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.

Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

System for Sewing Machine Industry Standard Innovation——Constructive Research for a Technical Standards System for Sewing Machines

Constructive research of a market-oriented industry's system of technical standardization by redefining those technical standards is the basis of innovation. Through considering and implementing innovation of the industry's standards, rapid development and standardization of the industry can be achieved.

An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries

Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.

Influence of aluminum content on machinability of AS series cast magnesium alloys

The influence of Al content on machinability of AS series cast Mg alloys was studied. The assessment of machinability of Mg alloys was performed by measuring the cutting forces and surface roughness. The microstructure and the tensile properties were also studied. The results reveal that cutting forces are increased with the increase of the Al content. Surface roughness and mechanical properties are the highest for AS91 Mg alloy. It is assumed that the main mechanism, which has an influence on the mechanical properties, is the presence of intermetallic phases(Mg2Si and Mg17Al12). Cutting forces increase with the increase of the cutting speed in machining of all Mg alloys. These measured data are in accordance with the mechanical properties of the machined alloys.

Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes

Two additional features are particularly useful in pixelwise satellite data segmentation using neural networks: one results from local window averaging around each pixel (MWA) and another uses a standard deviation estimator (MWSD) instead of the average. While the former’s complexity has already been solved to a satisfying minimum, the latter did not. This article proposes a new algorithm that can substitute a naive MWSD, by making the complexity of the computational process fall from O(N2n2) to O(N2n), where N is a square input array side, and n is the moving window’s side length. The Numba python compiler was used to make python a competitive high-performance computing language in our optimizations. Our results show efficiency benchmars

AED训练机辅助心肺复苏培训在麻醉科专业住院医师规范化培训中的效果观察

目的 探讨AED训练机辅助心肺复苏培训在麻醉科专业住院医师规范化培训中的效果。方法 选择该院麻醉科专业基地2022级住院医师共48人,随机分为观察组和对照组,观察组采用传统授课法+高级复苏模拟人配合监测反馈系统与AED训练机结合,对照组采用传统授课法+高级复苏模拟人配合监测反馈系统,比较两组理论及技能操作考核成绩,并进行满意度调查。结果 两组住院医师年龄、性别、理论考核成绩比较差异无统计学意义(P> 0.05),技能操作成绩差异有统计学意义(P<0.05)。观察组的技能学习、教学方法和教学效果满意度评分高于对照组,差异有统计学意义(P<0.05)。结论 传统授课法+高级复苏模拟人配合监测反馈系统与AED训练机结合模式应用于住院医师心肺复苏的日常培训能增强培训效果,使住院医师更好地掌握心肺复苏技术并能有效地应用于临床实践。

T/CMTBA 1010—2021《数控往复走丝电火花线切割机床性能评价规范》团体标准解读

介绍了《数控往复走丝电火花线切割机床性能评价规范》团体标准的制定背景、目的和主要内容,并在检验项目、关键指标等方面与现行行业标准进行了比对,解读了该团体标准的先进性、科学性和实用性。

基于管理标准化体系的医疗机构影像科机房项目改造研究

近年来,早期影像设备难以满足现阶段临床业务发展的问题凸显,基于工程项目管理标准化体系的医疗机构影像科机房改造建设至关重要。本文针对目前医疗机构影像科机房改造中普遍存在的工期紧迫、项目规模小但专业面广、新设备重量大、需协调部门多等问题,构建基于前期调研和设计-招标论证-施工-竣工验收的四阶段项目管理标准化体系。

机器旁教学在临床病理科住院医师常规技术教学中的应用

目的探讨临床病理科住院医师开展机器旁教学的可行性。方法对2019年—2021年在南京大学医学院附属鼓楼医院病理科接受住院医师规范化培训的57名住院医师开展机器旁教学,通过考核和问卷调查教学质量和满意度。结果结果显示,机器旁教学可巩固住院医师的理论知识、提高解决问题能力,并激发其对病理技术的学习兴趣。结论机器旁教学以自主动手实操为核心,能培养住院医师岗位胜任力和激发创新思维,可成为临床病理科常规技术教学的基本模式。

基于分类树的液化概率等级评估新方法

目前地震液化概率评估方法普遍公式形式复杂,多公式间概率计算值一致性不高,且在场地层面中液化概率意义体现不足。基于这些问题,在衡量多个概率公式方法的一致性及判别优势后,筛选各方法对现有实测历史样本的合理概率计算值标定历史样本的液化概率等级;以分类树方法建立初步液化预测模型,将其结合我国抗震设防推荐值优化后,构建一组液化概率等级评估新方法。新方法分为单孔与场地液化可能性评估两部分,为实际工程中的液化风险评估提供量化依据。与现有方法相比,新方法省略了传统方法的公式计算过程,评估过程简便,结果直观。选用新西兰地震液化数据和某实际工程场地作为检验样本,从单孔与工程场地两方面检验了新方法的可靠性和合理性。验证结果表明,新方法对单孔回判正确率达93%,对工程场地评价合理性优于现有规范。新方法可为工程场地液化可能性评估提供方法支持,具有实际工程意义。

