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Prediction models of bit errors for NAND flash memory using 200 days of measured data

REVIEW OF SCIENTIFIC INSTRUMENTS 2019年第6期90卷 064702-064702页

作者： Wei, Debao Qiao, Liyan Chen, Xiaoyu Feng, Hua Peng, Xiyuan Harbin Inst Technol Sch Elect & Informat Engn Harbin 150080 Heilongjiang Peoples R China

NAND flash memory, with its excellent storage performance, has become a leader in storage media for scientific instruments. With improvements in the manufacturing technique, its reliability is decreasing. Thus, methods for improving the storage reliability of scientific instruments has become a major topic. If a method to effectively evaluate NAND flash error distribution is found, it will provide significant guidance for error correction codes and the wear equalization algorithm to improve the performance of NAND flash memory. It will also be a good method to predict the flash memory life. Based on data measured over 200 days from an experimental platform for the NAND flash, in this study, the polynomial fitting method, an artificial neural network, and a support vector regression were adopted to build a NAND flash bit errors prediction model, and efficacies of the methods are compared. The simulation results show that the different evaluation models have their own advantages in different situations.

关键词： Flash memory optimization algorithms Statistical analysis Regression analysis Artificial neural networks Electrical properties and parameters

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A fast and accurate piezoelectric actuator modeling method based on truncated least squares support vector regression

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REVIEW OF SCIENTIFIC INSTRUMENTS 2019年第5期90卷 055004-055004页

作者： Liu, Xiangdong Ma, Zhibiao Mao, Xuefei Shan, Jinjun Wang, Yijun Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China York Univ Dept Earth & Space Sci & Engn Toronto ON M3J1P3 Canada CASICIoud Beijing 100080 Peoples R China

In order to improve the applicability of piezoelectric actuators (PEAs) in precision positioning, least squares support vector regression (LS-SVR) is applied to model hysteresis in PEAs due to its high modeling accuracy and fast convergence speed. However, low robustness of LS-SVR makes modeling accuracy susceptible to noises, which makes LS-SVR hysteresis models difficult to be applied in engineering environment. In this article, a robust truncated least squares support vector regression (T-LSSVR) is proposed. With the truncation strategy, redundancy in the training set is reduced and robustness is improved. Parameters required for T-LSSVR are optimized by particle swarm optimization and cross optimization algorithms. To test the proposed approach, it is applied to predict the hysteresis of PEAs. Results show that the proposed method is more accurate and robust than other versions of LS-SVR when the training set is polluted by noises, and meanwhile reduces the sample size and increases computational efficiency. Published under license by AlP Publishing.

关键词： Precision engineering optimization algorithms Piezoelectricity Hysteresis loop tracer

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Hyperparameter Search for Machine Learning algorithms for Optimizing the Computational Complexity

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PROCESSES 2023年第2期11卷 349页

作者： Ali, Yasser A. Awwad, Emad Mahrous Al-Razgan, Muna Maarouf, Ali King Saud Univ Dept Informat Syst Coll Comp & Informat Sci POB 51178 Riyadh 11543 Saudi Arabia King Saud Univ Coll Engn Elect Engn Dept POB 800 Riyadh 11421 Saudi Arabia King Saud Univ Dept Software Engn Coll Comp & Informat Sci POB 22452 Riyadh 11495 Saudi Arabia

For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms-the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale optimization, and the Particle Swarm optimization-to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyperparameter tuning. It was found that the Genetic Algorithm had a lower temporal complexity than other algorithms.

关键词： hyperparameter tuning machine learning optimization algorithms ant bee colony (ABC) genetic algorithm (GA) whale optimization (WO) particle swarm optimization (PSO) support vector machine (SVM)

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Parameter Estimation of Photovoltaic Cell/Modules Using Bonobo Optimizer

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ENERGIES 2022年第1期15卷 140页

作者： Al-Shamma'a, Abdullrahman A. Omotoso, Hammed O. Alturki, Fahd A. Farh, Hassan. M. H. Alkuhayli, Abdulaziz Alsharabi, Khalil Noman, Abdullah M. King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia

In this paper, a new application of Bonobo (BO) metaheuristic optimizer is presented for PV parameter extraction. Its processes depict a reproductive approach and the social conduct of Bonobos. The BO algorithm is employed to extract the parameters of both the single diode and double diode model. The good performance of the BO is experimentally investigated on three commercial PV modules (STM6-40 and STP6-120/36) and an R.T.C. France silicon solar cell under various operating circumstances. The algorithm is easy to implement with less computational time. BO is extensively compared to other state of the art algorithms, manta ray foraging optimization (MRFO), artificial bee colony (ABO), particle swarm optimization (PSO), flower pollination algorithm (FPA), and supply-demand-based optimization (SDO) algorithms. Throughout the 50 runs, the BO algorithm has the best performance in terms of minimal simulation time for the R.T.C. France silicon, STM6-40/36 and STP6-120/36 modules. The fitness results obtained through root mean square (RMSE), standard deviation (SD), and consistency of solution demonstrate the robustness of BO.

