High-precision Estimation Research Articles

Application of fuzzy logic control theory combined with target tracking algorithm in unmanned aerial vehicle target tracking

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

A method for phase estimation of X-ray pulsar signals: Combining a transformer network structure and a two-dimensional profile map

The high-precision phase estimation of X-ray pulsar signals is crucial for X-ray pulsar navigation. This paper introduces a novel feature modeling method named Two-Dimensional Profile Map (2D-PM), which enhances the time-domain information of the period axis using the epoch folding algorithm. Compared to traditional profiles, the newly proposed 2D-PM model exhibits richer information content, and greater robustness in terms of folding period precision. Furthermore, a new time-series neural network structure, inspired by groundbreaking advancements in the field of Natural Language Processing (NLP), is developed using the TensorFlow framework. This structure is designed to learn the proposed features and achieve precise phase estimation. The paper also outlines training schemes and debugging strategies for hyperparameters. To test the method, sample sets were created using simulated data and Rossi X-ray Timing Explorer (RXTE) observational data. These samples are specifically designed to enhance feature consistency and the generalization capability of the network. The effectiveness of the proposed method is demonstrated through a comparative analysis with traditional cross-correlation algorithms. The results confirm a high degree of alignment between the network outputs and the feature model, highlighting the significant potential of the proposed method for application in X-ray pulsar navigation.

Parameter-free super-resolution structured illumination microscopy via a physics-enhanced neural network.

With full-field imaging and high photon efficiency advantages, structured illumination microscopy (SIM) is one of the most potent super-resolution (SR) modalities in bioscience. Regarding SR reconstruction for SIM, spatial domain reconstruction (SDR) has been proven to be faster than traditional frequency domain reconstruction (FDR), facilitating real-time imaging of live cells. Nevertheless, SDR relies on high-precision parameter estimation for reconstruction, which tends to suffer from low signal-to-noise ratio (SNR) conditions and inevitably leads to artifacts that seriously affect the accuracy of SR reconstruction. In this Letter, a physics-enhanced neural network-based parameter-free SDR (PNNP-SDR) is proposed, which can achieve SR reconstruction directly in the spatial domain. As a result, the peak-SNR (PSNR) of PNNP-SDR is improved by about 4 dB compared to the cross-correlation (COR) SR reconstruction; meanwhile, the reconstruction speed of PNNP-SDR is even about five times faster than the fast approach based on principal component analysis (PCA). Given its capability of achieving parameter-free imaging, noise robustness, and high-fidelity and high-speed SR reconstruction over conventional SIM microscope hardware, the proposed PNNP-SDR is expected to be widely adopted in biomedical SR imaging scenarios.

High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework

High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework

Robust estimation of global horizontal irradiance with modified fuzzy regression functions with a noise cluster in Australia

The utilization of solar energy is picking up speed to counter climate change. New large-scale photovoltaic power stations are being constructed to increase solar utilization in the energy mix. A critical input of site selection for solar farms is the solar energy generation potential at a given location. Various physical and satellite-based inversion models are proposed to estimate the solar irradiation reaching the ground at potential locations, based on the meteorological features. However, the meteorological features generally contain outlier observations that distract the solar irradiation estimation models. To address this challenge, this study employs a robust fuzzy regression functions framework against the outliers to estimate the global horizontal irradiance (GHI) in Australia. Our framework is benchmarked with support vector machines, deep neural networks, and an adaptive network-based fuzzy inference system, and better GHI estimation performance is observed. The proposed framework provides 24 %, 18 %, and 23 % gain over the second-best method in terms of the rescaled mean absolute error, absolute percentage bias and rescaled root-mean-squared error. Monthly and annual GHI maps are created for Australia and compared to those from NASA POWER GHI estimates and Solargis annual GHI estimates. Our framework has an error range between 0.075 % and 2.9 % when validated against ground measurements. It provides at least an average of 40% lower monthly and annual error rates than POWER. This rate of gain rises to 69% when compared to Solargis. Our maps are not impacted by terrestrial characteristics and clear-sky conditions. This study’s results are beneficial in site selection and construction of high-precision GHI estimation models for practitioners.

