Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

Combinatorial Synthesis and High-Throughput Characterization of Microstructure and Phase Transformation in Ni-Ti-Cu-V Quaternary Thin-Film Library

Ni-Ti-based shape memory alloys(SMAs)have found widespread use in the last 70 years,but improving their functional stability remains a key quest for more robust and advanced applications.Named for their ability to retain their processed shape as a result of a reversible martensitic transformation,SMAs are highly sensitive to compositional variations.Alloying with ternary and quaternary elements to finetune the lattice parameters and the thermal hysteresis of an SMA,therefore,becomes a challenge in materials exploration.Combinatorial materials science allows streamlining of the synthesis process and data management from multiple characterization techniques.In this study,a composition spread of Ni-Ti-Cu-V thin-film library was synthesized by magnetron co-sputtering on a thermally oxidized Si wafer.Composition-dependent phase transformation temperature and microstructure were investigated and determined using high-throughput wavelength dispersive spectroscopy,synchrotron X-ray diffraction,and temperature-dependent resistance measurements.Of the 177 compositions in the materials library,32 were observed to have shape memory effect,of which five had zero or near-zero thermal hysteresis.These compositions provide flexibility in the operating temperature regimes that they can be used in.A phase map for the quaternary system and correlations of functional properties are discussed w让h respect to the local microstructure and composition of the thin-film library.

An efficient method for constructing a random insertional mutant library for forward genetics in Nannochloropsis oceanica

Insertional mutation,phenotypic evaluation,and mutated gene cloning are widely used to clone genes from scratch.Exogenous genes can be integrated into the genome during non-homologous end joining(NHEJ)of the double-strand breaks of DNA,causing insertional mutation.The random insertional mutant library constructed using this method has become a method of forward genetics for gene cloning.However,the establishment of a random insertional mutant library requires a high transformation efficiency of exogenous genes.Many microalgal species show a low transformation efficiency,making constructing random insertional mutant libraries difficult.In this study,we established a highly efficient transformation method for constructing a random insertional mutant library of Nannochloropsis oceanica,and tentatively tried to isolate its genes to prove the feasibility of the method.A gene that may control the growth rate and cell size was identified.This method will facilitate the genetic studies of N.oceanica,which should also be a reference for other microalgal species.

An Arabic Transformation Based Approach to Automatic Paraphrasing of Syntactic Sentences

The aim of this paper is to exploit the existing Lexicon-Grammar(LG)tables,as well as to assess their relative importance vis-à-vis the concept of transformation and automatic paraphrasing.These operations include multiple processes at the lexical,morpho-syntactic,and semantic levels.Our proposal is to model highly productive phenomena of the Arabic language,such as pronominalization and passivization,dedicated to the both Arabic verb classes and Multiword Expressions(MWEs),in order to formalize the relation between structures and their semantic properties and thus to represent the symmetry and pairs between sentences that share a predicate that links the noun and a support verb.Furthermore,the automatic process of paraphrasing involves both the distributional and transformative features of each class of verbs or other structures such as Arabic MWEs.This research in progress outlines how to build Lexicon-Grammar tables for Arabic syntactic sentences by using automatic paraphrasing in a large transformational grammar on the one hand,and to implement it into both NooJ electronic dictionaries and local grammars on the other hand.

基于SimPowerSystems的自动控制系统仿真探索

将Matlab/Simulink平台中SimPowerSystems用于自动控制系统仿真,使控制系统仿真的质和量都得到了很大提高。利用SimPowerSystems的各模块组,可以像实际电路接线一样,组成典型的控制环节及控制系统,所用的信号源、基本元器件以及各种测试仪器都与现场实物相仿,但功能更强,使用更方便。

Fault Diagnosis Method Based on Fractal Theory and Its Application in Wind Power Systems

The non-linear dynamic theory brought a new method for recognizing and predicting complex non-linear dynamic behaviors. The non-linear behavior of vibration signals can be described by using fractal dimension quantitatively. In this paper, a fractal dimension calculation method for discrete signals in the fractal theory was applied to extract the fractal dimension feature vectors and classified various fault types. Based on the wavelet packet transform, the energy feature vectors were extracted after the vibration signal was decomposed and reconstructed. Then, a wavelet neural network was used to recognize the mechanical faults. Finally, the fault diagnosis for a wind power system was taken as an example to show the method's feasibility.

AUTOMATIC SEARCHING OF CONTROL POINT IN NOAA AVHRR IMAGE

This paper discusses the approaches for automatical searching of control points in the NOAA AVHRR image on the basis of data rearrangement in the form of latitude and longitude grid. The vegetation index transformation and multi-level matching strategies have been proven effective and successful as the experiments show while the control point database is established.

A New Algorithm for Automatic Double Bright Fringe of Multiple-Beam Fizeau Fringe Skeletonization Using Fourier Transform Method of Fringe Pattern Analysis

The interferogram of multiple-beam Fizeau fringe technique plays an important role to investigate the optical properties of fiber because this interferogram provides us with useful information which can used to determine the dispersion curve of the fiber sample. A common problem in any interferogram analysis is the accuracy in locating fringe centers (fringe skeleton). There are a lot of computer-aided algorithms, which depend on the interferogram types, used to fringe skeleton extraction of various digital interferogram. In this paper, as far as I know, a novel algorithm for fringe skeleton extraction of double bright fringe of multiple-beam Fizeau fringe is presented. The proposed algorithm based on using the different order of Fourier transform and the derivative-sign binary image. Also the proposed algorithm has been successfully tested by using a computer simulation fringe and an experimental pattern. The results are compared with the original interferogram and shown a good agreement.

