• Journal of Advanced Navigation Technology
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• 제24권4호
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• pp.267-272
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• 2020

In order to monitor in large area of a ship without any concerns of spacial environments, there can be the use of many sensors that support wireless network technologies. Through the deployment of the wireless sensors and their autonomous network formations, the status of a ship can be easily monitored in real time. Moreover, if all the sensors are timely synchronized with each other, the locations and the moving directions of certain events can be estimated. Thus, the time synchronization amongst wireless sensors are crucial. In this paper, we propose an algorithm which supports reduced message transmissions between the sensors for synchronization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권11호
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• pp.5476-5496
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• 2016

Energy efficiency of resource-constrained wireless sensor networks is critical in applications such as real-time monitoring/surveillance. To improve the energy efficiency and reduce the energy consumption, the time series data can be compressed before transmission. However, most of the compression algorithms for time series data were developed only for single variate scenarios, while in practice there are often multiple sensor nodes in one application and the collected data is actually multivariate time series. In this paper, we propose to compress the time series data by the Lasso (least absolute shrinkage and selection operator) approximation. We show that, our approach can be naturally extended for compressing the multivariate time series data. Our extension is novel since it constructs an optimal projection of the original multivariates where the best energy efficiency can be realized. The two algorithms are named by ULasso (Univariate Lasso) and MLasso (Multivariate Lasso), for which we also provide practical guidance for parameter selection. Finally, empirically evaluation is implemented with several publicly available real-world data sets from different application domains. We quantify the algorithm performance by measuring the approximation error, compression ratio, and computation complexity. The results show that ULasso and MLasso are superior to or at least equivalent to compression performance of LTC and PLAMlis. Particularly, MLasso can significantly reduce the smooth multivariate time series data, without breaking the major trends and important changes of the sensor network system.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• 제8권3호
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• pp.122-131
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• 2009

Ubiquitous-Transportation sensor network is able to realize a vehicle ad-hoc network. Since there are some problems in an existing ITS system, the new technology and traffic information strategies are requirements in this advanced system, u-TSN. The purposes of this paper is to introduce the components on u-TSN system, establish new traffic strategies for this system, and then evaluate these strategies by making a comparative study of ITS and using micro traffic simulator, AIMSUN. The strategy evaluated by AIMSUN is position-based multicast strategy which provides traffic information to vehicles using V2I (vehicle to Infrastructure) communication. This paper focuses on the providing real-time route guidance information when congestion is occurred by the incidents. This study estimates total travel time on each route by API modules. Result from simulation experiments suggests that position-based multicast strategy can achieve more optimal network performance and increased driver satisfaction since the total accumulated travel times of both the major road and the total system on position-based multicast strategy are less than those on VMS.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.21-26
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• 2006

This paper focuses on modeling and predicting differential GPS corrections transmitted by marine radio-beacon systems using artificial neural networks. Various neural network structures with various training algorithms were examined, including Linear, Radial Biases, and Feedforward. Matlab Neural Network toolbox is used for this purpose. Data sets used in building the model are the transmitted pseudorange corrections and broadcast navigation message. Model design is passed through several stages, namely data collection, preprocessing, model building, and finally model validation. It is found that feedforward neural network with automated regularization is the most suitable for our data. In training the neural network, different approaches are used to take advantage of the pseudorange corrections history while taking into account the required time for prediction and storage limitations. Three data structures are considered in training the neural network, namely all round, compound, and average. Of the various data structures examined, it is found that the average data structure is the most suitable. It is shown that the developed model is capable of predicting the differential correction with an accuracy level comparable to that of beacon-transmitted real-time DGPS correction.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.619-639
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• 2010

There is increasing interest in using structural monitoring as a cost effective way of managing risks once an area of concern has been identified. However, it is challenging to deploy an effective, reliable, large-scale, long-term and real-time monitoring system in an underground railway environment (subway / metro). The use of wireless sensor technology allows for rapid deployment of a monitoring scheme and thus has significant potential benefits as the time available for access is often severely limited. This paper identifies the critical factors that should be considered in the design of a wireless sensor network, including the availability of electrical power and communications networks. Various issues facing underground deployment of wireless sensor networks will also be discussed, in particular for two field case studies involving networks deployed for structural monitoring in the Prague Metro and the London Underground. The paper describes the network design, the radio propagation, the network topology as well as the practical issues involved in deploying a wireless sensor network in these two tunnels.

