Improved adaptively robust estimation algorithm for GNSS spoofer considering continuous observation error

Once the spoofer has controlled the navigation sys-tem of unmanned aerial vehicle(UAV),it is hard to effectively control the error convergence to meet the threshold condition only by adjusting parameters of estimation if estimation of the spoofer on UAV has continuous observation error.Aiming at this problem,the influence of the spoofer’s state estimation error on spoofing effect and error convergence conditions is theoretically analyzed,and an improved adaptively robust estimation algo-rithm suitable for steady-state linear quadratic estimator is pro-posed.It enables the spoofer’s estimator to reliably estimate UAV status in real time,improves the robustness of the estima-tor in responding to observation errors,and accelerates the con-vergence time of error control.Simulation experiments show that the mean value of normalized innovation squared(NIS)is reduced by 88.5%,and the convergence time of NIS value is reduced by 76.3%,the convergence time of true trajectory error of UAV is reduced by 42.3%,the convergence time of estimated trajectory error of UAV is reduced by 67.4%,the convergence time of estimated trajectory error of the spoofer is reduced by 33.7%,and the convergence time of broadcast trajectory error of the spoofer is reduced by 54.8%when the improved algorithm is used.The improved algorithm can make UAV deviate from pre-set trajectory to spoofing trajectory more effectively and more subtly.

PARALLEL ADAPTIVELY MODIFIED CHARACTERISTIC BASIS FUNCTION METHOD BASED ON STATIC LOAD BALANCE

Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is suitable for parallel computing. In this paper, a static load balance parallel method is presented by combining Message Passing Interface (MPI) with Adaptively Modified CBFM (AMCBFM). In this method, the object geometry is partitioned into distinct blocks, and the serial number of blocks is sent to related nodes according to a certain rule. Every node only needs to calculate the information on local blocks. The obtained results confirm the accuracy and efficiency of the proposed method in speeding up solving large electrical scale problems.

Comparison of Two Fading Filters anc Adaptively Robust Filter

Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.

Satellite Constellation Design with Adaptively Continuous Ant System Algorithm

The ant system algorithm （ASA） has proved to be a novel meta-heuristic algorithm to solve many multivariable problems. In this paper, the earth coverage of satellite constellation is analyzed and a n ＋ 1^ -fold coverage rate is put forward to evaluate the coverage performance of a satellite constellation. An optimization model of constellation parameters is established on the basis of the coverage performance. As a newly developed method, ASA can be applied to optimize the constellation parameters. In order to improve the ASA, a rule for adaptive number of ants is proposed, by which the search range is obviously enlarged and the convergence speed increased. Simulation results have shown that the ASA is more quick and efficient than other methodV211.71s.

Decoding on Adaptively Pruned Trellis for Correcting Synchronization Errors

Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.

Adaptively Secure Attribute-Based Encryption Supporting Attribute Revocation

Attribute revocation is inevitable and al- so important for Attribute-Based Encryption （ABE） in practice. However, little attention has been paid to this issue, and it retrains one of the rmin obsta-cles for the application of ABE. Most of existing ABE schemes support attribute revocation work under indirect revocation model such that all the users＇ private keys will be affected when the revo-cation events occur. Though some ABE schemes have realized revocation under direct revocation model such that the revocation list is embedded in the ciphertext and none of the users＇ private keys will be affected by revocation, they mostly focused on the user revocation that revokes the user＇s whole attributes, or they can only be proven to be selectively secure. In this paper, we first define a model of adaptively secure ABE supporting the at- tribute revocation under direct revocation model. Then we propose a Key-Policy ABE （KP-ABE） scheme and a Ciphertext-Policy ABE （CP-ABE） scheme on composite order bilinear groups. Finally, we prove our schemes to be adaptively secure by employing the methodology of dual system eno cryption.

Uniform Convergence Analysis of Finite Difference Scheme for Singularly Perturbed Delay Differential Equation on an Adaptively Generated Grid

Adaptive grid methods are established as valuable computational technique in approximating effectively the solutions of problems with boundary or interior layers. In this paper,we present the analysis of an upwind scheme for singularly perturbed differential-difference equation on a grid which is formed by equidistributing arc-length monitor function.It is shown that the discrete solution obtained converges uniformly with respect to the perturbation parameter.Numerical experiments illustrate in practice the result of convergence proved theoretically.

