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Stomata are microscopic pores on the plant epidermis that serve as a major passage for the gas and water exchange between a plant and the atmosphere. The formation of stomata requires a series of cell division and cell-fate transitions and some key regulators including transcription factors and peptides. Monocots have different stomatal patterning and a specific subsidiary cell formation process compared with dicots. Cell-to-cell symplastic trafficking mediated by plasmodesmata (PD) allows molecules including proteins, RNAs and hormones to function in neighboring cells by moving through the channels. During stomatal developmental process, the intercellular communication between stomata complex and adjacent epidermal cells are finely controlled at different stages. Thus, the stomata cells are isolated or connected with others to facilitate their formation or movement. In the review, we summarize the main regulation mechanism underlying stomata development in both dicots and monocots and especially the specific regulation of subsidiary cell formation in monocots. We aim to highlight the important role of symplastic connection modulation during stomata development, including the status of PD presence at different cell–cell interfaces and the function of relevant mobile factors in both dicots and monocots. [ABSTRACT FROM AUTHOR]

In flight control actuation systems, electromechanical actuators (EMAs) as energy-efficient components play critical roles in next-generation aircraft. Reliable degradation estimation models based on sensing data from sensors installed in EMA can help to improve its availability and safety, which need to be studied. However, the dynamic operating conditions, which will lead to the mismatch issues of the sensing data-based degradation estimation models, reduce the overall accuracy of the degradation estimation. To address this issue, an improved adaptive Wiener process model for EMA degradation estimation based on sensing data with dynamic operating conditions is proposed. First, a health indicator (HI) is extracted from sensing data through physical analysis or extracted using data processing techniques. Then, a novel model for EMA degradation estimation with the improved strong tracking filter and the Wiener process model (i.e., ISTF-WPM) is formulated based on the obtained HI. By introducing a saturation function to the improved strong tracking filter (STF), the performance of sensing data-based degradation estimation with dynamic operating conditions can be improved. The proposed model presents a novel routine for EMA degradation estimation based on sensing data with dynamic operating conditions. In the experiments of this study, the effectiveness of the ISTF-WPM is validated using simulation data as well as practical data collected by sensors of EMA. Experimental results indicate that EMA sensing data-based degradation estimation with the proposed model has a high overall accuracy. [ABSTRACT FROM AUTHOR]

Compared with polymers and nanoparticles, fatty alcohols can not only increase the stability of foam, but also maintain better foamability at pH < 2, which is beneficial to reduce waste liquid and increase decontamination efficiency for radioactive surface pollution. However, different fatty alcohols have different hydrophobic chain lengths. The effects of fatty alcohols with different chain lengths on the performance of decontamination foam were studied at pH < 2, to assist in the selection of suitable fatty alcohols as foam stabilizers. Combined with betaine surfactant and phytic acid, biomass-based foams were synthesized using fatty alcohols with different chain lengths. When the hydrophobic tail groups of the fatty alcohol and the surfactant were the same, the foam showed the best performance, including the lowest surface tension, the highest liquid film strength, the greatest sag-resistance and the best stability. However, when the hydrophobic tail groups were different, the space between adjacent surface active molecules was increased by thermal motion of the excess terminal tail segments (a tail-wagging effect), and the adsorption density reduced on the gas-liquid interface, leading to increased surface tension and decreased liquid film strength, sag-resistance and stability. The use of decontamination foam stabilized by fatty alcohols with the same hydrophobic group as the surfactant was found to increase the decontamination rate of radioactive uranium pollution from 64 to over 90% on a vertical surface. [ABSTRACT FROM AUTHOR]

