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Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based network

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Computers, Materials & Continua 2024年第3期78卷 3349-3376页

作者： Zihao Song Yan Zhou Wei Cheng Futai Liang Chenhao Zhang Department of early warning Intelligence Early Warning AcademyWuhan430019China

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven ***,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of *** method for solving the RM may experience performance degradation or failure when applied to RTT-AT *** autoregressive deep learning methods are prone to error accumulation and long-term dependency *** this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is *** model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion *** learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual *** PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation *** weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final *** experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing *** to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same ***,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design

关键词： Missing value imputation time-series tracks probabilistic sparsity diagonal masking self-attention weight fusion

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early warning of core network capacity in space-terrestrial integrated networks

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Journal of systems Engineering and Electronics 2024年第4期35卷 855-864页

作者： HAN Sai LI Ao ZHANG Dongyue ZHU Bin WANG Zelin WANG Guangquan MIAO Jie MA Hongbing China Unicom Research Institute Beijing 100044China China United network Communications Group Company Limited Beijing 100033China

With the rapid development of low-orbit satellite com-munication networks both domestically and internationally,space-terrestrial integrated networks will become the future development *** space and terrestrial networks with limi-ted resources,the utilization efficiency of the entire space-terres-trial integrated networks resources can be affected by the core network *** order to improve the response efficiency of core networks expansion construction,early warning of the core network elements capacity is *** on the inte-grated architecture of space and terrestrial network,multidimen-sional factors are considered in this paper,including the number of terminals,login users,and the rules of users’migration during *** artifical intelligence(AI)technologies,the regis-tered users of the access and mobility management function(AMF),authorization users of the unified data management(UDM),protocol data unit(PDU)sessions of session manage-ment function(SMF)are predicted in combination with the num-ber of login users,the number of ***,the core network elements capacity can be predicted in *** proposed method is proven to be effective based on the data from real network.

关键词： space-terrestrial integrated networks core network element capacity artificial intelligent(AI)

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A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

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Petroleum Science 2024年第1期21卷 286-301页

作者： Yan-Wei Wang Zhen-Xue Dai Gui-Sheng Wang Li Chen Yu-Zhou Xia Yu-Hao Zhou College of Construction Engineering Jilin UniversityChangchun130026JilinChina Institute of Intelligent Simulation and early warning for Subsurface Environment Jilin UniversityChangchun130026JilinChina School of Energy Resources China University of GeosciencesBeijing100083China Department of Safety Environmental Protection and Quality ManagementShengli OilfieldSinopecDongying257000ShandongChina Key Laboratory of Thermo-Fluid Science and Engineering of MOE School of Energy and Power EngineeringXi'an Jiaotong UniversityXi'an710049ShaanxiChina College of Computer Science and Technology China University of Petroleum(East China)Qingdao266580ShandongChina Department of Physics University of FribourgFribourg1700Switzerland

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport ***,most current CO_(2) sequestration models do not adequately consider multiple transport ***,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and *** this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training *** this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and *** incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion ***,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated *** validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise *** study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

关键词： Deep learning Physics-informed data-driven neural network Depleted shale reservoirs CO_(2)storage Transport mechanisms

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Parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on micro-Doppler features using CNN

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Journal of systems Engineering and Electronics 2020年第5期31卷 884-889页

作者： WANG Wantian TANG Ziyue CHEN Yichang SUN Yongjian Department of early warning Technology Air Force Early Warning AcademyWuhan 430019China

This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network(CNN) using micro Doppler features. Firstly, the time-frequency spectrograms are acquired from the radar echo by the short-time Fourier ***, based on the obtained spectrograms, a seven-layer CNN architecture is built to recognize the blade-number parity and classify the manoeuvre intention of the rotor target. The constructed architecture contains a leaky rectified linear unit and a dropout layer to accelerate the convergence of the architecture and avoid over-fitting. Finally, the spectrograms of the datasets are divided into three different ratios, i.e., 20%, 33% and 50%,and the cross validation is used to verify the effectiveness of the constructed CNN architecture. Simulation results show that, on the one hand, as the ratio of training data increases, the recognition accuracy of parity and manoeuvre intention is improved at the same signal-to-noise ratio(SNR);on the other hand, the proposed algorithm also has a strong robustness: the accuracy can still reach 90.72% with an SNR of – 6 dB.

