Unknown Interference Research Articles

Performance Analysis of Reconnaissance Coverage for HUAV Swarms under Communication Interference Based on Different Architectures

In environments with unknown communication interference, the mission efficiency of heterogeneous unmanned aerial vehicle (HUAV) swarms is often impacted by communication disruptions due to regions of strong interference encountered when executing reconnaissance and coverage missions. Existing research has rarely focused on communication interference or on the differences in HUAV characteristics under various control architectures; thus, in this paper we explore the performance differences between HUAV swarms based on centralized, distributed, and centralized-distributed architectures when executing reconnaissance and coverage missions in environments with unknown communication interference. First, a communication model in an unknown interference environment is constructed to reflect the real-time communication status of the swarm. Second, in response to the limitations of the traditional artificial potential field (APF) algorithm in this environment, a coverage-oriented artificial potential field (COAPF) algorithm is proposed. Finally, based on the COAPF algorithm, a multi-dimensional comparison of the mission completion efficiency of HUAV swarms with three different architectures is conducted. Our simulation results indicate that the distributed architecture is suitable for large-scale environments with strong interference, while the centralized–distributed architecture performs better in small-scale environments with weak interference. Conversely, the centralized architecture exhibits poor performance in all interference scenarios due to its lack of decision-making capabilities.

A fitting method of unlabeled interference for spectra based on multi-stage cross optimization

When constructing a spectral detection model for a substance, it will be very difficult to ensure the accuracy of the model if there is an unlabeled interference in the spectrum. To overcome the issue, we used a mathematical model to describe the impact mode of single interference on spectra, and pointed out the difficulties of spectral analysis in the case of unknown interference label. Then, a fitting method based on multi-stage cross optimization was proposed, which can fit the impact of single interference on spectra without interference label. Finally, we applied the proposed method on cobalt ion spectral detection with temperature interference. The application results indicate that although the influence modes of temperature interference and cobalt ion concentration on spectra are similar, this method can still accurately fit their influence modes, and the effectiveness of the fitting model was verified through spectral detection for blank sample.

Beam-space spatial spectrum reconstruction under unknown stationary near-field interference: Algorithm design and experimental verification.

In acoustic array signal processing, spatial spectrum estimation and the corresponding direction-of-arrival estimation are sometimes affected by stationary near-field interferences, presenting a considerable challenge for the target detection. To address the challenge, this paper proposes a beam-space spatial spectrum reconstruction algorithm. The proposed algorithm overcomes the limitations of common spatial spectrum estimation algorithms designed for near-field interference scenarios, which require knowledge of the near-field interference array manifold. The robustness and efficacy of the proposed algorithm under strong stationary near-field interference are confirmed through the analysis of simulated and real-life experimental data.

Causal message-passing for experiments with unknown and general network interference

Randomized experiments are a powerful methodology for data-driven evaluation of decisions or interventions. Yet, their validity may be undermined by network interference. This occurs when the treatment of one unit impacts not only its outcome but also that of connected units, biasing traditional treatment effect estimations. Our study introduces a framework to accommodate complex and unknown network interference, moving beyond specialized models in the existing literature. Our framework, termed causal message-passing, is grounded in high-dimensional approximate message-passing methodology. It is tailored for multiperiod experiments and is particularly effective in settings with many units and prevalent network interference. The framework models causal effects as a dynamic process where a treated unit's impact propagates through the network via neighboring units until equilibrium is reached. This approach allows us to approximate the dynamics of potential outcomes over time, enabling the extraction of valuable information before treatment effects reach equilibrium. Utilizing causal message-passing, we introduce a practical algorithm to estimate the total treatment effect, defined as the impact observed when all units are treated compared to the scenario where no unit receives treatment. We demonstrate the effectiveness of this approach across five numerical scenarios, each characterized by a distinct interference structure.

