基于声音可视化技术的扎染图案创新设计

针对扎染染色过程中造成的材料浪费问题,提出通过声音可视化技术模拟扎染的染色效果实现图案生成。在分析卷压扎染技法染色效果基础上进行线条抽象元素提取,利用Processing声音可视化技术实现抽象元素程序化表达,为使生成图案效果丰富,加入设计变量与参数化设计,结合音频最终生成4种风格的扎染图案。经多次生成实验发现,图案截取过程中速度间隔数值与圆点直径数值成正比时,其生成效果最优,形成的晕染效果与扎染相似。将该技术应用于服装与交互设计领域,可为相关产品图案设计提供新的设计灵感与思路。

NADARAYA-WATSON ESTIMATORS FOR REFLECTED STOCHASTIC PROCESSES

We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.

Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

Audio-visual keyword transformer for unconstrained sentence-level keyword spotting

As one of the most effective methods to improve the accuracy and robustness of speech tasks,the audio-visual fusion approach has recently been introduced into the field of Keyword Spotting(KWS).However,existing audio-visual keyword spotting models are limited to detecting isolated words,while keyword spotting for unconstrained speech is still a challenging problem.To this end,an Audio-Visual Keyword Transformer(AVKT)network is proposed to spot keywords in unconstrained video clips.The authors present a transformer classifier with learnable CLS tokens to extract distinctive keyword features from the variable-length audio and visual inputs.The outputs of audio and visual branches are combined in a decision fusion module.As humans can easily notice whether a keyword appears in a sentence or not,our AVKT network can detect whether a video clip with a spoken sentence contains a pre-specified keyword.Moreover,the position of the keyword is localised in the attention map without additional position labels.Exper-imental results on the LRS2-KWS dataset and our newly collected PKU-KWS dataset show that the accuracy of AVKT exceeded 99%in clean scenes and 85%in extremely noisy conditions.The code is available at https://github.com/jialeren/AVKT.

In situ digital testing method for quality assessment of soft soil improvement with polyurethane

This study purposes an in situ testing method on quality assessment of soil improvement.Factual drilling data includes the spatial distribution and in situ strength of untreated and treated soil along three different drillholes measured by on-site drilling monitoring method.These factual drilling data can characterize the degree of soil improvement by penetration injection with permeable polyurethane.Result from on-site drilling monitoring shows that the linear zones represent constant drilling speeds shown in the plot of drill bit advancement vs.net drilling time,which indicates the spatial distributions of soil profile.The soil profile at the study site is composed of four layers,which includes fill,untreated silty clay,treated silty clay,and mucky soil.The results of soil profile are verified by the parallel site loggings.The constant drilling speeds profile the coring-resistant strength of drilled soils.By comparing with the untreated silty clay,the constant drilling speeds of the treated silty clay have been decreased by 13.0-62.8%.Two drilling-speed-based indices of 61.2%and 65.6%are proposed to assess the decreased average drilling speed and the increased in situ strength of treated silty clay.Laboratory tests,i.e.uniaxial compressive strength(UCS)test,have been performed with core sample to investigate and characterize in situ strength by comparing that with drilling speeds.Results show that the average predicted strengths of treated silty clay are 2.4-6.9 times higher than the average measured strength of untreated silty clay.The UCS-based indices of 374.5%and 344.2%verified the quality assessment(QA)results by this new in situ method.This method provides a cost-effective tool for quality assessment of soil improvement by utilizing the digital drilling data.

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

A New Method for Monitoring Scattered Stray Light of an Inner-occulted Coronagraph

The scattered stray light of a coronagraph is a type of stray light that is generated by the objective lens as its surface defects are irradiated by sunlight.The defects mainly include dust and blemishes on the lens surface,microroughness of the lens surface,and impurity and inhomogeneity of the glass.Unlike the other types of relatively stable defects introduced when the objective lens is being manufactured,the scattered stray light caused by dusts on the lens surface is difficult to quantify accurately due to the disorder and randomness of the dust accumulation.The contribution of this type of stray light to the overall stray light level is difficult to determine through simulations and experiments.This can result in continuous deterioration of the stray light level of a coronagraph and thus affect the observation capabilities of the instrument.To solve this issue,through analyzing the forming mechanism of scattered stray light and ghost image generated by the inner-occulted coronagraph,we propose a novel method to monitor the scattered stray light from dusts by utilizing different stray light correlation coefficients.In this method,we first simulate and measure the level of stray light from the ghost image of the objective lens,and then determine the flux ratio of scattered light and ghost image on the conjugate plane.Although the flux ratio varies with the accumulation of dusts on the lens surface,it remains constant on the image plane.Therefore,the level of dust scattering light on the image plane can be obtained by using this ratio together with the level of ghost image stray light.The accuracy of this method has been validated in a laboratory by applying the objective lens with numerous surface cleanliness levels.

