Search

Critical industrial infrastructures are currently facing increasing cyberspace threats in their underlying information and communication systems. The advanced monitoring, control, and management functionalities of the industrial systems firmly rely on the reliable and secure operations of the industrial control system (ICS) network. This article characterizes the ICS network traffic and presents a scalable and efficient solution for real-time ICS network traffic anomaly detection, considering various forms of ICS anomaly events. The events due to the cyberattacks, malicious operating behaviors, and network anomalies can be effectively detected without sophisticated computational requirements and retrieval of communication protocols. The proposed hybrid statistical-machine learning model integrates a seasonal autoregressive integration moving average (SARIMA)-based dynamic threshold model and a long short-term memory (LSTM) model to jointly identify the abnormal traffic patterns with low false omission rates. The proposed solution is extensively evaluated at a realistic ICS cyber-physical system (CPS) testbed, and the numerical results confirm its high detection accuracy and low computational complexity.

As industrial technology develops, industrial processes become increasingly large and complex, the traditional methods are difficult to extract features that can represent the condition of the whole process and the effect of fault on quality indicators. Therefore, a novel multi-block decouple convolutional neural network (multi-block DCN) algorithm is proposed. First, key process variables are selected, and process variables are grouped into multiple blocks for the following monitoring. Then, in each block, the proposed DCN constructs a regression model between key process variables and quality indicators, in which the regression model utilizes an improved convolutional neural network as feature extractor and a decoupling layer as feature regularizer. Afterward, the monitoring results of each block are integrated into a global monitoring index based on Bayesian theory. After fault detection, variable oblivion contribution plot is presented to locate faulty variables. Finally, an industrial case is used to demonstrate the effectiveness of multi-block DCN.

The incipient fault is difficult to detect because of its small amplitude and insignificant impact, however, ignoring such fault may cause irreversible damage to the system. In this paper, a hierarchical latent variable extraction and multisegment probability density analysis method is proposed to detect the incipient fault. Firstly, three data subspaces are constructed, which are named dominant, intermediate and residual spaces, and key latent variables which contain more offline variance or online variation information will be retained. Afterwards, the expanded data distribution interval and multiple data segments are constructed for the probability density estimation. Based on the improved symmetric divergence index, the distribution distance between the online data and offline modeling data can be evaluated, which has achieved 95.3% and 86.8% average detection rates for the faults in numerical case and Tennessee Eastman process. Finally, a real multiphase flow facility is used to demonstrate the effectiveness of the proposed method.

In real‐world applications, large amounts of data from multiple sources come in the form of streams. This makes multi‐view feature learning cost much time when new instances rise incrementally. Dealing with these growing multi‐view data becomes a challenging problem. Some single‐view methods focus on processing the data dynamically, but they are not suitable for multi‐view data. Some online multi‐view methods are proposed to tackle it, but they ignore the influence of uncorrelated items in each view. Therefore, in this study, the authors propose a new algorithm, called Incremental Multi‐view Correlated Feature Learning (IMCFL) based on non‐negative matrix factorisation, to learn the common feature across views. By separating uncorrelated items of new instances and constructing incremental joint learning of correlated and uncorrelated features, the proposed IMCFL can eliminate the influence of uncorrelated information in the individual view and improve the effectiveness of incremental multi‐view common feature learning. Extensive experiments on real‐world datasets confirm its superiority by comparing it with other state‐of‐the‐art incremental and non‐incremental methods.

Due to the closed-loop feedback control system, the data collected in the industry process is always temporal serial correlated. Moreover, different types of sensor data (such as temperature, pressure, liquid level, flow and so on) would show different temporal serial correlations. Meanwhile, the current process monitoring method considers all types of sensor data equally, ignoring the difference in temporal serial correlations caused by various sensor types. In response to this, serial correlated–uncorrelated concurrent space method is proposed in this work, where process variable original space is divided into serial correlated space and serial uncorrelated space via the degree of temporal serial correlations. In addition, the proposed method not only considers the local monitoring within space via principal component analysis and slow feature analysis respectively, but also monitors the information between-space based on the moving window and the mutual information. Finally, on the basis of the monitoring statistic within space and between-space, the comprehensive monitoring indicator is constructed via the local outlier factor method. The advantage and superiority of the proposed method is illustrated via testing on Tennessee Eastman process and Continuous Stirred Tank Reactor process.

