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Symmetric Learning Data Augmentation Model for Underwater Target Noise Data Expansion

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Computers, Materials & Continua 2018年第12期57卷 521-532页

作者： Ming He Hongbin Wang lianke zhou Pengming Wang Andrew Ju College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of Computer and Information Engineering Heilongjiang University of Science and TechnologyHarbin150022China School of Software and Microelectronics Harbin University of Science and TechnologyHarbin150080China Department of Computer Science and Information Systems University of LimerickLimerickIreland

An important issue for deep learning models is the acquisition of training of data.Without abundant data from a real production environment for training,deep learning models would not be as widely used as they are today.However,the cost of obtaining abundant real-world environment is high,especially for underwater environments.It is more straightforward to simulate data that is closed to that from real environment.In this paper,a simple and easy symmetric learning data augmentation model(SLDAM)is proposed for underwater target radiate-noise data expansion and generation.The SLDAM,taking the optimal classifier of an initial dataset as the discriminator,makes use of the structure of the classifier to construct a symmetric generator based on antagonistic generation.It generates data similar to the initial dataset that can be used to supplement training data sets.This model has taken into consideration feature loss and sample loss function in model training,and is able to reduce the dependence of the generation and expansion on the feature set.We verified that the SLDAM is able to data expansion with low calculation complexity.Our results showed that the SLDAM is able to generate new data without compromising data recognition accuracy,for practical application in a production environment.

关键词： Data augmentation symmetric learning data expansion underwater target noise data

ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network

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Computers, Materials & Continua 2019年第1期58卷 169-181页

作者： Nianbin Wang Ming He Jianguo Sun Hongbin Wang lianke zhou Ci Chu Lei Chen College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of Computer and Information Engineering Heilongjiang University of Science and Technology Harbin150022China College of Engineering and Computing Georgia Southern UniversityGeorgia30458USA

Underwater target recognition is a key technology for underwater acoustic countermeasure.How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals.In this paper,the deep learning model is applied to underwater target recognition.Improved anti-noise Power-Normalized Cepstral Coefficients(ia-PNCC)is proposed,based on PNCC applied to underwater noises.Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity.The method is combined with a convolutional neural network in order to recognize the underwater target.Experiment results show that the acoustic feature presented by ia-PNCC has lower noise and are wellsuited to underwater target recognition using a convolutional neural network.Compared with the combination of convolutional neural network with single acoustic feature,such as MFCC(Mel-scale Frequency Cepstral Coefficients)or LPCC(Linear Prediction Cepstral Coefficients),the combination of the ia-PNCC with a convolutional neural network offers better accuracy for underwater target recognition.

关键词： Noise processing underwater target recognition convolutional neural network

A RELIABILITY ENHANCED DENSITY ADAPTIVE DATA DISSEMINATION SCHEME FOR VANETS

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Journal of Electronics(China) 2011年第1期28卷 44-52页

作者： zhou lianke Cui Gang Luo Danyan Liu Hongwei School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

In this paper,a reliability enhanced and density adaptive data disseminating scheme is proposed for Vehicular Ad hoc NETworks(VANETs).The distributed on demand inquiring and responding mechanism is employed to get nodes' connectivity information.The announcing-listening process is also designed to find the nodes with bigger additional degree to rebroadcast,by which the relaying node is selected freely from density's influence.Simultaneously,a reliability parameter is designed to choose redundant relays for each hop.According to the importance of the broadcast,the parameter is set by the source node properly.Simulation results show that the scheme has achieved good performances such as low forwarding ratio,short latency and low load.The broadcast coverage ratio is ensured against the influence of key link errors and relaying nodes failure by paying suitable additional communication.

关键词： Vehicular Ad hoc NETworks(VANETs) Broadcast Communication Wireless network Density adaptive

PMS-Sorting:A New Sorting Algorithm Based on Similarity

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Computers, Materials & Continua 2019年第4期59卷 229-237页

作者： Hongbin Wang lianke zhou Guodong Zhao Nianbin Wang Jianguo Sun Yue Zheng Lei Chen College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Liren College of Yanshan University Yanshan UniversityQinhuangdao066004China College of Engineering and Computing Georgia Southern UniversityGeorgia30458USA

Borda sorting algorithm is a kind of improvement algorithm based on weighted position sorting algorithm,it is mainly suitable for the high duplication of search results,for the independent search results,the effect is not very good and the computing method of relative score in Borda sorting algorithm is according to the rule of the linear regressive,but position relationship cannot fully represent the correlation changes.aimed at this drawback,the new sorting algorithm is proposed in this paper,named PMS-Sorting algorithm,firstly the position score of the returned results is standardized processing,and the similarity retrieval word string with the query results is combined into the algorithm,the similarity calculation method is also improved,through the experiment,the improved algorithm is superior to traditional sorting algorithm.