Synergistic catalysis of the N-hydroxyphthalimide on flower-like bimetallic metal-organic frameworks for boosting oxidative desulfurization

Synergic catalytic effect between active sites and supports greatly determines the catalytic activity for the aerobic oxidative desulfurization of fuel oils.In this work,Ni-doped Co-based bimetallic metal-organic framework(CoNi-MOF)is fabricated to disperse N-hydroxyphthalimide(NHPI),in which the whole catalyst provides plentiful synergic catalytic effect to improve the performance of oxidative desulfurization(ODS).As a bimetallic MOF,the second metal Ni doping results in the flower-like morphology and the modification of electronic properties,which ensure the exposure of NHPI and strengthen the synergistic effect of the overall catalyst.Compared with the monometallic Co-MOF and naked NHPI,the NHPI@CoNi-MOF triggers the efficient activation of molecular oxygen and improves the ODS performance without an initiator.The sulfur removal of dibenzothiophene-based model oil reaches 96.4%over the NHPI@CoNi-MOF catalyst in 8 h of reaction.Furthermore,the catalytic product of this aerobic ODS reaction is sulfone,which is adsorbed on the catalyst surface due to the difference in polarity.This work provides new insight and strategy for the design of a strong synergic catalytic effect between NHPI and bimetallic supports toward high-activity aerobic ODS materials.

Reinforcement Learning in Process Industries:Review and Perspective

This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.

Metallogenesis of the Baidi Au-Sb deposit, southwest Guizhou Province, China: mineralogical and geochemical evidence from sulfur-bearing minerals

The Baidi Au-Sb deposit, which contains 8 t of Au and 10,979 Mt of Sb, is a typical and rare paragenetic deposit located in southwestern Guizhou Province, China.Previous studies have focused on individual ores, but have not combined them to identify their paragenetic mechanism or metallogenic regularity. Therefore, we used field investigations, microscopic observations, and in situ analyses to identify the spatial distribution, mineral paragenesis, compositional evolution, and metallogenic material sources of the ore bodies. We also determined the Au and Sb paragenetic characteristics and the metallogenesis of the deposit. The main Au-bearing minerals in the deposit were early(Apy1–2) and late(Apy3) stage arsenopyrites, as well as pre-mineralization(Py1), mineralization(Py2–5), and late mineralization(Py6–7) stage pyrites. The main Sb-bearing minerals were stibnite(Snt), skinnerite, bournonite,and valentinite. The minerals formed in the order of Py1,Py2–3 + Apy1, Py4–5 + Apy2, Snt, and Py6–7 + Apy3.The δ34S values of the arsenopyrites and pyrites ranged from-5 to 5‰, while those of stibnite were mostly less than-5‰ in the later mineralization stages. Sulfur was provided by deep magmatic hydrothermal fluids, but sedimentary sulfur was added in the later stages. Moreover,the trace elemental contents fluctuated and eventually became similar to those of the sedimentary strata. By comprehensively considering the ores along with the geological characteristics of the deposit, we determined that deep magma provided the Au during ore formation. Later tectonic changes provided Sb from the sedimentary strata,which precipitated along fault expansion areas and produced Au and Sb paragenesis.

The Impact of Bias Row Noise to Photometric Accuracy:Case Study Based on a Scientific CMOS Detector

We tested a new model of CMOS detector manufactured by the Gpixel Inc,for potential space astronomical application.In laboratory,we obtain some bias images under the typical application environment.In these bias images,clear random row noise pattern is observed.The row noise also contains some characteristic spatial frequencies.We quantitatively estimated the impact of this feature to photometric measurements,by making simulated images.We compared different bias noise types under strict parameter control.The result shows the row noise will significantly deteriorate the photometric accuracy.It effectively increases the readout noise by a factor of2-10.However,if it is properly removed,the image quality and photometric accuracy will be significantly improved.

Origin of tradeoff between movement velocity and attachment duration of kinesin motor on a microtubule

Kinesin-1 motor protein is a homodimer containing two identical motor domains connected by a common long coiledcoil stalk via two flexible neck linkers. The motor can step on a microtubule with a velocity of about 1 μm·s-1and an attachment duration of about 1 s under physiological conditions. The available experimental data indicate a tradeoff between velocity and attachment duration under various experimental conditions, such as variation of the solution temperature,variation of the strain between the two motor domains, and so on. However, the underlying mechanism of the tradeoff is unknown. Here, the mechanism is explained by a theoretical study of the dynamics of the motor under various experimental conditions, reproducing quantitatively the available experimental data and providing additional predictions. How the various experimental conditions lead to different decreasing rates of attachment duration versus velocity is also explained.

Transfer learning framework for multi-scale crack type classification with sparse microseismic networks

Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.