Plant-wide processes often have the characteristics of large-scale and multiple operating units. Moreover, due to the closed-loop control, it is possible that the fault never affects product quality. In this article, a novel data-driven method called multisubspace orthogonal canonical correlation analysis (CCA) is proposed, which can not only tell whether the fault occurs but can also judge whether the fault affects the product quality in real time. First, to reduce process analysis complexity and to construct an accurate monitoring model, the original process variable space is divided into four subspaces. Second, the developed orthogonal CCA is conducted on process data and quality data for correlation feature extraction. Then, the quality-related and quality-unrelated features are obtained. Afterward, a total of six monitoring statistics are constructed and integrated to four statistics with physical interpretation via the Bayesian fusion strategy. Finally, the developed method is tested under an industrial case.

Wireless sensor networks (WSNs) is associated with a new exemplification of a real-time embedded system used for various applications that make the traditional infrastructure-based network seem infeasible. Due to imbalanced energy consumption among nodes, WSN has challenges with better utilization of energy and system enhancement. Clustering has been a better approach in this sequence. Therefore, this paper will utilize a fuzzy logic-based clustering protocol (unequal clustering) with multi-hop transmission for load balancing, energy consumption minimization, and network lifetime prolongation. The protocol forms unequal clusters with cluster head (CH) being selected by fuzzy logic with competition radius. Node distance to the base station, concentration, and residual energy are input variables. The simulation and results section displays the outperformance of the proposed protocol, where the low-energy adaptive clustering hierarchy (LEACH), energy-aware multi-hop multi-path hierarchical (EAMMH), energy-aware unequal clustering fuzzy (EAUCF), and two-tier distributed fuzzy logic-based protocol (TTDFP) for efficient data aggregation in multi-hop wireless sensor networks algorithms.

In the production process of large-scale machinery and complex industries, the key performance indicator (KPI) prediction is an essential part of project scheduling and cost estimation. The continuous enrichment of sensor types and functions brings us massive soft-sensing parameters for regression, but also brings severe challenges to algorithm learning. In this paper, an improved ensemble feature selection based on decision tree (EFS-DT) strategy for KPI prediction is developed. On the one hand, the ensemble of multi-criteria filtering results broadens the selector’s perspective without the time cost of superposition. On the other hand, credibility and similarity analysis are designed to eliminate the concerns of Dempster’s combination rule about conflict. After re-evaluating the variable scores, more high-quality variables can be selected to build a more accurate and robust KPI prediction model. Finally, a realistic shield tunnel case in China is used to evaluate the feasibility and effectiveness of proposed approach.

As the deep learning technology develops, many process monitoring methods based on auto-encoder (AE) are designed for the nonlinear industrial processes. However, these methods mainly focus on process variables and ignore the quality indicator which is crucial for the final production. To extract the latent variables which represent both process information and quality information, this paper proposes a novel algorithm named information concentrated variational auto-encoder (IFCVAE). To concentrate the quality-related information, a loading matrix regularization based on mutual information is designed, so that the strongly quality-related variables tend to have larger weights in the loading matrix. In addition, to monitor processes from the quality-related and unrelated aspects, IFCVAE decomposes the original space into two subspaces that are mutually orthogonal based on variational auto-encoder (VAE). With the help of an additional regression network, the two subspaces can correspond to the quality-related and unrelated spaces. For process monitoring, two statistics are designed for the subspaces according to Kullback–Leibler divergence. Finally, the effectiveness of IFCVAE is demonstrated by a numerical case and an industrial case.

Efficiency and multi-simultaneous-frequency (MSF) output capability are two major criteria characterising the performance of a power amplifier in the application of multi-frequency eddy current testing (MECT). Switch-mode power amplifiers are known to have very high efficiency, yet they have rarely been adopted in the instrumental development of MECT. In addition, switch-mode power amplifiers themselves are lacking in the research literature for MSF. In this paper, a Class D power amplifier is designed so as to address the two issues. An MSF selective harmonic elimination pulse width modulation (MSF-SHEPWM) method is proposed to generate alternating magnetic fields which are rich in selected harmonics. A field-programmable gate array (FPGA) based experimental system has been developed to verify the design. Results show that the proposed methodology is capable of generating high MSF currents in the transmitting coil with a low distortion of signal.

