SERVICE INDUSTRIES INNOVATIVE DEVELOPMENT AS A BASIS FOR RUSSIAN SERVICE ECONOMY DEVELOPMENT by Irina А. DUDAKOVA, Yulia V. GLADKOVA

“…The Russian service practice innovations are featured.…”
Get full text

Family violence against children: intervention of nurses from the Family Health Strategy by Kelianny Pinheiro Bezerra, Akemi Iwata Monteiro

“…This study Aimed to analyze the performance of nurses of the Family Health Strategy by facing family violence against children and identifying actions to prevent the problem. It is a descriptive and exploratory research with qualitative feature, whose data were analyzed according to content analysis. 14 nurses from the Family Health Strategy of Mossoró-RN took part in the Study. …”
Get full text

Contemporary graphic art via Artificial Pixler Illustrator by Adnan Abdel Abbas Idan Radi

“…As for the third chapter, which consisted of the research procedures and methodology, the research community, and the reasons for choosing the samples, leading to the descriptive analysis that is compatible with the goal of the research, as for the fourth chapter, the results are presented, which are linked to the research problem, conclusions, sources, and references. One of the results of the research came, the Pixler program feature is a feature in which it is divided the image of the original model is divided into several different snapshots, up to ten slides out of one snapshot, and then it is transformed into an application proposal through which the manual printing formula is repeated within the mechanism of this artificial program …”
Get full text

Crack fault diagnosis of vibration exciter rolling bearing based on genetic algorithm–optimized Morlet wavelet filter and empirical mode decomposition by Xiaoming Han, Jin Xu, Songnan Song, Jiawei Zhou

“…To solve this problem, a fault feature recognition method based on genetic algorithm–optimized Morlet wavelet filter and empirical mode decomposition is proposed. …”
Get full text

基于MED-SVM的齿轮箱故障诊断方法 by 刘志川, 唐力伟, 曹立军, 张远刚

“…In order to solve the problem of gearbox fault diagnosis,a new method based on minimum entropy deconvolution（MED）and support vector machine（SVM）is proposed.MED is used for gearbox vibration acceleration signal under background noise,then feature parameters extracted on breadth domain,frequency domain and energy domain of decreased signal are carried out,and the feature vector is built.Taking the feature vector as input,the multi-classification support vector machine is established,and the model parameters optimized by cross validation method are used to identify gearbox fault types.The fault diagnosis result of practical gearbox vibration signals shows that the proposed method can effectively identify different fault types of gear and bearing,and the optimizing model parameters can evidently improve fault identification accuracy.…”
Get full text

基于EEMD与奇异熵增量谱的齿轮故障特征提取 by 杨望灿, 张培林, 吴定海, 陈彦龙

“…A method of feature extraction for gear fault based on Ensemble empirical mode decomposition（EEMD）and incremental spectrum of singularity entropy is put forward for the non-stationary and non-linear characteristics of gear vibration signal.Firstly,the gear vibration signal is decomposed into several smooth intrinsic mode functions（IMFs）by EEMD.The method of EEMD could take advantage of dyadic scale decomposition characteristics of the normal distribution white noise to suppress the problem of mode confusion in EMD.Because of the interference of background noise and residual assisted white noise,the gear fault feature is not extracted exactly from IMF.The method of singular value decomposition is used to remove the noise and reconstruct the IMF.The reconstruction order is determined according to the incremental spectrum of singularity entropy.Therefore the gear fault feature frequency could be extracted exactly.Results of simulation analysis and the gear fault test indicated that this method is accurate and effective for gear fault feature extraction.…”
Get full text

SCR-Net: A novel lightweight aquatic biological detection network. by Tao Li, Yijin Gang, Sumin Li, Yizi Shang

“…Second, a cross-scale feature fusion pyramid (CFFP) structure is introduced, which significantly reduces the number of parameters and computational cost during feature fusion. …”
Get full text

A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed Generation by Ronald Kfouri, Harag Margossian

“…State estimation is a challenging problem, particularly in distribution grids that have unique characteristics compared with transmission grids. …”
Get full text

Research on the detection of obstacles in front of unmanned vehicles in opencast mines based on binocular vision by Shunling RUAN, Huiguo ZHANG, Qinghua GU, Caiwu LU, Di LIU, Jing MAO

“…The Feffol network model proposed in this paper selects Efficient-v2 as the backbone network structure for feature extraction in the feature extraction stage, selects the Ebifpn feature pyramid module based on the SppCSP structure with SppCSP structure to improve the feature sensing field while enhancing the feature information of different sizes, uses the Focal Loss and CIoU Loss loss functions to balance positive and negative samples, and solve the problem of method failure when there is no intersection between the prediction frame and the real detection frame. …”
Get full text

Hedge Funds As a Alternative Investment Opportunities by Michał Falkowski

“…The paper deals with the current problem of the financial crisis and the effect it made worldwide for many institutional and private investors. …”
Get full text

Fusing Events and Frames with Coordinate Attention Gated Recurrent Unit for Monocular Depth Estimation by Huimei Duan, Chenggang Guo, Yuan Ou

“…Monocular depth estimation is a central problem in computer vision and robot vision, aiming at obtaining the depth information of a scene from a single image. …”
Get full text

Research on an underwater high-speed vehicle target recognition method based on deep learning by WU Yajun, LIU Liwen

“…Due to the complexity of marine environment and the constantly upgrading of new countermeasure equipment, underwater high-speed vehicles are currently faced with the problem of insufficient recognition ability in complex marine environment, and it is urgent to find a new way of feature extraction and target recognition. …”
Get full text

