External Interference Research Articles

Trajectory tracking of binocular vision system for picking robot based on fast non-singular terminal sliding mode control

This paper proposes a non-singular fast terminal sliding mode control method for a binocular active vision platform of a picking robot with unknown dynamics. The method uses radial basis function (RBF) neural networks to achieve trajectory tracking accuracy and enhance robustness against external interference. A non-singular fast terminal sliding mode controller is designed for the system’s convergence within a limited time. An adaptive neural network approximates the unknown nonlinear function of the dynamic model. Stability and finite-time convergence of the closed-loop system are established using Lyapunov theory. Experimental verification on the binocular vision platform demonstrates position and speed errors converging to the desired trajectory within 2 and 1 second, respectively. Moreover, when subjected to external interference, the position and velocity errors converge within 0.1 seconds. Simulation experiments confirm the method’s effectiveness in improving convergence speed, trajectory tracking accuracy, and robustness against external interference, while reducing system chattering.

Flexible Piezoelectric Sensors Based on Ionic Liquid‐doped Poly(L‐Lactic Acid) for Human Coughing Recognition

AbstractCough is a common symptom of various respiratory diseases. However, recognizing a subject's cough using acoustic imaging can be challenging due to the complexity of coughing and the vulnerability of acoustic acquisition to external interference. This research aims to address these issues by utilizing a flexible piezoelectric ionic liquid‐doped poly(L‐lactic acid) (PLLA) sensor to gather the pressure signals from the human surface. 1‐butyl‐3‐methylimidazolium bis (trifluoromethylsulfonyl)imide ([BMIm]TFSI) ionic liquid (IL) can enhance the piezoelectric performance of PLLA thin film, the corresponding output voltage, and impact sensitivity are improved to three times and 105.1% for the PLLA/IL thin film with 1 wt% IL, 140 °C annealing 2 h and four times drawing ratio. The effect of IL on the crystallization behavior and piezoelectric properties of thin films during their preparation is investigated. The flexible PLLA/IL sensor is integrated into a wearable electronic system which collects and transmits piezoelectric signals from cough‐induced vibrations to the filter for denoising, then the acquired cough data are decomposed using the Empirical Mode Decomposition method and trained in a multi‐layer perception model to detect human coughing. It's evident that the flexible piezoelectric PLLA/IL sensors can be applied to human coughing recognition and show great potential applications in intelligent healthcare.

The application of virtual synchronous generator technology in inertial control of new energy vehicle power generation

Introduction: With the rapid development of human society and economy, the power generation technology of various new energy vehicles has begun to receive widespread attention.Methods: Due to the lack of inertia and frequency stability in the new energy vehicle power generation system, this paper proposes a power generation control method that combines linear active disturbance rejection control technology and virtual synchronous generator technology. This method first introduces the control strategy and inertial response of the virtual synchronous generator. Then, it uses linear active disturbance rejection control technology to improve the virtual synchronous generator technology to deal with the uncertainty and external interference in the system.Results: The results showed that when the virtual inertia coefficient was 0, and the new energy vehicles would hardly intervene in the regulation of the grid voltage. When the virtual inertia coefficient was 5, the decline rate of the DC bus voltage of new energy vehicles had slowed down. When the virtual inertia coefficient increased, the power output of new energy vehicles can be increased to the grid. When the load suddenly increased, and the corresponding DC bus voltage decreased more slowly. In the VSG output power comparison, under the research method, the frequency fluctuation only increased by 0.09 Hz and returned to the rated frequency of 50 Hz. Additionally, the dynamic process of the system output power was the shortest, lasting only 0.05 s.Discussion: The above results show that the research method has significant superiority and effectiveness in improving the inertial response and overall stability of the new energy vehicle power system.