烟田宜机化改造发展现状问题及对策研究

农业规模化、机械化、标准化是当代农业生产的发展目标,宜机化改造是实现目标的有效途径。烟草是我国重要的经济作物之一,烟田宜机化改造是烟草行业发展的重要方向之一。烟田全程农机化装备供给不足、农机农艺融合不紧密、烟田宜机化改造标准体系不完善已成为烟田宜机化改造面临的重要问题。为此,应从烟草宜机化品种培育、栽培模式及配套技术研究、宜机化装备关键技术升级、宜机化改造标准体系构建、后期维护标准体系构建、政策和制度建设等方面解决目前所面临的问题,加快农田宜机化改造进度,推动烟草农业的规模化、机械化、标准化经营发展。

智慧矿山系统工程及关键技术研究与实践

针对智慧矿山复杂巨系统建设过程中存在的问题,提出了智慧矿山建设的总体技术架构,并分别从智慧矿山系统模型构建、地下空间重构与模型动态更新、机器视觉测量技术、瓦斯环境射频防爆测试方法、矿山安全闭环管控体系、智慧矿山标准体系等方面进行了详细阐述。将智慧矿山分为信息感知支撑层、边缘计算层、云数据中心、多型网络、矿山智能生产管控平台、矿山智能生产系统、智慧矿山运维管理系统,构建了基于多系统深度融合的智慧矿山技术架构。基于智慧矿山知识图谱及信息抽取技术,构建了以数据创新为驱动、通信网络为基础、数据算力为核心的智慧矿山系统模型。构建了以机器视觉感知信息为主、其他感知信息为辅的矿山井下三维视觉空间模型,提出了矿山井下场景三维视觉与空间重建框架,实现了矿山井下三维空间重构与动态更新。开发了基于机器视觉技术的井下设备位姿与煤岩界面识别算法,实现了对井下综采装备群及煤岩分界面的同时空测量。探讨了井下瓦斯环境下现行防爆标准对5G基站功率的限制及存在的不足,设计开发了射频电磁能防爆专用试验装置,为提升井下5G基站功率阈值提供了方法借鉴。提出了融合灾害信息全面感知、防控方案自主决策、防控装备协同控制的矿井灾害闭环管控系统技术架构,实现井下灾害的超前预测预警与协同防控。构建了智能化煤矿标准体系框架,剖析了我国煤矿与金属矿山智能化建设典型案例,提出了智慧矿山建设发展趋势及建议。

电力标准数字化:概念、核心挑战、治理路线图及发展趋势

标准数字化是关乎国家标准化高质量发展的重要因素,也是推进数字化转型发展的重要手段。电力标准数字化是面向电力标准的数字化转型研究,是标准数字化发展的重点内容与重点方向,对促进我国能源电力数字化转型具有重要意义。当前标准数字化相关研究集中在航空、智能制造等领域,缺乏电力标准数字化的概念、核心挑战、治理路线图等方面探索。该文首先综述了国内外标准数字化发展现状,并分析了不同行业开展标准数字化的发展需求,对比了电力同其他领域开展标准数字化的差异性需求。其次,围绕标准数字化内涵及外延2个方面,进一步阐述了电力标准数字化基本概念。并深入阐述了电力标准数字化核心能力、核心技术与核心挑战。同时,从电力标准数字化治理对象、治理模型、实施路径3个方面,提出了电力标准数字化治理路线图。最后,研判了未来电力标准数字化发展趋势,提出了电力标准数字化发展建议,可为开展电力标准数字化研究提供经验参考。

电加工机床安全国际标准解读

ISO 28881:2022是电加工机床安全领域的现行国际标准,主要规定了相关设备的安全要求和防护措施。为促进我国电加工机床产业更好地与国际接轨,对该国际标准进行了解读,在帮助相关单位了解国际标准的同时,引导其开展采标验证和风险评估等工作,不断提升国产电加工机床的安全与防护水平。与我国早期编制的相关安全技术标准相比,ISO 28881:2022在电磁兼容、防火、噪声及有害物质等方面的技术要求更规范、检验方法更科学,我国电加工机床制造企业应当积极探索国际标准的先进技术,从而减少技术性贸易壁垒,更好地适应国际贸易的需要。