The technique of recognising text in natural scene pictures is widely used in social production. For the existing identification methods, it is difficult to accurately identify in complex environments. The accuracy of the detection determines the efficiency of the identification. A text detection method based on Multiscale Connectionist Text Proposal Network is proposed. The Multiscale-Region Proposal Network regresses and classifies the extracted region to obtain the final candidate region. Taking a large number of commodity image samples as a dataset, the multi-scale joint text proposal network is used to detect and locate the text content area in the image. The experimental results show that the proposed algorithm improves the detection accuracy in complex environments.

The electro-hydraulic driven multi-dimensional force loading parallel mechanism can meet the requirements of complex force loading. However, the force loading performance is affected by the strong dynamic coupling due to flexible load and structure characteristics of parallel mechanism, which is analyzed from the mathematical model built by the Newton–Euler method. In order to solve the coupling problem, a novel control strategy, modal space sliding mode control, is proposed. It can realize multi-dimensional force loading decoupling by a coordinating modal space controller. The modal space control framework is established based on the vibration theory, and the decoupling characteristics of modal space are analyzed. Furthermore, it is discussed that the consistency of modal space channels is required for the decoupling condition in degree of freedom space. In order to improve the tracking performance of modal space channels, a sliding mode controller is designed in the modal space by using the decoupling property of modal space. The modal space sliding mode controller not only reduces the chattering caused by coupling force, but also greatly improves the dynamic performance of modal space channels, as well as the consistency of modal space channels. The stability condition of the modal space sliding mode controller is given and proved by Lyapunov theorem. The experimental results show the effectiveness of the proposed modal space sliding mode control, which can significantly reduce coupling force and improve the dynamic tracking performance.

Adaptive optics—optical coherence tomography (AO-OCT) is a non-invasive technique for imaging retinal vascular and structural features at cellular-level resolution. Whereas retinal blood vessel density is an important biomarker for ocular diseases, particularly glaucoma, automated blood vessel segmentation tools in AO-OCT have not yet been explored. One reason for this is that AO-OCT allows for variable input axial dimensions, which are not well accommodated by 2D-2D or 3D-3D segmentation tools. We propose a novel bidirectional long short-term memory (LSTM)-based network for 3D-2D segmentation of blood vessels within AO-OCT volumes. This technique incorporates inter-slice connectivity and allows for variable input slice numbers. We compare this proposed model to a standard 2D UNet segmentation network considering only volume projections. Furthermore, we expanded the proposed LSTM-based network with an additional UNet to evaluate how it refines network performance. We trained, validated, and tested these architectures in 177 AO-OCT volumes collected from 18 control and glaucoma subjects. The LSTM-UNet has statistically significant improvement (p &lt, 0.05) in AUC (0.88) and recall (0.80) compared to UNet alone (0.83 and 0.70, respectively). LSTM-based approaches had longer evaluation times than the UNet alone. This study shows that a bidirectional convolutional LSTM module improves standard automated vessel segmentation in AO-OCT volumes, although with higher time cost.

SLAM is a key technology in the field of mobile robot. However, it is difficult to meet the requirements of positioning accuracy by using single sensor to locate and navigate for a mobile robot. Multi-sensor information fusion technology has become an important method to solve the problem of mobile robot positioning and navigation. By establishing information fusion model, building sensor experimental platform such as laser radar, ultrasonic ranging module and monocular camera, based on the laser RBPF-SLAM algorithm, a SLAM algorithm based on multi-sensor information fusion is proposed. The mobile robot can locate and construct road sign map at the same time. The laser RBPF-SLAM positioning and monocular visual positioning are advanced by using the information fusion model Information fusion under the maximum posterior probability criterion. Through experiments, the accuracy, effectiveness and practicability of the proposed method are verified.

In this paper, we introduce the first simulation framework that provides cost-effective means to evaluate indoor navigation systems for different user groups (e.g., users with visual impairment), various positioning techniques, and navigation instructions algorithms. The simulation engine uses the Unity game engine, which tracks the virtual user interaction and motion in a virtual environment that represents the physical environment in which the user navigates. The framework includes the following modules that will be defined by the indoor navigation system developers: 1) Positioning module which simulates indoor localization techniques and their observed accuracy; 2) Indoor navigation algorithm module that generates the navigation instructions using natural language phrases, and 3) Virtual user model (VUM) that includes human perception and information processing that can understand navigation instructions, perceive the surrounding environment, and act based on this information. The framework also includes a performance analysis module that evaluates the indoor navigation system performance in terms of navigation success rate and route similarity.

