• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.517-532
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• 2022

Purpose: The purpose of this study is to propose useful suggestions by analyzing causal effect relationship between perceived usefulness (PU), perceived ease-of-use (PEOU), symbolic adoption (SA) which have four constructs, and ATCIS-II usage in mandatory context. Methods: Based on prior research, a research model was constructed using the variables of Technology Acceptance Model (TAM), the symbolic adoption theory, and the post-adoptive behavior variables. A structured questionnaire was conducted for those who use ATCIS-II in Republic of Korea Army (ROKA), and a total of 183 usable responses were collected and empirically analyzed using SmartPLS 3.3.9. Results: The results of this study are as follows; PEOU have a significant effect on PU and two constructs of SA (heightened enthusiasm, effort worthiness). PU have a significant effect on every construct of SA (heightened enthusiasm, mental acceptance, effort worthiness, use commitment). In addition, it was found that heightened enthusiasm have a significant effect on both expanded usage and exploratory usage. Also, mental acceptance and use commitment have a significant effect on exploratory usage. Conclusion: The findings of this empirical study have implications for proposing SA can explain mandated user's behavior and giving possible way that improve organization performance which adopt Information System (IS) by motivating end-user to extend IS's feature and explore new ways of using IS at work.

• Advances in concrete construction
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• v.13 no.5
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• Smart Structures and Systems
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• v.29 no.3
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• pp.499-512
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• 2022

This work proposes a novel contactless vibration damping and thermal isolation tripod platform based on Superconducting Magnetic Levitation (SML). This prototype is suitable for cryogenic environments, where classical passive, semi active and active vibration isolation techniques may present tribological problems due to the low temperatures and/or cannot guarantee an enough thermal isolation. The levitating platform consists of a Superconducting Magnetic Levitation (SML) with inherent passive static stabilization. In addition, the use of Operational Modal Analysis (OMA) technique is proposed to characterize the transmissibility function from the baseplate to the platform. The OMA is based on the Stochastic Subspace Identification (SSI) by using the Expectation Maximization (EM) algorithm. This paper contributes to the use of SSI-EM for SML applications by proposing a step-by-step experimental methodology to process the measured data, which are obtained with different unknown excitations: ambient excitation and impulse excitation. Thus, the performance of SSI-EM for SML applications can be improved, providing a good estimation of the natural frequency and damping ratio without any controlled excitation, which is the main obstacle to use an experimental modal analysis in cryogenic environments. The dynamic response of the 510 g levitating platform has been characterized by means of OMA in a cryogenic, 77 K, and high vacuum, 1E-5 mbar, environment. The measured vertical and radial stiffness are 9872.4 N/m and 21329 N/m, respectively, whilst the measured vertical and radial damping values are 0.5278 Nm/s and 0.8938 Nm/s. The first natural frequency in vertical direction has been identified to be 27.39 Hz, whilst a value of 40.26 Hz was identified for the radial direction. The determined damping values for both modes are 0.46% and 0.53%, respectively.

• Journal of KIBIM
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• v.12 no.4
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• pp.61-69
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• 2022

Earthmoving operations account for approximately 25% of construction cost, generally executed prior to the construction of buildings and structures with heavy equipment. For the successful completion of earthwork projects, it is crucial to constantly monitor earthwork equipment (e.g., trucks), estimate productivity, and optimize the construction process and equipment on a construction site. Traditional methods however require time-consuming and painstaking tasks for the manual observations of the ongoing field operations. This study proposed the use of a GPS sensor embedded in a smartphone for the tracking and visualization of equipment locations, which are in turn used for the estimation and simulation of cycle times and production rates of ongoing earthwork. This approach is implemented into a digital platform enabling real-time data collection and simulation, particularly in a 2D (e.g., maps) or 3D (e.g., point clouds) virtual environment where the spatial and temporal flows of trucks are visualized. In the case study, the digital platform is applied for an earthmoving operation at the site development work of commercial factories. The results demonstrate that the production rates of various equipment usage scenarios (e.g., the different numbers of trucks) can be estimated through simulation, and then, the optimal number of tucks for the equipment fleet can be determined, thus supporting the practical potential of real-time sensing and simulation for onsite equipment management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Smart Structures and Systems
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• v.29 no.3
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• pp.445-455
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• 2022

Steel anchor bolts are installed in concrete using a variety of methods. One of the most common methods of anchor bolt installation is using epoxy resin as an infill material injected into the drilled hole to act as a bonding material between the steel bolt and the surrounding concrete. Typical design standards assume uniform stress distribution along the length of the anchor bolt accompanied with single crack leading to pull-out failure. Experimental evidence has shown that the steel anchor bolts fail owing to the multiple failure patterns, hence these design assumptions are not realistic. In this regard, the presented research work details the analytical model that takes into consideration multiple micro cracks in the infill material induced via impact loading. The impact loading from the Schmidt hammer is used to evaluate the bond condition bond condition of anchor bolt and the epoxy material. The added advantage of the presented analytical model is that it is able to take into account the various type of end conditions of the anchor bolts such as bent or U-shaped anchors. Through sensitivity analysis the optimum stiffness and shear strength properties of the epoxy infill material is achieved, which have shown to achieve lower displacement coupled with reduced damage to the surrounding concrete. The accuracy of the presented model is confirmed by comparing the simulated deformational responses with the experimental evidence. From the comparison it was found that the model was successful in simulating the experimental results. The proposed model can be adopted by professionals interested in predicting and controlling the deformational response of anchor bolts.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.617-624
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• 2022

The small electric vehicles equipped with V2X(vehicle to everything) systems may provide more information and function to the existing navigation system of the vehicle. The key components of V2X technology include V2V (vehicle to vehicle), V2N(vehicle to network) and V2I (vehicle to infrastructure). This study is to design and implementation of VI type E-PTO which is interfaced with external equipments, the work designs the components of E-PTO such as DC/DC converter, DC/AC converter, battery bidirectional charging system etc. Also, it implements the devices and control systems for driving. The test results of VI type E-PTO components showed allowable 10% requirements of transient voltage variation rate and recovery time within 100ms for start/stop and normal operation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.233-252
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• 2022

Recently, various studies have been conducted to improve automated vehicle (AV) safety for AVs commercialization. In particular, the scenario method is directly related to essential safety assessments. However, the existing scenario do not have objectivity and explanability due to lack of data and experts' interventions. Therefore, this paper presents the AVs safety assessment extended scenario using real traffic accident data and vision transformer (ViT), which is explainable artificial intelligence (XAI). The optimal ViT showed 94% accuracy, and the scenario was presented with Attention Map. This work provides a new framework for an AVs safety assessment method to alleviate the lack of existing scenarios.