Extended Kalman filter-based robust roll angle estimation method for spinning vehicles

Attitude measurement is a basic technique for monitoring vehicle motion states and safety. The spin motion of a vehicle couples the attitude angles with each other, which has an impact on the navigation and control of the vehicle. Global navigation satellite system (GNSS) signals-based roll angle measurement methods are important for vehicle attitude measurement. Most of existing studies use continuous signal power, but the case of loop lock loss leading to discontinuous power reception has not been considered. A robust estimation method for the roll angle based on the Tukey weight function is proposed to improve the measurement accuracy in cases of discontinuous reception. The characteristics of the GNSS signals, the geometric relationship between the signal power and roll angle of the vehicle are discussed. By installing a GNSS receiver with a single patched antenna on a rotating platform with a controllable rolling speed, the proposed method was verified by experiments. The robust estimation errors of different weight functions are analyzed. According to the characteristics of the gross measurement errors, a robust estimation method of multisatellite power observations is proposed to obtain a high-precision and stable estimation of the vehicle roll angle. The results show that the proposed algorithm can improve the accuracy of roll angle estimation even with gross measurement errors. As a result of the experiments, the estimation errors of the algorithm are 6.57° at a confidence level of 68 % and 15.49°at the confidence level of 95 %. In contrast, they are 11.38° and 37.31° for the traditional LS method. Moreover, the estimation accuracy of the algorithm is not significantly correlated with the vehicle rotational speed. Therefore, the vehicle roll angle can be estimated with high accuracy under a variety of rotational speeds.

Prescribed-time active fault-tolerant control of dynamic positioning vessels based on improved iterative learning observer

Considering the dynamic positioning (DP) vessel control system with actuator faults and unknown environment disturbances, a fault-tolerant control scheme based on improved iterative learning observer (ILO) is proposed in this article. Firstly, the kinematics and dynamics models of DP vessels are established containing the actuator faults and environmental disturbances. Secondly, an improved ILO is designed to handle the unknown faults and disturbances concurrently with higher estimation precision by introducing the state information of the previous moment. Further, a prescribed-time active fault-tolerant control scheme is presented on the basis of the improved ILO and prescribed-time stability. And the stability of proposed ILO and DP fault-tolerant control system are analyzed based on Lyapunov theory. Finally, the effectiveness and advantages of the presented ILO and active fault-tolerant control scheme are illustrated by numerical simulations and comparisons.

Research on Estimating Potato Fraction Vegetation Coverage (FVC) Based on the Vegetation Index Intersection Method

The acquisition of vegetation coverage information is crucial for crop field management, and utilizing visible light spectrum vegetation indices to extract vegetation coverage information is a commonly used method. However, most visible light spectrum vegetation indices do not fully consider the relationships between the red, green, and blue bands during their construction, making it difficult to ensure the accurate extraction of coverage information throughout the crop’s entire growth cycle. To rapidly and accurately obtain potato vegetation coverage information, drones were used in this study to obtain high-resolution digital orthoimages of potato growth stages. Based on the differences in the grayscale values of potato plants, soil, shadows, and drip irrigation belts, this study presents a combination index of blue and green bands (BGCI) and a combination index of red and green bands (RGCI). The vegetation index intersection method was used with 10 vegetation information indices to extract vegetation coverage, and the differences in extraction accuracy were compared with those of the maximum entropy method and bimodal histogram method. Based on the high-precision fraction vegetation coverage (FVC) extraction results, the Pearson correlation coefficient method and random forest feature selection were used to screen 10 vegetation and 24 texture features, and the top six vegetation indices most strongly correlated with the FVC were selected for potato growth stage FVC estimation and accuracy verification. A high-precision potato vegetation coverage estimation model was successfully established. This study revealed that during the potato tuber formation and expansion stages, the BGCI combined with the vegetation index intersection method achieved the highest vegetation coverage extraction accuracy, with overall accuracies of 99.61% and 98.84%, respectively. The RGCI combined with the vegetation index intersection method achieved the highest accuracy, 98.63%, during the maturation stage. For the potato vegetation coverage estimation models, the model based on the BGCI achieved the highest estimation accuracy (R2 = 0.9116, RMSE = 5.7903), and the RGCI also achieved good accuracy in terms of vegetation coverage estimation (R2 = 0.8987, RMSE = 5.8633). In the generality verification of the models, the R2 values of the FVC estimation models based on the BGCI and RGCI were both greater than 0.94. A potato vegetation coverage estimation model was constructed based on two new vegetation information indices, demonstrating good accuracy and universality.

