• Computers and Concrete
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• v.32 no.5
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• pp.465-474
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• 2023

The point load test (PLT) is a widely-used alternative method in the field to determine the uniaxial compressive strength due to its simple testing machine and procedure. The point load test index can estimate the uniaxial compressive strength through conversion factors based on the rock types. However, the mechanism correlating these two parameters and the influence of the mechanical properties on PLT results are still not well understood. This study proposed a theoretical model to understand the mechanism of PLT serving as an alternative to the UCS test based on laboratory observation and literature survey. This model found that the point load test is a self-confined compression test. There is a compressive ellipsoid near the loading axis, whose dilation forms a tensile ring that provides confinement on this ellipsoid. The peak load of a point load test is linearly positive correlated to the tensile strength and negatively correlated to the Poisson ratio. The model was then verified using numerical and experimental approaches. In numerical verification, the PLT discs were simulated using flat-joint BPM of PFC3D to model the force distribution, crack propagation and BPM properties' effect with calibrated micro-parameters from laboratory UCS test and point load test of Berea sandstones. It further verified the mechanism experimentally by conducting a uniaxial compressive test, Brazilian test, and point load test on four different rocks. The findings from this study can explain the mechanism and improve the understanding of point load in determining uniaxial compressive strength.

• Structural Engineering and Mechanics
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• v.65 no.3
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• pp.263-274
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• 2018

In this study, we propose a frequency domain spectral element method (SEM) for the vibration analysis of a multi-span beam subjected to a moving point force. This study is an extension of the authors' previous study for a single-span beam subjected to a moving point force, where the two-element model-based SEM was applied. In this study, each span of a multi-span beam is represented by the Timoshenko beam model and the moving point force is transformed into the frequency domain as a series of each stationary point force distributed on the multi-span beam. The span at which a stationary point force is located is represented by two-element model, but all other spans are represented by one-element models. The vibration responses to a moving point force are obtained by superposing all individual vibration responses generated by each stationary point force. The high accuracy and computational efficiency of the proposed SEM are verified by comparing the solutions by SEM with exact analytical solutions by the integral transform method (ITM) as well as the solutions by the finite element method (FEM).

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.7
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• pp.633-641
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• 2015

In this study, we proposed a three-dimensional (3D) scanning system based on laser-vision technique and rotary mechanism for automatic 3D model reconstruction. The proposed scanning system consists of a laser projector, a camera, and a turntable. For laser-camera calibration a new and simple method was proposed. 3D point cloud data of the surface of scanned object was fully collected by integrating extracted laser profiles, which were extracted from laser stripe images, corresponding to rotary angles of the rotary mechanism. The obscured laser profile problem was also solved by adding an addition camera at another viewpoint. From collected 3D point cloud data, the 3D model of the scanned object was reconstructed based on facet-representation. The reconstructed 3D models showed effectiveness and the applicability of the proposed 3D scanning system to 3D model-based applications.

• Advances in aircraft and spacecraft science
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• v.1 no.4
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• pp.427-441
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• 2014

Orbit-raising is an important step to place spacecraft from parking orbits into working orbits. Attitude control system design is crucial in the success of orbit-raising. Several text books have discussed this design and focused mainly on the traditional methods based on single-input single-output (SISO) transfer function models. These models are not good representations for many orbit-raising control systems which have multiple thrusters and each thruster has impact on the attitude defined by all outputs. Only one published article is known to use a more suitable multi-input multi-output (MIMO) Euler angle model in spacecraft orbit-raising attitude control system design. In this paper, a quaternion based MIMO model for the orbit-raising attitude control system design is proposed. The advantages of using quaternion based model for orbit-raising control system designs are (a) there is no need for mathematical transformations because the attitude measurements are normally given by quaternion, (b) quaternion based model does not depend on rotational sequences, which reduces the chance of human errors, and (c) the singular point of reduced quaternion model is the farthest from the operational point where linearization is performed. We will show that performance of quaternion model based design will be as good as the performance of Euler angle model based design for orbit-raising problem.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.55-59
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• 2003

