Identification study of solar cell/module using recent optimization techniques

This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.

LEADD: Lamarckian evolutionary algorithm for de novo drug design

Given an objective function that predicts key properties of a molecule, goal-directed de novo molecular design is a useful tool to identify molecules that maximize or minimize said objective function. Nonetheless, a common drawback of these methods is that they tend to design synthetically unfeasible molecules. In this paper we describe a Lamarckian evolutionary algorithm for de novo drug design (LEADD). LEADD attempts to strike a balance between optimization power, synthetic accessibility of designed molecules and computational efficiency. To increase the likelihood of designing synthetically accessible molecules, LEADD represents molecules as graphs of molecular fragments, and limits the bonds that can be formed between them through knowledge-based pairwise atom type compatibility rules. A reference library of drug-like molecules is used to extract fragments, fragment preferences and compatibility rules. A novel set of genetic operators that enforce these rules in a computationally efficient manner is presented. To sample chemical space more efficiently we also explore a Lamarckian evolutionary mechanism that adapts the reproductive behavior of molecules. LEADD has been compared to both standard virtual screening and a comparable evolutionary algorithm using a standardized benchmark suite and was shown to be able to identify fitter molecules more efficiently. Moreover, the designed molecules are predicted to be easier to synthesize than those designed by other evolutionary algorithms. Graphical Abstract

Bilateral Personalized Dialogue Generation with Contrastive Learning

Generating personalized responses is one of the major challenges in natural human-robot interaction. Current researches in this field mainly focus on generating responses consistent with the robot’s pre-assigned persona, while ignoring the user’s persona. Such responses may be inappropriate or even offensive, which may lead to the bad user experience. Therefore, we propose a Bilateral Personalized Dialogue Generation (BPDG) method for dyadic conversation, which integrates user and robot personas into dialogue generation via designing a dynamic persona-aware fusion method. To bridge the gap between the learning objective function and evaluation metrics, the Conditional Mutual Information Maximum (CMIM) criterion is adopted with contrastive learning to select the proper response from the generated candidates. Moreover, a bilateral persona accuracy metric is designed to measure the degree of bilateral personalization. Experimental results demonstrate that, compared with several state-of-the-art methods, the final results of the proposed method are more personalized and consistent with bilateral personas in terms of both automatic and manual evaluations.

Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network

Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.

Upper limb involuntary tremor reduction using cantilever beam TMDs

Tuned mass dampers (TMDs) are proposed as a solution to reduce the involuntary tremor at the upper limb of a patient with postural tremor. The upper limb is modeled as a three-degrees-of-freedom rotating system in the vertical plane, with a flexion-extension motion at the joints. The measured extensor carpi radialis signal of a patient is used to excite the dynamic model. We propose a numerical methodology to optimize the parameters of the TMDs in the frequency domain combined with the response in the time domain. The objective function for the optimization of the dynamic problem is the maximum angular displacement of the wrist joint. The optimal stiffness and damping of the TMDs are obtained by satisfying the minimization of the selected objective function. The considered passive absorber is a cantilever beam–like TMD, whose length, beam cross-sectional diameter, and mass position reflect its stiffness for a chosen additional mass. A parametric study of the TMD is conducted to evaluate the effect of the TMD position along the hand segment, the number of TMDs, and the total mass of TMDs. The sensitivity of the TMD to a decrease of its modal damping ratio is studied to meet the range of stainless steel. TMDs are manufactured using stainless steel beams of the same length (9.1 cm) and cross-sectional diameter (0.79 mm), for which the mass (14.13 g) position is adjusted to match the optimal frequency. Three TMDs holding a mass of 14.13 g each cause 89% reduction in the wrist joint angular displacement.

