• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.2
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• pp.3-6
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• 2015

Recently, the collection of the sensor data and its analysis become important as the smart buildings equipped with the various sensors appear as a usual scene. The interconnection through the wire cable among the sensors is indispensible because of the information collections such as the temperature, the humidity, and the luminance in the rooms and the hallways for the effective management of the in-building energies. However, these interconnections through the cabling will be very costly, time-consuming, and a difficult task since they will cause some damages to the buildings. Therefore, the interconnections through the unwired connections are required in terms of the deployment effectiveness such as time and cost In this paper, the design and the operation appropriateness are confirmed through the simulation of the signal measurement system for in-building propagation characteristics based on signature sequence and the analysis of the collected measurement data is performed thereafter.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.2
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• pp.113-119
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• 2020

The IoT-based sensor network is one of the methods that can be efficiently applied to maintain the facilities, such as bridges, at a low cost. In this study, based on LoRa LPWAN, one of the IoT communications, sensor board for cable tension monitoring, data acquisition board for constructing sensor network along with existing measurement sensors, are developed to create bridge structural health monitoring system. In addition, we designed and manufactured a smart sensor node for LoRa communication and established a sensor network for monitoring. Further, we constructed a test bed at the Yeonggwang Bridge to verify the performance of the system. The test bed verification results suggested that the LoRa LPWAN-based sensor network can be applied as one of the technologies for monitoring the bridge structure soundness; this is excellent in terms of data rate, accuracy, and economy.

• Smart Structures and Systems
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• v.15 no.3
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• pp.665-682
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• 2015

Structural identification or St-Id is 'the parametric correlation of structural response characteristics predicted by a mathematical model with analogous characteristics derived from experimental measurements'. This paper describes a St-Id exercise on Humber Bridge that adopted a novel two-stage approach to first calibrate and then validate a mathematical model. This model was then used to predict effects of wind and temperature loads on global static deformation that would be practically impossible to observe. The first stage of the process was an ambient vibration survey in 2008 that used operational modal analysis to estimate a set of modes classified as vertical, torsional or lateral. In the more recent second stage a finite element model (FEM) was developed with an appropriate level of refinement to provide a corresponding set of modal properties. A series of manual adjustments to modal parameters such as cable tension and bearing stiffness resulted in a FEM that produced excellent correspondence for vertical and torsional modes, along with correspondence for the lower frequency lateral modes. In the third stage traffic, wind and temperature data along with deformation measurements from a sparse structural health monitoring system installed in 2011 were compared with equivalent predictions from the partially validated FEM. The match of static response between FEM and SHM data proved good enough for the FEM to be used to predict the un-measurable global deformed shape of the bridge due to vehicle and temperature effects but the FEM had limited capability to reproduce static effects of wind. In addition the FEM was used to show internal forces due to a heavy vehicle to to estimate the worst-case bearing movements under extreme combinations of wind, traffic and temperature loads. The paper shows that in this case, but with limitations, such a two-stage FEM calibration/validation process can be an effective tool for performance prognosis.

• The KIPS Transactions:PartC
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• v.18C no.3
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• pp.195-198
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• 2011

Recent expansion of the ubiquitous environment and improvement of the USN give lots of U-healthcare systems. In this paper, we design and implement a wireless ECG measurement system that can send ECG signals among the sensors and collector. It can also give almost the same precision as a hospital ECG system with mobility. The most important fact of the mobile ECG system is the signal data connectivity among the sensors and device such as signal cables or wires. we can eliminate the signal cable through the Zigbee sender and collector via implementing Zigbee-SD communication system that can receive the ECG signal data. We also implement ECG app software on the smart phone that can analyze and show the data results directly. It can give lots of mobility and usability under ubiquitous environment and would be a very efficient wireless ECG system for U-healthcare service.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.10-14
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• 2015

Recently, the collection of the sensor data and its analysis become important as the smart buildings equipped with the various sensors appear as a usual scene. The interconnection through the wire cable among the sensors is indispensible because of the information collections such as the temperature, the humidity, and the luminance in the rooms and the hallways for the effective management of the in-building energies. However, these interconnections through the cabling will be very costly, time-consuming, and a difficult task since they will cause some damages to the buildings. Therefore, the interconnections through the unwired connections are required in terms of the deployment effectiveness such as time and cost In this paper, the design and the short sequence operation appropriateness are confirmed through the simulation of the signal measurement system for in-building propagation characteristics based on short signature sequence and the analysis of the system characteristics based on the false alarm probability is performed thereafter.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Smart Structures and Systems
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• v.31 no.1
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• pp.57-68
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• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.

• The Journal of the Korea Contents Association
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• v.14 no.8
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• pp.342-364
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• 2014

The purpose of this paper is to provide an answer for the following question: What are the major shifts and implications of the unprecedently competitive and rapidly changing media ecosystem in the era of N-screen and OTT services? I've attempted to understand the complex and competitive nexus among media from an historical context by focusing on the displacement vs. complement thesis. The TPC model by Han has been employed for the analysis of the current dynamics of US media industries by triangulating three areas: Technology/industry, public policy and consumer/culture. More specifically, the US media landscape is initially divided into two competitive turfs - the competitors equipped with OTT services and the legacy media industry, and then the traditional media industry was grouped again into PayTV group(telecom service providers with IPTV and mobile TV, cable/Satellite TV networks) and Free (over-the-air) TV networks. Six major shifts were identified by the analysis: power shift in telecom carriers, power shifts in TV industry, Telecom/OTT partnership, time shifts, place shifts, and finally business model shifts.

• Journal of National Security and Military Science
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• s.7
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• pp.271-303
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• 2009

"Ubiquitous sensor network" definition is this-Someone attaches electro-magnetic tag everything which needs communication between man to man, man to material and material to material(Ubiquitous). By using attached every electro-magnetic tag, someone detects it's native information as well as environmental information such as temperature, humidity, pollution and infiltration information(Sensor). someone connects it realtime network and manage generated information(Network). 21st century's war is joint combined operation connecting with ground, sea and air smoothly in digitalized war field, and is systematic war provided realtime information from sensor to shooter. So, it needs dramatic development on watch reconnaissance, command and control, pinpoint strike etc. Ubiquitous computing and network technologies are essential in national defense to operate 21st century style war. It is possible to use many parts such as USN combined smart dust and sensor network to protect friend unit as well as to watch enemy's deep area by unmanned reconnaissance, wearable computer upgrading soldier's operational ability and combat power dramatically, RFID which can be used material management as well as on time support. Especially, unmanned watch system using USN is core part to transit network centric military service and to get national defense efficiency which overcome the dilemma of national defense person resource reducing, and upgrade guard quality level, and improve combat power by normalizing guardian's bio rhythm. According to the test result of sensor network unmanned watch system, it needs more effort and time to stabilize because of low USN technology maturity and using maturity. In the future, USN unmanned watch system project must be decided the application scope such as application area and starting point by evaluating technology maturity and using maturity. And when you decide application scope, you must consider not only short period goal as cost reduction, soldier decrease and guard power upgrade but also long period goal as advanced defense ability strength. You must build basic infra in advance such as light cable network, frequency allocation and power facility etc. First of all, it must get budget guarantee and driving force for USN unmanned watch system project related to defense policy. You must forwarded the USN project assuming posses of operation skill as procedure, system, standard, training in advance. Operational skill posses is come from step by step application strategy such as test phase, introduction phase, spread phase, stabilization phase and also repeated test application taking example project.