Auxiliary Signal Research Articles

An Online Non-Invasive Condition Assessment Method of Outdoor High-Voltage SF6 Circuit Breaker

Online monitoring of outdoor high voltage SF6 circuit breakers (HVCBs) is essential to detecting potential damages. To this end, the study of accurate and non-invasive monitoring methods has been significantly investigated in recent decades. Considering that HVCB vibration patterns carry important information about mechanical and electrical integrity and that vibration analysis requires a low level of invasiveness, this article presents methods of obtaining mechanism and reaction times using interference signals of outdoor high voltage SF6 circuit breakers (HVCBs). Compared to traditional methods of monitoring outdoor SF6 circuit breakers that are based on encoders, the proposed method presented ease of installation, as it only requires the insertion of accelerometers. The method of obtaining the mechanism time is based on the use of interference and auxiliary contact signals, in the time domain, where the accelerometer is installed, in the structure of the HVCB, makes it possible to guarantee the moment of the trip command. To obtain the reaction time of each HVCB pole, the envelope technique was applied with the Hilbert transform module of the hearing signal, filtered in a certain resonance band. The proof of the technique of analyzing the vibration of the signal in time was developed with laboratory tests of an HVCB, instrumented with accelerometers and angular encoders. The results obtained via vibration analysis were compared with those obtained via angular encoders and it was concluded that with the acceleration signals, in time, it is possible to obtain performance parameters of an HVCB from its displacement curve. Finally, the online monitoring of the circuit breaker applied in the field is presented, where the acquisition of trip current signals, the condition of the SF6 gas and extinguishing current signals were added to the instrumentation.

Adaptive control of hypersonic vehicles using intelligent allocation

This paper proposes an intelligent allocation-based adaptive controller for the longitudinal motion of air-breathing hypersonic vehicles (AHVs). A control-allocation (CA) module is developed to deal with composite actuator servo constraints that have usually been neglected in existing AHV control works. This CA process is realized with the help of the recent deep deterministic policy gradient algorithm without involving any online optimization. In the followed adaptive controller design, several auxiliary signals are constructed to compensate for the possible CA error, while adaptive super-twist differentiators are employed to fast estimate the lumped effect of uncertain aerodynamic coefficients and unknown external disturbances. As a result, the adaptive control algorithm only needs three parameter updating laws, whose dimension is much lower than traditional adaptive control strategies for AHVs. Simulations are provided to verify the proposed intelligent allocation-based adaptive controller.

Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering

Abstract Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint training of the retriever and generator components of RAG for the task of domain adaptation in ODQA. We propose RAG-end2end, an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. In addition, we introduce an auxiliary training signal to inject more domain-specific knowledge. This auxiliary signal forces RAG-end2end to reconstruct a given sentence by accessing the relevant information from the external knowledge base. Our novel contribution is that, unlike RAG, RAG-end2end does joint training of the retriever and generator for the end QA task and domain adaptation. We evaluate our approach with datasets from three domains: COVID-19, News, and Conversations, and achieve significant performance improvements compared to the original RAG model. Our work has been open-sourced through the HuggingFace Transformers library, attesting to our work’s credibility and technical consistency.

Laser frequency scanning interference nonlinear correction method based on Lomb-Scargle algorithm