Optimization of saliva sampling methods for analysis of bile acids by UHPLC-MS

This study investigated methods for sampling bile acids in saliva, a potential non-invasive diagnostic biofluid. Bile acids have been implicated in causing damage and permanent changes to the esophageal mucosa and increasing the risk of developing Barrett's esophagus, a condition that can potentially progress to esophageal cancer. Three saliva collection methods were compared: spitting, Salivette® swabs, and Salivette Cortisol® swabs. Spitting emerged as the superior method with the highest recoveries and the least interference, likely due to Salivette swabs retaining bile acids or introducing unknown interferences. All saliva samples were analyzed by UHPLC-MS/MS using the Zorbax RRHD Eclipse Plus C18 column (3 × 50 mm, 1.8 µm) in gradient elution of 0.1 % formic acid in water and methanol. Saliva sample stability was assessed over 14 days, reflecting typical storage times. The levels of detected bile acids were stable for the measured period (RSD ≤ 22 %) and no degradation was observed. Bile acid levels in saliva fluctuated throughout the day, with the greatest changes observed for glycine-conjugated bile acids after meals. To minimize sampling variability, saliva collection by spitting after overnight fasting is recommended for future studies. Our findings are applicable for standardized bile acid sampling and are currently applied in a large clinical study evaluating bile acids as potential susceptibility markers for Barrett's esophagus diagnostics.

Design, hydrodynamics analysis, and control of an underwater glider with controllable wing mechanism

Due to the effect of biofouling and different mission sensors that may be onboard, hydrodynamic coefficients of underwater glider (UG) may experience gradual or obvious changes, which increases the energy consumption and reduces the glide range. This paper proposes a controllable wing mechanism (CWM) for UG, which enables it adjust the hydrodynamic coefficients and kinematic performance without changing the net buoyancy. Only by two degrees of freedom (DoF), that CWM can realize the control of wingspan, sweep back angle and chord width. To apply the hydrodynamics controlled with CWM in glide model, the fitting precision of hydrodynamics function is improved based on backpropagation artificial neural network (BPANN). A nonlinear state observer based on finite impulse response (FIR) filter is employed for state estimation and random noise interference filtering to realize measurements and evaluation of velocity and reduce the attitude angle measurement error for UG. Then, the FIR-based fuzzy PID (FFPID) controller is established for a steady-state gliding motion control, which suppresses the unknown interference to stablize the system quickly. Finally, the effectiveness of the FFPID closed-loop controller and observer is verified through the simulation in matlab and the experimental test of the UG with CWMs in a water tank.

Green Approach for Simultaneous Quantitative Analysis of Fluoroquinolones in the Environment using Three-Dimensional Fluorescence Coupled with Second-Order Calibration Method.

In this study, a straightforward and quick analytical technique based on the self-weighted alternating trilinear decomposition (SWATLD) algorithm in conjunction with excitation-emission matrix (EEM) fluorescence for the simultaneous determination of the antibiotics levofloxacin (LVFX) and ciprofloxacin (CIP) in environmental waters and sediments was developed. This approach completely utilizes the "second-order advantage" and inherits the great sensitivity of classic fluorescence. It replaces or improves the conventional "physical/chemical separation" with "mathematical separation", enabling direct and quick quantification of the target analytes even in the presence of unknown interferences, greatly streamlining sample preparation procedures, consuming less solvent, and speeding up analysis time, and allows successful and environmentally friendly solution of overlapping fluorescence spectra of multiple components in complicated environmental matrices without cumbersome pretreatment steps and complex and expensive instrumentation. The limits of detection varied between 0.34 and 0.67 ng mL- 1, and the average spiking recoveries of LVFX and CIP in water and sediment ranged from 97.6 to 107.7% with relative standard deviations lower than 6.6%. The developed method shows the reliability of the technology and the ability to quickly detect trace antibiotics in lake water even in the presence of unidentified interferents.

