Probability Distribution Of Points Research Articles

Multi-parameter information detection of aircraft taxiing on an airport runway based on an ultra-weak FBG sensing array.

Tracking the moving state and analyzing the distribution feature of an aircraft taxiing on the pavement are crucial for runway safety management. One challenge is to provide a reliable methodology that would enable such needs to be addressed in long-distance range runway with high sensitivity and spatial resolution. This paper proposed a novel monitoring approach for acquiring multi-parameter information of aircraft taxiing on airport runway based on distributed vibration detected by ultra-weak fiber Bragg grating (FBG) sensing array. For an actual engineering equipped with the smart runway system, the ultra-weak FBG sensing technology was applied to collect the distributed vibration responses of taxiing aircraft in takeoff and landing stages. The monitoring data of the vibration sensing optical cables beneath the entire pavement were directly utilized to evaluate the identification feasibility and effectiveness of taxiing speed and position, landing distribution, and taxiing acceleration and distance based on samples of five representative aircraft types. The results indicated that the speed, position, aiming point offset, acceleration changes and probability distribution of the taxiing distance can be revealed based on actual measurements concerning the distribution of vibration intensity with respect to space and time. Moreover, the analysis demonstrated that the experimental results based on ultra-weak FBG sensing array was able to account for the characteristic of different aircraft types participating in the study and the current operating conditions of the airport.

Hayate: photometric redshift estimation by hybridizing machine learning with template fitting

ABSTRACT Machine learning photo-z methods, trained directly on spectroscopic redshifts, provide a viable alternative to traditional template-fitting methods but may not generalize well on new data that deviates from that in the training set. In this work, we present a Hybrid Algorithm for WI(Y)de-range photo-z estimation with Artificial neural networks and TEmplate fitting (hayate), a novel photo-z method that combines template fitting and data-driven approaches and whose training loss is optimized in terms of both redshift point estimates and probability distributions. We produce artificial training data from low-redshift galaxy spectral energy distributions (SEDs) at z < 1.3, artificially redshifted up to z = 5. We test the model on data from the ZFOURGE surveys, demonstrating that hayate can function as a reliable emulator of eazy for the broad redshift range beyond the region of sufficient spectroscopic completeness. The network achieves precise photo-z estimations with smaller errors (σNMAD) than eazy in the initial low-z region (z < 1.3), while being comparable even in the high-z extrapolated regime (1.3 < z < 5). Meanwhile, it provides more robust photo-z estimations than eazy with the lower outlier rate ($\eta _{0.2}\lesssim 1~{{\ \rm per\ cent}}$) but runs ∼100 times faster than the original template-fitting method. We also demonstrate hayate offers more reliable redshift probability density functions, showing a flatter distribution of Probability Integral Transform scores than eazy. The performance is further improved using transfer learning with spec-z samples. We expect that future large surveys will benefit from our novel methodology applicable to observations over a wide redshift range.

B-ROS re-balanced learning method for PS-A-RoF FWA communication

The enhanced remote area communication (eRAC) scenario is an important growth point in the communication market. In some remote areas where optical fiber access cannot be realized or the laying cost is too high, fixed wireless access (FWA) is an appropriate supplementary solution for eRAC. Adopting analog radio over fiber (A-RoF) technology to implement FWA can overcome the bandwidth limitation of electronic devices and realize high-frequency carrier communication economically to achieve high-capacity wireless communication. Also, probabilistic shaping (PS) technology can be combined with A-RoF to further improve the flexibility of the network and coverage of service provision. However, in the PS-A-RoF network, the high RF power introduces more undesired nonlinear effects into the network, and it is often necessary to deploy supervised machine learning (ML) compensation modules in wireless receivers (WRs). But the module performances are affected by the uneven probability distribution of PS-QAM constellation points. In this paper, we employ the PS-A-RoF nonlinear model to theoretically investigate the correlation between the distribution of training symbols and the wireless A-RoF system’s performance. Our analysis reveals that reducing the variance of training symbol power contributes to a lower BER in the A-RoF network. We introduce a borderline random over-sampling (B-ROS) that matches with the PS-A-RoF nonlinear model, instead of the mainstream ROS, which is only at the data level. Based on the B-ROS scheme, only the minority examples below the borderline are over-sampled to reach a better variance performance. Introducing the B-ROS method into the supervised complex value nonlinear compensation module can further improve the decision accuracy of WRs with the restoration of phase information, without increasing additional computational resource consumption. The vector noise power, training symbol power variance, and noise factor metrics have been calculated to optimize the borderline value of our ML-based approach. We also present experimental data on the proof-of-concept A-RoF experiment for PS-64QAM. The results demonstrate a promising nonlinear compensation performance of the B-ROS WR, and the optimal borderline agrees well with the one deduced from the theoretical model under certain transmission conditions. Our proposed B-ROS scheme lessens the training size demand and can improve the receiver sensitivity by 0.51 dB compared to the common ML-based WR and by 0.7 dB compared to the conventional ROS scheme.

