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CFRP/Ti stacks composed of carbon fiber-reinforced plastic composites (CFRP) and titanium alloys (Ti) are widely used in aerospace fields. However, in the integrated hole-making process of CFRP/Ti stacks, the machining characteristics of various materials are significantly different, and constant machining parameters cannot simultaneously meet the high-quality machining requirements of two materials. In addition, errors exist between the actual thickness of each material layer and the theoretical value, which causes an impediment to the monitoring of the machining interface and the corresponding adjustment of parameters. An adaptive machining method for the helical milling of CFRP/Ti stacks based on interface identification is proposed in this paper. The machining characteristics of the pneumatic spindle and the interface state in the helical milling of CFRP/Ti stacks are analyzed using self-developed portable helical milling equipment, and a new algorithm for the real-time monitoring of the machining interface position and adaptive adjustment of the machining parameters according to the interface identification result is proposed. Helical milling experiments were carried out, the results show that the proposed method can effectively identify the position of the machining interface with good identification accuracy. Moreover, the proposed parameter-adaptive optimized machining method for CFRP/Ti stacks can significantly improve hole diameter accuracy and machining quality.

Points of Subjective Equality (PSE) are commonly measured using staircase or constant stimuli methods. However, the staircase method is highly dependent on the step size, and the constant stimuli method is time-consuming. Thus, we wanted to develop an efficient and quick method to estimate both the PSE and the slope of the psychometric function. We developed a random-step algorithm in which a one-up-one-down rule is followed but with a random step size in a pre-defined range of test levels. Each stimulus would be chosen depending on the previous response of the subject. If the subject responded "up", any random level in the lower range would be picked for the next trial. And if the subject responded "down", any random level in the upper range would be picked for the next trial. This procedure would result in a bell-shaped distribution of the test levels around the estimated PSE, while a substantial amount of trials would still be dispersed at both bounds of the range. We then compared this method with traditional constant stimuli procedure on a task based on the Pulfrich phenomenon while the PSEs of participants could be varied using different neutral density filters. Our random-step method provided robust estimates of both the PSE and the slope under various noise levels with small trial counts, and we observed a significant correlation between the PSEs obtained with the two methods. The random-step method is an efficient way to measure the full psychometric function when testing time is critical, such as in clinical settings.

As we all know, YOLOv4 can achieve excellent detection performance in object detection and has been effectively applied in many fields. However, the inconsistency of scale features affects the prediction accuracy of the path aggregation network (PANet) in YOLOv4 for small objects, resulting in low detection accuracy. This paper presents YOLOv4, which uses an adaptive recursive path aggregation network (AR-PANet) to improve the detection accuracy of small objects. First, the output characteristics of the PANet are fed back into the backbone network by using a recursive structure to enrich the characteristic information of the object. Second, an adaptive approach is developed to eliminate conflicting information in multi-scale feature space, thereby enhancing scale invariance and promoting feature extraction accuracy for small objects. Finally, the CBAM is used to map the multi-scale features obtained from the AR-PANet to independent channels and spatial dimensions to achieve feature refinement, thus improving the detection accuracy of small objects. Experimental results show that our proposed method can effectively improve the accuracy of small object detection in multiple datasets, addressing this challenging problem with impressive results. Thus, our proposed approach has great potential and valuable applications in the fields of remote sensing and intelligent transportation., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Three-dimensional deformation is an important input to explore seismic mechanisms and geodynamics. The GNSS and InSAR technologies are commonly used to obtain the co-seismic three-dimensional deformation field. This paper focused on the effect of calculation accuracy caused by the deformation correlation between the reference point and the points involved in the solution, to build a high-accuracy three-dimensional deformation field for a detailed geological explanation. Based on the variance component estimation (VCE) method, the InSAR LOS, azimuthal deformation, and the GNSS horizontal and vertical deformation were integrated to solve the three-dimensional displacement of the study area in combination with the elasticity theory. The accuracy of the three-dimensional co-seismic deformation field of the 2021 Maduo M S 7.4 earthquake, which provided the direct evidence for the theoretical hypothesis that large earthquakes could not only produce surface rupture on seismogenic faults, but also trigger pre-existing faults or new faults to produce surface rupture or weak deformation in areas far from seismogenic faults. An adaptive method was proposed in GNSS and InSAR integration, which could take into account the correlation distance and the efficiency of homogeneous point selection. Meanwhile, deformation information of the decoherent region could be recovered without interpolation of the GNSS displacement. This series of findings formed an essential supplement to the field surface rupture survey and provided a novel idea for the combination of the various spatial measurement technologies to improve the seismic deformation monitoring.M S 7.4 earthquake, which provided the direct evidence for the theoretical hypothesis that large earthquakes could not only produce surface rupture on seismogenic faults, but also trigger pre-existing faults or new faults to produce surface rupture or weak deformation in areas far from seismogenic faults. An adaptive method was proposed in GNSS and InSAR integration, which could take into account the correlation distance and the efficiency of homogeneous point selection. Meanwhile, deformation information of the decoherent region could be recovered without interpolation of the GNSS displacement. This series of findings formed an essential supplement to the field surface rupture survey and provided a novel idea for the combination of the various spatial measurement technologies to improve the seismic deformation monitoring.