Three-dimensional line-of-sight-angle-constrained leader-following cooperative interception guidance law with prespecified impact time

To address the problem of multi-missile cooperative interception against maneuvering targets at a prespecified impact time and desired Line-of-Sight (LOS) angles in Three-Dimensional (3D) space, this paper proposes a 3D leader-following cooperative interception guidance law. First, in the LOS direction of the leader, an impact time-controlled guidance law is derived based on the fixed-time stability theory, which enables the leader to complete the interception task at a prespecified impact time. Next, in the LOS direction of the followers, by introducing a time consensus tracking error function, a fixed-time consensus tracking guidance law is investigated to guarantee the consensus tracking convergence of the time-to-go. Then, in the direction normal to the LOS, by combining the designed global integral sliding mode surface and the second-order Sliding Mode Control (SMC) theory, an innovative 3D LOS-angle-constrained interception guidance law is developed, which eliminates the reaching phase in the traditional sliding mode guidance laws and effectively saves energy consumption. Moreover, it effectively suppresses the chattering phenomenon while avoiding the singularity issue, and compensates for unknown interference caused by target maneuvering online, making it convenient for practical engineering applications. Finally, theoretical proof analysis and multiple sets of numerical simulation results verify the effectiveness, superiority, and robustness of the investigated guidance law.

Command filtered backstepping control of multiple transmission control protocol/active queue management networks with user datagram protocol flows

AbstractIn this study, the congestion control problem for multiple transmission control protocol/active queue management (TCP/AQM) networks with user datagram protocol (UDP) flows is investigated. Taking the UDP stream as the unknown interference and the maximum interference is obtained by the minimax theorem. A novel control algorithm is designed for multi‐TCP/AQM networks through backstepping control and command filtered technology. Simulation studies are carried out to verify the effectiveness and feasibility of the proposed method.

Super-resolution spatial spectrum reconstruction under unknown near-field interferences

In underwater acoustic signal processing, conventional spatial spectrum estimation and the associated direction-of-arrival estimation are often impaired by strong near-field interferences at the same frequency as the far-field target signal, leading to significant performance degradation and even detection failure. In contrast to prevalent near-field interference suppression algorithms that require prior knowledge of near-field interference positions, this paper proposes a super-resolution spatial spectrum reconstruction algorithm designed for more general scenarios where crucial information about the near-field interference, such as positions and magnitudes, is unknown. The proposed algorithm demonstrates its adaptability in mitigating unknown near-field interference and achieves this by leveraging rank constraint-based relaxation and alternating minimization, resulting in an effective spatial spectrum reconstruction strategy. The efficacy of the proposed spatial spectrum reconstruction method in handling strong near-field interference is confirmed through analysis of simulated and synthetic experimental data. It exhibits superiority over traditional competitors in terms of resolution, denoising capabilities, and estimation accuracy. Moreover, it achieves comparable results to algorithms that utilize prior information about near-field interference positions. The enhanced performance remains consistent even in challenging scenarios such as snapshot deficiency and low signal-to-noise ratios.

Gas rudder 2DOF FOPID position control based on CSFLA optimisation

Aiming at the problems of parameter variety, rigid-flexible coupling and external unknown factors interference in the process of spacecraft propulsion, a two-degree-of-freedom fractional-order PID (2DOF FOPID) controller based on a coevolutionary shuffled frog leaping algorithm (CSFLA) is designed for the high-precision control of the gas rudder servo control system. The anti-interference and target tracking capabilities of 2DOF PID is combined with the advantage of FOPID, which is insensitive to the parameters of the controlled object, to improve the performance of the control system as a whole. CSFLA is formed by integrating the self-learning evolution of elite populations and the interactive learning strategy among populations into the SFLA to enhance the global searching ability and searching accuracy of SFLA, and CSFLA is used to optimize the parameters of the 2DOF FOPID controller. The simulation results show that the designed CSFLA-2DOF FOPID controller has the advantages of fast response speed, small overshoot, strong tracking performance and strong anti-disturbance ability, which improves the dynamic and static performance and anti-disturbance ability of the gas rudder servo control system.

