Assessment for Production and Operation Ability of Medium and Small-sized Enterprises Based on Neural Network

In order to improve production and operation ability of medium and small-sized enterprises, an assessment-index system of production and operation ability was proposed, and a corresponding assessment model was established based on BP neural network. The conjunction weights of the neural network were continuously modified from output layer to input layer in the process of neural network training to reduce the errors between the anticipated and actual outputs. The results from an example show that this method is reliable and feasible. The production and operation abilitv of an enterorise with assessed result of 0.833 is fairly oowerful, and that with assessed result of 0.644 is average.

Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimi- zation of mine safety assessment and getting reasonable accurate safety assessment result.

The Construction of Linux System and Network Programming Course to Develop Engineering Ability

To meet society’s needs for undergraduate students to have engineering skills and to develop students’ability to operate Linux and engage in network software development,this paper proposes the construction of a new specialized course for network engineering major--Linux system and network programming.This paper analyzes the course’s advantages,presents the contents of this course,designs a series of teaching methods aimed at improving students’engineering ability,proposes a course assessment method that will encourage students to practice,lists the development requirements for an examination software designed for this course,and finally,presents the results of our practice in teaching this course.

An Analysis of College Students＇ Autonomous English Learning Ability under the Network Environment

Referring to the questionnaire survey and literature materials, this paper describes the current situation of college students＇ insufficient autonomous English learning ability and thefactors that influence this ability; it also suggests feasible suggestions under the network environment. Students （N= 178） from different grades, majors, universities and areas responded to a questionnaire survey, and the collected data were analyzed. The results indicate that complex external environment and insufficient internal subject cognition influence college students＇ autonomous English learning ability. Finally, this paper suggests that a good network environment is necessary for autonomous learners to enhance insufficient autonomous English learning ability and interaction, continuous thinking and cultural introduction are also indispensable to develop this ability.

Gated Neural Network-Based Unsteady Aerodynamic Modeling for Large Angles of Attack

Modeling of unsteady aerodynamic loads at high angles of attack using a small amount of experimental or simulation data to construct predictive models for unknown states can greatly improve the efficiency of aircraft unsteady aerodynamic design and flight dynamics analysis.In this paper,aiming at the problems of poor generalization of traditional aerodynamic models and intelligent models,an intelligent aerodynamic modeling method based on gated neural units is proposed.The time memory characteristics of the gated neural unit is fully utilized,thus the nonlinear flow field characterization ability of the learning and training process is enhanced,and the generalization ability of the whole prediction model is improved.The prediction and verification of the model are carried out under the maneuvering flight condition of NACA0015 airfoil.The results show that the model has good adaptability.In the interpolation prediction,the maximum prediction error of the lift and drag coefficients and the moment coefficient does not exceed 10%,which can basically represent the variation characteristics of the entire flow field.In the construction of extrapolation models,the training model based on the strong nonlinear data has good accuracy for weak nonlinear prediction.Furthermore,the error is larger,even exceeding 20%,which indicates that the extrapolation and generalization capabilities need to be further optimized by integrating physical models.Compared with the conventional state space equation model,the proposed method can improve the extrapolation accuracy and efficiency by 78%and 60%,respectively,which demonstrates the applied potential of this method in aerodynamic modeling.

Damage Diagnosis of Bleacher Based on an Enhanced Convolutional Neural Network with Training Interference

Bleachers play a crucial role in practical engineering applications, and any damage incurred during their operationposes a significant threat to the safety of both life and property. Consequently, it becomes imperative to conductdamage diagnosis and health monitoring of bleachers. The intricate structure of bleachers, the varied types ofpotential damage, and the presence of similar vibration data in adjacent locations make it challenging to achievesatisfactory diagnosis accuracy through traditional time-frequency analysis methods. Furthermore, field environmentalnoise can adversely impact the accuracy of bleacher damage diagnosis. To enhance the accuracy and antinoisecapabilities of bleacher damage diagnosis, this paper proposes improvements to the existing ConvolutionalNeural Network with Training Interference (TICNN). The result is an advanced Convolutional Neural Networkmodel with superior accuracy and robust anti-noise capabilities, referred to as Enhanced TICNN (ETICNN).ETICNN autonomously extracts optimal damage-sensitive features from the original vibration data. To validatethe superiority of the proposed ETICNN, experiments are conducted using the bleacher model from Qatar Universityas the subject. Comparative studies under identical experimental conditions involve TICNN, Deep ConvolutionalNeural Networks with wide first-layer kernels (WDCNN), and One-Dimensional ConvolutionalNeural Network (1DCNN). The experimental findings demonstrate that the ETICNN model achieves the highestaccuracy, approximately 99%, and exhibits robust classification abilities in both Phases I and II of the damagediagnosis experiments. Simultaneously, the ETICNN model demonstrates strong anti-noise capabilities, outperformingTICNN by 3% to 4% and surpassing other models in performance.