Objective: The aim of this study is to demonstrate an adaptive method that is robust toward environmental fluctuations and provides a real-time measure of plant growth by measuring CO 2 consumption. To verify the validity of the proposed method, the relation between the plant growth and variation in light conditions with a closed experimental system was investigated., Results: The proposed method was used to measure the photosynthetic rate induced by photosynthetic photon flux density (PPFD) and to evaluate plant growth under continuous and pulsed light in arugula plants. The PPFD-dependent change in photosynthetic rate was measured. And in the condition range of 200-10,000 μs pulse period and 50% duty ratio of pulsed light, there was no change in the growth rate of plants assuming the same PPFD as continuous light. These experiments showed the validity of the adaptive method in removing environmental fluctuations without precise control of temperature and humidity., (© 2022. The Author(s).)

A bilateral neural network adaptive controller is designed for a class of teleoperation systems with constant time delay, external disturbance and internal friction. The stability of the teleoperation force feedback system with constant communication channel delay and nonlinear, complex, and uncertain constant time delay is guaranteed, and its tracking performance is improved. In the controller design process, the neural network method is used to approximate the system model, and the unknown internal friction and external disturbance of the system are estimated by the adaptive method, so as to avoid the influence of nonlinear uncertainties on the system.

Concept drift is an important issue in the field of streaming data mining. However, how to maintain real-time model convergence in a dynamic environment is an important and difficult problem. In addition, the current methods have limited ability to deal with the problem of streaming data classification for complex nonlinear problems. To solve these problems, a selective ensemble-based online adaptive deep neural network (SEOA) is proposed to address concept drift. First, the adaptive depth unit is constructed by combining shallow features with deep features and adaptively controls the information flow in the neural network according to changes in streaming data at adjacent moments, which improves the convergence of the online deep learning model. Then, the adaptive depth units of different layers are regarded as base classifiers for ensemble and weighted dynamically according to the loss of each classifier. In addition, a dynamic selection of base classifiers is adopted according to the fluctuation of the streaming data to achieve a balance between stability and adaptability. The experimental results show that the SEOA can effectively contend with different types of concept drift and has good robustness and generalization., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Relation Extraction systems train an extractor by aligning relation instances in Knowledge Base with a large amount of labeled corpora. Since the labeled datasets are very expensive, Distant Supervision Relation Extraction (DSRE) utilizes rough corpus annotated with Knowledge Graph to reduce the cost of acquisition. Nevertheless, the data noise problem limits the performance of the DSRE. Dependency trees can be used to filter the wrong-labeled instances in the distant supervision bag. However, existing dependency tree relation extraction strategies are all based on manually-set paths between the subject and object entities, and suffer from the problem of pruning the trees too aggressively or too insufficiently. To circumvent the shortcomings, in this paper, we propose a novel DSRE framework A 2 DSRE, based on the Adaptive dependency-path and Additional KG supervision. To obtain the dependency paths related to entity relations adaptively, we introduce an advanced graph neural network-GeniePath into DSRE, which assigns higher weights to those direct neighbor words that contribute more to relation prediction through breadth exploration, and conducts depth exploration to determine the correlation between relations and high-order neighbors. In this way, the irrelevant nodes are pruned while the relevant nodes are kept, our method can obtain more appropriate paths associated with relations. At the same time, to further reduce the noises in the data, we incorporate additional supervision information from the knowledge graph by retracting the margin between the representation of the bag and the pre-training knowledge graph embedding. Extensive numerical experiments validate the effectiveness of our new method., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Accurate gait event detection is necessary for control strategies of gait rehabilitation robots. However, due to personal diversity between individuals, it is a challenge for robots to detect a gait event at various stride frequencies. This paper proposes a novel method for gait event detection of a gait rehabilitation robot using a single inertial sensor mounted on the thigh. A self-adaptive threshold for detecting heel strike is obtained in real time via a linear regression model. Observable thresholds for toe off detection are constant at various stride frequencies. Experiments are conducted based on 20 healthy subjects and six hemiplegic patients wearing a gait rehabilitation robot and walking at various kinds of stride frequencies. The experimental results show that the proposed method can detect heel strike and toe off gait events within an average 2% gait cycle temporal errors and never miss two-gait event detection. Compared to the conventional thresholding method, this work presents a simple and robust application for gait event detection in healthy and hemiplegic subjects by one inertial sensor. The linear regression model can be applicable to different subjects walking at various stride frequencies., (Copyright © 2020 Ye, Wu, Wu, Long, Zhang, Chen, Wang, Luo, Hou and Xu.)

