On-line Estimation of Biomass in Fermentation Process Using Support Vector Machine

Biomass is a key factor in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine （SVM） for an on-line biomass estimation was analyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.

On-line Estimation in Fed-batch Fermentation Process Using State Space Model and Unscented Kalman Filter

On-line estimation of unmeasurable biological variables is important in fermentation processes,directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product.In this study,a novel strategy for state estimation of fed-batch fermentation process is proposed.By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model,a state space model is developed.An improved algorithm,swarm energy conservation particle swarm optimization(SECPSO) ,is presented for the parameter identification in the mechanistic model,and the support vector machines(SVM) method is adopted to establish the nonlinear measurement model.The unscented Kalman filter(UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process.The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.

On-line Estimation of Biomass in Fermentation Process Using Support Vector Machine

Biomass is a key factor in fermentation process, directly influencing the performance of the fermenta- tion system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine (SVM) for an on-line biomass estimation was ana- lyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.

Brain age estimation:premise,promise,and problems

Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North America.Since old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.

On-Line Structural Breaks Estimation for Non-stationary Time Series Models

Non-stationary time series could be divided into piecewise stationary stochastic signal. However, the number and locations of breakpoints, as well as the approximation function of the respective segment signal are unknown. To solve this problem, a novel on-line structural breaks estimation algorithm based on piecewise autoregressive processes is proposed. In order to find the "best" combination of the number, lengths, and orders of the piecewise autoregressive (AR) processes, the Akaikes Information Criterion (AIC) and Yule-Walker equations are applied to estimate an AR model fit to the data. Numerical results demonstrate that the proposed estimation algorithm is suitable for different data series. Furthermore, the algorithm is used in a clinical study of electroencephalogram (EEG) with satisfactory results, and the ability to deal with real-time data is the most outstanding characteristic of on-line structural breaks estimation algorithm proposed.

A SCR method for uncertainty estimation in geodesy non-linear error propagation: Comparisons and applications

We review three derivative-free methods developed for uncertainty estimation of non-linear error propagation, namely, MC(Monte Carlo), SUT(scaled unscented transformation), and SI(sterling interpolation). In order to avoid preset parameters like as these three methods need, we introduce a new method to uncertainty estimation for the first time, namely, SCR(spherical cubature rule), which is no need for setting parameters. By theoretical derivation, we prove that the precision of uncertainty obtained by SCR can reach second-order. We conduct four synthetic experiments, for the first two experiments, the results obtained by SCR are consistent with the other three methods with optimal setting parameters, but SCR is easier to operate than other three methods, which verifies the superiority of SCR in calculating the uncertainty. For the third experiment, real-time calculation is required, so the MC is hardly feasible. For the forth experiment, the SCR is applied to the inversion of seismic fault parameter which is a common problem in geophysics, and we study the sensitivity of surface displacements to fault parameters with errors. Our results show that the uncertainty of the surface displacements is the magnitude of ±10 mm when the fault length contains a variance of 0.01 km^(2).

State Estimation for Non-linear Sampled-Data Descriptor Systems:A Robust Extended Kalman Filtering Approach

This paper proposes a state estimation method for a class of norm bounded non linear sampled data descriptor systems using the Kalman filtering method. The descriptor model is firstly discretized to obtain a discrete time non singular one. Then a model of robust extended Kalman filter is proposed for the state estimation based on the discretized non linear non singular system. As parameters are introduced in for transforming descriptor systems into non singular ones there exist uncertainties in the state of the systems. To solve this problem an optimized upper bound is proposed so that the convergence of the estimation error co variance matrix is guaranteed in the paper. A simulating example is proposed to verify the validity of this method at last.

Locating the position of objects in non-line-of-sight based on time delay estimation

Non-line-of-sight imaging detection is to detect hidden objects by indirect light and intermediary surface（diffuser）.It has very important significance in indirect access to an object or dangerous object detection, such as medical treatment and rescue. An approach to locating the positions of hidden objects is proposed based on time delay estimation. The time delays between the received signals and the source signal can be obtained by correlation analysis, and then the positions of hidden objects will be located. Compared with earlier systems and methods, the proposed approach has some modifications and provides significant improvements, such as quick data acquisition, simple system structure and low cost, and can locate the positions of hidden objects as well： this technology lays a good foundation for developing a practical system that can be used in real applications.