Laser frequency scanning interference technology has become a research hotspot due to its high precision and strong anti-interference capability and other advantages. The nonlinear problem of laser frequency modulation has always been a key factor affecting the accuracy of the measurement system. The most direct result of the nonlinearity of frequency modulation is that the spectrum of the beat signal is severely broadened, resulting in a decrease in the ranging accuracy. In order to solve this problem, this paper proposes a nonlinear correction method based on the Lomb-Scargle algorithm, and builds a laser frequency sweep interferometry system with an auxiliary interferometer. The phase is extracted by performing Hilbert transform on the auxiliary path beat signal, thereby generating a new time series based on the extracted phase information. The generated time series carries the phase change information of the auxiliary path beat signal, and it is combined with the Lomb-Scargle algorithm to perform the nonlinear correction of the measurement system and the frequency calculation of the beat signal simultaneously. As a verification, the targets in the range of 0.5–1.3 m are measured with a maximum error of 14 μm. The traditional frequency sampling method is limited by the Nyquist sampling theorem, and the laser emission and reception need to travel a round-trip distance, which means that the frequency sampling method must meet the requirement that the distance of the measured target cannot exceed a quarter of the optical path difference of the auxiliary interferometer. Therefore, the range of distance measurement is limited when the optical path difference of the auxiliary interferometer is constant. Different from the correction principle of the traditional frequency sampling method, the correction method proposed in this paper does not use the beat signal of the auxiliary path to resample the measurement path, so there is no need to satisfy the condition that the optical path difference of the auxiliary interferometer is greater than four times the measuring distance. Therefore, in the case of a certain optical path difference of the auxiliary interferometer, it can provide a way to increase the ranging range of the system.

Active Fault Detection Based on Auxiliary Input Signal Design

Active fault detection (AFD) is considered an effective incipient failure-detection method. In this paper, an auxiliary input resonance signal proposal that can detect different incipient faults quickly while keeping the system stable is proposed for AFD. To maintain the stability of the system, auxiliary input signals with a sinusoidal form, which should minimize and maximize the effect in fault-free cases and fault cases, are designed on the YJBK parameterization architecture. Subsequently, the fault detection signal is transferred from the traditional system output signal to the cumulative sum of the designed residual signal. Finally, some illustrative examples are given to indicate the effectiveness of the proposed unified operation architecture and some encourage results have been obtained, the simulation results show that the designed auxiliary input signal can not only ensure the normal operation of the system, but also quickly and effectively detect the occurrence of minor faults.

Active Fault-Tolerant Control Based on MPC and Reinforcement Learning for Quadcopter with Actuator Faults

In this paper, we propose an active fault-tolerant control (AFTC) method combining model predictive control (MPC) and reinforcement learning (RL) for the quadcopter with actuator faults. We take a data-based discriminant model as the fault detection and diagnosis (FDD) module indicating a system fault mode based on the state error. With the information of the fault mode and the state error, the RL controller generates auxiliary control signals to correct the system. To configure the MPC controller quickly, we propose an auxiliary signal-based method for estimation of fault parameters and prove its convergence. The AFTC framework reduces requirements for accurate modeling, and avoids the instability of the RL controller under a continuous operation. To validate the effectiveness of the proposed framework, two trajectory tracking simulations with single and multiple faults are carried out. The simulation results show satisfactory performance and verify that the proposed framework is real-time applicable.

Initial-Rectification Neuro-Adaptive Iterative Learning Control for Robot Manipulators With Input Deadzone and Nonzero Initial Errors

An initial-rectification adaptive iterative learning control scheme is proposed to solve the angle tracking problem of robot manipulators with input deadzone under nonzero initial errors. Lyapunov approach is utilized to design the controller. First, the initial-rectification auxiliary reference signal is constructed to overcome the obstacle caused by nonzero initial errors during ILC design. Second, adaptive ILC strategy and robust control strategy are adopted for dealing with deadzone nonlinearity. In addition, adaptive learning neural network is applied to approximate for uncertainties. The stability of closed-loop robotic system is rigorously proven by theoretical analysis. In the end, numerical simulation results are provided to verify the effectiveness of the proposed adaptive iterative learning control scheme.

