The role of polyurethane foam compressible layer in the mechanical behaviour of multi-layer yielding supports for deep soft rock tunnels

The polyurethane foam(PU)compressible layer is a viable solution to the problem of damage to the secondary lining in squeezing tunnels.Nevertheless,the mechanical behaviour of the multi-layer yielding supports has not been thoroughly investigated.To fill this gap,large-scale model tests were conducted in this study.The synergistic load-bearing mechanics were analyzed using the convergenceconfinement method.Two types of multi-layer yielding supports with different thicknesses(2.5 cm,3.75 cm and 5 cm)of PU compressible layers were investigated respectively.Digital image correlation(DIC)analysis and acoustic emission(AE)techniques were used for detecting the deformation fields and damage evolution of the multi-layer yielding supports in real-time.Results indicated that the loaddisplacement relationship of the multi-layer yielding supports could be divided into the crack initiation,crack propagation,strain-hardening,and failure stages.Compared with those of the stiff support,the toughness,deformability and ultimate load of the yielding supports were increased by an average of 225%,61%and 32%,respectively.Additionally,the PU compressible layer is positioned between two primary linings to allow the yielding support to have greater mechanical properties.The analysis of the synergistic bearing effect suggested that the thickness of PU compressible layer and its location significantly affect the mechanical properties of the yielding supports.The use of yielding supports with a compressible layer positioned between the primary and secondary linings is recommended to mitigate the effects of high geo-stress in squeezing tunnels.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

Learning Performance of Linear and Exponential Activity Function with Multi-layered Neural Networks

This paper presents a study on the improvement of MLNNs(multi-layer neural networks)performance by an activity function for multi logic training patterns.Our model network has L hidden layers of two inputs and three,four to six output training using BP(backpropagation)neural network.We used logic functions of XOR(exclusive OR),OR,AND,NAND(not AND),NXOR(not exclusive OR)and NOR(not OR)as the multi logic teacher signals to evaluate the training performance of MLNNs by an activity function for information and data enlargement in signal processing(synaptic divergence state).We specifically used four activity functions from which we modified one and called it L&exp.function as it could give the highest training abilities compared to the original activity functions of Sigmoid,ReLU and Step during simulation and training in the network.And finally,we propose L&exp.function as being good for MLNNs and it may be applicable for signal processing of data and information enlargement because of its performance training characteristics with multiple training logic patterns hence can be adopted in machine deep learning.

Defence Against Adversarial Attacks Using Clustering Algorithm

Deep learning model is vulnerable to adversarial examples in the task of image classification. In this paper, a cluster-based method for defending against adversarial examples is proposed. Each adversarial example before attacking a classifier is reconstructed by a clustering algorithm according to the pixel values. The MNIST database of handwritten digits was used to assess the defence performance of the method under the fast gradient sign method (FGSM) and the DeepFool algorithm. The defence model proposed is simple and the trained classifier does not need to be retrained.

基于深度学习的防空反导拦截决策研究

针对复杂海战场护航的任务场景,现有防空反导系统战术辅助决策功能在火控决策与武器火力分配方面分别具有对敌方模型依赖度高、拦截决策准确性差、无法有效利用战场历史数据和研究对象简单等问题,本文提出一种基于深度学习的反导拦截智能决策模型。首先,搭建战场仿真平台并分别对作战单元进行建模;然后,基于长短时记忆神经网络设计反导拦截智能决策模型;接着,利用匀速比例导引质点模型构建战前模拟数据以训练战前模型;最后,将战前模型迁移到战场模型中,并基于实际战场数据增强后的实时数据进行小样本在线训练。仿真结果表明,本文设计的反导拦截智能决策模型能够有效降低敌方模型依赖性,从而提升防空反导决策准确性。

Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model

Soil moisture content （SMC） is a key hydrological parameter in agriculture,meteorology and climate change,and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise irrigation scheduling.However,the hybrid interaction of static and dynamic environmental parameters makes it particularly difficult to accurately and reliably model the distribution of SMC.At present,deep learning wins numerous contests in machine learning and hence deep belief network （DBN） ,a breakthrough in deep learning is trained to extract the transition functions for the simulation of the cell state changes.In this study,we used a novel macroscopic cellular automata （MCA） model by combining DBN to predict the SMC over an irrigated corn field （an area of 22 km^2） in the Zhangye oasis,Northwest China.Static and dynamic environmental variables were prepared with regard to the complex hydrological processes.The widely used neural network,multi-layer perceptron （MLP） ,was utilized for comparison to DBN.The hybrid models （MLP-MCA and DBN-MCA） were calibrated and validated on SMC data within four months,i.e.June to September 2012,which were automatically observed by a wireless sensor network （WSN） .Compared with MLP-MCA,the DBN-MCA model led to a decrease in root mean squared error （RMSE） by 18%.Thus,the differences of prediction errors increased due to the propagating errors of variables,difficulties of knowing soil properties and recording irrigation amount in practice.The sequential Gaussian simulation （s Gs） was performed to assess the uncertainty of soil moisture estimations.Calculated with a threshold of SMC for each grid cell,the local uncertainty of simulated results in the post processing suggested that the probability of SMC less than 25% will be difference in different areas at different time periods.The current results showed that the DBN-MCA model performs better than the MLP-MCA model,and the DBN-MCA model provides a powerful tool for predicting SMC in highly non-linear forms.Moreover,because modeling soil moisture by using environmental variables is gaining increasing popularity,DBN techniques could contribute a lot to enhancing the calibration of MCA-based SMC estimations and hence provide an alternative approach for SMC monitoring in irrigation systems on the basis of canals.

Forecasting Damage Mechanics By Deep Learning

We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems.The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays.Relied on learning an amount of information from given data,the long short-term memory(LSTM)method and multi-layer neural networks(MNN)method are applied to predict solutions.Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio,single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale.The predicted results by deep learning algorithms are well-agreed with experimental data.

CHANGED CONTENTS OF MONOAMINES AND THEIR METABOLITES OF CEREBROSPINAL FLUID DURING INHIBITION OF DEFENCE PRESSOR RESPONSE BY INPUTS OF DEEP PERONEAL NERVE IN RABBITS

It has been demonstrated that excitation of hypothalamic defence area (HDA) causes an increase in sympathetic activity of the cardiovascular system and changes of other functions, which may be related to the activities of central monoamines; electroacupuncture applied to "Zusanli" or deep peroneal nerve stimulation (DPNS) can inhibit HDA stimulation-induced pressor, ventricular extrasystoles and other de-

RELATIONSHIP BETWEEN THE BRAIN MONO-AMINE NEUROTRANSMITTERS AND ENDOGE-NOUS OPIOID PEPTIDES DURING INHIBITION OF DEFENCE PRESSOR RESPONSE BY INPUTS OF DEEP PERONEAL NERVE IN RABBITS

It has been reported that stimulation of hypothalamic defence area (HDA) led to the increased release of central NA and other monoamine neurotransmitters,

Intelligent Detection Method of Substation Environmental Targets Based on MD-Yolov7

The complex operating environment in substations, with different safety distances for live equipment, is a typical high-risk working area, and it is crucial to accurately identify the type of live equipment during automated operations. This paper investigates the detection of live equipment under complex backgrounds and noise disturbances, designs a method for expanding lightweight disturbance data by fitting Gaussian stretched positional information with recurrent neural networks and iterative optimization, and proposes an intelligent detection method for MD-Yolov7 substation environmental targets based on fused multilayer feature fusion (MLFF) and detection transformer (DETR). Subsequently, to verify the performance of the proposed method, an experimental test platform was built to carry out performance validation experiments. The results show that the proposed method has significantly improved the performance of the detection accuracy of live devices compared to the pairwise comparison algorithm, with an average mean accuracy (mAP) of 99.2%, which verifies the feasibility and accuracy of the proposed method and has a high application value.