Time-Frequency Bandwidth Product Estimation of Sinusoidal Non-Linear Chirp Keying Scheme

Sine Non-linear Chirp Keying(SNCK) is a kind of high-efficient modulation scheme, which provides a potential new beamforming method in communication and radar systems. It has been proved to have advantages in some parameter estimation issues over conventional modulation schemes. In this paper, a novel transform termed as Discrete Sinusoidal Frequency Modulation transform(DSFMT) is proposed. Then, the DSFMT of SNCK signal is deduced and classified into three types, based on which, the time-bandwidth product is estimated by the proposed algorithm. Simulation results show that the noise has a signifi cant impact on the localization of the peak value and the time-bandwidth product can be estimated by using local ratio values when.

Graph-based structural joint pose estimation in non-line-of-sight conditions

In post-earthquake surveys,it is difficult(and often infeasible)to observe and quantify displacements beyond line-of-sight(LOS),given seismic force-resisting and gravity systems exist completely or partially within a building′s enclosure.To overcome this limitation,we develop a novel framework that generalizes graph-based state estimation towards structural joint localization via engineered landmarks.These landmarks provide an indirect means to estimate residual displacements where direct LOS is unavailable.Within our framework,engineered landmarks define topologies of uniquely identifiable landmarks that are either visible or non-visible to a robot performing simultaneous localization and mapping(SLAM).Within the SLAM approach,factors encoding robot odometry and robot-to-visible landmark measurements are formulated for the cases of wireless sensing and fiducial object detection and tracking.Visible landmarks are rigidly attached to non-visible landmark subsets for each engineered landmark,where the complete set of non-visible landmarks form globally rigid and localizable connectivity graphs via range-based factors.Complimentary subsets of non-visible landmarks are embedded within the base structure and uniquely define joint pose via geometric factors.All factors are unified within a common graph to solve for the maximum a posteriori estimate of robot,landmark,and joint states via nonlinear least squares optimization.To demonstrate the applicability of our approach,we apply the Monte Carlo method over a parameterization of system noise to calculate residual joint pose error distributions,maximum average inter-story drift ratios,and related summary statistics for a 19-story nonlinear structural model.By performing nonlinear time history analyses over sets of service-level and maximum considered earthquakes,our parametric study gives insight into our method′s application towards post-earthquake building evaluation in non-LOS conditions.

Model-driven full system dynamics estimation of PMSM-driven chain shell magazine

Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Efficient Unsupervised Image Stitching Using Attention Mechanism with Deep Homography Estimation

Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.

Cascaded ELM-Based Joint Frame Synchronization and Channel Estimation over Rician Fading Channel with Hardware Imperfections

Due to the interdependency of frame synchronization(FS)and channel estimation(CE),joint FS and CE(JFSCE)schemes are proposed to enhance their functionalities and therefore boost the overall performance of wireless communication systems.Although traditional JFSCE schemes alleviate the influence between FS and CE,they show deficiencies in dealing with hardware imperfection(HI)and deterministic line-of-sight(LOS)path.To tackle this challenge,we proposed a cascaded ELM-based JFSCE to alleviate the influence of HI in the scenario of the Rician fading channel.Specifically,the conventional JFSCE method is first employed to extract the initial features,and thus forms the non-Neural Network(NN)solutions for FS and CE,respectively.Then,the ELMbased networks,named FS-NET and CE-NET,are cascaded to capture the NN solutions of FS and CE.Simulation and analysis results show that,compared with the conventional JFSCE methods,the proposed cascaded ELM-based JFSCE significantly reduces the error probability of FS and the normalized mean square error(NMSE)of CE,even against the impacts of parameter variations.

High-Precision Doppler Frequency Estimation Based Positioning Using OTFS Modulations by Red and Blue Frequency Shift Discriminator

Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.

STRONGLY CONVERGENT INERTIAL FORWARD-BACKWARD-FORWARD ALGORITHM WITHOUT ON-LINE RULE FOR VARIATIONAL INEQUALITIES

This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.

Side Information-Based Stealthy False Data Injection Attacks Against Multi-Sensor Remote Estimation

Dear Editor,This letter investigates a novel stealthy false data injection(FDI)attack scheme based on side information to deteriorate the multi-sensor estimation performance of cyber-physical systems(CPSs).Compared with most existing works depending on the full system knowledge,this attack scheme is only related to attackers'sensor and physical process model.The design principle of the attack signal is derived to diverge the system estimation performance.Next,it is proven that the proposed attack scheme can successfully bypass the residual-based detector.Finally,all theoretical results are verified by numerical simulation.

Protocol-Based Non-Fragile State Estimation for Delayed Recurrent Neural Networks Subject to Replay Attacks

Dear Editor, This letter focuses on the protocol-based non-fragile state estimation problem for a class of recurrent neural networks(RNNs). With the development of communication technology, the networked systems have received particular attentions. The networked system brings advantages such as easy to implement.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.