• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.33-40
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• 2012

This paper proposes a method to implement biological early warning systems(BEWS). This system generates periodically data event using a monitoring daemon and it extracts the feature parameters from this data sets. The feature parameters are derived with 6 variables, x/y coordinates, distance, absolute distance, angle, and fractal dimension. Specially by using the fractal dimension theory, the proposed algorithm define the input features represent the organism characteristics in non-toxic or toxic environment. And to find a moderate algorithm for learning the extracted feature data, the system uses an extended learning algorithm(UChoo) popularly used in machine learning. And this algorithm includes a learning method with the extended data expression to overcome the BEWS environment which the feature sets added periodically by a monitoring daemon. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression. Experimental results show that the proposed BEWS is available for environmental toxicity detection.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.95-102
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• 2012

The development of applications for vehicular ad hoc networks (VANETs) has very specific and clear goals such as providing intellectual safe transport systems. An emergency warning technic for public safety is one of the applications which requires an intelligent broadcast mechanism to transmit warning messages quickly and efficiently against the time restriction. The broadcast storm problem causing several packet collisions and extra delay has to be considered to design a broadcast protocol for VANETs, when multiple nodes attempt transmission simultaneously at the access control layer. In this paper, we propose an early intelligent alert broadcasting (EI-CAST) algorithm to resolve effectively the broadcast storm problem and meet time-critical requirement. The proposed algorithm uses not only the early alert technic on the basis of time to collision (TTC) but also the intelligent broadcasting technic on the basis of fuzzy logic, and the performance of the proposed algorithm was compared and evaluated through simulation with the existing broadcasting algorithms. It was demonstrated that the proposed algorithm shows a vehicle can receive the alert message before a collision and have no packet collision when the distance of alert region is less than 4 km.

• Water for future
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• v.18 no.2
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• pp.153-161
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• 1985

The methods for hydrologic prediction of streamflows for more efficient and functional operations and automation of the flood warning and forecasting system have been studiedand which have been widely used in the control engineering have been studied and investigated for representation of the dynamic behavior of rainfall-runoff precesses, and formulated into mathematical model form. The applicabilities of the model using the adaptive Kalman filter algorithm to the on-line, real-time prediction of river flows have been worked out. The computer programs in FORTRAN which are developed here can be utilized for more efficient operations and better prediction abilities of flood warning and forecasting systems, and also should be modified for better model performance.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.440-448
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• 2015

A pedestrian collision warning system which makes a judgement of pedestrian's intention to help avoiding hitting accidents is proposed. This system uses the image sequences obtained from a car black box as well as vehicle's speed obtained from a GPS. It detects pedestrians, if any, based on the Histogram of Gradient method and extracts several information such as the pedestrian's relative positions, the direction of motion vectors, and distance between vehicle and pedestrian . A fuzzy logic based on these extracted information is applied to analyze the pedestrian's safety levels. When the safety level is determined to be danger, an alarm is triggered to the driver. The performance of the proposed algorithm is tested under various driving scenarios, which shows it works successfully in real-time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1203-1222
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• 2012

To provide active notification and enhance drivers'awareness of their surroundings, a vision-based collision warning system that detects and monitors surrounding vehicles is proposed in this paper. The main objective is to prevent possible vehicle collisions by monitoring the status of surrounding vehicles, including the distance to the other vehicles in front, behind, to the left and to the right sides. In addition, the proposed system collects and integrates this information to provide advisory warnings to drivers. To offer the correct notification, an algorithm based on features of edge and morphology to detect vehicles is also presented. The proposed system has been implemented in embedded systems and evaluated on real roads in various lighting and weather conditions. The experimental results indicate that the vehicle detection ratios were higher than 97% in the daytime, and appropriate for real road applications.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.526-533
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• 2014

In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.3
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• pp.137-144
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• 2015

The purpose of this study is to develop an warning system to detect real-time water quality abnormality using a multivariate statistical approach. In this study, we applied principal component analysis among multivariate data analyses which was used for the correlation between water quality parameters considering the real-time algorithm to determine abnormality in water quality. We applied our approach to real field data and showed the utilization of algorithm for the real-time monitoring to find water quality abnormality. In addition, our approach with Korea Meterological Adminstration database identified heavy rain data due to climate change is one of the most important factors to explain water quality abnormality.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.88-98
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• 2006

VANET has several different characteristics from MANET such as high mobility of nodes and frequent change of node density and network topology. Due to these characteristics, the network topology based protocol, often used in MANET, can not be applied to VANET. In this paper, we propose an emergency warning message broadcast protocol using range based relay node selecting algorithm which determines the minimal waiting time spent by a given node before rebroadcasting the received warning message. Because the time is randomly calculated based on the distance between sender node and receiver node, a node chosen as a relay node is assured to have a minimal waiting time, even though it is not located at the border of radio transmission range. The proposed emergency warning message broadcast protocol has low network traffic because it does not need to exchange control messages for message broadcasting. In addition, it can reduce End-to-End delay under circumstances of low node density and short transmission range in VANET.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.180-182
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• 2022

As various crimes such as theft, assault, and sex crimes are increasing, each local government is installing CCTVs to prevent them, and operating and managing control centers for emergency response. When the control center detects a dangerous situation in the field, it responds immediately in connection with the police or 911. However, since it is managed by humans, the response speed is anomalous and the reality is that it is mainly used for post-processing. Therefore, through the artificial intelligence LED signage, it notifies the emergency situation at the site, and it serves as a warning function before getting help from passers-by or an accident occurs. In this paper, we design and research a warning system such as changing the lighting color of the LED signboard or making a sound by reflecting the artificial intelligence algorithm. We intend to contribute to public safety and social safety through this study.

• ETRI Journal
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• v.45 no.3
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• pp.379-393
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• 2023

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized leastmean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted l₁-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.