• Current Optics and Photonics
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• v.2 no.1
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• pp.69-78
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• 2018

This paper presents a precise edge detection algorithm for the critical dimension (CD) measurement of a Thin-Film Transistor Liquid-Crystal Display (TFT-LCD) pattern. The sigmoid surface function is proposed to model the blurred step edge. This model can simultaneously find the position and geometry of the edge precisely. The nonlinear least squares fitting method (Levenberg-Marquardt method) is used to model the image intensity distribution into the proposed sigmoid blurred edge model. The suggested algorithm is verified by comparing the CD measurement repeatability from high-magnified blurry and noisy TFT-LCD images with those from the previous Laplacian of Gaussian (LoG) based sub-pixel edge detection algorithm and error function fitting method. The proposed fitting-based edge detection algorithm produces more precise results than the previous method. The suggested algorithm can be applied to in-line precision CD measurement for high-resolution display devices.

• Journal of the Korean Society of Combustion
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• v.5 no.2
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• pp.19-27
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• 2000

A turbulent combustion model, based on edge flame dynamics, is discussed in order to predict global extinction of turbulent flames. The model is applicable to the broken flamelet regime of turbulent combustion, in which global extinction of turbulent flame is achieved by gradual expansion of flame holes. The edge flame dynamics is the key mechanism to describe the flame hole expansion or contraction. For flames with Lewis numbers near unity, there is a $Damk{\ddot{o}}hler$ number, namely the crossover $Damk{\ddot{o}}hler$ number, at which edge flame changes its direction of propagation. The parametric region between the quasi-steady extinction condition and the edge-flame crossover condition is a metastable region, in that flames without edge can stay in their burning states while flames with edge have to retract to expand quenching holes. Using the above properties of edge flame, Hartley and Dold proposed a Lagrangian hole dynamics, which allows us to simulate transient variation of quenching holes. In their model, each stoichiometric surface is subjected to a random sequence of scalar dissipation rate compatible to the equilibrium turbulence. Then, each stoichiometric surface will evolve, according to the combustion map, dependent on the scalar dissipation rate and existence of flame edge, If all the burning surfaces are annihilated, the event can be declared as a global extinction. The consequence obtained from the above model also can be used as a subgrid model to determine local extinction occurring in a calculation grid.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.159-161
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• 2003

Fitting the projected wire-frame model to the detected edge pixels on images by using least-squares approach, called Least-squares Model-image Fitting (LSMIF), is the key of the Model-based Building Extraction (MBBE). It is implemented by iteratively adjusting the model parameters to minimize the squares sum of distances from the extracted edge pixels to the projected wire-frame. This paper describes a series of experiments and studies on various factors affect the fitting results, including the edge detectors, the weighting rules, the initial value of parameters, and the number of overlapped images. The experimental result is not only helpful to clarify the influences of each factor, but is also able to enhance the robustness of the LSMIF algorithm.

• ETRI Journal
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• v.30 no.5
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• pp.723-728
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• 2008

In this paper, a simple transmission line model for an edge-coupled patch antenna is presented. The coupled section is modeled with a lump network which represents the mutual admittance between patches and from patch to ground. Theoretical analysis of two edge-coupled patch antenna models are compared by simulation and experiment in antennas designed to operate at the 2 GHz band. The proposed model predicts the return loss of the antenna accurately.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.89-100
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• 2023

Recently, on-device artificial intelligence (AI) solutions using mobile devices and embedded edge devices have emerged in various fields, such as computer vision, to address network traffic burdens, low-energy operations, and security problems. Although vision transformer deep learning models have outperformed conventional convolutional neural network (CNN) models in computer vision, they require more computations and parameters than CNN models. Thus, they are not directly applicable to embedded edge devices with limited hardware resources. Many researchers have proposed various model compression methods or lightweight architectures for vision transformers; however, there are only a few studies evaluating the effects of model compression techniques of vision transformers on performance. Regarding this problem, this paper presents a performance evaluation of vision transformers on embedded platforms. We investigated the behaviors of three vision transformers: DeiT, LeViT, and MobileViT. Each model performance was evaluated by accuracy and inference time on edge devices using the ImageNet dataset. We assessed the effects of the quantization method applied to the models on latency enhancement and accuracy degradation by profiling the proportion of response time occupied by major operations. In addition, we evaluated the performance of each model on GPU and EdgeTPU-based edge devices. In our experimental results, LeViT showed the best performance in CPU-based edge devices, and DeiT-small showed the highest performance improvement in GPU-based edge devices. In addition, only MobileViT models showed performance improvement on EdgeTPU. Summarizing the analysis results through profiling, the degree of performance improvement of each vision transformer model was highly dependent on the proportion of parts that could be optimized in the target edge device. In summary, to apply vision transformers to on-device AI solutions, either proper operation composition and optimizations specific to target edge devices must be considered.

