Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.

Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalography

Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.

Role of Advanced Materials in Electrical Machines

There has been a revived and growing role for electrical machines and drives across a wide range of applications.Such applications include,hybrid/electrical traction applications,aerospace applications,and renewable energy.All these applications present different set of requirements and challenges.The common trend is that there is a need for higher-performance electrical machines in terms of higher power/torque density,and higher efficiency while keeping cost under control.There has been a lot of work done around coming up with novel machine topologies,optimizing more conventional topologies as well as improved thermal management schemes.Like many other areas of engineering/research,advanced materials can play a key role in opening up the design space for electrical machines leading to a step improvement in their performance.This paper will present an overview of some of the key advanced materials that are either recently developed or are currently under development and their potential impact on electrical machines.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms

This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are used.Five geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.

Prediction of multifaceted asymmetric radiation from the edge movement in density-limit disruptive plasmas on Experimental Advanced Superconducting Tokamak using random forest

Multifaceted asymmetric radiation from the edge(MARFE) movement which can cause density limit disruption is often encountered during high density operation on many tokamaks. Therefore, identifying and predicting MARFE movement is meaningful to mitigate or avoid density limit disruption for the steady-state high-density plasma operation. A machine learning method named random forest(RF) has been used to predict the MARFE movement based on the density ramp-up experiment in the 2022’s first campaign of Experimental Advanced Superconducting Tokamak(EAST). The RF model shows that besides Greenwald fraction which is the ratio of plasma density and Greenwald density limit, dβp/dt,H98and d Wmhd/dt are relatively important parameters for MARFE-movement prediction. Applying the RF model on test discharges, the test results show that the successful alarm rate for MARFE movement causing density limit disruption reaches ~ 85% with a minimum alarm time of ~ 40 ms and mean alarm time of ~ 700 ms. At the same time, the false alarm rate for non-disruptive and non-density-limit disruptive discharges can be kept below 5%. These results provide a reference to the prediction of MARFE movement in high density plasmas, which can help the avoidance or mitigation of density limit disruption in future fusion reactors.

HSP超前探测技术在煤矿TBM掘进巷道中的应用研究

随着全断面掘进机TBM(Tunnel Boring Machine)逐渐应用于煤矿岩巷掘进,对不良地质构造进行超前准确快速预测的需求日益迫切。通过对主动源地震波超前探测方法的特点和TBM破岩震源超前探测技术的适用性进行分析,结合煤矿巷道地质和生产条件,提出了适用于煤矿巷道TBM掘进的HSP超前探测方法。以河南平顶山首山一矿TBM掘进底板瓦斯治理巷道为工程背景,选用防爆硬件一体化设计的探测仪器在煤矿巷道中进行应用。构建了空间型观测方式对煤矿巷道近水平煤线进行探测,优化了双护盾TBM掘进巷道狭小空间检波器阵列式布置参数;基于时频分析、互相关干涉处理、反射与散射联合反演等方法处理原始信号并进行探测结果成像。研究表明:采用空间型观测方式可实现与巷道小角度斜交煤线的识别,设计震源与首检波器间距离为15 m时最优。通过时频分析提取有效信号,利用互相关干涉法获取虚拟震源道和反射特征曲线,并结合反射与散射联合反演成像得到探测区域地层反射能量分布图,能够较准确地推测得到围岩存在的不良地质构造。通过比较现场开挖揭露情况与探测结果发现两者吻合度较高,表明HSP超前探测方法可实现掘进工作面前方100 m范围内超前无损地质预测,有助于提高煤矿岩巷TBM掘进速度。

机械原理课程高阶教学模式探索与实践——以运动副的分析与设计为例

结合新工科、工程教育专业认证,探索适合于机械原理课程特点的高阶教学模式,并结合课程中重要的知识点——运动副的分析与设计进行实践。实践证明:高阶教学模式能够培养学生的高阶思维,契合新工科、工程教育专业认证背景下工科学生的培养要求;高阶教学模式,可以培养学生自主学习,最终达到“教为不教”的教学目的。

