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Investigation on parallel auxiliary adjusting machine of spatial filter lens for a high-power laser facility

OPTICS AND LASER TECHNOLOGY 2022年 147卷 107597-107597页

作者： Zhang, Donghui Dong, Ziming Zhang, Chao Liu, Zhigang Zheng, Liunian Pang, Xiangyang Zhu, Jianqiang Chinese Acad Sci Shanghai Inst Opt & Fine Mech Natl Lab High Power Laser & Phys Shanghai 201800 Peoples R China Chinese Acad Sci China Acad Engn Phys Natl Lab High Power Laser & Phys Shanghai 201800 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

As a key part in high-power laser facility, the spatial filter is used to determine the optical axis of the entire laser system, which consists of two conjugate lenses at both ends and filter hole in the middle. The lenses usually require precise adjustment, and the adjustment mechanism is integrated to both ends of the filter currently. To this end, a novel parallel adjusting machine for the filter lenses was proposed as an auxiliary tool in the spatial filter process. The machine was designed to realize the five-dimensional adjustment functions of the filter lenses with X-Y-Z axial translation, pitch, and yaw motion. The first-order ghost point of the filter lens was used as the feedback reference for the parallel machine to adjust Z-position of the lens. A more comprehensive optimizationoriented framework was established to improve the sensitivity of the lens to the posture error and to optimize the positioning speed based on the steepest descent method (SDM). The experimental results verified the superiority of the proposed adjustment machine and feedback reference point.

关键词： Spatial filter Parallel machine Ghost reference point optimization algorithm

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Hybrid Model of Gravitational Fields Around Small Bodies for Efficient Trajectory Propagations

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2020年第2期43卷 232-249页

作者： Wei, Bingwei Shang, Haibin Qiao, Dong Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Beijing Inst Technol Key Lab Dynam & Control Flight Vehicle Beijing 100081 Peoples R China

When modeling the gravitational fields around small bodies, the highly irregular shapes of such objects make it difficult to balance accuracy with efficiency. In this paper, a novel and general hybrid model for evaluating the gravity of a small body is proposed with the aim of reducing errors and the computational cost. The hybrid model separates dominant effects and smaller undulations. The dominant effects are represented by the gravity-best-fit ellipsoid, which is generated by solving an optimization problem in the sense of minimizing the contribution of undulations. The undulations are then approximated using the serendipity interpolation technique, which is computationally efficient. Furthermore, a discretization scheme for the region of interest is developed, and this guarantees the evaluation of gravity in the close proximity of a small body. Based on this discretization strategy, a rapid element tracking scheme is developed to improve the efficiency of trajectory propagations. The most remarkable advantage of this tracking strategy is that the runtime for identifying elements remains the same when the region of interest is arbitrarily broadened. Simulations on asteroids 1999KW4 and 1620 Geographos show that the hybrid model is highly accurate and around 196 times faster than the conventional polyhedral model.

关键词： 4179 Toutatis Iterative algorithm Spherical Harmonics Frozen Orbit Random Access Memory Finite Element Program Tetrahedral Element Probability Distribution optimization algorithm Solar System

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Scheduling Home Appliances with Integration of Hybrid Energy Sources using Intelligent algorithms

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AIN SHAMS ENGINEERING JOURNAL 2022年第4期13卷

作者： Tantawy, El Sayed F. Amer, Ghada M. Fayez, Hanaa M. Benha Univ Fac Eng Dept Elect Engn Banha Egypt

This paper presents Home Energy Management System(HEMS) for smart homes. This system includes Photovoltaic Sources and Battery Storage Units(BSU)and scheduling Smart Home(SH) appliances with various kinds and types of residential users. In addition, a classification algorithm of energy response programs, which relies on the electricity prices, is introduced for scheduling appliances of single and multiple homes using combined the Time of Use(TOU) and Inclining Block Rate(IBR) to control the demand under the grid station capacity and energy efficacious integration of hybrid power sources in Smart Grid(SG). Four different optimization algorithms used and compared for scheduling appliances. The simulation results show that the proposed algorithm achieves the desired objectives, in which the electricity cost and the Peak Average Ratio(PAR) were lowered, waiting time to the user is minimized, reducing the electricity bill per month/year. In addition, the proposed system increases the stability and reliability of the grid and consumers satisfaction. (C) 2022 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University

关键词： Home Appliances Scheduling Hybrid energy system Renewable Energy Sources Demand Side Management optimization algorithm Home Energy Management System Smart Home