Simultaneous Active and Passive Information Transfer for RIS-Aided MIMO Systems: Iterative Decoding and Evolution Analysis

This paper investigates the potential of reconfigurable intelligent surface (RIS) for passive information transfer in a RIS-aided multiple-input multiple-output (MIMO) system. We propose a novel simultaneous active and passive information transfer (SAPIT) scheme. In SAPIT, the transmitter (Tx) and the RIS deliver information simultaneously, where the RIS information is carried through the RIS phase shifts embedded in reflected signals. We introduce the coded modulation technique at the Tx and the RIS. The main challenge of the SAPIT scheme is to simultaneously detect the Tx signals and the RIS phase coefficients at the receiver. To address this challenge, we introduce appropriate auxiliary variables to convert the original signal model into two linear models with respect to the Tx signals and one entry-by-entry bilinear model with respect to the RIS phase coefficients. With this auxiliary signal model, we develop a message-passing-based receiver algorithm. Furthermore, we analyze the fundamental performance limit of the proposed SAPIT-MIMO transceiver. Notably, we establish the state evolution to predict the receiver performance in a large-size system. We further analyze the achievable rates of the Tx and the RIS, which provides insight into the code design for sum-rate maximization. Numerical results validate our analysis and show that the SAPIT scheme outperforms the passive beamforming counterpart in achievable sum rate of the Tx and the RIS.

Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation.

The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on "micro-motion" such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive "macro-motion". Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive "macro-motion", exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error (MAE) of 1.22|0.57 BPM, Mean Absolute Error Percentage (MAEP) of 0.95|0.38%, and performance index (PI) (which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run.

Disentangled Representations for Short-Term and Long-Term Person Re-Identification.

We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class variations, as different persons could have the same attribute, and persons' appearances look different, e.g., with viewpoint changes. Recent reID methods focus on learning person features discriminative only for a particular factor of variations (e.g., human pose), which also requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to factorize person images into identity-related and -unrelated features. Identity-related features contain information useful for specifying a particular person (e.g., clothing), while identity-unrelated ones hold other factors (e.g., human pose). To this end, we propose a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN). It disentangles identity-related and -unrelated features from person images through an identity-shuffling technique that exploits identification labels alone without any auxiliary supervisory signals. We restrict the distribution of identity-unrelated features, or encourage the identity-related and -unrelated features to be uncorrelated, facilitating the disentanglement process. Experimental results validate the effectiveness of IS-GAN, showing state-of-the-art performance on standard reID benchmarks, including Market-1501, CUHK03 and DukeMTMC-reID. We further demonstrate the advantages of disentangling person representations on a long-term reID task, setting a new state of the art on a Celeb-reID dataset. Our code and models are available online: https://cvlab-yonsei.github.io/projects/ISGAN/.

Deep Unfolding Network for PAPR Reduction in Multicarrier OFDM Systems

In this letter, we propose a Deep Unfolding Network called PR-DUN to reduce the peak-to-average power ratio (PAPR), which is a thorny problem in Orthogonal Frequency-Division Multiplexing (OFDM) systems. The proposed multi-layer model is constructed by unrolling an iterative algorithm resulting in layers with trainable parameters, which are optimized to minimize a loss function related to the PAPR value. The deep unfolding model uses the backpropagation algorithm to transfer gradients backwards to adjust parameters. Furthermore, the proposed scheme can accommodate any transmit power constraint, and therefore can control the power increase caused by the auxiliary signal. Simulation results show that the proposed PR-DUN model achieves a larger PAPR reduction and a smaller bit error rate while being less computationally intensive than related solutions.

28-GHz Dual-Mode Phase-Invariant Variable-Gain Amplifier With Phase Compensators

A 28 GHz dual-mode phase-invariant wide-dynamic-range variable-gain amplifier (VGA) with adaptive phase compensators is presented for use as a gain control element in a beamforming IC, which must have a small chip size and low power consumption. It consists of a single-stage differential amplifier with main and auxiliary signal paths, where the auxiliary paths are introduced to achieve most-significant-bit (MSB) gain control. The wide-gain dynamic range is also covered through a least-significant-bit (LSB) gain-control scheme, which is implemented with bias control of common gate (CG) transistors in the main signal paths. In addition, the phase variations due to the gain controls are drastically reduced by introducing phase compensators for both gain controls. The proposed VGA shows a gain dynamic range of 24 dB, maximum gain of 13 dB and root-mean-square (RMS) phase error of 2.77 degrees at 28 GHz. The maximum DC power consumption is 24 mW at the maximum gain, the minimum is 4 mW at the minimum gain, and the chip size is 0.066 mm2.

