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In the fully mechanized mining face, the proper chain tension of chain transmission system is the key to reduce the failure rate of scraper conveyor and improve the service life of expensive chain. However, the interference of random load and variable stiffness in the chain transmission system of the scraper conveyor make it very difficult for tensionable tail drive (TTD) to control the chain tension. In this research, the mathematical model of the TTD based on catenary theory is established, and the dynamic characteristics are analyzed to investigate the influence of load and stiffness on the tension control. An adaptive neural command filtering backstepping algorithm with parameter identification is proposed to control the chain tension of the chain transmission system. The combination of stiffness identification with the linearized tension model weakens the influence of variable stiffness and load on the tension control. Based on state observation and stiffness estimation, radial basis function neural network is introduced into the tension controller to compensate the load randomness caused by tail sprocket drive. Comparative experimental results are completed to verify the high-accuracy tension tracking performance and a strong robustness of the proposed controller in the random load mode.

Acoustic levitation is considered one of the most effective noncontact particle manipulation methods, along with aerodynamic, ferromagnetic, and optical levitation techniques. It is not restricted by the material properties of the target. However, existing acoustic levitation techniques have some drawbacks that limit their potential applications. Therefore, in this article, an innovative approach is proposed to manipulate objects more intuitively and freely. By taking advantage of the transition periods between the acoustic pulse trains and electrical driving signals, acoustic traps can be created by switching the acoustic focal spots rapidly. Since the high-energy-density points are not formed simultaneously, the computation of the acoustic field distribution with complicated mutual interference can be eliminated. Therefore, compared to the existing approaches that created acoustic traps by solving pressure distributions using iterative methods, the proposed method simplifies the computation of time delay and makes it possible to be solved even with a microcontroller. In this work, three experiments have been demonstrated successfully to prove the capability of the proposed method including lifting a Styrofoam sphere, transportation of a single target, and suspending two objects. Besides, simulations of the distributions of acoustic pressure, radiation force, and Gor'kov potential were conducted to confirm the presence of acoustic traps in the scenarios of lifting one and two objects. The proposed tactic should be considered effective since the results of the practical experiments and simulations support each other.

In recent years, more and more deep learning frameworks are being applied to hyperspectral image classification tasks and have achieved great results. However, the existing network models have higher model complexity and require more time consumption. Traditional hyperspectral image classification methods tend to ignore the correlation between local spatial features. In this paper, a new hyperspectral image classification method is proposed, which combines two-dimensional Gabor filter with random patch convolution (GRPC) feature extraction to obtain spatial-spectral feature information. The method firstly performs dimensionality reduction through principal component analysis and linear discriminant analysis and extracts the edge texture and spatial information of the image using a Gabor filter for the reduced-dimensional image. Next, the extracted information is convolved with random patches to extract spectral features. Finally, the spatial features and multi-level spectral features are fused to classify the images using the Support Vector Machine classifier. In order to verify the performance of this method, experiments were conducted on three widely used datasets of Indian Pines, Pavia University and Kennedy Space Center. The overall classification accuracy reached 98.09%, 99.64% and 96.53%, which are all higher than other comparison methods. The experimental results reveal the superiority of the proposed method in classification accuracy.

Existing studies for multi-source neural machine translation (NMT) either separately model different source sentences or resort to the conventional single-source NMT by simply concatenating all source sentences. However, there exist two drawbacks in these approaches. First, they ignore the explicit word-level semantic interactions between source sentences, which have been shown effective in the embeddings of multilingual texts. Second, multiple source sentences are simultaneously encoded by an NMT model, which is unable to fully exploit the semantic information of each source sentence. In this paper, we explore multi-stage information interactions for multi-source NMT. Specifically, we first propose a multi-source NMT model that performs information interactions at the encoding stage. Its encoder contains multiple semantic interaction layers, each of which sequentially consists of (1) monolingual semantic interaction sub-layer, which is based on the self-attention mechanism and used to learn word-level monolingual contextual representations of source sentences, and (2) cross-lingual semantic interaction sub-layer, which leverages word alignments to perform fine-grained semantic transitions among hidden states of different source sentences. Furthermore, at the training stage, we introduce a mutual distillation based training framework, where single-source models and ours perform information interactions. Such framework can fully exploit the semantic information of each source sentence to enhance our model. Extensive experimental results on the WMT14 English-German-French dataset show our model exhibits significant improvements upon competitive baselines.

Occupying the most significant portion of global data traffic, video is being generated in almost every aspect of our life. Because of its huge volume, we are depending much more heavily on machine intelligence based analysis. In the meantime, video coding technology has been continuously improved for better compression efficiency. However, the state-of-the-art video coding standards, such as H.265/HEVC and versatile video coding (VVC), are still designed assuming that the compressed video will be watched by a human later. Such a design is not optimal when the compressed video will be used by computer vision applications. While the human visual system (HVS) is consistently sensitive to the content with high contrast, the impact of pixels on computer vision algorithms is task driven. For example, because of the different categories of objects used to train detection algorithms, the influence of the same image content on those detectors also varies. Therefore, human oriented video coding strategies may not be optimal when the compressed signal is further processed by algorithms, as the encoder is unaware of the task specific information. In this paper, taking object detection as an example, we propose a novel video coding strategy for computer vision. By protecting the information according to its importance for an object detector rather than for the human visual system, our proposed method has the potential to achieve a better object detection performance with the same bandwidth. The main contributions of our paper are: 1) the modeling of the relationship between object detection accuracy and bit rate; 2) a back propagation based method to analyze the influence of each pixel on the detection of target objects; 3) an object detection oriented bit allocation and codec control parameter determination scheme; 4) an evaluation metric to compare the impact of video coding strategies on a given object detector over a predefined range of bit rate. Experimental results demonstrate that our proposed algorithm can better preserve the video content vital for object detection than state-of-the-art video coding schemes.

