Auxiliary Signal Research Articles

Active Fault Diagnosis Based on Dual-Observer for Leakage Detection and Estimation With Adaptive Auxiliary Signal Mechanism in Industrial Gas Networks

Active Fault Diagnosis Based on Dual-Observer for Leakage Detection and Estimation With Adaptive Auxiliary Signal Mechanism in Industrial Gas Networks

Unactuated and Actuated States Simultaneously Constrained Optimal Trajectory Planning-Based Path-Following Control for Underactuated Robots.

For underactuated robots working in complex environments, an important objective is to drive all variables (particularly for unactuated end-effectors) to move along the specific path and restrict positions/velocities to avoid obstacles, rather than using only point-to-point control. Unfortunately, most path planning methods are only suitable to fully actuated systems or depend on linearized models. The main motivations of our work are to directly fulfill motion constraints and achieve path following for both actuated and unactuated states (e.g., payload swing of cranes) when lacking effective control inputs. To this end, this article presents a new time-optimal trajectory planning-based motion control method for general underactuated robots. By constructing auxiliary signals (in Cartesian space) to express all actuated/unactuated variables (in joint space), their position/velocity constraints are converted into some convex/nonconvex inequalities related to a to-be-optimized path parameter and its derivatives. Then, an optimization algorithm is constructed to solve the available path parameter and derive a group of time-optimal trajectories for actuated states. As we know, this is the first study to ensure path following and necessary full-state constraints for actuated/unactuated states. Then, a tradeoff among path-constrained motions, time optimization, and state constraints is achieved together. This article takes the rotary crane as an example and provides detailed analysis of calculating desired trajectories based on the proposed planning frame, whose effectiveness is also verified through hardware experiments.

Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge Encoder-Decoder Framework

Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge Encoder-Decoder Framework

Defending UAV Networks Against Covert Attacks Using Auxiliary Signal Injections

Defending UAV Networks Against Covert Attacks Using Auxiliary Signal Injections

Command filter‐based adaptive fixed‐time fault‐tolerant control for stochastic nonlinear systems with actuator hysteresis

AbstractIn this paper, an adaptive fault‐tolerant fixed‐time control problem is considered via command‐filter technique for stochastic nonlinear systems with sensor fault and actuator hysteresis. With the application of command‐filtering technique, a novel command‐filter compensate mechanism is designed, which implies that the improved control scheme not only eliminates “the explosion of complexity” but also realizes the compensate signal is bounded within fixed‐time interval. The unavailability of state variables caused by sensor fault is solved by applying parameter separation and regrouping approach. Meanwhile, an adaptive auxiliary signal is designed to cope with the backlash‐like hysteresis phenomenon, which can avoid singularity, reduce chattering, and facilitate controller design. Combining backstepping technique and Lyapunov stability theorem, an adaptive fault‐tolerant control approach is developed, which can guarantee all closed‐loop signals remain semi‐globally practical fixed‐time stable (SGPFS) in probability. The validity of the proposed strategy is illustrated by simulation examples.

Compensation of the material temperature influence on specific electrical conductivity during eddy current structurescopy

The importance of eddy current measurements of specific electrical conductivity (SEC) of materials in non-destructive testing for solving problems of structuroscopy is discussed. In particular, their significant role is shown for estimation of the degree of changes in the mechanical characteristics of structural materials due to degradation processes in their structure. The influence of the temperature of metallic structural materials on the results of eddy current measuring of SEC has been studied. An analytical expression for the output signal of the SEC measuring channel of an electrical eddy current conductivity meter for real temperature of material is obtained. It depends on the value of the material SEC at a nominal temperature value (usually 20 C), temperature coefficient of electrical resistance of the material and the deviation of the material real temperature from the nominal value. It is shown that in order to tune out this effect and bring the measurement results to the nominal temperature, it is necessary to use eddy current meters with an additional channel for measuring the material temperature in the testing zone. The output signal of this channel is used to correct the SEC measurement results. This is carried out by multiplying the output signal of the SEC measurement channel and some auxiliary signal. This auxiliary signal is generated from the output signal of the temperature measurement channel by it scaling and summation with the signal of the direct current voltage reference source. A functional diagram of the eddy current SEC meter with temperature correction is proposed. A detailed description of it operation is given. In the absence of preliminary information about the grade of material and the value of its temperature coefficient of electrical resistance, is proposed to suppress the influence of temperature by test changes in the temperature of the material in the testing zone. At the same time, it is necessary to adjust the gain of the scale converter. This is done until the readings of the eddy current SEC meter do not depend on the material temperature in the testing zone.