A modified U-Net convolutional neural network for segmenting periprostatic adipose tissue based on contour feature learning

ObjectiveThis study trains a U-shaped fully convolutional neural network (U-Net) model based on peripheral contour measures to achieve rapid, accurate, automated identification and segmentation of periprostatic adipose tissue (PPAT). MethodsCurrently, no studies are using deep learning methods to discriminate and segment periprostatic adipose tissue. This paper proposes a novel and modified, U-shaped convolutional neural network contour control points on a small number of datasets of MRI T2W images of PPAT combined with its gradient images as a feature learning method to reduce feature ambiguity caused by the differences in PPAT contours of different patients. This paper adopts a supervised learning method on the labeled dataset, combining the probability and spatial distribution of control points, and proposes a weighted loss function to optimize the neural network's convergence speed and detection performance. Based on high-precision detection of control points, this paper uses a convex curve fitting to obtain the final PPAT contour. The imaging segmentation results were compared with those of a fully convolutional network (FCN), U-Net, and semantic segmentation convolutional network (SegNet) on three evaluation metrics: Dice similarity coefficient (DSC), Hausdorff distance (HD), and intersection over union ratio (IoU). ResultsCropped images with a 270 × 270-pixel matrix had DSC, HD, and IoU values of 70.1%, 27 mm, and 56.1%, respectively; downscaled images with a 256 × 256-pixel matrix had 68.7%, 26.7 mm, and 54.1%. A U-Net network based on peripheral contour characteristics predicted the complete periprostatic adipose tissue contours on T2W images at different levels. FCN, U-Net, and SegNet could not completely predict them. ConclusionThis U-Net convolutional neural network based on peripheral contour features can identify and segment periprostatic adipose tissue quite well. Cropped images with a 270 × 270-pixel matrix are more appropriate for use with the U-Net convolutional neural network based on contour features; reducing the resolution of the original image will lower the accuracy of the U-Net convolutional neural network. FCN and SegNet are not appropriate for identifying PPAT on T2 sequence MR images. Our method can automatically segment PPAT rapidly and accurately, laying a foundation for PPAT image analysis.

A digital twin-based analysis method to assess geometric variations for parts in batch production

Background In mass production, engineers are more concerned with the statistical distribution accuracy of parts in mass production rather than just the qualification of individual parts. However, currently, the statistical analysis methods designed for product accuracy are relatively scattered, and most of them focus on nominal part models. Therefore, there is a need to design a statistical analysis method for parts in mass production based on the Digital Twin model. >Methods This paper presents a novel method to analyse the geometric variations of parts in batch production in the production line, which is based on digital twins to model and evaluate deviations contributed by the geometrical condition, assembly condition and material condition. Firstly, the statistical descriptions of the parts, particularly the features of a digital twin for parts in batch production related to the geometry and position, are classified into various hierarchies. Secondly, a covariance method is employed to analyse the law of their shape from the descriptions. Thirdly, the parts' shape feature similarity for different terms is derived, including the linear features of pose constraint, rotation deviation, and geometric deviation and the curve features like a geometric deviation. Finally, the probability distribution of discrete points on the manufacturing error caused by different reasons is calculated. Results Two case studies of reducer and rail highlight the applicability of the proposed approach. The standard deviation of the points has similar trend with sample cases according to normal distribution. Conclusions This paper categorizes the deviations of batch parts into the linear features of pose constraint, rotation deviation, and geometric deviation. When batch parts exhibit any of these deviation types, the eigenvalues and eigenvectors of their covariance matrix show certain patterns, enabling the identification of the deviation type and calculation of the statistical deviation probability distribution for the corresponding features.