A Convolutional and Transformer Based Deep Neural Network for Automatic Modulation Classification

Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.

Strategic Transformation of Special Libraries

Under the shocks of digitalization and networking,National Science Library of the Chinese Academy of Sciences(NSLC)has executed the transformation strategy for knowledge-based services,and fully pushed forward the literature information services of NSLC.Supervised by the Chinese Academy of Sciences(CAS),NSLC has established the Literature and Information Branch of Chinese Alliance of

Research on Part Experssion Methodology of Part Library Based on ISO13584

ISO13584(PLIB) is an international standard, which is established to realize computer’s identification of data expression and data exchange of part library. In this international standard, part expression methodology of part library is an important characteristic, which distinguishes itself from STEP. So, the methodology is a focus in the research of part library. This article describes the principles of part information expression of part library based on ISO13584, and the research results of the methodology in details.

Robust Radiometric Normalization of the near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis

Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

新时代 新征程 新伟业——大阅读时代图书馆高质量发展思考

结合近年来国内外图书馆的发展理念与创新实践,从形势与任务、使命与愿景、理念与范式、战略与路径4个维度,对大阅读时代图书馆高质量发展这一议题进行阐述与思考。

面向RISC-V的基础数学库实现

RISC-V指令集架构(Instruction Set Architecture,ISA)作为一种新兴的精简ISA,因免费、开源、自由等特点而得到快速发展.由于国内外对RISC-V的研究主要集中在硬件开发,软件生态相较于成熟ISA还很薄弱,实现一套RISC-V指令集高性能基础数学库可以进一步丰富RISC-V软件生态.本文基于自动化移植技术实现申威数学库到RISC-V的移植,为RISC-V指令架构提供首个使用向量指令优化的基础数学库系统.本文提出向量寄存器自动分支查表法与路径标记插入法,重点解决不同架构间寄存器映射过程中的寄存器复用问题,实现寄存器正确高效映射,并依据不同指令等价转换策略自动化移植数学函数69个.测试结果表明,RISC-V基础数学库函数可实现正确计算,最大误差为1.90ULP,函数性能平均为157.03节拍.

数字化转型背景下高校图书馆数字基座的构建探索:以复旦大学图书馆为例

高校图书馆数字基座是一种支撑图书馆数字化转型的数字基础设施,其核心结构由数据中心、业务中心、基础服务中心、物联中心和AI服务中心组成,为业务转型和服务创新提供数据能力、通用业务能力和数字技术应用能力的支撑。本文以复旦大学图书馆数字基座的建设和成效为例,探索高校图书馆数字基座的建设思路、策略和方法,提出数字基座的建设需要在数字化转型战略指引下,适度超前规划并完善总体信息技术架构;围绕用户需求,从平台工具、数据资源、运营机制、场景创新、标准规范等方面进行系统性的构建和持续演进。

智能技术驱动下的图书馆服务现代化:转型与创新

智能技术是图书馆服务现代化转型的重要助推器,文章提出了三种转型模式:“图书馆+AI”的增强型变革、“图书馆×AI”的解构型变革以及“图书馆∞AI”的新生型变革,现代图书馆正朝着多模态感知、个性化精准化、多模态交互式服务方向转变。文章从AI赋能下的学习支持服务、科研服务创新、学科情报决策服务创新等方面展开讨论,强调了作为“软智能”的图书馆馆员在AI环境下能力重塑的重要性,并对未来图书馆服务的发展趋势和挑战进行展望。

基于改进YOLOv7的自动驾驶目标检测方法

针对自动驾驶场景下,车辆目标密集、相互遮挡和目标过小导致的误检、漏检问题,提出一种改进YOLOv7的车辆目标检测算法。在主干网络SPPCSPC后加入ACmix混合注意力机制,充分挖掘特征信息,增强网络对车辆信息的关注度,减少其他目标的干扰,提高检测精度;在Neck端中加入Swin Transformer,收集全局信息;添加160×160尺寸目标检测头,以增加锚点的数量和密度,提高网络对小目标的感知能力;最后利用Soft-NMS柔性非极大值抑制剔除冗余候选框,改善漏检能力。通过实验验证了改进的可行性并与五种主流网络进行了对比,平均精度达到91.5%,与基础网络YOLOv7相比,平均精度提高7.1%,运行速度达到105 FPS,证明了改进方法的有效性。

数字化转型背景下鲁班工坊教学资源库建设研究

优质数字化教学资源库的建设、应用及共享,是促进高职院校人才培养质量提高的有效途径。文章阐述了在数字化背景下鲁班工坊教学资源库的建设目标、建设内容、建设条件,以及建设理念和思路,分析了鲁班工坊教学资源库的设计和开发过程,介绍了天津渤海职业技术学院依托鲁班工坊进行的数字化教学资源库建设情况。