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  • 1
    Online Resource
    Online Resource
    The cutting parameters dependent vibration monitoring method for machine tools
    Acoustical Society of America (ASA) ; 2020
    In:  The Journal of the Acoustical Society of America Vol. 148, No. 4_Supplement ( 2020-10-01), p. 2793-2793
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 148, No. 4_Supplement ( 2020-10-01), p. 2793-2793
    Abstract: The on-line monitoring of the machine tools attracts growing interest for the operation of the smart factories. The quality of the workpieces and the productivity of the manufacturing can be maintained through the monitoring and optimization. The vibration signal reflects irregular characteristics of the fault conditions, such as the tool wear and chatter. The corresponding features can be extracted and recognized using the conventional signal processing method in the time and frequency domain. However, the vibration features are process-dependent. Due to the dynamic changes of cutting parameters, the variance of the cutting forces and the consequent frequency response between different cutting parameters could be larger than that between the regular conditions and fault conditions in the identical cutting parameters. This paper combined the vibration signal with the real-time cutting parameters and toolpath calculated by the numerical control codes to analysis the cutting condition of the machining process. A sub-band segmentation method based on spindle speed is proposed. The accuracy and applicability are also discussed.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/1.5147774
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2020
    detail.hit.zdb_id: 1461063-2
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  • 2
    Online Resource
    Online Resource
    Fast compressive beamforming with a modified fast iterative shrinkage-thresholding algorithm
    Acoustical Society of America (ASA) ; 2021
    In:  The Journal of the Acoustical Society of America Vol. 149, No. 5 ( 2021-05-01), p. 3437-3448
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 149, No. 5 ( 2021-05-01), p. 3437-3448
    Abstract: Compressive beamforming has been successfully applied to direction-of-arrival estimation with sensor arrays. The results demonstrated that this technique achieves superior performance when compared with traditional high-resolution beamforming methods. The existing compressive beamforming methods use classical iterative optimization algorithms in their compressive sensing theories. However, the computational complexity of the existing compressive beamforming methods tend to be excessively high, which has limited the use of compressive beamforming in applications with limited computing resources. To address this issue, this paper proposes a fast compressive beamforming method which combines the shift-invariance of the array beam patterns with a fast iterative shrinkage-thresholding algorithm. The evaluation shows that the proposed fast compressive beamforming method successfully reduces the number of floating-point operations by 3 orders of magnitude when compared with the existing methods. In addition, both the simulations and experiments demonstrate that the resolution limit for discerning closely spaced sources of the introduced fast method is comparable to those of the existing compressive beamforming methods, which use classical iterative optimization algorithms.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/10.0004997
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2021
    detail.hit.zdb_id: 1461063-2
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  • 3
    Online Resource
    Online Resource
    Machining process monitoring with vibration signal based manifold learning
    Acoustical Society of America (ASA) ; 2020
    In:  The Journal of the Acoustical Society of America Vol. 148, No. 4_Supplement ( 2020-10-01), p. 2766-2766
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 148, No. 4_Supplement ( 2020-10-01), p. 2766-2766
    Abstract: Machining process monitoring based on vibration sensing is a growing demand in smart manufacturing. However, in real factories, massive process conditions which include thousands of shapes of workpieces and thousands of combinations of cutting parameters, such as spindle speed, feed rate and cutting depth, are designed and used in manufacturing. Manifold learning is able to extract essential and distinct features from the vibration signal and helps to monitor and recognize different process conditions. In this paper, the dataset, including slight and huge variation of cutting parameters and workpiece shapes, are collected for analysis. Different manifold learning algorithms are utilized and compared to mine the essential features and reduce the interference of non-sensitive features. The generalization ability of different manifold learning algorithms are discussed to fit the various process conditions. Convolutional neural networks are employed to evaluate the monitoring accuracy. The experimental results show that the features obtained by the manifold learning distinguish vibration signals of different cutting parameters in low dimensional space and give a protentional way to construct effective monitoring systems. The generalization ability to different workpieces and cutting parameters and its limitation are discussed.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/1.5147701
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2020
    detail.hit.zdb_id: 1461063-2
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  • 4
    Online Resource
    Online Resource
    A three-dimensional finite difference model for ocean acoustic propagation and benchmarking for topographic effects
    Acoustical Society of America (ASA) ; 2021
    In:  The Journal of the Acoustical Society of America Vol. 150, No. 2 ( 2021-08-01), p. 1140-1156
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 150, No. 2 ( 2021-08-01), p. 1140-1156
