DL

摘要： The 21st Century era and new modern technologies surrounding us day-in and day-out have opened a new door to“Pandora Box”,that we do know it as AI(artificial intelligence)and its two essential integrated components namely ML(machine learning)and DL(deep learning).However,the strive and progress in AI,ML,and DL pretty much has taken over any industry that we can think of,when it comes to dealing with cloud of structured data in form of BD(big data).A NPP(nuclear power plant)has multiple complicated dynamic system-of-components that have nonlinear behaviors.For controlling the plant operation under both normal and abnormal conditions,the different systems in NPPs(e.g.,the reactor core components,primary and secondary coolant systems)are usually monitored continuously,which leads to very huge amounts of data.Of course Nuclear Power Industry in form of GEN-IV(Generation IV)has not been left behind in this 21st century era by moving out of GEN-III(Generation III)to more modulars form of GEN-IV,known as SMRs(small modular reactors),with a lot of electronic gadgets and electronics that read data and information from it to support safety of these reactor,while in operation with a built in PRA(probabilistic risk assessment),which requires augmentation of AI in them to enhance performance of human operators that are engaged with day-to-day smooth operation of these reactors to make them safe and safer as well as resilience against any natural or man-made disasters by obtaining information through ML from DL that is collecting massive stream of data coming via omni-direction.Integration of AI with HI(human intelligence)is not separable,when it comes to operation of these smart SMRs with state of the art and smart control rooms with human in them as actors.This TM(technical memorandum)is describing the necessity of AI playing with nuclear reactor power plant of GEN-IV being in operation within near term sooner than later,when specially we are facing today’s cyber-attacks with their smart malware agents at work.

摘要： Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient SDN-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

摘要： Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis applications.There are numerous ways to identify positive COVID-19 cases.One of the fundamental ways is radiology imaging through CXR,or CT images.The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality.Hence,automated classification techniques are required to facilitate the diagnosis process.Deep Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical images.The deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image datasets.In this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is offered.Three activation functions as well as three optimizers are tested and compared for this task.The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it.The performance is tested and investigated on the CT and CXR datasets.Three activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch sizes.Different optimizers are studied with different batch sizes and a constant learning rate.Finally,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is determined.Hence,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage.The proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR and CT datasets,respectively.The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10−5 and a minibatch size of 16.

摘要： The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of areas.In recent years,a lot of interest has been generated in researching remote sensing image scene classification.Remote sensing image scene retrieval,and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding(RSISU)research.In the last several years,the number of deep learning(DL)methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs,providing new research and development possibilities for RS image classification.A new network called Pass Over(POEP)is proposed that utilizes both feature learning and end-to-end learning to solve the problem of picture scene comprehension using remote sensing imagery(RSISU).This article presents a method that combines feature fusion and extraction methods with classification algorithms for remote sensing for scene categorization.The benefits(POEP)include two advantages.The multi-resolution feature mapping is done first,using the POEP connections,and combines the several resolution-specific feature maps generated by the CNN,resulting in critical advantages for addressing the variation in RSISU data sets.Secondly,we are able to use Enhanced pooling tomake the most use of themulti-resolution feature maps that include second-order information.This enablesCNNs to better cope with(RSISU)issues by providing more representative feature learning.The data for this paper is stored in a UCI dataset with 21 types of pictures.In the beginning,the picture was pre-processed,then the features were retrieved using RESNET-50,Alexnet,and VGG-16 integration of architectures.After characteristics have been amalgamated and sent to the attention layer,after this characteristic has been fused,the process of classifying the data will take place.We utilize an ensemble classifier in our classification algorithm that utilizes the architecture of a Decision Tree and a Random Forest.Once the optimum findings have been found via performance analysis and comparison analysis.

