检索结果-南通市图书馆

基于L-ConvNeXt网络的变电站人员安全操作检测方法

在线全文

学校读者我要写书评

暂无评论

广西师范大学学报（自然科学版） 2024年第1期42卷 102-110页

作者：曾亮胡谦杨腾飞谭微微湖北工业大学电气与电子工程学院湖北武汉430068 太阳能高效利用及储能运行控制湖北省重点实验室(湖北工业大学) 湖北武汉430068

针对变电站复杂环境中作业人员操作检测网络参数量过大、作业人员部分特征不明显等问题,本文提出一种基于L-ConvNeXt网络的变电站人员检测方法。首先,网络的主干特征提取部分由轻量化ConvNeXt模块搭建而成,保证网络特征提取能力同时使... 详细信息

针对变电站复杂环境中作业人员操作检测网络参数量过大、作业人员部分特征不明显等问题,本文提出一种基于L-ConvNeXt网络的变电站人员检测方法。首先,网络的主干特征提取部分由轻量化ConvNeXt模块搭建而成,保证网络特征提取能力同时使主干部分保持较低的参数量;其次,选择TPH(Transformer prediction head)作为网络末端检测头,加强网络对低分辨率特征的检测;最后,引用VariFocal Loss作为目标损失函数中的分类损失和置信度损失,进一步增加网络对正样本的损失权重。在天池公共数据集上的实验结果表明:本文网络模型获得较好的检测效果,其平均检测精度达到89.6%,模型参数量为13.2×10^(6),能够有效地检测变电站人员的作业情况,满足变电站复杂场景下的检测需求。

关键词：目标检测 ConvNeXt 轻量化模型变电站 VariFocal Loss

在线全文

学校读者我要写书评

暂无评论

基于旅伴效应的游客就餐行为分析

地球信息科学学报 2024年第2期26卷 408-423页

作者：牟乃夏卞书娣王艳慈张灵先郑允豪 Teemu Makkonen 杨腾飞山东科技大学测绘与空间信息学院青岛266590 北京大学遥感与地理信息系统研究所北京100871 东芬兰大学卡累利阿研究所约恩苏FI-801014 中国科学院空天信息创新研究院北京100094

美食已成为游客旅途中乐意分享的热点话题,进一步揭示了游客的旅游行为特征,分析不同旅伴角色下游客就餐行为的差异能更好地理解旅游中的旅伴效应。本文以2012—2020年重庆市美食游记为研究数据,采用社会网络分析等方法研究不同旅伴游... 详细信息

美食已成为游客旅途中乐意分享的热点话题,进一步揭示了游客的旅游行为特征,分析不同旅伴角色下游客就餐行为的差异能更好地理解旅游中的旅伴效应。本文以2012—2020年重庆市美食游记为研究数据,采用社会网络分析等方法研究不同旅伴游客的就餐行为差异,研究表明:(1)游客就餐网络(食物网络和餐厅网络)结构受旅伴影响显著。旅伴的加入有效提升了食物网络节点联系的紧密程度,并扩展了餐厅网络节点关联的空间范围;(2)旅伴角色影响游客在旅程中对特色食物类型的选择。其中,无旅伴的游客(独自一人)在体验特色食物类型时呈现“被动保守型”、旅伴角色为三五好友的游客呈现“遍历型”,而其他旅伴角色的游客呈现“随意/兴趣型”;(3)独自旅行游客和旅伴角色为闺蜜、家庭、情侣以及三五好友游客的就餐行为差异反映了旅伴和社会关系的交叉影响,是美食旅游动机下个体和群体面向多因素综合作用的就餐决策结果。本文从食物类型和餐厅2个视角揭示了不同旅伴角色下的游客就餐行为特征,为深度刻画人群移动行为机理和社会因素影响下的人群活动时空动态提供了科学指导。

