• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.577-582
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• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2002.11a
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• pp.91-95
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• 2002

This study is using the RADIANCE lighting simulation tool to determine the transmittance where the horizontal blind is installed. The transmittance is applied to DayDim program which is a lighting calculation and analysis tool. Parameters such as clear sky type, seasonal changes(the summer solstice), altitude, azimuth and horizontal blind angle at a 0$^{\circ}$, 45$^{\circ}$, 90$^{\circ}$ were considered. The simulation results present that measured directional transmittances have different values according to each directional property of the horizontal blind.

• Structural Engineering and Mechanics
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• v.48 no.2
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• pp.151-171
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• 2013

The current paper presents the numerical blind prediction of nonlinear seismic response of two full-scale, three dimensional, one-story reinforced concrete structures subjected to bidirectional earthquake simulations on shaking table. Simulations were carried out at the laboratories of LNEC (Laboratorio Nacional de Engenharia Civil) in Lisbon, Portugal. The study was motivated by participation in the blind prediction contest of shaking table tests, organized by the challenge committee of the 15th World Conference on Earthquake Engineering. The test specimens, geometrically identical, designed for low and high ductility levels, were subjected to subsequent earthquake motions of increasing intensity. Three dimensional nonlinear analytical models were implemented and subjected to the input base motions. Reasonably accurate reproduction of the measured displacement response was obtained through appropriate modeling. The goodness of fit between analytical and measured results depended on the details of the analytical models.

• International journal of steel structures
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• v.18 no.5
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• pp.1666-1683
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• 2018

To explore seismic behavior of blind bolted concrete-filled steel tube (CFST) frames infilled with precast sandwich composite wall panels (SCWPs), a series tests of blind bolted square CFST frames with precast SCWPs under lateral low-cyclic loading were conducted. The influence of the type of wall concrete, wall-to-frame connection and steel brace setting, etc. on the hysteretic curves and failure modes of the type of composite structure was investigated. The seismic behavior of the blind bolted CFST frames with precast SCWPs was evaluated in terms of lateral load-displacement relation curves, strength and stiffness degradation, crack patterns of SCWPs, energy dissipation capacity and ductility. Then, a finite element (FE) analysis modeling using ABAQUS software was developed in considering the nonlinear material properties and complex components interaction. Comparison indicated that the FE analytical results coincided well with the test results. Both the experimental and numerical results indicated that setting the external precast SCWPs could heighten the load carrying capacities and rigidities of the blind bolted CFST frames by using reasonable connectors between frame and SCWPs. These experimental studies and FE analysis would enable improvement in the practical design of the SCWPs in fabricated CFST structure buildings.

• Journal of Urban Science
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• v.7 no.2
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• pp.53-60
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• 2018

Digital Twin is a technology that creates a photocopy of real-world objects on a computer and analyzes the past and present operational status by fusing the structure, context, and operation of various physical systems with property information, and predicts the future society's countermeasures. In particular, 3D rendering technology (UAS, LiDAR, GNSS, etc.) is a core technology in digital twin. so, the research and application are actively performed in the industry in recent years. However, UAS (Unmanned Aerial System) and LiDAR (Light Detection And Ranging) have to be solved by compensating blind spot which is not reconstructed according to the object shape. In addition, the terrestrial LiDAR can acquire the point cloud of the object more precisely and quickly at a short distance, but a blind spot is generated at the upper part of the object, thereby imposing restrictions on the forward digital twin modeling. The UAS is capable of modeling a specific range of objects with high accuracy by using high resolution images at low altitudes, and has the advantage of generating a high density point group based on SfM (Structure-from-Motion) image analysis technology. However, It is relatively far from the target LiDAR than the terrestrial LiDAR, and it takes time to analyze the image. In particular, it is necessary to reduce the accuracy of the side part and compensate the blind spot. By re-optimizing it after fusion with UAS and Terrestrial LiDAR, the residual error of each modeling method was compensated and the mutual correction result was obtained. The accuracy of fusion-based 3D model is less than 1cm and it is expected to be useful for digital twin construction.