A digital twin-based analysis method to assess geometric variations for parts in batch production

Background: In mass production, engineers are more concerned with the statistical distribution accuracy of parts in mass production rather than just the qualification of individual parts. However, currently, the statistical analysis methods designed for product accuracy are relatively scattered, and most of them focus on nominal part models. Therefore, there is a need to design a statistical analysis method for parts in mass production based on the Digital Twin model. Methods: This paper presents a novel method to analyse the geometric variations of parts in batch production in the production line, which is based on digital twins to model and evaluate deviations contributed by the geometrical condition, assembly condition and material condition. Firstly, the statistical descriptions of the parts, particularly the features of a digital twin for parts in batch production related to the geometry and position, are classified into various hierarchies. Secondly, a covariance method is employed to analyse the law of their shape from the descriptions. Thirdly, the parts' shape feature similarity for different terms is derived, including the linear features of pose constraint, rotation deviation, and geometric deviation and the curve features like a geometric deviation. Finally, the probability distribution of discrete points on the manufacturing error caused by different reasons is calculated. Results: Two case studies of reducer and rail highlight the applicability of the proposed approach. The standard deviation of the points has similar trend with sample cases according to normal distribution. Conclusions: This paper categorizes the deviations of batch parts into the linear features of pose constraint, rotation deviation, and geometric deviation. When batch parts exhibit any of these deviation types, the eigenvalues and eigenvectors of their covariance matrix show certain patterns, enabling the identification of the deviation type and calculation of the statistical deviation probability distribution for the corresponding features.

A novel complex-valued classifier with a weighted loss function employed for W-band PS-16QAM transmission over 4.6 km

Probabilistic shaping(PS) technique can reduce Signal-Noise Ratio (SNR) requirement and save the transmission power, which is meaningful for millimeter wave (mm-wave) long-distance wireless transmission. Traditional Digital Signal Processing(DSP) algorithms are affected by the uneven probability distribution of constellation points when working on PS signals. We propose a novel complex-valued classifier with a weighted loss function to replace traditional algorithms in PS-16QAM W-band long-distance transmission. The proposed algorithm can provide a receiving sensitivity improvement of about 0.5 dBm. It can reduce the training sequence length by 4%–7%, the computational complexity by 10.53%–16.89%, and the number of training iterations by 23.08%, respectively.

Secure OFDM-PON using three-dimensional selective probabilistic shaping and chaos.

In this paper, a novel three-dimensional selective probabilistic shaping (3D-SPS) and chaos-based multi-stage encryption scheme is proposed for physical layer security enhancement and transmission performance improvement in orthogonal frequency division multiplexing-based passive optical network (OFDM-PON). On the basis of inherent randomness of symbol sub-sequences with low granularity, the SPS algorithm is performed on the employed cubic constellation within each sub-sequence. Consequently, the probability distribution of inner points significantly increases after the constellation region exchange according to various rules. The generated compressed shaping information (CSI) is encrypted and used as the synchronization head for transmission. Furthermore, 3D scrambling is performed while maintaining the shaping effect. The encrypted signals of 35.3 Gb/s are successfully transmitted over a 25-km standard single-mode fiber (SSMF) and a back-to-back (BTB) system. The results show that by selecting the appropriate system parameter, the proposed scheme can provide about 2.4 dB modulation gain on the received optical power at a bit error rate (BER) of 10‒3 compared with a conventional quadrature amplitude modulation (QAM) signal under the same bit rate, and 0.9 dB shaping gain is brought due to the SPS. The encryption method possesses a relatively low computational complexity and sufficient key space of 10120 is introduced to resist exhaustive attack.

Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification.

To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is difficult to monitor in real-time. Meanwhile, external uncertainties significantly impact bearing degradation. Therefore, this paper proposes a new bearing RUL prediction method based on long-short term memory (LSTM) with uncertainty quantification. First, a fusion metric related to runtime (or degradation) is proposed to reflect the latent degradation process. Then, an improved dropout method based on nonparametric kernel density is developed to improve estimation accuracy of RUL. The PHM2012 dataset is adopted to verify the proposed method, and comparison results illustrate that the proposed prediction model can accurately obtain the point estimation and probability distribution of the bearing RUL.

Hybrid photometric redshifts for sources in the COSMOS and XMM-LSS fields

ABSTRACT In this paper, we present photometric redshifts for 2.7 million galaxies in the XMM-LSS and COSMOS fields, both with rich optical and near-infrared data from VISTA and HyperSuprimeCam. Both template fitting (using galaxy and active galactic nuclei templates within LePhare) and machine learning (using gpz) methods are run on the aperture photometry of sources selected in the Ks-band. The resulting predictions are then combined using a Hierarchical Bayesian model, to produce consensus photometric redshift point estimates and probability distribution functions that outperform each method individually. Our point estimates have a root mean square error of ∼0.08–0.09, and an outlier fraction of ∼3–4 percent when compared to spectroscopic redshifts. We also compare our results to the COSMOS2020 photometric redshifts, which contain fewer sources, but had access to a larger number of bands and greater wavelength coverage, finding that comparable photo-z quality can be achieved (for bright and intermediate luminosity sources where a direct comparison can be made). Our resulting redshifts represent the most accurate set of photometric redshifts (for a catalogue this large) for these deep multisquare degree multiwavelength fields to date.

