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Frequency Domain Goodness of Fit Test Based Spectrum Sensing Method with Dynamically Varying Noise

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China Communications 2020年第12期17卷 172-179页

作者： Rui Gao peihan Qi Zhenghua Zhang School of Information Engineering Yangzhou University88 South Daxue RoadYangzhou 225009China State Key Laboratory of Integrated Service Networks Xidian UniversityNo.2 South Taibai RoadXi'an 710071China

In cognitive radio networks,spectrum sensing under circumstances of dynamically varying noise and lacking prior information is a key challenge to the conventional spectrum sensing *** the necessary information is rather difficult to obtain practically,most existing spectrum sensing methods are fettered in *** by these,in this paper,a Frequency domain Goodness of Fit Test(FGoF)based spectrum sensing method is *** FGoF makes full use of underlying information in Guard-Bands and the advantages of GoF test works for any *** and simulated results show that the FGoF is a robust spectrum sensing method in cognitive radio with the inherent advantages of invulnerability to dynamically varying noise.

关键词： spectrum sensing goodness of fit test dynamically varying noise noise uncertainty

Effective bias reduction methods for passive source localization using TDOA and GROA

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Science China(Information Sciences) 2013年第7期56卷 97-108页

作者： HAO BenJian LI Zan QI peihan GUAN Lei State Key Laboratory of Integrated Services Networks Xidian University

For passive source localization based on both TDOA and GROA, this paper proposes two bias reduction methods for the well-known Weighted-Least-Squares (WLS) estimator. We first derive the passive source localization bias from the two-step algebraic closed-form solution. This bias is found to be considerably larger than the Maximum Likelihood Estimator (MLE) and limits the WLS estimator's practical applications. In this paper, We develop two methods to reduce the bias. The first one called Bias-Subtraction-Method (BSM) directly subtracts the expected bias from the solution of the WLS estimator, and the second one called Bias-Reduction-Method (BRM) imposes a constraint to the equation error formulation to improve the source location estimate. The noise covariance matrix must be known exactly in calculating the expected bias in BSM, and we only need to know the structure of it in BRM. For far-field sources localization when the noise is Gaussian and not too large, both of the two proposed methods can reduce the localization bias effectively and achieve the Cramér-Rao Lower Bound (CRLB) performance very well, and the BRM almost has the same performance as the MLE estimator. Simulations corroborate the performance of the two proposed methods.

关键词： source localization bias reduction Time Differences of Arrival (TDOA) Gain Ratios of Arrival (GROA) Cramér-Rao Lower Bound (CRLB)

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

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China Communications 2020年第2期17卷 138-148页

作者： Shilian Zheng Shichuan Chen peihan Qi Huaji Zhou Xiaoniu Yang Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China State Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of ChinaXidian UniversityXi’an 710071China

Spectrum sensing is a key technology for cognitive *** present spectrum sensing as a classification problem and propose a sensing method based on deep learning *** normalize the received signal power to overcome the effects of noise power *** train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new *** also use transfer learning strategies to improve the performance for real-world *** experiments are conducted to evaluate the performance of this *** simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based *** addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new ***,the real-world signal detection experiment results show that the detection performance can be further improved by transfer ***,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.

关键词： spectrum sensing deep learning convolutional neural network cognitive radio spectrum management

Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM

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China Communications 2018年第12期15卷 55-63页

作者： Faquan Yang Ling Yang Dong Wang peihan Qi Haiyan Wang 无 School of Electrical and Information Engineering Foshan UniversityFoshan 528000China Xinhua College of Sun Yat-sen University Guanzuo 523133China State Key Laboratory of Integrated Service Networks Xidian UniversityXian 710071China

For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.

关键词： clustering algorithm feature extraction grading algorithm support vector machine modulation recognition

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含高浓度碱减量废水的印染废水治理实例

水处理技术 2025年第1期51卷 145-147,156页

作者：沈林叶曹奕挺徐璐于沛涵孙诗雨黄新文浙江环越科技有限公司浙江杭州310005 浙江工业大学环境学院浙江杭州310014 杭州佑源环保科技有限公司浙江杭州310005

碱度高、COD高和较差的生物处理性能是碱减量废水处理的主要难点,是常见的难处理废水。以浙江某涤纶织物企业为例,生产过程废水日处理总量2 000 m^(3),其中碱减量废水日处理量200 m^(3),综合废水日处理量1 800 m^(3)。企业采用酸析-Fen... 详细信息

碱度高、COD高和较差的生物处理性能是碱减量废水处理的主要难点,是常见的难处理废水。以浙江某涤纶织物企业为例,生产过程废水日处理总量2 000 m^(3),其中碱减量废水日处理量200 m^(3),综合废水日处理量1 800 m^(3)。企业采用酸析-Fenton预处理碱减量废水,混凝沉淀-水解酸化-A/O处理综合废水。该废水处理系统对COD、BOD5、SS、氨氮、TN的平均去除率分别为92.8%、96.7%、92.5%、76.3%、76.2%,出水水质优于GB 4287-2012规定的间接排放标准,运行成本为6.62元/m^(3)。

关键词：碱减量废水对苯二甲酸 Fenton氧化废水处理

Towards Semi-supervised Classification of Abnormal Spectrum Signals Based on Deep Learning

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Chinese Journal of Electronics 2024年第3期33卷 721-731页

作者： Tao JIANG Wanqing CHEN Hangping ZHOU Jinyang HE peihan QI School of Cyber Engineering Xidian University State Key Laboratory of Integrate Service Networks Xidian University Guangzhou Institute of Technology Xidian University