In this article, an adaptive dynamic surface control method with nonlinear disturbance observers is proposed for accurate position tracking of an electro-hydraulic servo system with unknown time-varying inner or external disturbances. The dynamic surface control approach adopted in the proposed controller is used to ameliorate the inherent complexity differentiation explosion of traditional backstepping method, which significantly simplifies the controller design process. The designed nonlinear disturbance observers are exploited to online estimate the inner or external disturbances of electro-hydraulic servo systems, and the performance degradation resulted from unknown time-varying disturbances is effectively suppressed. To further compensate for the system’s time-varying uncertain parameters, parameter adaptive updating laws are designed and combined in the proposed controller for accurate position tracking of electro-hydraulic servo systems. The closed-loop stability of the proposed controller is theoretically guaranteed by rigorous Lyapunov analysis. Comparative experimental results are carried out on a typical single-degree-of-freedom electro-hydraulic servo system, and the feasibility together with the superiority of the proposed controller is experimentally validated.

High-accuracy motion tracking of hydraulic systems is of great significance in industrial applications. Nevertheless, dynamic nonlinearity, modeling uncertainty, generalized disturbance, and measurement noise are inevitably existed in hydraulic systems, which severely deteriorates the practical control performance. Aimed at enhancing the motion-tracking accuracy of hydraulic systems, a novel command filtered adaptive backstepping controller with extended state observer is proposed in this article. On the basis of the established system’s nonlinear model, the extended state observer utilizing only position output feedback information is first designed to estimate the system’s unmeasurable states, and time-varying disturbances of the hydraulic system are also obtained for subsequent active disturbance compensation. Next, a second-order command filter is constructed to generate specific command signals and their derivatives, which significantly simplifies the controller design process by avoiding complicated analytical differential calculations in contrast to traditional adaptive backstepping algorithm. Subsequently, with consideration of system’s nonlinearity, parametric uncertainty, and time-varying disturbance, the developed extended state observer and command filter are introduced into the adaptive backstepping design process of the proposed controller, and theoretical stability of the proposed controller is guaranteed via Lyapunov analysis. Finally, the effectiveness and superiority of the proposed controller are demonstrated by comparative experimental results.

Reliability-based multidisciplinary design optimization (RBMDO) has been considered as an efficient technology for addressing complex engineering design optimization problems under uncertainties. C...

This paper deals with the speed synchronization control of a connected vehicle subject to a replay attack. A large number of replay attack signals are injected into controller area network (CAN) through external network, which greatly reduces the real-time control performance of a connected integrated motor-transmission (IMT) system. In order to ensure the performance of an IMT speed tracking system under large random message delays, a robust reset controller combined with a delay-robust speed synchronization controller satisfying energy-to-peak performance is designed in this paper. The uncertain impact caused by a replay attack is described by large random network delays which are modeled by polytopic inclusion. Then, a dynamic output-feedback controller considering the uncertainty caused by attack-delays is proposed for online speed tracking. Moreover, a robust reset controller is designed to obtain comparatively better transient response in the case of large attack-delays. In this control strategy, once the reset condition is triggered, the after-reset value calculated by linear matrix inequalities (LMIs) would replace the dynamic state vector. Finally, the effectiveness of the proposed controller is verified by comparing it with model predictive control (MPC), existing PD control considering delays and energy-to-peak robust control in terms of performance.

This paper analyses the mechanism of tensor projection transformation in depth and introduces a high-efficiency original algorithm developed in a quantum computing language for forward and backward projection between multidimensional tensors and one-dimensional vectors. Additionally, the author compares this algorithm with similar methods from both the Python scientific computing package and other relative development kits in method calls and source code to demonstrate the innovation of the tensor projection algorithm. On this basis, the classical convolution operation program commonly used in machine learning has been parallelized and improved, the analysis algorithm of the Beidou communication satellite view area has been parallelized and improved, and the actual operating efficiency has been greatly improved. After verification, the tensor projection transformation successfully solves the problem of location index mapping of the entity units among different dimensions, can provide a means for optimizing the traditional model of traversal algorithm, and can have significant reference value in eigenspace transformation against a tensor field.

