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Research on an ultrasonic detection method for weld defects based on neural network architecture search

MEASUREMENT 2023年第1期221卷

作者： Zhang, Rui Gao, Mei-Rong Zhang, Peng-Yun Zhang, Yong-Mei Fu, Liu-Hu Chai, Yan-Feng Taiyuan Univ Sci & Technol Coll Comp Sci & Technol Taiyuan 030024 Peoples R China North China Univ Technol Sch Informat Beijing 100144 Peoples R China Shanxi Design & Res Inst Mech & Elect Engn Co Ltd Taiyuan 030009 Peoples R China

In order to further reduce the subjectivity of network design and improve the ability of model feature extraction, an ultrasonic detection method for weld defects based on neural network architecture search is proposed. Through the designed multi-level and multi-branch search space and an untrained architecture search and evaluation method, an efficient defect classification network was automatically constructed to complete the task of weld defect classification. Experiments were carried out on a self-constructed data set, and compared with the manually designed model, the classification accuracy of defect types reached 95.26% when the number of parameters was only 7.3 M. Compared with the model constructed using neural network architecture search, the proposed method can reduce the searching time to 8.29% of the baseline model while weighing multiple conflicting objectives, which proved the efficiency and effectiveness of the proposed method.

关键词： Weld defects Ultrasonic testing Neural network architecture search multi-objective optimization algorithm

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Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

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ENERGIES 2019年第13期12卷 2467页

作者： Ni, Kailai Wang, Jianzhou Tang, Guangyu Wei, Danxiang Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management.

关键词： electricity load forecasting hybrid model data preprocessing strategy multi-objective optimization algorithm deep neural network

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A rapid diagnosis technology of short circuit fault in DC microgrid

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 147卷

作者： Wan, Qingzhu Zheng, Shuai Shi, Chenlu North China Univ Technol Sch Elect & Control Engn Beijing 100144 Peoples R China

With the introduction of new energy sources, the structure of DC microgrid is becoming more complex, and short circuit fault diagnosis is inefficient. A rapid diagnosis technology of short circuit fault in DC microgrid is pro-posed, which consists of two parts: fault classification and fault location. Firstly, the law of transient current change at the beginning and end of a line is analyzed when different faults occur in the line and the converter, and a classification criterion is constructed to judge the fault type by using the rate of change of transient current at the head and end of the line, and a converter fault location criterion is constructed to judge the location of fault converter. Then for line fault location, the mathematical model is established according to the specific type of line fault, the analytical expression of line parameters is given, multiple objective functions are constructed, and the parameters of the fault line are identified by the multi-objective optimization algorithm based on genetic algorithm, and fault location is carried out by using line parameters. Finally, a simulation model of the DC microgrid is built in Matlab/Simulink to prove the validity and practicability of the scheme.

关键词： New energy sources DC microgrid Short circuit fault Transient current change rate multi-objective optimization algorithm

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Research of a novel short-term wind forecasting system based on multi-objective Aquila optimizer for point and interval forecast

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ENERGY CONVERSION AND MANAGEMENT 2022年 263卷 1页

作者： Xing, Qianyi Wang, Jianzhou Lu, Haiyan Wang, Shuai Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sch Software Sydney NSW Australia Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features abundant deposits, extensive distribution, non-pollution, etc. In recent years, wind power generation occupies a non-negligible position in the electric power industry. Stable and reliable power system operation demands accurate wind speed prediction (WSP), but the inherent randomness of wind speed sequences complicates their fluctuations and causes them to be uncontrollable. In this paper, an innovative WSP system is proposed, which combines data pre-processing technique, benchmark model selection, an advanced optimizer for point forecast and interval forecast. Furthermore, this paper theoretically demonstrates that the weights allocated by this optimizer are Pareto optimal solutions. Six interval data from two sites in China are utilized to validate the forecasting performance of our developed model. The experimental results indicate that the developed model can achieve superior accuracy compared to the tested models in all cases for point forecast, and also obtains the forecasting interval with high coverage and low width error, which is an extremely crucial instruction to guarantee the security and stability of the power system.