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Differential Privacy-Preserving IoT Data Sharing Through Enhanced PSO

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JOURNAL OF COMPUTER INFORMATION SYSTEMS 2024年

作者： Dhavamani, Logeshwari Ananthavadivel, Devipriya Akilandeswari, P. Nanajappan, Manikandan St Josephs Coll Engn Chennai India SRM Inst Sci & Technol Chennai India

With the proliferation of Internet of Things (IoT) devices and the generation of massive amounts of sensitive data, preserving privacy while enabling data sharing has become a critical concern. In this research, we propose a novel approach for achieving differential privacy in IoT data sharing through an improved Particle Swarm optimization (PSO) algorithm. Our objective is to find the optimal configuration of privacy-preserving mechanisms that maximizes privacy while maintaining data utility. The research begins with a comprehensive overview of differential privacy in IoT data sharing, highlighting the limitations of existing optimization algorithms. We then present our proposed improvements to the traditional PSO algorithm, including the design of a suitable fitness function, the use of a dynamic inertia weight, exploration of different neighborhood topologies, and adaptive acceleration coefficients. We define a set of performance metrics, including privacy metrics (e.g. epsilon-differential privacy parameter) and utility measures (e.g. accuracy, utility loss), to assess the algorithm's effectiveness. The results of our experiments demonstrate that the improved PSO algorithm achieves higher privacy guarantees while maintaining competitive levels of data utility compared to existing approaches. The proposed algorithm exhibits faster convergence, better exploration of the search space, and improved scalability. Our research contributes to the field of privacy-preserving IoT data sharing by providing an efficient and effective optimization algorithm that enables secure and privacy-aware data sharing while facilitating valuable insights and analysis.

关键词： Internet of Things IoT data sharing privacy-preserving mechanisms Particle Swarm optimization optimization algorithm privacy guarantee data utility

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Individual mapping and asymmetric dual supervision for discrete cross-modal hashing

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 247卷

作者： Wang, Song Zhao, Huan Zhang, Zixing Li, Keqin Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Key Lab Embedded & Network Comp Hunan Prov Changsha 410082 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Cross -modal hashing has gained popularity in similarity search due to its excellent query efficiency and economical storage costs. However, current models frequently overlook the distinctive property of each modality, resulting in reduced accuracy due to inadequate utilization of these attributes. Moreover, there is a weak semantic relevance between modality attributes and multiple supervision knowledge (the labels and similarity constraints constructed by labels), accompanied by a cumulative quantization of the models. To address these issues, we propose an Individual Mapping and Asymmetric Dual Supervision method (IMADS). It merges specific and shared information to effectively learn a cross -modal representation space. Furthermore, we present an asymmetric dual supervision learning framework to produce discriminative hash codes. This framework achieves two primary goals: (1) Combing cross -modal representation and multiple supervision information to enhance the consistent relation of distinct modalities, and (2) developing a discrete optimization algorithm to mitigate the information loss caused by the hash code. Comprehensive experimental results illustrate that the introduced IMADS outperforms other stat-of-the-art hashing methods.

关键词： Asymmetric dual supervision Cross-modal hashing Individual mapping optimization algorithm

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Fast detection of volatile fatty acids in biogas slurry using NIR spectroscopy combined with feature wavelength selection

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SCIENCE OF THE TOTAL ENVIRONMENT 2023年第Part1期857卷 159282页

作者： Xu, Yonghua Liu, Jinming Sun, Yong Chen, Shaopeng Miao, Xinying Northeast Agr Univ Coll Elect & Informat Harbin 150030 Peoples R China Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Daqing 163319 Peoples R China Northeast Agr Univ Coll Engn Harbin 150030 Peoples R China Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn 5 Xinfeng Rd Hightech Zone Daqing Heilongjiang Peoples R China

To analyze the state of anaerobic digestion (AD), fast detection models of volatile fatty acids (VFAs) were constructed using near-infrared transmission spectroscopy combined with partial least squares regression to measure concentra-tions of the acetic acid (AA), propionic acid (PA) and total acid (TA) in biogas slurry. CARS-SA-BPSO algorithm was proposed based on competitive adaptive reweighted sampling (CARS) and simulated annealing binary particle swarm optimization algorithm (SA-BPSO) for selecting feature wavelengths of the AA, PA and TA. Regression models were established with the determination coefficient of prediction (Rp2) of 0.989, root mean squared error of prediction (RMSEP) of 0.111 and residual predictive deviation (RPD) of 9.706 for AA;Rp2 of 0.932, RMSEP of 0.116 and RPD of 3.799 for PA;Rp2 of 0.895, RMSEP of 0.689 and RPD of 3.676 for TA. It is sufficient to meet the fast detection needs of the AA and PA concentrations in biogas slurry, and basically meet the measuring demand of the TA concentration. CARS-SA-BPSO effectively improves the performance of the calibration model using sensitive wavelength selections, which provides theoretical support for establishing the spectral quantitative regression model to meet the require-ments of practical application.

