Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Study of real-time LWD data visual interpretation and geo-steering technology

LWD（logging while drilling） data has been used to explore complex subtle reservoirs by realtime visual interpretation and geo-steering.The method comprises of computer communication,well log data processing,formation recognition,reservoir modeling and model updating in real time.We studied the key technologies related to real-time LWD data visual interpretation and geo-steering and developed computer software with Chinese intellectual property rights covering the following important aspects： 1） real-time computer communication of well site LWD data;2） visualization of geological model and borehole information;3） real-time interpretation of LWD data;4） real-time geological model updating and geo-steering technology.We use field application examples to demonstrate the feasibility and validity of the proposed technologies.

Restoring force correction based on online discrete tangent stiffness estimation method for real-time hybrid simulation

In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation（online DTSE） to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that：（1） the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and（2） the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.

A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation

Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim-plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The me-thod is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.

Real-Time Maqam Estimation Model in Max/MSP Configured for the Nāy

Automatic maqam estimation is considered significant toward improving multimedia live music performances and automatic accompaniment. This contribution proposed a real-time maqam estimation model developed in the visual programming language MAX/MSP and configured for the nāydukah. The model’s design stood on basic formulas of Arab music maqamat as explained in theory and applied in practice. The model consisted of different layers of competition;the first was for the identification of the instant tonic of the melodic figure, and the second was for the recognition of its identifying E (E, E half-flat and E flat). Those two competitions were used to estimate the maqam in real-time. Then, accumulated estimation results were used to estimate the maqam in longer durations;five-second and full duration. The model was evaluated using professionally performed nāy improvisations. Results reflected a success in estimating all the studied maqamat when the full improvisation was considered. In addition, results were very good for real-time and five-second estimation where average estimation confidence was 75.98% and 80.04%, respectively.

FPGA Implementation of Extended Kalman Filter for Parameters Estimation of Railway Wheelset

It is necessary to know the status of adhesion conditions between wheel and rail for efficient accelerating and decelerating of railroad vehicle.The proper estimation of adhesion conditions and their real-time implementation is considered a challenge for scholars.In this paper,the development of simulation model of extended Kalman filter(EKF)in MATLAB/Simulink is presented to estimate various railway wheelset parameters in different contact conditions of track.Due to concurrent in nature,the Xilinx®System-on-Chip Zynq Field Programmable Gate Array(FPGA)device is chosen to check the onboard estimation ofwheel-rail interaction parameters by using the National Instruments(NI)myRIO®development board.The NImyRIO®development board is flexible to deal with nonlinearities,uncertain changes,and fastchanging dynamics in real-time occurring in wheel-rail contact conditions during vehicle operation.The simulated dataset of the railway nonlinear wheelsetmodel is tested on FPGA-based EKF with different track conditions and with accelerating and decelerating operations of the vehicle.The proposed model-based estimation of railway wheelset parameters is synthesized on FPGA and its simulation is carried out for functional verification on FPGA.The obtained simulation results are aligned with the simulation results obtained through MATLAB.To the best of our knowledge,this is the first time study that presents the implementation of a model-based estimation of railway wheelset parameters on FPGA and its functional verification.The functional behavior of the FPGA-based estimator shows that these results are the addition of current knowledge in the field of the railway.

A NEW HYBRID WAVELET-KALMAN FILTER METHOD FOR THE ESTIMATION OF DYNAMIC SYSTEM

A new hybrid wavelet-Kalman filter method for the estimation of dynamic system is developed, Using this method, the real-time multiscale estimation of the dynamic system is implemented, and the observation equation established is for the object signal itself rather than its wavelet decomposition. The simulation results show that this method can be used to estimate the object's state of the stacked system, which is the foundation of multiscale data fusion; besides the performance of the new algorithm developed in the letter is almost optimal.