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

An enzyme-free and ratiometric biosensor for single nucleotide polymorphism with portable electrochemical system

Portable, low-cost and efficient biosensing systems are valuable for single nucleotide polymorphisms (SNPs) analysis, particularly in resource-limited fields. Herein, a rapid, enzyme-free and ratiometric SNPs biosensor with portable electrochemical system enabling simultaneous recognition and signal amplification is proposed. To demonstrate its versatility and effectiveness, we have selected the human cancer-related KRAS gene and the soybean mosaic virus resistance Rsv3 gene as our analysis models. Our biosensor is constructed based on a combination of strand displacement reactions and hybridization chain reactions (HCR). This unique design enables to differentiate between mutant (MT) and wild (WD) sequences based on the differential release of HCR products on the electrode surfaces. Importantly, this enzyme-free SNPs biosensor concurrently achieves both SNPs recognition and signal amplification, significantly reducing the reaction time to just 40 minutes. Furthermore, we have developed a miniaturized biosensor system that incorporates screen printed gold electrodes (SPGE) and smartphones. This system is integrated with a ratiometric strategy with an limit of detection as low as 1.2 fM, enhancing accuracy and eliminating external interference. This simple SNPs biosensing system possesses high reliability, positioning it as a versatile biosensor for the determination of various SNPs mutations.

Predictor-based constrained fixed-time sliding mode control of multi-UAV formation flight

In this work, a predictor-based fixed-time sliding mode control is designed to tackle the problem of achieving precise trajectory tracking control of multiple unmanned aerial vehicle formation flight. The proposed approach simultaneously addresses various practical requirements, such as optimality, constraints, fixed-time convergence, external interferences, and computational burden. Initially, using the Taylor series concept, we derive the predictive models for the fixed-time sliding surface and reaching law. Subsequently, we formulate a constrained performance index by integrating these predictions with the control input. Ultimately, the optimal control input is computed through solving the constrained quadratic optimization problem. An outstanding feature of this study is the theoretical guarantee of closed-loop fixed-time system stability in the presence of external disturbances and constraints. Furthermore, the comparative analysis with sliding mode predictive control law and fixed-time sliding mode control methods demonstrates that the proposed approach achieves a straightforward control method with improved performance and acceptable accuracy. Finally, an experimental test is conducted to demonstrate the algorithm's potential for hardware implementation by employing the Hardware-in-the-Loop test.

Polyglycerol citrate: A novel coating and inoculation material for soybean seeds

The microbial inoculation of legumes such as soybeans is crucial for thriving plant growth due to symbiotic nitrogen (N) fixation and biological plant N fertilization. Soybean requires microbial pre-inoculation before sowing using the rhizobia strain Bradyrhizobium japonicum. Peat is typical for this purpose, although not sustainable since it is a finite resource. Here, we propose a straight-forwarding route to prepare and apply polyglycerol-citrate polymer (PGC), a biodegradable and fully renewable polymer, as a carrier for Bradyrhizobium japonicum inoculants for soybeans. This novel eco-friendly polymer combines the advantages of a polymeric, water-soluble structure based on biopolymers, which can protect the inoculant cells during the seed inoculation process, with protective properties of glycerol for bacterial cells and the contribution of citric acid for metabolic processes. A greenhouse study was conducted using soybean seeds coated with three different proportions of PGC with B. japonicum planted in a sand substrate free of external interference. Comparative results of N content and δ15N signature in soybean plant parts calculated from the natural abundance method associated with viability tests showed equal or superior symbiotic performance and nitrogen fixation rates to peat-based inoculants, considered the gold-standard carrier for inoculants. It ensured the shelf life of the inoculant formulations, offering convenience for farmers and environmental benefits through reduced fertilization.

Eye-Readable and Wearable Colorimetric Sensor Arrays for In Situ Monitoring of Volatile Organic Compounds.

Wearable sensors utilize changes in color as a response to physiological stimuli, making them easily recognizable by the naked eye. These colorimetric wearable sensors offer benefits such as easy readability, rapid responsiveness, cost-effectiveness, and straightforward manufacturing techniques. However, their applications in detecting volatile organic compounds (VOCs) in situ have been limited due to the low concentration of complex VOCs and complicated external interferences. Aiming to address these challenges, we introduced readable and wearable colorimetric sensing arrays with a microchannel structure and highly gas-sensitive materials for in situ detection of complex VOCs. The highly gas-sensitive materials were designed by loading gas-sensitive dyes into the porous metal-organic frameworks and further depositing the composites on the electrospun nanofiber membrane. The colorimetric sensor arrays were fabricated using various gas-sensitive composites, including eight dye/MOF composites that respond to various VOCs and two Pd2+/dye/MOF composites that respond to ethylene. This enables the specific recognition of multiple characteristic VOCs. A microfluidic channel made of polydimethylsiloxane (PDMS) was integrated with different colorimetric elements to create a wearable sensor array. It was attached to the surface of fruits to collect and monitor VOCs using the DenseNet classification method. As a proof of concept, we demonstrated the feasibility of the wearable sensing system in monitoring the ripening process of fruits by continuously measuring the VOC emissions from the skin of the fruit.

