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Efficient Amplification of Large-Signal Spoof Surface Plasmon Polaritons in a Subwavelength Size

ADVANCED MATERIALS TECHNOLOGIES 2024年第5期9卷

作者： Liao, Zhen Wu, Shaofei Guo, Jia Liu, Leilei Cai, Jialin Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Natl & Local Joint Engn Lab RF Integrat & Microass Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Flexible Elect Future Technol Nanjing 210023 Peoples R China Hangzhou Dianzi Univ Key Lab RF Circuit & Syst Minist Educ Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Zhejiang Key Lab Large Scale Integrated Circuit De Hangzhou 310018 Peoples R China

The advancement of communication systems demands ever-smaller sizes, leading to the integration of more circuits in compact forms. A promising solution lies in Spoof Surface Plasmon Polaritons (SSPPs), which enable strong confinement, low interference, and a significant wave vector, thereby facilitating smaller sizes. However, the amplification of large plasmonic signals in a compact size remains a daunting scientific challenge for the further development of SSPPs communication systems. In this study, this challenge is tackled by selecting GaN High Electron Mobility Transistors (HEMTs) and load/source-pull technologies. To ensure efficient operation, impedance-matching networks are designed, resulting in a remarkably small amplifier structure. The approach involves utilizing the bayesian optimization (BO) algorithm to fine-tune the matching network, leading to exceptional amplification performance. The results obtained in the frequency range of 1-3 GHz are highly promising, with both simulated and measured results demonstrating an impressive output power exceeding 40 dBm, a power added efficiency (PAE) surpassing 60%, and a gain exceeding 10 dB. These remarkable achievements underscore the viability of amplifying large-signal SSPPs and outputting high-power SSPPs waves within a subwavelength size, opening exciting possibilities for SSPPs communication, sensing, and imaging systems. A novel design of a high-power SSPPs amplifier at 1-3GHz utilizing HEMT is presented. The optimized structure is simulated, fabricated, and measured, resulting in an impressive output power exceeding 40 dBm, a PAE surpassing 60%, and a gain exceeding 10 dB. These results dvalidate the proposed design for amplifying large-signal SSPPs and its practical realization for high-power SSPPs ***

关键词： bayesian optimization algorithm power amplification spoof surface plasmon polaritons

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Optimal hybrid artificial intelligence models for predicting the adsorptive removal of dyes and phenols from aqueous solutions using an amine-functionalized graphene oxide/layered triple hydroxide nanocomposite

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JOURNAL OF MOLECULAR LIQUIDS 2023年第PartB期391卷

作者： Al-Jamimi, Hamdi A. Bahadi, Salem A. Binmakhashen, Galal M. Onaizi, Sagheer A. King Fahd Univ Petr & Minerals Comp Sci & Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Res Excellence Dhahran 31216 Saudi Arabia King Fahd Univ Petr & Minerals Dept Chem Engn Dhahran 31216 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Hydrogen & Energy Storag Dhahran 31216 Saudi Arabia

Water pollution is a serious environmental problem with a significant negative impact on human health and the ecosystem. Adsorption is an attractive process for water decontamination. Developing artificial intelligence models capable of predicting the adsorption performance of newly developed materials could save huge amounts of resources and efforts. In the present study, a novel polyethyleneimine/graphene oxide/layered triple hy-droxide (PEI/GO/LTH) nanocomposite was synthesized, characterized, and applied to adoptively remove harmful phenolic (bisphenol A) and azo dye (acid red 1) from wastewater samples. The results revealed that the PEI/GO/LTH nanocomposite is highly effective in removing these two pollutants. The adsorption isotherms and kinetics of these two pollutants are best fitted by the Langmuir isotherm and pseudo-second-order models, respectively. Additionally, the synthesized nanocomposite is easily and highly regenerable with an insignificant loss in performance when repeatedly used. Besides the above investigations, the present study also employs support vector machines (SVM) and bayesian optimization as tools for predicting the adsorptive removal of acid red 1 (AR1) dye and bisphenol A (BPA) from contaminated water samples by the synthesized PEI/GO/LTH nanocomposite. The models achieved promising results as the correlation coefficients, during the testing phase, reached 97.3 and 96.6 % for the AR1 and BPA data using BO-SVM models, respectively.

