A basis for solid modeling of gear teeth with application in design and manufacture

A new approach to modeling gear tooth surfaces is discussed. A computer graphics solid modeling procedure is used to simulate the tooth fabrication process. This procedure is based on the principles of differential geometry that pertain to envelopes of curves and surfaces. The procedure is illustrated with the modeling of spur, helical, bevel, spiral bevel, and hypoid gear teeth. Applications in design and manufacturing are discussed. Extensions to nonstandard tooth forms, to cams, and to rolling element bearings are proposed

Analyzing the influences of bicycle suspension systems on pedaling forces and human body vibration

Front and rear suspensions are commonly equipped on bicycles for the purpose of riding comfort especially for mountain bicycle. Suspension system includes damper for shock absorbing and spring for rebounding. Therefore suspension system would increase leg muscle forces for riding bicycle since damper dissipates some energy. ADAMS‎®‎/LifeMOD‎®‎ are proposed in this research to establish a bicycle-human integrated multibody dynamic model to investigate the impact of bicycle suspensions on cyclist’s leg muscle forces under various pedaling conditions and human body vibration for evaluation of riding comfort. Muscles studied include adductor magnus, rectus femoris, vastus lateralis and semitendinosus. Comfort analyses include the vibrating acceleration in vertical direction of lower torso and scapula. Pedaling conditions include riding on flat road, over a road bump, and climbing slope. The results indicate that suspension system increases the pedaling forces of vastus lateralis and semitendinosus. However suspension system decreases the pedaling forces of adductor magnus and rectus femoris. Suspension systems, especially the rear suspension, may effectively reduce human body vibrating acceleration. The integrated model built in this research may be used as reference for designing bicycle suspension systems. Also, the results of this study may be used as a basis of leg weight training to strengthen certain muscles for long-distance off-road cyclists

The Critical Behavior of Quantum Stirling Heat Engine

We investigate the performance of a Stirling cycle with a working substance (WS) modeled as the quantum Rabi model (QRM), exploring the impact of criticality on its efficiency. Our findings indicate that the criticality of the QRM has a positive effect on improving the efficiency of the Stirling cycle. Furthermore, we observe that the Carnot efficiency is asymptotically achievable as the WS parameter approaches the critical point, even when both the temperatures of the cold and hot reservoirs are finite. Additionally, we derive the critical behavior for the efficiency of the Stirling cycle, demonstrating how the efficiency asymptotically approaches the Carnot efficiency as the WS parameter approaches the critical point. Our work deepens the understanding of the impact of criticality on the performance of a Stirling heat engine.Comment: 7 pages, 3 figure

Error-Resilient Floquet Geometric Quantum Computation

We proposed a new geometric quantum computation (GQC) scheme, called Floquet GQC (FGQC), where error-resilient geometric gates based on periodically driven two-level systems can be constructed via a new non-Abelian geometric phase proposed in a recent study [V. Novi\^{c}enko \textit{et al}, Phys. Rev. A 100, 012127 (2019) ]. Based on Rydberg atoms, we gave possible implementations of universal single-qubit gates and a nontrivial two-qubit gate for FGQC. By using numerical simulation, we evaluated the performance of the FGQC Z and X gates in the presence of both decoherence and a certain kind of systematic control error. The gate fidelities of the Z and X gates are $F_{X,\text{gate}}\approx F_{Z,\text{gate}}\approx 0.9992$. The numerical results provide evidence that FGQC gates can achieve fairly high gate fidelities even in the presence of noise and control imperfection. In addition, we found FGQC is robust against global control error, both analytical demonstration and numerical evidence were given. Consequently, this study makes an important step towards robust geometric quantum computation.Comment: 12 pages,7 figure

Simulation of Riding a Full Suspension Bicycle for Analyzing Comfort and Pedaling Force

AbstractRecently, there is an increasing interest on bicycle riding for recreation or fitness purpose. Bicycles are also accepted as urban transportation due to the consciousness of environmental protection. For a more comfortable riding experience, many bicycles are equipped with a suspension system including a front suspension fork and/or rear suspension. However, when a suspension system is added to a bicycle, it makes riding a little heavier since suspension dissipates some pedalling energy. This paper discusses front and rear suspensions corresponding to rider comfort and pedalling effort when riding on a rough road and smooth road. A human body computer model LifeMOD® is employed to model the cyclist. Dynamic analysis software ADAMS® is employed to analyze human body vibration and leg muscle forces of bicycle riding. Human body acceleration vs. vibration frequencies are used as the comfort criteria. The results show that a suspension system may effectively reduce high frequency vibration of the human body when riding on a rough road. Pedalling forces are mostly contributed by the biceps femoris and semitendinosus. The suspension system would increase the pedaling forces of femoris and semitendinosus. Other leg muscles have a minor effect on pedaling forces. Results obtained from this research are useful for the design of bicycle suspension systems with better comfort and less loss of pedalling efficiency

Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline

Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image is challenging due to missing details in under-/over-exposed regions caused by quantization and saturation of camera sensors. In contrast to existing learning-based methods, our core idea is to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We then propose to learn three specialized CNNs to reverse these steps. By decomposing the problem into specific sub-tasks, we impose effective physical constraints to facilitate the training of individual sub-networks. Finally, we jointly fine-tune the entire model end-to-end to reduce error accumulation. With extensive quantitative and qualitative experiments on diverse image datasets, we demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.Comment: CVPR 2020. Project page: https://www.cmlab.csie.ntu.edu.tw/~yulunliu/SingleHDR Code: https://github.com/alex04072000/SingleHD