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.

Enhanced Domino Wireless Power Transfer for Transmission Line Monitoring: Overcoming External Metal Object Interference and Optimizing Coil Design

Enhanced Domino Wireless Power Transfer for Transmission Line Monitoring: Overcoming External Metal Object Interference and Optimizing Coil Design

Fixed-Time Trajectory Tracking Control of Fully Actuated Unmanned Surface Vessels with Error Constraints

This paper proposes a fixed-time prescribed performance control technique to address the challenge of precise trajectory tracking control for unmanned surface vessels (USVs) in the presence of external time-varying disturbances and input saturation. To begin with, a fixed-time disturbance observer is created to handle the time-varying external interference. The observer can accurately estimate and compensate for the disturbance in a fixed time, which effectively improves the robustness of the system. Furthermore, to guarantee both the transient and steady-state response of the system, we employed a specific control technology that ensures the trajectory tracking error remains within a preset bounded range. Then, combined with the fixed-time disturbance observer, the command filter, the prescribed performance control technology, and the fixed-time stability theory, a fixed-time trajectory tracking control law is designed to make the trajectory tracking error of the system converge in a fixed time. Finally, an experiment was designed to validate the suggested control scheme. The results show that under the same conditions, compared with the nonlinear controller and the finite-time controller, the absolute error tracking index of this paper is the lowest, which means that the presented control scheme has higher accuracy.

Optimizing natural gas pipeline risk assessment using hybrid fuzzy bayesian networks and expert elicitation for effective decision-making strategies

Natural gas pipelines are susceptible to external and internal risk factors, such as corrosion, environmental conditions, external interferences, construction and design faults, and equipment failures. Bayesian Networks (BN) is a promising risk assessment approach widely used to evaluate these risk factors. One of BN's inherent limitations is its inability to accurately capture statistical dependencies and causal relationships, which can be overcome by incorporating expert elicitation into BN. To account for uncertainty and vagueness in assessing pipeline failure risks, fuzzy set theory (FST) can be combined with BN, commonly known as Fuzzy Bayesian Networks (FBN). This study developed an FBN framework that uses linguistic variables to calculate fuzzy probability (FPr) through domain expert elicitation, and crisp probabilities (CPr) are computed using historical incident data from the Pipeline and Hazardous Materials Safety Administration (PHMSA). Based on the findings from the case study of the Midwest region of the USA, external factors, i.e., third-party interference, outside force, and other incidents, significantly impact pipeline performance and reliability. Diagnosis inference indicates that in the Midwest region of the USA, pipeline material and age are critical factors leading to corrosion failure by threatening pipeline integrity. The findings from this study suggested that a targeted risk mitigation strategy is paramount for minimizing the risks associated with pipeline networks.

Multiple ultrasonic partial discharge DOA estimation performance of KPCA Pseudo-Whitening mnc-FastICA

Empirical studies into partial discharge (PD) localization face the difficulty of signal contamination from external noise and interference. To extract relevant information from PD signals collected by acoustic sensor array, Kernel Principal Component Analysis (KPCA) pseudo-whitening with modified noncircular Fast Independent Component Analysis (mnc-FastICA) were used. By this approach, separated matrix and observed signals can be achieved. The reconstructed array manifold matrix created from the separated matrix generated by KPCA-mnc-FastICA may be used in the Direction of Arrival (DOA) estimation approach. An angle error correction matrix is designed to compensate for the sensor's phase inaccuracy. The simulation results show that angle identification accuracy is outstanding, with mistakes limited to a small margin of 2°. Furthermore, the results of trials show that this technique can efficiently isolate individual target signals even when they are polluted with significant noise and interference. The average errors in azimuth and pitch angle are less than 2° and 1.1°, respectively. These data support the effectiveness of the approach for effectively separating signals and estimating DOA of numerous signal sources.