教育考试信息化系统中的安全问题研究

在分析教育考试信息化技术发展的基础上,给出了教育考试信息化系统的总体架构;分析了教育考试信息化系统面临的安全风险,提出了多层次纵深防御的安全体系,以实现物理安全、网络安全、系统安全和信息安全,最后强调了建立严格有效的安全管理制度的重要性。

智能视频分析技术在综合安防系统中的应用

为了满足不断提升的地铁综合安防系统的运营管理要求,结合了某市大型地铁项目,对目前数字视频监控系统中采用的视频分析技术进行了分析,指出了这种技术在当前应用中的不足,提出了改进策略,通过把相关设备深度集成到监控系统中来,使视频分析系统更加智能化的同时提高了监控系统的精确度和整体可控度。详细介绍了智能视频分析系统的设计思想和技术选择、组成以及实现过程等,并阐述其未来的发展前景。

企业网络纵深防御体系

企业网络需要一种纵深的防御体系。本文提出了一种由安全策略、防护、检测、响应及加密所组成的企业网络安全模型 ,描述了系统结构并给出实现方法 ,同时对安全审计系统与入侵检测系统的集成 。

高校网站安全纵深防御体系研究

高校网站安全问题给校方、师生、家长及社会带来了严重的风险。设计一个3层次的高校网站安全纵深防御体系架构,包括基础设备防护、安全加固防护和健全的网站安全管理措施,为高校网站群变被动的为主动的安全防护体系提供借鉴。

伏核在躯体传入冲动抑制防御性心血管反应中的作用及机制

损毁伏核可明显削弱电刺激腓深神经（ＤＰＮ）对兴奋下丘脑背内侧核诱发的升压反应和心肌缺血的抑制作用（Ｐ＜０．０５，Ｐ＜０．０１）。电刺激伏核可引起明显的降压效应。中脑中央灰质腹侧部（ｖＰＡＧ）微量注射纳洛酮可明显衰减伏核的减压效应；损毁ｖＰＡＧ甚至可翻转伏核的减压效应，引起轻度升压（Ｐ＜０．０１）。损毁弓状核后伏核的减压效应基本消失；弓状核内微量注射纳洛酮明显衰减伏核的减压效应。故ＤＰＮ传入冲动可能通过伏核－弓状核－ｖＰＡＧ通路，继而下行抑制延髓、脊髓心血管中枢，内阿片肽参于伏核对弓状核与ｖＰＡＧ的调节过程。

Coupling Discriminating Statistical Analysis and Artificial Intelligence for Geotechnical Characterization of the Kampemba’s Municipality Soils (Lubumbashi, DR Congo)

This study focuses on the determination of physical and mechanical characteristics based on in vitro tests, by using field samples for the Kampemba urban area in the city of Lubumbashi. At the end of this study, we identified the soils according to their parameters, and established the geotechnical classification by determining their bearing capacity by the group index method using from the identification tests carried out. By using the AASHTO classification method (American Association for State Highway Transportation Official), the results obtained after our studies revealed five classes of soil: A-2, A-4, A-5, A-6, A-7 in a general way, and particularly eight subgroups of soil: A-2-4, A-2-6, A-2-7, A-4, A-5, A-6, A-7-5 and A-7-6 for the concerned area. The latter has given statistical analysis and deep learning based on multi-layer perceptron, the global values of the physical parameters. It’s about: 31.77% ± 1.05% for the limit of liquidity;18.71% ± 0.76% for the plastic limit;13.06% ± 0.79% for the plasticity index;83.00% ± 3.33% for passing of 2 mm sieve;76.22% ± 3.2% for passing of 400 μm sieve;89.07% ± 2.99% for passing of 4.75 mm sieve;70.62% ± 2.39% passing of 80 μm sieve;1.66 ± 0.61 for the consistency index;−0.67 ± 0.62 for the liquidity index and 8 ± 1 for the group index.

