三轴MEMS陀螺仪轨迹跟踪的自耦PID控制方法

为了有效提高三轴微机械(Micro-Electro-Mechanic System,MEMS)陀螺仪的抗干扰能力和检测精度,基于自耦PID控制理论提出了一种简单的陀螺仪控制方法。该方法将三轴MEMS陀螺仪控制问题分解为三个严格反馈子系统的控制问题,然后将三个子系统已知或未知动态、其不确定性和外部有界扰动分别定义为三个总扰动,进而将三个子系统等价映射为三个二阶线性扰动系统,据此分别构建了在总扰动反相激励下的三个受控误差系统,并根据自耦PID控制理论分别设计了三个自耦PD控制器,最后分析了每个子系统的鲁棒稳定性和抗扰动鲁棒性。仿真实验证明了所提控制方法的有效性,其在MEMS陀螺仪控制系统领域具有良好的实际应用前景。

A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

Introduction to the Special Issue on Machine Learning-Guided Intelligent Modeling with Its Industrial Applications

With the advancement of Artificial Intelligence(AI)technology,traditional industrial systems are undergoing an intelligent transformation,bringing together advanced computing,communication and control technologies,Machine Learning(ML)-based intelligentmodelling has become a newparadigm for solving problems in the industrial domain[1–3].With numerous applications and diverse data types in the industrial domain,algorithmic and data-driven ML techniques can intelligently learn potential correlations between complex data and make efficient decisions while reducing human intervention.However,in real-world application scenarios,existing algorithms may have a variety of limitations,such as small data volumes,small detection targets,low efficiency,and algorithmic gaps in specific application domains[4].Therefore,many new algorithms and strategies have been proposed to address the challenges in industrial applications[5–8].

A Quantized Kernel Least Mean Square Scheme with Entropy-Guided Learning for Intelligent Data Analysis

Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.

Effective short text classification via the fusion of hybrid features for IoT social data

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

Towards improving detection performance for malware with a correntropy-based deep learning method

With the rapid development of Internet of Things(IoT)technologies,the detection and analysis of malware have become a matter of concern in the industrial application of Cyber-Physical System(CPS)that provides various services using the IoT paradigm.Currently,many advanced machine learning methods such as deep learning are popular in the research of malware detection and analysis,and some achievements have been made so far.However,there are also some problems.For example,considering the noise and outliers in the existing datasets of malware,some methods are not robust enough.Therefore,the accuracy of malware classification still needs to be improved.Aiming at this issue,we propose a novel method that combines the correntropy and the deep learning model.In our proposed method for malware detection and analysis,given the success of the mixture correntropy as an effective similarity measure in addressing complex datasets with noise,it is therefore incorporated into a popular deep learning model,i.e.,Convolutional Neural Network(CNN),to reconstruct its loss function,with the purpose of further detecting the features of outliers.We present the detailed design process of our method.Furthermore,the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.

A Novel Named Entity Recognition Scheme for Steel E-Commerce Platforms Using a Lite BERT

In the era of big data,E-commerce plays an increasingly important role,and steel E-commerce certainly occupies a positive position.However,it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of staffs.In order to improve the efficiency of purchasers searching for commodities on the steel E-commerce platforms,we propose a novel deep learning-based loss function for named entity recognition(NER).Considering the impacts of small sample and imbalanced data,in our NER scheme,the focal loss,the label smoothing,and the cross entropy are incorporated into a lite bidirectional encoder representations from transformers(BERT)model to avoid the over-fitting.Moreover,through the analysis of different classic annotation techniques used to tag data,an ideal one is chosen for the training model in our proposed scheme.Experiments are conducted on Chinese steel E-commerce datasets.The experimental results show that the training time of a lite BERT(ALBERT)-based method is much shorter than that of BERT-based models,while achieving the similar computational performance in terms of metrics precision,recall,and F1 with BERT-based models.Meanwhile,our proposed approach performs much better than that of combining Word2Vec,bidirectional long short-term memory(Bi-LSTM),and conditional random field(CRF)models,in consideration of training time and F1.