Memristor Degradation Analysis Using Auxiliary Volt-Ampere Characteristics.

The memristor is one of the modern microelectronics key devices. Due to the nanometer scale and complex processes physic, the development of memristor state study approaches faces limitations of classical methods to observe the processes. We propose a new approach to investigate the degradation of six Ni/Si3N4/p+Si-based memristors up to their failure. The basis of the proposed idea is the joint analysis of resistance change curves with the volt-ampere characteristics registered by the auxiliary signal. The paper considers the existence of stable switching regions of the high-resistance state and their interpretation as stable states in which the device evolves. The stable regions’ volt-ampere characteristics were simulated using a compact mobility modification model and a first-presented target function to solve the optimization problem.

Rotational acoustofluidic fields induced by cross structures with asymmetric radiation surface arrangements

In this study, a novel strategy to generate sophisticated acoustic streaming vortices, which would be available for rotational manipulation of micro-/nano-scale objects, is proposed and simulated. All structural units in the microfluidic chamber are symmetric in design, and all radiation surfaces have the same settings of input frequency, oscillation amplitude, and initial phase. Different kinds of asymmetric acoustofluidic patterns can be generated in the originally static microfluidic chamber only because of the asymmetric arrangement of multiple radiation surfaces in space. The calculation results of kaleidoscopic acoustofluidic fields together with particle movement trajectories induced by cross structures with different radiation surface distributions further demonstrate the versatile particle manipulation capabilities of these functional microfluidic devices. In comparison to the existing oscillation modulation method, which requires multiple radiation surfaces with different initial phases, acoustofluidic devices with a same initial phase of all radiation surfaces can significantly reduce the required number of auxiliary signal generators and power amplifiers. The proposed generation method of acoustofluidic patterns is promising for microfluidic mixing without rotating machinery, driving operation of microrobots, and rotational manipulation of biological samples.

Decentralized Adaptive Fault-Tolerant Cooperative Control for Multiple UAVs with Input Saturation and State Constraints

This paper proposes a fault-tolerant cooperative control (FTCC) scheme for multiple UAVs in a distributed communication network against input saturation, full-state constraints, actuator faults, and unknown dynamics. Firstly, by considering physical limitations, an auxiliary control signal is designed to simplify the analysis process. Secondly, to avoid the difficulty in the back-stepping design caused by full-state constraints, virtual control signals are constructed to transform constrained variables, while the dynamic surface control is adopted to avoid the phenomenon of “differential explosion.” Thirdly, a disturbance observer (DO) is designed to estimate the unknown uncertainty caused by parameter uncertainty and actuator fault. Moreover, a recurrent wavelet fuzzy neural network (RWFNN) is used to compensate for the estimation errors of DO. Finally, it is proved that all states are uniformly ultimately bounded (UUB) by Lyapunov and invariant set theory. The effectiveness of the proposed scheme is further demonstrated by the simulation results.

EEG-Video Emotion-Based Summarization: Learning With EEG Auxiliary Signals

Video summarization is the process of selecting a subset of informative keyframes to expedite storytelling with limited loss of information. In this article, we propose an EEG-Video Emotion-based Summarization (EVES) model based on a multimodal deep reinforcement learning (DRL) architecture that leverages neural signals to learn visual interestingness to produce quantitatively and qualitatively better video summaries. As such, EVES does not learn from the expensive human annotations but the multimodal signals. Furthermore, to ensure the temporal alignment and minimize the modality gap between the visual and EEG modalities, we introduce a Time Synchronization Module (TSM) that uses an attention mechanism to transform the EEG representations onto the visual representation space. We evaluate the performance of EVES on the TVSum and SumMe datasets. Based on the rank order statistics benchmarks, the experimental results show that EVES outperforms the unsupervised models and narrows the performance gap with supervised models. Furthermore, the human evaluation scores show that EVES receives a higher rating than the state-of-the-art DRL model DR-DSN by 11.4% on the coherency of the content and 7.4% on the emotion-evoking content. Thus, our work demonstrates the potential of EVES in selecting interesting content that is both coherent and emotion-evoking.