In order to cope with the heavy labor cost challenge of the manual abnormal spectrum classification and improve the effectiveness of existing machine learning schemes for spectral datasets with interference-to-signal ratios,we proposes a semi-supervised classification of abnormal spectrum signals(SSC-ASS),aimed at addressing some of the challenges in abnormal spectrum signal(ASS) classification tasks.A significant advantage of SSC-ASS is that it does not require manual labeling of every abnormal data,but instead achieves high-precision classification of ASSs using only a small number of labeled ***,the method can to some extent avoid the introduction of erroneous information resulting from the complex and variable nature of abnormal signals,thereby improving classification ***,SSC-ASS uses a memory AutoEncoder module to efficiently extract features from abnormal spectrum signals by learning from the reconstruction ***,SSC-ASS combines convolutional neural network and the K-means using a DeepCluster framework to fully utilize the unlabeled ***,SSC-ASS also utilizes pre-training,category mean memory module and replaces pseudo-labels to further improve the classification accuracy of *** we verify the classification effectiveness of SSC-ASS on synthetic spectrum datasets and real on-air spectrum dataset.

关键词： Deep learning Costs Memory management Memory modules Manuals Feature extraction Labeling

A salt-induced tackifying polymer for enhancing oil recovery in highsalinity reservoirs:Synthesis,evaluation,and mechanism

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Green Energy & Environment 2024年第11期9卷 1747-1758页

作者： Yining Wu peihan Li Bin Yan Xiaohan Li Yongping Huang Juncong Yuan Xiang Feng Caili Dai Department of Petroleum Engineering China University of Petroleum(East China)Qingdao266580China Department of National Engineering Laboratory for Clean Technology of Leather Manufacture College of Biomass Science and EngineeringSichuan UniversityChengdu610065China Department of Civil and Environmental Engineering Princeton UniversityPrinceton08544USA Department of Chemical Engineering China University of Petroleum(East China)Qingdao266580China

Polymerﬂooding is an effective method widely applied for enhancing oil recovery(EOR)by reducing the mobility ratio between theinjected water and crude ***,traditional polymers encounter challenges in high salinity reservoirs due to their salt *** this challenge,we synthesized a zwitterion polymer(PAMNS)with salt-induced tackifying property through copolymerization ofacrylamide and a zwitterion monomer,methylacrylamide propyl-N,N-dimethylbutylsulfonate(NS).NS monomer is obtained from thereaction between 1,4-butanesultone and dimethylamino propyl *** this study,the rheological properties,salt responsiveness,and EOR efﬁciency of PAMNS were *** demonstrate that PAMNS exhibits desirable salt-induced tackifyingcharacteristics,with viscosity increasing up to 2.4 times as the NaCl concentration reaches a salinity of 30×10^(4)mg L^(-1).Furthermore,highvalence ions possess a much stronger effect on enhancing viscosity,manifested as Mg^(2+)>Ca^(2+)>Na^(+).Molecular dynamics simulations(MD)andﬂuid dynamics experiment results demonstrate that PAMNS molecules exhibit a more stretched state and enhanced intermolecularassociations in high-salinity *** is because of the salt-induced tackifying,PAMNS demonstrates superior performance inpolymerﬂooding experiments under salinity ranges from 5×10^(4)mg L^(-1)to 20×10^(4)mg L^(-1),leading to 10.38–19.83%higher EOR thantraditional polymers.

关键词： High salinity reservoirs Salt-induced tackifying polymer Anti-polyelectrolyte behavior Enhanced oil recovery

A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification

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China Communications 2024年第11期21卷 88-103页

作者： Zhou Xiaoyu Qi peihan Liu Qi Ding Yuanlei Zheng Shilian Li Zan State Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China The No.011 Research Center Science and Technology on Communication Information Security Control LaboratoryJiaxing 314033China

With the successive application of deep learning(DL)in classification tasks,the DL-based modulation classification method has become the preference for its state-of-the-art ***,once the DL recognition model is pre-trained with fixed classes,the pre-trained model tends to predict incorrect results when identifying incremental ***,the incremental classes are usually emergent without label information or only a few labeled samples of incremental classes can be *** this context,we propose a graphbased semi-supervised approach to address the fewshot classes-incremental(FSCI)modulation classification *** proposed method is a twostage learning method,specifically,a warm-up model is trained for classifying old classes and incremental classes,where the unlabeled samples of incremental classes are uniformly labeled with the same label to alleviate the damage of the class imbalance *** the warm-up model is regarded as a feature extractor for constructing a similar graph to connect labeled samples and unlabeled samples,and the label propagation algorithm is adopted to propagate the label information from labeled nodes to unlabeled nodes in the graph to achieve the purpose of incremental classes *** results prove that the proposed method is superior to other finetuning methods and retrain methods.

关键词： deep learning few-shot label propagation modulation classification semi-supervised learning

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High-Speed Railway 2024年第2期2卷 101-109页

作者： Jiaxu Guo Ding Ding peihan Yang Qi Zou Yaping Huang School of Computer Science and Technology Beijing Jiaotong UniversityBeijing 100044China Key Laboratory of Big Data&Artificial Intelligence in Transportation Ministry of EducationBeijing 100044China

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining ***,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale *** the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault *** algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent ***,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the *** experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.

关键词： High utility Quantitative Frequent pattern mining Related degree pruning Fixed pattern length

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