Error compensation based on surface reconstruction for industrial robot on two-dimensional manifold

Purpose This paper aims to present error compensation based on surface reconstruction to improve the positioning accuracy of industrial robots. Design/methodology/approach In previous research, it has been proved that the positioning error of industrial robots is continuous on the two-dimensional manifold of six-joint space. The point cloud generated by positioning error data can be used to fit the continuous surfaces, which makes it possible to apply surface reconstruction on error compensation. The moving least-squares interpolation and the B-spline method are used for the error surface reconstruction. Findings The results of experiments and simulations validate the effectiveness of error compensation by the moving least-squares interpolation and the B-spline method. Practical implications The proposed methods can control the average of compensated positioning error within 0.2 mm, which meets the requirement of a tolerance (±0.5 mm) for fastener hole drilling in aircraft assembly. Originality/value The error surface reconstruction based on the B-spline method has great superiority because fewer sample points are needed to use this method than others while keeping the compensation accuracy at the same level. The control points of the B-spline error surface can be adjusted with measured data, which can be applied for the error prediction in any temperature field.

Survey on Mitigating the Problems of Parabolic Reflectors for Efficient Communication

In this paper, various problems associated with parabolic reflectors, its causes and the approach to mitigate these problems are discussed. The problems include; side lobe radiations, edge diffraction, aperture blockage, cross polarisation, feed spill over, feed illumination taper, pointing error, surface error and phase error. These problems have adverse effect on the overall gain, efficiency and directivity of the antenna thereby inhibiting efficient communication process. The result of the survey reveals that, phase error tends to be the most difficult of the aforementioned problems due to the challenges associated with locating the phase centre at reflector’s focus. The aperture blockage seems to have the least method of solution, because the problem can be solved by changing the centre feed to an offset feed. Detailed investigation of these problems and the relevant solutions are necessary, since parabolic reflectors are among the most common antennas with diverse application.

On the Complementarity of Sparse L0 and CEL0 Regularized Loss Landscapes for DOA Estimation

L0 sparse methods are not widespread in [AD]DOADirection-Of-Arrival (DOA) estimation yet, [AD]althoughdespite their potential superiority over classical methods in difficult scenarios. This comes from the difficulties encountered for [AD]theglobal optimization on hill-climbing error surfaces. In this paper, we explore the loss landscapes of L0 and [AD]CEL0Continuous Exact L0 (CEL0) regularized problems in order to design a new optimization scheme. As expected, we observe that the recently introduced CEL0 penalty leads to an error surface with less local minima than the L0 one. This property explains the good behavior of [AD]the CEL0-regularized sparse DOA estimation problem for well-separated sources. Unfortunately, CEL0-regularized landscape enlarges L0-basins in the middle of close sources, and CEL0 methods are thus unable to resolve two close sources. Consequently, we propose to alternate between both error surfaces to increase the probability of reaching the global solution. Experiments show that the proposed approach offers better performance than existing ones, and particularly an enhanced resolution limit.

Investigating the Efficacy of Adhesive Tape for Drilling Carbon Fibre Reinforced Polymers

In the present research work, an effort has been made to explore the potential of using the adhesive tapes while drilling CFRPs. The input parameters, such as drill bit diameter, point angle, Scotch tape layers, spindle speed, and feed rate have been studied in response to thrust force, torque, circularity, diameter error, surface roughness, and delamination occurring during drilling. It has been found that the increase in point angle increased the delamination, while increase in Scotch tape layers reduced delamination. The surface roughness decreased with the increase in drill diameter and point angle, while it increased with the speed, feed rate, and tape layer. The best low roughness was obtained at 6 mm diameter, 130° point angle, 0.11 mm/rev feed rate, and 2250 rpm speed at three layers of Scotch tape. The circularity error initially increased with drill bit diameter and point angle, but then decreased sharply with further increase in the drill bit diameter. Further, the circularity error has non-linear behavior with the speed, feed rate, and tape layer. Low circularity error has been obtained at 4 mm diameter, 118° point angle, 0.1 mm/rev feed rate, and 2500 RPM speed at three layers of Scotch tape. The low diameter error has been obtained at 6 mm diameter, 130° point angle, 0.12 mm/rev feed rate, and 2500 rpm speed at three layer Scotch tape. From the optical micro-graphs of drilled holes, it has been found that the point angle is one of the most effective process parameters that significantly affects the delamination mechanism, followed by Scotch tape layers as compared to other parameters such as drill bit diameter, spindle speed, and feed rate.