关键词： Wind speed forecasting Data pre-processing Optimal benchmark model selection strategy multi-objective optimization algorithm

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Optimizing MMIC performance: The synergy of AI models and heterogeneous integration process

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2024年第3期37卷

作者： Liu, Jie Chen, Jiayu Wu, Yifan Su, Guodong Wang, Junchao Xu, Yuehang Liu, Jun Univ Elect & Sci Technol China Sch Elect Sci & Engn Chengdu Peoples R China China Elect Technol Grp Corp Informat Sci Acad Beijing Peoples R China Hangzhou Dianzi Univ Sch Integrated Circuit Sci & Engn Hangzhou Zhejiang Peoples R China

This study presents a novel approach for optimizing the parameters of monolithic microwave integrated circuit (MMIC) functional units using machine-learning techniques and multi-objective optimization algorithms. We utilize advanced machine-learning methods, including random forest, artificial neural networks (ANNs), and recurrent neural networks (RNNs), to construct highly accurate models that predict the performance of these units. These models are subsequently integrated with a multi-objective optimization algorithm, specifically the multi-objective particle swarm optimization (MOPSO), to generate inverse design solutions for both the geometric designs of the units and the fabrication parameters of the heterogeneous integration process. Our approach, which has been validated through chip fabrication and testing, has demonstrated its robustness as a tool for achieving optimal MMIC designs. It not only reduces the design time but also enhances the manufacturability of MMICs, thereby opening new avenues in microwave and RF circuit design.

关键词： heterogeneous integration machine learning MMIC multi-objective optimization algorithm

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Optimizing Electric Vehicle Charging Infrastructures in the Smart Grid with a multi-objective Approach

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IFAC-PapersOnLine 2024年第13期58卷 380-385页

作者： Meriem Belaid Said El Beid Anas Hatim CISIEV Team Cadi Ayyad University Marrakech 40160 Morocco TIM team Cadi Ayyad University Marrakech 40160 Morocco

This paper introduces a tool designed to optimize electric vehicle (EV) charging infrastructures within the smart grid framework. The tool utilizes a multi-objective approach and is programmed in Python. It enables dynamic management of energy distribution among different EV charging infrastructures, addressing scenarios where surplus photovoltaic (PV) power generation exceeds charging demands but faces challenges due to storage costs and electric energy transmission rates to alternative infrastructures. In instances of low PV production relative to charging demand, the algorithm strategically selects the optimal procurement strategy, either purchasing electric energy from neighboring infrastructures or utilizing surplus PV energy for direct charging. The tool empowers stakeholders to make informed decisions by facilitating comparisons between the cost of storing electric energy locally and the expense of procuring it from external sources, thereby enhancing the efficiency and cost-effectiveness of EV charging infrastructures in the smart grid ecosystem. Extensive simulations and case studies demonstrate the efficacy of the proposed approach, showcasing its potential to optimize energy distribution and promote sustainable practices within the EV charging domain.

关键词： Energy management multi-objective optimization algorithm Smart grid Charging infrastructure Electric vehicle Python PYPSA PV power

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A novel ensemble probabilistic forecasting system for uncertainty in wind speed

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APPLIED ENERGY 2022年 313卷 118796-118796页

作者： Wang, Jianzhou Wang, Shuai Zeng, Bo Lu, Haiyan Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Chongqing Technol & Business Univ Collaborat Innovat Ctr Chongqings Modern Trade Lo Chongqing 400067 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sch Comp Sci Sydney NSW Australia

The quantification of wind speed uncertainty is of great significance for real-time control of wind turbines and power grid dispatching. However, the intermittence and fluctuation of wind energy present great challenges in modeling its uncertainty;research in this field is limited. A quantile regression bi-directional long short-term memory network (QrBiLStm) and a novel ensemble probabilistic forecasting strategy are proposed in this study to explore ensemble probabilistic forecasting. To verify the reliability of the proposed ensemble probabilistic forecasting system, the uncertainties of wind speed at wind farms in China were modeled as a case study. The results of comparative experiments including 15 other models demonstrate the superiority of this ensemble probabilistic forecasting system in terms of sharpness while maintaining high interval coverage. More specifically, it was observed that the prediction interval coverage probability obtained by the proposed system is above 97%, and the sharpness is improved by at least 24.21% as compared with the commonly used single models. The proposed ensemble probabilistic forecasting system can accurately quantify the uncertainty of wind speed, and also reduce the operation cost of power systems by improving the efficiency of wind energy utilization.

