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- 2019
- low-temperature electronics
- class AB operation
- optimization of analog electronic circuit
- operational amplifier
- LTspice environment
- buffer amplifier
- junction field-effect transistors
- Electronic Science and Technology
- Don State Technical University
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Ant Colony Optimization and image model-based robot manipulator system for pick-and-place tasks
摘要： A visual servo control system combines with the model-based image segmentation and an Ant Colony Optimization (ACO) algorithm to design an excellent six-Degree-of-Freedom (6-DOF) robot manipulator for solving the complicated combinations of pick-and-place tasks. A simple but efficient vision-based segmentation methodology is developed to extract the object information by getting appropriate feature of the controlled platform when the robot is tracking the manipulated image patterns. The evolutionary ACO learning algorithm explores the near-optimal path selections to drive the 6 DOF robot arm kinematics model for completing the Pick-and-Place tasks as soon as possible. Inverse orientation kinematic machine is proposed to successfully guide the robot manipulator into the desired position. Several software simulations include image segmentations, the shortest path selection, and the performance validation in various experiments. These results are described and presented to demonstrate that the designed image model-based robot manipulator wins the excellent Pick-and-Place task. Not only the software simulation, the practical robot synchronously performed in real-world to reach the higher feasible functions in the eye-to-hand experiments.
关键词： image segmentation,pick-and-place task,Ant Colony Optimization,eye-to-hand,Robot manipulator
更新于2025-09-23 15:23:52
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Lightweight Design of Multi-Objective Topology for a Large-Aperture Space Mirror
摘要： For a large-aperture space telescope, one of the key techniques is the method for designing the lightweight primary mirror assembly (PMA). In order to minimize the mirror surface error under axial gravity, lateral gravity, and polishing pressure at the same time, a method for topology optimization with multi-objective function combined with parametric optimization is introduced in this paper. The weighted compliance minimum is selected as the objective function to maximum the mirror structural stiffness. Then sensitivity analysis method and size optimization are used to determine the mirror structure parameters. Compared with two types of commonly used lightweight configurations, the new configuration design shows obvious superiority. In addition, the surface figure root mean square (RMS) of the mirror mounted by given bipod flexure (BF) under 1 g lateral gravity is minimized only with a value of 3.58 nm, which proves the effectiveness of the design method proposed in this paper.
关键词： lightweight structure,space mirror,multi-objective topology optimization,sensitivity analysis
更新于2025-09-23 15:23:52
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Realizing Q> 300 000 in diamond microdisks for optomechanics via etch optimization
摘要： Nanophotonic structures in single–crystal diamond (SCD) that simultaneously con?ne and co-localize photons and phonons are highly desirable for applications in quantum information science and optomechanics. Here we describe an optimized process for etching SCD microdisk structures designed for optomechanics applications. This process allows the optical quality factor, Q, of these devices to be enhanced by a factor of 4 over previous demonstrations to Q ～ 335 000, which is suf?cient to enable sideband resolved coherent cavity optomechanical experiments. Through analysis of optical loss and backscattering rates, we ?nd that Q remains limited by surface imperfections. We also describe a technique for altering microdisk pedestal geometry which could enable reductions in mechanical dissipation.
关键词： surface roughness,optomechanics,quality factor,etch optimization,diamond microdisks
更新于2025-09-23 15:23:52
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Realization of effective laser blanking process by heat zone spread resistance coating and optimization methods
摘要： Laser blanking process is widely used for the ability to cut complex profiles on sheet metal without Die.Laser blanking process needs optimization methods to reduce wastage of raw material and cutting time. The work entails optimization of sheet metal nesting allocation to reduce wastage and also optimizes cutting time by reducing ideal travel distance. The laser irradiation induces heat affected zone in the cutting surface leading to poor service life of the components in the cutting edges. The development of AlN heat zone spread resistance coating over steel substrate through a reactive sputtering process is presented. The thin film preparation is carried out in two different combinations of Argon and Nitrogen ratio namely 1:1 and 2:1, respectively. The coating over the steel substrate is exposed to laser to analyze the micro-structural change induced by the laser in the cutting edges. The coating is observed to mitigate the spreading of heat zone. The coatings are further subjected to tafel polarization to analyze the corrosion resistance of the steel substrate.In this paper, an approach has been made to obtain an optimal allocation based on the selection of different dimensions of the AlN coated sheet and also to calculate the utilization and cutting time. The proposed method provides an optimal layout for parts using a software and to obtain minimum travel ideal distance a heuristic algorithm is used. Since the sheet is coated there is also an add-on advantage in minimizing cutting time. Finally, an Optimal Pareto front is developed between wastage and ideal cutting distance, in order to provide choices for the user to select the requirement in both cases.
