• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Structural Engineering and Mechanics
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• v.81 no.4
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• pp.411-428
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• 2022

This paper presents the Campbell diagram analysis of the rotordynamic system using the full order model (FOM) and the reduced order model (ROM) techniques to determine the critical speeds, identify the stability and reduce the computational time. Due to the spin-speed-dependent matrices (e.g., centrifugal stiffening matrix), several model order reduction (MOR) techniques may be considered, such as the modal superposition (MS) method and the Krylov subspace-based MOR techniques (e.g., Ritz vector (RV), quasi-static Ritz vector (QSRV), multifrequency quasi-static Ritz vector (MQSRV), multifrequency/ multi-spin-speed quasi-static Ritz vector (MMQSRV) and the combined Ritz vector & modal superposition (RV+MS) methods). The proposed MMQSRV method in this study is extended from the MQSRV method by incorporating the rotational-speed-dependent stiffness matrices into the Krylov subspace during the MOR process. Thus, the objective of this note is to respond to the question of whether to use the MS method or the Krylov subspace-based MOR technique in establishing the Campbell diagram of the shaft-disc-blade assembly systems in three-dimensional (3D) finite element analysis (FEA). The Campbell diagrams produced by the FOM and various MOR methods are presented and discussed thoroughly by computing the norm of relative errors (ER). It is found that the RV and the MS methods are dominant at low and high rotating speeds, respectively. More precisely, as the spinning velocity becomes large, the calculated ER produced by the RV method is significantly increased; in contrast, the ER produced by the MS method is smaller and more consistent. From a computational point of view, the MORs have substantially reduced the time computing considerably compared to the FOM. Additionally, the verification of the 3D FE rotordynamic model is also provided and found to be in close agreement with the existing solutions.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.199-208
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• 2022

Korean is an agglutinative language, and one or more morphemes are combined to form a single word. Part-of-speech tagging method separates each morpheme from a word and attaches a part-of-speech tag. In this study, we propose a new Korean part-of-speech tagging method based on the Head-Tail tokenization technique that divides a word into a lexical morpheme part and a grammatical morpheme part without decomposing compound words. In this method, the Head-Tail is divided by the syllable boundary without restoring irregular deformation or abbreviated syllables. Korean part-of-speech tagger was implemented using the Head-Tail tokenization and deep learning technique. In order to solve the problem that a large number of complex tags are generated due to the segmented tags and the tagging accuracy is low, we reduced the number of tags to a complex tag composed of large classification tags, and as a result, we improved the tagging accuracy. The performance of the Head-Tail part-of-speech tagger was experimented by using BERT, syllable bigram, and subword bigram embedding, and both syllable bigram and subword bigram embedding showed improvement in performance compared to general BERT. Part-of-speech tagging was performed by integrating the Head-Tail tokenization model and the simplified part-of-speech tagging model, achieving 98.99% word unit accuracy and 99.08% token unit accuracy. As a result of the experiment, it was found that the performance of part-of-speech tagging improved when the maximum token length was limited to twice the number of words.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.391-411
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• 2023

Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.

• Smart Structures and Systems
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• v.31 no.4
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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.

• Journal of Korean Home Economics Education Association
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• v.35 no.1
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• pp.73-88
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• 2023

Since the enactment of the School Meal Support Act in 2009, South Korean dietary education has been evolving, placing health, environment, and consideration as its core values. The 3rd Basic Plan for Dietary Education (2020-2024) aims to achieve a sustainable dietary lifestyle that civic agriculture for collective consumption, healthy citizens, and an inclusive society. However, the digital civilization of the Fourth Industrial Revolution is significantly impacting dietary education in schools. Therefore, this study examines sustainable dietary education content in South Korean school meals, diagnoses the phenomena of dietary education facing digital transformation in education due to the Fourth Industrial Revolution, and explores directions for inclusive dietary education through concrete structures and content systems for inclusive dietary education that foster sustainable dietary capabilities. To achieve inclusive dietary education, a structure and system that allow cognitive, normative, and practical learning to be combined in an inclusive way is required. Furthermore, to practice sustainable dietary education, alternative approaches that emphasize the development of learners' core competencies are necessary in the direction of inclusive dietary education that fosters inclusivity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2068-2082
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• 2023

With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multistage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.

• Journal of the Korean Geotechnical Society
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• v.22 no.1
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• pp.53-61
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• 2006

Soil liquefaction occurs by complex dynamic interaction between soil particles and pore fluid. Therefore, experimental researches have been widely performed to analyze liquefaction phenomena. In this research, centrifuge tests were performed to analyze the liquefaction behavior of horizontal sand ground. Centrifugal acceleration was 40g and the thickness of model ground was 25cm, which simulates 10m thickness in prototype scale. Viscous fluid was used as pore fluid to remove the time scaling difference between dissipation and dynamic shaking. Test results showed that the dissipation of excess pore pressure is the combined behavior of solidification and consolidation. In addition, the solidification rate, the ground acceleration amplitude, and the dynamic permeability during solidification were influenced by the confining pressure.

• Smart Structures and Systems
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• v.31 no.5
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• Structural Monitoring and Maintenance
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• v.10 no.1
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• pp.1-23
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• 2023

Various efforts have been made to reduce the weight of concrete slabs while preserving their flexural strength. This will result in reducing deflection and allows the utilization of longer spans. The top zone of the slab requires concrete to create the compression block for flexural strength, and the tension zone needs concrete to join with reinforcing for flexural strength. Also, the top and bottom slab faces must be linked to transmit stresses. Voided slab systems were and are still used to make long-span slab buildings lighter. Eight slab specimens of (1000^*1000 (1000^*1000 mm²) were cast and tested as two-way simply supported slabs in this research. The tested specimens consist of one solid slab and seven voided slabs with the following variables (type of slab solid and voided), thickness of slab (100 and 125 mm), presence of steel fibers (0% and 1%), and the number of GFRP layers). The voids in slabs were made using high-density polystyrene of dimensions (200^*200^*50 mm) with a central hole of dimensions (50^*50^*50 mm) at the ineffective concrete zones to give a reduction in weight by (34% to 38%). The slabs were tested as simply supported slabs under partial uniform loading. The results of specimens subjected to monotonic loading show that the combined strengthening by steel fibers and GFRP sheets of the concrete specimen (V-125-2GF-1%) shows the least deflection, deflection (4.6 mm), good ultimate loading capacity (192 MPa), large stiffness at cracking and at ultimate (57 and 41.74) respectively, more ductility (1.44), and high energy absorption (1344.83 kN.mm); so it's the best specimen that can be used as a voided slab under this type of loading.