Clustering is an effective method for reducing energy consumption in wireless sensor networks (WSNs). In a multihop clustered network scenario, each sensor node transmits data to its own cluster head (CH), and the CH aggregates the data from its member nodes and forwards it to the base station (BS) via other CHs. However, the “hot spot” problem is prone to occur in clustered WSNs because CHs closer to the BS have heavier intercluster forwarding tasks. To address this problem, this paper proposes an unequal clustering algorithm based on interval type-2 TSK fuzzy logic theory (UCT2TSK). The relative distance to the BS (RDB), residual energy (RE), and node density (ND) are considered as the inputs of an interval type-2 fuzzy logic system (FLS). Through fuzzy reasoning, outputs are acquired that can be used to optimize the CHs and determine the cluster sizes. Simulation results verify that UCT2TSK can effectively balance energy consumption and enhance energy efficiency because it has better performance in network lifetime and network throughput than other classical and recent clustering algorithms.

In this paper, a novel supervised nonlinear process monitoring method named comprehensive kernel principal component regression (C-KPCR) is proposed to monitor the quality-related/unrelated additive/multiplicative faults. Firstly, mutual information is used to classify the process variables into quality-related part and quality-unrelated part. Secondly, the original variables matrix and the variables variance matrix are constructed and the data is mapped into high-dimensional feature space to deal with the nonlinear problem. Then the quality-related additive and multiplicative faults can be detected based on the regression model using original variables matrix and variables variance matrix, respectively. Afterwards, the monitoring result of quality-unrelated fault is obtained through combining the quality-unrelated information in the regression model and the quality-unrelated process variables. Finally, the effectiveness of the proposed method is demonstrated by a numerical example and the Tennessee Eastman process.

Process monitoring is an effective means to ensure process safety and improve product quality. On the one hand, it is possible that the fault will not affect process safety or product quality. On the other hand, not every fault affects both process safety and product quality simultaneously. To make process monitoring more purposeful and more accurate, a novel performance-indicator-oriented concurrent subspace (PIOCS) process monitoring method containing three subspaces with different degrees of importance is proposed in this paper. The first one is safety-related subspace, the second one is safety-unrelated quality-related subspace, and the third one is safety quality unrelated subspace. Through subspace division and construction, the fault can be categorized as safety-related fault, safety-unrelated quality-related fault, and safety quality unrelated fault. For mining safety and quality related information, both mechanism analysis and data analysis are used. In order to show the effectiveness and superiority, the proposed PIOCS method is tested under a number example and an industrial case study. Compared with traditional process monitoring methods, the proposed PIOCS method can obtain richer information, which is easy to show process operation condition clearly.

In this paper, a novel method named distributed independent component–principal component regression (distributed ICPCR) is proposed to monitor the large-scale non-Gaussian process. First, multiple...

Tomato spotted wilt virus is a wide-spread plant disease in the world. It can threaten thousands of plants with a persistent and propagative manner. Early disease detection is expected to be able to control the disease spread, to facilitate management practice, and further to guarantee accompanying economic benefits. Hyperspectral imaging, a powerful remote sensing tool, has been widely applied in different science fields, especially in plant science domain. Rich spectral information makes disease detection possible before visible disease symptoms showing up. In the paper, a new hyperspectral analysis proximal sensing method based on generative adversarial nets (GAN) is proposed, named as outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). It is an all-in-one method, which integrates the tasks of plant segmentation, spectrum classification and image classification. The model focuses on image pixels, which can effectively visualize potential plant disease positions, and keep experts’ attention on these diseased pixels. Meanwhile, this new model can improve the performances of classic spectrum band selection methods, including the maximum variance principle component analysis (MVPCA), fast density-peak-based clustering, and similarity-based unsupervised band selection. Selecting spectrum wavebands reasonably is an important preprocessing step in spectroscopy/hyperspectral analysis applications, which can reduce the computation time for potential in-field applications, affect the prediction results and make the hyperspectral analysis results explainable. In the experiment, the hyperspectral reflectance imaging system covers the spectral range from 395 nm to 1005 nm. The proprosed model makes use of 83 bands to do the analysis. The plant level classification accuracy gets 96.25% before visible symptoms shows up. The pixel prediction false positive rate in healthy plants gets as low as 1.47%. Combining the OR-AC-GAN with three existing band selection algorithms, the performance of these band selection models can be significantly improved. Among them, MVPCA can leverage only 8 spectrum bands to get the same plant level classification accuracy as OR-AC-GAN, and the pixel prediction false positive rate in healthy plants is 1.57%, which is also comparable to OR-AC-GAN. This new model can be potentially transferred to other plant diseases detection applications. Its property to boost the performance of existing band selection methods can also accelerate the in-field applications of hyperspectral imaging technology.