With the integration of Artificial Intelligence (AI) and robots, unmanned and autonomous systems (UAS) have been widely used both in civil and military fields. However, the complexity of UAS increases with the improvement of its autonomous ability, which puts forward higher requirements for online condition monitoring (OCM) to timely confirm the safety and availability of UAS. To achieve UAS OCM, an embedded OCM instrument combing perception, intelligent algorithms, and edge computing should be developed, where perception is responsible for collecting UAS operation data through sensors, the intelligent algorithm uses the operation data to assess UAS status, and the edge computing is utilized to support the online operation of an intelligent algorithm. Since intelligent algorithms such as deep learning usually have high time complexity, it brings great challenges to assuring the timeliness of the embedded OCM instrument, especially under the limitations of size, weight, and power (SWaP). Therefore, in this paper, first, the UAS OCM is introduced. Then, the design guidelines for the embedded OCM instrument are given. Finally, the embedded OCM instrument of the unmanned aerial vehicle (UAV) is presented in detail as a case study. [ABSTRACT FROM AUTHOR]

The harmonic reducer is a key component of the industrial robot. Its reliability has significant influence on the consecutive operation of the industrial robot. However, its failure rate is high due to the complicated internal structure and long-term working conditions of large load and high torque. To identify the fault type of the harmonic reducer under different working conditions, this article proposes the joint wavelet regional correlation threshold denoising (WRCTD) algorithm and convolutional neural network-long short term memory (CNN-LSTM) fault detection method. Firstly, the WRCTD algorithm utilizes the regional correlation of the wavelet decomposition coefficients and the $3\sigma $ criterion to suppress noise in the raw sensor data. Then, the CNN-LSTM model could mine hidden features of the processed sensor data to identify fault correctly. To evaluate the proposed method, the vibration signals of the harmonic reducer under different working conditions are collected by establishing a test rig. Comparative experimental results show that the proposed WRCTD algorithm can significantly improve the accuracy of the fault detection method. The specific values for the CNN-LSTM method and the LSTM method are 9.5% and 12.2%, respectively. In addition, the proposed CNN-LSTM method has better performance than traditional methods. It achieves the fault detection over 94.0%, which is 1.5%, 2.0%, 2.0%, 1.9%, and, 1.9% higher than the highest accuracy values of CNN, LSTM, and SVM methods in five experiments. [ABSTRACT FROM AUTHOR]

Lithium-ion battery packs are critical to ensure the operational reliability and service life of spacecraft. The battery pack health diagnosis is meaningful for reasonable mission plan and proper control actions of spacecraft. However, the characters of space application scenarios include periodic charge and discharge, nearly constant discharge current, low electric current rate, and fixed depth of discharge, which bring challenges to battery pack state-of-health estimation. Considering these characters, this article proposes a health diagnosis approach for lithium-ion battery pack based on an improved equivalent circuit model. First, an empirical model is introduced into the standard first-order equivalent circuit model to improve the accuracy of discharge process modeling under constant current. Furthermore, a parameter lookup table is developed to simulate the complete discharge based on the in-orbit partial discharge. Finally, the battery pack complete discharge process is virtually conducted to realize battery pack capacity estimation. The proposed method just satisfies the special operating conditions in space applications. The proposed method is validated based on a real tested lithium-ion battery pack. And the results indicate that the proposed method can realize the high-accurate health diagnosis for both battery pack and its inner cells under space operating conditions, which illustrate the adaptability and applicability of the proposed method in space applications. [ABSTRACT FROM AUTHOR]

For the accelerated degradation test (ADT) of lithium-ion battery, the degradation path models are usually applied with the fixed parameters. To optimize the degradation path models, so many battery cells are required for the ADT models based on the degradation path. However, for some lithium-ion batteries with long cycle life and high reliability, the samples are limited due to experimental cost constraints. Meanwhile, owing to the differences in raw materials and manufacturing processes, it is easy to observe variations among the degradation path of lithium-ion battery cells. Fortunately, Wiener process model considering the individual difference can express the variations and be modeled with limited samples for lithium-ion battery. Motivated by this issue, an improved method with random effects Wiener process (REWP) is proposed in this article. First, to verify the degradation process following the Wiener process, the Shapiro–Wilk test is carried out on the basis of a significance level of 0.05. Second, we analyze the varieties of every lithium-ion battery cell. To select a suitable Wiener process model, we apply the coefficient of variation as a measure of random parameters selection for Wiener process. Meanwhile, the maximum likelihood estimation (MLE) is used to optimize parameters of the proposed model. Next, the three different REWP models and general Wiener process model are compared. Finally, to demonstrate the proposed method, the lithium-ion battery cells used for a spacecraft in low Earth orbit (LEO) are conducted for ADTs. The results suggest that the REWP model has a better performance for assessing reliability of lithium-ion battery with long cycle life. [ABSTRACT FROM AUTHOR]

