Using deep learning for detecting BotCloud by Guang KOU, Guang-ming TANG, Shuo WANG, Hai-tao SONG, Yuan BIAN

“…To solve this problem, a CNN(convolution neural network)-based method for detecting the BotCloud was pro-posed. …”
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RESEARCH ON EARLY WARNING METHOD OF ROTOR VIBRATION OF TURBOGENERATOR UNIT (MT) by HUANG YangSen, LIU YunPing, WANG Yong, FENG XiaoJian, LIU Xiong

“…Finally, it is tested and verified by the operation data of a power plant, and compared with the vibration early warning method based on back propagation（BP） neural network. The results show that the proposed method can accurately and effectively realize the rotor vibration early warning.…”
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Design of federated routing mechanism in cross-domain scenario by Peizhuang CONG, Yuchao ZHANG, Ye TIAN, Wendong WANG, Dan LI

“…With the development of multi-network integration,how to ensure efficient interconnections among multiple independent network domains is becoming a key problem.Traditional interdomain routing protocol fails due to the limitation of domain information privacy,where each autonomous domain doesn’t share any specific intra-domain information.A machine learning-based federated routing mechanism was proposed to overcome the existing shortcomings.Each autonomous domain shares intra-domain information implicitly via neural network models and parameters.It not only breaks data islands problems but also greatly reduces the amount of transmitted data shared between domains,then decreases convergence delay of entire network information.Based on the federated routing mechanism,border routers can formulate global optimal routing strategies according to the status of entire network.…”
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Forecasting Directions, Dates, And Causes of Future Technological Revolutions concerning the Growth of Human Capital by Ning Chen, Masoud Vaseei, Ali Peivandizadeh

“…Next, research gaps were analyzed by using the artificial neural network clustering method and also by analyzing covered and uncovered compounds. …”
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Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network by Yi Zhao, Satish V. Ukkusuri, Jian Lu

“…The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. …”
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Design and implementation of online learning assisted intelligent receiver by Lingjin KONG, Kai MEI, Xiaoran LIU, Jun XIONG, Haitao ZHAO, Jibo WEI

“…To address the issue of reliable communication under complicated scenarios, an online learning-assisted intelligent OFDM receiver was proposed.The variations of the channel environment could be precepted by the receiver, and the optimal parameters of the receiver under the current scenario were obtained by collecting data and training online.In the channel estimation module of the OFDM system, a performance comparator based on the mean square error of noisy channel samples was designed as the indicator of channel environment variations.To accelerate the online training progress, a lightweight neural network structure was applied.The proposed method was further implemented and verified based on universal software radio peripherals.The numerical simulation and over-the-air experimental results demonstrate that the proposed receiver can perceive and adapt to new environments effectively, and outperforms existing machine learning methods in terms of receiving performance and convergence rate with a limited number of pilots.…”
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Adaptive Neural Control of Hypersonic Vehicles with Actuator Constraints by Changxin Luo, Humin Lei, Dongyang Zhang, Xiaojun Zou

“…Secondly, on the basis of the implicit function theorem, the radial basis function neural network (RBFNN) is introduced to approximate the uncertain items of the model. …”
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Anomaly Detection in Moving Crowds through Spatiotemporal Autoencoding and Additional Attention by Biao Yang, Jinmeng Cao, Rongrong Ni, Ling Zou

“…We propose an anomaly detection approach by learning a generative model using deep neural network. A weighted convolutional autoencoder- (AE-) long short-term memory (LSTM) network is proposed to reconstruct raw data and perform anomaly detection based on reconstruction errors to resolve the existing challenges of anomaly detection in complicated definitions and background influence. …”
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Advanced Techniques and Antenna Design for Pulse Shaping in UWB Cognitive Radio by Lise Safatly, Mohammed Al-Husseini, Ali El-Hajj, Karim Y. Kabalan

“…The Parks-McClellan algorithm is employed, a neural network is trained, and a reconfigurable band stop filter is designed to generate an adaptive waveform with nulls at specific frequencies. …”
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Research on Architecture of LEO Satellite Internet of Things and Key Technologies by Xiaojin DING, Tao HONG, Rui LIU, Wentao PENG, Gengxin ZHANG

“…First, we analyzed the composition and demand characteristics of LEO satellite IoTs, and designed an architecture of LEO satellite IoTs.Second, to extracted characteristics of electromagnetic environment and channel, we studied electromagnetic environment and channel sensing methods through constructing neural network model.Then, we investigated an active terminal-identifi cation method for detecting active terminals.Finally, considered multiple-input multiple-output as the main approach to improved the capacity of terrestrial mobile communication systems, we explored a pair of algorithms of mining satellite spatial resources for realizing the collision mitigation and capacity improvement of LEO IoT terminals.Simulation results showed that the designed method sofmining spatial-domain resources could mitigated eff ectively signal collisions among IoT terminals, and improved signifi cantly the concurrent accessing number of LEO satellite IoT terminals.…”
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Bandwidth prediction of power communication network based on DBN-Softmax by Jia LI, Li CONG, Hua JIANG, Yang HU, Meng XU