Projection-Based Dimensional Reduction of Adaptively Refined Nonlinear Models

Adaptive mesh refinement (AMR) is fairly practiced in the context of high-dimensional, mesh-based computational models. However, it is in its infancy in that of low-dimensional, generalized-coordinate-based computational models such as projection-based reduced-order models. This paper presents a complete framework for projection-based model order reduction (PMOR) of nonlinear problems in the presence of AMR that builds on elements from existing methods and augments them with critical new contributions. In particular, it proposes an analytical algorithm for computing a pseudo-meshless inner product between adapted solution snapshots for the purpose of clustering and PMOR. It exploits hyperreduction—specifically, the energy-conserving sampling and weighting hyperreduction method—to deliver for nonlinear and/or parametric problems the desired computational gains. Most importantly, the proposed framework for PMOR in the presence of AMR capitalizes on the concept of state-local reduced-order bases to make the most of the notion of a supermesh, while achieving computational tractability. Its features are illustrated with CFD applications grounded in AMR and its significance is demonstrated by the reported wall-clock speedup factors.

Slip-draft embedded control system by adaptively adjusting the battery position for electric tractor

A slip-draft embedded control system was designed and developed for an independent developed 2WD(two-wheel drive)electric tractor,to improve the traction efficiency,operation performance and ploughing depth stability of the electric tractor.In this system,the battery of electric tractor was innovatively equivalent to the original counterweight of the fuel tractor.And through dynamic analysis of electric tractor during ploughing,the mathematical model of adjusting the center of gravity about draft force and slip rate was established.Then the automatic adjustment of the center of gravity for electric tractor was realized through the adaptive adjustment of battery position.Finally,the system was carried on electric tractor for performance evaluation under different ploughing conditions,the traction efficiency,slip rate and front wheel load of electric tractor were measured and controlled synchronously to make it close to the set range.And the comparative experiments of ploughing operation were carried out under the two modes of adaptive adjustment of center of gravity and fixed center of gravity.The test results showed that,based on the developed control system,the center of gravity of electric tractor can be adjusted in real time according to the complex changes of working conditions.During ploughing operation with adjusting adaptively battery position,the average values of traction efficiency,slip rate,front wheel load and relative error of tillage depth of electric tractor were 64.5%,22.2%,2045.0 N and 2.0%respectively.Which were optimized by 15.0%,29.5%,19.6%and 80.0%respectively,compared with electric tractor with fixed battery position.The slip-draft embedded control system can not only realize the adaptive adjustment of the center of gravity position in the ploughing process of electric tractor,but also improve the traction efficiency and the stability of ploughing depth,which can provide reference for the actual production operation of electric tractor.

Uniform Convergence of Multigrid V-Cycle on Adaptively Refined Finite Element Meshes for Elliptic Problems with Discontinuous Coefficients

The multigrid V-cycle methods for adaptive finite element discretizations of two-dimensional elliptic problems with discontinuous coefficients are considered.Under the conditions that the coefficient is quasi-monotone up to a constant and the meshes are locally refined by using the newest vertex bisection algorithm,some uniform convergence results are proved for the standard multigrid V-cycle algorithm with Gauss-Seidel relaxations performed only on new nodes and their immediate neighbours.The multigrid V-cycle algorithm uses O(N)operations per iteration and is optimal.

中国改编指南的现状分析

目的分析中国改编指南的制订现状及其方法学质量和报告质量,为我国改编指南的制订和发展提供参考。方法系统检索中国知网、万方数据知识服务平台、中国生物医学文献数据库、PubMed数据库,纳入2015—2023年由中国研究者或机构牵头制订的改编指南,提取改编指南的基本信息,采用AGREEⅡ和RIGHT-Ad@pt工具评价改编指南的方法学质量和报告质量。结果共纳入14部改编指南,主要集中于护理领域(42.9%,6/14),最常采用的改编方法是ADAPTE方法(71.4%,10/14),参照的原指南主要来自美国、英国和国际组织。AGREEⅡ工具的评估结果显示,改编指南的方法学质量平均得分为36.6%,表达明晰性和制订严谨性领域得分相对较高(44.4%和54.9%),应用性领域得分最低(15.3%);RIGHT-Ad@pt工具的评估结果显示,改编指南的报告质量相对较好,报告率最高为91.2%,最低为35.3%,其中7部报告率均高于70%。结论中国改编指南仍处于起步阶段,数量少且主要集中于护理领域,最常采用的改编方法是ADAPTE方法;改编指南的方法学质量和报告质量有待进一步提升,未来应针对改编指南制订的具体条件和时机开展更多相关研究。