Representing point clouds with generative conditional invertible flow networks

• We propose a novel generative model for 3D point clouds. • The model utilizes two normalizing flows - one produces an object descriptor and conditions the other to produce the shape. • Our method is insensitive to the ordering and cardinality and reduces training speed in comparison to other approaches. • Performed experiments show that the proposed model is a new state-of-the-art method for point cloud generation. In this paper, we propose a simple yet effective method to represent point clouds as sets of samples drawn from a cloud-specific probability distribution. This interpretation matches intrinsic characteristics of point clouds: the number of points and their ordering within a cloud is not important as all points are drawn from the proximity of the object boundary. We postulate to represent each cloud as a parameterized probability distribution of points in space, which is defined by a generative neural network. The network operates by composing several spatial transformations of point locations. Once trained, it provides a natural framework for point cloud manipulation. For instance we can decouple cloud shape from its orientation and provide routines for aligning a new cloud into a default spatial orientation. To exploit similarities between same-class objects and to improve model performance, we turn to weight sharing: networks that model densities of points belonging to objects in the same family share all parameters with the exception of a small, object-specific embedding vector. We show that these embedding vectors capture semantic relationships between objects. Our method leverages generative invertible flow networks to learn embeddings as well as to generate point clouds. Thanks to this formulation and contrary to similar approaches, we are able to train our model in an end-to-end fashion. As a result, our model offers competitive or superior quantitative results on benchmark datasets, while enabling unprecedented capabilities to perform cloud manipulation tasks, such as point cloud registration and regeneration, by a generative network.

SHM deformation monitoring for high-speed rail track slabs and Bayesian change point detection for the measurements

Fiber Bragg grating (FBG) technology has significant advantages in anti-electromagnetic interference and is suitable for structural health monitoring (SHM) in high-speed rail (HSR). In this paper, a deformation monitoring system based on FBG technology is designed for HSR track slab. Regarding to the possible large deformation of the track slab, a Bayesian change point detection (CPD) method is adopted to detect change points of the monitoring data sequence and the posterior probability distribution of change points can also be derived. The Bayesian CPD algorithm can directly sample and simulate from the posterior probability distribution of change point to determine corresponding parameters of regression model, as well as yielding uncertainty evaluation. The designed SHM system was implemented on a segment of HSR track on the Lanzhou-Xinjiang line in China to carry out the measurement of track slab deformation. Results shows that the designed SHM can resist strong electromagnetic interference in the operation environment of high-speed rail and versine deformation monitoring data over two months for the target track segment can be extracted based on SHM measurement. Through posterior distribution information of the Bayesian CPD method, change points which have the highest posterior probability can be identified for the versine deformation of HSR track slabs, which is essential for investigating the cause of the change point and providing maintenance plan in time.

Generalizing neural signal-to-text brain-computer interfaces

Objective: Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing. However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalizability of existing interfaces. The goal of this study is to evaluate the BCI performance of a robust speech decoding system that translates neural signals evoked by speech to a textual output. While previous studies have approached this problem by using neural signals to choose from a limited set of possible words, we employ a more general model that can type any word from a large corpus of English text. Approach: In this study, we create an end-to-end BCI that translates neural signals associated with overt speech into text output. Our decoding system first isolates frequency bands in the input depth-electrode signal encapsulating differential information regarding production of various phonemic classes. These bands form a feature set that then feeds into a Long Short-Term Memory (LSTM) model which discerns at each time point probability distributions across all phonemes uttered by a subject. Finally, a particle filtering algorithm temporally smooths these probabilities by incorporating prior knowledge of the English language to output text corresponding to the decoded word. The generalizability of our decoder is driven by the lack of a vocabulary constraint on this output word. Main result: This method was evaluated using a dataset of 6 neurosurgical patients implanted with intra-cranial depth electrodes to identify seizure foci for potential surgical treatment of epilepsy. We averaged 32% word accuracy and on the phoneme-level obtained 46% precision, 51% recall and 73.32% average phoneme error rate while also achieving significant increases in speed when compared to several other BCI approaches. Significance: Our study employs a more general neural signal-to-text model which could facilitate communication by patients in everyday environments.