关键词： Adsorption Polyethyleneimine/graphene oxide/layered triple hydroxide (PEI/GO/LTH) nanocomposite Wastewater treatment Artificial intelligence Machine learning bayesian optimization algorithm

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Using evolutionary algorithms for reachability analysis of complex software systems specified through graph transformation

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2019年 191卷 106577-000页

作者： Pira, Einollah Rafe, Vahid Nikanjam, Amin Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz *** Iran Arak Univ Fac Engn Dept Comp Engn Arak *** Iran KN Toosi Univ Technol Fac Comp Engn Tehran *** Iran

Assessing the reliability of safety-critical systems is an important and challenging task because even a minor failure in these systems may result in catastrophic consequences, like losing human life. A well-known and fully automatic technique in reliability assessing approaches is model checking. However, applying this technique to verify some properties such as safety may lead to the state space explosion problem in which all reachable states cannot be checked due to computational limitations. In such situations that the verification of a safety property is infeasible, it is possible to refute the safety property by searching a reachable state in which a special configuration (e.g., an error or an undesirable behaviour) occurs. Therefore, checking reachability can be done instead of refuting the corresponding safety property. Finding such reachable states, in the worst case, may cause the state space explosion problem again. Hence, using evolutionary algorithms to explore the state space efficiently can be a promising idea. In this paper, at first, we propose an evolutionary algorithm to check reachability properties and refute safety ones in software systems specified formally through graph transformations. Since the accuracy and convergence speed of the proposed approach can still be improved, we employ the bayesian optimization algorithm (BOA) to propose another approach. In BOA, a bayesian network is learnt from the population and then sampled to generate new solutions. The proposed approaches can be used to analyse the reachability and safety properties. The proposed approaches are implemented in GROOVE which is an open source toolset for designing and model checking graph transformation systems. To evaluate the efficiency of the proposed approaches, different benchmark problems are employed. Experimental results show that the proposed approaches are faster and more accurate than the existing methods.

关键词： bayesian optimization algorithm Genetic algorithm Model checking State space explosion Graph transformation system

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Short-Term Photovoltaic Power Forecasting Based on Historical Information and Deep Learning Methods

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SENSORS 2022年第24期22卷 9630页

作者： Guo, Xianchao Mo, Yuchang Yan, Ke Huaqiao Univ Fujian Prov Univ Key Lab Computat Sci Quanzhou 362021 Peoples R China Natl Univ Singapore Coll Design & Engn Dept Built Environm Singapore 117566 Singapore

The accurate prediction of photovoltaic (PV) power is essential for planning power systems and constructing intelligent grids. However, this has become difficult due to the intermittency and instability of PV power data. This paper introduces a deep learning framework based on 7.5 min-ahead and 15 min-ahead approaches to predict short-term PV power. Specifically, we propose a hybrid model based on singular spectrum analysis (SSA) and bidirectional long short-term memory (BiLSTM) networks with the bayesian optimization (BO) algorithm. To begin, the SSA decomposes the PV power series into several sub-signals. Then, the BO algorithm automatically adjusts hyperparameters for the deep neural network architecture. Following that, parallel BiLSTM networks predict the value of each component. Finally, the prediction of the sub-signals is summed to generate the final prediction results. The performance of the proposed model is investigated using two datasets collected from real-world rooftop stations in eastern China. The 7.5 min-ahead predictions generated by the proposed model can reduce up to 380.51% error, and the 15 min-ahead predictions decrease by up to 296.01% error. The experimental results demonstrate the superiority of the proposed model in comparison to other forecasting methods.