Study on Ill-Conditioned Total Least Squares Load Identification Method Based on the IGG Weight Function

To address the ill-conditioned problem and unknown noise interference in ship load identification, this study proposes the ill-conditioned Total Least Squares (TLS) load identification method using the anti-error principle of the IGG weight function (IGG-TLS). First, the weight factor is iteratively updated through the IGG weight function. The weight matrix is constructed adaptively by multiplying the weight factor with the initial unit weight matrix. Then, the regularization criterion for the ill-conditioned IGG-TLS model is established based on the Tikhonov regularization principle. And the Lagrange multiplier is employed to solve the IGG-TLS method. Finally, the load identification accuracies of the IGG-TLS method are investigated through the simulation analysis and experimental verification of the rectangular plate under different noise and SNR conditions. The results demonstrate that the IGG-TLS method can achieve high accuracy in load identification without the need to pre-construct the weight matrix, even when the SNR is unknown. Additionally, the IGG-TLS method can effectively correct the identification errors in the TLS method, demonstrating high accuracy and noise immunity under conditions of equal-weight and unequal-weight noise.

Robust predictive minmax fault‐tolerant control for multi‐phase batch processes under partial actuator faults and asynchronous switching

AbstractIn view of the uncertainties, partial actuator faults, asynchronous switching as well as unknown external interference in multi‐phase batch processes, a robust predictive minmax fault‐tolerant control approach is presented. First, considering that a controller and a system state cannot switch synchronously during switching, a match‐and‐mismatch switching model is constructed. Then, an output tracking error is introduced to establish an equivalent extended switching model. In this extended model, the robust control issue is transformed into the minmax optimization issue by establishing the minmax performance index. On this basis, the control laws with control input and interference input are designed. Second, sufficient conditions are given to assure the system separately has asymptotic and exponential stabilities in each phase and batch. By calculating the given sufficient conditions online, the gains of the control laws, and the minimum and maximum operating time under match and mismatch cases are obtained. With the maximum operating time, the controller receives the switching signal beforehand to eliminate the mismatch case of switching. Additionally, a robust criterion is supplied to confirm the system's robustness. Finally, a simulation study of an injection molding process shows that the approach is valid and feasible.

Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach

Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, a UAV, as a mobile device, still faces some challenges in optimizing its trajectory for data collection. Firstly, the high complexity of the movement action and state space of the UAV’s 3D trajectory is not negligible. Secondly, in unknown urban environments, a UAV must avoid obstacles accurately in order to ensure a safe flight. Furthermore, without a priori wireless channel characterization and ground device locations, a UAV must reliably and safely complete the data collection from the ground devices under the threat of unknown interference. All of these require the proposing of intelligent and automatic onboard trajectory optimization techniques. This paper transforms the trajectory optimization problem into a Markov decision process (MDP), and deep reinforcement learning (DRL) is applied to the data collection scenario. Specifically, the double deep Q-network (DDQN) algorithm is designed to address intelligent UAV trajectory planning that enables energy-efficient and safe data collection. Compared with the traditional algorithm, the DDQN algorithm is much better than the traditional Q-Learning algorithm, and the training time of the network is shorter than that of the deep Q-network (DQN) algorithm.

Event-triggered adaptive control for quadrotor unmanned aerial vehicle with prescribed performance

This paper proposes an event-triggered adaptive control algorithm with prescribed performance for quadrotor unmanned aerial vehicles (UAVs). The dynamic uncertainty and unknown external interference of the system are compensated by the property of approximation in a radial basis function (RBF) neural network. Based on this, introduce a performance constraint function to ensure that the trajectory tracking error of quadrotor UAV converges to a preset area, thereby guaranteeing transient and steady-state performance. In addition, design an event-triggered adaptive controller with the combination of the event-triggered mechanism, reducing the controller’s update frequency. Simulation results demonstrate that this algorithm exhibits strong anti-interference properties and effectively resolves the flight control problem of quadrotor UAV.