Maize inspection is an important and time-consuming task in the domain of food engineering. The human-based inspection strategy needs to be brought up to date with the rapid developments in the maize industry. In this paper, an automatic maize-inspection machine is proposed. Our proposed machine integrates several new designs in terms of both hardware and software components. First, a gravity-based dual-side camera design expands the machine's field-of-view to evaluate maize kernels more thoroughly. Second, touching kernels are pre-processed using a new k-means clustering guided-curvature method, which can improve the robustness of our machine. Next, a deep convolutional neural network, which has shown promise for application in image processing, is embedded into the system to evaluate maize kernels. In this work, the ResNet, which is a deep convolutional neural network architecture, was trained by fine-tuning with 1632 images. It achieved a 98.2% prediction accuracy for 408 test images, which outperforms existing approaches.

This paper proposes a novel approach for two-wheel differential spherical robot (TWDSR) design to monitor the power cable tunnel environment. A co-simulation of dynamic modeling and controller design is conducted based on ADAMS (Automatic Dynamic Analysis of Mechanical System) and MATLAB platforms, respectively. The combined simulation takes the advantages of dynamic modeling for complicated mechanical structure of ADAMS and various controller designs of MATLAB. We established the virtual prototype model of TWDSR composed of spherical shell, cart and driving wheels in ADAMS. The rectilinear motion and steering motion are modeled by loading different inputs on the driving wheel. Based on results of the model, we analyzed the basic open-loop motion control characteristics of TWDSR. Subsequently, the ADAMS virtual prototype model is imported into MATLAB to design the velocity controller by Simulink module. The co-simulation results demonstrate that the designed mechanism of TWDSR is suitable for power cable tunnel monitoring. The motion control simulation determines the parameter values of PI controller and evaluates the system motion performance. The co-simulation of TWDSR provides detailed references for the development and implementation of the physical prototype in the future.

This paper focuses on the study and application of a scissor structural element, termed non-crossing angulated element (NCAE). After studying a special NCAE with a constant subtended angle during motion, we present a family of novel deployable polyhedral mechanisms with radially reciprocating motion (RRM-DPM). Compared with previous RRM-DPMs, the presented RRM-DPMs have no singularity owing to the scissor structural property of NCAE. We also build 3D model both in computer and prototypes to demonstrate the feasibility of the proposed RRM-DPMs.

With the continuous expansion of the scale of the power grid and the gradual maturity of the application of drones, drones with the advantages of flexibility and convenience can overcome terrain constraints while reducing manpower and safety risks and can be well qualified for the power inspection work under the large-scale development of the power grid and improve the inspection efficiency. However, the massive amount of data collected by UAV power line inspection relies on the human judgment through the naked eyes of staff, and the recognition efficiency is very low and the accuracy rate is subjectively influenced. In recent years, the gradual introduction of traditional machine learning and deep learning has led to significant improvements in inspection efficiency and accuracy. The autonomous obstacle avoidance technology based on the inherent electromagnetic field characteristics of power lines makes it possible for UAVs to cruise autonomously. Based on a systematic summary of the achieved results, this paper briefly reviews the overall development of both power component detection and autonomous cruise in UAV power line inspection. At the same time, the current problems and future research trends are described, expecting to provide references for further research in this field.

Background Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1. Methods This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance. Findings Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680–0.814) in the 10-fold stratified cross-validation and 0.751 (0.635–0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted. Interpretation The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available. Funding This study was supported by the National Key Research and Development Program (2017YFC1001800).

