• Journal of KIISE:Databases
• /
• v.36 no.3
• /
• pp.215-225
• /
• 2009

Recently, various methods have been proposed to process k-NN (k-Nearest Neighbors) queries efficiently. However the previous methods have problems that they access additional cells unnecessarily and spend the high computation cost to find the nearest cells. In this paper, to overcome the problems, we propose a new method to process k-NN queries using the patterns of the distance relationship between the cells in a grid. The patterns are composed of the relative coordinates of cells sorted by the distance from certain points. Since the proposed method finds the nearest cells to process k-NN queries with traversing the patterns sequentially, it saves the computation cost. It is shown through the various experiments that out proposed method is much better than the existing method, CPM, in terms of the query processing time and the storage overhead.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.10
• /
• pp.2149-2156
• /
• 2011

In this paper, design of a new 3-dimensional (3-D) 16-ary signal constellation with constant envelope is presented and analyzed. Unlike the conventional 16-ary constellations, all signal points of the new constellation are uniformly located on the surface of a sphere so that they have a unique amplitude level and a symmetrical structure. When average power of the constellations is normalized, the presented 16-ary constellation has around 11.4% increased minimum Euclidean distance (MED) as compared to the conventional ones that have non-constant envelope. As a result, a digital communication system which exploits the presented constellation has 1.2dB improved symbol error rate (SER). While signal points of the conventional constant-envelope constellation are not distributed uniformly on the surface of a sphere, those of the proposed constellation has a completely symmetric distribution. In addition, the new signal constellation has much lower computational complexity for practical implementation than the conventional one. Hence, the proposed 3-D 16-ary signal constellation is appropriate for the application to a communication system which strongly requires a constant-envelope characteristic.

• Journal of Korea Spatial Information System Society
• /
• v.8 no.2 s.17
• /
• pp.109-120
• /
• 2006

This paper addresses techniques for predicting a future path of an object moving on a road network. Most prior methods for future prediction mainly focus their attention on objects moving in Euclidean space. A variety of applications such as telematics, however, deal with objects that move only over road networks in most cases, thereby requiring an effective method of future prediction of moving objects on road networks. In this paper, we propose a novel method for predicting a future path of an object by analyzing past trajectories whose changing pattern is similar to that of a current trajectory of a query object. We devise a new function that measures a similarity between trajectories by reflecting the characteristics of road networks. By using this function, we predict a future path of a given moving object as follows: First, we search for candidate trajectories that contain subtrajectories similar to a given query trajectory by accessing past trajectories stored in moving object databases. Then, we predict a future path of a query object by analyzing the moving paths along with a current position to a destination of candidate trajectories thus retrieved. Also, we suggest a method that improves the accuracy of path prediction by regarding moving paths that have just small differences as the same group.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.9
• /
• pp.1905-1912
• /
• 2009

Clustering results based on the FCM algorithm sometimes produces undesirable clustering result through data distribution in the clustered space because data is classified by comparison with membership degree which is calculated by the Euclidean distance between input vectors and clusters. Symmetrical measurement of clusters and fuzzy theory are applied to the classification to tackle this problem. The enhanced FCM algorithm has a low impact with the variation of changing distance about each cluster, middle of cluster and cluster formation. Improved hybrid network of applying FCM algorithm is proposed to classify patterns effectively. The proposed enhanced FCM algorithm is applied to the learning structure between input and middle layers, and normalized delta learning rule is applied in learning stage between middle and output layers in the hybrid network. The proposed algorithms compared with FCM-based RBF network using Max_Min neural network, FMC-based RBF network and HCM-based RBF network to evaluate learning and recognition performances in the two-dimensional coordinated data.

• Journal of Korean Society of Disaster and Security
• /
• v.9 no.2
• /
• pp.15-21
• /
• 2016

According to the disaster statistics issued by the Ministry of Public Safety and Security, traffic accidents, fire, collapses and others are classified into twenty-three (23) categories. In the past, disasters were mainly caused by the influence of nature, such as typhoon or drought. On the other hand, as society has become city-centered, social disasters' types, frequencies and scales are becoming more diversified and ever-increasing. However, there are no specific criteria and assessment methods that can measure degrees of social disasters-related risks objectively. Therefore, this study targeted traffic accidents, fire and collapses from major social disasters, utilized data that are related to occurrence rate, scale of casualties and scale of property loss in past eight years, and calculated the disaster risk index using the distance (Euclidean distance) between two points on the 3D spatial coordinates, in order to make the objective assessment by social disaster type possible. These results will enable the objective evaluation of risk index of major social disaster to be used as the foundational data when building the national disaster management system.