A Bayesian deep learning approach for video-based estimation and uncertainty quantification of urban rainfall intensity

Accurate, high-resolution spatiotemporal estimates of rainfall intensity (RI) are essential for effective prevention and control of urban flooding. Traditional methods are often costly and provide inadequate coverage. Meanwhile, the associated uncertainty of RI estimates is often overlooked, potentially leading to poor decisions in management of urban floods. Here we examine the potential of video imagery recorded by surveillance cameras in urban areas, for providing real-time estimates of RI. Specifically, we propose the use of Bayesian deep learning (DL) to estimate RI and its related uncertainty in a real-time manner. Our DL approach combines the strengths of convolutional neural network (CNN) and long short-term memory (LSTM) to construct a suitable model for this task, and uses variational inference to quantify the uncertainty of the CNN-LSTM model. The proposed approach is tested using video imagery captured under various light-intensity conditions, including daytime, nighttime, early morning, and early evening, and evaluated via experiments using both random samples and independent rainfall events. Further, we show that the temporal processing provided by the LSTM network is important to achieve good performance of RI estimation. The results indicate the strong potential for leveraging urban camera networks to obtain high-precision spatiotemporal RI estimates at low cost, which can be extremely valuable for implementing effective flood control measures and planning emergency responses in the urban areas.

Fixed-time synchronization of multilayered complex dynamic networks via quantized variable-gain saturated control

This paper studies fixed-time (FxT) quantitative synchronization of multilayered complex dynamic networks (CDNs). First, a new FxT stability theorem is established and two new estimations of the settling time of stability are acquired, which are more accurate than the existing ones. Then, by using the improved theorem, several sufficient conditions ensuring FxT synchronization of a multilayered CDN are derived via two classes of innovative quantized variable-gain saturated controllers, and some high-precision estimates of the synchronous settling time (SST) are attained. In general, the saturation function used to replace the signum function is nonlinear and even includes the odd-even requirement on the exponent, and no quantization is involved in the developed FxT controllers. However, in our design, the classical linear saturation function is employed and the input signals of the controllers are also quantized. Therefore, our proposed strategies are more practical and easier to implement. Besides, different from the traditional 2-norm/1-norm-based approaches, a novel nonsmooth Lyapunov function is constructed so that the resultant estimates of the SST are unrelated to either the network size or the node's dimension, which are less conservative and closer to the actual synchronized time. Lastly, some numerical examples are given to validate the theoretical results.

CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence.

In recent decades, the rapid advances in information technology have promoted a widespread deployment of medical cyber-physical systems (MCPS), especially in the area of digital healthcare. In digital healthcare, medical edge devices empowered by CPU-GPU (Graphics Processing Unit) cooperative multiprocessor system-on-chips (MPSoCs) have a great potential in processing and managing the massive amounts of health-related data. However, most of the existing works on CPU-GPU cooperative MPSoCs cannot maintain a high-precision workload estimation since they simply leverage the worst-case execution cycles to pessimistically predict the workload of digital healthcare applications. Besides, they neglect the personalized requirements of individual healthcare applications and the lifetime reliability demands of heterogeneous CPU-GPU cores. As a result, the normal functions of medical edge devices and the quality-of-services (QoS) of digital healthcare applications are likely to suffer from underlying failures and degradation. In this paper, we explore CPU-GPU cooperative QoS optimization of personalized digital healthcare applications running on reliability guaranteed edge devices with the help of machine learning and swarm intelligence techniques. We first develop two novel predictors: one is a machine learning based predictor for application workload estimation, and the other is a feature-driven predictor for application QoS estimation. We then incorporate the two predictors into a swarm intelligent application scheduling scheme upon the cooperative dual-population evolutionary algorithm (c-DPEA) to find optimal application mapping and partitioning settings. Experimental results show that our solution not only augments the average QoS of whole digital healthcare applications by 15.7%, but also balances the QoS of individual digital healthcare applications by 64.3%.

RNNPose: 6-DoF Object Pose Estimation via Recurrent Correspondence Field Estimation and Pose Optimization.

6-DoF object pose estimation from a monocular image is a challenging problem, where a post-refinement procedure is generally needed for high-precision estimation. In this paper, we propose a framework, dubbed RNNPose, based on a recurrent neural network (RNN) for object pose refinement, which is robust to erroneous initial poses and occlusions. During the recurrent iterations, object pose refinement is formulated as a non-linear least squares problem based on the estimated correspondence field (between a rendered image and the observed image). The problem is then solved by a differentiable Levenberg-Marquardt (LM) algorithm enabling end-to-end training. The correspondence field estimation and pose refinement are conducted alternately in each iteration to improve the object poses. Furthermore, to improve the robustness against occlusion, we introduce a consistency-check mechanism based on the learned descriptors of the 3D model and observed 2D images, which downweights the unreliable correspondences during pose optimization. We evaluate RNNPose on several public datasets, including LINEMOD, Occlusion-LINEMOD, YCB-Video and TLESS. We demonstrate state-of-the-art performance and strong robustness against severe clutter and occlusion in the scenes. Extensive experiments validate the effectiveness of our proposed method. Besides, the extended system based on RNNPose successfully generalizes to multi-instance scenarios and achieves top-tier performance on the TLESS dataset.