In this paper, we estimate the performance requirements of general-purpose DSP for Carrier Recovery of OFDM DVB-T receiver. Firstly, we transported the designed fixed-point OFDM DVB-T model to a floating-point software model written in C. Then, we measured the number of instruction cycles required for operation of Carrier Recovery in time. We use SignalMaster$\^$TM/ DSP platform of LYRtech Inc. as a environment of estimation, and Simulink$\^$TM/ as a graphical interface, Code Composer StudioTM of TI as profiler and compiler, and SPW$\^$TM/ for presenting functional reliability and comparing the performance distortion with fixed-point model. As a result, we show the required number of DSPs in our DSP-based system, and introduce the need of Multi-DSP-based system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1976-1995
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• 2023

The anchor-free object detector CenterNet regards the object as a center point and predicts it based on the Gaussian circle region. For each object's center point, CenterNet directly regresses the width and height of the objects and finally gets the boundary range of the objects. However, the critical range of the object's center point can not be accurately limited by using the Gaussian circle region to constrain the prediction region, resulting in many low-quality centers' predicted values. In addition, because of the large difference between the width and height of different objects, directly regressing the width and height will make the model difficult to converge and lose the intrinsic relationship between them, thereby reducing the stability and consistency of accuracy. For these problems, we proposed a center point prediction method based on the Gaussian elliptic region and a size component regression method based on the small solution space. First, we constructed a Gaussian ellipse region that can accurately predict the object's center point. Second, we recode the width and height of the objects, which significantly reduces the regression solution space and improves the convergence speed of the model. Finally, we jointly decode the predicted components, enhancing the internal relationship between the size components and improving the accuracy consistency. Experiments show that when using CenterNet as the improved baseline and Hourglass-104 as the backbone, on the MS COCO dataset, our improved model achieved 44.7%, which is 2.6% higher than the baseline.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.688-698
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• 2020

Point clouds have ability to express the spatial entities, however, the point clouds redundancy always involves some uncertainties in computer recognition and model construction. Therefore, point clouds thinning is an indispensable step in point clouds model reconstruction and other applications. To overcome the shortcomings of complex classification index and long time consuming in existing point clouds thinning algorithms, this paper proposes a point clouds fast thinning algorithm. Specifically, the two-dimensional index is established in plane linear array (x, y) for the scanned point clouds, and the thresholds of adjacent point distance difference and height difference are employed to further delete or retain the selected sample point. Sequentially, the index of sample point is traversed forwardly and backwardly until the process of point clouds thinning is completed. The results suggest that the proposed new algorithm can be applied to different targets when the thresholds are built in advance. Besides, the new method also performs superiority in time consuming, modelling accuracy and feature retention by comparing with octree thinning algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4355-4374
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• 2018

Crowd escape event detection has become one of the hottest problems in intelligent surveillance filed. When the 'escape event' occurs, pedestrians will escape in a disordered way with different velocities and directions. Based on these characteristics, this paper proposes a Direction-Collectiveness Model to detect escape event in crowd scenes. First, we extract a set of trajectories from video sequences by using generalized Kanade-Lucas-Tomasi key point tracker (gKLT). Second, a Direction-Collectiveness Model is built based on the randomness of velocity and orientation calculated from the trajectories to express the movement of the crowd. This model can describe the movement of the crowd adequately. To obtain a generalized crowd escape event detector, we adopt an adaptive threshold according to the Direction-Collectiveness index. Experiments conducted on two widely used datasets demonstrate that the proposed model can detect the escape events more effectively from dense crowd.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1349-1365
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• 2020

In recent years, in the fields of smart cities and digital twins, research on model generation is increasing due to the advantage of acquiring actual 3D coordinates by using point clouds. In addition, there is an increasing demand for a solid model that can easily modify the shape and texture of the building. In this paper, we propose a method to create a clustered solid building model based on point cloud data. The proposed method consists of five steps. Accordingly, in this paper, we propose a method to create a clustered solid building model based on point cloud data. The proposed method consists of five steps. In the first step, the ground points were removed through the planarity analysis of the point cloud. In the second step, building area was extracted from the ground removed point cloud. In the third step, detailed structural area of the buildings was extracted. In the fourth step, the shape of 3D building models with 3D coordinate information added to the extracted area was created. In the last step, a 3D building solid model was created by giving texture to the building model shape. In order to verify the proposed method, we experimented using point clouds extracted from unmanned aerial vehicle images using commercial software. As a result, 3D building shapes with a position error of about 1m compared to the point cloud was created for all buildings with a certain height or higher. In addition, it was confirmed that 3D models on which texturing was performed having a resolution of less than twice the resolution of the original image was generated.