A piezoelectric model based multi-objective optimization of robot gripper design

The field of robotics is evolving at a very high pace and with its increasing applicability in varied fields, the need to incorporate optimization analysis in robot system design is becoming more prominent. The present work deals with the optimization of the design of a 7-link gripper. As actuators play a crucial role in functioning of the gripper, the actuation system (piezoelectric (PZ), in this case) is also taken into consideration while performing the optimization study. A minimalistic model of PZ actuator, consisting different series and parallel assembly arrangements for both mechanical and electrical parts of the PZ actuators, is proposed. To include the effects of connector spring, the relationship of force with actuator displacement is replaced by the relation between force and the displacement of point of actuation at the physical system. The design optimization problem of the gripper is a non-linear, multi modal optimization problem, which was originally formulated by Osyczka (2002). In the original work, however, the actuator was a ‘constant output-force actuator model’ providing a constant output without describing the internal structure. Thus, the actuator model was not integrated in the optimization study. Four different cases of the PZ modelling have been solved using multi-objective evolutionary algorithm (MOEA). Relationship between force and actuator displacement is obtained using each set of non-dominated solutions. These relationships can provide a better insight to the end user to select the appropriate voltage and gripper design for specific application.     

Load and directional effects on microhardness and estimation of toughness and brittleness for flux-grown LaBO3 crystals

Results of microhardness measurements on (100) and (110) planes of flux-grown LaBO₃ crystals, in the applied load range of 10–100g, are presented. The microhardness was found to decrease with increasing load in a non-linear manner. By applying Hays and Kendall's law, the materials resistance pressure and other constants of the equation could be calculated. Hardness anisotropy, showing periodic variation of H _v with the maxima and minima repeating at every 15° change in orientation of the indentor, is described and discussed. H _max/H_min are estimated as 1.14 and 1.06 for (100) and (110) planes, respectively. The fracture toughness values, K _c, determined from measurements of crack lengths, are estimated to be 1.6, 1.7 MN m^–3/2 (for (100) planes) and 1.2, 1.5 MN m^–3/2 (for (110) planes) at 90 and 100g loads, respectively. The brittleness index, B _i, is estimated as 4.6, 4.0 m^–1/2 (for (100) planes) 6.0, 4.6 m^–1/2 (for (110) planes) at 90 and 100g, loads respectively.     

Prudence in vacillatory language identification

This paper settles a question about prudent vacillatory identification of languages. Consider a scenario in which an algorithmic deviceM is presented with all and only the elements of a languageL, andM conjectures a sequence, possibly infinite, of grammars. Three different criteria for success ofM onL have been extensively investigated in formal language learning theory. IfM converges to a single correct grammar forL, then the criterion of success is Gold's seminal notion ofTxtEx-identification. IfM converges to a finite number of correct grammars forL, then the criterion of success is calledTxtFex-identification. Further, ifM, after a finite number of incorrect guesses, outputs only correct grammars forL (possibly infinitely many distinct grammars), then the criterion of success is known asTxtBc-identification. A learning machine is said to beprudent according to a particular criterion of success just in case the only grammars it ever conjectures are for languages that it can learn according to that criterion. This notion was introduced by Osherson, Stob, and Weinstein with a view to investigating certain proposals for characterizing natural languages in linguistic theory. Fulk showed that prudence does not restrictTxtEx-identification, and later Kurtz and Royer showed that prudence does not restrictTxtBc-identification. This paper shows that prudence does not restrictTxtFex-identification.     

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Journal of Signal Processing Systems - Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful...     

Mechanical and Thermophysical Properties of Cerium Monopnictides

The ultrasonic attenuation due to phonon–phonon interaction, thermoelastic relaxation and dislocation damping mechanisms has been investigated in cerium monopnictides CeX (X: N, P, As, Sb and Bi) for longitudinal and shear waves along \({\langle }100{\rangle }\), \({\langle }110{\rangle }\) and \({\langle }111{\rangle }\) directions. The second- and third-order elastic constants of CeX have also been computed in the temperature range 0 K to 500 K using Coulomb and Born–Mayer potential upto second nearest neighbours. The computed values of these elastic constants have been applied to find out Young’s moduli, bulk moduli, Breazeale’s non-linearity parameters, Zener anisotropy, ultrasonic velocity, ultrasonic Grüneisen parameter, thermal relaxation time, acoustic coupling constants and ultrasonic attenuation. The fracture/toughness ratio is less than 1.75, which shows that the chosen materials are brittle in nature as found for other monopnictides. The drag coefficient acting on the motion of screw and edge dislocations due to shear and compressional phonon viscosities of the lattice have also been evaluated for both the longitudinal and shear waves. The thermoelastic loss and dislocation damping loss are negligible in comparison to loss due to Akhieser damping (phonon–phonon interaction). The obtained results for CeX are in qualitative agreement with other semi-metallic monopnictides.     

Machine Learning-Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high-quality datasets at appropriate volumes. This article extends the suitability of such ML-enabled approaches to a new technological domain of EHD spraying and drop-on-demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree-based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.     

Physiatric research fellows'' perceptions of the quality of their training

After being poisoned by eating the mushroom species Cortinarius speciosissimus, a twin developed interstitial nephritis with acute renal failure. He received a renal transplant from his living twin brother, who was presumed dizygotic on phenotypic grounds. Fifteen years later, the twins were zygosity tested by DNA "fingerprint analysis" and found to be monozygotic, despite important phenotypic discordances. The recipient has discontinued immunosuppression therapy and remains well after 9 months. We suggest that, for medical and other reasons, zygosity should be determined at birth on all like-sexed twins.     

PVC-Based 2,2,2-Cryptand Sensor for Zinc Ions

A PVC-based membrane of 2,2,2-cryptand exhibits a very good response for Zn(2+) in a wide concentration range (from 2.06 ppm to 6.54 × 10(3) ppm) with a slope of 22.0 mV/decade of Zn(2+) concentration. The response time of the sensor is <10 s, and the membrane can be used for more than 3 months without any observed divergence in potentials. The proposed sensor exhibits very good selectivity for Zn(2+) over other cations and can be used in a wide pH range (2.8-7.0). It has also been possible to use this assembly as an indicator electrode in potentiometric titrations involving zinc ions.     

Chaos in fractional ordered Liu system

Present paper deals with fractional version of a dynamical system introduced by C. Liu, L. Liu and T. Liu [C. Liu, L. Liu, T. Liu, A novel three-dimensional autonomous chaos system, Chaos Solitons Fractals 39 (4) (2009) 1950–1958]. Numerical investigations on the dynamics of this system have been carried out. Properties of the system have been analyzed by means of Lyapunov exponents. Furthermore the minimum effective dimensions have been identified for chaos to exist in commensurate and incommensurate orders. It is noteworthy that the results obtained are consistent with the analytical conditions given in the literature.