrid method consisting of a model-based algorithm and two neural networks. The model-based algorithm uses the first right singular vector to determine whether the image has a linear or complex structure. For an image with a linear structure, we remove the structure using the rank 1 approximation so that it has a homogeneous background. An image with a complex structure is inspected by two neural networks. We provide results of numerical experiments for the proposed methods.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.9-16
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• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• Bulletin of the Korean Mathematical Society
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• v.60 no.5
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• pp.1299-1320
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• 2023

In the present paper, firstly we obtain the general expression of the canal hypersurfaces that are formed as the envelope of a family of pseudo hyperspheres, pseudo hyperbolic hyperspheres and null hyper-cones whose centers lie on a non-null curve with non-null Frenet vector fields in E⁴₁ and give their some geometric invariants such as unit normal vector fields, Gaussian curvatures, mean curvatures and principal curvatures. Also, we give some results about their flatness and minimality conditions and Weingarten canal hypersurfaces. Also, we obtain these characterizations for tubular hypersurfaces in E⁴₁ by taking constant radius function and finally, we construct some examples and visualize them with the aid of Mathematica.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.1
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• pp.35-45
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• 1987

For the registration problem in which the matching of two images is made, a new algorithm using an 1-D LPC model was proposed. The proposed algorithm employed LPC coefficients as feature vector of an image. The similarity of two images was measured using an LPC distance, proposed by Itakura, between each image's feature vector. The comparision of performance with normalized correlation method and template matching method was made by a computer simulation with several real images. The results of simulation showed that the proposed algorithm was more robust to image intensity variation and computationall efficient.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.3
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• pp.340-344
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• 1986

This paper shows that we can design a vector-coordinate rotation processor and obtain the approximate evaluations of sine and cosine based upon the use of residue number systems. The algorithm results in the considerable improvement of the computation speed when compared to CORDIC algorithm. The results from computer simulation show that the mean error of sine and cosine is 0.0025 and the mean error of coordinate rotation arithmatic is 0.65. Also, the proposed processor has the efficiency for the design and fabrication of integrated circuit, because it consists of the array of idecntially structured look-up tables.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.540-553
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• 2023

The hollow letter CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is an optimized version of solid CAPTCHA, specifically designed to weaken characteristic information and increase the difficulty of machine recognition. Although convolutional neural networks can solve CAPTCHA in a single step, a good attack result heavily relies on sufficient training data. To address this challenge, we propose a seed filling algorithm that converts hollow characters to solid ones after contour line restoration and applies three rounds of detection to remove noise background by eliminating noise blocks. Subsequently, we utilize a support vector machine to construct a feature vector for recognition. Security analysis and experiments show the effectiveness of this algorithm during the pre-processing stage, providing favorable conditions for subsequent recognition tasks and enhancing the accuracy of recognition for hollow CAPTCHA.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.821-827
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• 2018

Affective computing has gained increasing interest in the recent years with the development of potential applications in Human computer interaction (HCI) and healthcare. Although momentous research has been done on human emotion recognition, however, in comparison to speech and facial expression less attention has been paid to physiological signals. In this paper, Electroencephalogram (EEG) signals from different brain regions were investigated using modified wavelet energy features. For minimization of redundancy and maximization of relevancy among features, mRMR algorithm was deployed significantly. EEG recordings of a publically available "DEAP" database have been used to classify four classes of emotions with Multi class Support Vector Machine. The proposed approach shows significant performance compared to existing algorithms.