关键词： Laser Blanking,Pareto Front,Coating,Optimization
更新于2025-09-23 15:23:52
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Photocatalytic degradation of yellow 2G dye using titanium dioxide/ultraviolet A light through a Box–Behnken experimental design: Optimization and kinetic study
摘要： Yellow 2G (Y2G), a type of anionic, synthetic monoazo dye that is widely used in household applications, textiles, and food industries, has been found to have cardiovascular and neurological effects on all living beings. In the present study, heterogeneous photocatalytic degradation of commercial Y2G was conducted using pure titanium dioxide (TiO2) in a batch reactor system under ultraviolet A (UVA) light for 180 min. TiO2 dosage, pH, and initial Y2G concentration were the three experimental parameters selected and studied to obtain preliminary information about the photocatalytic activities within a specified range. The Box–Behnken design method (BBD) was used to determine optimal values of the results using the above parameters of Y2G photocatalysis under response surface methodology (RSM). The optimum conditions were 0.914 g L?1 TiO2, pH 3.45, and an initial Y2G concentration of 20 mg L?1. The Y2G degradation efficiency was 96.19% using a second-order polynomial equation with R2 = 0.999. The experimental results also showed that the photocatalytic process could be successfully explained using the modified Langmuir–Hinshelwood model, where kc and KLH were 0.787 mg L?1 min and 0.010 L mg?1, respectively.
关键词： photocatalysis,Box–Behnken design (BBD),optimization,Yellow 2G (Y2G) dye,response surface methodology (RSM)
更新于2025-09-23 15:23:52
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Noise reduction for near-infrared spectroscopy data using extreme learning machines
摘要： The near infrared (NIR) spectra technique is an effective approach to predict chemical properties and it is typically applied in petrochemical, agricultural, medical, and environmental sectors. NIR spectra are usually of very high dimensions and contain huge amounts of information. Most of the information is irrelevant to the target problem and some is simply noise. Thus, it is not an easy task to discover the relationship between NIR spectra and the predictive variable. However, this kind of regression analysis is one of the main topics of machine learning. Thus machine learning techniques play a key role in NIR based analytical approaches. Pre-processing of NIR spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena (noise) in the spectra in order to improve the regression or classification model. In this work, we propose to reduce the noise using extreme learning machines which have shown good predictive performances in regression applications as well as in large dataset classification tasks. For this, we use a novel algorithm called C-PL-ELM, which has an architecture in parallel based on a non-linear layer in parallel with another non-linear layer. Using the soft margin loss function concept, we incorporate two Lagrange multipliers with the objective of including the noise of spectral data. Six real-life dataset were analyzed to illustrate the performance of the developed models. The results for regression and classification problems confirm the advantages of using the proposed method in terms of root mean square error and accuracy.
关键词： Parallel layers,Constrained optimization,Regression,Near-infrared spectroscopy,Classification
更新于2025-09-23 15:23:52
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Parametric Study and Optimization of Linear and Nonlinear Vibration Absorbers Combined with Piezoelectric Energy Harvester
摘要： In this work, a harmonically excited generalized two degree of freedom non-linear system is used to manifest the functions of both the vibration absorber and energy harvester simultaneously. The generalized system has been reduced to a linear primary system with linear/nonlinear absorber and harvester or nonlinear primary system with linear/nonlinear absorber and harvester. Multi-harmonic balance method (MHBM) along with arc length continuation is used for generating frequency response plots for different absorber and energy harvester system parameters with constant primary system parameters and excitation amplitude. The frequency response plots show multiple branches of stable periodic solutions and jump at certain frequency ranges for systems with nonlinearity. The absorber and energy harvester parameters are optimized using an optimization procedure based on genetic algorithm in combination with response surface methodology. The method is validated with analytical solutions available in the literature for a linear primary system with linear absorber and harvester and nonlinear primary system with nonlinear absorber alone. This study demonstrates that the proposed optimization framework along with MHBM is suitable for generating the optimal frequency response for multifunctional energy harvesting systems or systems with nonlinear absorber. The frequency response plots with optimal parameter values reiterates the fact that the absorber system with nonlinear element perform better compared to its linear counterpart over a wider band of frequencies. The study also reports the comparison of the performance of a combined nonlinear absorber harvester system with that of a nonlinear energy sink (NES) absorber harvester system.
关键词： vibration absorber,multi-harmonic balancing,energy harvesting,optimization,response surface method,genetic algorithm
更新于2025-09-23 15:23:52
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[IEEE 2018 15th International Conference on the European Energy Market (EEM) - Lodz (2018.6.27-2018.6.29)] 2018 15th International Conference on the European Energy Market (EEM) - Optimized Operational Management of an EV Sharing Community Integrated with Battery Energy Storage and PV Generation
摘要： Sharing schemes are emerging in residential and business sectors to reduce the purchase and operation cost of individuals. This paper proposes a framework to support the operational management of a shared EV fleet. An optimization algorithm is developed to coordinate the charging and reservation assignment using mixed integer programming. The integration with local PV production and battery storage is taken into account. A booking algorithm is also developed to determine whether a reservation can be accepted or not. Monte Carlo simulation is performed in the case study to demonstrate an application of the proposed framework with the Swedish travel patterns. The result provides an overview about the utilization rate of the fleet with different number of EVs, which can support the investment decision of an EV sharing community. The result also shows that the EVs and battery are effectively coordinated to minimize the total cost, satisfy the reservations and comply with grid limits.