Predictor-Based Fuzzy Adaptive Containment Control for Nonlinear Multiagent Systems With Actuator Nonlinearity and Unmeasurable States

This article investigates the containment control problem of nonlinear multiagent systems subject to actuator nonlinearity, unmeasured states, and unknown external disturbances. The predictor technique is employed for each subsystem, and the prediction error is utilized to update the adaptive law. The problem of actuator nonlinearity with dead zone and saturation input is solved by introducing an auxiliary control signal. Incorporating the state observer and disturbance ones, an extended state observer is proposed to compensate the effect of unmeasurable and unknown disturbances, simultaneously. Based on Lyapunov theory and graph theory, it is shown that the devised control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Moreover, the followers can enter the dynamic convex hull spanned by the dynamic leaders. A simulation example with comparison analysis is provided to illustrate the effectiveness of the theoretical results.

Auxiliary signal-guided knowledge encoder-decoder for medical report generation

Medical reports have significant clinical value to radiologists and specialists, especially during a pandemic like COVID. However, beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure. Secondly, there are many similar sentences used in each medical report to describe the normal regions of the image, which causes serious data bias. This deviation is likely to teach models to generate these inessential sentences on a regular basis. To address these problems, we propose an Auxiliary Signal-Guided Knowledge Encoder-Decoder (ASGK) to mimic radiologists’ working patterns. Specifically, the auxiliary patches are explored to expand the widely used visual patch features before fed to the Transformer encoder, while the external linguistic signals help the decoder better master prior knowledge during the pre-training process. Our approach performs well on common benchmarks, including CX-CHR, IU X-Ray, and COVID-19 CT Report dataset (COV-CTR), demonstrating combining auxiliary signals with transformer architecture can bring a significant improvement in terms of medical report generation. The experimental results confirm that auxiliary signals driven Transformer-based models are with solid capabilities to outperform previous approaches on both medical terminology classification and paragraph generation metrics.

Improved H[formula omitted] repetitive controller for current harmonics suppression of PMSM control system

The inverter owns nonlinearity characteristic, leading to high amount current harmonics in the Permanent Magnet Synchronous Motor (PMSM) control system. Hence, the operation performance of the PMSM control system will be affected. To suppress the current harmonics, the repetitive controller in the current loop is optimized through H∞ control theory, and an improved H∞ repetitive controller is proposed in this paper. Firstly, the state equation of the PMSM in dq reference frames is studied. To obtain the feedback compensator, estimated signals and auxiliary input signal are introduced into the control system. And then the transfer function matrix of the generalized controlled plant is established. Finally, the operation performances of the PMSM control system are carried out through Matlab/Simulink software, and the simulation results can verify the effectiveness and the superiority of the improved H∞ repetitive controller.

Event-triggered adaptive neural command-filter-based dynamic surface control for state constrained nonlinear systems

In this article, an event-triggered adaptive neural command-filter-based dynamic surface control (CFDSC) is discussed for states constrained nonstrict-feedback nonlinear systems with dynamic uncertainties. The order-reduced compensation signals are designed to compensate the tracking error of the filter in original dynamic surface control (DSC). The hyperbolic tangent function is adopted as the invertible mapping to deal with the full state constraints. An auxiliary signal is employed to estimate the dynamical uncertainties produced by the unmodeled dynamics. The unknown smooth nonlinear terms are approximated by radial basis function neural networks (RBFNNs) at recursive each step. An event-triggered input is constructed in this novel CFDSC framework. With the help of the defined compact set in the stability analysis, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the adaptive closed-loop system is proved. Moreover, each state can be strictly limited within the time-varying state conditions. Two simulation verifications are employed to verify and clarify the theoretical findings.