With the wide applications of the unmanned aerial vehicle (UAV) in the civilian and military fields, its operational safety has drawn much attention. A series of fault detection methods are studied to avoid disasters. Due to the capabilities of strong feature extraction and massive flight data processing, the deep learning-based methods have received extensive attention. However, restricted by UAV airborne size, weight, and power consumption, a significant challenge is posed to deploy these complicated detection methods in the airborne application, which requires to run in real time. In this article, a fault detection model acceleration engine (FDMAE) for UAV real-time fault detection is realized under the airborne constraint. First, a high-performance detection model is designed based on stacked long short-term memory networks, and fault detection is achieved by a statistical threshold in this method. Second, a model pruning method based on principal component analysis is proposed to improve computing efficiency. Finally, the pruned fault detection method is optimized and integrated as a flexible acceleration engine through high-level synthesis and deployed on an airborne embedded computing platform based on a field-programmable gate array. Real UAV flight data are used to verify the proposed FDMAE. By comparing accuracy, the area under the receiver operating characteristic curve, speed, and power consumption, the effectiveness of the FDMAE is proven. [ABSTRACT FROM AUTHOR]

With the widespread application of Unmanned Aerial Vehicle (UAV), more and more attention has been paid to the analysis of flight data, which can realize the condition monitoring and help improve operational safety. The UAV flight phase is dynamically switched, and flight phase identification is the vital basis for accurate flight data analysis. However, UAV flight data do not always contain a parameter that can be used for the direct division of flight phases, and it is difficult to give uniform thresholds for the flight phase identification. To realize the automatic identification of UAV flight phases based on airborne sensor data, an unsupervised Gaussian Mixture Model Recursive Clustering (GMMRC) method is proposed. GMMRC divides a complex flight phase identification task into simple subtasks which are corresponding to UAV operations. It can not only improve the performance of identification, but also enhance the interpretability of the results. In the recursive method, GMM is used as an unsupervised identification operator to obtain the desired identification results in each subtask. To further improve the identification effect, an interval detection method is used to eliminate false results. Experiments based on simulated flight data and real small fixed-wing UAV flight data demonstrate the potential of this method for accurate and robust flight phase identification. [ABSTRACT FROM AUTHOR]

The aircraft auxiliary power unit (APU) is mainly used to provide electricity and compressed air to the aircraft. Not only can it help to start main engines, but also it can provide essential thrust for the emergency landing. It is required to be reliable as high as possible when it is installed in the aircraft. However, the reality is that the degradation of APU is a nonlinear process. In this term, only a data-driven method or a physics-based method can hardly make accurate prediction for the remaining useful life (RUL) of the aircraft APU. Therefore, a hybrid RUL prediction method is proposed by fusing an artificial intelligence-based model and a physics-based model in this article. To be specific, Wiener process is used to formulate a physical model for generating degradation data. Then, the generated data and the corrected data are both utilized as the input of long short-term memory (LSTM) to enhance RUL prediction of the aircraft APU. The contributions of this study include: 1) A hybrid method for RUL prediction of the aircraft APU is proposed. 2) The influences of ambient environment and training data dependency of LSTM are solved. 3) The proposed method provides a novel RUL prediction method for the on-wing aircraft APU, which is evaluated by the real data. [ABSTRACT FROM AUTHOR]