Study on the Promotion Strategy of College Students’ Reading and Writing Ability Under the Micro Network Environment

Network media has become more and more involved in our daily life. It has become a part of modern life that cannot be stopped or rejected. Then, how to correctly face the changes in the Internet era is placed in front of everyone. Students need to “learn how to use the media to get their all-round development, effectively prevent the foreign information pollution, and actively guide students to a truly rational attitude for WeChat, blog and micro-blog”. It is a kind of media literacy being confronted with the network’s ability to consciously resist the harmful information, but also to benefit their own development and firmly grasp the information, so as to make full use of the convenience of network self promotion. “Screen reading” network plays an important role in college students’ life, and the fragmentation, the text features of the writing ability of college students also have a strong impact; this paper focuses on the influence of college students in the network reading and writing patterns on their writing ability and influence. From the perspective of media literacy education for college students, writing ability and reading ability provide a feasible way.

The structure and ability of the China Seismological Gravity Monitoring System

This paper assesses the structure and ability of Local Seismological Gravity Monitoring Network (LSGMN) in China main tectonic zone and China Seismological Gravity Monitoring System (CSGMS) which formed after the project of 'China Crustal Movement Observation Network (CCMON)' has been performed. The main conclusions drawn are as follows: ①LSGMN has good monitoring and prediction ability for the earthquake of M_s about 5. But it lacks ability to monitor and predict the strong earthquake of M_s>6 because of the little range of the observation network;②CSGMS has good ability to monitor and predict the earthquake of M_s>7, but the resolving power is not enough for the earthquake magnitude from M_s=6 to M_s=7 because the observation stations are too sparse.

An Investigation of College English Autonomous Learning in Network Multimodal Context

In the current society, based on the growing development of network information technology, the teaching in many colleges and universities has also introduced it to adapt to the situation. This trend can provide more useful conditions for students to learn, which requires students to master enough self-learning abilities to adapt to this model. The study in the paper shows that students are usually interested in autonomous learning in a multimodal environment, but the degree of strategy choice is relatively low, and the learning process is blind and passive with the lack of self-confidence. Facing the future, schools should actively integrate into network thinking, and teachers should change their roles and train and guide students’ learning strategies and learning motivations, so as to achieve better teaching results.

基于BP神经网络的高校教师精准教学能力评价模型构建

通过精准教学以促进学生个性化成长是教育理想和国家政策的不懈追求.高校教师是实施精准教学的“基”,现有关于其教学能力的评价体系中普遍存在概念不清和多采用主观构建评价指标的问题.为此,开展了基于BP神经网络的高校教师精准教学能力评价模型研究.首先,以理论研究为基础,对精准教学能力进行等级划分并构建评价指标框架,运用层级分析法建立指标权重;其次,利用BP神经网络智能学习的特性,以不同数据类型的指标值为输入,对应能力综合值为输出,检验精准教学能力分级及指标权重的合理性,进而生成较为客观的评价模型;最后,利用开发的评价系统和调查问卷进行样本数据采集和模型检验,从神经网络对数据的分类、拟合及仿真结果来看,模型能够对高校教师的精准教学能力进行客观评价,教师对模型测量结果的准确性也具有较高认可度.