Face matching technology is a key part in the applications of face recognition technology. It can be used for classroom behavior analysis, and then improve the teaching effect. The paper introduces the algorithm structure and loss function of face detection algorithm based on MTCNN, and realizes face comparison based on FaceNet model. According to the different tasks of training data set and algorithm in different convolution layer, the diversity of face samples in training set is adjusted. Through MTCNN's sub network training, the weight coefficient of loss function and various thresholds are changed to improve the training effect and enhance the robustness of face detection. Considering the problems of multi person detection, face recognition illumination robustness, algorithm implementation cost and application real-time, this paper integrates MTCNN detection algorithm, FaceNet model and image similarity comparison algorithm, and implements the algorithm on LFW data set. Then, under the condition of specific face segmentation, the selection method of the best matching threshold is analyzed, and the results of Euclidean distance comparison of different faces under the best threshold are given. At last, it gets the recognition accuracy improved by 0.21% on the LFW dataset, which lays a technical foundation for the realization of classroom behavior analysis of students' location attendance.

In the field of electronic nose(E-nose) research, the underlying gas sensor is affected by environmental changes, aging of its own devices, sensor poisoning and other factors, which will cause the detection value to drift. Besides, the need for a large number of labeled samples in the pattern recognition algorithm will lead to excessively high model training costs. In order to solve the problems mentioned above, a method that combines classifier integration and active learning to reduce the model training cost by reducing the number of labeled samples is proposed in this paper. Using this method, the trend of sensor drift is captured by classifier integration, the number of single-labeled samples is dynamically adjusted, and finally the drift of the gas sensor array is suppressed. From the experiment results, it can be found that the sensor drift can be satisfactorily solved by the proposed method.

"School-enterprise cooperation, work-study combination" talent training model is the core content of the "3+1" training model for application-based undergraduate education talents. By investigating the current situation of education in China, we analyze the main problems in the school-enterprise cooperation model at home and abroad, and propose a specific "3+1" training model program, and compare and analyze the "3+1" model and other models, highlighting the advantages of the "3+1" model in the direction of cultivating application-oriented talents, and laying the foundation for the next step in pilot applications and cultivating more application-oriented talents.

Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal and residual parts. However, as the principal part still contains many components unrelated to quality, if these components were not removed it could cause many false alarms. Besides, although these components do not affect product quality, they have a great impact on process safety and information about other faults. Removing and discarding these components will lead to a reduction in the detection rate of faults, unrelated to quality. To overcome the drawbacks of Standard PLS, a novel method, MI-PLS (mutual information PLS), is proposed in this paper. The proposed MI-PLS algorithm utilizes mutual information to divide the process variables into selected and residual components, and then uses singular value decomposition (SVD) to further decompose the selected part into quality-related and quality-unrelated components, subsequently constructing quality-related monitoring statistics. To ensure that there is no information loss and that the proposed MI-PLS can be used in quality-related and quality-unrelated fault detection, a principal component analysis (PCA) model is performed on the residual component to obtain its score matrix, which is combined with the quality-unrelated part to obtain the total quality-unrelated monitoring statistics. Finally, the proposed method is applied on a numerical example and Tennessee Eastman process. The proposed MI-PLS has a lower computational load and more robust performance compared with T-PLS and PCR.

The power supply mode of microelectronic equipment is still mainly battery. The difficulty in replacing the batteries affects the life time of these devices. Radio frequency energy harvesting (RFEH) technology provides a new way for the power supply of low-power microelectronic equipment. This paper designs a RFEH system which harvests 2.4 GHz radio frequency signal. The system is mainly composed of miniature patch antenna, impedance matching circuit, rectifier booster circuit, and storage circuit. The ADS simulation tool is used to verify the feasibility of the system design. When the load resistance is 100 KΩ and the input power is between −10 and 5 dBm, the energy conversion efficiency is above 30%. Continuous power supply for low-power microelectronic equipment can be realized.

Commercialized RGB-D and LiDAR cameras have been widely applied in food and agricultural engineering fields. However, the general-use 3D cameras cannot ensure high resolution 3D imaging because of their imaging principles. The accuracy of their generated height map ranges from 1cm – 10cm in the actual measurements, which cannot meet the requirement of accurate object height estimation in the food processing lines, especially for visualizing sub-centimeter food product shape variations. This research customized an active 3D scanning system, which utilized the line scanning method to ensure the scanning speed compared to the dot scanning and to minimize the cross talk in the frame scanning. Different from some industrial level 3D line scanning cameras, the system avoided synchronizing the imaging acquisition and processing line conveyor movement. Instead, to implement the 3D imaging for a static object, a programmable rotational mirror was integrated in the system to reflect the line laser for covering the scanning of the entire field of view. In our experimental setup, for a 5-cm calibration phantom, the standard derivation of height estimation from the system is 0.15mm in a 1m photographic distance, which is superior to commercialized RGB-D cameras including Intel RealSense and Kinect. The customized system can also automatically generate well-registered color and depth image pairs and its idea can easily fit into different food and agricultural applications.