Analysis of Bridge Dynamics and Response Characteristics Under The Influence of Axle Coupling Vibration

To ensure the safety and stability of high-speed rail lines and reduce external interference, it is essential to build a large number of elevated bridges. These elevated bridges account for a considerable proportion of the total length of high-speed rail lines. However, when high-speed rail lines pass through earthquake prone areas, the likelihood of earthquakes occurring when trains pass through bridges increases significantly. Therefore, it is necessary to study the dynamic response of bridge structures under earthquake action to ensure the safety of bridges during train operation and operation. The optimization scheme proposed in this article has undergone moderate impact tests, and the results show that the maximum lateral displacement of the bridge can reach 124 mm, while the maximum vertical acceleration is 5.16 m/s2, Exceeded the safety limit of 0.35 g. Through the analysis of train derailment coefficient, wheel load reduction rate, lateral sway force, lateral and vertical acceleration, and Spelling comfort index, we have come to the conclusion that bridges can ensure the safety of train operation in the absence of earthquakes and small earthquakes, and can also maintain certain stability under medium and large earthquakes. These research results have important guiding significance for the design and construction of high-speed rail lines. By optimizing the bridge structure and adopting relevant technical measures, the seismic disaster resistance of high-speed rail lines can be further improved, ensuring the safety and comfort of passengers during travel. At the same time, these research results also provide useful reference and inspiration for the construction and improvement of future high-speed rail lines.

Population Structures and Dynamics of Rhododendron Communities with Different Stages of Succession in Northwest Guizhou, China.

To explore the population structures and dynamics of Rhododendron shrub communities at different stages of succession in northwest Guizhou, China, this study examined the populations of Rhododendron annae and Rhododendron irroratum shrub with two different stages. A space-for-time substitution was employed to establish the diameter class/height structures, static life tables, and survival/mortality rate/disappearance rate curves of both Rhododendron populations with different orders of succession. Their structural and quantitative dynamics were analyzed, and their development trends were predicted. The results showed that, quantitatively, the populations of R. annae and R. irroratum in the two Rhododendron communities with different orders of succession were dominated by age classes one, two, and three as well as height classes i, ii, and iii. The number of Rhododendron plants at the three age classes and the three height classes accounted for 97.61-100% of the total. The quantitative dynamic indices of R. annae and R. irroratum were both greater than 0, with and without considering external interference. In terms of age class and height structures, both Rhododendron populations were expanding populations, presenting "inverted-J-shaped" and irregular pyramid patterns. There was a sufficient number of young individuals, but few or no old individuals. Both survival curves of the populations of R. annae and R. irroratum in the two Rhododendron communities with different orders of succession belonged to the Deevy-II type. In the late stage of succession, the mortality curves and disappearance curves of both Rhododendron populations in these communities presented a trend of increasing first and then decreasing with increasing age class. This result indicates that at each age class, R. annae and R. irroratum showed a trend of gradual increase after two, four, and six years. In brief, the populations of R. annae and R. irroratum have rich reserves of seedlings and saplings, but high mortality and disappearance rates. In this context, it is necessary to reduce human interference and implement targeted conservation measures to promote the natural renewal of Rhododendron populations.

Environmental Interference Suppression by Hybrid Segmentation Algorithm for Open-Area Electromagnetic Capability Testing

Compared with electromagnetic compatibility (EMC) testing in anechoic rooms, open-area EMC testing takes advantage of in situ and engine running status measurement but suffers from non-negligible external electromagnetic interference. This paper proposes a novel environmental interference suppression method (named the EMC environmental interference suppression algorithm (E2ISA)) that separates signals from backgrounds via image segmentation and recognizes the near–far site signal via a group of time-varying features based on the difference in the near-site EM radiative characteristic. We find that the proposed E2ISA method, which combines the deep learning segmentation network with the classical recognition methods, is able to suppress environmental interference signals accurately. The experiment results show that the accuracy of E2ISA reaches up to 95% in the face of VHF (Very High Frequency) EMC testing tasks.

Focal changes in alpha oscillations during short-term memorization of pain: a high-density electroencephalogram study with source localization.