Reading is slow and difficult for people with central vision loss who must rely on their peripheral vision. It has been shown that practicing on a letter-recognition task can increase peripheral reading speed, and that the training-related improvement is attributable mainly to reduced crowding. Since there is a high degree of variability in the vision conditions across people with central vision loss, a one-size-fits-all training protocol may not be adequate or appropriate for these patients. In this study, we target two aspects of training-training task and individual customization, and propose a training paradigm that focuses on reducing crowding and tailors training for each individual using an adaptive method. Seven normally-sighted adults were trained with four daily sessions of identifying crowded letters presented at various positions 10° below fixation in a pre/post design. During the training, a dynamic cue (jitter motion) was applied to target letters to modulate crowding. Amplitude of motion was varied on a block by block basis according to individual performance to maintain task difficulty near a pre-defined level (80% accuracy in letter recognition). We found that motion amplitude gradually reduced as training progressed, indicating a reduction in crowding. Following training, reading speed (measured using RSVP method) showed a significant improvement in both the trained (49%) and untrained (50%) visual fields. Despite showing similar improvement as observed in the previous training studies, our adaptive training method demands less effort and, most importantly, offers customization for each individual trainee., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

In this paper, an ensemble form of the semi-supervised Fisher Discriminant Analysis (FDA) model is developed for fault classification in industrial processes. This method uses the K Nearest Neighbor (KNN) algorithm to merge the metric level outputs, which are obtained by the sub-classifiers in the ensemble model, to get the final classification result. An adaptive form is further proposed to improve the classification performance by putting forward to a new method of weight adjustment. While semi-supervised learning can generate a better model by exploiting additional information contained in unlabeled data, ensemble learning achieves the promotion of algorithm robustness by integrating a series of weak learners. In addition, the property of diversity in ensemble learning can be boosted by incorporating different unlabeled data to different weak learners. Therefore, the combination of those two methods can achieve great generalization for the fault classification model. The performances of two proposed methods are evaluated through an industrial benchmark process., (Copyright © 2019 ISA. Published by Elsevier Ltd. All rights reserved.)

The modified CPMG (Carr-Purcell-Meiboom-Gill) pulse sequence is a common sequence used for measuring the internal magnetic field gradient distribution of formation rocks, for which t 0 (the duration of the first window) is a key acquisition parameter. In order to obtain the optimal t 0 , an adaptive method is proposed in this paper. By studying the factors influencing discriminant factor σ and its variation trend using T 2 -G forward numerical simulation, it is found that the optimal t 0 corresponds to the maximum value of σ. Then combining the constraint condition of SNR (Signal Noise Ratio) of spin echo, an optimal t 0 in modified CPMG pulse sequence is determined. This method can reduce the difficulties of operating T 2 -G experiments. Finally, the adaptive method is verified by the results of the T 2 -G experiments for four water-saturated sandstone samples., (Copyright © 2017 Elsevier Inc. All rights reserved.)

High-frequency 3-D ultrasound imaging is an informative tool for diagnosis, surgery planning and skin lesion examination. The purpose of this article was to describe a semi-automated segmentation tool providing easy access to the extent, shape and volume of a lesion. We propose an adaptive log-likelihood level-set segmentation procedure using non-parametric estimates of the intensity distribution. The algorithm has a single parameter to control the smoothness of the contour, and we describe how a fixed value yields satisfactory segmentation results with an average Dice coefficient of D = 0.76. The algorithm is implemented on a grid, which increases the speed by a factor of 100 compared with a standard pixelwise segmentation. We compare the method with parametric methods making the hypothesis of Rayleigh or Nakagami distributed signals, and illustrate that our method has greater robustness with similar computational speed. Benchmarks are made on realistic synthetic ultrasound images and a data set of nine clinical 3-D images acquired with a 50-MHz imaging system. The proposed algorithm is suitable for use in a clinical context as a post-processing tool., (Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