基于贝叶斯单源域领域泛化算法的天然气管道故障智能诊断

基于深度学习算法的故障智能诊断模型已被广泛应用于天然气管道运输安全领域,然而管道通常处于准稳态,使得训练集中的故障样本量受限。为此,针对天然气管道故障诊断中因训练集故障样本量有限,导致难以准确诊断的问题,提出了一种基于贝叶斯单源域领域泛化(BSDG)算法,部署了一种攻击防御策略,通过在攻击阶段明确伪目标域增强路径,并在防御阶段引导模型参数的后验分布向伪域样本得分更高的方向调整,增强模型在面对不同域扰动时的适应性和鲁棒性。研究结果表明:(1)基于贝叶斯网络建立的非定向攻击模型确保伪域样本既保留了与源域的相关性,又引入了足够的域差异来模拟潜在的目标域,由此提升了多源域和单源域设置下的领域泛化诊断准确率;(2)测试结果显示,BSDG算法在多源域泛化任务及两项单源域泛化任务中,相较于性能最优的对比算法,其准确率分别提高了9.79%、5.09%和27.98%;(3)裕度差异损失通过在学习决策边界的过程中引入不确定性,令分类器可以灵活且有效应对频繁的分布变化,显著性测试结果表明BSDG算法在多数场景下显著优于先进对比算法;(4)贝叶斯神经网络通过在权重上引入不确定性,有效提升了BSDG算法的泛化稳定性。结论认为,BSDG算法通过使用基于贝叶斯推理的攻击防御策略,有效扩展了源域模型的决策边界,解决了实际场景数据匮乏导致的深度神经网络泛化能力差的问题,为样本受限情形下的天然气管道故障诊断模型设计提供了理论支撑。

无源光网络开放性创新实验的探索与实践

实验教学是应用型创新人才培养的关键环节,开放性创新实验的设计与应用是延伸拓展课内实验教学的有益探索。针对学生差异化发展和自主研究探索的需求问题,设计涵盖时分复用无源网络和波分复用无源光网络的个性化开放性创新实验主题,建立针对实体实验资源和虚拟仿真资源特点的多元化开放共享机制。探索在开放性创新实验中应用小规模限制性在线课程与面授课程结合的融合式教学模式,设计过程育人理念引导的阶段化项目实施路线,推进有通信工程专业特色的无源光网络实验教学课程内涵建设。

基于LSTM神经网络的烟丝水分恒定控制系统设计

在烟丝加工过程中,水分分布受到温度、湿度多个因素的影响,控制系统无法准确反映整体水分情况;为全面提高加工型香烟的质量水平,设计基于LSTM神经网络的烟丝水分恒定控制系统;部署Profibus控制总线,并在线路体系中连接水分检测仪与水分恒定器,完成烟丝水分恒定控制系统的硬件设计;在系统软件设计方面,构建LSTM神经网络单元,根据烟叶吸湿能力分析条件,求解具体的水分分布模型,实现基于LSTM神经网络的烟丝水分模型建模;分别计算烟叶出口湿度与出口温度,并联合传递函数逼近参量与恒定时滞参数,完成对控制参数的整定处理,再联合相关应用部件,实现基于LSTM神经网络的烟丝水分恒定控制系统设计;实验结果表明,LSTM神经网络模型作用下,生丝含水量被稳定控制在13%~18%数值之间,不会因水分过量问题而导致香烟质量水平无法达到实际加工标准。

互联网时代大学生课外英语能力提升途径研究

随着互联网的发展,大学生利用互联网在课余时间提升英语能力的趋势逐渐扩大。研究以全国各地215名大学生为研究对象,通过网络问卷调查和线下访谈,调查在互联网时代背景下的大学生们在课外时间通过何种网络途径提升自己的英语能力及其有效性。调查结果显示大学生们通过各种互联网途径提升英语能力是有效的,但仍面临着如自制力不足、学习效率较低等问题。针对存在的主要问题,研究进行了分析并给出可行性建议,旨在帮助大学生们更科学、合理地通过互联网途径提升英语能力。

数字普惠金融促进农村居民创业了吗?——基于互联网支付能力和社会网络双重视角

基于我国2015年、2017年及2019年家庭金融调查数据(CHFS)与数字普惠金融指数,深入探讨数字普惠金融对农村居民创业活动的影响。研究发现:数字普惠金融对农村居民创业具有显著正向影响;数字普惠金融通过互联网支付能力的提升和社会网络传递间接影响农村居民创业;数字普惠金融对中年组农村居民创业的影响更显著,能促进具有低等和中等金融素养水平的农村居民创业,对东部地区农村居民创业具有显著促进作用。

研究生“自主学习与科研”能力评价体系构建及应用

目的:科学合理地构建研究生“自主学习与科研”能力评价体系,对高校培养研究生此方面能力具有重要参考价值。方法:文章以自主学习能力与科研能力的内涵与关系为基础,构建了研究生“自主学习与科研”能力评价指标体系,并以某高校为例运用网络层次分析法进行应用。结果:该校研究生自主学习能力低于科研能力;自主学习能力中,过程控制与结果反馈影响最大,自我认知影响最低;科研能力中,逻辑推理能力、写作能力和创新能力的影响均较高,实践能力影响最低。结论:该校可通过加大课程平时考核占比、利用线上资源优化学生自主学习过程、注重科研团队成员间的互助作用、优化课程模式保障教学质量等措施提升该能力的培养水平。