Video temporal perception characteristics based just noticeable difference model by Yafen1 XING, Haibing YIN, Hongkui WANG, Qionghua LUO

“…The existing temporal domain JND(just noticeable distortion) models are not sufficient to depict the interaction between temporal parameters and HVS characteristics, leading to insufficient accuracy of the spatial-temporal JND model.To solve this problem, feature parameters that can accurately describe the temporal characteristics of the video were explored and extracted, as well as a homogenization method for fusing heterogeneous feature parameters, and the temporal domain JND model based on this was improved.The feature parameters were investigated including foreground and background motion, temporal duration along the motion trajectory, residual fluctuation intensity along motion trajectory and adjacent inter-frame prediction residual, etc., which were used to characterize the temporal characteristics.Probability density functions for these feature parameters in the perception sense according to the HVS(human visual system) characteristics were proposed, and uniformly mapping the heterogeneous feature parameters to the scales of self-information and information entropy to achieve a homogeneous fusion measurement.The coupling method of visual attention and masking was explored from the perspective of energy distribution, and the temporal-domain JND weight model was constructed accordingly.On the basis of the spatial JND threshold, the temporal domain weights was integrated to develop a more accurate spatial-temporal JND model.In order to evaluate the performance of the spatiotemporal JND model, a subjective quality evaluation experiment was conducted.Experimental results justify the effectiveness of the proposed model.…”
Get full text

A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery by Songrong Luo, Junsheng Cheng, Kexiang Wei

“…The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. …”
Get full text

Video temporal perception characteristics based just noticeable difference model by Yafen1 XING, Haibing YIN, Hongkui WANG, Qionghua LUO

“…The existing temporal domain JND(just noticeable distortion) models are not sufficient to depict the interaction between temporal parameters and HVS characteristics, leading to insufficient accuracy of the spatial-temporal JND model.To solve this problem, feature parameters that can accurately describe the temporal characteristics of the video were explored and extracted, as well as a homogenization method for fusing heterogeneous feature parameters, and the temporal domain JND model based on this was improved.The feature parameters were investigated including foreground and background motion, temporal duration along the motion trajectory, residual fluctuation intensity along motion trajectory and adjacent inter-frame prediction residual, etc., which were used to characterize the temporal characteristics.Probability density functions for these feature parameters in the perception sense according to the HVS(human visual system) characteristics were proposed, and uniformly mapping the heterogeneous feature parameters to the scales of self-information and information entropy to achieve a homogeneous fusion measurement.The coupling method of visual attention and masking was explored from the perspective of energy distribution, and the temporal-domain JND weight model was constructed accordingly.On the basis of the spatial JND threshold, the temporal domain weights was integrated to develop a more accurate spatial-temporal JND model.In order to evaluate the performance of the spatiotemporal JND model, a subjective quality evaluation experiment was conducted.Experimental results justify the effectiveness of the proposed model.…”
Get full text

Gear Fault Diagnosis Based on EMD Decomposition and Levy-SSA-BP Neural Network by Xu Jingwen, Yang Ping, Yin Xiaojun

“…Secondly, calculating the correlation coefficient of each IMF with the original signal, and the feature extractions of each component are carried out to form a feature matrix. …”
Get full text

Adaptive Image-Based Leader-Follower Approach of Mobile Robot with Omnidirectional Camera by Dejun Guo, Hesheng Wang, Weidong Chen, Xinwu Liang

“…This paper focuses on the problem of the adaptive image-based leader-follower formation control of mobile robot with on-board omnidirectional camera. …”
Get full text

Multi-scale ship detection algorithm in SAR images in complex scenes by He Shun, Wang Yuzhu, Yang Zhiwei

“…Firstly, a multi-scale object feature extraction network (MFE-Net) is used to extract feature information to improve the detection capability of multi-scale objects and reduce redundant calculations. …”
Get full text

On OCT Image Classification via Deep Learning by Depeng Wang, Liejun Wang

“…In this paper, an automatic method based on deep learning is proposed to detect AME and AMD lesions, in which two publicly available OCT datasets of retina were adopted and a network model with effective feature of reuse feature was applied to solve the problem of small datasets and enhance the adaptation to the difference of different datasets of the approach. …”
Get full text

Joint beam hopping and coverage control optimization algorithm for multibeam satellite system by Guoliang XU, Feng TAN, Yongyi RAN, Feng CHEN

“…To improve the performance of multibeam satellite (MBS) systems, a deep reinforcement learning-based algorithm to jointly optimize the beam hopping and coverage control (BHCC) algorithm for MBS was proposed.Firstly, the resource allocation problem in MBS was transformed to a multi-objective optimization problem with the objective maximizing the system throughput and minimizing the packet loss rate of the MBS.Secondly, the MBS environment was characterized as a multi-dimensional matrix, and the objective problem was modelled as a Markov decision process considering stochastic communication requirements.Finally, the objective problem was solved by combining the powerful feature extraction and learning capabilities of deep reinforcement learning.In addition, a single-intelligence polling multiplexing mechanism was proposed to reduce the search space and convergence difficulty and accelerate the training of BHCC.Compared with the genetic algorithm, the simulation results show that BHCC improves the throughput of MBS and reduces the packet loss rate of the system, greedy algorithm, and random algorithm.Besides, BHCC performs better in different communication scenarios compared with a deep reinforcement learning algorithm, which do not consider the adaptive beam coverage.…”
Get full text