关键词： bayesian optimization algorithm bidirectional long short-term memory deep learning singular spectrum analysis short-term photovoltaic power forecasting

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A bayesian optimal convolutional neural network approach for classification of coal and gangue with multispectral imaging

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OPTICS AND LASERS IN ENGINEERING 2022年 156卷

作者： Hu, Feng Zhou, Mengran Yan, Pengcheng Liang, Zhe Li, Mei Anhui Univ Sci & Technol Sch Elect & Informat Engn 168 Taifeng Rd Huainan 232001 Anhui Peoples R China Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Anhui Peoples R China

The precise classification of coal and gangue is a crucial link for effective sorting and efficient utilization. However, there are some shortcomings in traditional methods, such as water consumption, coal slime pollution, and great influence of environmental factors, and so on. Here, multispectral imaging technology combined with the convolutional neural network (CNN) was applied to classify coal and gangue, in which the hyperparameters of the CNN model were optimized by bayesian algorithm. The multispectral images in the range of 675-975 nm of 209 pieces of coal and 201 pieces of gangue, which came from the Huainan mining area, were collected. The CNN and traditional modeling methods (combination strategy of image feature extraction and classifier) were employed to develop identification models, and the classification results were analyzed and compared on the multispectral dataset of coal and gangue. The identification analysis model based on CNN had the best performance, and the F1 score reached 1.00. At this time, the hyperparameters of the model are as follows: network depth was 1, initial learning rate was 0.012939, random gradient descent momentum was 0.83813, and L2 regularization intensity was 0.0099852. Moreover, the robustness of the CNN identification model was verified by introducing different levels of noise signals. The identification analysis model based on the CNN can quickly and accurately identify coal and gangue without complex image processing steps, and the model has certain anti-interference ability, which will promote the progress of automatic separation technology for coal and gangue.

关键词： Multispectral imaging Convolutional neural network Coal-gangue identification bayesian optimization algorithm

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Explainable highway performance degradation prediction model based on LSTM

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ADVANCED ENGINEERING INFORMATICS 2024年 61卷

作者： Sun, Xin Wang, Honglei Mei, Shilong Guizhou Univ Sch Management Guiyang 550025 Peoples R China Guizhou Minzu Univ Architectural Engn Coll Guiyang 550025 Peoples R China Key Lab Internet Collaborat Intelligent Mfg Guizho Guiyang 550025 Peoples R China Guizhou Univ Coll Architecture & Urban Planning Guiyang 550025 Peoples R China

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decisionmaking. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic l

关键词： Long Short-Term Memory (LSTM) Highway performance degradation bayesian optimization algorithm SHapley Additive exPlanations (SHAP) Explainable prediction model

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A data-driven model for predicting the mixed-mode stress intensity factors of a crack in composites

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ENGINEERING FRACTURE MECHANICS 2023年第1期288卷

作者： Zhang, Xinyu Zhao, Tingting Liu, Yifan Chen, Qingqing Wang, Zhiyong Wang, Zhihua Taiyuan Univ Technol Inst Appl Mech Coll Mech & Vehicle Engn Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Technol Shanxi Key Lab Mat Strength & Struct Impact Taiyuan 030024 Shanxi Peoples R China Beijing Inst Technol State Key Lab Explos Sci & Technol Beijing 100081 Peoples R China

A data-driven model is trained to predict mixed-mode stress intensity factors (SIFs) of composites through an artificial neural network (ANN) method. The model is based on a database generated by combining the interaction integral and the extended finite element method (XFEM). To reduce the input dimensionality and improve predictive performance, feature engineering is performed on the input data, and principal component analysis is conducted. Hyperparameters of the model are adjusted by using K-fold cross-validation and bayesian optimization algorithm (BOA) to enhance the adaptability and generalization of the model. To overcome the data scarcity issue, an active learning knowledge extraction framework is constructed, which allows for accurate knowledge extraction even with limited data. By utilizing data-driven models to solve mixed -mode SIFs, the computational cost and complexity are greatly reduced compared to numerical simulations, while computational stability and ability to deal with high-dimensional nonlinear problems are significantly improved.