Memories of painful events constitute the basis for assessing patients' pain. This study explores the brain oscillatory activity during short-term memorization of a nociceptive stimulus. High-density EEG activity (128 electrodes) was recorded in 13 healthy subjects during a match-to-sample sensory discrimination task, whereby participants compared the intensity of a thumb-located electric shock (S2) with a prior stimulus to the same location (S1) delivered 8-10s earlier. Stimuli were above or below the individual nociceptive threshold. EEG activity with intracortical source localization via LORETA source reconstruction was analysed during the inter-stimuli period and contrasted with a non-memory-related control task. The inter-stimulus memorization phase was characterized by a focal alpha-activity enhancement, significant during the nociceptive condition only, which progressed from bilateral occipital regions (cuneus and mid-occipital gyri) during the first encoding-memorization phase towards the right-superior and right mid-temporal gyri during the 2-4s immediately preceding S2. Initial alpha enhancement in occipital areas/cuneus is consistent with rapid non-specific inhibition of task-irrelevant visual processing during initial stimulus encoding. Its transfer to the right-temporal regions was concomitant to the temporary upholding of the stimulus perceptual representation, previous to receiving S2, and suggests an active and local blockade of external interferences while these regions actively maintain internal information. These results add to a growing field indicating that alpha oscillations, while indicating local inhibitory processes, can also indirectly reveal active stimulus handling, including maintenance in short-term memory buffers, by objectivizing the filtering out of irrelevant and potentially disrupting inputs in brain regions engaged in internally driven operations.

A tactical data link model based on CCSK-FMSK

In collaborative combat operations, the challenges brought by the communication adversities of tactical data links are becoming increasingly prominent. Ensuring the reliable connection of tactical data links has always been a key issue, especially considering the increasing impact of external interference on the signal at the receiving end of the system, which in turn affects the bit error rate (BER) of the tactical data link system. To address this challenge, this paper proposes a tactical data link anti-interference scheme based on cyclic code shift keying (CCSK) spread spectrum and minimum shift keying (MSK) modulation fusion frequency hopping technology, named CCSK-FMSK, and introduces turbo code and cyclic redundancy check (CRC) coding to construct a tactical data link model based on CCSK-FMSK. Specifically, CCSK-FMSK disperses interference power by expanding the signal spectrum, enhancing the anti-interference ability of tactical data links. CRC coding and Turbo coding achieve error detection and correction by introducing supervised symbols. In addition, this paper proposes a soft despreading and a soft demodulation algorithm corresponding to CCSK spread spectrum and MSK modulation to achieve cascade with Turbo decoder and improve the error rate performance and robustness of the model. Compared with existing models, the tactical data link based on CCSK-FMSK has better BER performance.

Dynamic stabilisation of magnetic fields measured inside a magnetically shielded room using an external coil system *

Magnetoencephalography (MEG) system based on optically pumped magnetometers (OPMs) requires a magnetically shielded room (MSR) to establish a stable near-zero field environment. Affected by external environmental electromagnetic interference, the magnetic noise in the MSR will become very severe. In order to overcome this problem, this paper proposes a method for dynamic stabilisation of magnetic fields measured inside a MSR using an external coil system. Firstly, the field form of the external compensation coil was analysed by taking the AC characteristics of the material into consideration. Then, the linear characteristic of the control system is studied and a high performance magnetic noise suppression controller is designed based on the environment noise characteristics. Finally, simulation and experimental are carried out through a self-developed 1250 mm × 1250 mm × 2100 mm MSR, which indicates that the proposed method can effectively suppress dynamic magnetic fluctuation and noise.

Analyses of unexplained faults in transmission lines in the power grid of Ethiopia

Ethiopian power transmission lines operating at 132 kV, 230 kV and 400 kV are encountering unacceptably high number of unexplained outages caused by external factors, which compromise the reliability of the system. It is therefore crucial to examine the characteristics and underlying causes of these outages through analyses of long-term records of the faults for identifying their probable causes for best targeting future mitigation measures.A method is presented to identify, in retrospect, the major possible root causes of unexplained outages that combines information on fault time-of-the day and time-of-the year records with flashover characteristic of known fault causes and local climactic and environmental information. The analyses are based on data covering an 8-year period from 2015 to 2022. A total of 8891 fault records were compiled and among them 4231 faults (47 %) were unexplained faults. Results show statistical variations in the frequency of unexplained faults with respect to spaciotemporal occurrence of the faults, voltage level and local climate.Moreover, the analyses indicate that out of the total number of 4231 unexplained faults, 787 faults could be attributed to lightning (19 %), 1135 faults to pollution events (27 %), 479 were fire related (11 %) and 1830 faults caused by external interferences (43 %).