• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.7
• /
• pp.2488-2511
• /
• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.

• International Journal of Contents
• /
• v.13 no.1
• /
• pp.53-61
• /
• 2017

Recently, the Social Internet of Things (IoT), the follow-up of the IoT, has been studied to expand the existing IoT services, by integrating devices into the social network of people. In the Social IoT environment, humans, devices and digital contents are connected with social relationships, to guarantee the network navigability and establish levels of trustworthiness. However, this environment handles massive data, including social data of humans (e.g., profile, interest and relationship), profiles of IoT devices, and digital contents. Hence, users and service providers in the Social IoT are exposed to arbitrary data when searching for specific information. A study about the recommender system for the Social IoT environment is therefore needed, to provide the required information only. In this paper, we propose the Social correlation group based Recommender System (SRS). The SRS generates a target group, depending on the social correlation of the service requirement. To generate the target group, we have designed an architecture, and proposed a procedure of the SRS based on features of social interest similarity and principles of the Collaborative Filtering and the Content-based Recommender System. With simulation results of the target scenario, we present the possibility of the SRS to be adapted to various Social IoT services.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.5
• /
• pp.511-519
• /
• 1997

This paper presents the automatic recognition algorithm of the license number in on vehicle image. The proposed algorithm uses the correlation coefficient and Hough transform to detect license plate. The m/n ratio reduction is performed to save time and memory. By the correlation coefficient between the standard pattern and the target pattern, licence plate area is roughly extracted. On the extracted local area, preprocessing and binarization is performed. The Hough transform is applied to find the extract outline of the plate. If the detection fails, a smaller or a larger standard pattern is used to compute the correlation coefficient. Through this process, the license plate of different size can be extracted. Two algorithms to each separate number are proposed. One segments each number with projection-histogram, and the other segments each number with the label. After each character is separated, it is recognized by the neural network. This research overlomes the problems in conventional methods, such as the time requirement or failure in extraction of outlines which are due to the processing of the entire image, and by processing in real time, the practical application is possible.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.10B
• /
• pp.1216-1221
• /
• 2011

We propose a multiplexed fiber Bragg grating (FBG) sensor network for vessel monitoring to measure the variation of strain and temperature by environmental perturbation based on code division multiple access (CDMA). The center wavelength of FBG was linearly changed by environmental perturbation such as strain and temperature variation so that we could be monitoring the state of sensors. A RSOA was used as optical broadband source and which was modulated by using pseudo random binary sequence (PRBS) signal. The correlation peak of reflected signal from sensor networks was measured. In this paper, we used the sliding correlation techniques for high speed response and dynamic rage of sensors.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.3 no.4
• /
• pp.23-27
• /
• 2010

This paper performs emotion classification test to find out the best parameter of electroencyphalogram(EEG) signal. Linear predictor coefficients, band cross-correlation coefficients of fast Fourier transform(FFT) and autoregressive model spectra are used as the parameters of 10-channel EEG signal. A multi-layer neural network is used as the pattern classifier. Four emotions for relaxation, joy, sadness, irritation are induced by four university students of an acting circle. Electrode positions are Fp1, Fp2, F3, F4, T3, T4, P3, P4, O1, O2. As a result, the Linear predictor coefficients showed the best performance.

• Journal of Information Technology Applications and Management
• /
• v.26 no.1
• /
• pp.95-102
• /
• 2019

Public transportation systems play key roles in supporting dynamic activities and interaction between urban places. Especially, high efficient public transportation systems are required in order to support large traffic demands in urban areas. In this paper, we define a new metric, structural activation level (SAL), to replace the conventional transportation share ratio (TSR) measuring efficiency of public transportation systems. First of all, we access the Korea Transport Database (KTDB) and download origin-destination data by transport types to construct traffic networks with respect to transport types for each city. Then, we calculate the QAP (Quadratic Assignment Procedure) correlation between each traffic network and the total traffic network for each city to investigate SAL by comparing cities one another. The results of our investigation reveal inconsistency between TSR and SAL. In Daegu, TSR of public transportation systems is relatively low while SAL is high. In Deajeon, however, SAL is low while TSR is high. Therefore, we suggest to take into consideration SAL as well as TSR in order to investigate the degree of activation of public transportation.

• Journal of Satellite, Information and Communications
• /
• v.9 no.3
• /
• pp.80-85
• /
• 2014

In this paper, we propose and analyze a novel synchronization scheme for ubiquitous home network system. In ubiquitous home network system, synchronization method is very important because many consumer electronic devices send the data simultaneously to each other through infrastructure such as access point. We employ digital watermarking sequence to improve synchronization performance without system overhead. The performance of proposed scheme is analyzed in terms of correlation performance. The results of the paper can be applied to design of various applications for ubiquitous home network systems.

• Nuclear Engineering and Technology
• /
• v.44 no.6
• /
• pp.639-652
• /
• 2012

A three-dimensional discrete fracture network model was developed in order to simulate the hydraulic characteristics of a granitic rock mass at Korea Atomic Energy Research Institute (KAERI) Underground Research Tunnel (KURT). The model used a three-dimensional discrete fracture network (DFN), assuming a correlation between the length and aperture of the fractures, and a trapezoid flow path in the fractures. These assumptions that previous studies have not considered could make the developed model more practical and reasonable. The geologic and hydraulic data of the fractures were obtained in the rock mass at the KURT. Then, these data were applied to the developed fracture discrete network model. The model was applied in estimating the representative elementary volume (REV), the equivalent hydraulic conductivity tensors, and the amount of groundwater inflow into the tunnel. The developed discrete fracture network model can determine the REV size for the rock mass with respect to the hydraulic behavior and estimate the groundwater flow into the tunnel at the KURT. Therefore, the assumptions that the fracture length is correlated to the fracture aperture and the flow in a fracture occurs in a trapezoid shape appear to be effective in the DFN analysis used to estimate the hydraulic behavior of the fractured rock mass.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.17 no.1
• /
• pp.57-66
• /
• 2007

Scanning worms increase network traffic load because they randomly scan network addresses to find hosts that are susceptible to infection. Since propagation speed is faster than human reaction, scanning worms cause severe network congestion. So we need to build an early detection system which can automatically detect and quarantine such attacks. We propose algorithms to detect scanning worms using network traffic characteristics such as variance, variance to mean ratio(VMR) and correlation coefficient. The proposed algorithm have been verified by computer simulation. Compared to existing algorithm, the proposed algorithm not only reduced computational complexity but also improved detection accuracy.

• Journal of information and communication convergence engineering
• /
• v.10 no.2
• /
• pp.135-140
• /
• 2012

Massive numbers of users of social networks share various types of information such as opinions, news, and ideas in real time. As a new form of social network, Twitter is a particularly useful information source. Studying influence can help us better understand the role of social networks. The popularity of social networks like Twitter is primarily measured by the number of followers. The number of followers in Twitter and the number of users exposed to news media are important factors in measuring influence. We chose Twitter and the New York Times as representative media to analyze the influence and present an empirical analysis of these datasets. When the correlation between the number of followers in Twitter and the number of users exposed to the New York Times is computed, the result is moderately high. The correlation between the number of users exposed to the New York Times and the number of sections including the users on it, was found to be very high. We measure the normalized influence score using our proposed expression based on the two correlation coefficients.