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The monitoring of harmful phytoplankton is very important for the maintenance of the aquatic ecological environment. Traditional algae monitoring methods require professionals with substantial experience in algae species, which are time-consuming, expensive, and limited in practice. The automatic classification of algae cell images and the identification of harmful phytoplankton images were realized by the combination of multiple convolutional neural networks (CNNs) and deep learning techniques based on transfer learning in this work. Eleven common harmful and 31 harmless phytoplankton genera were collected as input samples; the five CNNs classification models of AlexNet, VGG16, GoogLeNet, ResNet50, and MobileNetV2 were fine-tuned to automatically classify phytoplankton images; and the average accuracy was improved 11.9% when compared to models without fine-tuning. In order to monitor harmful phytoplankton which can cause red tides or produce toxins severely polluting drinking water, a new identification method of harmful phytoplankton which combines the recognition results of five CNN models was proposed, and the recall rate reached 98.0%. The experimental results validate that the recognition performance of harmful phytoplankton could be significantly improved by transfer learning, and the proposed identification method is effective in the preliminary screening of harmful phytoplankton and greatly reduces the workload of professional personnel., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Due to the outstanding performance, composite materials are widely used and analyzing their properties and designing them based on performance has become a crucial task in the field of many manufacturing industries. Composite materials possess complex multiscale structures, and traditional fine-scale finite element modeling and analysis may lead to severe computational resource challenges. To overcome this difficulty, breakthroughs in key technologies of multiscale accelerated analysis algorithms are required. In this study, an innovative approach based on artificial intelligence and multiscale finite element method is presented. This approach involves partitioning the entire composite material structure into coarse grids that resemble homogenous structures of similar size, providing results consistent to fine-grid finite element analysis. By utilizing CNN for image feature recognition and employing the CGAN adversarial method, coarse-grid equivalent stiffness matrices and multiscale shape functions from completely random microstructures of composite materials can be obtained. Consequently, this enables a rapid response process from microstructure to low-resolution grid to high-resolution physical field, with remarkably accurate physical field results. Moreover, compared to traditional fine-grid finite element methods, this approach significantly reduces memory usage and computation time. This method is applicable to composite materials with varying shaped inclusions, different component properties, and diverse geometric distributions, allowing these materials to perform high-fidelity finite element calculations on coarse grids and predict their mechanical behavior. Furthermore, this breakthrough opens avenues for accelerating the optimization design of composite materials with diverse mechanical functionalities, by employing a bottom-up approach. [ABSTRACT FROM AUTHOR]

For the ongoing revolution in developing intelligent and connected vehicles (ICVs), there is a lack of research for powertrain control systems using the latest artificial intelligence and vehicle-to-everything technology that have already been widely adopted in the autonomous driving systems. In this context, recent development of deep reinforcement learning (DRL) and one of the latest computing frameworks are coupled to facilitate an onboard-based intelligent powertrain control. Taking the boost control of a diesel engine equipped with variable geometry turbocharger as an example, the results show that the final control behavior indicated by the cumulated rewards is improved by 50.43% and the learning efficiency is improved by 74.29% for the proposed curiosity-driven DRL algorithm, compared with the same structure DRL algorithm with classic random exploration policy. In addition, unlike most of the DRL-based powertrain optimization algorithms, which have only been applied to single-machine architecture, this work manages the proposed DRL algorithm in parallel and, more importantly, from an edge computing perspective. This, in addition to greatly speeding up the algorithm training, can also realize a good balance of control accuracy and generality depending upon the selected training scenario. Moreover, unlike most of the cloud computing frameworks, which require low network latency, the proposed architecture can achieve a similar final control performance even if good network communication is not allowed. Compared with other existing powertrain control methods, the proposed algorithm is able to approximate a global powertrain control optimization autonomously in a connected manner, making it attractive to current ICVs with advanced automated driving and traditional powertrain control. [ABSTRACT FROM AUTHOR]

Frailty evaluation is of great significance for the specific population, which can speed up the treatment process and reduce the adverse effects after treatment. In this article, in order to make up for the shortcomings of the traditional evaluation methods, a comprehensive intelligent evaluation method that integrates physiological data from multiple perspectives, such as mobility, muscle level, and body composition data has been proposed. The multi-source physiological data of 174 participants has been collected, and a series of features related to frailty have been extracted. A recurrent stepping semi-supervised learning (RSSSL) framework is proposed to transmit the knowledge from the annotated data to the unannotated gait sequence utilizing various networks with varying architectures. Moreover, in order to enhance the frailty evaluation's performance, an approach based on subspace transfer learning integrating with heterogeneous features (STL-IHF) is proposed. The mean segmentation accuracy on the unannotated gait sequence reaches over 97.10% across recruited subjects with distinct gait patterns, which is significantly superior when compared with classical deep learning methods. And the recognition accuracy of the STL-IHF method for frailty evaluation reaches over 92.9%. The overall experimental results demonstrate that the proposed methods are effective in frailty evaluation tasks in clinics. • A novel and comprehensive evaluation method for frailty is proposed. • A recurrent stepping semi-supervised learning framework is proposed. • A subspace transfer learning integrating with heterogeneous features algorithm. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL), which excels at solving a wide variety of Atari and board games, is an area of machine learning that combines the deep learning approach and reinforcement learning (RL). However, to the authors' best knowledge, there seem to be few studies that apply the latest DRL algorithms on real-world powertrain control problems. If there are any, the requirement of classical model-free DRL algorithms typically for a large number of random exploration in order to realize good control performance makes it almost impossible to implement directly on a real plant. Unlike most of the other DRL studies, whose control strategies can only be trained in a simulation environment—especially when a control strategy has to be learned from scratch—in this study, a hybrid end-to-end control strategy combining one of the latest DRL approaches—i.e., a dueling deep Q-network and traditional Proportion Integration Differentiation (PID) controller—is built, assuming no fidelity simulation model exists. Taking the boost control of a diesel engine with a variable geometry turbocharger (VGT) and cooled (exhaust gas recirculation) EGR as an example, under the common driving cycle, the integral absolute error (IAE) values with the proposed algorithm are improved by 20.66% and 9.7% respectively for the control performance and generality index, compared with a fine-tuned PID benchmark. In addition, the proposed method can also improve system adaptiveness by adding another redundant control module. This makes it attractive to real plant control problems whose simulation models do not exist, and whose environment may change over time. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) is an area of machine learning that combines a deep learning approach and reinforcement learning (RL). However, there seem to be few studies that analyze the latest DRL algorithms on real-world powertrain control problems. Meanwhile, the boost control of a variable geometry turbocharger (VGT)-equipped diesel engine is difficult mainly due to its strong coupling with an exhaust gas recirculation (EGR) system and large lag, resulting from time delay and hysteresis between the input and output dynamics of the engine's gas exchange system. In this context, one of the latest model-free DRL algorithms, the deep deterministic policy gradient (DDPG) algorithm, was built in this paper to develop and finally form a strategy to track the target boost pressure under transient driving cycles. Using a fine-tuned proportion integration differentiation (PID) controller as a benchmark, the results show that the control performance based on the proposed DDPG algorithm can achieve a good transient control performance from scratch by autonomously learning the interaction with the environment, without relying on model supervision or complete environment models. In addition, the proposed strategy is able to adapt to the changing environment and hardware aging over time by adaptively tuning the algorithm in a self-learning manner on-line, making it attractive to real plant control problems whose system consistency may not be strictly guaranteed and whose environment may change over time. [ABSTRACT FROM AUTHOR]