标准数据形态变革视域下我国标准数字化转型发展综述

标准数字化是标准从文本形态迈向数据形态的变革性发展方向,也是当今世界数字化转型发展的必然趋势。鉴于学界对标准数字化的关键问题、核心挑战及对策尚未形成统一观点,梳理了标准数字化的概念和需求,提出了7个发展需求、4个现实逻辑、11个核心挑战,提出了标准数字化赋能国家标准化发展的建议。研究表明,标准数字化是一个工程问题,需根据实际情况判断是否有必要开展标准数字化转型;要重视数据安全及个人隐私保护;要意识到标准数字化涉及众多产业链的韧性问题;要根据存量标准和增量标准两种情况分别推进机器可读标准发展。

机器可识别标准实现路径及保障措施

标准作为经济活动和社会发展的技术支撑,是行业共同遵守的准则和依据。随着产业体系的数字化转型,标准也急需向网络化、数字化、智能化发展。标准数字化发展主要阶段中,机器可识别作为其数据源基础受到广泛关注。本文系统梳理机器可识别标准转化的关键技术路线,结合当前现状,提出传统标准向机器可识别标准转化的保障措施。最后,对机器可识别标准的挑战及发展进行总结,以期对标准数字化转型的发展提供借鉴。

冬小麦水分利用效率相关驱动因子及其预测模型构建

【目的】水分利用效率能够综合反映冬小麦生长适宜度和能量转化效率,筛选并探究冬小麦各生育时期响应标准化水分利用效率(WP*)的驱动因子,构建相关驱动因子的WP*预测模型,对黄淮海平原冬小麦水分利用效率监测及水资源高效利用具有重要意义。【方法】以冬小麦为研究对象,设置3个水分处理,包括水分亏缺处理(W1):35 mm和(W2):48 mm,对照处理(W3):68 mm,获取冬小麦拔节期、孕穗期和灌浆期的冠层温度参数、生理指标和标准化水分利用效率(WP*)。通过逐步回归和通径分析筛选各生育时期响应WP*变化的主要驱动因子,探究WP*与相关驱动因子间关系,最后采用偏最小二乘回归(PLSR)和支持向量机(SVM)方法构建各生育时期基于驱动因子的WP*预测模型。【结果】较对照处理(常规灌溉),水分亏缺处理下冬小麦冠层温度参数、生理指标和WP*均表现出显著差异。基于逐步回归方法筛选出了各生育时期响应WP*的主要驱动因子,并采用通径分析得到各驱动因子响应WP*敏感程度排序,即拔节期依次为冠层温度极差(MTD)、气孔导度(Gs)、叶片含水量(LWC)和POD;孕穗期依次为冠层相对温差(CRTD)、等效水厚度(EWT)、可溶性糖含量(SSC)和作物水分胁迫指数(CWSI);灌浆期依次为SSC、冠层温度标准差(CTSD)、LWC和Gs。最后,采用偏最小二乘回归(PLSR)和支持向量机(SVM)基于筛选后的驱动因子构建了各生育时期WP*预测模型,其中以SVM构建的孕穗期WP*预测模型精度最优,R2cal(R^(2)val)、RMSEcal(RMSEval)和n RMSEcal(n RMSE_(val))分别为0.945(0.926)、0.533 g·m^(-2)(0.580 g·m^(-2))和2.844%(3.075%)。【结论】通过筛选冬小麦各生育时期响应WP*的相关驱动因子信息及构建冬小麦水分利用效率预测模型,为黄淮海平原冬小麦水分精准监测与管理提供了理论基础。

船舶标准数字化发展趋势初探

随着技术的飞速发展,数字经济与实体经济深度交融,共同构筑起现代经济的骨架,质量基础设施扩建、产业价值链优化和企业造血能力提升对标准有了更高的要求。2021年《国家标准化发展纲要》提出标准化工作下一步发展方向为数字化、网络化和智能化。以机器可读标准为主导的未来标准新式形态涌现,标准数字化越发成为标准迎接数字化转型的关键和核心。本文通过分析船舶标准数字化现状,从标准化研究、标准应用模式和标准业务创新三个角度出发,探究了船舶标准数字化发展趋势,对顶层布局、人才建设、现状深挖、试点应用提出具有参考价值的思路与建议。

标准数字化转型中标准标签集发展与应用分析

本文聚焦标准数字化转型中标准标签集这一关键技术,系统回顾了标准标签集从ISO STS演变到NISO STS的发展历程,并分析了两者的区别。随后围绕NISO STS的组成,明确了标准标签集中元素和属性的特征及用法,并分析了<standard>和<adoption>两种根元素结构。最后结合标准标签集在国内外的应用情况,分别介绍了相关典型案例,并指出NISO STS需要进一步引入语义更丰富的元素,从而增强标准的机器可读能力。