Layerwise learning for quantum neural networks

With the increased focus on quantum circuit learning for near-term applications on quantum devices, in conjunction with unique challenges presented by cost function landscapes of parametrized quantum circuits, strategies for effective training are becoming increasingly important. In order to ameliorate some of these challenges, we investigate a layerwise learning strategy for parametrized quantum circuits. The circuit depth is incrementally grown during optimization, and only subsets of parameters are updated in each training step. We show that when considering sampling noise, this strategy can help avoid the problem of barren plateaus of the error surface due to the low depth of circuits, low number of parameters trained in one step, and larger magnitude of gradients compared to training the full circuit. These properties make our algorithm preferable for execution on noisy intermediate-scale quantum devices. We demonstrate our approach on an image-classification task on handwritten digits, and show that layerwise learning attains an 8% lower generalization error on average in comparison to standard learning schemes for training quantum circuits of the same size. Additionally, the percentage of runs that reach lower test errors is up to 40% larger compared to training the full circuit, which is susceptible to creeping onto a plateau during training.

Optimized LMS algorithm for system identification and noise cancellation

Optimization by definition is the action of making most effective or the best use of a resource or situation and that is required almost in every field of engineering. In this work, the optimization of Least Mean square (LMS) algorithm is carried out with the help of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Efforts have been made to find out the advantages and disadvantages of combining gradient based (LMS) algorithm with Swarm Intelligence SI (ACO, PSO). This optimization of LMS algorithm will help us in further extending the uses of adaptive filtering to the system having multi-model error surface that is still a gray area of adaptive filtering. Because the available version of LMS algorithm that plays an important role in adaptive filtering is a gradient based algorithm, that get stuck at the local minima of system with multi-model error surface considering it global minima, resulting in an non-optimized convergence. By virtue of the proposed method we have got a profound solution for the problem associated with system with multimodal error surface. The results depict significant improvements in the performance and displayed fast convergence rate, rather stucking at local minima. Both the SI techniques displayed their own advantage and can be separately combined with LMS algorithm for adaptive filtering. This optimization of LMS algorithm will further help to resolve serious interference and noise issues and holds a very important application in the field of biomedical science.

The Contributions of Gauge-Based Precipitation and SMAP Brightness Temperature Observations to the Skill of the SMAP Level-4 Soil Moisture Product

Soil Moisture Active Passive (SMAP) mission L-band brightness temperature (Tb) observations are routinely assimilated into the Catchment land surface model to generate Level-4 Soil Moisture (L4_SM) estimates of global surface and root-zone soil moisture at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment model in the L4_SM algorithm is driven with ¼-degree, hourly surface meteorological forcing data from the Goddard Earth Observing System (GEOS). Outside of Africa and the high latitudes, GEOS precipitation is corrected using Climate Prediction Center Unified (CPCU) gauge-based, ½-degree, daily precipitation. L4_SM soil moisture was previously shown to improve over land model-only estimates that use CPCU precipitation but no Tb assimilation (CPCU_SIM). Here, we additionally examine the skill of model-only (CTRL) and Tb assimilation-only (SMAP_DA) estimates derived without CPCU precipitation. Soil moisture is assessed versus in situ measurements in well-instrumented regions and globally through the Instrumental Variable (IV) method using independent soil moisture retrievals from the Advanced Scatterometer. At the in situ locations, SMAP_DA and CPCU_SIM have comparable soil moisture skill improvements relative to CTRL for the unbiased root-mean-square error (surface and root-zone) and correlation metrics (root-zone only). In the global average, SMAP Tb assimilation increases the surface soil moisture anomaly correlation by 0.10-0.11 compared to an increase of 0.02-0.03 from the CPCU-based precipitation corrections. The contrast is particularly strong in central Australia, where CPCU is known to have errors and observation-minus-forecast Tb residuals are larger when CPCU precipitation is used. Validation versus streamflow measurements in the contiguous U.S. reveals that CPCU precipitation provides most of the skill gained in L4_SM runoff estimates over CTRL.