The rapid increase in the number of chemical science literature has brought challenges to researchers in search and data analysis. For many chemical scientific literature, extracting information from text and using knowledge is the focus of research. However, the existing Chinese text word segmentation methods have low recognition rate for chemical terms. The reason is that the addition of many new vocabulary and mixed professional vocabulary of Chinese and English brings challenges to word segmentation. In this paper, we propose a word segmentation method of Chinese chemical literature based on hybrid feature fusion learning Model(HFFLM). HFFLM first establishes chemical science corpus (Chem-pku) to train Chinese word segmentation (CWS) tasks. In addition, HFFLM uses BiLSTM and CNN to extract document features and fuse them. Then, HFFLM combines boundary features to construct conditional random field to train the end-to-end CWS model. In the end, HFFLM makes visual analysis of the word segmentation results. The experimental results indicate that HFFLM has high accuracy and recall rate, and is suitable for chemical industry vocabulary extraction with mixed Chinese and English.

Crystal structure prediction is now playing an increasingly important role in the discovery of new materials or crystal engineering. Global optimization methods such as genetic algorithms (GAs) and particle swarm optimization have been combined with first-principles free energy calculations to predict crystal structures given the composition or only a chemical system. While these approaches can exploit certain crystal patterns such as symmetry and periodicity in their search process, they usually do not exploit the large amount of implicit rules and constraints of atom configurations embodied in the large number of known crystal structures. They currently can only handle crystal structure prediction of relatively small systems. Inspired by the knowledge-rich protein structure prediction approach, herein we explore whether known geometric constraints such as the atomic contact map of a target crystal material can help predict its structure given its space group information. We propose a global optimization-based algorithm, CMCrystal, for crystal structure (atomic coordinates) reconstruction based on atomic contact maps. Based on extensive experiments using six global optimization algorithms, we show that it is viable to reconstruct the crystal structure given the atomic contact map for some crystal materials, but more geometric or physicochemical constraints are needed to achieve the successful reconstruction of other materials.

The Joint Video Exploration Team (JVET) recently launched the standardization of the next-generation video coding named Versatile Video Coding (VVC) with the inherited technical framework from its predecessor High-Efficiency Video Coding (HEVC). The simplified Enhanced Multiple Transform (EMT) has been adopted as the primary residual coding transform solution, termed Multiple Transform Selection (MTS). In MTS, only the transform set consisting of DST-VII and DCT-VIII remains, excluding the other transform sets and the dependency on intra prediction modes. Significant coding gains are achieved by introducing new DST/DCT transforms, but the full matrix implementation is relatively costly compared to partial butterfly in terms of both software run-time and operation counts. In this work, we exploit the inherent features existing in DST-VII and DCT-VIII. Instead of repeating the element-wise additions and multiplications in full matrix operation, these features can be leveraged to achieve more efficient implementations which only use partial elements to derive the identical results. Existing transform matrices are further tuned to utilize these (anti-)symmetric features. A partial butterfly-type fast algorithm with dual-implementation support is proposed for DST-VII/DCT-VIII transform in VVC. Complexity analysis including operation counts and software run-time are conducted to validate the effectiveness. In addition, we prove the features are perfectly supported by theory. The proposed fast methods achieve noticeable software run-time savings without compromising on coding performance by comparing with the VVC Test Model VTM-3.0. It is shown that under Common Test Condition (CTC) with inter MTS enabled, an average of 9%, 0%, and 3% decoding time savings are achieved for All Intra (AI), Random Access (RA) and Low Delay B (LDB), respectively. Under low QP test condition with inter MTS enabled, the proposed fast methods achieve 1%, 2% and 4% decoding time savings on average for AI, RA, and LDB, respectively.

Feature selection is a technique to select a subset of the most relevant features for modeling training. In this paper, a new concept of TDR is firstly proposed to improve the classification accuracy. Then, a TDR-based algorithm for text classification is advanced. Finally, the extensive experiments are made on seven datasets (K1a, K1b, WAP, R52, R8, 20NewGroups, and Cade12) for two classifiers of Naive Bayes and Support Vector Machine. The experimental results indicate that the new approach can improve the classification accuracy by an average percent of 7.9%.

ConspectusFlexible integrated circuits, working as the core unit of information processing, have been a subject of extensive research; they are essential to realize fully flexible electronic system...

Missing values are common in clinical datasets which bring obstacles for clinical data analysis. Correctly estimating the missing parts plays a critical role in utilizing these analysis approaches. However, only limited works focus on the missing value estimation of multivariate time series (MTS) clinical data, which is one of the most challenge data types in this area. We attempt to develop a methodology (MD-MTS) with high accuracy for the missing value estimation in MTS clinical data. In MD-MTS, temporal and cross-variable information are constructed as multi-directional features for an efficient gradient boosting decision tree (LightGBM). For each patient, temporal information represents the sequential relations among the values of one variable in different time-stamps, and cross-variable information refers to the correlations among the values of different variables in a fixed time-stamp. We evaluated the estimation method performance based on the gap between the true values and the estimated values on the randomly masked parts. MD-MTS outperformed three baseline methods (3D-MICE, Amelia II and BRITS) on the ICHI challenge 2019 datasets that containing 13 time series variables. The root-mean-square error of MD-MTS, 3D-MICE, Amelia II and BRITS on offline-test dataset are 0.1717, 0.2247, 0.1900, and 0.1862, respectively. On online-test dataset, the performance for the former three methods is 0.1720, 0.2235, and 0.1927, respectively. Furthermore, MD-MTS got the first in ICHI challenge 2019 among dozens of competition models. MD-MTS provides an accurate and robust approach for estimating the missing values in MTS clinical data, which can be easily used as a preprocessing step for the downstream clinical data analysis.