• Journal of Internet Computing and Services
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• v.4 no.6
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• pp.33-42
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• 2003

A XML Instance repository model based on the Edge-Labeled Graph is suggested for storing the XML instance in Relational Databases, This repository model represents the XML instance as a data graph based on the Edge-Labeled Graph, extracts the defined value based on the structure of data path, element, attribute, and table index table presented as database schema, and stores these values using the Mapper module, In order to support querry, XML repository model offers the module translating XQL which is a query language under XPATH to SQL, and has DBtoXML generator module restoring the stored XML instance. As a result, it is possible to represent the storage relationship between the XML instances and the proposed repository model in terms of Graph-based Path, and it shows the possibility of easy search of specific element and attribute information.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.45-54
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• 2012

In order to predict trailing edge noise from a flat plate more effectively and accurately, the prediction algorithm based on semi-analytic model for point pressure spectrum is proposed. The semi-analytic model consists of empirical models for point pressure spectra and theoretical model to determine the boundary layer characteristics needed for the empirical models. The proposed methods are applied to predict the trailing edge noise of the flat plate located in the mean flow of speed 38 m/s, for which the measured data are available. In present study, six empirical models for point pressure spectra are utilized for the predictions of trailing edge noise and their prediction results are compared to the measured data. Through the analysis of these comparisons, it is revealed that the present method based on non-frozen formula using Efimtsov model and Smol'yakov-Tkachenko model can provide more accurate and efficient predictions of trailing edge noise.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.6
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• pp.524-534
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• 2012

In order to predict trailing edge noise from a flat plate more effectively and accurately, the prediction algorithm based on semi-analytic model for point pressure spectrum is proposed. The semi-analytic model consists of empirical models for point pressure spectra and theoretical model to determine the boundary layer characteristics needed for the empirical models. The proposed methods are applied to predict the trailing edge noise of the flat plate located in the mean flow of speed 38 m/s, for which the measured data are available. In present study, six empirical models for point pressure spectra are utilized for the predictions of trailing edge noise and their prediction results are compared to the measured data. Through the analysis of these comparisons, it is revealed that the present method based on non-frozen formula using Efimtsov model and Smol'yakov-Tkachenko model can provide more accurate and efficient predictions of trailing edge noise.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2120-2123
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• 2003

This paper presents the moving object detection and tracking algorithm using edge information base on human perceptual system The human visual system recognizes shapes and objects easily and rapidly. It's believed that perceptual organization plays on important role in human perception. It presents edge model(GCS) base on extracted feature by perceptual organization principal and extract edge information by definition of the edge model. Through such human perception system I have introduced the technique in which the computers would recognize the moving object from the edge information just like humans would recognize the moving object precisely.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.88-93
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• 2024

The main problem with existing edge detection techniques is that they have many limitations in detecting edges for complex and diverse images that exist in the real world. This is because only edges of a defined shape are discovered based on an accurate definition of the edge. One of the methods to solve this problem is the cost minimization method. In the cost minimization method, cost elements and cost functions are defined and used. The cost function calculates the cost for the candidate edge model generated according to the candidate edge generation strategy, and if the cost is found to be satisfactory, the candidate edge model becomes the edge for the image. In this study, we proposed an enhanced candidate edge generation strategy to discover edges for more diverse types of images in order to improve the shortcoming of the cost minimization method, which is that it only discovers edges of a defined type. As a result, improved edge detection results were confirmed.