• 디지털융복합연구
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• 제11권7호
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• pp.149-155
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• 2013

최근 차량 교통 시스템은 스마트 센서 및 외부와의 소통을 통해 교통의 효율성과 안정성을 향상시키는 교통 체계를 의미하는 지능형 교통시스템(Intelligent Transport Systems : ITS)의 형태로 진화하고 있다. 이러한 발전 추세에 반하여 국내의 ITS 서비스는 다른 주요 통신 서비스에 비해 많이 낙후되어 있다. 이러한 현상의 원인은 현재 국내에서는 국제적으로 배정된 ITS 주파수 대역 내에 방송용 주파수가 할당되어 있으며, 국내에서 ITS로 사용되고 있는 DSRC(Dedicated Short Range Communications) 방식의 주파수도 ISM(Industrial, scientific and medical) 대역을 사용하고 있기 때문에 그것의 활용에 있어서 제한적일 수밖에 없다. 본 논문에서는 국내 ITS의 기술적 현황을 분석하여 다음과 같은 ITS 활성화 방안을 제안하였다. 첫째, 기존의 DSRC방식을 포용하는 WAVE(Wireless Access in Vehicular Environments)방식의 ITS 관련 표준을 시급히 확정하여 표준 설치 사양을 마련해야 한다. 둘째, ITS 표준을 완성하기 위한 주파수 배정이 시급히 시행되어야 한다.

• Smart Structures and Systems
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• 제17권6호
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• pp.957-980
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• 2016

Structural damage detection (SDD) is a challenging task in the field of structural health monitoring (SHM). As an exploring attempt to the SDD problem, a hybrid self-adaptive Firefly-Nelder-Mead (SA-FNM) algorithm is proposed for the SDD problem in this study. First of all, the basic principle of firefly algorithm (FA) is introduced. The Nelder-Mead (NM) algorithm is incorporated into FA for improving the local searching ability. A new strategy for exchanging the information in the firefly group is introduced into the SA-FNM for reducing the computation cost. A random walk strategy for the best firefly and a self-adaptive control strategy of three key parameters, such as light absorption, randomization parameter and critical distance, are proposed for preferably balancing the exploitation and exploration ability of the SA-FNM. The computing performance of the SA-FNM is evaluated and compared with the basic FA by three benchmark functions. Secondly, the SDD problem is mathematically converted into a constrained optimization problem, which is then hopefully solved by the SA-FNM algorithm. A multi-step method is proposed for finding the minimum fitness with a big probability. In order to assess the accuracy and the feasibility of the proposed method, a two-storey rigid frame structure without considering the finite element model (FEM) error and a steel beam with considering the model error are taken examples for numerical simulations. Finally, a series of experimental studies on damage detection of a steel beam with four damage patterns are performed in laboratory. The illustrated results show that the proposed method can accurately identify the structural damage. Some valuable conclusions are made and related issues are discussed as well.

• Smart Structures and Systems
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• 제26권3호
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• pp.345-360
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• 2020

Modern swarm intelligence heuristic search methods are widely applied in the field of structural health monitoring due to their advantages of excellent global search capacity, loose requirement of initial guess and ease of computational implementation etc. To this end, a hybrid strategy is proposed based on butterfly optimization algorithm (BOA) and differential evolution (DE) with purpose of effective combination of their merits. In the proposed identification strategy, two improvements including mutation and crossover operations of DE, and dynamic adaptive operators are introduced into original BOA to reduce the risk to be trapped in local optimum and increase global search capability. The performance of the proposed algorithm, hybrid butterfly optimization and differential evolution algorithm (HBODEA) is evaluated by two numerical examples of a simply supported beam and a 37-bar truss structure, as well as an experimental test of 8-story shear-type steel frame structure in the laboratory. Compared with BOA and DE, the numerical and experimental results show that the proposed HBODEA is more robust to detect the reduction of stiffness with limited sensors and contaminated measurements. In addition, the effect of search space, two dynamic operators, population size on identification accuracy and efficiency of the proposed identification strategy are further investigated.

• 한국기계가공학회지
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• 제15권2호
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• pp.51-56
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• 2016

High brittleness is a characteristic of glass, and in many cases it is broken during the process of machining due to processing problems, such as scratches, chipping, and notches. Machining defects occur due to the vibration of the equipment. Therefore, design techniques are needed that can control the vibration generated in the equipment to increase the strength of tempered glass. The natural frequency of the machine tool via vibration analysis (computer simulation) must be accurately understood to improve the design to ensure the stability of the machine. To accurately understand the natural frequency, 3D modeling, which is the same as actual apparatus, was used and a constraint condition was also applied that was the same as that of the actual apparatus. The maximum speeds of ultrasonic and high frequency, which are 15,000 rpm and 60,000 rpm, respectively, are considerably faster than those of typical machine tools. Therefore, an improved design is needed so that the natural frequency is formed at a lower region and the natural frequency does not increase through general design reinforcement. By restructuring the top frame of the glass processing, the natural frequency was not formed in the operating speed area with the improved design. The lower-order natural frequency is dominant for the effects that the natural frequency has on the vibration. Therefore, the design improvement in which the lower-order natural frequency is not formed in the operating speed area is an optimum design improvement. It is possible to effectively control the vibrations by avoiding resonance with simple design improvements.