摘要： Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)classification.In any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature reduction.The downsampling is an improved form of the existing pooling layer to obtain better classification accuracy.The two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for downsampling.They convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature vectors.However,there are parameters which directly affect how a CNN model is trained.In this paper,some issues concerned with the training of CNNs have been handled.The CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of epochs.Training and assessment of the performance of CNNs are carried out on 16S rRNA bacterial sequences.Simulation results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

摘要： 尾矿库溃坝事故危险性大,尚缺乏有效的溃坝沙流预测机制。基于DL Breach模型,提出一种尾矿库溃坝沙流的计算方法,依据尾矿下泄沙流的性质,结合尾矿库溃坝的工程经验及规范,建立溃坝沙流运动模型,计算溃坝过程中的主要影响参数,分析下泄沙流的淹没范围、淹没深度的演变规律。结果表明,溃坝下泄沙流的堆积深度总体上先增大后减小,下泄沙流在钻天道沟谷内淹没深度较大,淤积较多,最大堆积深度达2.48 m;随着与初期坝的距离逐渐变大,下游的3#桥处的最大淹没深度不超过1 m,整体安全风险可控。利用该模型进行尾矿库溃坝数值模拟,可以预测尾矿坝溃决可能引起的灾害,对尾矿库的安全运行具有指导意义。

摘要： "大数据+云计算+区块链"智能技术带动了物流业转型,高职院校课程体系及人才培养要匹配市场的需求变化.本文主要调研及诊断台州区域高职院校物流专业课程体系及人才培养现状,采用TBL课程体系和DL人才培养方法解决问题,以准员工视角精准对接汽车物流企业,提出了融合核心技能和课程思政双因素,优化"四度统一"实训教学路径,建立校企双交替、岗位技能双递人才培养模式等实施策略.

摘要： "大数据+云计算+区块链"智能技术带动了物流业转型,高职院校课程体系及人才培养要匹配市场的需求变化。本文主要调研及诊断台州区域高职院校物流专业课程体系及人才培养现状,采用TBL课程体系和DL人才培养方法解决问题,以准员工视角精准对接汽车物流企业,提出了融合核心技能和课程思政双因素,优化"四度统一"实训教学路径,建立校企双交替、岗位技能双递人才培养模式等实施策略。

摘要： 一台东菱DL-100面包机,通电试机,按键、显示均正常,但和面时搅拌叶片不动,电机发出“嗡嗡”声。首先拆开上盖,取下金属框(此机外壳底部没有可拆螺丝),然后拆下按键操作板,左旋内胆桶,取下内胆,拆下内胆下方4颗螺丝发现,电机皮带已老化碎掉,如图1所示。更换皮带后试机,故障排除。

摘要： 随着更多的专业人像摄影师开始使用LED光源来进行创作,行业内涌现出众多的专业人像级LED灯光产品。市场上各类LED产品从外形设计、可变色温、功率、操控等已经处于多元化形态。今天我们将带来一款全新的影像专业级LED产品,那就是海力欧大功率LED双色摄影灯DL-400型。

摘要： 描述了用于无线通信的方法、系统和设备。用户装备(UE)可以在一个或多个下行链路(DL)波束上接收来自基站的DL信号。该UE可标识该UE处的一个或多个接收波束与该UE处的一个或多个发射波束之间的对应关系的特性。该UE可标识该一个或多个DL波束中的所选DL波束以用于从基站到UE的通信。该UE可使用至少部分地基于所选DL波束和该对应关系的特性所选择的资源和/或RACH波形来向该基站传送随机接入信道(RACH)消息。

摘要： 描述了用于无线通信的方法、系统和设备。用户装备(UE)可以在一个或多个下行链路(DL)波束上接收来自基站的DL信号。该UE可标识该一个或多个DL波束中的所选DL波束以用于从基站到UE的通信。该UE可以使用至少部分地基于所选DL波束所选择的资源或波形中的至少一者来向该基站传送调度请求消息。

摘要： 本发明公开了一种DL‑聚乳酸‑聚乙二醇‑DL‑聚乳酸嵌段共聚物的少溶剂制备方法，具体步骤如下：S1、添加溶剂：将原料聚乙二醇与溶剂混合并溶解；S2、共沸除水并精确控制剩余少量溶剂；S3、向原料聚乙二醇和剩余溶剂中，加入原料丙交酯和催化剂辛酸亚锡，加热并搅拌，得到生成物；S4、对生成物先减压蒸馏除去溶剂，再以良溶剂溶解稀释，并在不良溶剂中剧烈搅拌沉淀，倾去母液，重复1～2次，得到最终沉淀物；S5、最终沉淀物干燥后，即得所需产物。本发明的制备方法能够解决原料DL‑丙交酯升华凝华、导致产物不稳定的问题，且缩短反应时间、减少锡金属催化剂用量，使得整个制备流程简单方便，易于操作，耗时短，催化剂残留低，节约能耗。