With the wide applications of the unmanned aerial vehicle (UAV), operating safety becomes a critical issue. Thus, fault detection (FD) has been focused, which can realize fault alarm and schedule maintenance in time. Since the accurate physical model of UAV is usually difficult to obtain and flight data with random noise has both spatial and temporal correlation, a huge challenge is posed to FD. In this article, a data-driven multivariate regression approach based on long short-term memory with residual filtering (LSTM-RF) is proposed to fulfill UAV flight data FD and recovery. First, an LSTM network is designed as a regression model, which can extract the spatial–temporal features from the flight data and obtain an estimation of the monitored parameter. Second, a filter is utilized to smooth the residuals between real flight data and estimated values, which mitigates the effect of random noise and dramatically improves the detection performance. Finally, FD is achieved by comparing the smoothed residual with a statistical threshold. Then, fault recovery is fulfilled by replacing fault data with the estimated value. To validate the effectiveness of the proposed method, experiments are conducted based on simulation data and real flight data. The experimental results demonstrate that the proposed method has good performance in FD and recovery of UAV flight data. [ABSTRACT FROM AUTHOR]

The traditional satellite prognostic and health management (PHM) mostly relies on the satellite downlink telemetry data. Nowadays, the application of satellite becomes more diversification and its function becomes much more complicated. As a result, the telemetry data cannot meet the data requirements for satellite PHM due to the limited resources of telemetry channels and low sampling rate. Therefore, satellite in-orbit embedded testing was proposed. In order to satisfy the requirement of large volumes of data acquisition and transmission in satellite in-orbit embedded testing, this article proposes a new data agreement by the modification of the Modbus protocol to unified control 5-lane MLVDS bus. Therefore, a multi-channel parallel MLVDS data transmission bus is proposed to realise the data transmission from the data collection end to the information processing end of the PHM system on-board. The bus protocol designed here is implemented by using the Programmable Logic Part on the Xilinx XC7Z045. [ABSTRACT FROM AUTHOR]

Since the control laws require sensor feedback to set the current dynamic state of the unmanned aerial vehicle (UAV), incorrect readings may lead to potentially catastrophic conditions. Thus, automated detection and mitigation of UAV flight data anomaly is an important problem in the aviation domain. However, the conventional anomaly detection algorithms simply detect the outlier points and cannot provide estimated values. To address this challenge, anomaly detection and mitigation algorithm based on online subspace tracking, an online algorithm for flight data anomaly detection and mitigation is proposed. At every time instant, data subspace matrix is used as a meaningful data representation of raw multivariate heterogeneous flight data. Besides, in terms of outlier points, the subspace matrix is tracked with incomplete partial observation. Anomaly score is calculated based on the identification of a change in the underlying subspace. Utilizing the tracked subspace matrix, the detected outlier points are replaced with reasonable recovered estimations, which will help mitigate the influence of anomaly to UAV system control. Experimental results on real UAV flight data demonstrate its ability to maintain high accuracy for anomaly detection and low error for data recovery. [ABSTRACT FROM AUTHOR]

Lithium-ion battery is a critical part in various industrial applications. In practice, the performance of such batteries degrades over time. To maintain the battery performance and ensure their reliability, it is important to implement on-line life cycle health state assessment in a battery management system. However, two big challenges in on-line battery actual capacity estimation must be overcome. The first one is the on-line extraction of measurable degradation features. The other one is the on-line mapping from the degradation feature space to the battery capacity space. This paper proposes a self-adaptive life-cycle health state assessment method based on the on-line measurable parameters of lithium-ion battery. Ten different degradation features are extracted from the voltage, electric current and critical time during operation. These degradation features are fused to achieve a higher adaptability to complex operating conditions. The lithium-ion battery health state is assessed with a mapping model that links the feature space to the capacity space. The model is trained by the least squares support vector machine method for less computational complexity. The experimental results based on the real battery testing data show that the correlation between the degradation feature and the battery capacity is higher than 0.7 and the mean error of capacity estimation is less than 0.05. For the dynamic operation conditions, the mean error of capacity estimation is less than 11 mAh. This study illustrates the adaptability and applicability of the proposed on-line life-cycle health state assessment approach in various electric vehicle applications. [ABSTRACT FROM AUTHOR]