考虑站点耦合关系的大型城市轨道交通网络级联失效分析

为了解析站点分布密集的大型城市轨道交通网络级联失效传播机理,提出改进的耦合映射格子模型.该模型结合大型城市轨道交通网络站点分布密集特征,量化耦合站点间客流演化及耦合站点抵抗力.为了衡量级联失效在大型城市轨道交通网络中的传播过程与结果,提出失效站点数量比与网络效率比指标.以上海市城市轨道交通网络为例,验证该模型的可行性.结果表明:当节点度高或进站客流量大的站点失效后,耦合站点间的级联失效规模是邻接站点间的约2倍,且由于耦合站点间的级联失效的影响,介数中心性大的站点失效所引起的级联失效传播速度最快.在考虑耦合站点间级联失效时,客流对大型城市轨道交通网络级联失效的作用力是拓扑结构的约9倍.研究结论可以为城市轨道交通网络的规划设计与事故管控提供科学的建议.

PACS联合多媒体教学法在肾内科实习教学中的应用

目的探究在开展肾病内科实习教学的过程中按照医学图像存储与传输系统(picture archiving and communication system,PACS)联合网络多媒体教学法进行教学的价值。方法选取2020年2月—2022年8月苏州大学附属第二医院肾病内科60名实习医生为研究对象,随机分为对照组(30名,按照常规方式进行教学)和观察组(30名,按照PACS联合网络多媒体教学法进行教学)。针对2组教学效果进行评估。结果对比2组实习医生考核成绩,在教学前2组理论知识水平、实践操作能力、病例书写、体格检查考核差异无统计学意义(P>0.05)。教学后,观察组理论知识水平、实践操作能力、病例书写、体格检查考核考核成绩[(93.53±2.05)分;(93.63±2.11)分;(94.45±2.05)分;(94.36±2.32)分]高于对照组[(88.45±2.15)分;(88.64±1.78)分;(89.45±1.68)分;(86.14±1.48)分],差异有统计学意义(P<0.05)。对比2组实习医生对教学方式评价,在提升临床思维、提升学习兴趣、提升自学能力、降低理论知识理解难度方面评分,观察组均高于对照组,(P<0.05)。对比2组教学满意度,观察组高于对照组,(P<0.05)。结论在进行肾病内科实习教学的过程中开展PACS联合网络多媒体教学法,可以有效提升该方面教学质量,有助于实习医生对各方面专业知识进行掌握和运用,并增加教学满意度。

创新型产业集群如何提升区域创新能力?——基于网络构型的组态分析

创新型产业集群的形成过程呈现多样化特征,但其如何提升区域创新能力值得深入探讨。基于中国28个省级行政区(未包含港澳台、西藏、宁夏、海南)的案例进行QCA组态分析,研究创新型产业集群的产业层面、节点层面以及联结层面网络特征对区域创新能力的作用机理。根据集群网络层次及驱动方式,总结出全网络特征促进型、产业整体主导型、主体特征主导型、联结关系主导型、替代型以及软化型驱动路径,并以组态内案例分析验证驱动路径地域特征与时间演进规律,进一步发现创新驱动方式与集群“全要素集聚”“自上而下”“自下而上”及“中间互补”发展模式相契合。基于此,政府部门应精准施策,为创新型产业集群提供便利条件;完善信用体系,调动集群内企业积极性与创造性;把握发展机遇,贯通产业体系与创新体系。

基于神经网络的基层智慧型融媒体系统传播能力预测方法

随着基层智慧型融媒体系统在各地的应用越来越广泛,为实现系统长期传播能力的预测,设计基于神经网络的基层智慧型融媒体系统传播能力预测方法。基于改进数据集结构设计高效用数据挖掘算法,实施基层智慧型融媒体系统结构、时间、文本、用户信息的挖掘。对于挖掘信息,设计异常事件检测模型对其实施数据异常事件检测,使用差分综合移动平均自回归模型实施单点噪声的修复。构建一种基于上下文依赖的动态图注意网络作为基层智慧型融媒体系统传播能力预测模型,由建模传播动态图模块、时—空依赖学习模块与预测模块构成,其输入为处理后的信息,输出信息为基层智慧型融媒体系统传播规模预测增量。实验结果表明,该方法预测用户列表精度整体较高,最高达到98.95%,预测用户列表召回率整体较高,最高达到97.24%。