A Mean-Rate Event-Triggered Mechanism for Nonlinear Plants With Weak Time Regularization

This paper proposes a new event-triggered mechanism for nonlinear plants with multiple communication networks. An auxiliary signal that contains a component to measure the (weighted) mean changing rates of measurement errors is employed; and hence the induced event-triggered mechanism is termed as a mean-rate one. By incorporating this auxiliary signal into dynamic event-triggering conditions, the new mean-rate event-triggered mechanism can ensure closed-loop input-to-state stability with respect to external disturbances. To further improve transmission performance, a concept of weak time regularization is given and proved, where similar to traditional time regularization, no events can be triggered within a user-specified lower bound; but differently, the positiveness of this user-specified parameter is not necessary in excluding Zeno behavior. Finally, a nonlinear example is simulated to illustrate the feasibility and efficiency of the theoretical results.

System-level performance prognosis based on data augmentation for air brake systems of in-service trains

In order to prognose the performance for air brake systems of in-service trains, a data augmentation method based on correlation analysis and improved multivariate support vector regression was proposed in this paper. By using black box theory and correlation analysis, brake cylinder pressure was extracted as the performance identification signal of air brake system, and the input and output signals were used as the auxiliary signals to construct the black box model of air brake system. Meanwhile, in order to make full use of the obtained data information, a data augmentation prognosis model based on improved multivariate support vector regression was established. Moreover, the optimal parameters of prognosis model were selected by means of particle swarm optimization algorithm, and RMSE and MAE were used as evaluation indicators. Finally, case studies on test-rig performance experiment data of air brake system were conducted. The prognosis model was trained and verified by using the service 1-7N and emergency, service full brake-relief and stage brake-relief experiment data, respectively, and the modeling and calibration error at pressure-holding stage were both in the order of ±5 kPa, which demonstrated the proposed method to be quite accurate and effective.

Learning to compensate for lack of information: Extracting latent knowledge for effective temporal knowledge graph completion

The goal of temporal knowledge graph embedding (TKGE) is to represent the entities and relations in a given temporal knowledge graph (TKG) as low-dimensional vectors (i.e., embeddings), which preserve both semantic information and temporal dynamics of the factual information. In this paper, we posit that the intrinsic difficulty of existing TKGE methods lies in the lack of information in KG snapshots with timestamps, each of which contains the facts that co-occur at a specific timestamp. To address this challenge, we propose a novel self-supervised TKGE approach, THOR (Three-tower grapH cOnvolution netwoRks (GCNs)), which extracts latent knowledge from TKGs by jointly leveraging both temporal and atemporal dependencies between entities and the structural dependency between relations. THOR learns the embeddings of entities and relations, obtained from three-tower GCNs by (1) maximizing the likelihood of the facts in a TKG and (2) addressing the lack of information in a TKG based on the auxiliary supervision signals of each entity. Our experiments on three real-world datasets demonstrate that THOR significantly outperforms 17 competitors in terms of TKG completion tasks. THOR yields up to 9.37% higher accuracy than the best competitor.

