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| 1 | Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios显示文摘Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal power to overcome the effects of noise power uncertainty.We 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 signals.We also use transfer learning strategies to improve the performance for real-world signals.Extensive experiments are conducted to evaluate the performance of this method.The 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 method.In addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals.Furthermore,the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning.Finally,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. | Shilian Zheng Shichuan Chen Peihan Qi Huaji Zhou Xiaoniu Yang | 2020 | China Communications2020，17，2： | 16 |
| 2 | Ambient Backscatter Communications over NOMA Downlink Channels显示文摘In this paper,we investigate the performance of commensal ambient backscatter communications(AmBC)that ride on a non-ortho go nal multiple access(NOMA)downlink transmission,in which a backscatter device(BD)splits part of its received signals from the base station(BS)for energy harvesting,and backscatters the remaining received signals to transmit information to a cellular user.Specifically,under the power consumption constraint at BD and the peak transmit power constraint at BS,we derive the optimal reflection coefficient at BD,the optimal total transmit power at BS,and the optimal power allocation at BS for each transmission block to maximize the ergodic capacity of the ambient backscatter transmission on the premise of preserving the outage performance of the NOMA downlink transmission.Furthermore,we consider a scenario where the BS is restricted by a maximum allowed average transmit power and the reflection coefficient at BD is fixed due to BD’s low-complexity nature.An algorithm is developed to determine the optimal total transmit power and power allocation at BS for this scenario.Also,a low-complexity algorithm is proposed for this scenario to reduce the computational complexity and the signaling overheads.Finally,the performance of the derived solutions are studied and compared via numerical simulations. | Weiyu Chen Haiyang Ding Shilian Wang Daniel Benevides daCosta Fengkui Gong Pedro Henrique Juliano Nardelli | 2020 | China Communications2020，17，6： | 4 |
| 3 | Primary User Adversarial Attacks on Deep Learning-Based Spectrum Sensing and the Defense Method显示文摘The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been verified.In this paper,we propose primary user adversarial attack(PUAA)to verify the robustness of the deep learning based spectrum sensing model.PUAA adds a care-fully manufactured perturbation to the benign primary user signal,which greatly reduces the probability of detection of the spectrum sensing model.We design three PUAA methods in black box scenario.In or-der to defend against PUAA,we propose a defense method based on autoencoder named DeepFilter.We apply the long short-term memory network and the convolutional neural network together to DeepFilter,so that it can extract the temporal and local features of the input signal at the same time to achieve effective defense.Extensive experiments are conducted to eval-uate the attack effect of the designed PUAA method and the defense effect of DeepFilter.Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model.In addition,the experimen-tal results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA with-out affecting the detection performance of the model. | Shilian Zheng Linhui Ye Xuanye Wang Jinyin Chen Huaji Zhou Caiyi Lou Zhijin Zhao Xiaoniu Yang | 2021 | China Communications2021，18，12： | 3 |
| 4 | A Novel Anti-human DR5 Monoclonal Antibody with Tumoricidal Activity Induces Caspase-dependent and Caspase-independent Cell Death显示文摘 | Yabin Guo Caifeng Chen Yong Zheng Jinchun Zhang Xiaohui Tao Shilian Liu Dexian Zheng Yanxin Liu | 2006 | 中国生物学文摘2006，20，1： | 2 |
| 5 | A Method of Synthetical Appraisal With Interval Numbers | CHEN Shilian(Mathematics Department,Qujing Teacher’s College,Qujing,Yunnan,655000,China) | 1995 | Systems Science and Systems Engineering1995，5，1： | 1 |
| 6 | Target Channel Sequence Selection Scheme for Proactive-Decision Spectrum Handoff显示文摘 | ZHENG Shilian VANG Xiaoniu CHEN Shichuan | 2011 | IEEE Communications Letters2011，15，12： | 1 |
| 7 | Reusable sensor based on high magnetization carboxyl-modified graphene oxide with intrinsic hydrogen peroxide catalytic activity for hydrogen peroxide and glucose detection,Biosensors and Bioelectronics显示文摘 | Yang Hungwei Hua Muyi Chen Shilian | 2013 | 41:172-1792013，41，： | 1 |
| 8 | Loss or duplication of key regulatory genes coincides with environmental adaptation of the stomatal complex in Nymphaea colorata and Kalanchoe laxiflora显示文摘The stomatal complex is critical for gas and water exchange between plants and the atmosphere.Originating over 400 million years ago,the structure of the stomata has evolved to facilitate the adaptation of plants to various environments.Although the molecular mechanism of stomatal development in Arabidopsis has been widely studied,the evolution of stomatal structure and its molecular regulators in different species remains to be answered.In this study,we examined stomatal development and the orthologues of Arabidopsis stomatal genes in a basal angiosperm plant,Nymphaea colorata,and a member of the eudicot CAM family,Kalanchoe laxiflora,which represent the adaptation to aquatic and drought environments,respectively.Our results showed that despite the conservation of core stomatal regulators,a number of critical genes were lost in the N.colorata genome,including EPF2,MPK6,and AP2C3 and the polarity regulators BASL and POLAR.Interestingly,this is coincident with the loss of asymmetric divisions during the stomatal development of N.colorata.In addition,we found that the guard cell in K.laxiflora is surrounded by three or four small subsidiary cells in adaxial leaf surfaces.This type of stomatal complex is formed via repeated asymmetric cell divisions and cell state transitions.This may result from the doubled or quadrupled key genes controlling stomatal development in K.laxiflora.Our results show that loss or duplication of key regulatory genes is associated with environmental adaptation of the stomatal complex. | Meizhi Xu Fei Chen Shilian Qi Liangsheng Zhang Shuang Wu | 2018 | Horticulture Research2018，5，1： | 0 |
