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H∞控制；零和动态博弈；强化学习；自适应动态规划；极小极大Q-学习；策略迭代 1

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Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

《信息与电子工程前沿（英文）》 2015年第16卷第12期页码 1046-1058 doi: 10.1631/FITEE.1500085

摘要： With the development of face recognition using sparse representation based classification (SRC), many relevant methods have been proposed and investigated. However, when the dictionary is large and the representation is sparse, only a small proportion of the elements contributes to the 1-minimization. Under this observation, several approaches have been developed to carry out an efficient element selection procedure before SRC. In this paper, we employ a metric learning approach which helps find the active elements correctly by taking into account the interclass/intraclass relationship and manifold structure of face images. After the metric has been learned, a neighborhood graph is constructed in the projected space. A fast marching algorithm is used to rapidly select the subset from the graph, and SRC is implemented for classification. Experimental results show that our method achieves promising performance and significant efficiency enhancement.

关键词： Face recognition Sparse representation Manifold structure Metric learning Subset selection

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种基于特征选择与迁移学习的度量补偿软件缺陷预测方法 Research Article

陈锦富1,2,王小丽1,2,蔡赛华1,2,徐家平1,陈静怡1,陈海波1

《信息与电子工程前沿（英文）》 2022年第23卷第5期页码 715-731 doi: 10.1631/FITEE.2100468

摘要：跨项目软件缺陷预测解决了传统缺陷预测中训练数据不足的问题，克服了将多个不同源项目中学习的模型应用于目标项目的挑战。与此同时，出现两个新问题：（1）模型训练过程中过多无关和冗余特征影响训练效率，降低了模型预测精度；（2）由于开发环境等因素，度量值的分布因项目而异，当模型用于跨项目预测时，预测精度较低。本文引入皮尔逊特征选择方法解决数据冗余问题，采用基于迁移学习的度量补偿技术解决源项目和目标项目之间数据分布差异较大的问题。提出一种基于特征选择和迁移学习的度量补偿软件缺陷预测方法。实验结果表明，用该方法构建的模型在AUC（接收器工作特性曲线下面积）值和F1度量指标上取得较好结果。

关键词：缺陷预测；特征选择；迁移学习；度量补偿

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一种局部二次嵌入学习算法及其在软测量中的应用 Article

包垚垚, 朱远明, 钱峰

《工程（英文）》 2022年第18卷第11期页码 186-196 doi: 10.1016/j.eng.2022.04.025

摘要：首先，通过优化输入输出空间里样本间度量的全局一致性来改进马氏度量（Mahalanobis metric）学习算法；同时，通过引入松弛约束进一步证明了改进的度量学习问题等价于一个凸规划问题。

关键词：局部二次嵌入度量学习回归机软测量

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利用流形学习进行基于图像的三维模型检索 None

Pan-pan MU, San-yuan ZHANG, Yin ZHANG, Xiu-zi YE, Xiang PAN

《信息与电子工程前沿（英文）》 2018年第19卷第11期页码 1397-1408 doi: 10.1631/FITEE.1601764

摘要：提出一种基于图像的三维模型检索新框架。把查询的图像建模为欧氏空间的一个点，把三维模型所有投影视图建模为一个对称正定矩阵，并视该对称正定矩阵为黎曼流形上的一个点。于是，基于图像的三维模型检索简化为欧氏空间到黎曼流形上的度量学习。为解决异质匹配问题，把欧式空间和对称正定矩阵黎曼空间映射到同一个高维希尔伯特空间，极大缩小彼此之间语义鸿沟。最后，使用核方法设计一个优化算法学习映射。任何新图像描述符，比如深度学习特征描述符，可以很容易嵌入该框架。实验结果表明，该方法相较目前最新基于图像的三维模型检索方法有一定优势。

关键词：模型检索；欧式空间；黎曼流形；希尔伯特空间；度量学习

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基于特征-模式图的SDN下分布式拒绝服务攻击发现方法 Special Feature on Future Network-Research Article

Ya XIAO, Zhi-jie FAN, Amiya NAYAK, Cheng-xiang TAN

《信息与电子工程前沿（英文）》 2019年第20卷第9期页码 1195-1208 doi: 10.1631/FITEE.1800436

摘要：由于软件定义网络（software-defined networks, SDN）的开方式结构，软件定义网络环境下的安全威胁已成为一个重要问题。在所有威胁中，分布式拒绝服务攻击（distributed denial-of-service, DDoS）对网络具有巨大影响。本文提出一种基于特征-模式图模型的方法来发现软件定义网络环境下的DDoS攻击行为。所提出的特征-模式图采用网络模式作为节点，将其相似度作为加权边。该图模型可同时表示网络包的头信息和各网络模式之间的关系信息。节点之间的相似度由度量学习和马氏距离表示。所提方法可以基于图的邻近分类模型发现DDoS攻击，并具有自动发现未知攻击的能力且可通过全局或局部插入新节点的方式扩展已有图结构。两个数据集上的实验证明了所提方法在攻击行为检测和图更新任务上的可行性，并证明了本文基于图的模型在DDoS攻击检测上优于对比模型。

关键词：软件定义网络；分布式拒绝服务攻击；行为检测；距离度量学习；特征-模式图

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A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

《能源前沿（英文）》 2022年第16卷第2期页码 187-223 doi: 10.1007/s11708-021-0722-7

摘要： In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

关键词： forecasting techniques hybrid models neural network solar forecasting error metric support vector machine (SVM)

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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

《环境科学与工程前沿（英文）》 2023年第17卷第6期 doi: 10.1007/s11783-023-1677-1

摘要：

● MSWNet was proposed to classify municipal solid waste.