Structural Interpretation and Petrophysical Analysis for Reservoir Sand of Lower Goru, Miano Area, Central Indus Basin, Pakistan

The main purpose of this research is to estimate the structural analysis and hydrocarbon potential of Miano Block by using seismic and well log techniques. Miano area hosts a number of gas fields with structural and stratigraphic traps. The area is located in Central Indus Basin which is a part of an extensional regime exhibiting normal faulting due to the split of the Indian Plate firstly from Africa and then from Madagascar and Seychelles. Miano area recognized as a proven petroleum province which has complex tectonic history of Cretaceous extensional and overprints of Tertiary strike-slip tectonics. The area has prospect with accumulation of hydrocarbons in structural and stratigraphic traps including pinchouts. NW-SE oriented Khairpur and Mari Highs are main structural features with impact on the fault system. The sands of Lower Goru of Lower Cretaceous age are acting as a reservoir in the area. The area has great potential of hydrocarbons for which more exploratory wells are required to be drilled with better insight of structural and stratigraphic traps.

EFFECTS OF ORIGINAL PHASE AND TIME-DELAY ON THE HF CHANNEL PARAMETER ESTIMATION

It is proposed firstly that the original phase and the time-delay are the main factors which affect the measuring resolution of the multitone complex envelope method. The effects of these factors are analysed and checked by the computer simulation. Finally, three possible ways to eliminate these effects are given.

Optimal parameter estimation of open quantum systems

In quantum information technologies,quantum weak measurement is beneficial for protecting coherence of systems.In order to further improve the protection effect of quantum weak measurement on coherence,we propose an optimization scheme of quantum Fisher information(QFI)protection in an open quantum system by combing no-knowledge quantum feedback control with quantum weak measurement.On the basis of solving the dynamic equations of a stochastic two-level quantum system under feedback control,we compare the effects of different feedback Hamiltonians on QFI and find that via no-knowledge quantum feedback,the observation operatorσx(orσx andσz)can protect QFI for a long time.Namely,no-knowledge quantum feedback can improve the estimation precision of feedback coefficient as well as that of detection coefficient.

A New Interpretation of Quantum Mechanics

The Copenhagen interpretation is the most authorized interpretation of quantum mechanics, but there are a number of ideas that are associated with the Copenhagen interpretation. It is ceratin that this fact is not necessarily desirable. Thus, we propose a new interpretation of measurement theory, which is the linguistic aspect (or, the mathematical generalization) of quantum mechanics. Although this interpretation is superficially similar to a part of so-called Copenhagen interpretation, we show that it has a merit to be applicable to both quantum and classical systems. For example, we say that Bell’s inequality is broken even in classical systems.

随机工况下基于改进ANFIS的锂电池容量衰减实时估计

锂电池容量的衰减会影响其安全性和稳定性,准确的容量估计能够帮助用户进行更好的决策。目前,广泛使用的黑盒数据驱动模型因其不可解释性很难被应用于安全相关的领域中,并且大多方法都基于固定工况进行特征提取,对具有随机性的实际工况不具有普适性。因此,本文构建了一种基于随机工况数据的改进自适应模糊神将网络推理系统(ANFIS)。首先分析了容量衰减的影响因素,据此从电池监测数据中提取和筛选健康特征;其次系统内部利用激活机制简化系统结构,并引入衰减系数更好地拟合电池单体特性;然后通过自适应粒子滤波算法优化模糊聚类中心;最后使用NASA随机工况数据集验证了该系统的有效性,其容量估计RMSE为3.73%。与其他方法相比,本文提出的方法结果精度更高且具有一定的可解释性。

基于可解释随机森林的场地放大系数估算方法研究

场地放大系数(F_(pga))是反映地震动峰值在不同地区的差异,也是评估地震动影响范围的关键参数。F_(pga)的估算采用单一参数,靠经验选取,估算结果存在较强的主观性。为此,选取KiK-net台网40个台站的地震记录,基于随机森林算法提出一种综合多参数的F_(pga)估算方法,且可解释各参数对F_(pga)估算的影响。所提估算方法以震级、震中距、震源深度、震源距、覆盖层厚度h、场地周期T、等效剪切波速(V_(SE)、V_(S30)、V_(S20)、V_(S10))作为输入,F_(pga)为输出。结果表明:预测F_(pga)的相关系数为0.84,误差均值为0.01;震中距、震源深度、震源距对F_(pga)估算呈正相关,震级、h、T、V_(SE)、V_(S30)、V_(S20)、V_(S10)对F_(pga)估算呈负相关,其中T和h对模型的影响最大。该模型可有效预测场地放大效应。

