• Journal of Power System Engineering
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• v.3 no.2
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• pp.34-41
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• 1999

This paper identifies a linear combustion model of Refuse Incineration Plant(RIP) which characterizes its combustion dynamics, where the proposed model has thirteen-inputs and one-output. The structure of the RIP model is given as an ARX model which obtained from the theoretical analysis. And then, some unknown model parameters are decided from experimental input-output data sets, using system identification algorithm based on Instrumental Variables(IV) method. In result, it is shown that the proposed model well approximates the input-output combustion characteristics riven by experimental data sets.

• Smart Structures and Systems
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• v.8 no.1
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• pp.119-137
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• 2011

A bio-inspired two-mode structural health monitoring (SHM) system based on the Na$\ddot{i}$ve Bayes (NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

• Smart Structures and Systems
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• v.11 no.6
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• pp.569-587
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• 2013

This paper evaluates the closed loop performance of the reaching law based discrete sliding mode controller with multisensor data fusion (MSDF) in real time, by controlling the first two vibrating modes of a piezo actuated structure. The vibration is measured using two homogeneous piezo sensors. The states estimated from sensors output are fused. Four fusion algorithms are considered, whose output is used to control the structural vibration. The controller is designed using a model identified through linear Recursive Least Square (RLS) method, based on ARX model. Improved vibration suppression is achieved with fused data as compared to single sensor. The experimental evaluation of the closed loop performance of sliding mode controller with data fusion applied to piezo actuated structure is the contribution in this work.

• Smart Structures and Systems
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• v.14 no.2
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.457-465
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• 2015

Internet of Things(IoT) technologies should be able to communication with various embedded platforms. We will need to select an appropriate cryptographic algorithm in various embedded environments because we should consider security elements in IoT communications. Therefore the lightweight block cryptographic algorithm is essential for secure communication between these kinds of embedded platforms. However, the lightweight block cryptographic algorithm has a vulnerability which can be leaked in side channel analysis. Thus we also have to consider side channel countermeasure. In this paper, we will propose the scenario of side channel analysis and confirm the vulnerability for HIGHT algorithm which is composed of ARX structure. Additionally, we will suggest countermeasure for HIGHT against side channel analysis. Finally, we will explain how much the effectiveness can be provided through comparison between countermeasure for AES and HIGHT.

• Computers and Concrete
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• v.12 no.2
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• pp.211-228
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• 2013

The structure analyzed in this paper has particular building style and special structural system. It is a rigid-connected twin-tower skyscraper with asymmetrical distribution of stiffness and masses in two towers. Because of the different stiffness between the north and the south towers, the torsion seismic vibration is significant. In this paper, in order to study the seismic response of the structure under both frequent low-intensity earthquakes as well as rare earthquakes at the levels of intensity 7, the analysis model is built and analyzed with NosaCAD. NosaCAD is an nonlinear structure analysis software based on second-development of AutoCAD with ObjectARX. It has convenient modeling function, high computational efficiency and diversity post-processing functions. The deformations, forces and damages of the structure are investigated based on the analysis. According to the analysis, there is no damage on the structure under frequent earthquakes, and the structure has sufficient capacity and ductility to resist rare earthquakes. Therefore the structure can reach the goal of no damage under frequent earthquakes and no collapse under rare earthquakes. The deformation of the structure is below the limit in Chinese code. The time sequence and distribution of damages on tubes are reasonable, which can dissipate some dynamic energy. At last, according to forces, load-carrying capacity and damage of elements, there are some suggestions on increasing the reinforcement in the core tube at base and in stiffened stories.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2305-2307
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• 2001

Generally, The modeling method for the mathematical model is mdeled by using the physical laws and the system identification. In this paper, The arago's disk system of the operating principle of induction motors is selected as an example for identification. The system transfer function is derived from input/output data through experiment. Model is estimated by using ARX, ARMAX, BJ, OE model structure and compared each other.

• Journal of IKEEE
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• v.3 no.1 s.4
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• pp.109-117
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• 1999

This paper describes the quick and precise control of the wafer temperature essential in rapid thermal processing(RTP). The bang-bang plus PID controller structure is introduced to satisfy rapid ramp-up rate and reduce overshoot and steady state error. The controller employs the PID action when the magnitude of the error between reference signal and the output temperature signal is smaller than some prescribed value. To find PID gains, the plant(autoregressive) model is first identified and Kappa-Tau tuning rule is used. The developed controller is applied to experimental RTP apparatus, and performances are evaluated.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.2
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• pp.93-101
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• 2016

This paper presents the results obtained using time-series-based methods for structural damage assessment. The methods are applied to a wind turbine blade structure subjected to fatigue loads. A 9 m CX-100 (carbon experimental 100 kW) blade is harmonically excited at its first natural frequency to introduce a failure mode. Consequently, a through-thickness fatigue crack is visually identified at 8.5 million cycles. The time domain data from the piezoelectric active-sensing techniques are measured during the fatigue loadings and used to detect incipient damage. The damage-sensitive features, such as the first four moments and a normality indicator, are extracted from the time domain data. Time series autoregressive models with exogenous inputs are also implemented. These features could efficiently detect a fatigue crack and are less sensitive to operational variations than the other methods.