关键词：美食旅游游记数据旅伴角色社会网络就餐行为食物类型餐厅重庆

不同剂量阿替普酶对急性缺血性脑卒中患者认知功能、血清D-D、PLT的影响

在线全文

学校读者我要写书评

暂无评论

中国医学创新 2024年第11期21卷 42-47页

作者：王福平杨腾飞胡意瑞金市人民医院神经内科江西瑞金342500 瑞金市人民医院儿科江西瑞金342500

目的:探讨不同剂量阿替普酶对急性缺血性脑卒中患者血小板计数(PLT)、D-二聚体(D-D)、认知功能的影响。方法:选取瑞金市人民医院于2020年3月—2022年11月收治72例急性缺血性脑卒中患者,随机分为标准剂量组(n=36)和低剂量组(n=36),两组... 详细信息

目的:探讨不同剂量阿替普酶对急性缺血性脑卒中患者血小板计数(PLT)、D-二聚体(D-D)、认知功能的影响。方法:选取瑞金市人民医院于2020年3月—2022年11月收治72例急性缺血性脑卒中患者,随机分为标准剂量组(n=36)和低剂量组(n=36),两组均给予阿替普酶治疗,给药剂量分别为0.9 mg/kg、0.6 mg/kg,比较两组神经功能缺损、认知功能,脑循环动力学指标、血小板参数及血清指标水平。结果:两组临床治疗总有效率比较,差异无统计学意义(P>0.05)。治疗1、7、14、28 d后两组患者的美国国立卫生研究院卒中量表(NIHSS)评分均比治疗前下降,蒙特利尔认知评估量表(MoCA)评分均较治疗前升高(P0.05)。治疗7 d后,两组最小血流速度(V_(min))、最小血流量(Q_(min))、脑血管动态阻力(DR)水平均较治疗前升高,脑血管阻力(R)、脉搏波波速(WV)水平均较治疗前降低(P0.05)。治疗7 d后,两组PLT、血小板压积(PCT)水平均较治疗前升高,平均血小板体积(MPV)、血小板体积分布宽度(PDW)水平均较治疗前降低(P0.05)。治疗7 d后,两组血清纤维蛋白原(FIB)、D-D水平均较治疗前降低(P0.05)。结论:低剂量和标准剂量的阿替普酶在急性缺血性脑卒患者的治疗中,都可以提高神经功能和认知功能,降低脑循环阻力,提升脑血流量,改善血小板参数,降低血液高凝状态,二者效果相当。

关键词：急性缺血性脑卒中阿替普酶剂量认知功能 D-二聚体血小板

MBBR耦合多段AO工艺在大型市政污水处理厂的运行初探和实践成效

在线全文

学校读者我要写书评

暂无评论

净水技术 2024年第1期43卷 101-109页

作者：杨颂杨腾飞付炳炳杨伟滨深圳市环境水务集团有限公司广东深圳518000

新建城市污水处理厂或提标改造老旧污水处理厂普遍存在出水水质差、污染物负荷与占地面积等不可调和的矛盾,基于纵向扩大池容会增加建设成本、高负荷运行具有超标风险,因此,急需一种污泥/容积负荷高、运行稳定、操作简单的污水处理新技... 详细信息

新建城市污水处理厂或提标改造老旧污水处理厂普遍存在出水水质差、污染物负荷与占地面积等不可调和的矛盾,基于纵向扩大池容会增加建设成本、高负荷运行具有超标风险,因此,急需一种污泥/容积负荷高、运行稳定、操作简单的污水处理新技术。对此,某大型市政污水处理厂利用移动床生物膜反应器(MBBR)泥膜共混技术提高生化池有效污泥浓度、实现功能微生物的强化和富集,耦合多段AO工艺,经历雨/旱、夏/冬季,已稳定运行227 d,当进水COD_(Cr)、氨氮、TN质量浓度分别为277.97、34.92、45.95 mg/L,对应出水质量浓度分别降低至12.68、0.33、7.55 mg/L,MBBR挂膜前后的氨氮污泥负荷为0.011、0.015 g氨氮/(g MLSS·d)、TN污泥负荷为0.014、0.020 g TN/(g MLSS·d)。系统稳定运行期间,好氧Ⅰ段氨氮硝化速率为7.27 mg/(L·h)、缺氧Ⅰ段NO-3-N还原速率为3.48 mg/(L·h)。结果表明,通过负载微生物和游离污泥的交互作用,寻求复杂微生物群落功能的强化和调控,可以设计出更加合理、高效的污水处理新工艺。

