• Journal of Navigation and Port Research
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• v.39 no.6
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• pp.479-484
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• 2015

Network RTK is a precise positioning technique using carrier phase correction data from reference stations within the network, and is constantly being researched for improved performance. However, the study for the system accuracy has been performed but system integrity research has not been done as much as system accuracy, because network RTK has been mainly used on surveying for static or kinematic positioning. In this paper, adequate monitoring system for network RTK is designed as basis research for integrity monitoring on network RTK. To this, fault tree on network RTK is analyzed, and a countermeasure is prepared to detect and identify the each fault items. Based these algorithms, monitoring system to use on central processing facility is designed for network RTK service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1759-1772
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• 2023

Chinese named entity recognition (NER) is a challenging work that seeks to find, recognize and classify various types of information elements in unstructured text. Due to the Chinese text has no natural boundary like the spaces in the English text, Chinese named entity identification is much more difficult. At present, most deep learning based NER models are developed using a bidirectional long short-term memory network (BiLSTM), yet the performance still has some space to improve. To further improve their performance in Chinese NER tasks, we propose a new NER model, IDCNN-BiLSTM-Highway, which is a combination of the BiLSTM, the iterated dilated convolutional neural network (IDCNN) and the highway network. In our model, IDCNN is used to achieve multiscale context aggregation from a long sequence of words. Highway network is used to effectively connect different layers of networks, allowing information to pass through network layers smoothly without attenuation. Finally, the global optimum tag result is obtained by introducing conditional random field (CRF). The experimental results show that compared with other popular deep learning-based NER models, our model shows superior performance on two Chinese NER data sets: Resume and Yidu-S4k, The F1-scores are 94.98 and 77.59, respectively.

• Journal of Sensor Science and Technology
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• v.17 no.3
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• pp.168-177
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• 2008

An indoor environmental monitoring system using IEEE 802.15.4 based wireless sensor network is proposed to monitor the amount of pollutant entering to the room from outside and also the amount of pollutant that is generated in indoor by the building materials itself or human activities. Small-size, low-power wireless sensor node and low power electrochemical sensor board is designed to measure the condition of indoor environment in buildings such as home, offices, commercial premises and schools. In this paper, two query models, the broadcasting query protocol and flooding query protocol, were designed and programmed as a query-based routing protocol in wireless sensor network for an environment monitoring system. The flooding query routing protocol in environment monitoring is very effective as a power saving routing protocol and reliable data transmission between sensor nodes.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1727-1731
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• 2004

We propose an ART2 neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, fault classifier by ART2 NN (adaptive resonance theory 2 neural network) with uneven vigilance parameters is used for fault isolation. The performances of the proposed fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.6
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• pp.331-338
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• 2012

This paper presents a wireless sensor network protocol aimed for artificial structure monitoring. The proposed protocol enables the sensor network to operate at a low duty cycle for reducing power consumption with a high degree of synchronization accuracy. It also enables event-triggered measurement of environmental information with a high sampling rate and the transmission of the measured data with a low latency. The feasibility of the proposed protocol is demonstrated through experiments involving three sensor nodes and a sink node. Though a tunnel health monitoring was considered in the paper, the proposed protocol can be easily adopted in other areas.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.449-450
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• 2015

This paper describes performance evaluation results of a new dynamic bandwidth allocation algorithm for an XG-PON (10-Gbps-capable passive optical network) system without using an explicit request. An ONU (optical network unit) does not send its request to an OLT (optical line termination). The OLT monitors the upstream bandwidth usage of the ONU to estimate the request of the ONU.

• Smart Structures and Systems
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• v.10 no.3
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• pp.271-298
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• 2012

Researchers have made significant progress in recent years towards realizing effective structural health monitoring (SHM) utilizing wireless smart sensor networks (WSSNs). These efforts have focused on improving the performance and robustness of such networks to achieve high quality data acquisition and distributed, in-network processing. One of the primary challenges still facing the use of smart sensors for long-term monitoring deployments is their limited power resources. Periodically accessing the sensor nodes to change batteries is not feasible or economical in many deployment cases. While energy harvesting techniques show promise for prolonging unattended network life, low power design and operation are still critically important. This research presents the WiSeMote: a new, fully integrated ultra-low power wireless smart sensor node and a flexible base station, both designed for long-term SHM deployments. The power consumption of the sensor nodes and base station has been minimized through careful hardware selection and the implementation of power-aware network software, without sacrificing flexibility and functionality.

• Laser Solutions
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• v.2 no.3
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• pp.19-31
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• 1999

This paper introduces the monitoring scheme of laser welding quality using neural network. The developed monitoring scheme detects light signal emitting from plasma formed above the weld pool with optic sensor and DSP-based signal processor, and analyzes to give a guidance about the weld quality. It can automatically detect defects of laser weld and further give an information about what kind of defects it is, specially partial penetration and porosity among the interior defects. Those could be detected only by naked eyes or X-ray after welding, which needs more processes and costs in mass production. The monitoring scheme extracts four feature vectors from signal processing results of optical measuring data. In order to classify pattern for extracted feature vectors and to decide defects, it uses single-layer neural network with perceptron learning. The monitoring result using only the first feature vector shows confidence rate in recognition of 90%($\pm$5) and decides whether normal status or defects status in real time.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.268-276
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• 2003

In this paper, we present a new control and monitoring technique for a power system using CAN(Controller Area Network). Feedback control systems having co'ntrol loops closed through a network(i.e. Ethernet, ControlNet, CAN) are called NCSs(Networked Control Systems). The major problem of NCSs is the variation of stability property according to time delay including network-induced delay and computation delay in nodes. We present a new stability analysis method of NCSs with time delay exploiting a state-space model of LTI(Linear Time Invariant) interconnected systems. The proposed method can determine a proper sampling period of NCSs that preserves stability performance even in NCSs with a dynamic controller. We design CAN nodes which can transmit control and monitoring data through CAN bus and apply these to NCSs for a power system. The results of the experiment validate effectiveness of our control and monitoring technique for a power system.