• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.1939-1951
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• 1994

This study introduces a new tool breakage detecting technology comprised of an unsupervised neural network combined with adaptive time series autoregressive(AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(Recursive Least Square). Experiment indicates that AR parameters are good features for tool breakage, therefore it can be detected by tracking the evolution of the AR parameters during milling process. an ART 2(Adaptive Resonance Theory 2) neural network is used for clustering of tool states using these parameters and the network is capable of self organizing without supervised learning. This system operates successfully under the wide range of cutting conditions without a priori knowledge of the process, with fast monitoring time.

• IEMEK Journal of Embedded Systems and Applications
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• v.1 no.2
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• pp.43-55
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• 2006

A home network is a typical ubiquitous computing network that consists of various consumer devices and service environments. Home networks are requiring increasingly more complicated services, such as multimedia home theater and the monitoring and controlling of heterogeneous devices. Accordingly, a mutually connecting mechanism is needed among heterogeneous devices and services redundant. The current paper presents a Multimedia Room Bridge Adapter (MRBA) system that is designed to manage heterogeneous devices and support various services. In addition, a hardware and software prototype is implemented based on the proposed architecture.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.131-137
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• 1994

Detection of grinding trouble occuring during the grinding process is classified into two types, i.e, based on the quantitative and qualitative knowledge. But, since the grinding operation is especially related with a large amount of functional parameters, it is actually defficult to cope with the grinding troubles occuring during process. Therefore, grinding trouble-shooting has difficulty in satisfying the requirement from the user. To cope with the grinding troubles occuring during the process, the application of neural network is on effective way. In this study, we identify the four parameters derived from the AE(Acoustic Emission) signals and present the grinding trouble-shooting system utilizing a back-propagation model of the neural network.

• Annual Conference of KIPS
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• 2008.11a
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• pp.1012-1015
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• 2008

The packet capturing becomes a bottleneck in the network intrusion detection and monitoring system as the network performance developing. Many approaches, zero copy, interrupt coalescing and NAPI which attempt to improve packet capturing performance of Linux, are inefficient. PF_RING is a new type of network socket that dramatically improves the packet capture speed, but not perfect. This paper proposes some solutions which can improve the memory utilization and save some data copy time based on the commodity network adapters rather than on the commercial network adapters.

• Journal of Ecology and Environment
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• v.34 no.2
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• pp.149-156
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• 2011

Phenological variables derived from remote sensing are useful in determining the seasonal cycles of ecosystems in a changing climate. Satellite remote sensing imagery is useful for the spatial continuous monitoring of vegetation phenology across broad regions; however, its applications are substantially constrained by atmospheric disturbances such as clouds, dusts, and aerosols. By way of contrast, a tower-based ground remote sensing approach at the canopy level can provide continuous information on canopy phenology at finer spatial and temporal scales, regardless of atmospheric conditions. In this study, a tower-based ground remote sensing system, called the "Phenological Eyes Network (PEN)", which was installed at the Gwangneung Deciduous KoFlux (GDK) flux tower site in Korea was introduced, and daily phenological progressions at the canopy level were assessed using ratios of red, green, and blue (RGB) spectral reflectances obtained by the PEN system. The PEN system at the GDK site consists of an automatic-capturing digital fisheye camera and a hemi-spherical spectroradiometer, and monitors stand canopy phenology on an hourly basis. RGB data analyses conducted between late March and early December in 2009 revealed that the 2G_RB (i.e., 2G - R - B) index was lower than the G/R (i.e., G divided by R) index during the off-growing season, owing to the effects of surface reflectance, including soil and snow effects. The results of comparisons between the daily PEN-obtained RGB ratios and daily moderate-resolution imaging spectroradiometer (MODIS)-driven vegetation indices demonstrate that ground remote sensing data, including the PEN data, can help to improve cloud-contaminated satellite remote sensing imagery.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.352-358
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• 2012

A sensor network system can be an efficient tool for healthcare telemetry for multiple users due to its power efficiency. One drawback is its limited data size. This paper proposed a real-time application of data compression/decompression method in u-Health monitoring system in order to improve the network efficiency. Our high priority was given to maintain a high quality of signal reconstruction since it is important to receive undistorted waveform. Our method consisted of down sampling coding and differential Huffman coding. Down sampling was applied based on the Nyquist-Shannon sampling theorem and signal amplitude was taken into account to increase compression rate in the differential Huffman coding. Our method was successfully tested in a ZigBee and WLAN dual network. Electrocardiogram (ECG) had an average compression ratio of 3.99 : 1 with 0.24% percentage root mean square difference (PRD). Photoplethysmogram (PPG) showed an average CR of 37.99 : 1 with 0.16% PRD. Our method produced an outstanding PRD compared to other previous reports.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.1-8
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• 2006

The home network system guarantees families a safe, economical, socially integrated and healthy life by using information appliances. And it provides a family with domestic safety, control of instruments, controllable energy and health monitoring by connecting to home appliances. This study designs the broadband PLC home controller using broadband PLC(Power Line Communication) technology which can save much cost at a network infrastructure by using the existing power line at home. The broadband PLC home controller consists of the broadband PLC module, the embedded main controller module and I/O module. The broadband PLC home controller can control various domestic appliances such as an auto door-lock, a boiler, an oven, etc., because it has various I/O specifications. In this study, selected home network middleware for the broadband PLC home controller is Jini surrogate using Jini technology designed by means of access to easily a home network system without a limitation of the devices. And a client application program is supported java servlet program to manage and monitor the broadband PLC home controller via web browser of a PC or a PDA, etc. Finally, for an application, we implemented and tested a home security system using one broadband PLC home controller.

• ETRI Journal
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• v.38 no.6
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• pp.1145-1152
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• 2016

Because the nodes in a wireless sensor network (WSN) are mobile and the network is highly dynamic, monitoring every node at all times is impractical. As a result, an intruder can attack the network easily, thus impairing the system. Hence, detecting anomalies in the network is very essential for handling efficient and safe communication. To overcome these issues, in this paper, we propose a rule-based anomaly detection technique using roaming honeypots. Initially, the honeypots are deployed in such a way that all nodes in the network are covered by at least one honeypot. Honeypots check every new connection by letting the centralized administrator collect the information regarding the new connection by slowing down the communication with the new node. Certain predefined rules are applied on the new node to make a decision regarding the anomality of the node. When the timer value of each honeypot expires, other sensor nodes are appointed as honeypots. Owing to this honeypot rotation, the intruder will not be able to track a honeypot to impair the network. Simulation results show that this technique can efficiently handle the anomaly detection in a WSN.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.11
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• pp.872-879
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• 2017

This work presents a cable fault monitoring system based on the differential frequency domain reflectometry using a compact impedance analyser which is composed of a direct digital synthesizer, an op amp and a gain/phase detector with a micro controller. The proposed system can replace expensive vector network analysers for frequency domain reflectometry and therefore be deployed in sensor networks for long term multi-point cable monitoring. Effectiveness of the system is experimentally confirmed by diagnosing the status of the power cable.