关键词：市政污水处理厂移动床生物膜反应器(MBBR) 多段AO 工艺泥膜共混技术强化脱氮

分散型催化剂在煤/重油共炼体系中的加氢抑焦作用

在线全文

学校读者我要写书评

暂无评论

化工学报 2024年第7期75卷 2556-2564页

作者：杨露刘聪聪孟彤彤张博远杨腾飞邓文安王晓斌山东理工大学化学化工学院山东淄博255000 淄博市产品质量检验研究院山东淄博255000 中国石油大学(华东)化学化工学院山东青岛266580

采用马瑞常渣与印尼褐煤进行煤/重油加氢共炼反应,研究了马瑞常渣脱除沥青质和催化剂加量对共炼体系加氢转化深度的影响。利用XRD、XPS、TEM、TG、^(13)C NMR和SEM对共炼反应后固体残渣进行表征。结果表明,沥青质的存在会促进生焦过程... 详细信息

采用马瑞常渣与印尼褐煤进行煤/重油加氢共炼反应,研究了马瑞常渣脱除沥青质和催化剂加量对共炼体系加氢转化深度的影响。利用XRD、XPS、TEM、TG、^(13)C NMR和SEM对共炼反应后固体残渣进行表征。结果表明,沥青质的存在会促进生焦过程并显著降低共炼体系中煤的转化效率,达到同等转化深度时脱沥青油体系中所需催化剂加量大幅度降低。对于沥青质含量较高的马瑞常渣体系,催化剂反应后固体残渣中有机碳质组分芳香度降低,含氧组分比重下降,且残余有机碳也更易发生热解,表明催化剂对反应体系中碳质组分优异的氢解作用。固体残渣的微观形貌分析则显示出催化剂对生焦过程的良好抑制效果,有助于提高反应体系的加氢转化深度。

关键词：分散催化剂加氢沥青质生焦

维普期刊数据库博看期刊

SCChOA:Hybrid Sine-Cosine Chimp Optimization Algorithm for Feature Selection

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第12期77卷 3057-3075页

作者： Shanshan Wang Quan Yuan Weiwei Tan tengfei yang Liang Zeng School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China Xiangyang Industrial Institute of Hubei University of Technology Xiangyang441100China

Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification ***,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search ***,the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm(SCChOA)to address the feature selection *** this approach,firstly,a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm(SCA)and the Chimp Optimization Algorithm(ChOA),enabling a more effective search in the objective ***,an S-shaped transfer function is introduced to perform binary transformation on ***,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection *** evaluate the performance of the proposed method,16 datasets from different dimensions of the UCI repository along with four evaluation metrics of average fitness value,average classification accuracy,average feature selection number,and average running time are ***,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for *** results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection *** is capable of maximizing the reduction in the number of selected features while maintaining a high classification ***,the results of statistical tests also confirm the significant effectiveness of this method.

