The Mixed-Body Model: A Method for Predicting Large Deflections in Stepped Cantilever Beams

This paper presents a method for predicting endpoint coordinates, stress, and force to deflect stepped cantilever beams under large deflections. This method, the Mixed-Body Model or MBM, combines small deflection theory and the Pseudo-Rigid-Body Model for large deflections. To analyze the efficacy of the model, the MBM is compared to a model that assumes the first step in the beam to be rigid, to finite element analysis, and to the numerical boundary value solution over a large sample set of loading conditions, geometries, and material properties. The model was also compared to physical prototypes. In all cases, the MBM agrees well with expected values. Optimization of the MBM parameters yielded increased agreement, leading to average errors of <0.01 to 3%. The model provides a simple, quick solution with minimal error that can be particularly helpful in design.

Simulation and Implementation PID Controlling Buck Converter DC

Regulating the output voltage based on the desired set point is useful for many applications. However, getting the optimal value using fast computation with minimal error is still challenging. This paper aims to design, simulate, and implement a second-order Buck-Boost DC-DC converter circuit so that the voltage result according to the desired set point can be achieved. Initially, testing is conducted using Matlab Simulink. Then, Proteus is used to test the computation of the program on embedded systems in which the result is implemented in C. In low voltage power electronics applications, this approach has never been used to determine the output form. To determine the value of Kp, Ki, dan Kd, PID, Ziger Nichos (Guo, 2002). method is used. Meanwhile, tuning is done through Matlab. For simulation on Proteus, the output is tested by setting the setpoint values of 3.0, 2.5, and 1.7 volts. This aims to see the pattern of changes in the simulation. The simulation results with Proteus show that they have similar peak values but with different overshoot values. This is because the simulation must pass the reference voltage before it drops to the desired setpoint value. Proteus simulation can also help to prove embedded system programs are running correctly. On the other hand, the value of 1.7 volts is used as a setpoint in device implementation. This is due to the determination that the setpoint voltage in the implementation does not exceed the value of the source/power supply. The results show that for the rise time value of 378,770 ms, Overshoot and settling time are 11.798% and 0, respectively. This means the result produces an optimal value which is a return to the initial target. The optimal factor is assessed from the ability to minimize existing errors as well as having the shortest possible computational process.

Novel surface optimization for trajectory reconstruction in industrial robot tasks

This study presents a novel algorithm implementation that optimizes manually recorded toolpaths with the use of a 3D-workpiece model to reduce manual error induced. The novel algorithm has three steps: workpiece declaration, manual toolpath declaration, and toolpath optimization using steepest descent algorithm. Steepest descent finds the surface route wherein the manually recorded toolpaths traverse over a 3D-workpiece surface. The optimized toolpaths were simulated and tested with an industrial robot showing minimal error compared to the desired optimized toolpaths. The results obtained from the presented implementation on three different trajectories demonstrate that the proposed methodology can reduce the manual error induced using as a reference the CAD-workpiece surface.

OnePetri: accelerating common bacteriophage Petri dish assays with computer vision

IntroductionBacteriophage plaque enumeration is a critical step in a wide array of protocols. The current gold standard for plaque enumeration on Petri dishes is through manual counting. This approach is time-intensive, has low-throughput, is limited to Petri dishes which have a countable number of plaques, and can have variable results upon recount due to human error.MethodsWe present OnePetri, a collection of trained machine learning models and open-source mobile application for the rapid enumeration of bacteriophage plaques on circular Petri dishes.ResultsWhen compared against the current gold standard of manual counting, OnePetri was significantly faster, with minimal error. Compared against two other similar tools, Plaque Size Tool and CFU.AI, OnePetri had higher plaque recall and reduced detection times on most test images.ConclusionsThe OnePetri application can rapidly enumerate phage plaques on circular Petri dishes with high precision and recall.

An improved minimal error model for the robotic kinematic calibration based on the POE formula

Summary The conventional product of exponentials $\left(\rm POE\right)$ -based methods dissatisfy the parametric minimality for the kinematic calibration of serial robots due to overlooking the magnitude and pitch constraints. Thus, the minimal kinematic model is presented to solve this problem, which can be developed further. This paper puts forward an improved algorithm for the minimal parameter calibration. An actual kinematic model with the minimal parameters $\left(\rm MP\right)$ is constructed according to the geometric properties of actual joint twists in the auxiliary frames established on the basis of the nominal joint axes. Then, the initial pose error is defined in the tool coordinate frame, which is expressed as the exponential map of the twist, and all twist descriptions are unified, so as to give a unified kinematic model in mathematics. By differentiating the kinematic model, a minimal error model is derived in explicit form. Subsequently, we propose a novel parameter identification method, which identifies the orientation error and position error parameters separately by the iterative least-squares method and updates the MP uniformly. Finally, the simulations and experiments on the different serial robots are conducted to verify the correctness and effectiveness of the proposed algorithm. The simulation results show our calibration algorithm outperforms the existing ones in the accuracy aspect, and the experiment result shows that the absolute pose accuracy of the UR5 industrial robot is upgraded about 9 times under a statistics sense after the calibration.

The Mixed-Body Model: A Method for Predicting Large Deflections in Stepped Cantilever Beams

This paper presents a method for predicting endpoint coordinates, stress, and force to deflect stepped cantilever beams under large deflections. This method, the Mixed-Body Model or MBM, combines small deflection theory and the Pseudo-Rigid-Body Model for large deflections. To analyze the efficacy of the model, the MBM is compared to a model that assumes the first step in the beam to be rigid, to finite element analysis, and to the numerical boundary value solution over a large sample set of loading conditions, geometries, and material properties. The model was also compared to physical prototypes. In all cases, the MBM agrees well with expected values. Optimization of the MBM parameters yielded increased agreement, leading to average errors of < 0.01 to 3%. The model provides a simple, quick solution with minimal error that can be particularly helpful in design.

LOSS FUNCTIONS AND DESCENT METHOD

In this paper, we showed that it is possible to use gradient descent method to get minimal error values of loss functions close to their Bayesian estimators. We calculated Bayesian estimators mathematically for different loss functions and tested them using gradient descent algorithm. This algorithm, working on Normal and Poisson distributions showed that it is possible to find minimal error values without having Bayesian estimators. Using Python, we tested the theory on loss functions with known Bayesian estimators as well as another loss functions, getting results proving the theory.

Constraints for Time-Multiplexed Structured Light with a Hand-held Camera

Multi-frame structured light in projector-camera systems affords high-density and non-contact methods of 3D surface reconstruction. However, they have strict setup constraints which can become expensive and time-consuming. Here, we investigate the conditions under which a projective homography can be used to compensate for small perturbations in pose caused by a hand-held camera. We synthesize data using a pinhole camera model and use it to determine the average 2D reprojection error per point correspondence. This error map is grouped into regions with specified upper-bounds to classify which regions produce sufficiently minimal error to be considered feasible for a structured-light projector-camera system with a hand-held camera. Empirical results demonstrate that a sub-pixel reprojection accuracy is achievable with a feasible geometric constraints