Access control encryption (ACE) is a useful concept introduced by Damgard, Haagh and Orlandi in TCC 2016, which not only protects the data privacy but also controls the information flow. However, their DDH-based scheme suffered from the ciphertext revealing attack (CRA) introduced by Badertscher, Matt and Maurer in Asiacrypt 2017 and just satisfied chosen plaintext attack (CPA) security. Badertscher, Matt and Maurer strengthened the security model to chosen ciphertext attack (CCA) security and constructed a CCA secure scheme under Naor-Yung paradigm. However, they did not indicate how to fix the DDH-based scheme proposed by Damgard, Haagh and Orlandi. Their CCA secure scheme is inefficient and complicated due to the non-interactive zero knowledge proofs (NIZKs) of a very complicated relation. And their scheme is constructed just for limited communication policies.

Identity-Based Cryptography (IBC) is a useful tool for the security of IoT devices, but securely deploying this cryptographic technique to the IoT systems is quite challenging. For instance, a leakage of the master secret key will result in the leakage of all IoT devices’ private keys. SM9 is the only approved IBC algorithm standard in China. It is critical to have mechanisms to protect the SM9 master secret keys. In this work, to reduce the risk of the master secret key leakage, we propose a (t, n)-threshold distributed private key generation scheme for SM9 with some techniques from multiparty computation. Our scheme is compatible with all the three SM9 sub-algorithms (i.e., the encryption, signature and key agreement). It is also provably secure and completely eliminates the single point of failures in SM9 that is concerned by the industry. The experimental analysis indicates that the proposed scheme is efficient, e.g., up to 1 million private key generation requests can be handled per day.

Eddy current testing (ECT) is an effective technique for evaluating depth of metal surface defects. However, in practice, evaluation primarily relies on the experience of an operator and is often carried out by manual inspection. In this article, we address the challenges of automatic depth evaluation of metal surface defects by virtual of state-of-the-art deep learning (DL) techniques. The main contributions are threefold. First, a highly integrated portable ECT device is developed, taking the advantage of an advanced field-programmable gate array (Zynq-7020 system on chip) and provides fast data acquisition and in-phase/quadrature demodulation. Second, a dataset, termed metal defects of different depths by ECT (MDDECT), is constructed using the ECT device by human operators and made openly available. It contains 48000 scans from 18 defects of different depths and liftoffs. Third, the depth evaluation problem is formulated as a time series classification problem, and various state-of-the-art 1-D residual convolutional neural networks are trained and evaluated on the MDDECT dataset. A 38-layer 1-D ResNeXt achieves an accuracy of 93.58% in discriminating the surface defects in a stainless steel sheet with depths from 0.3 to 2.0 mm in the resolution of 0.1 mm. In addition, the results show that the trained ResNeXt1D-38 model is immune to liftoff signals.

Anonymous signature is a cryptographic tool where the signature of a message can hide its identity of the signer as long as part of the signature is hidden from the verifier. It gives a handy protection of signer identity and is useful in key exchange, auctions and voting.

As the epidemic of the COVID-19 continues, many venues need to be installed with recognition equipment at the entrance to recognize whether people wear masks. However, it is not economical to install multiple high-performance devices with a great recognition model only for mask recognition, and not fast to use the combination of camera shooting devices and cloud computing. Therefore, this paper put forward an edge-cloud computing system, whose purpose is to use the poor computing resources of the edge server to share the computing pressure of the cloud server. In other words, the recognition tasks are assigned not only to the cloud device but also to the edge devices with poor computing power and a simple recognition model. The scheduling optimization problem is solved by reinforcement learning method. In the case of insufficient and imperfect data sets, this system still shows high recognition accuracy and fast response speed.

The capacitated arc routing problem is a challenging combinatorial optimization problem with numerous real-world applications. In recent years, several multi-objective optimization algorithms have been applied to minimize both the total cost and makespan for capacitated arc routing problems, among which the decomposition-based memetic algorithm with extended neighborhood search has shown promising results. In this paper, we propose an improved decomposition-based memetic algorithm with extended neighborhood search, called D-MAENS2, which uses a novel method to construct a gene pool to measure and improve the diversity of solutions in decision variable space. Additionally, D-MAENS2 is capable of adapting online its hyper-parameters to various problem instances. Experimental studies show that our novel D-MAENS2 significantly outperforms D-MAENS on 81 benchmark instances and shows outstanding performance on instances of large size.

