Recognition of Bio-Structural Anisotropy by Polarization Resolved Imaging

In this paper, we develop a simple technique to identify material texture from far, by using polarization-resolved imaging. Such a technique can be easily implemented into industrial environments, where fast and cheap sensors are required. The technique has been applied to both isotropic references (Teflon bar) and anisotropic samples (wood). By studying the radiance of the samples illuminated by linearly polarized light, different and specific behaviours are identified for both isotropic and anisotropic samples, in terms of multipolar emission and linear dichroism, from which fibre orientation can be resolved.

A Multi-Curvature, Variable Stiffness Soft Gripper for Enhanced Grasping Operations

For soft grippers to be applied in atypical industrial environments, they must conform to an object’s exterior shape and momentarily change their stiffness. However, many of the existing grippers have limitations with respect to these functions: they grasp an object with only a single curvature and a fixed stiffness. Consequently, those constraints limit the stability of grasping and the applications. This paper introduces a new multicurvature, variable-stiffness soft gripper. Inspired by the human phalanx and combining the phalanx structure and particle jamming, this work guarantees the required grasping functions. Unlike the existing soft pneumatic grippers with one curvature and one stiffness, this work tries to divide the pressurized actuating region into three parts to generate multiple curvatures for a gripper finger, enabling the gripper to increase its degrees of freedom. Furthermore, to prevent stiffness loss at an unpressurized segment, this work combines divided actuation and the variable-stiffness capability, which guarantee successful grasping actions. In summary, this gripper generates multiple grasping curvatures with the proper stiffness, enhancing its dexterity. This work introduces the new soft gripper’s design, analytical modeling, and fabrication method and verifies the analytic model by comparing it with FEM simulations and experimental results.

An Improved Nesting Algorithm for Irregular Patterns

In industrial environments, nesting consists in cutting or extracting pieces from a material sheet, with the purpose of minimizing the surface of the sheet used. This problem is present in different types of industries, such as shipping, aeronautics, woodworking, footwear, and so on. In this work, the aim is to find an acceptable solution to solve complex nesting problems. The research developed is oriented to sacrifice accuracy for speed so as to obtain robust solutions in less computational time. To achieve this, a greedy method and a genetic algorithm have been implemented, being the latter responsible for generating a sequence for the placement of the pieces, where each piece is placed in its current optimal position with the help of a representation system for both the pieces and the material sheet.

Quantum-Inspired Recommendation System with Threshold Proportion Interception

Modern recommendation systems leverage historical behavior information to generate precise recommendation results for users. However, when the data scale of users and items is large, it is difficult to generate recommendation results in time. Tang proposed a quantum-inspired recommendation algorithm, which could solve the recommendation problem in constant time complexity. However, Tang’s approach is based on a set of assumptions which rely heavily on some empirical parameters. The time complexity for calculating parameters is high. Thus, this approach cannot be directly applied in industrial applications. In this paper, we propose a method, namely, Quantum-inspired Recommendation system with threshold Proportion Interception (QRPI), which is based on the quantum-inspired recommendation system and more suitable for industrial environments. Compared with the existing widely used recommendation algorithms, we show through numerical experiments that our solution can achieve almost the same performance with better efficiency.