Distributed fiber optic acoustic sensing system intrusion full event recognition based on 1-D MFEWnet

Proper detection of the full range of intrusion events is of paramount significance to distributed fiber optic sensing perimeter security systems. Traditional neural networks for intrusion event recognition are constrained by the training dataset, that is, they cannot detect intrusions outside of the training dataset. However, in real complex environments, the dataset by manually obtained is far fall short of encompassing all possible real-world data. This limitation can lead to inaccuracies of identification in the distributed fiber optic sensing system not being able to identify correctly, which causes immeasurable losses. In order to address the aforementioned issues, this paper presents a 1D MFEWnet model, which completes the effective differentiation of all datasets by means of a Multi-Feature branch 1-dimensional Convolution Neural Network, followed by fitting the activation vectors after the recognition of known datasets to a Weibull distribution, through the improved Euclidean distance tracing algorithm. This approach allows for the extraction and identification of additional intrusion signals while providing the ability to recognize and reject unknown interference events. In the experiments, a distributed fiber optic sensing system was established to collect event signals. For three known event categories, the highest recognition accuracy is up to 99.6%. After adding 2 unknown event categories randomly, the accuracy remained at a commendable 96.9%. This innovative methodology ensures the accuracy of target recognition under the introduction of all conceivable events and improves the robustness of the distributed fiber optic perimeter security system.

A fuzzy adaptive complementary filter for attitude estimation based on norm judgment

Unmanned aerial vehicle (UAV) has great application prospect because of its capability of three-dimensional space operation. The reliability of attitude and heading reference system (AHRS) for UAV attitude estimation is crucial for the application of UAV. When a UAV is subjected to unknown electromagnetic interference and force interference in flight, its attitude detection system can suffer from reduced accuracy or even failure. In this paper, a fuzzy adaptive complementary filter (CF) for attitude estimation based on norm judgment is proposed to solve the problem that the sensor is easily disturbed by the complex flight environment and the fixed filter parameters are difficult to obtain the UAV attitude accurately under different flight states. Firstly, the correction model of the gyroscope, which includes four filter parameters, namely accelerometer weight, magnetometer weight, proportional gain P and integral gain I, is established. Secondly, the influence of the four parameters on the estimation accuracy is analyzed. Finally, the adaptive adjustment rules are designed to adjust the filter parameters online and thus achieve the accurate and reliable measurement of UAV attitude. The feasibility of the proposed algorithm is verified through static, dynamic and interference experiments with the specially designed AHRS test platform. And the results show that the attitude estimation algorithm designed in this paper can ensure the high accuracy of the system in both steady state and high-speed rotation, shield the pitch and roll angles from the acceleration of motion, and keep the yaw angle from the external magnetic field.

Estimation of geomagnetic components under unknown interferences for drilling tools

The geomagnetic components measurement directly influences the azimuth and magnetic Toolface of downhole drilling tools. Accurate estimation of the geomagnetic components is crucial for wellbore trajectory control. However, the unknown magnetic interferences caused by ferromagnetic drilling tools introduce significant errors in the estimation of the geomagnetic components. Firstly, this paper considers the constraint of a constant total magnetic field value and introduces the unknown axial interferences of the drilling tools as a new state. At the same time, parameter uncertainties and radial interferences within the drilling tools are regarded as unknown but bounded, leading to the establishment of a linear time-varying system model for the measurement system. Then, a state estimation method based on the Zonotopic Kalman filter (ZKF) is proposed for geomagnetic components and attitude measurements. The simulation results indicate that, compared to the ZKF method without considering nonlinear constraints, the method proposed in this paper effectively separates the axial interference of the geomagnetic components from the drilling tools during dynamic measurements. The root mean square errors of the estimated three-axis geomagnetic components are reduced to 0.0249 Gauss, 0.0082 Gauss, and 0.0148 Gauss, resulting in improvements in accuracy of 96.06%, 28.6%, and 14.1%, respectively. Furthermore, the root mean square errors of the gravity Toolface, magnetic Toolface, and azimuth estimation decreased to 1.8296°, 1.8294°, and 3.2898°, leading to enhancements in accuracy of 9.61%, 9.71%, and 96.37%, respectively. Finally, experimental validation confirms the effectiveness of the proposed method, providing a reference basis for dynamic measurement while drilling.