摘要： 描述了用于无线通信的方法、系统和设备。用户装备(UE)可以在(诸)下行链路(DL)波束上接收来自基站的DL信号。UE可以标识该(诸)DL波束中的所选DL波束以用于从基站到UE的通信。该UE可以使用至少部分地基于所选DL波束所选择的资源或RACH波形中的至少一者来向基站传送随机接入信道(RACH)消息。

摘要： 描述了用于无线通信的方法、系统和设备。用户装备(UE)可以在一个或多个下行链路(DL)波束上接收来自基站的DL信号。该UE可标识该一个或多个DL波束中的所选DL波束以用于从基站到UE的通信。该UE可以使用至少部分地基于所选DL波束所选择的资源或波形中的至少一者来向该基站传送波束恢复或波束跟踪消息。

摘要： 本发明涉及了一种制备消旋体门冬氨酸鸟氨酸的方法。所述制备方法包括以下步骤：(1)以乙酸水溶液为溶剂，L‑门冬氨酸L‑鸟氨酸盐在催化剂作用下发生消旋反应，得到消旋液；(2)向消旋液中加入反溶剂，析晶，过滤，烘干，即得。本发明提供的制备方法原料易得，操作简便，易于控制，制备过程稳定可靠，适合大规模生产消旋体门冬氨酸鸟氨酸样品。

摘要： 描述了用于无线通信的方法、系统和设备。用户装备(UE)可以在一个或多个下行链路(DL)波束上接收来自基站的DL信号。该UE可标识该一个或多个DL波束中的所选DL波束以用于从基站到UE的通信。该UE可以使用至少部分地基于所选DL波束所选择的资源或波形中的至少一者来向该基站传送调度请求消息。

摘要： 本发明公开了一种DL‑聚乳酸‑聚乙二醇‑DL‑聚乳酸嵌段共聚物的少溶剂制备方法，具体步骤如下：S1、添加溶剂：将原料聚乙二醇与溶剂混合并溶解；S2、共沸除水并精确控制剩余少量溶剂；S3、向原料聚乙二醇和剩余溶剂中，加入原料丙交酯和催化剂辛酸亚锡，加热并搅拌，得到生成物；S4、对生成物先减压蒸馏除去溶剂，再以良溶剂溶解稀释，并在不良溶剂中剧烈搅拌沉淀，倾去母液，重复1～2次，得到最终沉淀物；S5、最终沉淀物干燥后，即得所需产物。本发明的制备方法能够解决原料DL‑丙交酯升华凝华、导致产物不稳定的问题，且缩短反应时间、减少锡金属催化剂用量，使得整个制备流程简单方便，易于操作，耗时短，催化剂残留低，节约能耗。

摘要： 提供了有关在下行链路(DL)无执照频带和上行链路(UL)有执照频带中部署的网络中操作的无线通信系统和方法。第一无线通信设备在网络的下行链路无执照频带中与第二无线通信设备传达第一下行链路通信信号。第一无线通信设备在该网络的上行链路有执照频带中与第二无线通信设备传达第二下行链路通信信号。第二下行链路通信信号包括同步信号块(SSB)、系统信息、随机接入响应、争用解决消息、寻呼信息、下行链路反馈信号、或低等待时间数据中的至少一者。

摘要： 描述了用于无线通信的方法、系统和设备。用户装备(UE)可以在一个或多个下行链路(DL)波束上接收来自基站的DL信号。该UE可标识该一个或多个DL波束中的所选DL波束以用于从基站到UE的通信。该UE可以使用至少部分地基于所选DL波束所选择的资源或波形中的至少一者来向该基站传送调度请求消息。