城市复杂环境基坑深孔控制爆破开挖

介绍了城市复杂环境基坑石方爆破开挖工程,采用深孔控制爆破技术,在主爆区采取大孔径,在爆区四周实施预裂爆破,并严格控制单段药量的爆破方式,能够满足设计施工要求,有效缩短工期。通过优化爆破技术参数,加强安全防护技术措施,可将爆破有害效应控制在允许范围内。

深基坑工程遇位置不明废弃人防空洞处理措施

旧城区地下工程建设过程中常遇废弃地下人防工程,不同工程遇人防工程的处理方式各异,如何合理处理需要工程技术人员根据工程自身特点选择适宜的处理方式。以某管廊工程深基坑为依托,对比研究了几种处理方案,分析可能存在的风险点及重难点,选择的处理方案在实际实施中取得了很好的效果。在该工程经验的基础上对本问题进行了拓展延伸,对类似工程的设计和施工具有一定的指导意义。

Voice to Face Recognition Using Spectral ERB-DMLP Algorithms

Designing an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented equipment.This authentication is also vital to prevent any security threats or risks like compromises of business server,release of confidential data etc.Though conventional works attempted to accomplish better authentication,they lacked with respect to accuracy.Hence,the study aims to enhance the recognition rate by introducing a voice recognition system as a personal authentication based on Deep Learning(DL)due to its ability to perform effective learning.The study proposes Equivalent Rectangular Bandwidth and Deep Multi-Layer Perceptron(ERB-DMLP)as it has the ability to perform efficient and relevant feature extraction and faster classification.This algorithm also has the ability to establish effective correlation between voices and images and achieve the semantic relationship between them.Voice preprocessing is initially performed to make it suitable for further processing by removing the noise and enhancing the quality of signal.This process is also vital to minimize the extra computations so that the overall efficacy of the system can be made flexible by considering the audio files as features and the images as labels to identify a person’s voice by classifying the extracted features from the ERB Feature Extraction.This is then passed as the input into DMLP model to classify the persons,and trained the model to make an accurate classification of audio with corresponding image labels,and perform the performance test based on the trained model.Flexibility,relevant feature extraction and faster classification ability of the proposed work has made it explore better outcomes that is confirmed through results.

Cross Intelligence Evaluation for Effective Emotional Intelligence Estimat

Afamous psychologist or researcher,Daniel Goleman,gave a theory on the importance of Emotional Intelligence for the success of an individual’s life.Daniel Goleman quoted in the research that“The contribution of an individual’s Intelligence Quotient(IQ)is only 20%for their success,the remaining 80%is due to Emotional Intelligence(EQ)”.However,in the absence of a reliable technique for EQ evaluation,this factor of overall intelligence is ignored in most of the intelligence evaluation mechanisms.This research presented an analysis based on basic statistical tools along with more sophisticated deep learning tools.The proposed cross intelligence evaluation uses two different aspects which are similar,i.e.,EQ and SQ to estimate EQ by using a trained model over SQ Dataset.This presented analysis ensures the resemblance between the Emotional and Social Intelligence of an Individual.The research authenticates the results over standard statistical tools and is practically inspected by deep learning tools.Trait Emotional Intelligence Questionnaire-Short Form(TEIQue-SF)and Social IQ dataset are deployed over aMulti-layered Long-Short TermMemory(M-LSTM)based deep learning model for accessing the resemblance between EQ and SQ.The M-LSTM based trained deep learning model registered,the high positive resemblance between Emotional and Social Intelligence and concluded that the resemblance factor between these two is more than 99.84%.This much resemblance allows future researchers to calculate human emotional intelligence with the help of social intelligence.This flexibility also allows the use of Big Data available on social networks,to calculate the emotional intelligence of an individual.