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Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status

Jian HUANG,Gui-xiong LIU

《机械工程前沿（英文）》 2016年第11卷第3期页码 311-315 doi: 10.1007/s11465-016-0376-z

摘要：

The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm (k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample S_r was classified by the k-NN algorithm with training set T_z according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler’s numbers. Last, while the classification confidence coefficient equaled k, made S_r as one sample of pre-training set T_z′. The training set T_z increased to T_z₊₁ by T_z′ if T_z′ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65% identification accuracy, also selected five groups of samples to enlarge the training set from T₀ to T₅ by itself.

关键词： multi-color space k-nearest neighbor algorithm (k-NN) self-learning surge test

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模糊基函数神经网络在线跟踪自学习算法研究

许飞云,钟秉林,黄仁

《中国工程科学》 2007年第9卷第11期页码 48-53

摘要：

提出了一种用于分类的模糊基函数（FBF）神经网络在线跟踪自学习算法，通过带有遗忘因子的样本均值和样本协方差矩阵，保存了原始样本所包含的类可能性分布信息，并在此基础上产生新增样本的目标输出用于训练FBF网络，以实现分类边界的在线跟踪；给出了带有遗忘因子的样本均值和样本协方差矩阵的递推算法，以克服传统方法需要保存大量以往训练样本带来的困难。所提出的方法用于旋转机械的故障识别，结果表明是可行的和有效的。

关键词：模糊基函数自学习故障诊断

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智能阀门定位系统的设计

吴爱国,王立石

《中国工程科学》 2005年第7卷第4期页码 69-73

摘要：

介绍了一种基于ARM的智能阀门定位系统的硬件设计和定位控制方法。其中应用Philips公司的带CAN总线接口的ARM控制器作为系统的控制核心，既满足了现场实时数据的采集、计算和处理，又可以通过CAN总线使得阀门控制器和控制中心保持实时通信；控制策略采用带智能积分的自学习模糊控制算法，提高了系统的定位精度和智能化水平。

关键词： ARM 控制器 CAN总线智能积分自学习模糊控制

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基于iMOEA/D-DE的组合权重模型 Research Article

董铭涛,程建华,赵琳

《信息与电子工程前沿（英文）》 2022年第23卷第4期页码 604-616 doi: 10.1631/FITEE.2000545

摘要：为准确求解评估方法的权重，提出一种基于iMOEA/D-DE（基于差分进化分解的改进多目标进化算法）的组合权重模型。多专家权重仅考虑主观权重，导致客观性差。为解决此问题，考虑组合系数的不确定性，设计了基于改进博弈论的组合权重多目标优化模型。引入改进变异算子提高收敛速度，进而获得更好优化结果。同时，设计了具有自学习能力的自适应变异系数和交叉概率系数，以提高MOEA/D-DE算法的鲁棒性。由于现有权重评价方法不能单独评价权重，提出一种基于相对熵的新权重评价方法。以组合导航系统评估方法为例开展实验。实验证明，该算法具有良好性能。

关键词：组合权重；MOEA/D-DE；博弈论；自学习能力；相对熵

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

《能源前沿（英文）》 2023年第17卷第4期页码 527-544 doi: 10.1007/s11708-023-0880-x

摘要： Intelligent power systems can improve operational efficiency by installing a large number of sensors. Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources. However, the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data. The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms. Moreover, sensor data faults in power systems are dynamically changing and pose another challenge. Therefore, a fault detection method based on self-supervised feature learning was proposed to address the above two challenges. First, self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data. The self-supervised representation learning uses a sequence-based Triplet Loss. The extracted features of large amounts of normal data are then fed into a unary classifier. The proposed method is validated on exhaust gas temperatures (EGTs) of a real-world 9F gas turbine with sudden, progressive, and hybrid faults. A comprehensive comparison study was also conducted with various feature extractors and unary classifiers. The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults. The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms of F1 score.

关键词： fault detection unary classification self-supervised representation learning multivariate nonlinear time series

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Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

《结构与土木工程前沿（英文）》 2023年第17卷第2期页码 284-305 doi: 10.1007/s11709-022-0901-6

摘要： Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressive strength (CS) is a critical mechanical property that must be adequately determined. In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps include the limitations of samples in databases, the applicability constraints of models owing to limited mixture components, and the possibility of applying recently proposed models. This study developed different ML models for predicting the CS of FRSCC to address these limitations. Artificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique. A database of 381 samples was created, representing the most significant FRSCC dataset compared with previous studies, and it was used for model development. The findings indicated that CatBoost outperformed the other two models with excellent predictive abilities (root mean square error of 2.639 MPa, mean absolute error of 1.669 MPa, and coefficient of determination of 0.986 for the test dataset). Finally, a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of FRSCC. The results showed that the cement content, testing age, and superplasticizer content are the most critical factors affecting the CS.