竖井巷道掘进超前地质探测研究进展与展望

以掘进机为代表的机械破岩是未来竖井巷道掘进技术发展的方向,为保障竖井巷道快速机械智能化掘进的安全,超前地质探测是不可或缺的工序。从探测范围、适用条件以及优势不足等方面对常规超前地质探测技术和随掘超前探测技术的发展现状与特点进行了分类总结,常规超前探测技术各有一定的适用范围,在钻爆法施工环境中得到了较好地应用。在面对掘进机复杂的施工环境时,常规超前探测技术难以适用。而随掘超前探测可以实现掘进与超前地质探测同步进行,实时预测工作面前方的不良地质,是井巷机械智能化掘进超前地质探测技术研究的重点。其中,竖井全断面掘进机是综合机械化凿井的发展方向和趋势,但其施工环境非常复杂,基于掘进机破岩震源地震波的超前地质探测是有效的预测方法。针对全断面竖井掘进机破岩震源超前探测方法的难点,即施工环境和破岩震源地震波场的双重复杂性,从多个方面提出了解决思路:针对震源先导信号,采用多种方法联合去噪,压制破岩震源干扰波;对于地震记录信号,采用以互相关为核心的地震记录重构方法恢复有效波场;开展竖井全空间三维立体探测和高精度成像的研究等。此外,开展多种随掘物探方法联合反演能够提高地质判识的可靠性和解释精度。研发竖井掘进机的掘探一体化装备是未来深入研究的方向。

掌上型核磁共振控制台的设计与实现

常规的核磁共振仪器具有体积大,不易携带等缺点,限制了其在现场石油勘探、食品安全、环境污染、质检等领域的应用.为此,本文提出了一种掌上型核磁共振控制台设计方案,在一块可编程片上系统芯片Zynq-7000上,通过高级精简指令集计算机(Advanced RISC Machine,ARM)核构建、现场可编程门阵列(Field Programmable Gate Array,FPGA)逻辑设计和控制程序设计,完成了整个掌上型核磁共振控制台的设计与实现.全部设计完成后,在课题组自研的0.5 T桌面式核磁共振系统上,进行了自由感应衰减(Free Induction Decay,FID)、自旋回波(Spin Echo,SE)和CPMG(Carr-Purcel1-Meiboom-Gill)几个基本脉冲序列的测试,验证了其整体架构设计的正确性和各个模块之间的协调性.设计的核磁共振控制台长10.6 cm,宽6.0 cm,高1.9 cm,在缩小体积的同时,还提高了脉冲序列的实时性和控制台的稳定性,为进一步研制便携式核磁共振仪器奠定了基础.

AutoML: A systematic review on automated machine learning with neural architecture search

AutoML(Automated Machine Learning)is an emerging field that aims to automate the process of building machine learning models.AutoML emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine learning is applied.In particular,research has been conducted for a long time on technologies that can effectively develop high-quality models by minimizing the intervention of model developers in the process from data preprocessing to algorithm selection and tuning.In this semantic review research,we summarize the data processing requirements for AutoML approaches and provide a detailed explanation.We place greater emphasis on neural architecture search(NAS)as it currently represents a highly popular sub-topic within the field of AutoML.NAS methods use machine learning algorithms to search through a large space of possible architectures and find the one that performs best on a given task.We provide a summary of the performance achieved by representative NAS algorithms on the CIFAR-10,CIFAR-100,ImageNet and wellknown benchmark datasets.Additionally,we delve into several noteworthy research directions in NAS methods including one/two-stage NAS,one-shot NAS and joint hyperparameter with architecture optimization.We discussed how the search space size and complexity in NAS can vary depending on the specific problem being addressed.To conclude,we examine several open problems(SOTA problems)within current AutoML methods that assure further investigation in future research.

From tunnel boring machine to tunnel boring robot: perspectives on intelligent shield machine and its smart operation

Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional shield machine industry arising from construction environment and manual operations.This study presents a systematic review of intelligent shield machine technology,with a particular emphasis on its smart operation.Firstly,the definition,meaning,contents,and development modes of intelligent shield machines are proposed.The development status of the intelligent shield machine and its smart operation are then presented.After analyzing the operation process of the shield machine,an autonomous operation framework considering both stand-alone and fleet levels is proposed.Challenges and recommendations are given for achieving autonomous operation.This study offers insights into the essence and developmental framework of intelligent shield machines to propel the advancement of this technology.

