• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.199-205
• /
• 2022

The extensive growth in data rate demand by the smart gadgets and mobile broadband application services in wireless cellular networks. To achieve higher data rate demand which leads to aggressive frequency reuse to improve network capacity at the price of Inter Cell Interference (ICI). Fractional Frequency Reuse (FFR) has been recognized as an effective scheme to get a higher data rate and mitigate ICI for perfect geometry network scenarios. In, an irregular geometric multicellular network, ICI mitigation is a challenging issue. The purpose of this paper is to develop distributed dynamic power allocation scheme for FFR based on game theory to mitigate ICI. In the proposed scheme, each cell region in an irregular multicellular scenario adopts a self-less behavior instead of selfish behavior to improve the overall utility function. This proposed scheme improves the overall data rate and mitigates ICI.

• Korean Journal of Remote Sensing
• /
• v.19 no.6
• /
• pp.457-464
• /
• 2003

Triangulated irregular network is one of the most frequently used terrain modeling methods. It represents terrain precisely using only a small amount of data, and enables fast rendering of terrain. In this paper, ridges and valleys are extracted from the terrain, and used as a set of vertices for the construction of triangulated irregular network. An experiment shows the new method reduces the time for construction of the triangulated irregular network significantly with almost equal amount of induced error.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.8 no.2
• /
• pp.1-8
• /
• 1990

A regular grid or a triangulated irregular network is generally used as the data structure of digital terrain models. A Regular grid is simple and easy to manipulate, but it can't describe well terrain surface features and requires vast volumes of data. In the meantime, a triangulated irregular network has complex data structure, but it can describe well terrain surface features and can achieve the accuracy suitable to its application with relatively little data. This paper aims at the construction of efficient digital terrain models by the improvment of a triangulated irregular network based on Delaunay triangulation. Regular and irregular data set are sampled from existing contour maps, and the efficiency and the accuracy of the two data structures are compared.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.12 no.2
• /
• pp.209-218
• /
• 1994

This study aims to generate triangulated irregular network in a form of digital terrain model which is being increasingly used. In general, grid digital elevation model and triangulated irregular network are reasonable units for solving terrain problems. But, triangulated irregular network is an efficient alternative to grid digital elevation model because of their efficiency in storing data and their convenient data structure for accommodating irregularly spaced elevation data. Various methods represented for extracting triangulated irregular networks from grid digital elevation model, and then algorithm that get accurate results for triangulation with their data set was introduced. The new approach for triangulation in this study uses the Elevation and Changeable Distance criterion, and adding interpolation points and restricted constraint lines can generate triangulated irregular network which is more close to real surface. This made database efficient construction and could be used for many applications of geo-spatial information system.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.48 no.4
• /
• pp.3-12
• /
• 2006

Watershed is the land area that contributes runoff to an outlet point. To delineate an watershed, watershed delineation using GIS that contains grid data structure is the most general method. Some researchers have studied to implement algorithms that revise the TIN topography since it is difficult to delineate watershed boundary more accurately. In this study kruskal's greedy algorithm and triangulated irregular network (TIN) were used to delineate a watershed. This method does not require a conversion from to DEM in grid and automatically obtain(generates) the oulet points. Delineation algorithm was tested in Geosan-gun, Chung-cheongbuk-do and get small watershed areas. Finally, kruskal's algorithm could operate more precisely with revision algorithm.

• Korean Journal of Construction Engineering and Management
• /
• v.6 no.1 s.23
• /
• pp.65-72
• /
• 2005

The 3D objects far representing artificial element in building project can be easily organized in 4D system. However, the civil engineering projects need a composing technology of irregular triangulate network because the projects generally include earthwork of large scale that consists of cutting, filling, and excavating work. It Is very difficult to represent earthwork status as 3D object type because the earthwork in highway or railway projects is being progressed on the natural geographical features. That is, a composing technology of irregular triangulate networks necessary function to simulate natural site condition as 3D object in 4D system. This study suggests a new composing technology of irregular triangulate network that 3D objects can be automatically generated by attribute in each layer.

• Structural Engineering and Mechanics
• /
• v.49 no.6
• /
• pp.687-703
• /
• 2014

This paper develops a new nonlinear model for active control of three-dimensional (3D) irregular building structures. Both geometrical and material nonlinearities with a neuro-controller training algorithm are applied to a multi-degree-of-freedom 3D system. Two dynamic assembling motions are considered simultaneously in the control model such as coupling between torsional and lateral responses of the structure and interaction between the structural system and the actuators. The proposed control system and training algorithm of the structural system are evaluated by simulating the responses of the structure under the El-Centro 1940 earthquake excitation. In the numerical example, the 3D three-story structure with linear and nonlinear stiffness is controlled by a trained neural network. The actuator dynamics, control time delay and incident angle of earthquake are also considered in the simulation. Results show that the proposed control algorithm for 3D buildings is effective in structural control.

• The Journal of Engineering Geology
• /
• v.33 no.3
• /
• pp.439-459
• /
• 2023

Numerical analysis has been adopted in nearly all modern scientific and engineering fields due to the rapid and ongoing evolution of computational technology, with the number of grid or mesh points in a given data field also increasing. Some values must be extracted from large data fields to evaluate and supplement numerical analysis results and observational data, thereby highlighting the need for a fast and effective interpolation approach. The quadrilateral irregular network (QIN) proposed in this study is a fast and reliable interpolation method that is capable of sufficiently satisfying these demands. A comparative sensitivity analysis is first performed using known test functions to assess the accuracy and computational requirements of QIN relative to conventional interpolation methods. These same interpolation methods are then employed to produce simple numerical model results for a real-world comparison. Unlike conventional interpolation methods, QIN can obtain reliable results with a guaranteed degree of accuracy since there is no need to determine the optimal parameter values. Furthermore, QIN is a computationally efficient method compared with conventional interpolation methods that require the entire data space to be evaluated during interpolation, even if only a subset of the data space requires interpolation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.233-236
• /
• 2010

This paper describes the packet traffic management of the Ubiquitous Healthcare System. In this system, ECG signal and accelerometer signal is transmitted from a wearable health shirt (WHS) to the base station. However, with the increment of users in this system, traffic over-load issue occurs. The main aim of this paper is to reduce the traffic over-load issue between sensor nodes by only transmitting the required signals to the base station when irregular activities are observed. In order to achieve this, in-network processing is adapted where the process of observation is conducted inside the sensor node of WHS. Results shows that irregular activities such as fall can be detected on real-time inside the sensor node and thus resolves traffic over-load issue.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.11a
• /
• pp.625-627
• /
• 2022

In the construction and development of modern industrial production technology, the traditional technology management mode is faced with many problems such as low qualification rates and high application costs. In the research, an improved workpiece defect detection method based on deep learning is proposed, which can control the application cost and improve the detection efficiency of irregular defects. Based on the research of the current situation of deep learning applications, this paper uses the improved Faster R-CNN network structure model as the core detection algorithm to automatically locate and classify the defect areas of the workpiece. Firstly, the robustness of the model was improved by appropriately changing the depth and the number of channels of the backbone network, and the hyperparameters of the improved model were adjusted. Then the deformable convolution is added to improve the detection ability of irregular defects. The final experimental results show that this method's average detection accuracy (mAP) is 4.5% higher than that of other methods. The model with anchor size and aspect ratio (65,129,257,519) and (0.2,0.5,1,1) has the highest defect recognition rate, and the detection accuracy reaches 93.88%.