• Journal of Sensor Science and Technology
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• v.9 no.2
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• pp.127-133
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• 2000

We propose a novel monitoring system for a recycling piggery slurry treatment system through neural networks. Here we tried to model treatment process for each tank(influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) in the system based on population densities of heterotrophic and lactic acid bacteria. Principle component analysis(PCA) was first applied to identify a relation between input(microbial densities and parameters for the treatment) and output, and then multilayer neural networks were employed to model the treatment process for each tank. PCA filtration of input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent combinatorial data analysis allowed a successful prediction of the treatment system for at least two days.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3C
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• pp.281-292
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• 2008

A main application of sensor networks are to monitor and to send information about a possibly hostile environment to a powerful base station connected to a wired network. To conserve power from each sensor, intermediate network nodes should aggregate results from individual sensors. However, it can make it that a single compromised sensor can render the network useless, or worse, mislead the operator into trusting a false reading. In this paper, we propose a protocol to give us a key aggregation mechanism that intermediate network nodes could aggregate data more safely. The proposed protocol is more helpful at multi-tier network architecture in secure sessions established between sensor nodes and gateways. From simulation study, we compare the amount of the energy consumption overhead, the time of key transmission and the ratio of of key process between the proposed method and LHA-SP. The simulation result of proposed protocol is low 3.5% a lord of energy consumption than LHA-SP, the time of key transmission and the ration of key process is get improved result of each 0.3% and 0.6% than LHA-SP.

• IE interfaces
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• v.20 no.3
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• pp.288-297
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• 2007

Success of semiconductor/LCD industry depends on its yield and quality of product. For the purpose, FDC (Fault Detection and Classification) system is used to diagnose fault state in main manufacturing processes by monitoring time series data collected by equipment sensors which represent various conditions of the equipment. The data set is segmented at the start and end of each product lot processing by a trigger event module. However, in practice, segmented sensor data usually have the features of data asynchronization such as different start points, end points, and data lengths. Due to the asynchronization problem, false alarm (type I error) and missed alarm (type II error) occur frequently. In this paper, we propose a robust process fault detection system by integrating a process event detection method and a similarity measuring method based on dynamic time warping algorithm. An experiment shows that the proposed system is able to recognize abnormal condition correctly under the asynchronous data situation.

• Journal of KIISE
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• v.42 no.1
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• pp.54-67
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• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.629-644
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• 2010

A real-time image georeferencing system requires all inputs to be determined in real-time. The intrinsic camera parameters can be identified in advance from a camera calibration process while other control information can be derived instantaneously from real-time GPS/INS data. The bottleneck process is tie point acquisition since manual operations will be definitely obstacles for real-time system while the existing extraction methods are not fast enough. In this paper, we present a fast-and-automated image matching technique based on the KLT tracker to obtain a set of tie-points in real-time. The proposed work accelerates the KLT tracker by supplying the initial guessed tie-points computed using the GPS/INS data. Originally, the KLT only works effectively when the displacement between tie-points is small. To drive an automated solution, this paper suggests an appropriate number of depth levels for multi-resolution tracking under large displacement using the knowledge of uncertainties the GPS/INS data measurements. The experimental results show that our suggested depth levels is promising and the proposed work can obtain tie-points faster than the ordinary KLT by 13% with no less accuracy. This promising result suggests that our proposed algorithm can be effectively integrated into the real-time image georeferencing for further developing a real-time surveillance application.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.219-226
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• 2018

IIoT(Industrial Internet of Thing) means that all manufacturing processes are informed beyond the conventional automation of process automation. The objective of the system is to build an information system based on the data collected from the sensors installed in each process and to maintain optimal productivity by managing and automating each process in real time. Data collected from sensors in each process is unstructured and many studies have been conducted to collect and process such unstructured data effectively. In this paper, we propose a system using Node-RED as middleware for effective big data collection and processing.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.315-323
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• 2010

In Wireless Sensor Networks (WSNs), various Data-Centric Storages (DCS) schemes have been proposed to store the collected data and to efficiently process a query. A DCS scheme assigns distributed data regions to sensor nodes and stores the collected data to the sensor which is responsible for the data region to process the query efficiently. However, since the whole data stored in a node will be lost when a fault of the node occurs, the accuracy of the query processing becomes low, In this paper, we propose an in-network aggregation query processing method that assures the high accuracy of query result in the case of data loss due to the faults of the nodes in the DCS scheme. When a data loss occurs, the proposed method creates a compensation model for an area of data loss using the linear regression technique and returns the result of the query including the virtual data. It guarantees the query result with high accuracy in spite of the faults of the nodes, To show the superiority of our proposed method, we compare E-KDDCS (KDDCS with the proposed method) with existing DCS schemes without the data-loss correction method. In the result, our proposed method increases accuracy and reduces query processing costs over the existing schemes.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.307-312
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• 2007

Wireless Sensor Network(WSN) is considered as a core technology necessary for Ubiquitous computing, with its numerous possible applications in many practical areas, is being researched and studied actively by many around the world. WSN utilizes wireless sensors spatially placed to gather information regarding temperature, light condition, motion and change in speed of the objects within their surrounding environment. This paper implements an environmental information monitoring and indexing system based on spatial indexing technique by constructing a WSN system. This Multi-Query Indexing Technique coupled with wireless sensors provides an output based on the pre-defined built-in data index and new input from the sensors. If environment data is occured, system have to perform a proper action after collecting and analyzing this data. This is the purpose of implementing environment data monitoring system. We constructed environmental application using TinyOS and built tested with MICAz sensor bords. We designed and implemented a monitoring system which detects and multi-indexing process environmental data from distributed sensors.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.68-73
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• 2007

Recently, WSN(wireless sensor networks) consists of several sensor nodes in sensor field. And each sensors have the enforced energy constraint. Therefore, it is important to manage energy efficiently. In WSN application system, FEC(Forward error correction) increases the energy efficiency and data reliability of the data transmission. LDPC(Low density parity check) code is one of the FEC code. It needs more encoding operation than other FEC code by growing codeword length. But this code can approach the Shannon capacity limit and it is also can be used to increase the data reliability and decrease the transmission energy. In this paper, the author adopt Linear-Congruence method at generating parity check matrix of LDPC(Low density parity check) codes to reduce the complexity of encoding process and to enhance the energy efficiency in the WSN. As a result, the proposed algorithm can increase the encoding energy efficiency and the data reliability.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.43-48
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• 2016

Wireless sensor networks (WSN) can be more reliable and easier to program and use with the help of sensor database management systems (SDMS). SDMS establish a user-friendly SQL-based interface to process declarative user-defined queries over sensor readings from WSN. Typical queries in SDMS are ad-hoc snapshot queries and long-running, continuous queries. In SDMSs queries are flooded to all nodes in the sensor net, and query results are sent back from nodes that have qualified results to a base station. For query flooding to all nodes, and result flooding to the base station, a lot of communication energy consuming is required. This paper suggests an efficient in-network index solution, named Distributed Information Gathering (DIG) to process range queries in a sensor net environment that can save energy by reducing query and result flooding.