• 한국산학기술학회논문지
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• 제16권9호
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• pp.5789-5794
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• 2015

최근 스마트 폰, 모바일 PC 제품의 외관에 필요한 가벼운 금속제품으로 제조를 하기 위하여 알루미늄 압출 공정과 CNC 가공기법을 적용한 생산방식이 널리 사용되고 있다. 하지만, 알루미늄 압출법은 외관 디자인의 제약이 있으며, 특히 CNC 가공 프로세스가 상대적으로 높은 생산 비용 및 낮은 생산성으로 생산단가가 많이 높은 단점이 있다. 본 연구에서, 새로운 처리 방법을 순서 재료비를 대폭 감소시키고, 제조 속도를 향상시키기 위해 판재성형과 부피성형의 두가지 공정을 섞어 새로운 판단조 공정을 개발하였다. 새로운 판단조 공법(hybrid plate forging)이란 우선 일반적인 딥드로잉으로 중간 모양을 만든 후 원하는 벽 부위만 증육을 하는 방법을 의미한다. 이러한 판단조 공법을 활용하여 재료의 낭비와 제조 시간을 최소화하는 것이 가능하게 된다. 본 연구에서는 상용 유한 요소 프로그램 AFDEX-2D를 통해 판단조공정을 설계하였고 최적의 사용 가능한 소재의 두께와 초기 폭을 설계하였다. 최종적으로 실제 노트북 케이스 금형을 제작하여 제안한 방법의 타당성을 검증하였다.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.731-748
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• 2010

Impact detection and health monitoring are very important tasks for civil infrastructures, such as bridges. Piezoceramic based transducers are widely researched for these tasks due to the piezoceramic material's inherent advantages of dual sensing and actuation ability, which enables the active sensing method for structural health monitoring with a network of piezoceramic transducers. Wireless sensor networks, which are easy for deployment, have great potential in health monitoring systems for large civil infrastructures to identify early-age damages. However, most commercial wireless sensor networks are general purpose and may not be optimized for a network of piezoceramic based transducers. Wireless networks of piezoceramic transducers for active sensing have special requirements, such as relatively high sampling rate (at a few-thousand Hz), incorporation of an amplifier for the piezoceramic element for actuation, and low energy consumption for actuation. In this paper, a wireless network is specially designed for piezoceramic transducers to implement impact detection and active sensing for structural health monitoring. A power efficient embedded system is designed to form the wireless sensor network that is capable of high sampling rate. A 32 bit RISC wireless microcontroller is chosen as the main processor. Detailed design of the hardware system and software system of the wireless sensor network is presented in this paper. To verify the functionality of the wireless sensor network, it is deployed on a two-story concrete frame with embedded piezoceramic transducers, and the active sensing property of piezoceramic material is used to detect the damage in the structure. Experimental results show that the wireless sensor network can effectively implement active sensing and impact detection with high sampling rate while maintaining low power consumption by performing offline data processing and minimizing wireless communication.

• Smart Structures and Systems
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• 제23권2호
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• pp.107-122
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• 2019

As one of the most important parameters in structural health monitoring, structural frequency has many advantages, such as convenient to be measured, high precision, and insensitive to noise. In addition, frequency-change-ratio based method had been validated to have the ability to identify the damage occurrence and location. However, building a precise enough finite elemental model (FEM) for the test structure is still a huge challenge for this frequency-change-ratio based damage detection technique. In order to overcome this disadvantage and extend the application for frequencies in structural health monitoring area, a novel method was developed in this paper by combining the cross-model cross-mode (CMCM) model updating algorithm with the frequency-change-ratio based method. At first, assuming the physical parameters, including the element mass and stiffness, of the test structure had been known with a certain value, then an initial to-be-updated model with these assumed parameters was constructed according to the typical mass and stiffness distribution characteristic of shear buildings. After that, this to-be-updated model was updated using CMCM algorithm by combining with the measured frequencies of the actual structure when no damage was introduced. Thus, this updated model was regarded as a representation of the FEM model of actual structure, because their modal information were almost the same. Finally, based on this updated model, the frequency-change-ratio based method can be further proceed to realize the damage detection and localization. In order to verify the effectiveness of the developed method, a four-level shear building was numerically simulated and two actual shear structures, including a three-level shear model and an eight-story frame, were experimentally test in laboratory, and all the test results demonstrate that the developed method can identify the structural damage occurrence and location effectively, even only very limited modal frequencies of the test structure were provided.

• Smart Structures and Systems
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• 제30권6호
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Smart Structures and Systems
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• 제31권4호
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Smart Structures and Systems
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• 제30권4호
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• pp.339-351
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• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.