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Transient pulses generated by high-energy particles can cause soft errors in circuits, resulting in spacecraft malfunctions and posing serious threats to the normal operation of spacecraft. For integrated circuits used in space applications, it is necessary to first evaluate soft errors caused by transient pulses. Conventional soft-error-rate evaluation tools are designed to simulate the generation of transient pulses using many accurate models, while the propagation of transient pulses is primarily simulated by circuit-level simulation tools. Due to the limitations of simulation tools, conventional evaluation approaches are limited to the circuit scale. The simulation runtime is unbearable for large-scale integrated circuits. This paper presents an approach for evaluating the soft error rate using machine learning. A back propagation neural network is implemented in the proposed approach. It helps to determine the probability of transient pulse propagation. Compared with the conventional soft-error-rate evaluation results, the proposed approach demonstrates a strong correlation in both trend and magnitude. The average difference between the results obtained using the proposed evaluation method and the experimental results is 23.5%, which is 7.5% higher than that between the results obtained using the conventional evaluation method and the experimental results. Compared to the conventional evaluation method, the proposed approach improves the runtime by an order of magnitude. The proposed approach also benefits the locating of highly sensitive circuit nodes in large-scale integrated circuits. Circuit design and radiation hardening are both useful applications. [ABSTRACT FROM AUTHOR]

• Seepage failure probability of high core rockfill dams during RDD is investigated. • The randomness of the parameters is estimated by Bayesian inference. • Seepage failure is most likely to occur in the mid-term of RDD. Rapid drawdown (RDD) is an abnormal reservoir flood control operation that may cause seepage failure of high core rockfill dams. In this paper, a novel framework combining Bayesian inference and transient unsaturated seepage simulation is proposed to evaluate the seepage failure probability of the core during RDD. The dynamic hydraulic boundaries utilized in the simulation are first determined by the performance of water release structures. The extreme learning machine (ELM) is then employed as the surrogate model to establish the mapping relationship between material parameters with simulated seepage pressure and hydraulic gradient. The Bayesian inference method is applied to characterize the uncertainty of material parameters based on seepage monitoring data. Finally, the posterior distribution samples of random parameters are utilized to calculate the seepage failure probability of the core. The seepage safety of the core under different drawdown rates is studied with a real 186 m high core rockfill dam. The results reveal that a faster RDD rate can lead to an increased probability of seepage failure. The maximum failure probability is 35.56 % for the four release structures working together (about 0.316 m/h), higher than 23.16 % for the three release structures (about 0.165 m/h). It is also found that the peak of seepage failure probability often occurs in the middle stage of RDD. Therefore, to ensure the safety of the project, it is important to control the RDD rate and carefully monitor the seepage pressure. From the point of view of seepage failure, the proposed method for evaluating the safety of core rockfill dams under RDD is of high guiding significance to the risk assessment of similar projects under extreme conditions. [ABSTRACT FROM AUTHOR]

Here, we show the fabrication of the carbon dots (CDs) with green and orange emissions from PTCDA (perylene‐3,4,9,10‐tetracarboxylic dianhydride). Using these CDs as emitters, the orange (or green) CDs LEDs were fabricated, which show electroluminescence (EL) spectra centered at 560 nm (or 498 nm) with an external quantum efficiency (EQE) of 1.98 % (1.76 %) adhering a luminescence of 626 cd m−2 (or 519 cd m−2). The machine learning was successfully used to predict PL CCT value. With the model, the white photoluminescence (PL) emission with adjustable correlated color temperature (CCT) from 3093 to 11018 K via combining blue, green, and orange CDs was achieved. Then, we obtained the warm white CDs LEDs with CCT of 3107, 4071 and 4548 K, and cold white CDs LEDs with CCT of 5632 (CIE coordinates of (0.33, 0.33), EQE: 1.18 %, luminescence: 598 cd m−2) and 6034 K accurately. [ABSTRACT FROM AUTHOR]