关键词： Volatile fatty acids Near-infrared spectroscopy Partial least squares Competitive adaptive reweighted sampling Simulated annealing binary particle swarm optimization algorithm

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Exploring a homotopy approach for the design of nanometer digital circuits tolerant to process variations

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IEICE ELECTRONICS EXPRESS 2018年第14期15卷 20180475-20180475页

作者： Dominguez Rodriguez, Gilberto Luis Garcia-Gervacio, Jose Vazquez Leal, Hector Univ Veracruzana Xalapa Veracruz Mexico

It is known that process parameter variation degrades the performance of nanometer integrated circuits. Process variations reduce the maximum clock frequency operation of the chips. Diverse strategies have been proposed in the literature to overcome this issue, especially optimization algorithms (gate-sizing algorithms). However, convergence related problems have limited their use. In this work, a homotopy approach for the design of nanometer digital circuits tolerant to process variations is proposed. Two optimization strategies are developed in this work, the first based on the homotopy continuation method (HCM) and the second based on a modification of HCM, called in this work reboot homotopy continuation method (RHCM). Three logic paths were implemented to validate the algorithms. The optimization results obtained with the proposed strategies are compared with a Lagrange-Multipliers-based framework. Results obtained from HCM method are equivalent to the obtained with Lagrange Multipliers. On the other side, results obtained from the RHCM method are more accurate than the obtained with HCM and Lagrange Multipliers. Furthermore, the area used to implement the logic paths is lower when RHCM is applied. Moreover, the number of Newton-Rhapson iterations required to find the solutions are lower when RHCM is used;consequently, time computing is also lower.

关键词： process variation homotopy continuation method Lagrange Multiplier gate-sizing optimization algorithm

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Genetic algorithm-Optimized Extreme Learning Machine Model for Estimating Daily Reference Evapotranspiration in Southwest China

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ATMOSPHERE 2022年第6期13卷 971-971页

作者： Liu, Quanshan Wu, Zongjun Cui, Ningbo Zhang, Wenjiang Wang, Yaosheng Hu, Xiaotao Gong, Daozhi Zheng, Shunsheng Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Sichuan Peoples R China Chinese Acad Agr Sci Inst Environm & Sustainable Dev Agr Beijing 100081 Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Xianyang Peoples R China

Reference evapotranspiration (ET0) is an essential component in hydrological and ecological processes. The Penman-Monteith (PM) model of Food and Agriculture Organization of the United Nations (FAO) model requires a number of meteorological parameters;it is urgent to develop high-precision and computationally efficient ET0 models with fewer parameter inputs. This study proposed the genetic algorithm (GA) to optimize extreme learning machine (ELM), and evaluated the performances of ELM, GA-ELM, and empirical models for estimating daily ET0 in Southwest China. Daily meteorological data including maximum temperature (T-max), minimum temperature (T-min), wind speed (u(2)), relative humidity (RH), net radiation (R-n), and global solar radiation (R-s) during 1992-2016 from meteorological stations were used for model training and testing. The results from the FAO-56 Penman-Monteith formula were used as a control group. The results showed that GA-ELM models (with R-2 ranging 0.71-0.99, RMSE ranging 0.036-0.77 mm center dot d(-1)) outperformed the standalone ELM models (with R-2 ranging 0.716-0.99, RMSE ranging 0.08-0.77 mm center dot d(-1)) during training and testing, both of which were superior to empirical models (with R-2 ranging 0.36-0.91, RMSE ranging 0.69-2.64 mm center dot d(-1)). ET0 prediction accuracy varies with different input combination models. The machine learning models using T-max, T-min, u(2), RH, and R-n/R-s (GA-ELM5/GA-ELM4 and ELM5/ELM4) obtained the best ET0 estimates, with R-2 ranging 0.98-0.99, RMSE ranging 0.03-0.21 mm center dot d(-1), followed by models with T-max, T-min, and R-n/R-s (GA-ELM3/GA-ELM2 and ELM3/ELM2) as inputs. The machine learning models involved with R-n outperformed those with R-s when the quantity of input parameters was the same. Overall, GA-ELM5 (T-max, T-min, u(2), RH and R-n as inputs) outperformed the other models during training and testing, and was thus recommended for daily ET0 estimation. With the estimation accuracy,

