• Journal of information and communication convergence engineering
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• v.10 no.1
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• pp.1-4
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• 2012

Given separated points divided by a line, called a wall, in a plane, we aim to make a gate in the wall to connect the separated points to each other. In this setting, the problem is to find a location for the gate that minimizes the average distance between the points. The problem is a variant of the well-known facility location problem, which is extensively studied in the fields of operations research, location theory, theoretical computer science, and so on. In this paper, we consider the $L^1$-distance of the points in the plane. The points are projected onto the wall and so the problem is transformed to a proximity problem of points on a line. Then it is shown that the transformed problem is related to the weighted median problem of points on the line. Therefore, we obtain an O(n log n)-time algorithm to solve our problem.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.105-115
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• 2012

In this paper, we propose a correction method using phoneme unit segmentation to solve misrecognition of Korean Texts in signboard images using weighted Disassemble Levenshtein Distance. The proposed method calculates distances of recognized texts which are segmented into phoneme units and detects the best matched texts from signboard text database. For verifying the efficiency of the proposed method, a database dictionary is built using 1.3 million words of nationwide signboard through removing duplicated words. We compared the proposed method to Levenshtein Distance and Disassemble Levenshtein Distance which are common representative text string comparison algorithms. As a result, the proposed method based on weighted Disassemble Levenshtein Distance represents an improvement in recognition rates 29.85% and 6% on average compared to that of conventional methods, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2414-2433
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• 2019

The aim of this paper is to design a 3D laser radar prototype based on laser triangulation. The mathematical model of distance sensitivity is deduced; a pixel-distance conversion formula is discussed and used to complete 3D scanning. The center position extraction algorithm of the spot is proposed, and the error of the linear laser, camera distortion and installation are corrected by using the proposed weighted average algorithm. Finally, the three-dimensional analytic computational algorithm is given to transform the measured distance into point cloud data. The experimental results show that this 3D laser radar can accomplish the 3D object scanning and the environment 3D reconstruction task. In addition, the experiment result proves that the product of the camera focal length and the baseline length is the key factor to influence measurement accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.95-101
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• 2012

Layout Criteria are developed for an access mode's on and off facility at multiple transfer centers in this paper. Layout Criteria are location issues between a main mode and access modes in terms of the on and off facility one another. The total distance between them has been suggested to be minimized. In the distance calculation stairs are considered to be more difficult and than open space. On the other hand an escalator and an elevator are treated as easier than open space. Considering the number of people between on and off facility, the weighted average distance is suggested to be a MOE(Measure of effectiveness) for layout criteria at multiple transfer centers. Finally, the layout criteria are applied to the existing Kimpo airport terminal and some improvement ideas are suggested.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.210-216
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• 2006

In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.43-50
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• 2013

This paper aims to determine the optimal location of expressway patrol vehicle stations that minimizes additional troubles caused by the delay of crash treatments. To do this, we formulate a maximum covering problem and a p-center problem weighted by crash frequency, using the shortest distance as the criteria for allocating service district, and we employ the Lagrangian relaxation algorithm to solve the former and Daskin's heuristic algorithm to solve the latter, respectively. Based on crash data of Korean expressways, the results from the proposed models are compared with the current location of patrol vehicle stations by using several indices as the level of service for crash treatment, such as maximum crash-weighted distance, average crash-weighted distance, and average access distance. The results show that the proposed models improve average access distance and time by about 10km and 10min, respectively. When allocation for service district is changed only with the fixed current location, the level of service can be also improved. The models and results proposed in this paper can contribute to improving the level of service for crash treatment on expressways. They can also provide the theoretical basis on the location decision for other various emergency facilities, and the allocation decision for floating service districts according to time-period crash data.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.139-148
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• 2022

Research into wireless sensor networks (WSNs) is a trendy issue with a wide range of applications. With hundreds to thousands of nodes, most wireless sensor networks interact with each other through radio waves. Limited computational power, storage, battery, and transmission bandwidth are some of the obstacles in designing WSNs. Clustering and routing procedures have been proposed to address these concerns. The wireless sensor network's most complex and vital duty is routing. With the Greedy Perimeter Stateless Routing method (GPSR), an efficient and responsive routing protocol is built. In packet forwarding, the nodes' locations are taken into account while making choices. In order to send a message, the GPSR always takes the shortest route between the source and destination nodes. Weighted directed graphs may be constructed utilising four distinct distance metrics, such as Euclidean, city block, cosine, and correlation distances, in this study. NS-2 has been used for a thorough simulation. Additionally, the GPSR's performance with various distance metrics is evaluated and verified. When compared to alternative distance measures, the proposed GPSR with correlation distance performs better in terms of packet delivery ratio, throughput, routing overhead and average stability time of the cluster head.

• Journal of Forest and Environmental Science
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• v.28 no.3
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• pp.170-178
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• 2012

Dipterocarps or Dipterocarpaceae is a commercially important timber producing and dominant keystone tree family in the rain forests of Borneo. Borneo's landscape is changing at an unprecedented rate in recent years which affects this important biodiversity. This paper attempts to model the natural occurrence (distribution including those areas with natural forests before being converted to other land uses as opposed to current distribution) of dipterocarp species in Sarawak which is important for forest biodiversity conservation and management. Local modeling method of Inverse Distance Weighting was compared with commonly used statistical method (Binary Logistic Regression) to build the best natural distribution models for three genera (12 species) of dipterocarps. Database of species occurrence data and pseudoabsence data were constructed and divided into two halves for model building and validation. For logistic regression modeling, climatic, topographical and edaphic parameters were used. Proxy variables were used to represent the parameters which were highly (p>0.75) correlated to avoid over-fitting. The results show that Inverse Distance Weighting produced the best and consistent prediction with an average accuracy of over 80%. This study demonstrates that local interpolation method can be used for the modeling of natural distribution of dipterocarp species. The Inverse Distance Weighted was proven a better method and the possible reasons are discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.118-120
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• 2021

In modern society, with the development of IoT technology, various digital equipment is being distributed in a wide range of fields such as CCTV and exploration robots. Accordingly, the importance of data processing is increasing, and various studies are being conducted to remove noise generated in the process of receiving data in the imaging field. Representative noise includes additive white Gaussian noise (AWGN), and existing filters for removing noise include an average filter (AF), an alpha trimmed average filter (A-TAF), and a median filter (MF). However, existing filters have a disadvantage in that they show somewhat insufficient performance in noise removal characteristics in high frequency areas. Therefore, in this paper, in order to effectively remove AWGN existing in the high frequency region, a weight filter according to a distance based on the standard deviation is proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.366-373
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• 2017

In this paper, we propose a variable weighted average (VWA) learning method in order to improve the performance of the fuzzy ART neural network that has been developed by Grossberg. In a conventional method, the Fast Commit Slow Recode (FCSR), when an input pattern falls in a category, the representative pattern of the category is updated at a fixed learning rate regardless of the degree of similarity of the input pattern. To resolve this issue, a variable learning method proposes reflecting the distance between the input pattern and the representative pattern to reduce the FCSR's category proliferation issue and improve the pattern recognition rate. However, these methods still suffer from the category proliferation issue and limited pattern recognition rate due to inevitable excessive learning created by use of fuzzy AND. The proposed method applies a weighted average learning scheme that reflects the distance between the input pattern and the representative pattern when updating the representative pattern of a category suppressing excessive learning for a representative pattern. Our simulation results show that the newly proposed variable weighted average learning method (VWA) mitigates the category proliferation problem of a fuzzy ART neural network by suppressing excessive learning of a representative pattern in a noisy environment and significantly improves the pattern recognition rates.