Multi-hop question answering of bridge inspection by adopting knowledge graph embedding technology

The complexity of relationships between bridge inspection presents a significant challenge for the answer system. Sparse knowledge graph (KG) due to limited triples further compounds the issue. To overcome this, this paper proposes a dynamic reasoning strategy for bridge inspection question answering. The framework comprises a teacher-student network and dynamic reasoning strategy. The teacher network, based on the neural state machine (NSM), acquires auxiliary intermediate supervision signals. Its output provides probability distribution and entity embedding as input for the student network. The student network, also NSM-based, provides accurate answers with the aid of intermediate supervision signals. To handle incomplete KG, the dynamic reasoning strategy incorporates knowledge embedding, updating the KG by capturing contextual information of each related entity node in relation to the question. Experiments on the bridge inspection dataset demonstrated the effectiveness of this method, outperforming other approaches.

Auxiliary Diagnostic Signal for Deep-Level Detection.

We propose and demonstrate that temperature-dependent curve-fitting error values of the Schottky diode I-V curve in the forward regime can be an auxiliary diagnostic signal as the temperature-scan Capacitance DLTS (CDLTS) signals and helps to work time-efficiently with high accuracy when using the Laplace Transform (LT)-DLTS or Isothermal Capacitance transient spectroscopy (ICTS) method. Using Be-doped GaAs showing overlapping DLTS signals, we verify that the LT-DLTS or ICTS analysis within a specific temperature range around the characteristic temperature Tpeak coincides well with the results of the CDLTS and Fourier Transform DLTS performed within the whole temperature range. In particular, we found that the LT-DLTS signals appeared intensively around Tpeak, and we confirmed it with the ICTS result. The occurrence of the curve fitting error signal is attributed to the relatively increased misfit error by the increased thermal emission from the deep-level trap in the case near the Tpeak, because the applied transport model excludes defect characteristics.

Adaptive neural optimised control for stochastic nonlinear systems with time-varying input delay via self-triggered mechanism

In this paper, an adaptive tracking control strategy based on neural networks (NNs) is proposed for the stochastic nonlinear strict-feedback system with a time-varying input delay and unmeasurable states. A state observer is constructed to solve the problem of unmeasurable state and the effect of the time-varying input delay is compensated by using an auxiliary signal. All the virtual controllers and the actual controller are designed as the optimised solution of the corresponding subsystems to ensure the entire obtained controllers are optimised. To ensure that the states of the system are constrained within some given compact sets, the barrier Lyapunov function (BLF) is used to design the controller and perform stability analysis. Meanwhile, this paper adds a self-triggered mechanism to reduce the communication burden of the data transfer. Thus, the proposed control strategy can achieve optimised control and all system states can be constrained within the given ranges. On the other hand, all closed-loop signals of system are bounded in probability. Finally, it is demonstrated that the proposed control strategy can achieve the expected system performance by a simulation experiment.

Dipole and quadrupole resonance excitation in linear quadrupoles.

In the development of commercial quadrupole mass spectrometers, there is an interest in improving the performance characteristics such as transmission, resolution, and mass range. In particular, parametric and dipolar resonance excitation of trapping ions are used for linear quadrupole mass filters. Theoretical methods and numerical simulation of ion trajectories were applied for study of ion-optical properties. The review is devoted to description of different excitation methods to improve QMF performance and consists of three parts. The first part presents the results of a linear ion trap simulation for various operating conditions and excitation methods. The second part considers the effects of dipole excitation (DE) on the performance of the quadrupole mass filter. The last part analyzes the formation of stability islands by different methods of quadrupole excitation. To date conditions of mass separation in quadrupole mass filters with sin wave supply were described for stability islands of the first and third stability regions formed by quadrupole and DE. By complicating the electronics such methods allow to overcome the destructive influence of electric field distortions and obtain a resolving power and ion transmission efficiency comparable with commercial devices. At quadrupole resonance excitation by a two-frequency signal, it is possible to reduce the length of electrodes three times without losses in resolution and transmission, which reduces the cost of rod set production with micrometer accuracy. Dipole resonance excitation allows controlling the shape of the mass peak by changing amplitude and phase of the auxiliary AC signal. The main factors affecting the resolving power of a linear ion trap are described theoretically. The numerical modeling results are confirmed by experiment.

Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation.

Colony-Forming Unit (CFU) counting is a complex problem without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by researchers. However, U-Net remains the most frequently cited and used deep learning method in these domains. The latter approach provides a segmentation output map and requires an additional counting procedure to calculate unique segmented regions and detect microbial colonies. However, due to pixel-based targets, it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, this paper proposes a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. Firstly, a unique innovation lies in the multi-loss U-Net reformulation. An additional loss term is introduced in the bottleneck U-Net layer, focusing on the delivery of an auxiliary signal that indicates where to look for distinct CFUs. Secondly, the novel localization algorithm automatically incorporates an agar plate and its bezel into the CFU counting techniques. Finally, the proposition is further enhanced by the integration of a fully automated solution, which comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application directly receives images from the camera, processes them, and sends the segmentation results to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the deep learning model. Through extensive experimentation, the authors of this paper have found that all probed multi-loss U-Net architectures incorporated into the proposed hybrid approach consistently outperformed their single-loss counterparts, as well as other comparable models such as self-normalized density maps and YOLOv6, by at least 1% to 3% in mean absolute and symmetric mean absolute percentage errors. Further significant improvements were also reported through the means of the novel localization algorithm. This reaffirms the effectiveness of the proposed hybrid solution in addressing contemporary challenges of precise in vitro CFU counting.

Vibration Control for a Three-Dimensional Variable Length Flexible String With Time-Varying Actuator Faults and Unknown Control Directions

In this study, actuator faults and unknown control directions are taken into account concurrently for a three-dimensional variable length flexible string system modeled by partial differential equations (PDEs). Different from the unknown fault parameters being restricted to just positive constants in the previous fault-tolerance research on PDEs systems, the time-varying actuator faults are considered, which is more practical but challenging. Additionally, the control directions of the system are unknown which can also be different in three directions. To overcome the difficulties, adaptive control laws employing the Nussbaum function are designed, and some auxiliary control signals are intended for service. Under the proposed boundary control scheme, uniform ultimate boundedness of the closed-loop system is guaranteed, and the deflections of the flexible string can be suppressed to a small neighborhood of zero. Finally, the simulations are implemented to indicate the control effect. <i>Note to Practitioners</i>&#x2014;String structural systems are widely used in fields of indoor and outdoor industrial and mining enterprises, ports, wharves, and seabeds. To improve work efficiency and safety, effectively suppressing the vibration of the flexible string is a crucial problem to be solved. This paper proposes a novel adaptive boundary control scheme to reduce the deflections of the flexible string system subject to time-varying actuator faults and unknown control directions in three-dimensional space. The control scheme is designed without simplification, which can provide a theoretical basis and a feasible solution for the control of flexible string systems in practical engineering. In the future, we will address the modeling and control problems of moving three-dimensional variable length flexible string systems.

Generating radiology reports via auxiliary signal guidance and a memory-driven network

Automatically generating medical image reports is a gratifying task. For doctors, it can reduce the heavy burden of writing reports, and for patients, it can reduce the waiting time for reports; it can also avoid misdiagnosis and missed diagnoses caused by human factors. However, this task still faces enormous challenges due to the problem of visual and textual data bias and the complex relationships among the components of medical reports. To this end, in this work, we propose an auxiliary signal guidance and memory-driven (ASGMD) network that can be used to generate medical reports automatically. It includes three modules: an Auxiliary Signal Guidance Module (ASG), a text sequential attention mechanism (TSAM) module, and a Memory Mechanism-Driven Decoding Module (MMDD). Given a medical image of a patient, radiologists usually focus on the abnormal area first, then browse the global information included in the image and write a corresponding report. Similar to the above working mode, the ASG module enhances the features of the abnormal areas of medical images by introducing auxiliary signals that alleviate the problem of visual data bias. We design a novel TSAM module that explores the consistency of medical report context and enhances essential medical information in reports to reduce textual data bias. Finally, the MMDD module integrates visual and textual knowledge to achieve dynamic decoding and generate a final report. The experimental results show that the proposed method outperforms state-of-the-art models on various evaluation metrics on the two public datasets, IU-Xray and MIMIC-CXR. To make our results reproducible, our code has been released at https://github.com/shangchengLu/ASGMDN.