Strong negative type in spheres

It is known that spheres have negative type, but only subsets with at most one pair of antipodal points have strict negative type. These are conditions on the (angular) distances within any finite subset of points. We show that subsets with at most one pair of antipodal points have strong negative type, a condition on every probability distribution of points. This implies that the function of expected distances to points determines uniquely the probability measure on such a set. It also implies that the distance covariance test for stochastic independence, introduced by Sz\'ekely, Rizzo and Bakirov, is consistent against all alternatives in such sets. Similarly, it allows tests of goodness of fit, equality of distributions, and hierarchical clustering with angular distances. We prove this by showing an analogue of the Cram\'er--Wold theorem.

Discussion of 16QAM with 3bit Information

Aiming at the improvement of the modulation scheme of the underlying link of the optical transport network, under the condition of 3 bits of information entropy, the possible improvement of the 16QAM modulation format is explored from the aspects of the number, position and probability distribution of constellation points. The effectiveness of the improved 16QAM modulation is illustrated by comparison with the original 16QAM and 8QAM modulation.

A re-evaluation of the term spread as a leading indicator

Forecasting the evolution path of macroeconomic variables has always been of keen interest to policy makers and market participants. A common tool used in the relevant forecasting literature is the term spread of Treasury bond yields. In this paper, we decompose the term spread into an expectation and a term premium component and evaluate the informational content of each component in forecasting the GDP growth rate and inflation in various forecasting horizons. In doing so, we employ alternative decomposition procedures and introduce the Support Vector Regression (SVR) methodology from the field of Machine Learning, coupled with linear and non-linear kernels as a novel forecasting method in the field. Using rolling windows in producing point and conditional probability distribution forecasts we find that neither the term spread, nor its decomposition components possess the ability to accurately forecast output growth or inflation. Our findings extend the existing literature, since they are focused on an explicit out-of-sample evaluation in contrast to most existing empirical studies that produce only in-sample forecasts. To strengthen our findings, we also consider several control variables suggested in the relevant literature without significant qualitative differences from the initial results. The main innovation of our approach stems from the use of the non-linear Support Vectors Machine methodology, that is introduced for the first time in this line of research for forecasting out-of-sample.

Degradation assessment for the ball screw with variational autoencoder and kernel density estimation

The ball screw is an important component of machine tools, and its degradation assessment is therefore critical for the health management of the entire machine tool. Generally, the degradation assessment includes health indicator construction and degradation modeling. However, the health indicator is often constructed manually with prior knowledge, and its sensitivity can be affected by various factors. In addition, most existing degradation models rely on a large amount of failure data, which is not practical for the ball screw due to its high reliability. To solve these problems, this article presents a novel ball screw performance evaluation method. First, the raw data collected in the normal status are used to train the variational autoencoder, and then, the online raw signals are input into the learned variational autoencoder to construct health indicators. After that, the kernel density estimation is utilized to estimate the probability distribution of health indicator points in a dynamic sliding window, and then, the deterioration can be evaluated by summarizing the probability distribution that exceeds a predefined threshold. Experimental results show that the presented methodology can establish the health indicator automatically and adaptively. Also, it can evaluate the ball screw performance effectively and quantitatively when only data in healthy state are available.

Anomaly detection in time series based on interval sets

Anomaly detection in time series is a popular topic focusing on a variety of applications, which achieves a wealth of results. However, there are many cases of missing anomalies and false alarms for most existing works. Inspired by the concept of interval sets, this paper proposes an anomaly detection algorithm called probability interval and tries to detect the anomaly data in time series from a new perspective. In the proposed algorithm, a time series is divided into several subsequences. Each subsequence is regarded as an interval set depending on its value space and boundary of the subsequence. The similarity measurements between interval sets adopt interval operations and point probability distributions of the interval bounds. In addition, based on similarity results, the anomaly score is defined. The experimental results on artificial and real datasets indicate that the proposed algorithm has better discriminative performance than the piecewise aggregate approximation method and greatly reduces the false alarm rate. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Data Representation Based on Interval-Sets for Anomaly Detection in Time Series

Anomaly detection in time series is a popular topic focusing on a variety of applications, which achieves a wealth of results. However, there are many cases of missing anomaly and increased false alarm in most of the existing works. Inspired by the concept of interval-sets, this paper proposes an anomaly detection algorithm called a fuzzy interval-set and tries to detect the potential value of the time series from a new perspective. In the proposed algorithm, a time series will be divided into several subsequences. Each subsequence is regarded as an interval-set depending on its value space and boundary of the subsequence. The similarity measurements between interval sets adopt interval operations and point probability distributions of the interval bounds. In addition, an anomaly score is defined based on similarity results. The experimental results on synthetic and real data sets indicate that the proposed algorithm has better discriminative performance than the piecewise aggregate approximation method and reduces the false alarm rate significantly.

Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

Abstract We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i-magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.