Colour Recognition Algorithm Based on Colour Mapping Knowledge for Wooden Building Image

In the cross-media image reproduction technology, the accurate transfer and reproduction of colour between different media are an important issue in the reproduction process, and the colour mapping technology is the key technology to effectively maintain the image details and improve the level of colour reproduction. Wooden structure in the image colour and colour piece is different, the image of each colour of visual perception is not independent, and every colour in the image pixels is affected by the surrounding pixels, but in the process of image map, without thinking of the pixel space, adjacent pixels of mutual influence in particular, do not let a person particularly be satisfied with the resulting map figure. In the process of image processing by traditional colour mapping algorithm, the colour distortion caused by colour component is ignored and the block diagram of colour mapping system is constructed. With the continuous development of mapping recognition algorithms, the maximum and minimum brightness values in the image are mapped to the maximum and minimum brightness values of the display device by linear mapping algorithm according to the flow of the established recognition algorithm. By establishing the colour adjustment method of the colour mapping image, the processing effect of the mapping algorithm is analysed. The results show that the brightness deviation of the image is reduced and the colour resolution is improved by the colour brightness compensation.

Jet stream meandering in the Northern Hemisphere winter: an advection-diffusion perspective

Large meridional excursions of a jet stream are conducive to blocking and related midlatitude weather extremes, yet the physical mechanism of jet meandering is not well understood. This paper examines the mechanisms of jet meandering in boreal winter through the lens of a potential vorticity (PV)-like tracer advected by reanalysis winds in an advection-diffusion model. As the geometric structure of the tracer displays a compact relationship with PV in observations and permits a linear mapping from tracer to PV at each latitude, jet meandering can be understood by the geometric structure of tracer field that is only a function of prescribed advecting velocities. This one-way dependence of tracer field on advecting velocities provides a new modeling framework to quantify the effects of time mean flow versus transient eddies on the spatiotemporal variability of jet meandering. It is shown that the mapped tracer wave activity resembles the observed spatial pattern and magnitude of PV wave activity for the winter climatology, interannual variability, and blocking-like wave events. The anomalous increase in tracer wave activity for the composite over interannual variability or blocking-like wave events is attributed to weakened composite mean winds, indicating that the low-frequency winds are the leading factor for the overall distributions of wave activity. It is also found that the tracer model underestimates extreme wave activity, likely due to the lack of feedback mechanisms. The implications for the mechanisms of jet meandering in a changing climate are also discussed.

Design of an Automatic English Pronunciation Error Correction System Based on Radio Magnetic Pronunciation Recording Devices

In this paper, a system for automatic detection and correction of mispronunciation of native Chinese learners of English by speech recognition technology is designed with the help of radiomagnetic pronunciation recording devices and computer-aided software. This paper extends the standard pronunciation dictionary by predicting the phoneme confusion rules in the language learner’s pronunciation that may lead to mispronunciation and generates an extended pronunciation dictionary containing the standard pronunciation of each word and the possible mispronunciation variations, and automatic speech recognition uses the extended pronunciation dictionary to detect and diagnose the learner’s mispronunciation of phonemes and provides real-time feedback. It is generated by systematic crosslinguistic phonological comparative analysis of the differences in phoneme pronunciation with each other, and a data-driven approach is used to do automatic phoneme recognition of learner speech and analyze the mapping relationship between the resulting mispronunciation and the corresponding standard pronunciation to automatically generate additional phoneme confusion rules. In this paper, we investigate various aspects of several issues related to the automatic correction of English pronunciation errors based on radiomagnetic pronunciation recording devices; design the general block diagram of the system, etc.; and discuss some key techniques and issues, including endpoint detection, feature extraction, and the system’s study of pronunciation standard algorithms, analyzing their respective characteristics. Finally, we design and implement a model of an automatic English pronunciation error correction system based on a radiomagnetic pronunciation recording device. Based on the characteristics of English pronunciation, the correction algorithm implemented in this system uses the similarity and pronunciation duration ratings based on the log posterior probability, which combines the scores of both, and standardizes this system scoring through linear mapping. This system can achieve the purpose of automatic recognition of English mispronunciation correction and, at the same time, improve the user’s spoken English pronunciation to a certain extent.