| 9 | The Superiority Analysis on Characteristics and Relative Factorsof Systems | Chen Shilian Kunming University of Science and Technology, Kunming 650093 | 1998 | Systems Science and Systems Engineering1998，8，3： | 0 |
| 10 | In situ strain electrical atomic force microscopy study on two-dimensional ternary transition metal dichalcogenides显示文摘Atomically thin two-dimensional(2D)alloys have attracted wide interests of study recently due to their potential in flexible electronic and optoelectronic applications.In particular,monolayer transition metal dichalcogenide(TMD)alloys have emerged as unique 2D semiconductors with tunable bandgaps,by means of alloying.However,response of surface electrical potential and barrier height to strain for 2D TMD alloys–electrode interface is rarely explored.Apparently,revealing such strain-dependent evolution of electrical properties is crucial for developing advanced 2D TMD based flexible electronics and opto-electronics.Here we performed in situ strain Kelvin probe force microscopy(KPFM)and conductive atomic force microscopy(C-AFM)investigations of monolayer Mo_(0.4)W_(0.6)Se_(2) on Au coated flexible substrate,where controlled uni-axial tensile strain is applied.Both contact potential difference(CPD)and Schottky barrier heights(SBH)of monolayer Mo_(0.4) W_(0.6)Se_(2) show obvious decreases with the increase of strain,which is mainly due to the strain-induced increment of TMD electron affinity.Our in situ strain photoluminescence(PL)measurements also indicate the changes of electronic band structures under strain.We further exploit the substrate effects on CPD by study the monolayer alloy on the mostly used substrates of SiO 2/Si and indium tin oxide(ITO)/glass.Our findings could strengthen the foundation for the potential applications of 2D TMD and their alloys in the fields of strain sensors,flexible photodetectors,and other wearable electronic devices. | Li Ma Rui Chen Shilian Dong Ting Yu | 2022 | InfoMat2022，4，7： | 0 |
| 11 | The Fuzzy Incidence Degree in Systems Analysis | CHEN Shilian (Mathematics Department. Qujing Teacher’s College, Qujing, Yunnan, China)CAO Guiying(Branch Factory of Kunming Cigarette factory, Kunming, Yunnan, China) | 1994 | Systems Science and Systems Engineering1994，4，2： | 0 |
| 12 | Contrastive Clustering for Unsupervised Recognition of Interference Signals显示文摘Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms.However,there is no unsupervised interference signals recognition algorithm at present.In this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is proposed.Specifically,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive learning.In the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive learning.In addition,a dynamic entropy parameter strategy is proposed.The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC effectively.The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms.In particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 dB.In addition,our method is close to the supervised learning algorithm. | Xiangwei Chen Zhijin Zhao Xueyi Ye Shilian Zheng Caiyi Lou Xiaoniu Yang | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 13 | Incremental Learning of Radio Modulation Classification Based on Sample Recall显示文摘Radio modulation classification has always been an important technology in the field of communications.The difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old tasks.In this paper,we propose a sample memory and recall framework for incremental learning of radio modulation classification.For data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for memorizing.We compare the performance of our proposed method with three baselines through a large number of simulation experiments.Results show that our method achieves far higher classification accuracy than finetuning method and feature extraction method.Furthermore,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting. | Yan Zhao Shichuan Chen Tao Chen Weiguo Shen Shilian Zheng Zhijin Zhao Xiaoniu Yang | 2023 | China Communications2023，20，7： | 0 |
| 14 | Open World Recognition of Communication Jamming Signals显示文摘To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming. | Yan Tang Zhijin Zhao Jie Chen Shilian Zheng Xueyi Ye Caiyi Lou Xiaoniu Yang | 2023 | China Communications2023，20，6： | 0 |
| 15 | Analysis on Formation Reason of '0902' Blizzard in Northeast China显示文摘[Objective]The research aimed to analyze formation reason of ' 0902' blizzard in northeast China. [Method]By using timely observation data,NCEP reanalysis data and Doppler radar data at Baishan station,blizzard process in southeast part of northeast China during 12-13 February,2009 was analyzed. [Result]Snowfall zone of the blizzard process was wide,snowfall was more,snowfall gradient was big,and snowfall time relatively concentrated. These characteristics reflected that the blizzard process had significant convection characteristics. Baroclinic disturbance at high-altitude straight frontal zone and ground warm frontogenesis caused by eastward movement and northward advancement of North China low vortex at low altitude were the circulation characteristics in the process. Water vapor from the sea went northward as southwest airflow,and strongly converged in blizzard zone,which provided sufficient water vapor condition for the blizzard. Before heavy snowfall occurred,there was accumulation process of heat and energy. Conditional symmetric instability was main unstable mechanism of the blizzard. During heavy snowfall period,ascending branch of secondary vertical circulation at exit zone of high-altitude jet coupled with ascending branch of secondary vertical circulation of warm frontegenesis at low layer,inducing strong development of the vertical motion. Doppler radar intensity echo revealed that it was easy to generate blizzard in the area where echo intensity was consistently above 20 dBz. Strong wind velocity convergence zone at radical velocity field especially adverse wind zone was favorable for the generation of blizzard. [Conclusion]The research could provide reference for blizzard forecast in northeast China. | Changsheng Chen Shilian Chou Lixin Su Min Liu | 2013 | Meteorological and Environmental Research2013，4，12： | 0 |
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