• Interaction and multiscale mechanics
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• v.1 no.3
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• pp.357-368
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• 2008

The result of damage detection from on-site measurements is commonly polluted by unavoidable measurement noises. It is widely recognized that this side influence could be reduced to some extent if the sensor placement was properly designed. Although many methods have been proposed to find the optimal number and location of mono-type sensors, the optimal layout of multi-type sensors need further investigation, because a network of heterogeneous sensors is commonly used in engineering. In this paper, a new criterion of the optimal placement for different types of sensors is proposed. A corresponding heuristic is developed to search for good results. In addition, Monte Carlo simulation is suggested to design a robust damage detection system which contains certain redundancies. The validity of these methods is illustrated by two bridge examples.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2688-2691
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• 2008

This study addresses a microfluidic method to uniformly form diacetylene (DA) liposomes and control their size. DA liposomes are biochemical sensor materials with a unique property such that when they are polymerized to polydiacetylene (PDA) they exhibit non-fluorescent blue to fluorescent red phase transition upon chemical or thermal stress. The liposome size and distribution are important because they significantly affect the phase transition. So far, DA Liposomes, have been prepared by mixing of bulk phases leading to heterogeneous, polydisperse distribution in size. Therefore, additional post-processes are required such as sonication or membrane extrusion to obtain an appropriate size of liposomes. Here, we report a novel strategy using a microfluidic chip and hydrodynamic focusing to form DA liposomes and control their size. Preliminary results obtained by scanning electron microscope (SEM) and dynamic light scattering (DLS) show that the microfluidic strategy generates more monodispersed liposomes than a bulk method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3753-3771
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• 2016

Increasingly, numerous and various Internet-capable devices are connected in end user networks, such as a home network. Most devices use the combination of TCP and 802.11 DCF as a system platform, but whereas some devices such as a streaming video persistently generate traffic, others such as a motion sensor do so only intermittently with lots of pauses. This study addresses the issue of performance in this heterogeneous traffic wireless LAN environment from the perspective of fairness. First, instantaneous fairness is introduced as a notion to indicate how immediately and how closely a user obtains its fair share, and a new time-based metric is defined as an index. Second, extensive simulation experiments have been made with TCP Reno, Vegas, and Westwood to determine how each TCP congestion control corresponds to the instantaneous fairness. Overall, TCP Vegas yields the best instantaneous fairness because it keeps the queue length shorter than the other TCPs. In the simulations, about 60% of a fair share of the effective user bandwidth is immediately usable in any circumstance. Finally, we introduce two simple strategies for adjusting TCP congestion controls to enhance instantaneous fairness and validate them through simulation experiments.

• Smart Structures and Systems
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• v.22 no.5
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• pp.631-641
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• 2018

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.7-14
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• 2022

Sensor Registry System (SRS) has been devised for maintaining semantic interoperability of data on heterogeneous sensor networks. SRS measures the connectability of the mobile device to ambient sensors based on positions and only provides metadata of sensors that may be successfully connected. The step of identifying the ambient sensors which can be successfully connected is called sensor filtering. Improving the performance of sensor filtering is one of the core issues of SRS research. In reality, GPS sometimes shows the wrong position and thus leads to failed sensor filtering. Therefore, this paper proposes a new sensor filtering strategy using geographical embedding and neural network-based path prediction. This paper also evaluates the service provision rate with the Monte Carlo approach. The empirical study shows that the proposed method can compensate for position abnormalities and is an effective model for sensor filtering in SRS.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.7
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• pp.62-70
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• 2015

The energy of a sensor in a Wireless Sensor Network (WSN) puts a limit on the lifetime of the network. To prolong the lifetime, a clustering plan is used. Clustering technology gets its energy efficiency through reducing the number of communication occurrences between the sensors and the base station (BS). In the distributed clustering protocol, LEACH-like (Low Energy Adaptive Clustering Hierarchy - like), the number of sensor's cluster head (CH) roles is different depending on the sensor's residual energy, which prolongs the time at which half of nodes die (HNA) and network lifetime. The position of the CH in each cluster tends to be at the center of the side close to BS, which forces cluster members to consume more energy to send data to the CH. In this paper, a protocol, pseudo-LEACH, is proposed, in which a cluster with a CH placed at the center of the cluster is formed. The scheme used allows the network to consume less energy. As a result, the timing of the HNA is extended and the stable network period increases at about 10% as shown by the simulation using MATLAB.

• Smart Structures and Systems
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• v.5 no.4
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• pp.415-426
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• 2009

This study focuses on the concept of multi-scale wireless sensor networks for damage detection in civil infrastructure systems by first over viewing the general network philosophy and attributes in the areas of data acquisition, data reduction, assessment and decision making. The data acquisition aspect includes a scalable wireless sensor network acquiring acceleration and strain data, triggered using a Restricted Input Network Activation scheme (RINAS) that extends network lifetime and reduces the size of the requisite undamaged reference pool. Major emphasis is given in this study to data reduction and assessment aspects that enable a decentralized approach operating within the hardware and power constraints of wireless sensor networks to avoid issues associated with packet loss, synchronization and latency. After over viewing various models for data reduction, the concept of a data-driven Bivariate Regressive Adaptive INdex (BRAIN) for damage detection is presented. Subsequent examples using experimental and simulated data verify two major hypotheses related to the BRAIN concept: (i) data-driven damage metrics are more robust and reliable than their counterparts and (ii) the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.32-38
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• 2020

The deployment of geographically distributed wireless sensors has greatly elevated the capability of monitoring structural health in social-overhead capital (SOC) public infrastructures. This paper deals with the utilization of a distributed mobility management (DMM) approach for the deployment of wireless sensing devices in a structural health monitoring system (SHM). Then, a wireless sensing mechanism utilizing low-energy adaptive clustering hierarchy (LEACH)-based clustering algorithm for smart sensors has been analyzed to support the seamless data transmission of structural health information which is essentially important to guarantee public safety. The clustering of smart sensors will be able to provide real-time monitoring of structural health and a filtering algorithm to boost the transmission of critical information over heterogeneous wireless and mobile networks.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.85-90
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• 2016

In this paper, we study the domain-independent sensor data structuring method and the derivations about ' Filter Criteria' expansion factors of heterogeneous domain systems. For this purpose, we propose a 5W1H based information search and retrieval method without knowledges about the target domains. And we also suggest a resource management model that can support expanding search of information. By the suggested method, the oneM2M standard able to implement the use-cases like 'data exchanging between the heterogeneous domains'.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.78-80
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• 2011

In the area of data fusion, dealing with heterogeneous data sources, numerous models have been proposed in last three decades to facilitate different application domains i.e. Department of Defense (DoD), monitoring of complex machinery, medical diagnosis and smart buildings. All of these models shared the theme of multiple levels processing to get more reliable and accurate information. In this paper, we consider five most widely acceptable fusion models (Intelligence Cycle, Joint Directors of Laboratories, Boyd control, Waterfall, Omnibus) applied to different areas for data fusion. When they are exposed to a real scenario, where large dataset from heterogeneous sources is utilize for object monitoring, then it may leads us to non-efficient and unreliable information for decision making. The proposed variation works better in terms of time and accuracy due to prior data diminution.