关键词： Wind speed forecasts multi-objective optimization algorithm Deep learning Ensemble probabilistic strategy Forecast uncertainty

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Modelling of carbon price in two real carbon trading markets

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JOURNAL OF CLEANER PRODUCTION 2020年 244卷

作者： Hao, Yan Tian, Chengshi Wu, Chunying Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Carbon price forecasting plays a vital role in establishing a reasonable and stable carbon market. A number of carbon price forecasting models have been developed to improve the effectiveness of the predictions. However, most of the previous studies failed to focus on the role of choosing the appropriate input features and only aimed to improve the forecasting accuracy. In this paper, a novel hybrid model based on feature selection and a multi-objective optimization algorithm is proposed for carbon price forecasting. More specifically, the main novel contributions of this study are as follows. A two-stage feature selection method is developed to obtain the appropriate input variables to enhance the forecasting ability. In addition, the weighted regularized extreme learning machine is optimized using a multi-objective optimization algorithm, named the multi-objective grasshopper optimization algorithm, which can obtain better forecasting results. To demonstrate the effectiveness of the developed carbon price forecasting model, two daily carbon price datasets that were collected from the China and European Union Emissions Trading Scheme, are used in this study. The results revealed that the mean absolute percentage errors of the proposed model utilizing data from the China and European Union Emissions Trading Scheme are 2.4923% and 0.8418%, respectively, which are lower than those of other compared models. In addition, the variances of the forecasting errors of the developed model are 1.1419 and 0.0038 for the data from the China and European Union Emissions Trading Scheme, respectively. These results reflect the superior forecasting ability of this method compared to other methods. Therefore, the proposed method is more effective than other models in carbon price forecasting. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Carbon price forecasting Hybrid model Two-stage feature selection multi-objective optimization algorithm

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Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study

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PLANT METHODS 2020年第1期16卷 112-112页

作者： Hesami, Mohsen Naderi, Roohangiz Tohidfar, Masoud Yoosefzadeh-Najafabadi, Mohsen Univ Guelph Dept Plant Agr Guelph ON Canada Univ Tehran Fac Agr Dept Hort Sci Karaj Iran Shahid Beheshti Univ Fac Sci & Biotechnol Dept Plant Biotechnol GC Tehran Iran

Background Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy. Results The results showed that SVR (R-2 > 0.92) had better performance accuracy than MLP (R-2 > 0.82). Moreover, the Non-dominated Sorting Genetic algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10 mu M 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70 mu M kinetin (KIN), and 18.73 mu M sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum's somatic embryogenesis accurately. Conclusions SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.

关键词： Artificial intelligence Support vector regression multi-objective optimization algorithm Machine learning algorithms multilayer perceptron Somatic embryogenesis Chrysanthemum Nitric oxide

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Point and interval prediction for non-ferrous metals based on a hybrid prediction framework

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RESOURCES POLICY 2021年 73卷 1页

作者： Wang, Jianzhou Niu, Xinsong Zhang, Linyue Lv, Mengzheng Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

As a bulk product with huge international circulation, non-ferrous metals have frequent and severe price fluctuations, which have attracted great attention from academia and industry. However, the non-ferrous metal price series has strong volatility and nonlinear characteristics, which makes the realization of high-precision forecasts still a difficult and challenging problem. In this paper, a hybrid point prediction system is constructed to achieve high precision point prediction results. Moreover, uncertain forecasts contain more information and can provide market participants with more detailed guidance, but uncertainty forecasting is often ignored in practice. Based on the high precision point prediction system, the uncertainty prediction framework is proposed in this paper. Different distribution functions were used to analyze the distribution characteristics of the data, and the uncertainty prediction at different levels was successfully realized according to point prediction results. To verify prediction performance of the proposed prediction framework, multiple contrast experiments have been carried out using the London Metal Exchange daily future prices of Zinc, Copper and Lead. The empirical results show that the developed prediction framework has better predictive power for non-ferrous metals price prediction.

关键词： Non-ferrous metals Interval prediction Artificial intelligence multi-objective optimization algorithm Distribution function

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