TBM窄刃刀与标准刀现场性能对比测试与分析

极硬岩条件下如何进行刀具选型,使TBM掘进速度和刀具消耗取得综合效益,是有待解决的具有争议性的问题。为此,依托新疆YE工程现场TBM实际掘进,在岩石单轴抗压强度为100~150 MPa的完整Ⅱ类凝灰岩条件下,对比测试刀盘主要区域窄刃刀和标准刀的掘进性能表现以及刀具消耗情况,探索降低滚刀刀圈刃宽,提升TBM贯入度和掘进速度的可行性与合理性。结果表明:1)使用窄刃刀破岩对贯入度和施工速度有着较为明显的提升,且随着围岩抗压强度的增加,窄刃刀对掘进速度的提升效果明显;2)与标准刀相比,窄刃刀消耗数量也有较大增加,但2种滚刀的失效形式占比情况大致相同,对TBM作业利用率影响极小;3)经综合成本估算,窄刃刀通过提升施工速度节省的成本远高于刀具消耗增加的成本。

煤矿岩巷TBM掘进随掘地震信号特征及其应用

随掘地震超前探测技术可实现探掘平行,为巷道快速智能掘进场景下实时、精准地质保障提供了可能。随掘震源产生的是复杂、变频、连续信号,信号特征认知直接影响数据处理与成像精度,而目前针对岩巷全断面掘进机(TBM)随掘地震信号特征的认知仍不清晰,且暂时还没有针对性开展过信号处理与成像研究工作。针对上述问题,以谢桥煤矿瓦斯治理巷TBM随掘地震超前探测试验为例,分析了刀盘先导信号与岩壁接收信号的时间域、频率域及时频域特征:岩巷TBM随掘地震信号中不同振幅能量成分比例呈现金字塔形,但分布随机,不对称程度较高;机械运转信号能量较大,刀盘先导信号强度是岩壁接收信号的200倍左右;频率域变频特征明显;机械运转信号基础频率较低,刀盘先导信号频率成分主要集中在10~80 Hz与150~200 Hz,主频为36.99 Hz,岩壁接收信号频率成分主要集中在50~200 Hz,主频为137.97 Hz;刀盘先导信号较岩壁接收信号时频域能量团分布更为规则,多次震源激发现象明显,能量团之间的差异性特征表明了多次震源激发时振幅能量与持续时间的随机性。利用脉冲化算法与绕射叠加偏移成像方法对岩巷TBM随掘地震信号进行数据处理与成像试验,结果表明:(1)脉冲化等效单炮记录与利用常规震源得到的超前探测单炮记录特征一致性较强,同相轴清晰且连续性较好,可满足现场探测分析需要。(2)对探测范围内岩体情况的超前预报结果与实际揭露情况一致,说明岩巷TBM随掘地震超前探测可提供有效地质保障。

融合STPA及有限状态机的ADAS触发条件生成机制

现有高级辅助驾驶系统(Advanced Driver Assistance Systems,ADAS)功能不断增多且系统复杂性不断提高,不可避免带来了预期功能安全(Safety of the Intended Functionality,SOTIF)问题。触发条件的识别与生成是预期功能安全活动中重要的一环,然而现有对触发条件识别仅借助系统过程理论分析方法(System Theoretic Process Analysis,STPA)进行分析,未充分考虑系统功能状态转换中存在的问题。本文以知识驱动的方式构建触发条件识别机制,将STPA及有限状态机(Finite State Machine,FSM)理论融合构建拓展型系统控制结构,针对拓展型控制架构及功能状态转换进行安全分析,根据系统存在的功能局限及人为误用,完成触发条件的识别、生成、规范化描述、分类及标签化。最后将本文提出的触发条件生成机制应用于集成式巡航辅助系统(Integrated Cruise Assistance,ICA),得到了该系统的触发条件及其分类,并将本文所提出的生成机制与现有相关触发条件生成方法进行对比分析,证明了本机制的实用性、可行性及有效性。