Transferable Features from 1D-Convolutional Network for Industrial Malware Classification

With the development of information technology,malware threats to the industrial system have become an emergent issue,since various industrial infrastructures have been deeply integrated into our modern works and lives.To identify and classify new malware variants,different types of deep learning models have been widely explored recently.Generally,sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ability.However,in current practical applications,an ample supply of data is absent in most specific industrial malware detection scenarios.Transfer learning as an effective approach can be used to alleviate the influence of the small sample size problem.In addition,it can also reuse the knowledge from pretrained models,which is beneficial to the real-time requirement in industrial malware detection.In this paper,we investigate the transferable features learned by a 1D-convolutional network and evaluate our proposed methods on 6 transfer learning tasks.The experiment results show that 1D-convolutional architecture is effective to learn transferable features for malware classification,and indicate that transferring the first 2 layers of our proposed 1D-convolutional network is the most efficient way to reuse the learned features.

A Novel Hidden Danger Prediction Method in CloudBased Intelligent Industrial Production Management Using Timeliness Managing Extreme Learning Machine

To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction.Compared with traditional learning algorithms,extreme learning machine(ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed.Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM.When using the timeliness managing ELM scheme to predict hidden dangers,newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data,because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions.Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.

Magnetic phase transition and continuous spin switching in a high-entropy orthoferrite single crystal

Rare-earth orthoferrite REFeO_(3)(where RE is a rare-earth ion)is gaining interest.We created a high-entropy orthoferrite(Tm_(0.2)Nd_(0.2)Dy_(0.2)Y_(0.2)Yb_(0.2))FeO_(3)(HEOR)by doping five RE ions in equimolar ratios and grew the single crystal by optical floating zone method.It strongly tends to form a single-phase structure stabilized by high configurational entropy.In the low-temperature region(11.6‒14.4 K),the spin reorientation transition(SRT)ofΓ_(2)(F_(x),C_(y),G_(z))‒Γ_(24)‒Γ_(4)(G_(x),A_(y),F_(z))occurs.The weak ferromagnetic(FM)moment,which comes from the Fe sublattices distortion,rotates from the a-to c-axis.The two-step dynamic processes(Γ_(2)‒Γ_(24)‒Γ_(4))are identified by AC susceptibility measurements.SRT in HEOR can be tuned in the range of 50‒60000 Oe,which is an order of magnitude larger than that of orthoferrites in the peer system,making it a candidate for high-field spin sensing.Typical spin-switching(SSW)and continuous spin-switching(CSSW)effects occur under low magnetic fields due to the strong interactions between RE‒Fe sublattices.The CSSW effect is tunable between 20‒50 Oe,and hence,HEOR potentially can be applied to spin modulation devices.Furthermore,because of the strong anisotropy of magnetic entropy change()and refrigeration capacity(RC)based on its high configurational entropy,HEOR is expected to provide a novel approach for refrigeration by altering the orientations of the crystallographic axes(anisotropic configurational entropy).

Damaged apple detection with a hybrid YOLOv3 algorithm

This paper proposes an improved You Only Look Once(YOLOv3)algorithm for automatically detecting damaged apples to promote the automation of the fruit processing industry.In the proposed method,a clustering method based on Rao-1 algorithm is introduced to optimize anchor box sizes.The clustering method uses the intersection over the union to form the objective function and the most representative anchor boxes are generated for normal and damaged apple detection.To verify the feasibility and effectiveness of the proposed method,real apple images collected from the Internet are employed.Compared with the generic YOLOv3 and Fast Region-based Convolutional Neural Network(Fast R-CNN)algorithms,the proposed method yields the highest mean average precision value for the test dataset.Therefore,it is practical to apply the proposed method for intelligent apple detection and classification tasks.

OsFAD1–OsMYBR22 modulates clustered spikelet through regulating BRD3 in rice

Grain number per panicle,a crucial component for rice grain yield,is usually determined by the primary and secondary branches of spikelets.Normally,the tips of the primary and secondary branches of spikelets are occupied by a single grain.Occasionally,this phenotype can be disrupted by two or three complete spikelets or grains clustered on the branch tips,significantly altering the spatial arrangement of spikelet and the structure of the panicle,thus impacting grain yield (Ren et al.,2017,2020;Zhang et al.,2017).

Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management

This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household appliances.Model-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV generation.However,discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous actions.Thus,a mixed deep reinforcement learning(MDRL)algorithm is proposed,which integrates deep Q-learning(DQL)algorithm and deep deterministic policy gradient(DDPG)algorithm.The DQL algorithm deals with discrete actions,while the DDPG algorithm handles continuous actions.The MDRL algorithm learns optimal strategy by trialand-error interactions with the environment.However,unsafe actions,which violate system constraints,can give rise to great cost.To handle such problem,a safe-MDRL algorithm is further proposed.Simulation studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy management.The proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test dataset.Moreover,the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.

Towards Rehabilitation at Home After Total Knee Replacement

In this paper,we present the design and implementation of an avatar-based interactive system that facilitates rehabilitation for people who have received total knee replacement surgeries.The system empowers patients to carry out exercises prescribed by a clinician at the home settings more effectively.Our system helps improve accountability for both patients and clinicians.The primary sensing modality is the Microsoft Kinect sensor,which is a depth camera that comes with a Software Development Kit(SDK).The SDK provides access to 3-dimensional skeleton joint positions to software developers,which significantly reduces the challenges in developing accurate motion tracking systems,especially for use at home.However,the Kinect sensor is not wellequipped to track foot orientation and its subtle movements.To overcome this issue,we augment the system with a commercial off-the-shelf Inertial Measurement Unit(IMU).The two sensing modalities are integrated where the Kinect serves as the primary sensing modality and the IMU is used for exercises where Kinect fails to produce accurate measurement.In this pilot study,we experiment with four rehabilitation exercises,namely,quad set,side-lying hip abduction,straight raise leg,and ankle pump.The Kinect is used to assess the first three exercises,and the IMU is used to assess the ankle pump exercise.

A Heterogeneous Ensemble of Extreme Learning Machines with Correntropy and Negative Correlation

The Extreme Learning Machine（ELM） is an effective learning algorithm for a Single-Layer Feedforward Network（SLFN）. It performs well in managing some problems due to its fast learning speed. However, in practical applications, its performance might be affected by the noise in the training data. To tackle the noise issue, we propose a novel heterogeneous ensemble of ELMs in this article. Specifically, the correntropy is used to achieve insensitive performance to outliers, while implementing Negative Correlation Learning（NCL） to enhance diversity among the ensemble. The proposed Heterogeneous Ensemble of ELMs（HE2 LM） for classification has different ELM algorithms including the Regularized ELM（RELM）, the Kernel ELM（KELM）, and the L2-norm-optimized ELM（ELML2）. The ensemble is constructed by training a randomly selected ELM classifier on a subset of the training data selected through random resampling. Then, the class label of unseen data is predicted using a maximum weighted sum approach. After splitting the training data into subsets, the proposed HE2 LM is tested through classification and regression tasks on real-world benchmark datasets and synthetic datasets. Hence, the simulation results show that compared with other algorithms, our proposed method can achieve higher prediction accuracy, better generalization, and less sensitivity to outliers.

LIDAR:learning from imperfect demonstrations with advantage rectification

In actor-critic reinforcement learning(RL)algorithms,function estimation errors are known to cause ineffective random exploration at the beginning of training,and lead to overestimated value estimates and suboptimal policies.In this paper,we address the problem by executing advantage rectification with imperfect demonstrations,thus reducing the function estimation errors.Pretraining with expert demonstrations has been widely adopted to accelerate the learning process of deep reinforcement learning when simulations are expensive to obtain.However,existing methods,such as behavior cloning,often assume the demonstrations contain other information or labels with regard to performances,such as optimal assumption,which is usually incorrect and useless in the real world.In this paper,we explicitly handle imperfect demonstrations within the actor-critic RL frameworks,and propose a new method called learning from imperfect demonstrations with advantage rectification(LIDAR).LIDAR utilizes a rectified loss function to merely learn from selective demonstrations,which is derived from a minimal assumption that the demonstrating policies have better performances than our current policy.LIDAR learns from contradictions caused by estimation errors,and in turn reduces estimation errors.We apply LIDAR to three popular actor-critic algorithms,DDPG,TD3 and SAC,and experiments show that our method can observably reduce the function estimation errors,effectively leverage demonstrations far from the optimal,and outperform state-of-the-art baselines consistently in all the scenarios.