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

直接抽吸取栓术与支架取栓术治疗急性大脑中动脉粥样硬化性闭塞的疗效对比研究

目的 比较首选直接抽吸技术(a direct aspiration first-pass technique, ADAPT)与支架取栓(stent-retriever thrombectomy, SRT)治疗急性大脑中动脉粥样硬化性闭塞(intracranial atherosclerotic stenosis related large vessel occlusion, ICAS-LVO)所致急性缺血性卒中(acute ischemic stroke, AIS)患者的临床疗效。方法 回顾性连续纳入2020年1月至2023年1月西安交通大学第一附属医院神经内科及陕西省中医药大学第二附属医院脑病二科接受血管内治疗的急性大脑中动脉ICAS-LVO所致AIS患者,根据首选取栓装置将患者分为ADAPT组和SRT组,比较两组患者的基线资料、检验及检查结果、手术过程及临床预后等。结果 共纳入117例患者,其中ADAPT组48例,SRT组69例,两组患者的基线资料无统计学差异。SRT组首次取栓成功再通率(P=0.014)及首选取栓装置成功再通率(P<0.001)均高于ADAPT组,且医源性夹层(P<0.001)及血管痉挛(P=0.003)的发生率显著降低;而ADAPT组更换取栓装置补救治疗比例显著高于SRT组(P<0.001);但在最终血管成功再通率、症状性及无症状性颅内出血发生率和90 d良好预后率等方面,两组无统计学差异。结论 对于急性大脑中动脉ICAS-LVO患者,首选SRT治疗相较于ADAPT治疗具有更高的即刻成功再通率,且血管损伤风险更小,但二者改善患者预后的作用相似。

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Interplay between mesenchymal stromal cells and the immune system after transplantation: implications for advanced cell therapy in the retina

Advanced mesenchymal stromal cell-based therapies for neurodegenerative diseases are widely investigated in preclinical models.Mesenchymal stromal cells are well positioned as therapeutics because they address the underlying mechanisms of neurodegeneration,namely trophic factor deprivation and neuroinflammation.Most studies have focused on the beneficial effects of mesenchymal stromal cell transplantation on neuronal survival or functional improvement.However,little attention has been paid to the interaction between mesenchymal stromal cells and the host immune system due to the immunomodulatory properties of mesenchymal stromal cells and the long-held belief of the immunoprivileged status of the central nervous system.Here,we review the crosstalk between mesenchymal stromal cells and the immune system in general and in the context of the central nervous system,focusing on recent work in the retina and the importance of the type of transplantation.

Revolutionizing gastric cancer treatment:The potential of immunotherapy

Gastric cancer,a prevalent malignancy worldwide,ranks sixth in terms of frequency and third in fatality,causing over a million new cases and 769000 annual deaths.Predominant in Eastern Europe and Eastern Asia,risk factors include family medical history,dietary habits,tobacco use,Helicobacter pylori,and Epstein-Barr virus infections.Unfortunately,gastric cancer is often diagnosed at an advanced stage,leading to a grim prognosis,with a 5-year overall survival rate below 5%.Surgical intervention,particularly with D2 Lymphadenectomy,is the mainstay for early-stage cases but offers limited success.For advanced cases,the National Comprehensive Cancer Network recommends chemotherapy,radiation,and targeted therapy.Emerging immunotherapy presents promise,especially for unresectable or metastatic cases,with strategies like immune checkpoint inhibitors,tumor vaccines,adoptive immunotherapy,and nonspecific immunomodulators.In this Editorial,with regards to the article“Advances and key focus areas in gastric cancer immunotherapy:A comprehensive scientometric and clinical trial review”,we address the advances in the field of immunotherapy in gastric cancer and its future prospects.

Vibration Control of A Flexible Marine Riser System Subject to Input Dead Zone and Extraneous Disturbances

An observer-based adaptive backstepping boundary control is proposed for vibration control of flexible offshore riser systems with unknown nonlinear input dead zone and uncertain environmental disturbances.The control algorithm can update the control law online through real-time data to make the controller adapt to the environment and improve the control precision.Specifically,based on the adaptive backstepping framework,virtual control laws and Lyapunov functions are designed for each subsystem.Three direction interference observers are designed to track the timevarying boundary disturbance.On this basis,the inverse of the dead zone and linear state transformation are used to compensate for the original system and eliminate the adverse effects of the dead zone.In addition,the stability of the closed-loop system is proven by Lyapunov stability theory.All the system states are bounded,and the vibration offset of the riser converges to a small area of the initial position.Finally,four examples of flexible marine risers are simulated in MATLAB to verify the effectiveness of the proposed controller.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive Data

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be preserved.Data owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the requester.We prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file keepers.We also provide security analysis in the context of four typical attacks.