    Abstract: A three-dimensional (3D) finite difference (FD) model with formal fourth-order accuracy has been developed for the ocean acoustic Helmholtz equation (HE), which can be used to address arbitrary bathymetry and provide more accurate benchmark solutions for other 3D underwater acoustic approximate models. The derivatives in the acoustic HE are numerically discretized based on regular grids, and the perfectly matched layer is introduced to absorb unphysical reflections from the boundaries where Sommerfeld radiation conditions are deployed. The system of linear equations is solved using a parallel matrix-free geometric multigrid preconditioned biconjugate gradient stabilized iteration method, and the code (named COACH) is run on the Tianhe-2 supercomputer in China. Four 3D topographic benchmark acoustic cases—a wedge waveguide, Gaussian canyon, conical seamount, and corrugated seabed—are simulated to test the present FD model, and the maximum number of grid points reaches 33.15 × 109 in the wedge waveguide case, running in parallel with 988 central processing unit cores. Furthermore, the accuracy and generality of the present model have been verified by solution comparisons with other available 3D acoustic propagation models, and the two-dimensional and 3D transmission loss contours are presented to facilitate the distinguishing among the acoustic field features of these cases.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/10.0005853
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2021
    detail.hit.zdb_id: 1461063-2
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  • 5
    Online Resource
    Online Resource
    Dissociation of posture remapping and cognitive load in level-2 perspective-taking
    Elsevier BV ; 2021
    In:  Cognition Vol. 214 ( 2021-09), p. 104733-
    Details
    In: Cognition, Elsevier BV, Vol. 214 ( 2021-09), p. 104733-
    Type of Medium: Online Resource
    ISSN: 0010-0277
    URL: Article
    DOI: 10.1016/j.cognition.2021.104733
    RVK:
    EQ 2127.5
    RVK:
    CL 1000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 1499940-7
    detail.hit.zdb_id: 184702-8
    SSG: 5,2
    SSG: 7,11
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  • 6
    Online Resource
    Online Resource
    A typed resolution principle for deduction with conditional typing theory
    Elsevier BV ; 1995
    In:  Artificial Intelligence Vol. 75, No. 2 ( 1995-06), p. 161-194
    Details
    In: Artificial Intelligence, Elsevier BV, Vol. 75, No. 2 ( 1995-06), p. 161-194
    Type of Medium: Online Resource
    ISSN: 0004-3702
    URL: Article
    DOI: 10.1016/0004-3702(94)00027-X
    RVK:
    EQ 1000
    RVK:
    SA 2940
    Language: English
    Publisher: Elsevier BV
    Publication Date: 1995
    detail.hit.zdb_id: 1468341-6
    detail.hit.zdb_id: 218797-8
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  • 7
    Online Resource
    Online Resource
    Low-rank decomposition meets kernel learning: A generalized Nyström method
    Elsevier BV ; 2017
    In:  Artificial Intelligence Vol. 250 ( 2017-09), p. 1-15
    Details
    In: Artificial Intelligence, Elsevier BV, Vol. 250 ( 2017-09), p. 1-15
    Type of Medium: Online Resource
    ISSN: 0004-3702
    URL: Article
    DOI: 10.1016/j.artint.2017.05.001
    RVK:
    EQ 1000
    RVK:
    SA 2940
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2017
    detail.hit.zdb_id: 1468341-6
    detail.hit.zdb_id: 218797-8
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  • 8
    Online Resource
    Online Resource
    Hierarchical deduction
    Springer Science and Business Media LLC ; 1987
    In:  Journal of Automated Reasoning Vol. 3, No. 1 ( 1987-3), p. 35-77
    Details
    In: Journal of Automated Reasoning, Springer Science and Business Media LLC, Vol. 3, No. 1 ( 1987-3), p. 35-77
    Type of Medium: Online Resource
    ISSN: 0168-7433 , 1573-0670
    URL: Article
    DOI: 10.1007/BF00381144
    RVK:
    EQ 1000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 1987
    detail.hit.zdb_id: 1479376-3
    SSG: 7,11
    SSG: 17,1
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  • 9
    Online Resource
    Online Resource
    Solving open problems in right alternative rings with Z-module reasoning
    Springer Science and Business Media LLC ; 1989
    In:  Journal of Automated Reasoning Vol. 5, No. 2 ( 1989-6)
    Details
    In: Journal of Automated Reasoning, Springer Science and Business Media LLC, Vol. 5, No. 2 ( 1989-6)
    Type of Medium: Online Resource
    ISSN: 0168-7433 , 1573-0670
    URL: Article
    DOI: 10.1007/BF00243001
    RVK:
    EQ 1000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 1989
    detail.hit.zdb_id: 1479376-3
    SSG: 7,11
    SSG: 17,1
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  • 10
    Online Resource
    Online Resource
    Bayesian inversion and sequential Monte Carlo sampling techniques applied to nearfield acoustic sensor arrays
    Acoustical Society of America (ASA) ; 2014
    In:  The Journal of the Acoustical Society of America Vol. 136, No. 4_Supplement ( 2014-10-01), p. 2084-2084
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 136, No. 4_Supplement ( 2014-10-01), p. 2084-2084
    Abstract: This paper demonstrates that inverse source reconstruction can be performed using a methodology of particle filters that relies primarily on the Bayesian approach of parameter estimation. The proposed approach is applied in the context of nearfield acoustic holography based on the equivalent source method (ESM). A state-space model is formulated in light of the ESM. The parameters to estimate are amplitudes and locations of the equivalent sources. The parameters constitute the state vector which follows a first-order Markov process with the transition matrix being the identity for every frequency-domain data frame. The implementation of recursive Bayesian filters involves a sequential Monte Carlo sampling procedure that treats the estimates as point masses with a discrete probability mass function (PMF) which evolves with iteration. It is evident from the results that the inclusion of the appropriate prior distribution is crucial in the parameter estimation.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/1.4899486
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2014
    detail.hit.zdb_id: 1461063-2
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