基于Swin Transformer-CNN的单目遥感影像高程估计方法及其在公路建设场景中的应用

目前,在遥感影像几何条件和辐射质量良好的情况下,通过多视遥感影像的逐像素立体密集匹配对场景进行高程估计的技术相对比较成熟,无论是精度还是效率均达到了较高水平。然而,当具有良好几何条件和辐射质量的多视遥感影像难以获取时,经典摄影测量和计算机视觉的几何处理方法可能会面临较大的挑战。本文对该问题进行了研究,针对大幅遥感图像中各部分高程分布差异大,模型训练难度大的问题,提出了一种基于Swin Transformer和卷积神经网络(convolutional neural network, CNN)的单目遥感影像高程估计方法。一方面Swin Transformer利用滑动窗口和层级设计,兼具了卷积神经网络处理大尺寸图像和提取多尺度特征的能力及Transformer的全局信息交互能力。另一方面针对大幅遥感图像中各部分高程分布差异大带来的训练不稳定问题,本文方法能针对每张输入图像自适应地划分高程值,将高程估计问题转化为分类-回归问题,最终图像各像素点的高程值由划分的高程值及其分布概率得到。试验结果表明:本文所提出的基于Swin Transformer-CNN的遥感影像高程估计方法无论是定性还是定量的结果都取得了很好的效果,且能应用于公路建设施工场景中,具有良好的泛化性。

GeeNet:robust and fast point cloud completion for ground elevation estimation towards autonomous vehicles

Ground elevation estimation is vital for numerous applications in autonomous vehicles and intelligent robotics including three-dimensional object detection,navigable space detection,point cloud matching for localization,and registration for mapping.However,most works regard the ground as a plane without height information,which causes inaccurate manipulation in these applications.In this work,we propose GeeNet,a novel end-to-end,lightweight method that completes the ground in nearly real time and simultaneously estimates the ground elevation in a grid-based representation.GeeNet leverages the mixing of two-and three-dimensional convolutions to preserve a lightweight architecture to regress ground elevation information for each cell of the grid.For the first time,GeeNet has fulfilled ground elevation estimation from semantic scene completion.We use the SemanticKITTI and SemanticPOSS datasets to validate the proposed GeeNet,demonstrating the qualitative and quantitative performances of GeeNet on ground elevation estimation and semantic scene completion of the point cloud.Moreover,the crossdataset generalization capability of GeeNet is experimentally proven.GeeNet achieves state-of-the-art performance in terms of point cloud completion and ground elevation estimation,with a runtime of 0.88 ms.

An improved GNSS ambiguity best integer equivariant estimation method with Laplacian distribution for urban low-cost RTK positioning

The integer least squares(ILS)estimation is commonly used for carrier phase ambiguity resolution(AR).More recently,the best integer equivariant(BIE)estimation has also attracted an attention for complex application scenarios,which exhibits higher reliability by a weighted fusion of integer candidates.However,traditional BIE estimation with Gaussian distribution(GBIE)faces challenges in fully utilizing the advantages of BIE for urban low-cost positioning,mainly due to the presence of outliers and unmodeled errors.To this end,an improved BIE estimation method with Laplacian distribution(LBIE)is proposed,and several key issues are discussed,including the weight function of LBIE,determination of the candidates included based on the OIA test,and derivation of the variance of LBIE solutions for reliability evaluation.The results show that the proposed LBIE method has the positioning accuracy similar to the BIE using multivariate t-distribution(TBIE),and significantly outperforms the ILS-PAR and GBIE methods.In an urban expressway test with a Huawei Mate40 smartphone,the LBIE method has positioning errors of less than 0.5 m in three directions and obtains over 50%improvements compared to the ILS-PAR and GBIE methods.In an urban canyon test with a low-cost receiver STA8100 produced by STMicroelectronics,the positioning accuracy of LBIE in three directions is 0.112 m,0.107 m,and 0.252 m,respectively,with improvements of 17.6%,27.2%,and 26.1%compared to GBIE,and 23.3%,28.2%,and 30.6%compared to ILS-PAR.Moreover,its computational time increases by 30–40%compared to ILS-PAR and is approximately half of that using TBIE.