A position-sensitive detector/device (PSD) is a sensor that is capable of tracking the location of a laser beam on its surface. PSDs are used in many scientific instruments and technical applications including but not limited to atomic force microscopy, human eye movement monitoring, mirrors or machine tool alignment, vibration analysis, beam position control and so on. This work intends to propose a new application using the PSD. That is a new microscopy system called scanning PSD microscopy. The working mechanism is about putting an object on the surface of the PSD and fast scanning its area with a laser beam. To achieve a high degree of accuracy and precision, a reliable framework was designed using the PSD. In this work, we first tried to improve the PSD reading and its measurement performance. This was done by minimizing the effects of noise, distortion and other disturbing parameters. After achieving a high degree of confidence, the microscopy system can be implemented based on the improved PSD measurement performance. Later to improve the scanning efficiency, we developed an adaptive local scanning system to scan the whole area of the PSD in a short matter of time. It was validated that our comprehensive and adaptive local scanning method can shorten the scanning time in order of hundreds of times in comparison with the traditional raster scanning without losing any important information about the scanned 2D objects. Methods are also introduced to scan very complicated objects with bifurcations and crossings. By incorporating all these methods, the new microscopy system is capable of scanning very complicated objects in the matter of a few seconds with a resolution that is in order of a few micrometers.

An adaptive integration method is proposed for computing cardiac action potential models accurately and efficiently. Time steps are adaptively chosen by solving a quadratic formula involving the first and second derivatives of the membrane action potential. To improve the numerical accuracy, we devise an extremum-locator (el) function to predict the local extremum when approaching the peak amplitude of the action potential. In addition, the time step restriction (tsr) technique is designed to limit the increase in time steps, and thus prevent the membrane potential from changing abruptly. The performance of the proposed method is tested using the Luo-Rudy phase 1 (LR1), dynamic (LR2), and human O'Hara-Rudy dynamic (ORd) ventricular action potential models, and the Courtemanche atrial model incorporating a Markov sodium channel model. Numerical experiments demonstrate that the action potential generated using the proposed method is more accurate than that using the traditional Hybrid method, especially near the peak region. The traditional Hybrid method may choose large time steps near to the peak region, and sometimes causes the action potential to become distorted. In contrast, the proposed new method chooses very fine time steps in the peak region, but large time steps in the smooth region, and the profiles are smoother and closer to the reference solution. In the test on the stiff Markov ionic channel model, the Hybrid blows up if the allowable time step is set to be greater than 0.1ms. In contrast, our method can adjust the time step size automatically, and is stable. Overall, the proposed method is more accurate than and as efficient as the traditional Hybrid method, especially for the human ORd model. The proposed method shows improvement for action potentials with a non-smooth morphology, and it needs further investigation to determine whether the method is helpful during propagation of the action potential., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Gene set analysis (GSA) aims to assess the overall association of a set of genetic variants with a phenotype and has the potential to detect subtle effects of variants in a gene or a pathway that might be missed when assessed individually. We present a new implementation of the Adaptive Rank Truncated Product method (ARTP) for analyzing the association of a set of Single Nucleotide Polymorphisms (SNPs) in a gene or pathway. The new implementation, referred to as globalARTP, improves the original one by allowing the different SNPs in the set to have different modes of inheritance. We perform a simulation study for exploring the power of the proposed methodology in a set of scenarios with different numbers of causal SNPs with different effect sizes. Moreover, we show the advantage of using the gene set approach in the context of an Alzheimer's disease case-control study where we explore the endocytosis pathway. The new method is implemented in the R function globalARTP of the globalGSA package available at http://cran.r-project.org., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Auditory scene analysis describes the ability to segregate relevant sounds out from the environment and to integrate them into a single sound stream using the characteristics of the sounds to determine whether or not they are related. This study aims to contrast task performances in objective threshold measurements of segregation and integration using identical stimuli, manipulating two variables known to influence streaming, inter-stimulus-interval (ISI) and frequency difference (Δf). For each measurement, one parameter (either ISI or Δf) was held constant while the other was altered in a staircase procedure. By using this paradigm, it is possible to test within-subject across multiple conditions, covering a wide Δf and ISI range in one testing session. The objective tasks were based on across-stream temporal judgments (facilitated by integration) and within-stream deviance detection (facilitated by segregation). Results show the objective integration task is well suited for combination with the staircase procedure, as it yields consistent threshold measurements for separate variations of ISI and Δf, as well as being significantly related to the subjective thresholds. The objective segregation task appears less suited to the staircase procedure. With the integration-based staircase paradigm, a comprehensive assessment of streaming thresholds can be obtained in a relatively short space of time. This permits efficient threshold measurements particularly in groups for which there is little prior knowledge on the relevant parameter space for streaming perception.