A method of constructing an inspiration library driven by user-perceived preference evaluation data for biologically inspired design

Biologically inspired design (BID) is one of the common methods for product design. To solve the problem of inaccurate acquisition of inspirational creatures due to the lack of user perception preference analysis, a data-driven intelligent service model for BID considering user perception needs is proposed based on Kansei engineering. Firstly, by extracting the perceptual features of creatures from the semantic source elements of products through mapping and encodes them, we proposed a data acquisition method based on intuitionistic fuzzy sets considering different customer preference distributions, bridging the gap caused by the asymmetry between designers and users. Secondly, the functional relationship between biometric features and user-perceived attributes is identified and predicted, and a predictive model of biodata considering user preferences is obtained by multiple linear regression analysis. Finally, based on the data clustering and reorganization theory to understand the organization and dynamics of the database, the construction of a BID library was completed, and the design resources in the library were used as analyzed knowledge for designers to plan design activities. Taking the bionic design of a UAV product as an example, a prototype of a computer-aided design service system was developed based on the theory proposed in the article, and the analyzed knowledge was used to improve the efficiency and science of the design, effectively verifying the usefulness of this study for design. To a certain extent, this study addresses the problem of cognitive limitations of designers and cognitive differences between designers and users, promotes the application of bioinspiration in product design, and improves the marketability of design solutions.     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.     

Biologically Inspired Design of Context-Aware Smart Products

The rapid development of information and communication technologies (ICTs) and cyber–physical systems (CPSs) has paved the way for the increasing popularity of smart products. Context-awareness is an important facet of product smartness. Unlike artifacts, various bio-systems are naturally characterized by their extraordinary context-awareness. Biologically inspired design (BID) is one of the most commonly employed design strategies. However, few studies have examined the BID of context-aware smart products to date. This paper presents a structured design framework to support the BID of context-aware smart products. The meaning of context-awareness is defined from the perspective of product design. The framework is developed based on the theoretical foundations of the situated function–behavior–structure ontology. A structured design process is prescribed to leverage various biological inspirations in order to support different conceptual design activities, such as problem formulation, structure reformulation, behavior reformulation, and function reformulation. Some existing design methods and emerging design tools are incorporated into the framework. A case study is presented to showcase how this framework can be followed to redesign a robot vacuum cleaner and make it more context-aware.     

Nature inspired 21 branches sneezewort/Achillea ptarmica plant growth pattern–shaped antenna for Ku‐band applications

This reported paper presents the design and analysis of wideband nature inspired planar antenna of 46 × 18 × 1.6 mm³ size, designed using FR4 (Flame Retardant) substrate. The antenna patch is delineated from the growth pattern of sneezewort plant with supplementary notion of golden ratio in branching and branch width. The total number of branches in each stage follows the Fibonacci series and the branch width is calculated from the golden ratio concept. Starting from one feed line and two branches, the maximum numbers of branches are increased to 20. There has been significant enhancement in the radiation performance of proposed geometry by increasing the number of branches to 21 and by modifying ground with T‐shaped slot. The antenna covers wide impedance bandwidth of 8.2 to 16.5 GHz by possessing stable radiation characteristics.     

Enhanced grouping league championship and optics inspired optimization algorithms for scheduling a batch processing machine with job conflicts and non-identical job sizes

This research aims to address scheduling of a single batch processing machine, where jobs are in different sizes and have a conflicting nature with each other, in the sense that two conflicting jobs cannot share the same batch and hence, cannot be processed simultaneously by the machine. The problem is referred to as SBMC. After formulating the problem, a strong lower bound procedure is developed by transforming a relaxed version of the scheduling problem to the multiple bottleneck transportation problem (MTP) with conflicts. An efficient Lagrangian relaxation procedure is proposed, which takes advantage of decomposable nature of the relaxed problem, to attain a lower bound for MTP which in essence is a lower bound for SBMC. To solve the problem, two metaheuristic algorithms, namely the League Championship Algorithm (LCA) and Optic Inspired Optimization (OIO), are adapted and fundamentally modified to match the problem unique structure (i.e., the grouping structure) and accordingly the grouping version of these algorithms is developed. The effectiveness of the lower bound procedure, as well as algorithms, are evaluated through extensive computational experiments. To show they perform efficiently in running times and effective in finding near-optimal bounds for most of the problem instances, we generate 20 different classes of problems according to variability in job sizes, number of jobs and density of incompatibility matrix. Then, we make comparison with two of extensively used algorithms from literature, SA and GA. Our proposed algorithms obtain solutions with objective values less than 6% gap from the lower bound and outperform SA and GA algorithms, especially for large instances. Moreover, values of the lower bound procedure lie within less than 4% of objective values provided by CPLEX.     

Structural design of carbon/epoxy bio‐inspired wind turbine blade using fluid/structure simulation

The purpose of this paper is to present the structural design procedure of a low‐speed, horizontal axis, bio‐inspired wind turbine blade made of carbon/epoxy. The methodology initiates with the mechanical characterization of the carbon fiber composite material. An aerodynamic simulation using Computational Fluid Dynamics (CFD) method is performed in order to obtain the pressure distribution profile of the blade. This result is coupled with a Finite Element Analysis (FEA) to carry out an iterative design process through a Fluid‐Structure Interaction (FSI) simulation. Different stacking sequences of laminates are evaluated to find a configuration which allows balance between aerodynamic and dynamic inertial loads, ensuring an almost undeformed geometry during wind turbine's operation. The final structural design of the blade consists in six regions with different laminates. These are balanced and symmetric with distinct thickness characteristics and stacking sequences, which vary in three different orientations: 0^°, ± 45^° and 90^°, achieving a minimum deflection at the tip close to 3.11 cm, and a total weight of 3.6 kg of a 1.8 m radius blade, even with the restrictions imposed by the non‐conventional geometry. Copyright © 2016 John Wiley & Sons, Ltd.     

Physics inspired algorithms for (co)homology computations of three-dimensional combinatorial manifolds with boundary

The issue of computing (co)homology generators of a cell complex is gaining a pivotal role in various branches of science. While this issue may be rigorously solved in polynomial time, it is still overly demanding for large scale problems. Drawing inspiration from low-frequency electrodynamics, this paper presents a physics inspired algorithm for first cohomology group computations on three-dimensional complexes. The algorithm is general and exhibits orders of magnitude speed up with respect to competing ones, allowing to handle problems not addressable before. In particular, when generators are employed in the physical modeling of magneto-quasistatic problems, this algorithm solves one of the most long-lasting problems in low-frequency computational electromagnetics. In this case, the effectiveness of the algorithm and its ease of implementation may be even improved by introducing the novel concept of lazy cohomology generators.     

1-氨基环丙烷-1-羧酸的合成方法及其应用

1_氨基环丙烷-1-羧酸是一种非蛋白氨基酸,具有独特的生物活性。本文概述了1一氨基环丙烷一卜羧酸的主要合成方法及其应用。