Biological signals generated during various biological processes are critically important for providing insight into the human physiological status. Recently, there have been many great efforts in developing flexible and stretchable sensing systems to provide biological signal monitoring platforms with intimate integration with biological surfaces. Here, this review summarizes the recent advances in flexible and stretchable sensing systems from the perspective of electronic system integration. A comprehensive general sensing system architecture is described, which consists of sensors, sensor interface circuits, memories, and digital processing units. The subsequent content focuses on the integration requirements and highlights some advanced progress for each component. Next, representative examples of flexible and stretchable sensing systems for electrophysiological, physical, and chemical information monitoring are introduced. This review concludes with an outlook on the remaining challenges and opportunities for future fully flexible or stretchable sensing systems.

Modern industrial processes are becoming more and more complex and contain linear and nonlinear parts as well as some unknown disturbances, which poses the difficulty for product quality prediction...

Graphs are a rich and fundamental object of study, of interest from both theoretical andapplied points of view. This thesis is in two parts and gives a treatment of graphs from twodiffering points of view, with the goal of doing inference on graphs. The first is a mathematicalapproach. We create a formal framework to investigate the quality of inference ongraphs given partial observations. The proofs we give apply to all graphs without assumptions.In the second part of this thesis, we take on the problem of clustering with the aid ofdeep neural networks and apply it to the problem of community detection. The results arecompetitive with the state of the art, even at the information theoretic threshold of recoveryof community labels in the stochastic blockmodel.

Video display processing is an indispensable module in a variety of multimedia systems. With the increase of video format to 4K $\times$ 2K@60fps, the design of efficient display processing circuit faces severe challenges. This paper presents a video display processor which supports a variety of video formats and integrates multiple advanced algorithms for perpetual image quality improvement. By leveraging algorithm and architecture co-design, this paper efficiently implements these computational and memory intensive display processing algorithms in a single chip. In particular, we describe three representative design modules in detail, including motion compensated frame rate up-conversion, content-adaptive contrast enhancement, and backlight local dimming. Extensive experiments demonstrate that the proposed design modules outperform the previous works in terms of either image quality or hardware efficiency. The overall chip is fabricated in GF 55nm CMOS technology, and can work at the maximum operating frequency of 594 MHz. The maximum input and output video formats reach up to 4K $\times$ 2K@60fps.

Facial beauty prediction (FBP) has become an emerging area in the field of artificial intelligence. However, the lacks of data and accurate face representation hinder the development of FBP. Multi-task transfer learning can effectively avoid over-fitting, and utilize auxiliary information of related tasks to optimize the main task. In this paper, we present a network named Multi-input Multi-task Beauty Network (2M BeautyNet) and use transfer learning to predict facial beauty. In the experiment, beauty prediction is the main task, and gender recognition is the auxiliary. For multi-task training, we employ multi-task loss weights automatic learning strategy to improve the performance of FBP. Finally, we replace the softmax classifier with a random forest. We conduct experiments on the Large Scale Facial Beauty Database (LSFBD) and SCUT-FBP5500 database. Results show that our method has achieved good results on LSFBD, the accuracy of FBP is up to 68.23%. Our 2M BeautyNet structure is suitable for multiple inputs of different databases.

In this paper, a modified double-rope winding hoisting system (MDWHS) is proposed for ultra-deep mines. Nevertheless, the MDWHS has wire rope tension imbalance issues due to lift inconsistency of two wire ropes. A combined control algorithm is designed for wire rope tension active control considering the nonlinear uncertainties subjected to the MDWHS, where a low gain state observer (LGSO) and a robust nonlinear adaptive controller are employed via backstepping theory. Firstly, to investigate the wire rope tension active control method, the dynamics model of the MDWHS is established. Then, the LGSO is employed to estimate unmeasured system state variables and to optimize perturbed system state variables. Finally, a robust nonlinear control method is employed to acquire prospective control input. The combined control algorithm can ensure the desired transient and final tracking accuracy, which is essential for tracking control of wire rope tension active adjustment. The stability of the overall closed-loop system with the presented control algorithm can be verified referring to the Lyapunov theory. Experimental results conducted on the established double-rope winding hoisting simulation experimental system demonstrate that the proposed active control method exhibit more advantageous performance on wire rope tension balance control compared with the conventional Proportion Integration (PI) and adaptive backstepping controller (ABC).

This article focuses on a novel robust adaptive sliding mode control strategy of an electro-hydraulic force loading system with consideration of external disturbances and parameter uncertainties. To obtain the proposed controller, the nonlinear dynamic model of the electro-hydraulic force loading system is firstly provided, where external disturbances and system’s uncertain parameters are merged. Then, with consideration of external disturbances and parameter uncertainties, a robust adaptive sliding mode controller for the force control system is proposed and designed according to Lyapunov stability theory. The proposed controller has the merit of suppressing chattering phenomenon in traditional adaptive backstepping controllers when designing sign functions. In order to verify effectiveness and feasibility of the proposed controller, experimental studies are implemented on an electro-hydraulic force loading system by xPC rapid prototyping technology. Experimental results demonstrate that the proposed controller exhibits more excellent performance on force tracking control in comparison of a traditional proportional-integral controller and an adaptive backstepping controller.