关键词： photovoltaic parameter extraction Bonobo optimizer renewable energy optimization algorithms

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A Literature Review on the Optimal Placement of Static Synchronous Compensator (STATCOM) in Distribution Networks

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ENERGIES 2023年第17期16卷 6122页

作者： Mumtahina, Umme Alahakoon, Sanath Wolfs, Peter Cent Queensland Univ Sch Engn & Technol Rockhampton 4701 Australia EleXsys Ptd Ltd Brisbane 4072 Australia

The existing distribution networks were designed at a time when there was virtually no embedded generation. The design methods ensured the voltage at various parts of the network remained within the limits required by standards, and for the most part, this was very successfully achieved. As Distributed Energy Resources (DERs) started to grow, the rise in voltage due to injected currents and the local impedances started to push network voltages toward, and even above, the desired upper limits. Voltage limits are based on typical appliance requirements, and long-term over-voltages will ultimately result in unacceptably short appliance life spans. Distribution Static Compensators (dSTATCOMs) are shunt-connected devices that can improve low-voltage networks' performance by injecting currents that do not transfer real power. The currents can be reactive, negative or zero sequence, or harmonic. System performance can be improved by reducing conduction loss, improving voltage profile and voltage balance, or reducing Total Harmonic Distortion (THD). To obtain these benefits, optimal sizes of dSTATCOMs need to be placed at optimal locations within the distribution network. This paper has considered seventy research articles published over the past years related to the optimal placement and sizing of dSTATCOMs. In this study, minimization of power losses, voltage profile improvement, loadablity factor, voltage sag mitigation, and reduction in annual operating costs are considered fitness functions that are subjected to multiple constraint sets. The optimization algorithms found in the literature are categorized into six methods: analytical methods, artificial neural network-based methods, sensitivity approaches, metaheuristic methods, a combination of metaheuristic and sensitivity analysis, and miscellaneous. This study also presents a comparison among distribution network types, load flow methods optimization tools, etc. Therefore, a comprehensive review of optimal allocatio

关键词： optimal placement optimal sizing dSTATCOM distribution network optimization algorithms

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Advanced Optical Imaging Based on Metasurfaces

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ADVANCED OPTICAL MATERIALS 2024年第6期12卷

作者： Zou, Xiujuan Lin, Ruoyu Fu, Yunlai Gong, Guangxing Zhou, Xuxi Wang, Shuming Zhu, Shining Wang, Zhenlin Nanjing Univ Sch Phys Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Key Lab Intelligent Opt Sensing & Manipulat Minist Educ Nanjing 210093 Peoples R China Collaborat Innovat Ctr Adv Microstruct Nanjing 210093 Peoples R China

Recently, metasurfaces have arisen as next-generation optics with various merits, such as being ultrathin and lightweight with multi-function integration. Numerous applications of metasurfaces have been demonstrated up to now, including wavefront shaping, optical imaging, hologram display, structured light generation, polarization detection, and so on. Among them, optical imaging components and techniques have been substantially promoted with the advent of metasurfaces due to their superior spatial modulation properties over electromagnetic waves. Here, the forward and reverse design approaches of metasurface and recent advances in various imaging applications based on them are reviewed. Due to the ability of arbitrary wavefront encoding, metasurfaces have been widely applied to numerous imaging applications, including diffraction-limited imaging with aberration correction, polarization-related imaging, high-resolution spectral imaging, three-dimentional (3D) imaging, and even algorithm-assisted imaging, etc. Optical imaging applications combined with novel metasurfaces can make breakthroughs with the development of nanofabrication technologies and the improvement of computer algorithms.

关键词： imaging applications metasurfaces optimization algorithms phase-modulation

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Shapley Additive Explanations of Multigeometrical Variable Coupling Effect in Transonic Compressor

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JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME 2022年第4期144卷 041015-041015页

作者： Wang, Junying He, Xiao Wang, Baotong Zheng, Xinqian Tsinghua Univ Turbomachinery Lab State Key Lab Automot Safely & Energy Beijing 100084 Peoples R China Imperial Coll London Dept Mech Engn London SW7 2AZ England Tsinghua Univ Inst Aero Engine Beijing 100084 Peoples R China Tsinghua Univ Dept Aerodynam & Thermodynam Inst Aero Engine Beijing 100084 Peoples R China

optimization algorithms in the compressor detailed design stage generate big data of geometries and corresponding performances, but these data are often not exploited efficiently to unveil hidden compressor design guidance. In this work, the Shapley additive explanations (SHAP) method from game theory is proposed as an efficient methodology to extract design guidelines from databases. A database was generated when optimizing the blade features (sweep, lean, and end-bend) of Rotor 37. Based on this, a neural network is trained to predict compressor efficiency. The SHAP method is then applied to explain the neural network behavior, which provides information on the sensitivity of single geometrical variables and the coupling effect between multiple geometrical variables. Results show that the near-tip sweep and midspan lean angles are most influential on efficiency. Within the same group of variables, the adjacent variables tend to present strong positive coupling effects on efficiency. Among different groups, evident coupling effects are observed between sweep and lean and between lean and end-bend, but the coupling effect between sweep and end-bend is negligible. Flow mechanisms behind the coupling effects are discussed. For near-tip lean angles L3 and L4, the positive coupling effect is due to the change of the passage shock. For near-tip lean angle L4 and sweep angle S4, the change of detached shock leads to a negative coupling effect. The proposed data mining method based on the neural network and SHAP is promising and transferable to other turbomachinery optimization databases in the future.