关键词： compressive strength self-compacting concrete artificial neural network decision tree CatBoost

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Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

《结构与土木工程前沿（英文）》 2022年第16卷第7期页码 928-945 doi: 10.1007/s11709-022-0837-x

摘要： The compressive strength of self-compacting concrete (SCC) needs to be determined during the construction design process. This paper shows that the compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machine learning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid and hybrid models. Nine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees (DTR), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network using two training algorithms LBFGS and SGD (denoted as ANN_LBFGS and ANN_SGD), are also compared with the XGB model. Moreover, the hybrid models of eight ML techniques and Particle Swarm Optimization (PSO) are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model. The highest number of SCC samples available in the literature is collected for building the ML techniques. Compared with previously published works’ performance, the proposed XGB method, both hybrid and non-hybrid models, is the most reliable and robust of the examined techniques, and is more accurate than existing ML methods (R² = 0.9644, RMSE = 4.7801, and MAE = 3.4832). Therefore, the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.

关键词： compressive strength self-compacting concrete machine learning techniques particle swarm optimization extreme gradient boosting

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Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

《化学科学与工程前沿（英文）》 2023年第17卷第6期页码 759-771 doi: 10.1007/s11705-022-2269-5

摘要： This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), for modeling refining units comprised of two reactors and a separation train. The model is comprised of self-organizing-map and the neural network parts. The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part. In the neural network part, residual blocks enhance the convergence and accuracy, ensuring that the structure will not be overfitted easily. Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products. The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model, thus leading to more accurate optimization of the hydrocracker operation. Moreover, the MISR model has smoother error convergence than the previous model. Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms. Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.

关键词： hydrocracking convolutional neural networks self-organizing map deep learning data-driven optimization

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A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

《结构与土木工程前沿（英文）》 2022年第16卷第8期页码 990-1002 doi: 10.1007/s11709-022-0864-7

摘要： This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.

关键词： artificial neural network grey wolf optimize algorithm compressive strength self-compacting concrete

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自主创新技术学习与产业竞争力的提高——以台湾IC业为例

卢锐,盛昭瀚

《中国工程科学》 2007年第9卷第8期页码 35-39

摘要：

自主创新、技术学习是台湾集成电路（IC）产业遵循比较优势的产业政策和技术政策的结果，是基于本土市场的自主创新以及企业在技术学习上的努力，是发展中国家的企业能够在开放市场条件下获得竞争优势的原因。

关键词：技术学习自主创新产业竞争力 IC产业

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融合自监督图学习与目标自适应屏蔽的会话型推荐方法 Research Article

王祎童,蔡飞,潘志强,宋城宇

《信息与电子工程前沿（英文）》 2023年第24卷第1期页码 73-87 doi: 10.1631/FITEE.2200137

摘要：会话型推荐旨在根据用户在短时间内有限的交互来预测下一个时间戳将要进行交互的物品。现有模型主要使用循环神经网络（RNN）或图神经网络（GNN）来建模顺序序列或物品之间的传递关系。然而，此类模型要么忽略了GNN的过度平滑问题，要么直接利用交叉熵损失和softmax层进行模型优化，容易导致过拟合问题。为了解决上述问题，本文提出一种融合自监督图学习与目标自适应屏蔽的会话型推荐方法(SGL-TM)。具体来说，首先根据所有涉及到的会话构建全局图，然后从物品之间的全局连接中捕捉自监督信号，用来监督模型生成当前会话中准确的物品表示。之后，通过比较真值与经过我们设计的目标自适应屏蔽模块调整后的物品的预测分数来计算主监督损失。最后，将主监督组件与辅助自监督模块相结合，以获得用来优化模型参数的最终损失。在两个真实数据集（Gowalla和Diginetica）上的大量实验结果表明，SGL-TM在Recall@20和MRR@20方面的性能优于最先进的基准模型，尤其体现在短会话上。

关键词：会话型推荐；自监督学习；图神经网络；目标自适应屏蔽

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Exploring self-organization and self-adaption for smart manufacturing complex networks

《工程管理前沿（英文）》 2023年第10卷第2期页码 206-222 doi: 10.1007/s42524-022-0225-1

摘要： Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.