Primer design is essential to conduct whole plasmid site‐directed mutagenesis for protein study. Traditionally, primers of mutagenesis are designed manually that is time‐consuming and fallible. Here, we present a Python script for searching primers by presetting parameters of nucleotide composition and percentage of guanine‐cytosine (GC content). The running results showed that the script is able to search primers with mutations of target residue automatically. This script may facilitate primer design for whole plasmid site‐directed mutagenesis and aid protein mutant construction.

The significant effect of path loss on the reliability of very high frequency (VHF) and ultrahigh frequency (UHF) bands propagation has drawn much attention. Previous works mainly focus on the reliability evaluation for infrastructures and basic equipment, however, its propagation reliability has not been taken into full consideration. This paper proposes a new method for evaluating the reliability of the wireless communication based on the analysis of the traditional outdoor wave propagation loss models. In the reliability evaluation of the radio communication, we firstly consider the transmission frequency, the antenna height, the cell type and the communication distance. Then, we use a lognormal distribution to fit the random distribution curve of the communication distance so that the relationship between the path loss value and the reliability can be analysed. We further derive the probability distribution function (PDF) and the cumulative distribution function (CDF) of the path loss value from different antenna correction factors, cell type correction factors and terrain correction factors. Finally, we calculate the radio communication reliability values at different frequencies based on the threshold of the propagation loss value. Compared with the reliability degree only considering the communication distance threshold, the influence of environmental factors on the reliability of the VHF and the UHF radio propagation has been analysed

In this article, the position control problem of electro-hydraulic servo systems with feedback signal transmission delay is studied. In order to improve the control accuracy of the system, a hybrid controller which combines a delay observer, a nonlinear disturbance observer and a backstepping controller is proposed. The controller has the characteristics of compensating the delay of signal transmission, restraining the uncertain disturbance of control systems and high control precision. In order to verify the stability and validity of the proposed hybrid controller, a single-degree-of-freedom electro-hydraulic shaking table is used to verify the experimental results. The experimental results show that the proposed controller has better control effects than proportional integral derivative and backstepping controller.

© 2019 Elsevier Ltd This paper presents an innovative approach for damage and residual performance evaluation of ultra-high performance steel fiber reinforced concrete (UHPSFRC) piers after earthquakes utilizing low-level vibration tests. A nonlinear fiber section element model is constructed in OpenSees to simulate the hysteretic behavior of a UHPSFRC bridge pier. Experimental data from a UHPSFRC column is utilized to verify the accuracy of the nonlinear numerical model. Based on the nonlinear fiber section element model, a new technique of nonlinear finite element model updating involving two updating stages is developed. This new method is designed to incorporate the maximum and minimum strains of section fibers as the updating parameters. By forming the objective function from the modal information, the damage parameters related to the nonlinear material model can be updated by solving the constrained optimization problem. To validate the efficiency of this updating approach, it has been applied to a numerically simulated UHPSFRC pier. With using the updated nonlinear finite element model, the residual axial loading capacity and post-seismic performance of the UHPSFRC pier are examined. The numerical results indicate that the updated nonlinear finite element model can be used not only to assess the current damage state of the UHPSFRC pier but also to predict its future performance after an earthquake. Finally, the noise effect on the proposed method is also investigated. The results reveal that the post-earthquake evaluation approach for UHPSFRC piers based on this study's updating algorithm is robust to noise.

Predicting data with noise and high correlation using latent structural projection or partial least-squares (PLS) is a commonly used linear regression method. In order to solve the nonlinear problem existing in the industrial processes, many methods for extending the PLS to the nonlinear space have been proposed. In this paper, the support vector machine (SVM) is integrated into the PLS to get a nonlinear PLS model. In order to reduce information in process variables that are independent of quality variables, a orthogonal signal correction (OSC) method has been introduced. Nonlinear data from industrial processes is used to build nonlinear PLS models and SVM methods are adopted to approximate nonlinear internal relationships between the input and the output. The model proposed in this paper can predict industrial process quality variables and improve prediction accuracy compared with traditional PLS.

Large-scale grid-connected generation of renewable energy makes power grid reserve optimization more important. In this paper the reserve optimization for multi-area interconnected power systems (MAIPSs) with wind power is studied. Firstly, the uncertainty set of wind power output with budget constraints is constructed to represent the spatial correlation of wind power output. On this basis, the reserve demand of wind power is calculated by solving some simple linear programming problems. Then the total reserve demand for wind power and loads in all areas is considered synthetically, and an optimization model for reserve capacity of MAIPSs is established, which takes maximum wind power accommodation of the whole system as the optimization objective and considers reserve sharing and various operational constraints of the grid. Subsequently, combined with the characteristics of global optimization variables in the system, the existing distributed (sub)-gradient optimization algorithm based on consensus is improved to achieve a fully distributed solution of the model faster. The new global variable consensus calculation is proposed and additional global variable (sub)-gradient consensus operation is added, which ensures the convergence of the improved algorithm under fixed step size. Finally, the IEEE 24-bus system is divided into 3-area interconnected power systems to perform the numerical tests. The results show that the improved distribution optimization algorithm can obtain the global optimal solution more quickly, and the proposed reserve optimization model can improve the level of wind power accommodation.

