Hybrid moment/position control of a parallel robot

In this paper, a hybrid moment/position controller in task space is proposed for tasks involving a contact between a robot and its environment. We consider a contour-tracking task performed by a six DOF (Degrees Of Freedom) parallel robot. The task space dynamic model of the robot in contact with its environment, seen as a black box, is estimated by a MLP-NN (MultiLayer Perceptron Neural Network). The neural network non-linearity is treated using Taylor series expansion. An adaptation algorithm of the neural parameters resulting from a closed-loop stability analysis is proposed. The performance of the proposed controller is validated on the C5 parallel robot by considering two different environments: rigid and compliant.     

Filtering Algorithms for the NValue Constraint

The NValue constraint counts the number of different values assigned to a vector of variables. Propagating generalized arc consistency on this constraint is NP-hard. We show that computing even the lower bound on the number of values is NP-hard. We therefore study different approximation heuristics for this problem. We introduce three new methods for computing a lower bound on the number of values. The first two are based on the maximum independent set problem and are incomparable to a previous approach based on intervals. The last method is a linear relaxation of the problem. This gives a tighter lower bound than all other methods, but at a greater asymptotic cost.     

Electrolytic equipment for reductions in liquid ammonia

We have developed static and circulating electrolytic equipment for liquid ammonia (or low molecular weight amines) to perform reductions of compounds bearing at least one reducible group. The equipment contains undivided electrochemical cells including consumable magnesium anodes. Birch and Bouveault-Blanc reductions are performed with substrate weight percents up to 10%. A circulating electrolytic cell working at room temperature is described which may be adaptable to large scale production.     

Hierarchical decomposition and optimization of thermal transformer performances

An optimization under constraints of heat transformer systems is presented. The hierarchical decomposition and endoreversibility principle are integrated to avoid the great number of variables in the analyzed optimization problem. The analysis method is applied on a solar absorption refrigerator in the purpose to define the optimum design parameters. The decomposition levels of an equivalent model are defined. The equation system governing the behavior of the chosen model is established using Lagrange method for optimization under constraints. These constraints are defined according to thermodynamic laws. The couplings between the optimal functional and conceptual parameters are defined. The temperature and pinch distributions through heat exchangers are determined. The performance limits of an endoreversible cycle are defined for different values of the cooling load. The contribution of the different component heat transfer areas and the times of transfer are deducted according to technical an economic analysis.     

Residual Tensile Properties of GFRP Reinforcing Bars after Loading in Severe Environments

The long-term behavior of glass fiber-reinforced polymer (GFRP) reinforcing bars is one of the most critical issues for the acceptance of these materials as reinforcement for concrete structures. There is a high demand for experimental studies to investigate the stability of the tensile strength, ultimate elongation, and elasticity modulus. GFRP reinforcing bars inherently have a low elasticity modulus, which must not significantly decrease over time under loading or the serviceability behavior of the concrete element containing them will be jeopardized. This paper evaluates the residual tensile properties of three sizes of sand-coated GFRP reinforcing bars in alkaline and water environments combined with sustained loading and elevated temperature. Bar diameters of 15.9 (No. 5), 12.7 (No. 4), and 9.5?mm (No. 3) were loaded for different durations, then tested in axial tension for residual tensile properties. The test periods varied from 1?to?4?months under elevated temperature to hasten degradation and simulate extended service periods. The reduction in tensile strength was found to be 7–13% of the guaranteed strength for the three bar sizes under elevated temperature, which is at least 26% higher than the specified design strength as recommended by ACI 440.1R-03. More importantly, no significant change in the elastic modulus was observed.     

