In order to improve the alignment accuracy of a Cassegrain system, to the best of our knowledge, a novel computer-aided alignment method based on torque sensitivity is proposed. Different from the traditional position sensitivity curve guiding scheme, the accurate position of the secondary mirror is not necessary while the torque sensitivity curve is generated. By establishing the relationship between the torque of the secondary mirror setting screw and the Zernike coefficients of the system, a practical quantitative alignment scheme for the Cassegrain system can be realized. For a two-mirror Cassegrain optical-mechanical system, an alignment scheme based on torque sensitivity is designed. The results show that the wavefront aberrations of three Cassegrain systems reach 0.0479,0.0537, and 0.0698 respectively. It proves that the torque sensitivity curves can well guide the real alignment process.
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http://dx.doi.org/10.1364/AO.459163 | DOI Listing |
Materials (Basel)
December 2024
Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, A. Boboli 8, 02-525 Warsaw, Poland.
The magnetoelastic effect is known as the dependence between the magnetic properties of the material and applied mechanical stress. The stress might not be applied directly but rather generated by the applied torque. This creates the possibility of developing a torque-sensing device based on the magnetoelastic effect.
View Article and Find Full Text PDFAdv Healthc Mater
January 2025
Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UPS), 205 Route de Narbonne, Toulouse, 31400, France.
Phys Rev E
November 2024
Department of Fibre and Polymer Technology, KTH Royal Institute of Technology, SE10044 Stockholm, Sweden and Wallenberg Wood Science Center, KTH Royal Institute of Technology, SE10044 Stockholm, Sweden.
Motivated by the limitations of conventional coarse-grained molecular dynamics for simulation of large systems of nanoparticles and the challenges in efficiently representing general pair potentials for rigid bodies, we present a method for approximating general rigid body pair potentials based on a specialized type of deep neural network that maintains essential properties, such as conservation of energy and invariance to the chosen origins of the particles. The network uses a specialized geometric abstraction layer to convert the relative coordinates of the rigid bodies to input more suitable to a conventional artificial neural network, which is trained together with the specialized layer. This results in geometric representations of the particles optimized for the specific potential.
View Article and Find Full Text PDFFront Hum Neurosci
November 2024
The Research Center for Brain Function and Medical Engineering, Asahikawa Medical University, Asahikawa, Japan.
MethodsX
December 2024
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Tamil Nadu 602105, India.
The Model Reference Adaptive System (MRAS) is effective for speed control in sensorless Induction Motor (IM) drives, particularly at zero and very low speeds. This study enhances MRAS's resilience and dynamic performance by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into sensorless vector-controlled IM drives. The research addresses challenges related to parameter uncertainties, load variations, and external disturbances through the combination of MRAS and ANFIS.
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