The smart grid requires real-time, reliable, and secure communication. Based on 5G reference point architecture, this paper has proposed a heterogeneous wireless non-public network, and added I-SMF, I-UPF to ensure the continuity of network switching business sessions. This paper described session switching process and the deployment plan of a heterogeneous integrated network. In order to ensure transmission reliability, the end-to-end monitoring token bucket algorithm is used to monitor network performance. Once a fault is found, the switching action is executed immediately. And random backoff algorithm is adopted to avoid end-to-end switching conflicts. Finally, experimental simulation and field test results show that as the number of backoff increases, the handover success rate is closer to one. During field test, businesses communication channel switching and transmission delay are only 100ms level.

The gaming market has kept growing significantly in recent years. Driven by multiple technology advances, such as cloud computing and video technologies, new gaming applications, e.g. AR, VR and cloud gaming, are becoming more and more practical and popular. Among different types of gaming, the emergence of cloud gaming is driving the market with enhanced gamer experience as well as new challenges to the services. One of the key technological challenges of gaming applications is the video coding, which is the foundation of several popular gaming applications, including cloud gaming and game live streaming. Comparing to the typical camera captured content and screen content, gaming content presents unique features that directly lead to different preferences on the selection of coding tool sets. To better understand the behaviors of known video coding tools and provide test materials for research and development on future coding tools that benefits more on gaming content, in this paper, a dataset consists of a set of gaming video is proposed together with analysis of the performances of existing coding tools on these materials. It is observed that, several known coding tools are exceptionally beneficial for gaming content and the rational is analyzed in this paper.

Due to the operation characteristics of the power grid, there is a demand for power grid peak regulation every day, and the compressed air energy storage (CAES), having the characteristic of large energy storage capacity, can meet the demand well. This paper formulates the automatic control process of CAES energy storage stage and energy release stage by analyzing the process flow and operation process of the two stages. According to the operation characteristics of CAES system, this paper works out the system, process and concrete logic diagram for the automatic control. It provides a specific implementation method for the application of CAES in peak regulation of power grid, and has a broad application prospect.

The spread of smart meters has given grid companies access to a large amount of power load data. Load data contains a great deal of knowledge that helps distinguish load patterns from user categories. It is common practice to cluster daily load curves to mine load patterns. However, the load data collected by smart meters are high-dimensional data, and the common clustering algorithm has poor effect. Therefore, this paper presents an algorithm based on feature engineering and uniform manifold approximation to solve this problem. This algorithm consists of three stages. First, feature extraction is carried out on the pre-processed intelligent meter data, then the extracted statistical features are mixed with the original data as the input data of the dimensionality reduction algorithm, and finally, k-means algorithm is used to cluster the data after dimensionality reduction. Experimental results show that the proposed method has higher clustering accuracy than other methods.

When the double-rope winding hoisting system (DWHS) is in operation, tensions of two wire ropes of the DWHS will not be symmetrical because of some factors such as different manufacturing deviation between the twin winding drum and two wire ropes, different winding groove depths, the winding asynchronism of two wire ropes, and elastic modulus difference of two wire ropes and so on. Therefore, an electro-hydraulic servo system (EHSS) is employed to actively control two wire ropes tensions to guarantee operation security of the DWHS. Dynamic models of the hoisting system and the EHSS are introduced, of which dynamic model of the DWHS is expressed with state representation. The flatness-based controller (FBC) is designed for the hoisting system. A disturbance observer is utilized to deal with the external disturbance and unmodeled characteristics of the EHSS. Hence, a disturbance observer based integral backstepping controller (DO-BIBC) is designed for the EHSS. The stability of the overall control system is proved by de-fining an overall Lyapunov function. To investigate the property of the proposed controller, an experimental setup of the DWHS is established. As well, comparative experimental results indicate that the proposed controller exhibits a better performance on leveling control of the conveyance and tension coordination control on the two wire ropes than a conventional PI controller.

The "National Vocational Education Reform Implementation Plan" puts forward the "degree certificate + vocational skill level certificate" system, which points out the connotation and development trend of vocational education from the logical root. Starting with the trend of quality development and market demand, the specific implementation method of the 1+X certificate system was proposed, and specific issues such as the standard formulation of the X certificate, certificate level development, dynamic assessment and query management were answered, and the "1+X certificate system" was centered on to carry out in-depth exploration and research on the construction of vocational education, and strive to contribute to the enrichment and development of vocational education theory with Chinese characteristics and the construction of a national qualifications framework.

Taking into account the external natural disaster probability and the space and electrical coupling characteristics between electrical equipment, a combined probability calculation method of group failure and sequential failure under natural conditions is proposed. And a multi-dimensional failure based on grid structure vulnerability, grid state vulnerability and grid accident risk is proposed. Consequence evaluation method provides a basis for effectively screening high-risk expected failure sets; constructs a disaster-problem equipment failure probability state tree model, uses the multiplicity of failures, combined probability threshold, and multi-dimensional failure consequence evaluation indicators as constraints, and uses a backtracking algorithm to achieve natural disasters. The high-risk prediction sets are quickly generated to ensure that small-probability and high-risk faults are included in the calculation, and the real-time and targeted nature of power grid safety analysis and control is improved. The simulation analysis of an actual power grid online example verifies the effectiveness and practicability of the method.

