Data-driven Robust State Estimation Through Off-line Learning and On-line Matching

To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods;then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.

Parametic Model for Optimization of Battery Capacity and Power Transmitters of On-line Electric Vehicles in Closed/Open Environments

An on-line electric vehicle(OLEV)uses a wireless charging phenomenon,in which power transmitters are installed beneath the road and the OLEV’s battery is charged remotely.This paper deals with the optimization of two key economic and design parameters,i.e.,the size of the battery and the power transmitters allocation.A complete model configuration of the OLEV system,including the vehicle design and power transmitter,is implemented using MATLAB/Simulink.The battery’s state of charge(SOC)rises and drops according to the vehicle’s velocity and power collection and consumption.The mixed integer programming(MIP)model is used for cost calculation.Therefore,with the help of the SOC graph and MIP model,the battery size and the number of power transmitters,along with their placements,are optimized.The proposed model is applicable to both closed and open environments as it accepts both regulated and deregulated velocities.Two test cases are performed for this purpose.The first test case deals with regulated velocity for which we have applied the KAIST campus OLEV’s velocity along with its 13 kWh battery size and 4 power transmitters,and then applied the suggested solution with the same velocity and route i.e.,8 power transmitters with shorter lengths and reduced battery size(3.25 kWh;one-fourth of the first case).SOC is found within limits at the end of the route,saving$1600 and validating the proposed model in this paper.For the second test case,we use deregulated velocity and optimize both parameters,using the same approach.

Measurement methods of particle size distribution in emulsion polymerization

The particle size distribution of polymer always develops in emulsion polymerization systems,and certain key phenomena/mechanisms as well as properties of the final product are significantly affected by this distribution.This review mainly focuses on the measurement methods of particle size distribution rather than average particle size during the emulsion polymerization process,including the existing off-line,on-line,and in-line measurement methods.Moreover,the principle,resolution,performance,advantages,and drawbacks of various methods for evaluating particle size distribution are contrasted and illustrated.Besides,several possible development directions or solutions of the in-line measurement technology are explored.

Fuzzy-GA PID controller with incomplete derivation and its application to intelligent bionic artificial leg

An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part andthe on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step re-sponse as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p , Ti* ,and Tj are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-linepart, based on K; , Ti* , and T*d and according to the current system error e and its time derivative, a dedicatedprogram is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mech-anism to ensure that the system response has optimal dynamic and steady-state performance. The controller has beenused to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computersimulation shows that this kind of optimal PID controller has excellent control performance and robust performance.

Recent Progress of Commercially Available Biosensors in China and Their Applications in Fermentation Processes

Biosensors, which are the products of the biotechnology industry, are among the key projects of the 7th, 8th, and 9th Fiveyear Plans of China Science ＆ Technology Developing Programs, respectively, and they play an important role in developing and reforming traditional biotechnology. SBA series biosensor analyzer, as the only one commercial biosensor in China, has attracted lots of attention in the process of information gathering and measurement for biological industry with the development of technology and society. In this paper, we presented an overview of the most important contributions dealing with the monitoring of the biochemical analytes in fermentation processes using SBA series biosensor analyzers in China. Future trends of the biosensor analyzer in China were also mentioned in the last section.

Gain deeper insights into traditional Chinese medicines using multidimensional chromatography combined with chemometric approaches

Traditional Chinese medicines(TCMs)possess a rich historical background,unique theoretical framework,remarkable therapeutic efficacy,and abundant resources.However,the modernization and internationalization of TCMs have faced significant obstacles due to their diverse ingredients and unknown mechanisms.To gain deeper insights into the phytochemicals and ensure the quality control of TCMs,there is an urgent need to enhance analytical techniques.Currently,two-dimensional(2D)chromatography,which incorporates two independent separation mechanisms,demonstrates superior separation capabilities compared to the traditional one-dimensional(1D)separation system when analyzing TCMs samples.Over the past decade,new techniques have been continuously developed to gain actionable insights from complex samples.This review presents the recent advancements in the application of multidimensional chromatography for the quality evaluation of TCMs,encompassing 2D-gas chromatography(GC),2D-liquid chromatography(LC),as well as emerging three-dimensional(3D)-GC,3D-LC,and their associated data-processing approaches.These studies highlight the promising potential of multidimensional chromatographic separation for future phytochemical analysis.Nevertheless,the increased separation capability has resulted in higher-order data sets and greater demands for data-processing tools.Considering that multidimensional chromatography is still a relatively nascent research field,further hardware enhancements and the implementation of chemometric methods are necessary to foster its robust development.