This paper presents an automated method for solving the initial structure of compact, high-zoom-ratio mid-wave infrared (MWIR) zoom lenses. Using differential analysis, the focal length variation process of zoom lense...
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This paper presents an automated method for solving the initial structure of compact, high-zoom-ratio mid-wave infrared (MWIR) zoom lenses. Using differential analysis, the focal length variation process of zoom lenses under paraxial conditions is investigated, and a model for the focal power distribution and relative motion of three movable lens groups is established. The particle swarm optimization (PSO) algorithm is introduced into the zooming process analysis, and a program is developed in MATLAB to solve for the initial structure. This algorithm integrates physical constraints from lens analysis and evaluates candidate solutions based on key design parameters, such as total lens length, zoom ratio, Petzval field curvature, and focal length at tele end. The results demonstrate that the proposed method can efficiently and accurately determine the initial structure of compact MWIR zoom lenses. Using this method, a mid-wave infrared zoom lens with a zoom ratio of 50x, a total length of less than 530 mm, and the ratio of focal length to total length approximately 2:1 was successfully designed. The design validates the effectiveness and practicality of this method in solving the initial structure of zoom lenses that meet complex design requirements.
In the process of author name disambiguation (AND), varying characteristics and noise of different blocks significantly impact disambiguation performance. In this paper, we propose a block-based adaptive hyperparamete...
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In the process of author name disambiguation (AND), varying characteristics and noise of different blocks significantly impact disambiguation performance. In this paper, we propose a block-based adaptive hyperparameter optimization method that assigns optimal hyperparameters to each block without altering the original AND model structure. Based on this, a random forest model is trained using the optimized results to fit the relationship between the block's data features and its optimal hyperparameters, thereby enabling the prediction of hyperparameters for new blocks. Empirical studies on 6 state-of-the-art AND algorithms, 11 public datasets, and a manually labeled dataset of China's information and communication technology (ICT) industry patents demonstrate that the proposed method significantly outperforms the original algorithms across multiple standard performance evaluation metrics (Cluster F1/Pairwise F1, B-Cubed F1, and K metrics). The results of the random forest regression indicate that the selected 16 features effectively predict the optimal hyperparameters. Further analysis reveals a power-law relationship between relative block size and both relative performance and relative optimized performance across all datasets and evaluation metrics, and the relative performance improvement of the adaptive hyperparameter optimization algorithm is particularly significant for smaller blocks. These findings provide theoretical support and practical guidance for the development of AND algorithms.
Integrated and sustainable river basin management requires accurate assessment and optimized allocation of available water resources, considering the impacts of climate and anthropogenic changes. It can benefit from c...
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Integrated and sustainable river basin management requires accurate assessment and optimized allocation of available water resources, considering the impacts of climate and anthropogenic changes. It can benefit from coupled modeling frameworks combining simulation models and optimization algorithms (OAs). This article reviews simulation-based optimization (SbO) techniques developed for integrated water resources management (IWRM) classified into four distinct categories: i) coupled Simulation-only Models (SM-o) and Simulation Models with water allocation skill (SM-wa);ii) coupled Simulation Models with water allocation skill (SM-wa) and OAs;iii) coupled SM-o and OAs, and iv) simultaneous coupling of SM-o, SM-wa, and OAs. These simulation-based optimization frameworks are constructed using various coupling strategies-isolated, loose, tight, and integrated-based on the required level of data exchange and interactions between model components. The first category (SM-o-SM-wa) is predominantly based on applying offline/isolated coupling methods, whereas subsequent categories witness the adoption of loose and tight coupling approaches. The findings of this review underscore the value of SbO frameworks in promoting comprehensive and sustainable management of water resources capable of addressing single-period and multi-period allocations, as well as optimizing reservoir operation policies within a single framework. Additionally, this review can be a valuable resource for researchers, modelers, and policymakers in selecting suitable simulation models, OAs, and coupling methods for effective decision-making in IWRM.
Sorptive bioprocesses are the basis for numerous biotechnological applications such as enzyme immobilization, biosensors, controlled drug delivery, water treatment, and molecular purification. Yet due to the complexit...
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Sorptive bioprocesses are the basis for numerous biotechnological applications such as enzyme immobilization, biosensors, controlled drug delivery, water treatment, and molecular purification. Yet due to the complexity of these processes, their optimization is still time, labor, and cost-intensive. This research presents a flexible self-driving laboratory (SDL) designed for the accelerated development and optimization of solid-phase extraction processes. As a use case, the SDL was used to optimize a DNA purification process using silica magnetic beads. Through the integration of robotics, machine learning, and data-driven experimentation, the SDL demonstrates a highly accelerated process optimization with minimal human intervention. In the multistep purification approach, the system is able to optimize buffer compositions for DNA extraction from complex samples, demonstrating effectiveness in both conventional chaotropic salt-based methods and innovative chaotropic salt-free buffers. The study highlights the SDL's capability to autonomously refine process parameters, achieving significant enhancements in yield and purity of the product. This blueprint for future self-driving optimization of bioprocess parameters showcases the potential of autonomous systems to revolutionize biochemical process development, offering insights into scalable, environmentally sustainable, and cost-effective solutions.
In the last decades, the field of global optimization has experienced significant growth, leading to the development of various deterministic and stochastic algorithms designed to tackle a wide range of optimization p...
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In this paper, we presented a full-Newton short-step interior-point algorithm, which is based on a new algebraically equivalent transformation technique, for a linear optimization problem. This technique offers a new ...
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In recent years, quadrotors have emerged as essential roles in robotics, and their diversity and usefulness emphasize their importance. This research work presents an in-depth analysis of a quadcopter in terms of mode...
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In recent years, quadrotors have emerged as essential roles in robotics, and their diversity and usefulness emphasize their importance. This research work presents an in-depth analysis of a quadcopter in terms of modeling, control, and optimization, where central to the operation of quadcopters and all robotics systems is the idea of stability response. This paper discusses the possibility of providing quadcopter stability by demonstrating the impact of the fractional controller in sensitive systems. The five fractional parameters for each engine are also improved using the Bonobo optimization (BO) algorithm. The optimized results in this paper are compared with the algorithms used, such as Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Grey Wolf optimization (GWO). The fractional-order proportional integral derivative (FOPID) controller has greater control power compared to its classic counterpart, PID control, as it provided improvement in minimizing overshoot by 90%, and it showed great improvement in settling and rising times using GWO 25% and BO 50% with some superiority of BO. By examining both the advantages and constraints inherent in these methodologies, we seek to advance the field forward, promoting more breakthroughs in this crucial area.
Fuzzy mathematical theory is widely used, fuzzy optimization is a branch of fuzzy mathematical theory, the significant application area is artificial intelligence in computer science, especially machine learning (deep...
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In this article, the sparrow search algorithm (SSA) is extended to the multiobjective SSA (MOSSA) with the purpose of efficiently solving the multiobjective optimization problems (MOPs). First, the MOSSA adaptively ev...
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In this paper, we consider nonconvex uncertain vector optimization problems, and discuss the properties of their robust efficient solution sets. First, existence conditions of the robust efficient solutions under cons...
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