The majority of the traditional methods deal with text matching at the word level which remains uncertain as the text semantic features are ignored. This also leads to the problems of low recall and high space utilization of text matching while the comprehensiveness of matching results is poor. The resultant method, thus, cannot process long text and short text simultaneously. The current study proposes a text matching algorithm for Korean Peninsula language knowledge base based on density clustering. Using the deep multiview semantic document representation model, the semantic vector of the text to be matched is captured for semantic dependency which is utilized to extract the text semantic features. As per the feature extraction outcomes, the text similarity is calculated by subtree matching method, and a semantic classification model based on SWEM and pseudo-twin network is designed for semantic text classification. Finally, the text matching of Korean Peninsula language knowledge base is carried out by applying density clustering algorithm. Experimental results show that the proposed method has high matching recall rate with low space requirements and can effectively match long and short texts concurrently.

Versatile Video Coding (VVC) is the latest video coding standard jointly developed by ITU-T VCEG and ISO/IEC MPEG. In this paper, technical details and experimental results for the VVC block partitioning structure are provided. Among all the new technical aspects of VVC, the block partitioning structure is identified as one of the most substantial changes relative to the previous video coding standards and provides the most significant coding gains. The new partitioning structure is designed using a more flexible scheme. Each coding tree unit (CTU) is either treated as one coding unit or split into multiple coding units by one or more recursive quaternary tree partitions followed by one or more recursive multi-type tree splits. The latter can be horizontal binary tree split, vertical binary tree split, horizontal ternary tree split, or vertical ternary tree split. A CTU dual tree for intra-coded slices is described on top of the new block partitioning structure, allowing separate coding trees for luma and chroma. Also, a new way of handling picture boundaries is presented. Additionally, to reduce hardware decoder complexity, virtual pipeline data unit constraints are introduced, which forbid certain multi-type tree splits. Finally, a local dual tree is described, which reduces the number of small chroma intra blocks.

Large oil-immersed transformers are an important part of the transmission and distribution network in power systems. Power transformers are the main noise source of substations. Because of the uneven manufacturing process, aging equipment, long-term operation, and close distance from sensitive points, the problem of transformer noise pollution has become increasingly prominent. In this paper, the transmission and analysis model is established for transformer sound waves on the interface between insulating oil and tank body according to the sound wave propagation rule in complicated medium, and the simplified acoustic simulation model is constructed for large oil-immersed transformers by simulating the vibration noise of transformer core with monopole sound source, with which, the sound field distribution rule inside and outside the transformer tank structure is obtained, and finally, the influence factors for noise distribution are given. The results of the study provide control basis for reducing transformer noise.

With the increasing development of metro transportation in cities, the accurate prediction of station traffic poses a challenge for data mining and analysis. Machine learning has been gradually applied to various traffic prediction due to its low computational complexity, easy interpretation and accurate prediction results. In this paper, based on ARIMA and the gray prediction model, the data of a city's various subway gates are selected to predict the passenger flow of the subway. Then, the MAE (Mean Absolute Error) is used to compare the differences between the two machine learning algorithms on passenger flow prediction. Finally, this paper discusses the training set and model fitting to provide a technical reference for further improving the prediction performance.

This paper overviews the motion vector coding and block merging techniques in the Versatile Video Coding (VVC) standard developed by the Joint Video Experts Team (JVET). In general, inter-prediction techniques in VVC can be classified into two major groups: ""whole block-based inter prediction"" and ""subblock-based inter prediction"". In this paper, we focus on techniques for whole block-based inter prediction. As in its predecessor, High Efficiency Video Coding (HEVC), whole block-based inter prediction in VVC is represented by adaptive motion vector prediction (AMVP) mode or merge mode. Newly introduced features purely for AMVP mode include symmetric motion vector difference and adaptive motion vector resolution. The features purely for merge mode include pairwise average merge, merge with motion vector difference, combined inter-intra prediction and geometric partitioning mode. Coding tools such as history-based motion vector prediction and bidirectional prediction with coding unit weights can be applied on both AMVP mode and merge mode. This paper discusses the design principles and the implementation of the new inter-prediction methods. Using objective metrics, simulation results show that the methods overviewed in the paper can jointly achieve 6.2% and 4.7% BD-rate savings on average with the random access and low-delay configurations, respectively. Significant subjective picture quality improvements of some tools are also reported when comparing the resulting pictures at same bitrates.

In response to the development needs of wireless sensor intelligent sensing in structural health monitoring, embedding slots and adding short-circuit vias are used to design and improve a small anti-metal antenna with FR4 substrate. Using Ansoft HFSS 15.0 simulation platform modeling, simulation experiments show that the improved antenna could monitor the expansion information of defects generated on the surface of the metal plate in the width and depth directions with a single tag. As the width and depth of metal structure defects increase, the antenna resonant frequency shifts to low frequencies. This research is of great significance to the online assessment of metal life cycle in service in structural health monitoring field. The passive antenna sensor is of great significance to the online evaluation of the service metal life cycle in the structural health monitoring field. It can provide valuable information for structural fault diagnosis and prediction, and engineering practice reference value for sensor antenna design.