基于Arduino与Processing的心率检测计设计研究

随着社会经济的高速发展,人们的物质生活也有了极大的提高,但同时也伴随着各种疾病的到来,身体健康已经成为人们普遍关注的焦点,因此,心率检测仪、血压计、血糖仪等各种家用医疗监测仪器已经逐渐融入日常生活。心脏病是人们难以预防的突发致命疾病之一,本文介绍的是一款基于Arduino【是基于易用硬件和软件的原型开源平台,包由可编程的电路板(简称微控制器),以及集成开发环境(称为Arduino IDE)的现成软件组成】Processing(开源编程语言,包括编辑器、编译器、展示器)的简易心率检测计系统,其功能实用、操作简单,可以测量心率,当超出正常心率范围时及时预警,是一款便携的实时心率测试仪。

Shallow Convection Dataset Simulated by Three Different Large Eddy Models

Shallow convection plays an important role in transporting heat and moisture from the near-surface to higher altitudes,yet its parameterization in numerical models remains a great challenge,partly due to the lack of high-resolution observations.This study describes a large eddy simulation(LES)dataset for four shallow convection cases that differ primarily in inversion strength,which can be used as a surrogate for real data.To reduce the uncertainty in LES modeling,three different large eddy models were used,including SAM(System for Atmospheric Modeling),WRF(Weather Research and Forecasting model),and UCLA-LES.Results show that the different models generally exhibit similar behavior for each shallow convection case,despite some differences in the details of the convective structure.In addition to grid-averaged fields,conditionally sampled variables,such as in-cloud moisture and vertical velocity,are also provided,which are indispensable for calculation of the entrainment/detrainment rate.Considering the essentiality of the entraining/detraining process in the parameterization of cumulus convection,the dataset presented in this study is potentially useful for validation and improvement of the parameterization of shallow convection.

Balancing the Quantum Speed Limit and Instantaneous Energy Cost in Adiabatic Quantum Evolution

Adiabatic time-optimal quantum controls are extensively used in quantum technologies to break the constraints imposed by short coherence times.However,practically it is crucial to consider the trade-off between the quantum evolution speed and instantaneous energy cost of process because of the constraints in the available control Hamiltonian.Here,we experimentally show that using a transmon qubit that,even in the presence of vanishing energy gaps,it is possible to reach a highly time-optimal adiabatic quantum driving at low energy cost in the whole evolution process.This validates the recently derived general solution of the quantum Zermelo navigation problem,paving the way for energy-efficient quantum control which is usually overlooked in conventional speed-up schemes,including the well-known counter-diabatic driving.By designing the control Hamiltonian based on the quantum speed limit bound quantified by the changing rate of phase in the interaction picture,we reveal the relationship between the quantum speed limit and instantaneous energy cost.Consequently,we demonstrate fast and high-fidelity quantum adiabatic processes by employing energy-efficient driving strengths,indicating a promising strategy for expanding the applications of time-optimal quantum controls in superconducting quantum circuits.

Observation of Giant Topological Hall Effect in Room-Temperature Ferromagnet Cr_(0.82)Te

Novel magnetic materials with non-trivial magnetic structures have led to exotic magnetic transport properties and significantly promoted the development of spintronics in recent years.Among them is the Crx Tey family,the magnetism of which can persist above room temperature,thus providing an ideal system for potential spintronic applications.Here we report the synthesis of a new compound,Cr_(0.82)Te,which demonstrates a record-high topological Hall effect at room temperature in this family.Cr_(0.82)Te displays soft ferromagnetism below the Curie temperature of 340 K.The magnetic measurement shows an obvious magneto-crystalline anisotropy with the easy axis located in the ab plane.The anomalous Hall effect can be well explained by a dominating skew scattering mechanism.Intriguing,after removing the normal Hall effect and anomalous Hall effect,a topological Hall effect can be observed up to 300 K and reaches up to 1.14μΩ·cm at 10 K,which is superior to most topological magnetic structural materials.This giant topological Hall effect possibly originates from the noncoplanar spin configuration during the spin flop process.Our work extends a new Cr_(x)Te_(y)system with topological non-trivial magnetic structure and broad prospects for spintronics applications in the future.

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

Modeling and analysis of air combustion and steam regeneration in methanol to olefins processes

Light olefins is the incredibly important materials in chemical industry.Methanol to olefins(MTO),which provides a non-oil route for light olefins production,received considerable attention in the past decades.However,the catalyst deactivation is an inevitable feature in MTO processes,and regeneration,therefore,is one of the key steps in industrial MTO processes.Traditionally the MTO catalyst is regenerated by removing the deposited coke via air combustion,which unavoidably transforms coke into carbon dioxide and reduces the carbon utilization efficiency.Recent study shows that the coke species over MTO catalyst can be regenerated via steam,which can promote the light olefins yield as the deactivated coke species can be essentially transferred to industrially useful synthesis gas,is a promising pathway for further MTO processes development.In this work,we modelled and analyzed these two MTO regeneration methods in terms of carbon utilization efficiency and technology economics.As shown,the steam regeneration could achieve a carbon utilization efficiency of 84.31%,compared to 74.74%for air combustion regeneration.The MTO processes using steam regeneration can essentially achieve the near-zero carbon emission.In addition,light olefins production of the MTO processes using steam regeneration is 12.81%higher than that using air combustion regeneration.In this regard,steam regeneration could be considered as a potential yet promising regeneration method for further MTO processes,showing not only great environmental benefits but also competitive economic performance.

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