Multiobjective Optimization of Earthmoving Operations

This paper presents a framework for optimizing earthmoving operations using computer simulation and genetic algorithms. It provides a multiobjective optimization tool geared towards selection of near-optimum fleet configurations. The optimization aims at minimizing time and cost of earthmoving operations. The proposed framework considers factors that influence earthmoving operations including equipment availability and project indirect cost. The simulation process, in the proposed methodology, utilizes discrete event simulation and object oriented modeling. The optimization process uses a recently developed genetic algorithm to search for a near-optimum fleet configuration employing Pareto optimality to account for multiobjective optimization. The algorithm considers a set of qualitative and quantitative variables that influence the production of earthmoving operations. The developed framework supports time–cost tradeoff analysis and can assist users in considering what if scenarios with respect to fleet configurations. A numerical example is presented to illustrate a number of practical features of the proposed framework and to demonstrate its capabilities in selecting near-optimum fleet configurations.     

Photovoltaics with Ferroelectrics: Current Status and Beyond

Ferroelectrics carry a switchable spontaneous electric polarization. This polarization is usually coupled to strain, making ferroelectrics good piezoelectrics. When coupled to magnetism, they become so‐called multiferroic systems, a field that has been widely investigated since 2003. While ferroelectrics are birefringent and non‐linear optically transparent materials, the coupling of polarization with optical properties has received, since 2009, renewed attention, triggered notably by low‐bandgap ferroelectrics suitable for sunlight spectrum absorption and original photovoltaic effects. Consequently, power conversion efficiencies up to 8.1% were recently achieved and values of 19.5% were predicted, making photoferroelectrics promising photovoltaic alternatives. This article aims at providing an up‐to‐date review on this emerging and rapidly progressing field by highlighting several important issues and parameters, such as the role of domain walls, ways to tune the bandgap, consequences arising from the polarization switchability, and the role of defects and contact electrodes, as well as the downscaling effects. Beyond photovoltaicity, other polarization‐related processes are also described, like light‐induced deformation (photostriction) or light‐assisted chemical reaction (photostriction). It is hoped that this overview will encourage further avenues to be explored and challenged and, as a byproduct, will inspire other research communities in material science, e.g., so‐called hybrid halide perovskites.     

First-Principles Prediction of Structural,Magnetic, Electronic,and Elastic Properties of Full-Heusler Compounds Co<Subscript>2</Subscript>YIn (Y = Ti,V)

Density functional theory based on the full-potential linearized augmented plane wave (FP-LAPW) method is used to investigate the structural, magnetic, electronic, and elastic properties of Heusler alloys Co₂YIn (Y = Ti, V). It is shown that the calculated spin magnetic moments using the local spin-density approximation (LSDA), generalized gradient approximation (GGA), LSDA + U, and Tran–Blaha (TB)-modified Becke–Johnson (mBJ)-local density approximations (LDA) are in good agreement with the Slater–Pauling rule. The obtained results with LSDA, GGA-PBE, and LSDA + U of the density of states illustrate that both compounds have a metal behavior; however, mBJ-LDA predicts Co₂VIn alloy to be a half metal. The band structure obtained with mBJ-LDA has an indirect band gap along the Γ–X symmetry with energy of 0.4 eV for Co₂VIn, and E _F lies in the middle of the gap; the electrons at the Fermi level are fully spin-polarized. The calculation of elastic properties indicates the stability of these compounds, and they have a ductile behavior. The 3D dependences of Young’s modulus exhibit a strong anisotropic character. The high values of the elastic constant C ₁₁ reflect the strength of the bonding Ti (V)–In.     

Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

Recently, Internet of Things (IoT) devices produces massive quantity of data from distinct sources that get transmitted over public networks. Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved. The development of automated tools for cyber threat detection and classification using machine learning (ML) and artificial intelligence (AI) tools become essential to accomplish security in the IoT environment. It is needed to minimize security issues related to IoT gadgets effectively. Therefore, this article introduces a new Mayfly optimization (MFO) with regularized extreme learning machine (RELM) model, named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment. The presented MFO-RELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment. For accomplishing this, the MFO-RELM model pre-processes the actual IoT data into a meaningful format. In addition, the RELM model receives the pre-processed data and carries out the classification process. In order to boost the performance of the RELM model, the MFO algorithm has been employed to it. The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.