In recent years, Deep learning based methods have proven their excellent performance in generic object detection. However, underwater object detection is still a challenge, especially from underwater optical images. In contrast to generic datasets, underwater images usually have color shift and low contrast, underwater detection datasets are scarce and the objects in the available underwater datasets and real applications are usually small. To address these issues, we propose a underwater object detection algorithm which choose Yolov3-tiny network as backbone and pre-train on Pascal VOC datasets. Experiments show that our proposed method improves the performance of region-based object detectors on CHINAMM2019 datasets, compared with SSD algorithm and Yolov3.

The rollover of buses has received considerable attention and the integrated control of multiple active security systems has become a hot spot. Due to good performance, the active front steering (AFS) control, direct yaw-moment control (DYC), and active roll control (ARC) are integrated to enhance rollover prevention capability. In this work, we propose a mixed H 2 /H ∞ controller design method based on the linear quadratic regulator (LQR) tracking control to coordinate integrated systems and keep vehicle roll and yaw stability. The problem is described in the form of an augment discrete-time tracking model integrated with AFS, DYC, and ARC. A mixed H 2 /H ∞ tracking controller is proposed and the controller gain is obtained by solving linear matrix inequalities (LMIs). Co-simulations with Simulink and TruckSim are carried out. The results show that the designed tracking controller achieves good control performance in both roll and yaw stability.

In order to improve the computational efficiency of structural reliability analysis under random and interval uncertainties, a hybrid sampling method is proposed in this paper. The basic idea of the proposed method is to integrate the Bucher experimental design and Latin hypercube sampling (LHS). Firstly, the sampling of random variables and the sampling of interval variables are respectively conducted according to Bucher experimental design and LHS. Then each random sample will be combined with each interval sample to form the new sample point. One e example is used to demonstrate the effectiveness of the proposed sampling method.

In high switching frequency applications, for the purpose of the high efficiency and low electromagnetic interference (EMI), an adaptive soft switching technique is proposed in this paper. According to the resonance characteristics of system, a valley point detection circuit is designed. Besides, considering the driving delay of power MOSFET, an adaptive control circuit is proposed to generate a flag signal of driver, by which the adaptive zero voltage switching (AZVS) can be achieved. With the help of proposed AZVS, the precise zero voltage switching independent of driving delay and type of power MOSFET can be automatically realized. An AZVS control circuit is implemented in a 0.35μm BCD process. It allows high efficiency and low EMI, as well as good thermal performance.

The magnetic Barkhausen noise (MBN) signal provides interesting clues about the evolution of microstructure of the magnetic material (internal stresses, level of degradation, etc.). This makes it widely used in non-destructive evaluation of ferromagnetic materials. Although researchers have made great effort to explore the intrinsic random characteristics and stable features of MBN signals, they have failed to provide a deterministic definition of the stochastic quality of the MBN signals. Because many features are not reproducible, there is no quantitative description for the stochastic nature of MBN, and no uniform standards to evaluate performance of features. We aim to make further study on the stochastic characteristics of MBN signal and transform it into the quantification of signal uncertainty and sensitivity, to solve the above problems for fatigue state prediction. In the case of parameter uncertainty in the prediction model, a prior approximation method was proposed. Thus, there are two distinct sources of uncertainty: feature(observation) uncertainty and model uncertainty were discussed. We define feature uncertainty from the perspective of a probability distribution using a confidence interval sensitivity analysis, and uniformly quantize and re-parameterize the feature matrix from the feature probability distribution space. We also incorporate informed priors into the estimation process by optimizing the Kullback&ndash, Leibler divergence between prior and posterior distribution, approximating the prior to the posterior. Thus, in an insufficient data situation, informed priors can improve prediction accuracy. Experiments prove that our proposed confidence interval sensitivity analysis to capture feature uncertainty has the potential to determine the instability in MBN signals quantitatively and reduce the dispersion of features, so that all features can produce positive additive effects. The false prediction rate can be reduced to almost 0. The proposed priors can not only measure model parameter uncertainties but also show superior performance similar to that of maximum likelihood estimation (MLE). The results also show that improvements in parameter uncertainties cannot be directly propagated to improve prediction uncertainties.

The compressed air energy storage(CAES) system can participate in the primary frequency modulation of the power grid during the two stages of power generation and energy storage, but how to carry out a primary frequency modulation performance test has not yet been reported in relevant studies. The continuous output signal generator and multi-channel multi-range signal acquisition device are developed by analyzing the primary frequency modulation function of CAES in two stages. The primary frequency modulation performance of the compressed air energy storage system on the power generation side and the power consumption side was comprehensively tested by constructing a test system with a switch and developing static and dynamic test methods. By constructing a test system with a switch and developing static and dynamic test methods, the primary frequency modulation performance of CAES system on the power generation side and the power consumption side was comprehensively tested, and it can provide reliable guarantee for energy storage system to participate in power grid frequency modulation.