关键词： Municipal solid waste sorting Deep residual network Transfer learning Cyclic learning rate Visualization

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Spatial prediction of soil contamination based on machine learning: a review

《环境科学与工程前沿（英文）》 2023年第17卷第8期 doi: 10.1007/s11783-023-1693-1

摘要：

● A review of machine learning (ML) for spatial prediction of soil contamination.

关键词： Soil contamination Machine learning Prediction Spatial distribution

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Machine learning in building energy management: A critical review and future directions

《工程管理前沿（英文）》 2022年第9卷第2期页码 239-256 doi: 10.1007/s42524-021-0181-1

摘要： Over the past two decades, machine learning (ML) has elicited increasing attention in building energy management (BEM) research. However, the boundary of the ML-BEM research has not been clearly defined, and no thorough review of ML applications in BEM during the whole building life-cycle has been published. This study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research directions. An integrated framework of ML-BEM, composed of four layers and a series of driving factors, is proposed. Then, based on the hype cycle model, this paper analyzes the current development status of ML-BEM and tries to predict its future development trend. Finally, five research directions are discussed: (1) the behavioral impact on BEM, (2) the integration management of renewable energy, (3) security concerns of ML-BEM, (4) extension to other building life-cycle phases, and (5) the focus on fault detection and diagnosis. The findings of this study are believed to provide useful references for future research on ML-BEM.

关键词： building energy management machine learning integrated framework knowledge evolution

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网络系统行为效用计算——概念与原理 Article

胡昌振

《工程（英文）》 2018年第4卷第1期页码 78-84 doi: 10.1016/j.eng.2018.02.010

摘要：

网络的服务与应用是一个面向业务与任务的行为过程，其度量与评估目前尚处于粗略比较状态。本文把网络行为场景刻画为微分流形，根据光滑微分流形的同胚变换，给出了网络行为的数学定义，提出了网络行为路径及行为效用的数学描述。基于微分几何原理，提出了网络行为效用的计算原理与方法，并构建了基于行为效用的网络攻防对抗评估计算框架，从而奠定了网络行为定量度量和评估的数学基础。

关键词：网络度量评估微分流形网络行为效用网络攻防对抗

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Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

《医学前沿（英文）》 doi: 10.1007/s11684-023-0982-1

摘要： Previous studies have revealed that patients with hypertrophic cardiomyopathy (HCM) exhibit differences in symptom severity and prognosis, indicating potential HCM subtypes among these patients. Here, 793 patients with HCM were recruited at an average follow-up of 32.78 ± 27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features. Furthermore, we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data. Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings. Consequently, two subtypes characterized by different clinical outcomes were identified in HCM. Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course, while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression. Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities. Furthermore, the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction. By employing echocardiography and genetic screening for the 46 genes, HCM can be classified into two subtypes with distinct clinical outcomes.

关键词： machine learning methods hypertrophic cardiomyopathy genetic risk

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Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

《化学科学与工程前沿（英文）》 2022年第16卷第2期页码 183-197 doi: 10.1007/s11705-021-2073-7

摘要： Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations, so that solvability becomes a practical issue. Additional constraints from technical, economic, environmental, and safety considerations may further limit the feasible solution space beyond the convergence requirement. A priori, the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice. To support the exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning models has recently been proposed. However, that approach only considers the exploration of the convergent domain and ignores additional constraints. In this paper, we present an improvement which particularly takes the fulfillment of constraints into account. We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.

关键词： machine learning flowsheet simulations constraints exploration

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Machine learning for fault diagnosis of high-speed train traction systems: A review

《工程管理前沿（英文）》 doi: 10.1007/s42524-023-0256-2

摘要： High-speed trains (HSTs) have the advantages of comfort, efficiency, and convenience and have gradually become the mainstream means of transportation. As the operating scale of HSTs continues to increase, ensuring their safety and reliability has become more imperative. As the core component of HST, the reliability of the traction system has a substantially influence on the train. During the long-term operation of HSTs, the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures, thus threatening the running safety of the train. Therefore, performing fault monitoring and diagnosis on the traction system of the HST is necessary. In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis. Machine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensive systematic review is still lacking in this field. This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint. First, the structure and function of the HST traction system are briefly introduced. Then, the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed. Finally, the challenges for accurate fault diagnosis under actual operating conditions are revealed, and the future research trends of machine learning in traction systems are discussed.

关键词： high-speed train traction systems machine learning fault diagnosis

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

《结构与土木工程前沿（英文）》页码 994-1010 doi: 10.1007/s11709-023-0942-5

摘要： The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

关键词： dynamic prediction moving trajectory pipe jacking GRU deep learning

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿（英文）》 2022年第17卷第2期 doi: 10.1007/s11465-022-0673-7

摘要： Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词： deep reinforcement learning hyper parameter optimization convolutional neural network fault diagnosis