2-D DOA Estimation of Coherent Signals Using the Dual-Size Spreading Array

Abstract We introduce a novel approach for high-precision estimation of two-dimensional (2-D) Direction-of-Arrival (DOA) under conditions of coherent interference. This study innovatively devises an antenna configuration, termed the Dual-Size Spreading Array, specifically tailored to estimate DOAs in such environments. The method involves integrating a subarray with the primary array to create a Dual-Size Extended Array structure. This integration allows for a transformation of the covariance matrix into a new form, where its rank solely depends on the number of impinging waves. Both the subarray and primary array must have a scale no less than the number of incoming wavefronts. This proposed technique not only amplifies the effective aperture but also significantly reduces the computational intricacy associated with DOA estimation procedures. The efficacy of our methodology has been substantiated through simulation results, which demonstrate that it outperforms the conventional spatial smoothing techniques. Moreover, we provide several practical guidelines designed to enhance estimation accuracy while concurrently minimizing computational demands. In summary, this research presents an advanced solution that effectively addresses challenges in 2D DOA estimation under coherent scenarios, achieving improved performance and computational efficiency compared to existing methods.

Characterisation of human penile tissue properties using experimental testing combined with multi-target inverse finite element modelling

This paper presents an inverse finite element (FE) approach aimed at estimating multi-layered human penile tissues. The inverse FE approach integrates experimental force–displacement and boundary deformation data of penile tissues with a developed FE model and uses new experimental data on human penile tissue. The experimental study encompasses whole organ plate-compression tests and individual layer tensile and compression tests, providing comprehensive insights into the tissue's mechanical behaviour. The biomechanical characterisation of penile tissue is of crucial significance for understanding its mechanical behaviour under various physiological and pathological conditions. The FE model is constructed using the realistic geometry of the penile segment and appropriate constitutive models for each tissue layer to leverage the accuracy and consistency of the model. Through systematic variation of tissue parameters in the inverse FE algorithm, simulations achieve the best match with both force–displacement and deformed boundary results obtained from the whole organ plate-compression tests. Test results from individual tissue layers are also utilised to assess the estimated parameters. The proposed inverse FE approach allows for the estimation of penile tissue parameters with high precision and reliability, shedding light on the mechanical properties of this complex biological organ. This work has applications not only in urology but also for researchers in various disciplines of biomechanics. As a result, our study contributes to advancing the understanding of human penile tissue mechanics whilst the methodology could also be applied to a range of other soft biological tissues. Statement of significanceThis research uses a multi-target inverse finite element (FE) approach for estimating the material parameters of human penile tissues. By integrating experimental data and a realistic FE model, this study achieves high-precision constitutive model parameter estimation, offering key insights into penile tissue mechanics under various loading conditions. The significance of this work lies in the use of this inverse FE approach for fresh-frozen human penile tissues, to identify the mechanical properties and constitutive models for both segregated tunica albuginea and corpus cavernosum as well as intact penile tissue segments. The study's scientific impact lies in its advancement of the understanding of human urological tissue mechanics, impacting researchers and clinicians alike.

Myocardial T1 mapping using an instantaneous signal loss simulation modeling and a Bayesian estimation method: A robust T1 extraction method free of tuning parameters

The Instantaneous Signal Loss Simulation (InSiL) model is a promising alternative to the classical mono-exponential fitting of the Modified Look-Locker Inversion-recovery (MOLLI) sequence in cardiac T1 mapping applications, which achieves better accuracy and is less sensitive to heart rate (HR) variations. Classical non-linear least squares (NLLS) estimation methods require some parameters of the model to be fixed a priori in order to give reliable T1 estimations and avoid outliers. This introduces further bias in the estimation, reducing the advantages provided by the InSiL model. In this paper, a novel Bayesian estimation method using a hierarchical model is proposed to fit the parameters of the InSiL model. The hierarchical Bayesian modeling has a shrinkage effect that works as a regularizer for the estimated values, by pulling spurious estimated values toward the group-mean, hence reducing greatly the number of outliers. Simulations, physical phantoms, and in-vivo human cardiac data have been used to show that this approach estimates accurately all the InSiL parameters, and achieve high precision estimation of the T1 compared to the classical MOLLI model and NLLS InSiL estimation.