False-positive MDA findings in HRMS-based screening of putrefied postmortem blood samples-Identification of the interference as N-acetyltyramine.

An unidentified compound in putrefied postmortem blood samples showed identical accurate mass and chromatographic behavior as 3,4-methylenedioxyamphetamine (MDA) and led to false-positive preliminary screening results. The aim of the study was to identify this unknown interference. Postmortem blood samples were analyzed after protein precipitation on a QExactive Focus high-resolution mass spectrometer (Thermo Fisher, Germany) coupled to a RP C18 column (Macherey-Nagel, Germany). Based on the analysis of mass spectrometry (MS) adducts and isotope ratios using fullscan (m/z 134-330) information, the empiric formula of the protonated molecule [M + H]+ of the unknown compound was found to be C10H14O2N (+ 0.6 ppm). Product ion spectra recorded using normalized collision energy 22% showed a base peak of C8H9O1 (+ 1.5 ppm) and a low-abundant water loss to C7H9 (+ 1.9 ppm), neutral losses of C2H2O and NH3 were found. Based on fullscan and MS-MS information and under consideration of the observed order of neutral losses, the compound was presumptively identified as N-acetyltyramine. This assumption was supported by SIRIUS software showing a SIRIUS score of 99.43% for N-acetyltyramine. Finally, the putative structure annotation was confirmed by a reference compound. The described false-positive MDA findings could be attributed to the presence of N-acetyltyramine in putrefied blood samples. Being an isomer of MDA, N-acetyltyramine could not be distinguished by high-resolution data of the protonated molecules. The presented results once again highlight that false-positive findings may occur even in hyphenatedhigh-resolution mass spectrometry (HRMS) when using full-scan information only.

An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of phenobarbital in human serum and plasma.

Phenobarbital serves as an antiepileptic drug (AED) and finds application in the treatment of epilepsy either as monotherapy or adjunctive therapy. This drug exhibits various pharmacodynamic properties that account for its beneficial effects as well as potential side effects. Accurate measurement of its concentration is critical for optimizing AED therapy through appropriate dose adjustments. Therefore, our objective was to develop and validate a new reference measurement procedure (RMP) for the accurate quantification of phenobarbital levels in human serum and plasma. A sample preparation protocol based on protein precipitation followed by a high dilution step was established in combination with a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method using a C8 column to separate target analytes from known and unknown interferences. Assay validation and determination of measurement uncertainty were performed based on current guidelines. Selectivity and Specificity were assessed using spiked serum and plasma samples; to investigate possible matrix effects (MEs) a post-column infusion experiment and a comparison of standard line slopes was performed. Precision and accuracy were determined within a multiday precision experiment. The RMP was shown to be highly selective and specific, with no evidence of matrix interferences. It can be used to quantify phenobarbital in the range of 1.92 to 72.0 μg/mL. Intermediate precision was less than 3.2 %, and repeatability coefficient of variation (CV) ranged from 1.3 to 2.0 % across all concentration levels. The relative mean bias ranged from -3.0 to -0.7 % for native serum levels, and from -2.8 to 0.8 % for Li-heparin plasma levels. The measurement uncertainties (k=1) for single measurements and target value assignment were 1.9 to 3.3 % and 0.9 to 1.6 %, respectively. A novel LC-MS/MS-based candidate RMP for the quantification of phenobarbital in human serum and plasma is presented which can be used for the standardization of routine assays and the evaluation of clinically relevant samples.