Optimal Path Analysis for Solving Nonlinear Equations with Finite Local Error

Because the traditional method of solving nonlinear equations takes a long time, an optimal path analysis method for solving nonlinear equations with limited local error is designed. Firstly, according to the finite condition of local error, the optimization objective function of nonlinear equations is established. Secondly, set the constraints of the objective function, solve the optimal solution of the nonlinear equation under the condition of limited local error, and obtain the optimal path of the nonlinear equation system. Finally, experiments show that the optimal path analysis method for solving nonlinear equations with limited local error takes less time than other methods, and can be effectively applied to practice

BER-Aware Reflecting Elements Allocation for RIS-aided NOMA Networks over Ricean Fading

Reconfigurable intelligent surface (RIS) as a supportive technology for aiding downlink non-orthogonal multiple access (NOMA) can enhance the bit error rate (BER) performance. In this paper, a novel BER-aware reflecting elements allocation (REA) on an RIS is proposed to maintain the BER order among paired RIS-NOMA users. The RIS REA is useful for minimizing the average user BER, ompared with a system that allocates the same number of elements to all users. Additionally, the Ricean fading is considered instead of Rayleigh fading as it is more practical and general. Furthermore,an REA optimization objective function for equalizing the user BER is proposed. In order to solve the problem, a modified exhaustive search is proposed to reduce complexity. The distribution of the objective function is observed first; subsequently, the exhaustive search range is determined. Both the analytical and simulation results show that the proposed algorithm can minimize the average user BER.

Computational Scale-Up for Flood Fed/Starve Fed Single Screw Extrusion of Polymers

A novel scaling-up computer system for single screw extrusion of polymers has been developed. This system makes it possible to scale-up extrusion process with both starve feeding and flood feeding. Each of the scale-up criteria can be an objective function to be minimized, represented by single values or functional dependencies over the screw length. The basis of scaling-up is process simulation made with the use of the GSEM program (Global Screw Extrusion Model). Scaling-up is performed using the GASES program (Genetic Algorithms Screw Extrusion Scaling) based on Genetic Algorithms. Scaling-up the extrusion process has been performed to increase extrusion output according to the scaling-up criteria defined by the single parameters of unit energy consumption, polymer plasticating rate and polymer temperature, as well as by the process parameters profiles of the temperature and plasticating. The global objective function reached the lowest value for the selected process parameters, and extrusion throughput was significantly increased.

BER-Aware Reflecting Elements Allocation for RIS-aided NOMA Networks over Ricean Fading

Reconfigurable intelligent surface (RIS) as a supportive technology for aiding downlink non-orthogonal multiple access (NOMA) can enhance the bit error rate (BER) performance. In this paper, a novel BER-aware reflecting elements allocation (REA) on an RIS is proposed to maintain the BER order among paired RIS-NOMA users. The RIS REA is useful for minimizing the average user BER, ompared with a system that allocates the same number of elements to all users. Additionally, the Ricean fading is considered instead of Rayleigh fading as it is more practical and general. Furthermore,an REA optimization objective function for equalizing the user BER is proposed. In order to solve the problem, a modified exhaustive search is proposed to reduce complexity. The distribution of the objective function is observed first; subsequently, the exhaustive search range is determined. Both the analytical and simulation results show that the proposed algorithm can minimize the average user BER.

AIEADA 1.0: Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction, and is a crucial building block that has allowed dramatic improvements in weather forecasting over the past few decades. DA is commonly framed in a variational setting, where one solves an optimization problem within a Bayesian formulation using raw model forecasts as a prior, and observations as likelihood. This leads to a DA objective function that needs to be minimized, where the decision variables are the initial conditions specified to the model. In traditional DA, the forward model is numerically and computationally expensive. Here we replace the forward model with a low-dimensional, data-driven, and differentiable emulator. Consequently, gradients of our DA objective function with respect to the decision variables are obtained rapidly via automatic differentiation. We demonstrate our approach by performing an emulator-assisted DA forecast of geopotential height. Our results indicate that emulator-assisted DA is faster than traditional equation-based DA forecasts by four orders of magnitude, allowing computations to be performed on a workstation rather than a dedicated high-performance computer. In addition, we describe accuracy benefits of emulator-assisted DA when compared to simply using the emulator for forecasting (i.e., without DA). Our overall formulation is denoted AIAEDA (Artificial Intelligence Emulator Assisted Data Assimilation).