Telemetry data, sent by the satellite, is the only basis for ground staffs to monitor on-board equipment status. In addition, the pattern discovery and operating state identification of telemetry data are very essential for automatic anomaly detection and problem diagnosis for satellites. Clustering, as an important data mining method for time series, can realize pattern discovery of satellite telemetry data automatically and intelligently, whereas the large amount of raw data and pseudo-period characteristic make the clustering on raw data inefficient and susceptible to noise interference. Thus, based on the prominent shape features and Time-Spatial specialty, a clustering framework is proposed for telemetry data mining with physical-based segmentation and improved time series representation. Moreover, different distance measures are introduced to this framework to realize the time series clustering. The experiments are firstly performed on the public data sets which have high similarity with the real satellite telemetry to quantify the clustering accuracy, then a case study on the real satellite telemetry verifies the effectiveness and applicability of the proposed framework. [ABSTRACT FROM AUTHOR]

On-line anomaly detection is critical for the safety of unmanned aerial vehicles (UAVs). However, the flight status assessment still depends on ground control stations, which cannot meet the time requirement for autonomous and safe flight. The lack of on-board intelligent anomaly detection systems makes it rather difficult for on-line flight status estimation and assessment. In order to achieve real-time monitoring of UAV flight status and enhance the reliability and safety of UAVs, an embedded intelligent system is designed to address the challenging issues of UAV on-line anomaly detection in this paper. During the flight, the status of sensors and key components are continuously detected via flight data which can reflect the current status of the UAV. The proposed embedded anomaly detection system includes two main parts: (1) a general heterogeneous computing architecture which is based on Xilinx Zynq-7000 SoC with dual-core Cortex A9 processors and Field Programmable Gate Arrays (FPGA), (2) an on-line anomaly detection intelligent algorithm which is based on least squares support vector machine (LS-SVM) prediction model and utilized as a demonstration that needs high computing performance. The simulation flight data are used to verify the proposed system, and the experimental results show that the proposed intelligent system is capable of effective UAV on-line anomaly detection. [ABSTRACT FROM AUTHOR]

Health status monitoring of flight-critical sensors is crucial to the flight safety of unmanned aerial vehicles (UAVs). While many flight data anomaly detection algorithms have been proposed, most do not consider data source information and cannot identify which data sources contribute most to the anomaly, hindering proper fault mitigation. To address this challenge, a structured sparse subspace learning (SSL) anomaly detection (SSSLAD) algorithm, which reformulates anomaly detection as a structured SSL problem, is proposed. A structured norm is imposed on the projection coefficients matrix to achieve structured sparsity and help identify anomaly sources. Utilizing an efficient optimization method based on Nesterov’s method and a subspace tracking approach considering temporal dependence, the computation is efficient. Experiments on real UAV flight data sets illustrate that the proposed SSSLAD algorithm can accurately and quickly detect and identify anomalous sources in flight data, outperforming state of art algorithms, both in terms of accuracy and speed. [ABSTRACT FROM PUBLISHER]

In the past decades, fault diagnosis and prognosis (FDP) approaches were developed in the Riemann sampling (RS) framework, in which samples are taken and algorithms are executed in periodic time intervals. With the increase of system complexity, a bottleneck of real-time implementation of RS-based FDP is limited calculation resources, especially for distributed applications. To overcome this problem, a Lebesgue sampling-based FDP (LS-FDP) is proposed. LS-FDP takes samples on the fault dimension axis and provides a need-based FDP philosophy in which the algorithm is executed only when necessary. In previous LS-FDP, the Lebesgue length is a constant. To accommodate the nonlinear fault dynamics, it is desirable to execute FDP algorithm more frequently when the fault growth is fast while less frequently when fault growth is slow. This requires to change the Lebesgue length adaptively and optimize the selection of Lebesgue length based on fault state and fault growth speed. The goal of this paper is to develop an improved LS-FDP method with adaptive Lebesgue length, which enables the FDP to be executed according to fault dynamics and has low cost in terms of computation and hardware resource needed. The design and implementation of adaptive LS-FDP (ALS-FDP) based on a particle filtering algorithm are illustrated with a case study of Li-ion batteries to verify the performances of the proposed approach. The experimental results show that ALS-FDP keeps close monitoring of fault growth and is accurate and time-efficient on long-term prognosis.

