• Korean Journal of Applied Biomechanics
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• v.27 no.4
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• pp.287-288
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• 2017

• Journal of the Korean Society for Precision Engineering
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• v.33 no.2
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• pp.129-138
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• 2016

This paper presents a natural corners-based SLAM (Simultaneous Localization and Mapping) with a robust data association algorithm in a real unknown environment. Corners are extracted from raw laser sensor data, which are chosen as landmarks for correcting the pose of mobile robot and building the map. In the proposed data association method, the extracted corners in every step are separated into several groups with small numbers of corners. In each group, local best matching vector between new corners and stored ones is found by joint compatibility, while nearest feature for every new corner is checked by individual compatibility. All these groups with local best matching vector and nearest feature candidate of each new corner are combined by partial compatibility with linear matching time. Finally, SLAM experiment results in an indoor environment based on the extracted corners show good robustness and low computation complexity of the proposed algorithms in comparison with existing methods.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1343-1358
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• 2020

In the stagewise arithmetic orthogonal matching pursuit algorithm, the weak threshold used in sparsity estimation is determined via maximum iterations. Different maximum iterations correspond to different thresholds and affect the performance of the algorithm. To solve this problem, we propose an improved variable weak threshold based on the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the residual error value to control the weak threshold. When the residual value decreases, the threshold value continuously increases, so that the atoms contained in the atomic set are closer to the real sparsity value, making it possible to improve the reconstruction accuracy. In addition, we improved the generalized Jaccard coefficient in order to replace the inner product method that is used in the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the covariance to replace the joint expectation for two variables based on the generalized Jaccard coefficient. The improved generalized Jaccard coefficient can be used to generate a more accurate calculation of the correlation between the measurement matrixes. In addition, the residual is more accurate, which can reduce the possibility of selecting the wrong atoms. We demonstrate using simulations that the proposed algorithm produces a better reconstruction result in the reconstruction of a one-dimensional signal and two-dimensional image signal.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.29-34
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• 2001

This paper is devoted to investigating direct adaptive neural control of nonlinear systems with uncertain or unknown dynamic models. In the direct adaptive neural networks control area, theoretical issues of the existing backpropagation-based adaptive neural networks control schemes. The major contribution is proposing the variable index control approach, which is of great significance in the control field, and applying it to derive new stable robust adaptive neural network control schemes. This new schemes possess inherent robustness to system model uncertainty, which is not required to satisfy any matching condition. To demonstrate the feasibility of the proposed leaning algorithms and direct adaptive neural networks control schemes, intensive computer simulations were conducted based on the flexible joint robot systems and functions.

• Computers and Concrete
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• v.26 no.2
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• pp.161-173
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• 2020

Shear keys in precast concrete segmental bridges (PCSBs) are usually match-casting which is very labour intensive. In this research, an innovative match-casting-free construction was proposed by leaving small gap between the convex and the concave castellated shear keys in the joints of PCSBs. Specimen experiment, shear strength analysis and numerical simulation were conducted, investigating the loading performance of this new type of dry joints, the gap dry joints. Compared with match-casting joint specimens, it has been found from experiment that shear capacity of gap joint specimens significantly decreased ranging from 17.75% to 42.43% due to only partially constrained and contacted in case of gap dry joints. Through numerical simulation, the effects of bottom contacting location, the heights of the gap and the shear key base were analyzed to investigate strength reduction and methods to enhance shear capacity of gap joint specimens. Numerical results proved that shear capacity of gap dry joints under full contact condition was higher than that under partial contact. In addition, left contact destroyed the integrity of shear keys, resulting in significant strength reduction. Larger shear key base remarkably increased shear capacity of the gap joint. Experimental tests indicated that AASHTO provision underestimated shear capacity of the match-casting dry joint specimens, while the numerical results for the gap dry joint showed that AASHTO provision underestimated shear capacity of full contact specimens, but overestimated that of left contact specimens.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.550-555
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• 1993

The aim of this paper is to present the development of visual seam tracking system equipped with visual range finder. The visual range finder, which consists of a CCD camera and a diode laser system with line generating optics, developed to recognize the types of weld joints and detect the location of weld joints. In practical applications, however, images of the weld joints are often degraded due to spatters, are flares, surface specularity, and welding smoke. To overcome the problem, this paper proposes a syntactic approach which is a class of artificial intelligence techniques. In the approach, the type of weld joint is inferred based upon the production rules which are linguiques grammars consisting of a set of line and junction primitives of laser strip image projected on weld joint. The production rules eliminate several noisy primitives to create new primitives through the merging process of primitives. After the recognition of weld joint, arc welding is started and the location of weld joints is repeatedly detected using a spring model-based template matching in which the template model is a by-product of the recognition process of weld joint. To show the effectiveness of the proposed approach a series of experiments-identification and robotic tracking-are conducted for four different types of weld joints.

