Patent Statistics and IPR Laws Update Online

Patent Statistics Beginning from No.2 of 2005 of China Patents & Trademarks, the Statistics on Patent Applications & Grants in China, previously published under the column of Statistics, will be updated online, including the monthly Statistics on Patent Applications by Patent Category, the Patent Grants by Patent Category, the Domestic Patent Applications by Province, and the Overseas Patent Applications by Country, and their yearly statistics at www.cpt.cn or www.cpahkltd.com/cn/ Publications/staten.htm...

Online RGB-D person re-identification based on metric model update

Person re-identification （re-id） on robot platform is an important application for human-robot- interaction （HRI）, which aims at making the robot recognize the around persons in varying scenes. Although many effective methods have been proposed for surveillance re-id in recent years, re-id on robot platform is still a novel unsolved problem. Most existing methods adapt the supervised metric learning offline to improve the accuracy. However, these methods can not adapt to unknown scenes. To solve this problem, an online re-id framework is proposed. Considering that robotics can afford to use high-resolution RGB-D sensors and clear human face may be captured, face information is used to update the metric model. Firstly, the metric model is pre-trained offline using labeled data. Then during the online stage, we use face information to mine incorrect body matching pairs which are collected to update the metric model online. In addition, to make full use of both appearance and skeleton information provided by RGB-D sensors, a novel feature funnel model （FFM） is proposed. Comparison studies show our approach is more effective and adaptable to varying environments.

A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

To overcome the large time-delay in measuring the hardness of mixed rubber, rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model, the strategy could dramat- ically save the storage cost and overcome the adverse influence of low signal-to-noise ratio samples. Moreover, it could be applied to any statistical process monitoring system without drastic changes to them, which is practical for industrial practices. As examples, the Q-based strategy was integrated with three popular algorithms （partial least squares （PIE）, recursive PIE （RPLS）, and kernel PIE （KPIE）） to form novel regression ones, QPLS, QRPIE and QKPLS, respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations.

Two-Stage Online Debiased Lasso Estimation and Inference for High-Dimensional Quantile Regression with Streaming Data

In this paper,the authors propose a two-stage online debiased lasso estimation and statistical inference method for high-dimensional quantile regression(QR)models in the presence of streaming data.In the first stage,the authors modify the QR score function based on kernel smoothing and obtain the online lasso smoothed QR estimator through iterative algorithms.The estimation process only involves the current data batch and specific historical summary statistics,which perfectly accommodates to the special structure of streaming data.In the second stage,an online debiasing procedure is carried out to eliminate biases caused by the lasso penalty as well as the accumulative approximation error so that the asymptotic normality of the resulting estimator can be established.The authors conduct extensive numerical experiments to evaluate the performance of the proposed method.These experiments demonstrate the effectiveness of the proposed method and support the theoretical results.An application to the Beijing PM2.5 Dataset is also presented.

Hierarchical Bayesian Calibration and On-line Updating Method for Influence Coefficient of Automatic Dynamic Balancing Machine

Measurement error of unbalance＇s vibration response plays a crucial role in calibration and on-line updating of influence coefficient（IC）. Focusing on the two problems that the moment estimator of data used in calibration process cannot fulfill the accuracy requirement under small sample and the disturbance of measurement error cannot be effectively suppressed in updating process, an IC calibration and on-line updating method based on hierarchical Bayesian method for automatic dynamic balancing machine was proposed. During calibration process, for the repeatedly-measured data obtained from experiments with different trial weights, according to the fact that measurement error of each sensor had the same statistical characteristics, the joint posterior distribution model for the true values of the vibration response under all trial weights and measurement error was established. During the updating process, information obtained from calibration was regarded as prior information, which was utilized to update the posterior distribution of IC combined with the real-time reference information to implement online updating. Moreover, Gibbs sampling method of Markov Chain Monte Carlo（MCMC） was adopted to obtain the maximum posterior estimation of parameters to be estimated. On the independent developed dynamic balancing testbed, prediction was carried out for multiple groups of data through the proposed method and the traditional method respectively, the result indicated that estimator of influence coefficient obtained through the proposed method had higher accuracy; the proposed updating method more effectively guaranteed the measurement accuracy during the whole producing process, and meantime more reasonably compromised between the sensitivity of IC change and suppression of randomness of vibration response.

Digital twin modeling method for lithium-ion batteries based on data-mechanism fusion driving

Lithium-ion batteries have been rapidly developed as clean energy sources in many industrial fields,such as new energy vehicles and energy storage.The core issues hindering their further promotion and application are reliability and safety.A digital twin model that maps onto the physical entity of the battery with high simulation accuracy helps to monitor internal states and improve battery safety.This work focuses on developing a digital twin model via a mechanism-data-driven parameter updating algorithm to increase the simulation accuracy of the internal and external characteristics of the full-time domain battery under complex working conditions.An electrochemical model is first developed with the consideration of how electrode particle size impacts battery characteristics.By adding the descriptions of temperature distribution and particle-level stress,a multi-particle size electrochemical-thermal-mechanical coupling model is established.Then,considering the different electrical and thermal effect among individual cells,a model for the battery pack is constructed.A digital twin model construction method is finally developed and verified with battery operating data.

Long-term Visual Tracking: Review and Experimental Comparison

As a fundamental task in computer vision,visual object tracking has received much attention in recent years.Most studies focus on short-term visual tracking which addresses shorter videos and always-visible targets.However,long-term visual tracking is much closer to practical applications with more complicated challenges.There exists a longer duration such as minute-level or even hour-level in the long-term tracking task,and the task also needs to handle more frequent target disappearance and reappearance.In this paper,we provide a thorough review of long-term tracking,summarizing long-term tracking algorithms from two perspectives:framework architectures and utilization of intermediate tracking results.Then we provide a detailed description of existing benchmarks and corresponding evaluation protocols.Furthermore,we conduct extensive experiments and analyse the performance of trackers on six benchmarks:VOTLT2018,VOTLT2019(2020/2021),OxUvA,LaSOT,TLP and the long-term subset of VTUAV-V.Finally,we discuss the future prospects from multiple perspectives,including algorithm design and benchmark construction.To our knowledge,this is the first comprehensive survey for long-term visual object tracking.The relevant content is available at https://github.com/wangdongdut/Long-term-Visual-Tracking.