Note to Practitioners—Traditional fault diagnosis and prognosis (FDP) approaches are based on the Riemann sampling (RS) method, in which samples are taken and algorithms are executed in periodic time intervals no matter if it is necessary. To reduce computation and make optimal use of computational resources, Lebesgue sampling method is introduced into FDP. In this Lebesgue sampling-based approach, Lebesgue states are defined on the fault dimension axis and algorithm is executed only when the measurement causes a transition from one Lebesgue state to another, or an event happens. Different from RS-based approach, this is a need-based FDP philosophy in which the algorithm is executed only when necessary. This paper studies a parameter adaption method to optimally adjust the Lebesgue state length, which results in the changes of number and location of Lebesgue states, according to fault state and fault growth speed to accommodate the nonlinearity of fault dynamics and achieve a balance between computation and performance. The application to the capacity degradation of a set of Li-ion batteries is presented to demonstrate the effectiveness of the proposed adaptive Lebesgue state length FDP method. [ABSTRACT FROM PUBLISHER]

Waxy (Wx) protein is a key enzyme for synthesis of amylose in endosperm. Amylose content in wheat grain influences the quality of end-use products. Seven alleles have been described at the Wx-D1 locus, but only two of them ( Wx-D1b, Wx-D1e) were genotyped with codominant markers. The waxy wheat line K107Wx1 developed by treating 'Kanto 107' seeds with ethyl methanesulphonate carries the Wx-D1d allele. However, no molecular basis supports this nomenclature. In the present study, DNA sequence analysis confirmed that a single nucleotide polymorphism in the sixth exon of Wx-D1 changed tryptophan at position 301 into a termination codon. Based on this sequence variation, a PCR-based KASP marker was developed to detect this point mutation using 68 BC8F1 plants and 297 BC8F2 lines derived from the cross 'Ningmai 14'*9/K107Wx1. Combined with codominant markers for the Wx-A1 and Wx-B1 alleles, waxy and non-waxy near-isogenic lines were distinguished. The KASP marker was efficient in identifying the mutant allele and can be used to transfer waxiness to elite lines. [ABSTRACT FROM AUTHOR]

Detecting the anomalies timely in the condition monitoring data, which are highly relevant to the potential system faults, has become a research focus in many domains. Among the various detection methods available, the prediction-based algorithms are popular without using prior knowledge and expert labels. Additionally, these methods can take the time-ordered specialty into account which is highly significant for time-series-based anomaly detection. However, the detected feedback is binary, especially, due to the influence of inaccurate confidence interval (CI), the false alarm phenomena occur frequently based on predicted models. Thus this paper proposes an Uncertainty Fusion method to realize anomaly detection. Firstly, in order to estimate the data uncertainty, the Gaussian Process Regression (GPR) is applied to perform the prediction with uncertainty presentation. Then, based on the GPR model, the improved k-fold cross-validation is combined to represent the model uncertainty. Moreover, the quantitative error index is designed to provide more detecting information for decision-making. Eventually, the effectiveness of the proposed method are verified by different simulated and open-source data sets, as well as the real application in mobile traffic data detecting. The quantitative results on simulated data experiments show the proposed method can largely eliminate the false alarms without sacrificing much detection rate compared with the basic GPR model. Especially, the experiments on periodic data sets with high Signal Noise Ratio have the better performance. And the mobile traffic data detecting proves the Uncertainty Fusion method can expand the basic GPR model to meet the real industrial requirements. [ABSTRACT FROM AUTHOR]