关键词： micro-Doppler convolutional neural network(CNN) parity recognition of blade number manoeuvre intention classification

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Analysis of Urban Agglomeration network Structure Based on Baidu Migration Data: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Urban Agglomeration

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Journal of Landscape Research 2024年第4期16卷 47-50页

作者： XIA Yuan WANG Bin Guangzhou Urban Planning and Design Co. Ltd.GuangzhouGuangdong 510060China Guangzhou Urban Planning&Design Survey Research Institute Co. Ltd.GuangzhouGuangdong 510060China Guangdong Enterprise Key Laboratory for Urban Sensing Monitoring and Early WarningGuangzhouGuangdong 510060China

The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure of urban agglomeration in the Greater Bay Area through the use of social network analysis *** is the inaugural application of big data based on location services in the study of urban agglomeration network structure,which represents a novel research perspective on this *** study reveals that the density of network linkages in the Greater Bay Area urban agglomeration has reached 100%,indicating a mature network-like spatial *** structure has given rise to three distinct communities:Shenzhen-Dongguan-Huizhou,Guangzhou-Foshan-Zhaoqing,and ***,cities within the Greater Bay Area urban agglomeration play different roles,suggesting that varying development strategies may be necessary to achieve staggered *** study demonstrates that large datasets represented by LBS can offer novel insights and methodologies for the examination of urban agglomeration network structures,contingent on the appropriate mining and processing of the data.

关键词： Baidu migration data Social network analysis Urban agglomeration network structure Greater Bay Area urban agglomeration

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Anal pruritus:Don’t look away

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World Journal of Gastrointestinal Endoscopy 2024年第3期16卷 112-116页

作者： Andreia Albuquerque Department of Gastroenterology Fernando Pessoa Teaching HospitalGondomarPorto 4420-096Portugal Precancerous Lesions and early Cancer Management Research Group RISE@CI-IPO(Health Research network) Portuguese Oncology Institute of PortoPorto 4200-072Portugal

Anal pruritus is a common anorectal symptom that can significantly impair a patient’s quality of life,including their mental *** can be one of the most difficult proctological conditions to *** often delay seeking medical attention,since it is an embarrassing but non-life-threatening *** ani can be associated with idiopathic and secondary causes,such as anorectal diseases,cancer(anal or colorectal),dermatological and sexually transmitted diseases,fungal infections and systemic *** patients are referred for a colonoscopy,this can sometimes provide the first opportunity to evaluate the perianal *** of anal pruritus are based on the abnormalities of the perianal skin,one of the most commonly used being the Washington classi-fication.A proper digital anorectal examination is important,as well as an anoscopy to help to exclude anorectal diseases or suspicious *** should be aware of the common etiologies,and classification of the perianal area abnormalities should be provided in the colonoscopy *** on treatment possibilities and follow-up can also be *** treatment normally consists of a triple approach:proper hygiene,elimination of irritants,and skin care and *** topical therapies have been described as possible treatments,including steroids,capsaicin,tacrolimus and methylene blue intradermal injections.

关键词： Anal pruritus Colonoscopy Washington classification Hemorrhoids Fissure Cancer

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User space transformation in deep learning based recommendation

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Journal of systems Engineering and Electronics 2020年第4期31卷 674-684页

作者： WU Caihua MA Jianchao ZHANG Xiuwei XIE Dang Radar Non-Commissioned Officer School Air Force Early Warning AcademyWuhan 430345China

Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.

关键词： recommender system collaborative filtering time heterogeneous feedback recurrent neural network gated recurrent unit(GRU) user space transformation

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Rangeland water requirement satisfaction index under rainfall variability and predicting future rainfall scenarios: implication for availability of feed resources

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Ecological Processes 2017年第1期6卷 264-272页

作者： Hassen M.Ahmed Zewdu K.Tessema Adugna Tolera Diriba Korecha School of Animal and Range Sciences College of Agriculture and Environmental SciencesPO Box 138Dire DawaEthiopia School of Animal and Range Sciences Hawassa UniversityPO Box 222HawassaEthiopia famine early warning systems network PO Box 1090Addis AbabaEthiopia School of Animal and Range Sciences College of Agriculture and Environmental SciencesHaramaya UniversityPO Box 1632Dire DawaEthiopia