Traditionally, the museum statically displays its art collections by means of windows. Such approach is limited in certain aspects, such as insufficient information expression, poor user interaction, and limited space. By leveraging Virtual Reality (VR) technology, this paper proposes a new museum exhibition system to address the above limitations. Our design is demonstrated in the exhibition system at China Arts and Crafts Master Museum of Nanchang Institute of Science & Technology. Experiments show that the VR-based system can display the information of the museum's collections more comprehensively and more effectively. Visitors can then better understand the artistic essence of museum collections, inspire visitors' observation and thinking, and consequently achieve a deeper level of interaction experience.

The epidemic of novel coronavirus pneumonia (COVID-19) has spread worldwide, which greatly affected people's daily life, study and office. Especially the distance education needs better video communication software. Among them, video chat technology is the key technology. At present, there are a large number of video chat systems in the market. Based on the analysis of the technical background of video chat system, this paper proposes a new P2P video chat system design, aiming at the problem of unstable video communication in the current video chat system. Based on VNN technology, this paper presents the logic and model design of P2P video chat system, including login and call model, and analyzes its workflow. Finally, the prototype system of video chat room is realized. The video communication of the system is smooth and the picture quality is good. Next, we will use the technology in the research and development of distance education system, which can be applied in the teaching of colleges and universities.

Data-based process monitoring methods have received tremendous attention in recent years, and modern industrial process data often exhibit dynamic and nonlinear characteristics. Traditional autoencoders, such as stacked denoising autoencoders (SDAEs), have excellent nonlinear feature extraction capabilities, but they ignore the dynamic correlation between sample data. Feature extraction based on manifold learning using spatial or temporal neighbors has been widely used in dynamic process monitoring in recent years, but most of them use linear features and do not take into account the complex nonlinearities of industrial processes. Therefore, a fault detection scheme based on temporal-spatial neighborhood enhanced sparse autoencoder is proposed in this paper. Firstly, it selects the temporal neighborhood and spatial neighborhood of the sample at the current time within the time window with a certain length, the spatial similarity and time serial correlation are used for weighted reconstruction, and the reconstruction combines the current sample as the input of the sparse stack autoencoder (SSAE) to extract the correlation features between the current sample and the neighborhood information. Two statistics are constructed for fault detection. Considering that both types of neighborhood information contain spatial-temporal structural features, Bayesian fusion strategy is used to integrate the two parts of the detection results. Finally, the superiority of the method in this paper is illustrated by a numerical example and the Tennessee Eastman process.

In this paper, face recognition technology is studied and explored, and the classical local binary pattern (LBP) algorithm is studied and improved. An improved LBP algorithm is proposed to solve the deficiency of LBP: a “double circle” LBP algorithm is proposed, which improves the rotation invariability of LBP to a greater extent and has a good recognition effect on small-pose face images. On the basis of good recognition effect of LBP based on partitioning, the idea of “multiple partitioning + intermediate partitioning” is put forward, which effectively makes up for the shortage of incomplete extraction of peripheral information of original segmentation line. Facial features are the key features of face recognition. Different organs contribute differently to face image recognition. The idea of “key block” based on the original block is proposed. Finally, the application of an LBP algorithm for face recognition is taken as an example, and the experimental results verify the reliability of the face recognition method based on LBP for weak light and expression change images.

Many lattice-based schemes are built from the hardness of the learning with errors problem, which naturally comes in two flavors: the decision LWE and search LWE. In this paper, we investigate the decision LWE and search LWE by Renyi divergence respectively and obtain the following results: For decision LWE, we apply RD on LWE variants with different error distributions (i.e., center binomial distribution and uniform distribution, which are frequently used in the NIST PQC submissions) and prove the pseudorandomness in theory. As a by-product, we extend the so-called public sampleability property and present an adaptively public sampling property to the application of Renyi divergence on more decision problems. As for search LWE, we improve the classical reduction proof from GapSVP to LWE. Besides, as an independent interest, we also explore the intrinsic relation between the decision problem and search problem.