• ETRI Journal
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• v.41 no.4
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• pp.426-436
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• 2019

In time division duplex (TDD)-based multiuser massive multiple input multiple output (MIMO) systems, the uplink channel is estimated and the results are used in downlink for signal detection. Owing to noisy uplink channel estimation, the downlink channel should also be estimated for accurate signal detection. Therefore, recently, a blind method was developed, which assumes the use of a linear high-power amplifier (HPA) in the base station (BS). In this study, we extend this method to a scenario with a nonlinear HPA in the BS, where the Bussgang decomposition is used for HPA modeling. In the proposed method, the average power of the received signal for each user is a function of channel gain, large-scale fading, and nonlinear distortion variance. Therefore, the channel gain is estimated, which is required for signal detection. The performance of the proposed method is analyzed theoretically. The simulation results show superior performance of the proposed method compared to that of the other methods in the literature.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.814-822
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• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

• Steel and Composite Structures
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• v.27 no.5
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• pp.647-659
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• 2018

Through extensive research, there exist a new type of connection between railway bridge girders and steel-concrete composite panels. In addition to conventional shear connectors, newly developed blind bolts have been recently adopted for retrofitting. However, the body of knowledge on their influence and application to railway structures has not been thoroughly investigated. This study has thus placed a particular emphasis on the application of blind bolts on the Sydney Harbour Bridge as a feasible alternative constituent of railway track upgrading. Finite element modeling has been used to simulate the behaviours of the precast steel-concrete panels with common types of bolt connection using commercially available package, ABAQUS. The steel-concrete composite track slabs have been designed in accordance with Australian Standards AS5100. These precast steel-concrete panels are then numerically retrofitted by three types of most practical bold connections: head studded shear connector, Ajax blind bolt and Lindapter hollow bolt. The influences of bolt connections on load and stress transfers and structural behaviour of the composite track slabs are highlighted in this paper. The numerical results exhibit that all three bolts can distribute stresses effectively and can be installed on the bridge girder. However, it is also found that Lindapter hollow bolts are superior in minimising structural responses of the composite track slabs to train loading.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6A
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• pp.473-479
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• 2012

In this paper, blind equalization technique using Gaussian two-cluster model is proposed. The proposed approach, by modeling the received M-QAM signals as Gaussian distributed two-cluster, minimizes the computational complexity and enhances the reliability of the signal estimates. In addition, by using a nonlinear estimator with variable parameters to estimate the transmitted signal, and by selectively applying the reduced constellation and the original constellation when estimating the signals, the reliability of the signal estimation was further improved. As a result, the proposed approach has improved the performance while reducing the complexity of the equalizer. Through computer simulations for blind equalization of higher-order signals of 64-QAM, it was confirmed that the proposed method showed better performance than traditional approaches.

• Journal of Multimedia Information System
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• v.7 no.4
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• pp.249-256
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• 2020

Learning methods using various assistive and smart devices have been developed to enable independent learning of the disabled. Pointer control is the most important consideration for the disabled when controlling a device and the contents of an existing graphical user interface (GUI) environment; however, difficulties can be encountered when using a pointer, depending on the disability type; Although there are individual differences depending on the blind, low vision, and upper limb disability, problems arise in the accuracy of object selection and execution in common. A multimodal interface pilot solution is presented that enables people with various disability types to control web interactions more easily. First, we classify web interaction types using digital devices and derive essential web interactions among them. Second, to solve problems that occur when performing web interactions considering the disability type, the necessary technology according to the characteristics of each disability type is presented. Finally, a pilot solution for the multimodal interface for each disability type is proposed. We identified three disability types and developed solutions for each type. We developed a remote-control operation voice interface for blind people and a voice output interface applying the selective focusing technique for low-vision people. Finally, we developed a gaze-tracking and voice-command interface for GUI operations for people with upper-limb disability.