关键词： cyber–physical systems Industrial Internet of Things smart manufacturing complex networks self-organization and self-adaption analytical target cascading collaborative optimization

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忘得少，数得好：一种域增量式自蒸馏终身人群计数基准 Research Article

高佳琪1,李婧琦1,单洪明2,3,曲延云4,王则5,王飞跃6,张军平1

《信息与电子工程前沿（英文）》 2023年第24卷第2期页码 187-202 doi: 10.1631/FITEE.2200380

摘要：人群计数在公共安全和流行病控制方面具有重要应用。一个鲁棒且实用的人群计数系统须能够在真实场景中不断学习持续到来的新域数据，而非仅仅拟合某一单域的数据分布。现有方法在处理多个域的数据时有一些不足之处：（1）由于来自不同域的固有数据分布之间的差异，模型在训练来自新域的图像数据后在旧域中的性能可能会变得十分有限（甚至急剧下降），这种现象被称为灾难性遗忘；（2）由于域分布的偏移，在某一特定域数据中训练好的模型在其他未见域中通常表现不佳；（3）处理多个域的数据通常会导致存储开销的线性增长，例如混合来自所有域的数据进行训练，或者是简单地为每一个域的数据单独训练一个模型。为克服这些问题，我们探索了在域增量式训练设置下一种新的人群计数任务，即终身人群计数。它的目标是通过使用单个模型持续不断地学习新域数据以减轻灾难性遗忘并提高泛化能力。具体来说，提出一种自蒸馏学习框架作为终身人群计数的基准模型（forget less，count better，FLCB），这有助于模型可持续地利用之前学到的有意义的知识来更好地对人数进行估计，以减少训练新数据后对旧数据的遗忘。此外，设计了一种新的定量评价指标，即归一化后向迁移（normalized Backward Transfer，nBwT），用于评估模型在终身学习过程中的遗忘程度。大量实验结果证明了该模型的优越性，即较低的灾难性遗忘度和较强的泛化能力。

关键词：人群计数；知识蒸馏；终身学习

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基于自适应置信度校准的交互式医疗图像分割框架

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

《信息与电子工程前沿（英文）》 2023年第24卷第9期页码 1332-1348 doi: 10.1631/FITEE.2200299

摘要：基于人机交互的医疗图像分割方法是一种新的范式，其通过引入专家交互信息来指导算法完成图像分割任务。然而，现有医疗图像分割模型往往容易产生“交互误解”，即无法合理权衡短期和长期交互信息的重要性。为更好地利用不同时间尺度上的交互信息，本文提出一种基于自适应置信度校准的交互式医疗图像分割框架MECCA，其结合了基于分割决策的置信度学习技术和多智能体强化学习技术，并通过预测分割决策与短期交互信息的对齐水平来学习一个新颖的置信度网络。随后，提出一种基于置信度的奖励塑造机制，在策略梯度计算中引入置信度，从而直接纠正模型产生的交互误解。MECCA还通过标签生成和交互指导来降低交互强度和难度，从而实现用户友好交互。实验结果表明，MECCA在不同分割任务中可以显著提高短期和长期交互信息的利用效率，且仅需较少的标注样本。演示视频可通过https://bit.ly/mecca-demo-video访问。

关键词：医疗图像分割交互式分割多智能体强化学习置信度学习半监督学习

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Emerging trends in self-healable nanomaterials for triboelectric nanogenerators: A comprehensive review

《能源前沿（英文）》页码 727-750 doi: 10.1007/s11708-023-0896-2

摘要： A thorough analysis of triboelectric nanogenerators (TENGs) that make use of self-healable nanomaterials is presented in this review. These TENGs have shown promise as independent energy sources that do not require an external power source to function. TENGs are developing into a viable choice for powering numerous applications as low-power electronics technology advances. Despite having less power than conventional energy sources, TENGs do not directly compete with these. TENGs, on the other hand, provide unique opportunities for future self-powered systems and might encourage advancements in energy and sensor technologies. Examining the many approaches used to improve nanogenerators by employing materials with shape memory and self-healable characteristics is the main goal of this review. The findings of this comprehensive review provide valuable information on the advancements and possibilities of TENGs, which opens the way for further research and advancement in this field. The discussion of life cycle evaluations of TENGs provides details on how well they perform in terms of the environment and identifies potential improvement areas. Additionally, the cost-effectiveness, social acceptability, and regulatory implications of self-healing TENGs are examined, as well as their economic and societal ramifications.

关键词： triboelectric nanogenerator (TENG) self-healable nanomaterials self-powered devices energy