关键词： Stress intensity factors Composites Data driven Active learning bayesian optimization algorithm

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Abnormal data detection for industrial processes using adversarial autoencoders support vector data description

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MEASUREMENT SCIENCE AND TECHNOLOGY 2022年第5期33卷

作者： Qiu, Kepeng Song, Weihong Wang, Peng Tsinghua Univ Dept Precis Instrument State Key Lab Precis Measurement Technol & Instru Beijing 100084 Peoples R China Heilongjiang North Tool Co Ltd Mudanjiang 157000 Heilongjiang Peoples R China

Abnormal data detection for industrial processes is essential in industrial process monitoring and is an important technology to ensure production safety. However, for most industrial processes, it is a challenge to establish an effective abnormal data detection model due to the following issues: (a) weak model performance due to the small amount of process data;(b) trade-offs between model sparsity and accuracy;and (c) weak generalization ability of abnormal data detection model. To address these issues, a method based on adversarial autoencoders support vector data description (AAESVDD) is presented in this work. First, a novel construction strategy is designed for a hybrid feature dataset based on the adversarial autoencoder (AAE). The hybrid feature dataset utilizes the latent feature and reconstruction residual extracted by the AAE to enhance the feature diversity of the process data. Then, combining the support vector data description (SVDD) and bayesian optimization algorithm (BOA), an automatic detection model for abnormal data of the hybrid feature dataset is established. Meanwhile, a BOA objective function based on the criterion of the hybrid risk minimization is proposed to automatically optimize the model parameters, which further enhances the generalization ability of the SVDD-based model. Finally, the effectiveness of the proposed AAESVDD method is illustrated with the UCI benchmark datasets and an industrial penicillin fermentation process.

关键词： industrial processes abnormal detection support vector data description adversarial autoencoders bayesian optimization algorithm

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A novel approach to solve AI planning problems in graph transformations

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 92卷 103684-103684页

作者： Pira, Einollah Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz *** Iran

The aim of AI planning is to solve the problems with no exact solution available. These problems usually have a big search space, and planning may not find plans with the least actions and in the shortest time. Recent researches show that using suitable heuristics can help to find desired plans. In planning problems specified formally through graph transformation system (GTS), there are dependencies between applied rules (actions) in the search space. This fact motivates us to solve the planning problem for a small goal (instead of the main goal), extract dependencies from the searched space, and use these dependencies to solve the planning problem for the main goal. In GTS based systems, the nodes of a state (really is a graph) can be grouped due to their type. To create a small (refined) goal, we use a refinement technique to remove the predefined percent of nodes from each group of the main goal. bayesian optimization algorithm (BOA) is then used to solve the planning problem for the refined goal. BOA is an Estimation of Distribution algorithm (EDA) in which bayesian networks are used to evolve the solution populations. Actually, a bayesian network is learned from the current population, and then this network is employed to generate the next population. Since the last bayesian network learned in BOA has the knowledge about dependencies between applied rules, this network can be used to solve the planning problem for the main goal. Experimental results on four well-known planning domains confirm that the proposed approach finds plans with the least actions and in the lower time compared with the state-of-the-art approaches.

关键词： bayesian optimization algorithm AI planning Graph transformation system bayesian network Refinement

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State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles

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ENERGY 2023年第PartE期263卷

作者： Li, Guanzheng Li, Bin Li, Chao Wang, Shuai Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Tianjin Univ Natl Ind Educ Platform Energy Storage Tianjin Peoples R China

Lithium-ion batteries are playing an increasingly important role in industrial applications such as electrical vehicles and energy storage systems. Their working performance and operation safety are significantly impacted by state of health (SOH), which will decrease after cycles of charging and discharging. This paper has proposed a novel two-stage SOH estimation method that can realize SOH estimation flexibly, rapidly and robustly. In the first stage, eight typical 300-s voltage profiles are used for describing the whole charging process and multiple aging features are extracted. Then, a novel stacking ensemble model with five base models is proposed. In the second stage, a Shapley additive explanation approach is introduced to obtain the contributions of features and understand why a prediction is made, thus reducing the concern of applying black-box model. The performance of the proposed model is verified using two different battery degradation datasets and the results show that the accuracy of the proposed model is better than conventional machine learning models including lightGBM, XGBoost, RF, SVR, and GPR. In addition, with various forms of noise interference, the proposed stacking model is proved to be more robust than conventional machine learning models.

关键词： Stacking ensemble model Interpretable machine learning Short-term voltage profile State of health bayesian optimization algorithm Battery aging

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