MultiMAP: dimensionality reduction and integration of multimodal data

Multimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, a novel algorithm for dimensionality reduction and integration. MultiMAP can integrate any number of datasets, leverages features not present in all datasets, is not restricted to a linear mapping, allows the user to specify the influence of each dataset, and is extremely scalable to large datasets. We apply MultiMAP to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data and show that it outperforms current approaches. On a new thymus dataset, we use MultiMAP to integrate cells along a temporal trajectory. This enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of expression versus binding site opening kinetics.

Retentive capacity of power output and linear versus non-linear mapping of power loss in the isotonic muscular endurance test

The limit of dynamic endurance during repetitive contractions has been referred to as the point of muscle fatigue, which can be measured by mechanical and electrophysiological parameters combined with subjective estimates of load tolerance for revealing the human real-world capacity required to work continuously. In this study, an isotonic muscular endurance (IME) testing protocol under a psychophysiological fatigue criterion was developed for measuring the retentive capacity of the power output of lower limb muscles. Additionally, to guide the development of electrophysiological evaluation methods, linear and non-linear techniques for creating surface electromyography (sEMG) models were compared in terms of their ability to estimate muscle fatigue. Forty healthy college-aged males performed three trials of an isometric peak torque test and one trial of an IME test for the plantar flexors and knee and hip extensors. Meanwhile, sEMG activity was recorded from the medial gastrocnemius, lateral gastrocnemius, vastus medialis, rectus femoris, vastus lateralis, gluteus maximus, and biceps femoris of the right leg muscles. Linear techniques (amplitude-based parameters, spectral parameters, and instantaneous frequency parameters) and non-linear techniques (a multi-layer perception neural network) were used to predict the time-dependent power output during dynamic contractions. Two mechanical manifestations of muscle fatigue were observed in the IME tests, including power output reduction between the beginning and end of the test and time-dependent progressive power loss. Compared with linear mapping (linear regression) alone or a combination of sEMG variables, non-linear mapping of power loss during dynamic contractions showed significantly higher signal-to-noise ratios and correlation coefficients between the actual and estimated power output. Muscular endurance required in real-world activities can be measured by considering the amount of work produced or the activity duration via the recommended IME testing protocol under a psychophysiological termination criterion. Non-linear mapping techniques provide more powerful mapping of power loss compared with linear mapping in the IME testing protocol.

A multichannel learning-based approach for sound source separation in reverberant environments

In this paper, a multichannel learning-based network is proposed for sound source separation in reverberant field. The network can be divided into two parts according to the training strategies. In the first stage, time-dilated convolutional blocks are trained to estimate the array weights for beamforming the multichannel microphone signals. Next, the output of the network is processed by a weight-and-sum operation that is reformulated to handle real-valued data in the frequency domain. In the second stage, a U-net model is concatenated to the beamforming network to serve as a non-linear mapping filter for joint separation and dereverberation. The scale invariant mean square error (SI-MSE) that is a frequency-domain modification from the scale invariant signal-to-noise ratio (SI-SNR) is used as the objective function for training. Furthermore, the combined network is also trained with the speech segments filtered by a great variety of room impulse responses. Simulations are conducted for comprehensive multisource scenarios of various subtending angles of sources and reverberation times. The proposed network is compared with several baseline approaches in terms of objective evaluation matrices. The results have demonstrated the excellent performance of the proposed network in dereverberation and separation, as compared to baseline methods.