Data-driven fault diagnostics of industrial systems suffer from class-imbalanced problems, which is a common challenge for machine learning algorithms as it is difficult to learn the features of the minority class samples. Synthetic oversampling methods are commonly used to tackle these problems by generating minority class samples to balance the majority and minority classes. Two major issues will influence the performance of oversampling methods which are how to choose the most appropriate existing minority seed samples, and how to synthesize new samples from seed samples effectively. However, many existing oversampling methods are not accurate and effective enough to generate new samples when dealing with high-dimensional faulty samples with different imbalanced ratios, since they do not take these two factors into consideration at the same time. This article develops a novel adaptive oversampling technique: expectation maximization (EM)-based local-weighted minority oversampling technique for industrial fault diagnostics. This method uses a local-weighted minority oversampling strategy to identify hard-to-learn informative minority fault samples and an EM-based imputation algorithm to generate fault samples based on the distribution of minority samples. To validate the performance of the developed method, experiments were conducted on two real-world datasets. The results show that the developed method can achieve better performances, in terms of F-measure, Matthews correlation coefficient (MCC), and Mean (average of F-measure and MCC) values, on multiclass imbalanced fault diagnostics in different imbalance ratios than state-of-arts’ baseline sampling techniques. [ABSTRACT FROM AUTHOR]

• Data-based models with EMD are proposed for deformation monitoring of concrete dams. • The proposed models can well simulate the irreversible component of concrete dam deformation and significantly decrease the over-fitting levels. • A new model selection criterion, namely Over-fitting coefficient, is proposed for evaluating and selecting dam health monitoring models. • The Over-fitting coefficient can better reflect over-fitting levels of individual monitoring models compared to traditional model selection criteria. Data-based models are extensively applied in concrete dam health monitoring. In this paper, the Empirical Mode Decomposition (EMD) is adopted to extract and visualize the irreversible component of deformation monitoring sequence. Consequently proper mathematical formulation of the irreversible component of deformation monitoring model for concrete dams can be established, and the problem of improperly assuming the mathematical form of irreversible component when constructing data-based models is well addressed. The periodic component of the deformation time series is modelled by regression and three machine learning algorithms (i.e., Extreme learning machine, Support Vector Machine based on Grid search and Cross-Verification, and Back Propagation Neural Networks optimized by Genetic Algorithm and Simulated Annealing) respectively. The data-based models with the EMD are able to effectively improve the performance of formulating irreversible component of concrete dam deformation and decrease the over-fitting levels. A new model selection criterion, namely Over-fitting coefficient, is proposed for evaluating and selecting the optimum model among candidate monitoring models. The Over-fitting coefficient can better reflect over-fitting levels of individual monitoring models compared to traditional model selection criteria. As a case study, the displacement and strain monitoring data from the YL gravity dam are modeled by data-based models with the EMD and machine learning algorithms. It is shown that data-based models with the EMD are of higher prediction accuracy and lower rate of false alarm in deformation monitoring than traditional models. The Over-fitting coefficient can better reflect over-fitting levels of monitoring models compared to the error measurement criteria and information criteria. The case study highlights the merits of the proposed data-based models and the model selection criterion for health monitoring of concrete dams. [ABSTRACT FROM AUTHOR]

To solve the problems of poor exploration ability and convergence speed of traditional deep reinforcement learning in the navigation task of the patrol robot under indoor specified routes, an improved deep reinforcement learning algorithm based on Pan/Tilt/Zoom(PTZ) image information was proposed in this paper. The obtained symmetric image information and target position information are taken as the input of the network, the speed of the robot is taken as the output of the next action, and the circular route with boundary is taken as the test. The improved reward and punishment function is designed to improve the convergence speed of the algorithm and optimize the path so that the robot can plan a safer path while avoiding obstacles first. Compared with Deep Q Network(DQN) algorithm, the convergence speed after improvement is shortened by about 40%, and the loss function is more stable. [ABSTRACT FROM AUTHOR]

IR-drop is a fundamental constraint by almost all integrated circuits (ICs) physical designs, and many iterations of timing engineer change order (ECO), IR-drop ECO, or other ECO are needed before design signoff. However, IR-drop analysis usually takes a long time and wastes so many resources. In this work, we develop a fast dynamic IR-drop predictor based on a machine learning technique, XGBoost, and the prediction method can be applied to vector-based and vectorless IR-drop analysis simultaneously. Correlation coefficient is often used to characterize the symmetry of prediction data and golden data, and our experiments show that the prediction correlation coefficient is more than 0.96 and the average error is no more than 1.3 mV for two industry designs, which are of 2.4 million and 3.7 million instances, respectively, and that the analysis is speeded up over 4.3 times compared with the IR-drop analysis by commercial tool, Redhawk. [ABSTRACT FROM AUTHOR]