关键词： PV generation,battery storage,optimization,Monte Carlo simulation,operational management,electric vehicle sharing,mixed integer programming
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE Industry Applications Society Annual Meeting (IAS) - Portland, OR, USA (2018.9.23-2018.9.27)] 2018 IEEE Industry Applications Society Annual Meeting (IAS) - Design and Optimization of a Solar Power Conversion System for Space Applications
摘要： This manuscript details a design method for a 500kW solar power based microgrid system for space applications. The design method utilizes multi-objective optimization with the Genetic Algorithm considering four parameters that characterize solar power based microgrids (battery voltage, PV maximum power, PV maximum power point voltage, and number of panels per string). The final optimization metric is the ratio of daily average deliverable power to total system mass (W/kg) metric. The microgrid system is composed of a number of modular DC-DC micro-converters, of which four topologies (buck, boost, buck-boost and non-inverting buck-boost) are evaluated and compared. The non-inverting buck-boost converter is determined to be the best candidate, and the optimal system characteristics are provided and analyzed. The final system design achieves a specific power of 35.56W/kg, with optimized result of 743.7V battery voltage, 439.5W PV maximum power, 182.7V PV maximum voltage, and three panels per string. Based on the optimizations results, a prototype is designed, tested, and analyzed in terms of efficiency and low temperature reliability. The converter achieved a peak efficiency of 98.4%, a power density of 3.54W/cm3, a specific power of 3.76W/g, and operated for over 267 hours of 11-minute low temperature cycles from 0oC to -140oC.
关键词： wide band gap semiconductors,microgrids,non-inverting buck-boost,maximum power point trackers,space exploration,photovoltaic systems,design optimization,DC-DC power converters,system-level design,low temperature testing
更新于2025-09-23 15:23:52
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Concurrent Monte Carlo transport and fluence optimization with fluence adjusting scalable transport Monte Carlo
摘要： Purpose: The future of radiation therapy will require advanced inverse planning solutions to support single-arc, multiple-arc, and '4π' delivery modes, which present unique challenges in finding an optimal treatment plan over a vast search space, while still preserving dosimetric accuracy. The successful clinical implementation of such methods would benefit from Monte Carlo (MC) based dose calculation methods, which can offer improvements in dosimetric accuracy when compared to deterministic methods. The standard method for MC based treatment planning optimization leverages the accuracy of the MC dose calculation and efficiency of well-developed optimization methods, by precalculating the fluence to dose relationship within a patient with MC methods and subsequently optimizing the fluence weights. However, the sequential nature of this implementation is computationally time consuming and memory intensive. Methods to reduce the overhead of the MC precalculation have been explored in the past, demonstrating promising reductions of computational time overhead, but with limited impact on the memory overhead due to the sequential nature of the dose calculation and fluence optimization. The authors propose an entirely new form of 'concurrent' Monte Carlo treat plan optimization: a platform which optimizes the fluence during the dose calculation, reduces wasted computation time being spent on beamlets that weakly contribute to the final dose distribution, and requires only a low memory footprint to function. In this initial investigation, the authors explore the key theoretical and practical considerations of optimizing fluence in such a manner. Methods: The authors present a novel derivation and implementation of a gradient descent algorithm that allows for optimization during MC particle transport, based on highly stochastic information generated through particle transport of very few histories. A gradient rescaling and renormalization algorithm, and the concept of momentum from stochastic gradient descent were used to address obstacles unique to performing gradient descent fluence optimization during MC particle transport. The authors have applied their method to two simple geometrical phantoms, and one clinical patient geometry to examine the capability of this platform to generate conformal plans as well as assess its computational scaling and efficiency, respectively. Results: The authors obtain a reduction of at least 50% in total histories transported in their investigation compared to a theoretical unweighted beamlet calculation and subsequent fluence optimization method, and observe a roughly fixed optimization time overhead consisting of ～10% of the total computation time in all cases. Finally, the authors demonstrate a negligible increase in memory overhead of ～7–8 MB to allow for optimization of a clinical patient geometry surrounded by 36 beams using their platform. Conclusions: This study demonstrates a fluence optimization approach, which could significantly improve the development of next generation radiation therapy solutions while incurring minimal additional computational overhead.
关键词： Monte Carlo,optimization concurrent Monte Carlo optimization,fluence optimization,concurrent optimization
更新于2025-09-23 15:22:29