Introduction:Rangeland ecosystems provide multiple ecosystem services,including feed resources for wild and domestic herbivores in semi-arid ***,under the ever increasing environmental changes,the impact of rainfall variability on the productivity and vegetation dynamics of rangelands are the great challenges that pastoral community are facing *** a result,the potentials of most rangelands in semi-arid ecosystems affect the ***,we studied the interconnections between the long-term rainfall variation and the rangeland Water Requirement and Satisfaction Index(WRSI)in Mieso,Jigjiga,and Shinile districts under pastoral conditions of ***:The base period rainfall data(1984–2015)was obtained from the National Meteorological Agency of Ethiopia,whereas the future rainfall trend was predicted using MarkSim software(Representative Concentration Pathways 4.5 GHG concentration trajectory).Mann-Kendall’s statistical tests,coefficient of variation,LEAP software(version 2.61),and Minitab Software(version 15)were used to assess the relationship between rangeland WRSI and long-term rainfall ***:The result indicated that mean annual rainfall anomaly had strong positive correlation with rangeland WRSI in Mieso(P<0.05),Jigjiga(P<0.001),and Shinile(P<0.001)pastoral ***,short and long rainy seasons had positive association(P<0.001)with rangeland WRSI,especially in Jigjiga and Shinile pastoral *** base period rainfall as well as the predicted annual rainfall showed variability in amount and distribution in all studied districts in pastoral areas of ***:The mean annual rainfall anomaly is correlated with the rangeland ***,the future rainfall trend analysis indicated that variability of rainfall would be expected in between the years 2020–2049,2040–2069,and 2070–***,the future rainfall variability would limit future rangeland WRSI under pastoral conditions of Ethiopia.

关键词： LEAP software Pastoralist Rangeland water requirement Trend analysis

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Demonstrating Effect of Rainfall Characteristics on Wheat Yield: Case of Sinana District, South Eastern Ethiopia

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Agricultural Sciences 2017年第5期8卷 371-384页

作者： Fitsum Bekele Diriba Korecha Lisanework Negatu National Meteorological Agency of Bale Branch Directorate Bale-Robe Ethiopia USGS/famine early warning systems network Addis Ababa Ethiopia Colleague of Agriculture Haramaya University Haramaya Ethiopia

Demonstrating Effects of Current and Projected Rainfall Characteristics on Wheat yield has been investigated in Sinana district for the period 1995-2016. Data on rainfall and crop yield for the period 1995-2016 were obtained from National Meteorological Agency and Sinana District Agricultural Offices, respectively. Following data quality checking, rainfall data (current and future), correlation and regression studies were analyzed using Statistical software like Instat V3.36 and SPSS V20. Downscaling the output of CSIRO-Mk3.6.0 GCM model (daily rainfall data) for RCP8.5 emission scenario using a web based software tool (Marksim GCM) for the period 2020-2049 were used to determine seasonal total rainfall and ascertain its impact on yield. This study used mean, coefficient of variation, correlation and regression analysis to ascertain the relation, cause and effect relationship between rainfall characteristics and wheat yields. The results indicated that the mean onset date of the main rainy season (JJAS) for Robe and Sinana station was June 30 and July 3. Furthermore, the results of Pearson Correlation Coefficients indicated that kiremt rainfall total (JJAS) had moderate positive relationship (r = 0.499) with wheat yield in the study area. It was also observed that nearly fifty percent of total variance of crop yield is explained jointly by kiremt rainfall total and rainy day (R2 value was 47.9%). The result of projected wheat yield indicated that there will be a slight decrease in wheat yield (qt/ha) after 2030 years due to the impact of expected weakening of kiremt rainfall total. Taking in to account the above findings, it could be suggested that the farmers’ community will be encouraged encouraged to utilize timely climate information issued from National Meteorological Agency of Ethiopia (NMA) and other centers for farm level decision to enhance their crop production.

关键词： CSIRO-Mk3.6.0 Impact Kiremt (JJAS) Wheat Yield

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Circadian rhythm disruption and retinal dysfunction:a bidirectional link in Alzheimer’s disease?

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Neural Regeneration Research 2024年第9期19卷 1967-1972页

作者： Laura Carrero DesireéAntequera Cristina Municio Eva Carro Group of Neurodegenerative Diseases Hospital Universitario 12 de Octubre Research Institute(imas12)MadridSpainNetwork Center for Biomedical Research in Neurodegenerative Diseases(CIBERNED)ISCIIIMadridSpain PhD Program in Neuroscience Autonoma de Madrid UniversityMadridSpain Neurobiology of Alzheimer’s Disease Unit Functional Unit for Research into Chronic DiseasesInstituto de Salud Carlos IIIMadridSpainNetwork Centre for Biomedical Research in Neurodegenerative Diseases(CIBERNED)ISCIIIMadridSpain

Dysfunction in circadian rhythms is a common occurrence in patients with Alzheimer’s disease.A predominant function of the retina is circadian synchronization,carrying information to the brain through the retinohypothalamic tract,which projects to the suprachiasmatic ***,Alzheimer’s disease hallmarks,including amyloid-β,are present in the retinas of Alzheimer’s disease patients,followed/associated by structural and functional ***,the mechanistic link between circadian dysfunction and the pathological changes affecting the retina in Alzheimer’s disease is not fully understood,although some studies point to the possibility that retinal dysfunction could be considered an early pathological process that directly modulates the circadian rhythm.

关键词： Alzheimer’s disease amyloid circadian rhythm neurodegeneration retina

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