• Journal of the Korean Society of Physical Medicine
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• v.13 no.1
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• pp.99-105
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• 2018

PURPOSE: The aim of this study was to evaluate the concurrent validity and clinical usefulness of the universal plastic goniometer to measure the range of motion of the internal and external rotation of the hip joint using the three dimensional motion analysis which can analyze the joints and segment movements in the most objective and quantitative method. METHODS: Clinical and kinematic data were collected from thirty individuals using a universal plastic goniometer and a ten camera motion analysis system. Passive hip rotation range was obtained three trials for left and right hip joints using two measure methods simultaneously. RESULTS: There were significant differences between all matching measures of the two measures of internal and external rotation of the hip joint (p<.05). The relationship between the two tests for all measurements of the internal and external rotation of the hip was statistically significant with correlation coefficient form r=.87 to .96. (p<.01). CONCLUSION: Clinical measurement of the internal and external rotation of the hip using a universal plastic goniometer is effective to assess the hip condition. However, application of universal plastic goniometer requires careful attention in more accurate evaluation and research verification of the internal and external rotation of hip joint.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2400-2413
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• 2019

Doubly-selective (DS) fading channel is often occurred in many orthogonal frequency division multiplexing (OFDM) communication systems, such as high-speed rail communication systems and underwater acoustic (UWA) wireless networks. It is challenging to provide an accurate and fast estimation over the doubly-selective channel, due to the strong Doppler shift. This paper addresses the doubly selective channel estimation problem based on complex exponential basis expansion model (CE-BEM) in OFDM systems from the perspective of distributed compressive sensing (DCS). We propose a novel DCS-based improved sparsity adaptive matching pursuit (DCS-IMSAMP) algorithm. The advantage of the proposed algorithm is that it can exploit the joint channel sparsity information using dynamic threshold, variable step size and tailoring mechanism. Simulation results show that the proposed algorithm achieves 5dB performance gain with faster operation speed, in comparison with traditional DCS-based sparsity adaptive matching pursuit (DCS-SAMP) algorithm.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.95-104
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• 2022

This study investigates the complex mechanism of work-family conflict affecting counterproductive behavior of employees based on resource conservation theory and 417 valid samples by using polynomial regression and response surface analysis. Counterproductive work behavior refers to any intentional behavior of an individual that has potential harm to the legitimate interests of the organization or its stakeholders. Results show that first, work-to-family conflict (WFC) and family-to-work conflict (FWC) had four matching types. Compared with "high WFC-low FWC," "low WFC-high FWC" and "low WFC-low FWC" matching conditions, the employee self-control resource depletion and counterproductive work behavior (CWB) are at their highest under "high WFC-high FWC" congruence matching condition. Second, the joint effect of WFC and FWC has a U-shaped relationship with counterproductive behavior. Compared with the "high WFC-low FWC" match state, the level of CWB in the "low WFC-high FWC" match state is higher. Third, the depletion of self-control resources played a mediating role in the effect of WFC on counterproductive behavior. Fourth, emotional intelligence moderated the relationship between the congruence of WFC and FWC and self-control resource depletion. Emotional intelligence was higher, and the positive relationship between the congruence of WFC and FWC and self-control resource depletion was weaker.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5015-5038
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• 2018

In this paper, a novel spectral-spatial joint sparse representation algorithm for hyperspectral image classification is proposed based on multi-layer superpixels in various scales. Superpixels of various scales can provide complete yet redundant correlated information of the class attribute for test pixels. Therefore, we design a joint sparse model for a test pixel by sampling similar pixels from its corresponding superpixels combinations. Firstly, multi-layer superpixels are extracted on the false color image of the HSI data by principal components analysis model. Secondly, a group of discriminative sampling pixels are exploited as reconstruction matrix of test pixel which can be jointly represented by the structured dictionary and recovered sparse coefficients. Thirdly, the orthogonal matching pursuit strategy is employed for estimating sparse vector for the test pixel. In each iteration, the approximation can be computed from the dictionary and corresponding sparse vector. Finally, the class label of test pixel can be directly determined with minimum reconstruction error between the reconstruction matrix and its approximation. The advantages of this algorithm lie in the development of complete neighborhood and homogeneous pixels to share a common sparsity pattern, and it is able to achieve more flexible joint sparse coding of spectral-spatial information. Experimental results on three real hyperspectral datasets show that the proposed joint sparse model can achieve better performance than a series of excellent sparse classification methods and superpixels-based classification methods.