The attitude tracking control with unwinding-free performance for rigid spacecraft is studied in this article. A full-state feedback control law based on a hyperbolic sine function is developed such that the resulted ...
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Malware detection has been a hot spot in cyberspace security and academic research. We investigate the correlation between the opcode features of malicious samples and perform feature extraction, selection and fusion ...
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Malware detection has been a hot spot in cyberspace security and academic research. We investigate the correlation between the opcode features of malicious samples and perform feature extraction, selection and fusion by filtering redundant features, thus alleviating the dimensional disaster problem and achieving efficient identification of malware families for proper classification. Malware authors use obfuscation technology to generate a large number of malware variants, which imposes a heavy analysis burden on security researchers and consumes a lot of resources in both time and space. To this end, we propose the MalFSM framework. Through the feature selection method, we reduce the 735 opcode features contained in the Kaggle dataset to 16, and then fuse on metadata features(count of file lines and file size)for a total of 18 features, and find that the machine learning classification is efficient and high accuracy. We analyzed the correlation between the opcode features of malicious samples and interpreted the selected features. Our comprehensive experiments show that the highest classification accuracy of MalFSM can reach up to 98.6% and the classification time is only 7.76 s on the Kaggle malware dataset of Microsoft.
In recent years, large language models (LLMs) have driven advances in natural language processing. Still, their growing scale has increased the computational burden, necessitating a balance between efficiency and perf...
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This paper introduces a novel risk evaluation and prediction model based on the GABP neural network. Initially, a K-means clustering model categorizes events into three risk levels: high, medium, and low. Subsequently...
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In this paper we present the effective strategy of Fe^(3+)and Li^(+)co-doping to enhance the electrostrain of ZnO thin ***_(0.88)(Fe_(0.06)Li_(0.06))O thin film exhibits an excellent d_(33)^(*)value of 415 pm/V and la...
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In this paper we present the effective strategy of Fe^(3+)and Li^(+)co-doping to enhance the electrostrain of ZnO thin ***_(0.88)(Fe_(0.06)Li_(0.06))O thin film exhibits an excellent d_(33)^(*)value of 415 pm/V and large linear electrostrain of 0.68%after thermal-electric *** the X-ray diffraction and first principle calculation results have indicated that the Fe^(3+)and Li^(+)ions are inclined to adopt a preferential alignment along the edges approximately parallel to[001]direction in the zinc-oxygen tetrahedron and constitute a defect dipole(ionic pairs).It is considered that the local lattice distortion and the electric field-triggered polarization extension and rotation generated by preferential distributed Fe^(3+)-Li^(+)ionic pairs are responsible for the outstanding piezoelectric properties and large linear electrostrain.
The coronavirus disease 2019(COVID-19)has severely disrupted both human life and the health care *** diagnosis and treatment have become increasingly important;however,the distribution and size of lesions vary widely ...
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The coronavirus disease 2019(COVID-19)has severely disrupted both human life and the health care *** diagnosis and treatment have become increasingly important;however,the distribution and size of lesions vary widely among individuals,making it challenging to accurately diagnose the *** study proposed a deep-learning disease diagnosismodel based onweakly supervised learning and clustering visualization(W_CVNet)that fused classification with ***,the data were *** optimizable weakly supervised segmentation preprocessing method(O-WSSPM)was used to remove redundant data and solve the category imbalance ***,a deep-learning fusion method was used for feature extraction and classification recognition.A dual asymmetric complementary bilinear feature extraction method(D-CBM)was used to fully extract complementary features,which solved the problem of insufficient feature extraction by a single deep learning ***,an unsupervised learning method based on Fuzzy C-Means(FCM)clustering was used to segment and visualize COVID-19 lesions enabling physicians to accurately assess lesion distribution and disease *** this study,5-fold cross-validation methods were used,and the results showed that the network had an average classification accuracy of 85.8%,outperforming six recent advanced classification models.W_CVNet can effectively help physicians with automated aid in diagnosis to determine if the disease is present and,in the case of COVID-19 patients,to further predict the area of the lesion.
GeTe has attracted extensive research interest for thermoelectric *** this paper,we first train a neuroevolution potential(NEP)based on a dataset constructed by ab initio molecular dynamics,with the Gaussian approxima...
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GeTe has attracted extensive research interest for thermoelectric *** this paper,we first train a neuroevolution potential(NEP)based on a dataset constructed by ab initio molecular dynamics,with the Gaussian approximation potential(GAP)as a *** phonon density of states is then calculated by two machine learning potentials and compared with density functional theory results,with the GAP potential having higher ***,the thermal conductivity of a GeTe crystal at 300 K is calculated by the equilibrium molecular dynamics method using both machine learning potentials,and both of them are in good agreement with the experimental results;however,the calculation speed when using the NEP potential is about 500 times faster than when using the GAP ***,the lattice thermal conductivity in the range of 300 K-600 K is calculated using the NEP *** lattice thermal conductivity decreases as the temperature increases due to the phonon anharmonic *** study provides a theoretical tool for the study of the thermal conductivity of GeTe.
Tapered roller bearings often experience thermal deformation and stress concentration due to high temperature during prolonged operation. However, the traditional model for calculating bearing heat does not consider t...
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Aiming at the problems of difficulty in testing and observing the boot time of embedded devices, a method based on CPLD is proposed to accurately measure the boot time of various types of embedded devices (FPGA, MCU, ...
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Quick Access Recorder(QAR),an important device for storing data from various flight parameters,contains a large amount of valuable data and comprehensively records the real state of the airline ***,the recorded data h...
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Quick Access Recorder(QAR),an important device for storing data from various flight parameters,contains a large amount of valuable data and comprehensively records the real state of the airline ***,the recorded data have certain missing values due to factors,such as weather and equipment *** missing values seriously affect the analysis of QAR data by aeronautical engineers,such as airline flight scenario reproduction and airline flight safety status ***,imputing missing values in the QAR data,which can further guarantee the flight safety of airlines,is *** data also have multivariate,multiprocess,and temporal ***,we innovatively propose the imputation models A-AEGAN("A"denotes attention mechanism,"AE"denotes autoencoder,and"GAN"denotes generative adversarial network)and SA-AEGAN("SA"denotes self-attentive mechanism)for missing values of QAR data,which can be effectively applied to QAR ***,we apply an innovative generative adversarial network to impute missing values from QAR *** improved gated recurrent unit is then introduced as the neural unit of GAN,which can successfully capture the temporal relationships in QAR *** addition,we modify the basic structure of GAN by using an autoencoder as the generator and a recurrent neural network as the *** missing values in the QAR data are imputed by using the adversarial relationship between generator and *** introduce an attention mechanism in the autoencoder to further improve the capability of the proposed model to capture the features of QAR *** mechanisms can maintain the correlation among QAR data and improve the capability of the model to impute missing ***,we improve the proposed model by integrating a self-attention mechanism to further capture the relationship between different parameters within the QAR *** results on real datasets demonstrate that the model can rea
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