RRVPE:A Robust and Real-Time Visual-Inertial-GNSS Pose Estimator for Aerial Robot Navigation

Self-localization and orientation estimation are the essential capabilities for mobile robot navigation.In this article,a robust and real-time visual-inertial-GNSS(Global Navigation Satellite System)tightly coupled pose estimation(RRVPE)method for aerial robot navigation is presented.The aerial robot carries a front-facing stereo camera for self-localization and an RGB-D camera to generate 3D voxel map.Ulteriorly,a GNSS receiver is used to continuously provide pseudorange,Doppler frequency shift and universal time coordinated(UTC)pulse signals to the pose estimator.The proposed system leverages the Kanade Lucas algorithm to track Shi-Tomasi features in each video frame,and the local factor graph solution process is bounded in a circumscribed container,which can immensely abandon the computational complexity in nonlinear optimization procedure.The proposed robot pose estimator can achieve camera-rate(30 Hz)performance on the aerial robot companion computer.We thoroughly experimented the RRVPE system in both simulated and practical circumstances,and the results demonstrate dramatic advantages over the state-of-the-art robot pose estimators.

Near real-time spatial prediction of earthquake-induced landslides:A novel interpretable self-supervised learning method

Near real-time spatial prediction of earthquake-induced landslides(EQILs)can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake;thus,EQIL prediction is very crucial to the 72-hour‘golden window’for survivors.This work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau,a famous seismically-active zone,and proposes a novel interpretable self-supervised learning(ISeL)method for the near real-time spatial prediction of EQILs.This new method innovatively introduces swap noise at the unsupervised mechanism,which can improve the generalization performance and transferability of the model,and can effectively reduce false alarm and improve accuracy through supervisedfine-tuning.An interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial distribution.Experimental results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning methods.Furthermore,according to the interpretable module in the ISeL method,the critical controlling and triggering factors are revealed.The ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability.

Distributed Real-time State Estimation for Combined Heat and Power Systems

This paper proposes a distributed real-time state estimation(RTSE)method for the combined heat and power systems(CHPSs).First,a difference-based model for the heat system is established considering the dynamics of heat systems.This heat system model is further used along with the power system steady-state model for holistic CHPS state estimation.A cubature Kalman filter(CKF)-based RTSE is developed to deal with the system nonlinearity while integrating both the historical and present measurement information.Finally,a multi-timescale asynchronous distributed computation scheme is designed to enhance the scalability of the proposed method for largescale systems.This distributed implementation requires only a small amount of information exchange and thus protects the privacy of different energy systems.Simulations carried out on two CHPSs show that the proposed method can significantly improve the estimation efficiency of CHPS without loss of accuracy compared with other existing models and methods.

Real-Time Facial Pose Estimation and Tracking by Coarse-to-Fine Iterative Optimization

We present a novel and efficient method for real-time multiple facial poses estimation and tracking in a single frame or video.First,we combine two standard convolutional neural network models for face detection and mean shape learning to generate initial estimations of alignment and pose.Then,we design a bi-objective optimization strategy to iteratively refine the obtained estimations.This strategy achieves faster speed and more accurate outputs.Finally,we further apply algebraic filtering processing,including Gaussian filter for background removal and extended Kalman filter for target prediction,to maintain real-time tracking superiority.Only general RGB photos or videos are required,which are captured by a commodity monocular camera without any priori or label.We demonstrate the advantages of our approach by comparing it with the most recent work in terms of performance and accuracy.