Two-step detection method for actuator and sensor failures based on self-repairing PID control

Our previous works have proposed a self-repairing PID (SRPID) control for plants with sensor failures. This method uses an I-controller (integrator) with a well-designed auxiliary signal to detect failures. However, the SRPID has not addressed actuator failures. This paper presents a two-step detection method for actuator and sensor failures based on the SRPID and confirms the effectiveness of the proposed detection method through experiments on a ball-and-beam system.

Multiplexed and amplified electrochemical aptasensor for sensitive assay of piperaquine and mefloquine

Simultaneous detection and quantification of antimalarial drugs is important for evaluating the spread of their resistance. On the base of the target-induced assembly of branched DNA nanostructure signal amplification and different electrochemical coding tags at distinct potentials, an aptasensing method for multiplexed and highly sensitive detection of piperaquine (PQ) and mefloquine (MQ) antimalarial drugs is established. The target drugs associate with and switch the conformation of the aptamers to release free ssDNA trigger sequences, which initiate the self-assembly of many methylene blue (MB)- and ferrocene (Fc)-labeled auxiliary signal sequences to form branched nano-assemblies on electrode. The electrode-captured MB and Fc tags thus yield highly magnified current reponses at well-resolved potentials for simultaneously monitoring PQ and MQ with respective detection limits as low as 0.061 ng mL−1 (i.e., 0.114 nM) and 0.17 ng mL−1 (i.e., 0.449 nM). Moreover, the simultaneous detection of PQ and MQ in diluted human serum samples has also been verified by the developed sensor, revealing its potential for multiplexed monitoring of different drugs in connection with the corresponding aptamers for various application purposes.

A novel ESO-based adaptive RISE control for asymptotic position tracking of electro-hydraulic actuator systems

This paper is focused on asymptotic tracking control of electro-hydraulic actuator (EHA) systems subject to matched and mismatched time-varying disturbances. To counteract the matched disturbance, a novel extended state observer (ESO) is proposed to achieve asymptotic convergence of the estimation error, by incorporating the strictly positive real (SPR) Lyapunov design method and a Nussbaum function. To further suppress the mismatched disturbance, an adaptive robust integral of the sign of the error (RISE) controller is formulated in the backstepping framework based on the proposed ESO. Asymptotic tracking performance is theoretically achieved via closed-loop system stability analysis. The efficacy of the proposed control scheme is verified through comparative experiments executed on an EHA test rig. In this study, a priori bounds of the disturbances and their higher-order derivatives are no longer needed, and only one auxiliary error signal is introduced. This approach loosens the restrictions on the disturbances and reduces the design conservativeness, thus making it promising in practice.

Bumpless Transfer Control for Switched Systems via a Dynamic Feedback and a Bump-Dependent Switching Law.

This article investigates the bumpless transfer (BT) control problem of switched systems with unmeasured states. Note that the control input signal of the switched system jumps when switching occurs, which may adversely affect the performance of switched systems. By introducing a modified state observer and an auxiliary continuous control signal, a novel description of the BT performance for the system is presented. Moreover, a compensation-based dynamic BT controller and a bump-dependent switching law are designed to solve the BT control problem. As a result, the developed control method ensures the stability of the closed-loop system while maintaining BT performance. The validation of the results can be provided by applying the proposed BT control method to an example of an aero-engine control system.