关键词： optimization Blades Rotors Design Flow (Dynamics) Computational fluid dynamics Compressors optimization algorithms Artificial neural networks Data mining Databases

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Development of a BIM-Based Framework Using Reverberation Time (BFRT) as a Tool for Assessing and Improving Building Acoustic Environment

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BUILDINGS 2022年第5期12卷 542页

作者： Aguilar, Antonio J. de la Hoz-Torres, Maria L. Martinez-Aires, Ma Dolores Ruiz, Diego P. Univ Granada Dept Appl Phys Av Severo Ochoa S-N Granada 18071 Spain Univ Granada Dept Bldg Construct Av Severo Ochoa S-N Granada 18071 Spain

Both the building design and the construction process determine the indoor acoustic quality of enclosures. A suitable indoor acoustic environment is crucial for the productivity and well-being of users. For this purpose, Reverberation Time (RT) is often calculated or measured in situ. Recently, Building Information Modelling (BIM) has provided a new paradigm to face building projects. Nevertheless, little research has been conducted on the optimisation of indoor acoustics using BIM methodology. In this context, the objective of this work is to propose and develop a BIM-based framework for the analysis, evaluation and optimization of the RT. The proposed procedure allows designers to explore alternatives in order to achieve an adequate acoustic performance without any further needs of specific software. This proposal is devised to consider some important characteristics of the project, such as its location, applicable regulations, room uses, materials and costs. This framework calculates the solution set that meets the requirements, showing the set of optimal solutions according to the minimization of both the cost and the optimum absorbent surface area. BFRT contributes by offering a tool to support the decision making process of designers during the initial design phase in the field of acoustic conditioning of buildings.

关键词： acoustic performance building Building Information Modelling (BIM) built environment optimization algorithms reverberation time

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Comparing Mechanical Neural-Network Learning algorithms

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JOURNAL OF MECHANICAL DESIGN 2023年第7期145卷 071704-071704页

作者： Lee, Ryan H. H. Sainaghi, Pietro Hopkins, Jonathan B. Univ Calif Los Angeles Dept Mech & Aerosp Engn 420 Westwood PlazaRoom 46-127 Los Angeles CA 90095 USA

The purpose of this work is to compare learning algorithms to identify which is the fastest and most accurate for training mechanical neural networks (MNNs). MNNs are a unique class of lattice-based artificial intelligence (AI) architected materials that learn their mechanical behaviors with repeated exposure to external loads. They can learn multiple behaviors simultaneously in situ and re-learn desired behaviors after being damaged or cut into new shapes. MNNs learn by tuning the stiffnesses of their constituent beams similar to how artificial neural networks (ANNs) learn by tuning their weights. In this work, we compare the performance of six algorithms (i.e., genetic algorithm, full pattern search, partial pattern search, interior point, sequential quadratic progression, and Nelder-Mead) applied to MNN leaning. A computational model was created to simulate MNN learning using these algorithms with experimentally measured noise included. A total of 3900 runs were simulated. The results were validated using experimentally collected data from a physical MNN. We identify algorithms like Nelder-Mead that are both fast and able to reject noise. Additionally, we provide insights into selecting learning algorithms based on the desired balance between accuracy and speed, as well as the general characteristics that are favorable for training MNNs. These insights will promote more efficient MNN learning and will provide a foundation for future algorithm development.

关键词： mechanical neural network optimization algorithms learning materials AI materials architected materials metamaterials smart materials

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Optimal design of the dual-axis tracking system used for a PV string platform

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JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 2019年第4期11卷 043501-043501页

作者： Alexandru, Catalin Transilvania Univ Brasov Brasov Romania

This work deals with the optimal design of the dual-axis solar tracker used for a PV (photovoltaic) string platform. The solution of the dual-axis mechanism was developed starting from the monoaxis version (for the diurnal movement), which was outlined in a previous paper. With reference to the initial monoaxis mechanism, the diurnal movement subsystem was simplified from a structural point of view;while adding the actuating and motion transmitting mechanisms for adjusting the elevation angle of the panels, the same type of linear actuator is being used for both the movements. The optimization study was carried out in a virtual prototyping environment, through the development of complex digital models (which integrate not only mechanical, actuating, and control components, but also algorithms for the solar radiation modeling), and two major optimization directions are being targeted: The geometric optimization of the mechanism, with the purpose of determining the optimal arrangement of two linear actuators while respecting several design constraints;the optimal design of the tracking program, with the purpose of maximizing the energy gain of the dual-axis system with reference to the equivalent fixed system (without tracking). The physical prototype of the tracking system was developed on the basis of the virtual prototype specifications, the experimental and simulation results, which are quite well correlated (especially in sunny days), proving the performance of the proposed design.

关键词： Tracking devices Atmospheric radiation Solar tracking system Programmable logic controller optimization algorithms Energy consumption Photovoltaics Solar panels

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