Investigating the Upper-Bound Performance of Sparse-Coding-Based Spectral Reconstruction from RGB Images

In Spectral Reconstruction (SR), we recover hyperspectral images from their RGB counterparts. Most of the recent approaches are based on Deep Neural Networks (DNN), where millions of parameters are trained mainly to extract and utilize the contextual features in large image patches as part of the SR process. On the other hand, the leading Sparse Coding method ‘A+’—which is among the strongest point-based baselines against the DNNs—seeks to divide the RGB space into neighborhoods, where locally a simple linear regression (comprised by roughly 102 parameters) suffices for SR. In this paper, we explore how the performance of Sparse Coding can be further advanced. We point out that in the original A+, the sparse dictionary used for neighborhood separations are optimized for the spectral data but used in the projected RGB space. In turn, we demonstrate that if the local linear mapping is trained for each spectral neighborhood instead of RGB neighborhood (and theoretically if we could recover each spectrum based on where it locates in the spectral space), the Sparse Coding algorithm can actually perform much better than the leading DNN method. In effect, our result defines one potential (and very appealing) upper-bound performance of point-based SR.

LIE-TYPE DERIVATIONS OF NEST ALGEBRAS ON BANACH SPACES AND RELATED TOPICS

Let $\mathcal {X}$ be a Banach space over the complex field $\mathbb {C}$ and $\mathcal {B(X)}$ be the algebra of all bounded linear operators on $\mathcal {X}$ . Let $\mathcal {N}$ be a nontrivial nest on $\mathcal {X}$ , $\text {Alg}\mathcal {N}$ be the nest algebra associated with $\mathcal {N}$ , and $L\colon \text {Alg}\mathcal {N}\longrightarrow \mathcal {B(X)}$ be a linear mapping. Suppose that $p_n(x_1,x_2,\ldots ,x_n)$ is an $(n-1)\,$ th commutator defined by n indeterminates $x_1, x_2, \ldots , x_n$ . It is shown that L satisfies the rule $$ \begin{align*}L(p_n(A_1, A_2, \ldots, A_n))=\sum_{k=1}^{n}p_n(A_1, \ldots, A_{k-1}, L(A_k), A_{k+1}, \ldots, A_n) \end{align*} $$ for all $A_1, A_2, \ldots , A_n\in \text {Alg}\mathcal {N}$ if and only if there exist a linear derivation $D\colon \text {Alg}\mathcal {N}\longrightarrow \mathcal {B(X)}$ and a linear mapping $H\colon \text {Alg}\mathcal {N}\longrightarrow \mathbb {C}I$ vanishing on each $(n-1)\,$ th commutator $p_n(A_1,A_2,\ldots , A_n)$ for all $A_1, A_2, \ldots , A_n\in \text {Alg}\mathcal {N}$ such that $L(A)=D(A)+H(A)$ for all $A\in \text {Alg}\mathcal {N}$ . We also propose some related topics for future research.

Estimating subsurface properties using a semi-supervised neural networks approach

Estimating static rock properties (e.g., density and porosity) from seismic and well logs is one of the essential but challenging tasks in subsurface interpretation and characterization. To compensate for the sparsity of well logs and the limited bandwidth of seismic data, a semi-supervised learning workflow is presented for efficiently integrating seismic and logs and simultaneously estimating multiple subsurface properties. It consists of two components: (1) unsupervised seismic feature engineering and (2) supervised seismic-well integration, each of which is implemented as a convolutional neural network (CNN). Compared to most of the existing methods, it advances in three aspects. First, it allows the use of local 3D seismic patterns for building an optimal non-linear mapping function with 1D logs, which is more noise robust and significantly improves the lateral consistency of machine prediction throughout the entire seismic survey. Second, it is capable of automatically bridging the gap of vertical resolution between seismic and well logs, which simplifies the workflow of data preparation, such as log upscaling. Additionally, it enables Monte Carlo (MC) dropout-based epistemic uncertainty analysis. The performance of the proposed solution is evaluated through two examples, relative acoustic impedance and porosity estimation in a synthetic PreSDM dataset of 36 pseudo wells and sonic and density estimation in the Groningen dataset of 375 wells. The good match between the machine predictions and the actual measurements demonstrates the capability of the proposed semi-supervised learning in providing reliable seismic and well integration and delivering robust estimation of subsurface properties, including those of a relatively weak physical link with seismic, such as density and porosity.