• The KIPS Transactions:PartD
• /
• v.10D no.3
• /
• pp.547-558
• /
• 2003

From the viewpoint of that music melody has the similar features to time-series data, music melody is transformed to a time-series data with normalization and corrections and the similarity between melodies is defined as the Euclidean distance between the transformed time-series data. Then, based the similarity between melodies of a music object, melodies are clustered and the representative of each cluster is extracted as one of theme melodies for the music. To construct the theme melody index, a theme melody is represented as a point of the multidimensional metric space of M-tree. For retrieval of user's query melody, the query melody is also transformed into a time-series data by the same way of indexing phase. To retrieve the similar melodies to the query melody given by user from the theme melody index the range query search algorithm is used. By the implementation of the prototype system using the proposed theme melody index we show the effectiveness of the proposed methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.1
• /
• pp.291-299
• /
• 2016

This paper raises a question of whether a simple periodic phenomenon is associated with the topology and provides the convincing answers to it. A variety of music instrumental sound signals are used to prove our assertion, which are embedded in Euclidean space to analyze their topologies by computing the homology groups. A commute time embedding is employed to transform segments of waveforms into the corresponding geometries, which is implemented by organizing patches according to the graph-based metric. It is shown that commute time embedding generates the intrinsic topological complexities although their geometries are varied according to the spectrums of the signals. This paper employs a persistent homology to determine the topological invariants of the simplicial complexes constructed by randomly sampling the commute time embedding of the waveforms, and discusses their applications.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.6
• /
• pp.648-653
• /
• 2010

The non-metric multidimensional scaling (nMDS) is a method for analyzing the relation among objects by mapping them onto the Euclidean space. The nMDS is useful when it is difficult to use the concept of distance between pairs of objects due to non-metric dissimilarities between objects. The nMDS can be regarded as an optimization problem in which there are many local optima. Since the conventional nMDS algorithm utilizes the steepest descent method, it has a drawback in that the method can hardly find a better solution once it falls into a local optimum. To remedy this problem, in this paper, we applied the simulated annealing to the nMDS and proposed a new optimization algorithm which could search for a global optimum more effectively. We examined the algorithm using benchmarking problems and found that improvement rate of the proposed algorithm against the conventional algorithm ranged from 0.7% to 3.2%. In addition, the statistical hypothesis test also showed that the proposed algorithm outperformed the conventional one.

• Journal of Broadcast Engineering
• /
• v.8 no.1
• /
• pp.37-44
• /
• 2003

The standard approach to signal resampling is to fit the original image to a continuous model and resample the function at a desired rate. We used the compact B-spline function as the continuous model which produces less oscillatory behavior than other tails functions. In the case of nonuniform resampling based on a B-spline model, the digital signal is fitted to a spline model, and then the fitted signal is resampled at a space varying rate determined by the transformation function. It is simple to implement but may suffer from artifacts due to data loss. The main purpose of this paper is the derivation of optimal nonuniform resampling algorithm. For the optimal nonuniform formulation, the resampled signal is represented by a combination of shift varying splines determined by the transformation function. This optimal nonuniform resampling algorithm can be verified from the experiments that It produces less errors.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.2 s.302
• /
• pp.113-120
• /
• 2005

3D reconstruction of a scene structure from un-calibrated image sequences has been long one of the central problems in computer vision. For 3D reconstruction in Euclidean space, projective reconstruction, which is classified into the merging method and the factorization, is needed as a preceding step. By calculating all camera projection matrices and structures at the same time, the factorization method suffers less from dia and error accumulation than the merging. However, the factorization is hard to analyze precisely long sequences because it is based on the assumption that all correspondences must remain in all views from the first frame to the last. This paper presents a new projective reconstruction method for recovery of 3D structure over long sequences. We break a full sequence into sub-sequences based on a quantitative measure considering the number of matching points between frames, the homography error, and the distribution of matching points on the frame. All of the projective reconstructions of sub-sequences are registered into the same coordinate frame for a complete description of the scene. no experimental results showed that the proposed method can recover more precise 3D structure than the merging method.