关键词： reference evapotranspiration extreme learning machine optimization algorithm empirical model solar radiation

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An algorithmic multiple attribute decision-making method for heart problem analysis under neutrosophic hypersoft expert set with fuzzy parameterized degree-based setting

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1607页

作者： Ihsan, Muhammad Saeed, Muhammad Alanzi, Agaeb Mahal Khalifa, Hamiden El-Wahed Univ Management & Technol Dept Math Lahore Pakistan Qassim Univ Coll Sci & Arts Dept Math Al Badaya Saudi Arabia Cairo Univ Fac Grad Studies Stat Res Dept Operat & Management Res Giza Egypt

A fuzzy parameterized neutrosophic hypersoft expert set (FpNHse-set) is one of the family members of fuzzy parameterized structures and a valuable extension of the neutrosophic soft expert set as well as the neutrosophic hypersoft set. This structure involves a multi-argument approximate function that has the ability to change the sub-characteristic pairs in the form of power set of universe. The main function of this structure is the classification of each character into sub-characteristic valued sets. Due to this prominent property, this mathematical structure is useful for uncertainties and also helps make the decision-making process more adaptable and dependable. By using the algebraic and basic ideas of the FpNHse-sets, a useful strategy, especially for medical diagnosis, known as Sanchez's method has been used in this study. To see a reformed process for the medical diagnosis of heart disease, a useful combination of FpNHse-set and a modified Sanchez's method has been made in this context. By using the real data from the Cleveland data set for heart disease, the implementation of the reform process has been made to see its veracity. Finally, a clear comparison of the used study with its existing studies has been made for the purpose of benefits.

关键词： Fuzzy set Fuzzy soft expert set Hypersoft expert set optimization algorithm Decision-making

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Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes

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JOURNAL OF NEURAL ENGINEERING 2018年第2期15卷 026005-026005页

作者： Anderson, Daria Nesterovich Osting, Braxton Vorwerk, Johannes Dorval, Alan D. Butson, Christopher R. Univ Utah Dept Bioengn Salt Lake City UT 84112 USA Univ Utah Sci Comp & Imaging SCI Inst Salt Lake City UT 84112 USA Univ Utah Dept Math Salt Lake City UT 84112 USA Univ Utah Dept Neurol Salt Lake City UT 84112 USA Univ Utah Dept Neurosurg Salt Lake City UT 84112 USA

Objective. Deep brain stimulation (DBS) is a growing treatment option for movement and psychiatric disorders. As DBS technology moves toward directional leads with increased numbers of smaller electrode contacts, trial-and-error methods of manual DBS programming are becoming too time-consuming for clinical feasibility. We propose an algorithm to automate DBS programming in near real-time for a wide range of DBS lead designs. Approach. Magnetic resonance imaging and diffusion tensor imaging are used to build finite element models that include anisotropic conductivity. The algorithm maximizes activation of target tissue and utilizes the Hessian matrix of the electric potential to approximate activation of neurons in all directions. We demonstrate our algorithm's ability in an example programming case that targets the subthalamic nucleus (STN) for the treatment of Parkinson's disease for three lead designs: the Medtronic 3389 (four cylindrical contacts), the direct STNAcute (two cylindrical contacts, six directional contacts), and the Medtronic-Sapiens lead (40 directional contacts). Main results. The optimization algorithm returns patient-specific contact configurations in near real-time-less than 10 s for even the most complex leads. When the lead was placed centrally in the target STN, the directional leads were able to activate over 50% of the region, whereas the Medtronic 3389 could activate only 40%. When the lead was placed 2 mm lateral to the target, the directional leads performed as well as they did in the central position, but the Medtronic 3389 activated only 2.9% of the STN. Significance. This DBS programming algorithm can be applied to cylindrical electrodes as well as novel directional leads that are too complex with modern technology to be manually programmed. This algorithm may reduce clinical programming time and encourage the use of directional leads, since they activate a larger volume of the target area than cylindrical electrodes in central and of

关键词： deep brain stimulation optimization algorithm hessian activating function Parkinson's disease finite element model directional electrodes

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