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots.

A high-precision view of intercompartmental drug transport via simultaneous, seconds-resolved, in situ measurements in the vein and brain.

The ability to measure specific molecules at multiple sites within the body simultaneously, and with a time resolution of seconds, could greatly advance our understanding of drug transport and elimination. As a proof-of-principle demonstration, here we describe the use of electrochemical aptamer-based (EAB) sensors to measure transport of the antibiotic vancomycin from the plasma (measured in the jugular vein) to the cerebrospinal fluid (measured in the lateral ventricle) of live rats with temporal resolution of a few seconds. In our first efforts, we made measurements solely in the ventricle. Doing so we find that, although the collection of hundreds of concentration values over a single drug lifetime enables high-precision estimates of the parameters describing intracranial transport, due to a mathematical equivalence, the data produce two divergent descriptions of the drug's plasma pharmacokinetics that fit the in-brain observations equally well. The simultaneous collection of intravenous measurements, however, resolves this ambiguity, enabling high-precision (typically of ±5 to ±20% at 95% confidence levels) estimates of the key pharmacokinetic parameters describing transport from the blood to the cerebrospinal fluid in individual animals. The availability of simultaneous, high-density 'in-vein' (plasma) and 'in-brain' (cerebrospinal fluid) measurements provides unique opportunities to explore the assumptions almost universally employed in earlier compartmental models of drug transport, allowing the quantitative assessment of, for example, the pharmacokinetic effects of physiological processes such as the bulk transport of the drug out of the CNS via the dural venous sinuses.

A high precision estimation algorithm for multi‐channel wide‐area surveillance ground moving target indication mode based on maximum likelihood method

A high precision estimation algorithm for multi‐channel wide‐area surveillance ground moving target indication mode based on maximum likelihood method

State-of-Charge Estimation of Lithium-Ion Battery Based on Convolutional Neural Network Combined with Unscented Kalman Filter

Estimation of the state-of-charge (SOC) of lithium-ion batteries (LIBs) is fundamental to assure the normal operation of both the battery and battery-powered equipment. This paper derives a new SOC estimation method (CNN-UKF) that combines a convolutional neural network (CNN) and an unscented Kalman filter (UKF). The measured voltage, current and temperature of the LIB are the input of the CNN. The output of the hidden layer feeds the linear layer, whose output corresponds to an initial network-based SOC estimation. The output of the CNN is then used as the input of a UKF, which, using self-correction, yields high-precision SOC estimation results. This method does not require tuning of network hyperparameters, reducing the dependence of the network on hyperparameter adjustment and improving the efficiency of the network. The experimental results show that this method has higher accuracy and robustness compared to SOC estimation methods based on CNN and other advanced methods found in the literature.

Mapping Topsoil Carbon Storage Dynamics of Croplands Based on Temporal Mosaicking Images of Landsat and Machine Learning Approach

Understanding changes of soil organic carbon (SOC) in top layers of croplands and their driving factors is a vital prerequisite in decision-making for maintaining sustainable agriculture. However, high-precision estimation of SOC of croplands at regional scale is still an issue to be solved. Based on soil samples, synthetic image of bare soil and geographical data, this paper predicted SOC density of croplands using Random Forest model in the Black Soil Region of Jilin Province, China in 2005 and 2020, and analyzed its influencing factors. Results showed that random forest model that integrates bare soil composite images improve the accuracy and robustness of SOC density prediction. From 2005 to 2020, the total SOC storage in croplands decreased from 89.96 to 82.79 Tg C with an annual decrease of 0.48 Tg C yr−1. The mean value of SOC density of croplands decreased from 3.42 to 3.32 kg/m2, and high values are distributed in middle parts. Changes of SOC represented significant heterogeneity spatially. 62.14% of croplands with SOC density greater than 4.0 kg/m2 decreased significantly, and 38.60% of croplands with SOC density between 2.5 and 3.0 kg/m2 significantly increased. Climatic factors made great contributions to SOC density, however, their relative importance (RI) to SOC density decreased from 44.65% to 37.26% during the study period. Synthetic images of bare soil constituted 23.54% and 26.29% of RI in the SOC density prediction, respectively, and the contribution of each band was quite different. The RIs of topographic and vegetation factors were low but increased significantly from 2005 to 2020. This study can aid local land managers and governmental agencies in assessing carbon sequestration potential and carbon credits, thus contributing to the protection and sustainable use of black soils.