“…With the change of the power communication network,the data of the bearer service of the power communication network has increased exponentially,which puts higher requirements on the processing capability of the power communication network.In order to guarantee the service quality of communication network,aiming at the current unreasonable distribution of network bandwidth,a bandwidth prediction algorithm based on deep confidence for power communication network was proposed.The deep confidence network formed by the Boltzmann machine was used to obtain the characteristics that could perfectly express the network bandwidth,and the reasonable prediction of the bandwidth of the power communication network planning stage was realized.The implementation results show that the proposed algorithm is more accurate and robust than neural network.It has the advantage of improving the carrying capacity of the power communication network and providing a powerful guarantee for the safe and stable operation of the power system.…”
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Coordinated Control of Slip Ratio for Wheeled Mobile Robots Climbing Loose Sloped Terrain by Zhengcai Li, Yang Wang

“…To improve the robustness and adaptability of the control system, an adaptive neural network is designed. Analytical results and those of a simulation using Vortex demonstrate the significantly improved mobile performance of the WMR using the proposed control system.…”
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Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model by Nhat-Duc Hoang, Anh-Duc Pham, Quoc-Lam Nguyen, Quang-Nhat Pham

“…Based on experimental outcomes, prediction results of the GPR model are superior to those of the Least Squares Support Vector Machine and the Artificial Neural Network. Furthermore, GPR model is strongly recommended for estimating HPC strength because this method demonstrates good learning performance and can inherently express prediction outputs coupled with prediction intervals.…”
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Intelligent power control for covert communication in cognitive Internet of things by Zan LI, Xiaomin LIAO, Jia SHI, Pei XIAO

“…In order to solve the security problem of cognitive Internet of things (IoT),an intelligent power control algorithm of covert communication in cognitive IoT based on generative adversarial network was proposed.Firstly,the covert communication optimization problem in the cognitive IoT was transformed into a dynamic game between the cognitive IoT user and the eavesdropper.Then,the generator imitated the cognitive IoT user,while the discriminator imitated the eavesdropper.The generator and the discriminator were constructed by the three-layer neural network respectively.Through the two-person zero-sum game,the learning optimization process was realized to achieve the Nash equilibrium,and finally the covert power control scheme was obtained.The simulation results show that the proposed algorithm can not only obtain near-optimal covert power control scheme with rapid convergence ability,but also be more practical in the future cognitive IoT.…”
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An algorithm of blood typing using serological plate images by S.A. Korchagin, E.E. Zaychenkova, D.A. Sharapov, E.I. Ershov, Y.V. Butorin, Y.Y. Vengerov

“…The proposed recognition algorithm allows the alveolus boundaries to be accurately determined and the agglutination degree to be evaluated using a lightweight convolutional neural network. A unique dataset was collected with the independent assessment of agglutination degree conducted by medical experts. …”
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Fast QTMT partition decision based on deep learning by Shuang PENG, Xiaodong WANG, Zongju PENG, Fen CHEN

“…Compared with the predecessor standards, versatile video coding (VVC) significantly improves compression efficiency by a quadtree with nested multi-type tree (QTMT) structure but at the expense of extremely high coding complexity.To reduce the coding complexity of VVC, a fast QTMT partition method was proposed based on deep learning.Firstly, an attention-asymmetric convolutional neural network was proposed to predict the probability of partition modes.Then, the fast decision of partition modes based on the threshold was proposed.Finally, the cost of coding performance and time was proposed to obtain the optimal threshold, and the threshold decision method was proposed.Experimental results at different levels show that the proposed method achieves an average time saving of 48.62%/52.93%/62.01% with the negligible BDBR of 1.05%/1.33%/2.38%.Such results demonstrate that the proposed method significantly outperforms other state-of-the-art methods.…”
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Command Filtering and Barrier Lyapunov Function-Based Adaptive Control for PMSMs with Core Losses and All-State Restrictions by Xiaoling Wang, Jinpeng Yu

“…To begin with, the RBF neural network technique is utilized to get close to the uncharted nonlinear terms which existed in PMSM’s mathematical model. …”
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Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images by Anne M. Davis, Asahi Tomitaka

“…The comparative analysis identified that random forest and convolutional neural network (CNN) models performed well in classifying the lateral flow assay results compared to other well-established machine learning models. …”
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Image Sequence Fusion and Denoising Based on 3D Shearlet Transform by Liang Xu, Junping Du, Zhenhong Zhang

“…The high-pass subbands are then combined to employ the fusion rule of the selecting maximum based on 3D pulse coupled neural network (PCNN), and the low-pass subband is fused to use the fusion rule of the weighted sum. …”
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Epilepsy: The Quintessential Pathology of Consciousness by Andrea Eugenio Cavanna, Fizzah Ali

“…This article provides a description of the phenomenology of ictal consciousness and reviews the underlying shared neural network, dubbed the 'consciousness system', which overlaps with the 'default mode' network. …”
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