• Generalization of diagnosis model is improved on new hard disks without faults. • New faulty samples of hard disks could be generated from healthy samples. • Distribution discrepancy between different types of hard disks could be decreased. • End-end EM-based training strategy guarantees a good accuracy and convergence. Intelligent fault diagnosis of hard disks becomes significantly important to guarantee reliability of current cloud-based industrial systems. Most intelligent diagnostic methods are commonly based on assumptions that data from different disks are subject to the same distribution and there are sufficient faulty samples for training the models. However, in reality, there are types of hard disks from different manufacturers and their SMART encoding varies widely across manufacturers. It results in distribution discrepancy among disks and influences the generalization of machine learning methods. Moreover, hard disks usually work in healthy state that faulty events rarely happen on most of them, or especially never occur on new ones. Thus, this paper proposes a deep generative transfer learning network (DGTL-Net) for intelligent fault diagnostics on new hard disks. The DGTL-Net combines the deep generative network that generates fake faulty samples and the deep transfer network that solves the problem of distribution discrepancy between hard disks. An iterative end-end training strategy is also proposed for DGTL-Net to get the most optimal parameters of generative and transfer network simultaneously. Experiments have been conducted to prove that our method achieves better performance. [ABSTRACT FROM AUTHOR]

• An method of multi-source data-driven unsaturated seepage parameter inversion. • NSGA-II is used to generate the pareto-optimal solutions of parameters. • Parameter decision based on a comprehensive index composed of multi-source data. • Inversion results are more in line with engineering practice and have higher accuracy. The inverse parameter's estimation of unsaturated seepage models is important to ensure the safety of earth dams, while most existing studies determine the hydraulic characteristics only based on seepage pressure monitoring data, which may lead to inversion results deviating from the intrinsic properties of dam materials. This study proposed an advanced methodology for unsaturated seepage parameter inversion of earth dams driven by multi-source data. The transient unsaturated seepage behavior of earth dams is simulated using the Van Genuchten (VG) model, and the extreme learning machine (ELM) is adopted as the surrogate model to estimate the approximate simulation results. Then the multi-objective function for parameter inversion is constructed by combining the observed transient pressure data and ELM models, and the Pareto-optimal solutions are generated using the elitist non-dominated sorting genetic algorithm (NSGA-II). Multi-source information, including parameter design values, in-situ testing results, and monitoring data, are fused to construct a comprehensive index to drive the parameter decision. The proposed method is applied to the PB core rockfill dam, which has a well-established seepage pressure monitoring system. In addition, the design values and in-situ testing results of the hydraulic conductivity of the dam material are also available. The results showed that the inversion parameters of the proposed method are closer to the physical characteristics of materials and have advantages in prediction accuracy. [ABSTRACT FROM AUTHOR]

• A temperature component is given by theoretical inference and parameter optimization. • Separate modeling technique is proposed based on ICEEMDAN and clustering analysis. • The ET-SMT-DELM model is established by a multi-output deep extreme learning machine. • The proposed model has high accuracy, credibility and interpretability. The anatomy of service status through health monitoring models is essential to long-term structural safety. Since deformation is the intuitive representation of the dam's operating condition, it is crucial to investigate the deformation monitoring model with high accuracy and strong interpretability for the safety management of high arch dams. A novel deformation monitoring model is proposed by incorporating impulse response-based equivalent temperature (ET) and machine learning-aided separate modeling technique (SMT). This methodology has three main sources of novelty. First, the impulse response-based equivalent temperature and corresponding temperature component are derived from heat transfer and temperature convolution theories. Then, the model parameters are identified by an improved firefly algorithm. Second, the components in the monitored displacement are progressively stripped out by clustering analysis, separation under equal water level conditions, and a robust signal decomposition technique. Third, the separated deformation components and environmental factors are modeled by the multi-output deep extreme learning machine (DELM) with autoencoder, and the monitoring model ET-SMT-DELM is thus established. The world's highest arch dam is selected to illustrate the proposed model, and the prediction accuracy, early warning performance, component shares, and impulse response mechanism are comprehensively investigated by comparing with several typical baseline models. The results show that the overfitting of the proposed model is reduced, and the prediction and early warning performance is significantly improved. The resulting temperature impulse response function and component shares imply that the interpretability of the model is also enhanced. [ABSTRACT FROM AUTHOR]