基于数字钻探与多尺度模型融合的隧道岩体完整性自动解译技术研究及应用

在多岩性与多指标钻探数据收集的基础上,综合考虑解译精度与预报效果,借助机器学习工具,提出一种基于数字钻探与多尺度模型融合的隧道岩体完整性自动解译技术。首先,对原始钻探数据有针对性的进行降噪与等距分割(0.5,1,2 m)等预处理,形成多尺度、高质量机器学习数据集;然后,进行模型参数自动寻优、训练、评估与可解释性等操作,验证模型的准确性与可靠性;最后,采用加权平均的方法进行多尺度模型解译结果的融合,以增强该技术的工程实用效果。为方便实际工程应用,以上述技术为核心开发轻量化数字钻探智能解译平台,经多条灰岩与砂岩隧道应用结果表明:对比地质雷达与常规钻探解译,多尺度模型融合解译在解译效率、预测效果等方面总体表现优异,可为隧道施工的开挖与支护提供可靠的岩体完整性信息。

石灰岩地层下土压平衡盾构机推进速度预测与分析

[目的]盾构机的推进速度不仅关系到隧道开挖的快慢,对于施工周期与施工成本的预测也具有重要意义,因此需对盾构机的推进速度进行预测和分析。[方法]以济南地铁3号线龙奥站—奥体中心西站区间全断面石灰岩地层为研究对象,收集盾构机掘进数据,采用空推值、异常值剔除及数据去噪等多种方法对数据进行预处理。为了提高预测精度,将数据集划分为训练集和验证集,通过线性回归建立了基于刀盘扭矩、总推进力的推进速度预测模型,并分析了掘进载荷对推进速度的影响规律。[结果及结论]在石灰岩地层下,3组推进速度预测模型的线性回归预测精度均在0.70以上,说明根据刀盘扭矩和总推进力可以正确预测推进速度。土压平衡盾构机在掘进过程中,刀盘转速和推进速度波动均较小,随着刀盘转速增大,刀盘扭矩逐渐减小;刀盘扭矩对推进速度有积极的影响,刀盘扭矩越大,推进速度越大;在同一地层和掘进条件下,由于刀盘挤土作用,仅增加总推进力不利于推进速度的提高,此时需同时增加刀盘转速。

桌面式电火花加工机床数控系统硬件设计

针对桌面式电火花加工机床数控系统的兼容性和加工放电要求,课题组设计了一款基于ARM和现场可编辑门阵列(field programmable gate array, FPGA)的电火花加工机床数控系统硬件平台。该平台任务分配均匀合理,以ARM处理器为核心搭建上位机,移植Linux系统进行多任务处理、人机界面、插补运算等任务;以FPGA为核心搭建的下位机系统负责运动控制、放电脉冲控制、辅助模块控制等任务;硬件平台的搭建采用了核心板+基板的方式并采用以太网口进行通信。试验结果表明该硬件平台搭建的数控系统可以较好地完成电火花小孔加工。该研究提供了一种移植性好、迭代升级成本低、适配多种脉冲电源的电火花加工机床数控系统硬件平台设计方案。

红外识别闸机控制系统的设计与实现

针对闸机通行逻辑算法数据处理复杂、实时性差等问题,设计红外识别闸机控制系统。该系统集成了通行逻辑、闸门控制、声光报警等功能;引入红外传感识别技术,结合旅客通行事件集,优化闸机通道传感器布局;基于位置识别思想,设计通行逻辑算法;采用32位AdvancedRISC Machine(ARM)微控制器,作为主控芯片。在国家铁路多个客运车站应用的结果表明,该系统误报率低,旅客行为检测精准,通行效率高,验证了其可靠性与实用性。

转支运移一体机的研制与应用

综采工作面超前支护是整个支护系统中的薄弱环节。传统的20 m超前支护采用液压单体支柱。由于液压单体支柱在支设和回撤时完全依靠人力,工人劳动强度大,耗工费时,安全性较差。为了提高液压单体支柱的支设和回撤效率、减轻工人的劳动强度,研制一种具有转载、支护、运输、自移功能的转支运移一体机。