关键词： Meta search engine result sorting query similarity Borda sorting algorithm position relationship

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考虑节点交通特性的VANET分簇广播协议

高技术通讯 2012年第5期22卷 468-476页

作者：周连科左德承崔刚刘宏伟哈尔滨工业大学计算机科学与技术学院哈尔滨150001

结合车用自组织网络（VANET）的道路交通特点，针对车辆节点的交通行为特性，提出了一种适合VANET道路交通场景的基于综合加权的分簇广播协议。该协议在簇建立和维护过程中都充分考虑了交通规则和结点运动规律对网络的影响，力求以较小... 详细信息

结合车用自组织网络（VANET）的道路交通特点，针对车辆节点的交通行为特性，提出了一种适合VANET道路交通场景的基于综合加权的分簇广播协议。该协议在簇建立和维护过程中都充分考虑了交通规则和结点运动规律对网络的影响，力求以较小的控制开销形成稳定的分簇网络。并根据交通流理论中车头间距与速度关系模型设计了符合实际的节点移动模型以进行仿真实验。实验结果表明，采用该协议形成的分簇网络结构简单、稳定，控制开销小，同时能够保证广播的可靠性，并有效降低了网络负载，适合多种交通场景下的VANET高效广播。

关键词：移动自组网(MANET) 车用自组织网络(VANET) 广播协议分簇交通特性

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关系数据库关键字查询结果排序方法

哈尔滨工程大学学报 2017年第12期38卷 1937-1942,1963页

作者：王瑛琦周连科王念滨哈尔滨工程大学计算机科学与技术学院黑龙江哈尔滨150001

为了提高传统排序方法的排序准确率及效率,本文提出一种列表级的并行学习排序方法,用于关系数据库关键字查询结果的排序。采用一种列表级学习排序算法并在此基础上引入并行框架,使训练过程能够在多个节点上并行执行,有效地减少训练时间... 详细信息

为了提高传统排序方法的排序准确率及效率,本文提出一种列表级的并行学习排序方法,用于关系数据库关键字查询结果的排序。采用一种列表级学习排序算法并在此基础上引入并行框架,使训练过程能够在多个节点上并行执行,有效地减少训练时间;并且提出一种基于贪婪搜索算法和排序性能的弱排序器分层构建策略,提高排序模型的训练效率和有效性;分别在公开数据集IMDB和Wikipedia上进行实验。实验结果表明:与传统的排序算法相比,本文方法具有较高的训练效率及排序准确率。

关键词：关系数据库关键字查询学习排序并行算法排序模型特征关联图排序函数弱排序器

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交通密度自适应的VANET广播协议

高技术通讯 2012年第4期22卷 355-362页

作者：周连科崔刚刘宏伟左德承哈尔滨工业大学计算机科学与技术学院哈尔滨150001

为了实现车用自组织网络（VANET）在交通流实时变化的场景下对安全信息的快速、有效广播，提出了一种能够适应交通流密度变化的VANET广播协议——交通密度自适应广播协议（TDABP）。该协议针对VANET环境的特点，采用了基于节点覆盖信息... 详细信息

为了实现车用自组织网络（VANET）在交通流实时变化的场景下对安全信息的快速、有效广播，提出了一种能够适应交通流密度变化的VANET广播协议——交通密度自适应广播协议（TDABP）。该协议针对VANET环境的特点，采用了基于节点覆盖信息的分布式广播策略，采取按需交换邻节点覆盖信息的方式来选择转发节点，有效地降低了更新邻节点信息的通信开销、减小了广播风暴的影响，使广播通信不受节点密度的影响，能够适应道路交通中交通流不断变化的VANET广播场景。理论分析和仿真实验结果表明，该VANET广播协议能够满足VANET广播的可靠性和实时性要求，能够适应交通流引起的节点密度变化，具有良好的可扩展性和较高的广播效率。

关键词：移动自组网车用自组织网络(VANET) 广播协议节点覆盖交通流密度

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基于查询意图的数据空间预取方法

哈尔滨工程大学学报 2016年第2期37卷 236-241页

作者：祝官文周连科王念滨刘丹哈尔滨工程大学计算机科学与技术学院黑龙江哈尔滨150001

为在查询前预取用户可能访问的数据,提出了一种利用查询日志的数据空间预取方法。该方法从查询日志中提取意图特征,并采用聚类技术对其进行聚类,识别用户查询意图,并基于该意图预取查询结果。实验结果表明:该方法在预取准确率和查询效... 详细信息

为在查询前预取用户可能访问的数据,提出了一种利用查询日志的数据空间预取方法。该方法从查询日志中提取意图特征,并采用聚类技术对其进行聚类,识别用户查询意图,并基于该意图预取查询结果。实验结果表明:该方法在预取准确率和查询效率方面均显著优于已有方法。

关键词：数据空间预取查询意图聚类查询日志查询结果预取准确率查询效率

电沉积制备多孔Ni-Fe-Sn合金电极及其析氧性能

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过程工程学报 2019年第1期19卷 159-164页

作者：高莹吴艺辉周连科马春生中南大学粉末冶金国家重点实验室湖南长沙410083 北京中材人工晶体研究院有限公司北京100018

采用直流电沉积法在铜箔表面合成了多孔结构的Ni-Fe-Sn合金,用扫描电子显微镜、X射线能谱仪和X射线衍射仪对合金的微观组织形貌和相态进行了表征,用电化学工作站测试了合金电极在碱性环境中的析氧性能。结果表明,Ni-Fe-Sn合金电极主要由... 详细信息

采用直流电沉积法在铜箔表面合成了多孔结构的Ni-Fe-Sn合金,用扫描电子显微镜、X射线能谱仪和X射线衍射仪对合金的微观组织形貌和相态进行了表征,用电化学工作站测试了合金电极在碱性环境中的析氧性能。结果表明,Ni-Fe-Sn合金电极主要由Ni3Sn2和FeNi3相组成,电极表面形成了多孔结构。在30wt%KOH溶液中,Ni-Fe-Sn合金的析氧过电位仅为261 mV(电流密度10 mA/cm2),Tafel斜率为69.9 mV/dec。电极在10 mA/cm2电流密度下能稳定工作12 h以上,具有良好的电化学稳定性。

关键词： Ni-Fe-Sn合金析氧反应电解水电沉积