关键词： Metaheuristics chimp optimization algorithm sine-cosine algorithm feature selection and classification

“Swimming Ducks Forecast the Coming of Spring”—The predictability of aggregate insider trading on future market returns in the Chinese market

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

China Journal of Accounting Research 2014年第3期7卷 179-201页

作者： Chafen Zhu Li Wang tengfei yang Department of Accounting and Financial Management School of Management Zhejiang University School of Accountancy University of Akron College of Humanities and Social Sciences National University of Defense Technology

This study systematically examines the ability of aggregate insider trading to predict future market returns in the Chinese A-share market. After controlling for the contrarian investment strategy, aggregate executive(large shareholder)trading conducted over the past six months can predict 66%(72.7%) of market returns twelve months in advance. Aggregate insider trading predicts future market returns very accurately and is stronger for insiders who have a greater information advantage(e.g., executives and controlling shareholders).Corporate governance also affects the predictability of insider trading. The predictability of executive trading is weakest in central state-owned companies,probably because the "quasi-official" status of the executives in those companies effectively curbs their incentives to benefit from insider *** predictive power of large shareholder trading in private-owned companies is higher than that in state-owned companies, probably due to their stronger profit motivation and higher involvement in business operations. This study complements the literature by examining an emerging market and investigating how the institutional context and corporate governance affect insider trading.

关键词： Aggregate insider trading Large shareholder trading Information hierarchy Corporate governance Emerging market

Personalized tourist route recommendation model with a trajectory understanding via neural networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

International Journal of Digital Earth 2022年第1期15卷 1738-1759页

作者： Naixia Mou Qi Jiang Lingxian Zhang Jiqiang Niu Yunhao Zheng Yanci Wang tengfei yang College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdaoPeople’s Republic of China School of Geographic Sciences Xinyang Normal UniversityXinyangPeople’s Republic of China Institute of Remote Sensing and Geographical Information Systems School of Earth and Space SciencesPeking UniversityBeijingPeople’s Republic of China Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China

Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features,for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by the success of deep learning in sequence learning,a personalized recurrent neural network (P-RecN) is proposed for tourist route recommendation. It is data-driven and adaptively learns the unknown mapping of historical trajectory input to recommended route output. Specifically,a trajectory encoding module is designed to mine the semantic information of trajectory data,and LSTM neural networks are used to capture the sequence travel patterns of tourists. In particular,a temporal attention mechanism is integrated to emphasize the main behavioral intention of tourists. We retrieve a geotagged photo dataset in Shanghai,and evaluate our model in terms of accuracy and ranking ability. Experimental results illustrated that P-RecN outperforms other baseline approaches and can effectively understand the travel patterns of tourists.

关键词： Recommendation system travel trajectory recurrent neural networks Flickr geotagged photos

An OpenFlow-Based Load Balancing Strategy in SDN

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2020年第1期62卷 385-398页

作者： Xiaojun Shi yangyang Li Haiyong Xie tengfei yang Linchao Zhang Panyu Liu Heng Zhang Zhiyao Liang Department of Science and Technology China Electronics Technology Group CorporationBeijingChina National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data China Academy of Electronics and Information TechnologyBeijingChina University of Science and Technology of China HefeiChina National University of Defense Technology ChangshanChina Macao University of Science and Technology Avenida WaiLongTaipaMacao

In today’s datacenter network,the quantity growth and complexity increment of traffic is unprecedented,which brings not only the booming of network development,but also the problem of network performance degradation,such as more chance of network congestion and serious load *** to the dynamically changing traffic patterns,the state-of the-art approaches that do this all require forklift changes to data center networking *** root of problem is lack of distinct strategies for elephant and mice *** this condition,it is essential to enforce accurate elephant flow detection and come up with a novel load balancing solution to alleviate the network congestion and achieve high bandwidth *** paper proposed an OpenFlow-based load balancing strategy for datacenter networks that accurately detect elephant flows and enforce distinct routing schemes with different flow types so as to achieve high usage of network *** prototype implemented in Mininet testbed with POX controller and verify the feasibility of our load-balancing strategy when dealing with flow confliction and network *** results show the proposed strategy can adequately generate flow rules and significantly enhance the performance of the bandwidth usage compared against other solutions from the literature in terms of load balancing.

关键词： Load balancing OpenFlow data center network elephant flow multi-path routing