• Journal of Institute of Control, Robotics and Systems
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• 제10권4호
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• pp.377-384
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• 2004

Nowadays, control systems consist of smart sensors, smart actuators, and controllers connected via fieldbus. Some devices such as motors in plant environments generate high degrees of EMI or noise. This noise may cause communication errors and make the successful transmission of data longer. Therefore, the noise condition has to be considered at the design of a reliable control system based on a network. This paper presents a scheduling method of task and message to guarantee the given end-to-end constraints under noise environments. A noise model with multi-sources of noise is used, and the analysis method of message's response time is presented when the noise model is applied to CAN (Controller Area Network). Two kinds of noise models are applied to an example system, and the effect to each control loop s end-to-end response time is analyzed. We believe that the proposed method help system designers design the control system guaranteeing its requirements under noise environment.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 한국마린엔지니어링학회 2002년도 춘계학술대회논문집
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• pp.213-217
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• 2002

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to courier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier, and more useful method than the FFW (Fast courier Transform).ransform).

• Journal of the Korean Institute of Telematics and Electronics B
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• 제31B권10호
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• pp.44-52
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• 1994

In this paper, we assume that the dynamics of DC motor and nonlinear load are unknown. We propose an inverse dynamic model of DC motor and nonlinear load using the artificial neural network and construck speed control system based on the proposed dynamic model. We also propose another dynamic model with speed prediction scheme using the artificial neural network that removes the undesirable time delay effect caused by the computation time during the real-time control. We suggest a dynamic model which has arbitrary number of speed arguments and is especially effective when the motor and load has large moment of inertia. Next, we suggest a controller that combine the neurocontrol and PID control with constant gain. We show that the proposed neurocontrol systems have capabilities of noise rejection and generalization to have good velocity tracking through computer simulations and experiments.

• Proceedings of the Safety Management and Science Conference
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• 대한안전경영과학회 2003년도 추계학술대회
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• pp.129-142
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• 2003

The SCM is that Supply Chain Network is Promptly and Voluntarily Optimized in Unstable Market Change Environment. The Cash flow Efficiency of Hole Supply Chain Network is Improved by Changing the Information and Changing the Foundation of Business Processes. The Role of WMS has been Changing Importantly with the Introduction of SCM. WMS Needed to Change to the Information Center in Order to Change Information in Real Time and the WMS of Information Storing in Order to Support an Idea Decision. This Development was Defined about the Importance of WMS in SCM Environment. The Criterion of Valuation is Normally Measured Time between Taking a Order Receive and Bringing the Items to Customer. The Decreasing Move Time of Order Picker in Warehouse is Directly Influence to the Job Execution. So, this Research is Defined about the Optimized Route of Order Picker and Suggests Algorithm. To do this, Past Algorithm is Studied. It's Easy to Introduce and this Study is Looking for Method about the Noticing of Order Picker. The Algorithm will Improve to be Adapt to Standard Process System.

• Journal of the Korean Institute of Intelligent Systems
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• 제13권5호
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• pp.606-613
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• 2003

As a technical method for controlling chaotic dynamics, this paper presents a predictive control for chaotic systems based on radial basis function networks(RBFNs). To control the chaotic systems, we employ an on-line identification unit and a nonlinear feedback controller, where the RBFN identifier is based on a suitable NARMA real-time modeling method and the controller is predictive control scheme. In our design method, the identifier and controller are most conveniently implemented using a gradient-descent procedure that represents a generalization of the least mean square(LMS) algorithm. Also, we introduce a projection matrix to determine the control input, which decreases the control performance function very rapidly. And the effectiveness and feasibility of the proposed control method is demonstrated with application to the continuous-time and discrete-time chaotic nonlinear system.