bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-AGV Task Allocation with Attention Based on Deep Reinforcement Learning
Authors: Yin Zuozhong , Liu Jihong , Wang Dianpeng
Published: June 2022

Automated guided vehicle (AGV) is an important transportation equipment, which is widely used in warehouses and factories. In the scenarios of multi-AGVs application, an efficient AGVs task assignment strategy is beneficial for transportation costs, balance of workload and increasing distribution efficiency. The traditional method usually depends on a powerful scheduling system, which solves the task assignment problem in a regular way. In this paper, we present a decentralized framework of multi-task allocation with attention (MTAA) in deep reinforcement learning, which combines with the methods of task assignment in balance and path planning in cooperation for distribution application. As for task assignment balance, we adopt DNN network to achieve task assignment equilibrium. In multi-AGVs path planning, methods of A3C are embedded in MTAA framework, which are instrumental in improving the stationarity and performance in deep reinforcement learning application. In experiments, we designed two different scenarios under different obstacles to verify the performance of MTAA-A3C and MTAA-DQN methods. The experiments show that the proposed approach has feasibility and effectiveness used in multi-AGVs application.
A Keypoint-Based and Block-Based Fusion Method for Image Copy–Move Forgery Detection
Authors: Zhong Jun-Liu , Gan Yan-Fen , Zou Cai-Feng
Published: June 2022

Currently, image copy–move forgery issues have brought increasingly serious problems to the social fields. Many research have been devoted to address the image copy–move forgery issues. However, image copy–move forgery detection (CMFD) is still a challenging problem to image forensics. This paper proposes a coarse-to-fine detection method fusing the superiorities of both keypoint-based and block-based methods. The fusion method gets good geometric invariances of keypoint-based methods and good matching robustness with the invariant moment of block-based methods. In the coarse detection stage, a robust SIFT descriptor is used to extract the candidate keypoints, and then the 2NN test is applied to match the suspicious keypoint couples. The proposed three-pass filtering relying on the Euclidean distance, scaling coefficient ratio, and correlation coefficient of block-based features, remove the false-positive outlier couples. Finally, the scaling coefficient ratio statistics of the remaining keypoint couples get the scaling coefficient ratio between the size of copied/pasted or pasted/copied snippets. In the fine detection stage, the block-based thought relying on the scaling coefficient ratio uses the DAFMT to extract the block-based features of multiple scaling levels. Subsequently, LSH is presented to classify block features and finally indicate the fine forgery snippets. Finally, the morphological operations are presented to indicate the forgery accurately. The benchmark IMD and CoMoFoD image copy–move datasets are used to measure the performances between the proposed fusion method and the state-of-the-art CMFD methods. The numerous experimental results demonstrate that the proposed fusion method achieves nearly the best performances to resist various attacks, especially large-scaling attacks, among the compared state-of-the-art methods.
Enhancement of Hand Gesture Recognition Using Convolutional Neural Networks Integrating a Combination of an Autoencoder Network and PCA
Authors: Bousbai Khalil , Merah Mostefa
Published: June 2022

Hand gestures offer people a convenient way to interact with computers, in addition to give them the ability to communicate without physical contact and at a distance, which is essential in today’s health conditions, especially during an epidemic infectious viruses such as the COVID-19 coronavirus. However, factors, such as the complexity of hand gesture patterns, differences in hand size and position, and other aspects, can affect the performance of hand gesture recognition and classification algorithms. Some deep learning approaches such as convolutional neural networks (CNN), capsule networks (CapsNets) and autoencoders have been proposed by researchers to improve the performance of image recognition systems in this particular field: While CNNs are arguably the most widely used networks for object detection and image classification, CapsNets and Autoencoder seem to resolve some of the limitations identified in the first approach. For this reason, in this work, a specific combination of these networks is proposed to effectively solve the ASL problem. The results obtained in this work show that the proposed group with a simple data augmentation process improves precision performance by 99.43%.
DRCP: Dimensionality Reduced Chess Pattern for Person Independent Facial Expression Recognition
Authors: Kartheek Mukku Nisanth , Prasad Munaga V. N. K. , Bhukya Raju
Published: June 2022

Automatic Facial Expression Recognition (FER) has become essential today as it has many applications in real time such as animation, driver mood detection, lie detection, and clinical psychology. The effectiveness of FER systems mainly depends on the extracted features. For extracting distinctive features with low dimensions, a new local texture-based image descriptor named Dimensionality Reduced Chess Pattern (DRCP) is proposed for recognizing facial expressions in a person independent scenario. DRCP, an improvement over Chess Pattern (CP), is mainly proposed for effectively reducing the feature vector length of CP. For feature extraction, DRCP also considers the movements of chessmen in a [Formula: see text] neighborhood, as like CP. As a part of feature extraction through DRCP, apart from the center pixel, the remaining 24 pixels are arranged into four groups in such a manner that each group contains the pixels corresponding to three chessmen. From each group, one feature is extracted and thus corresponding to four groups, four features are extracted in a [Formula: see text] neighborhood. The extracted features are fed into multi-class Support Vector Machine (SVM) for expression recognition. The experiments are performed on five “in the lab” datasets (MUG, TFEID, JAFFE, CK[Formula: see text] and KDEF) and on two “in the wild” datasets (RAF and SFEW) in person independent setup to simulate a real world scenario.
Context Data Fusion Model Enlightened Multi-Scale Capsule Network for Fruit Diseases Identification
Authors: Wang Yawei , Chen Yifei , Wang Dongfeng
Published: June 2022

Pests and diseases of tomato fruits are the foremost factors leading to the reduction in produce quantity. Effective and prompt differentiation of these lesion symptoms is essential for agricultural production. Due to the lack of a global receptive field, commonly used convolution neural network models restrict the capability on tomato images under complicated backgrounds. In this paper, we present an efficient and robust method for field diagnosis of tomato fruits based on the context data fusion and capsule network. First, in order to make better the point of inadequate images, the Cycle-GAN algorithm is used to realize the augmentation of the diseases dataset. To improve the quality of the generated images, the feature reconstruction loss function is proposed for the Cycle-GAN method, named Fruit-GAN. The proposed Fruit-GAN learns features of healthy and diseased tomatoes, respectively, and forms lesions based on healthy tomato fruit images. Adding lesion tomato images produced on Fruit-GAN to training sets improves the diagnostic capability compared to classical data augmentation techniques. Subsequently, context fusion studies were used to construct intermediate feature maps from different layers into the fruit diseases recognition compact representation to efficiently leverage the feature reuse mechanism. Finally, the fusion model’s enlightened multi-scale capsule network is integrated to form deep features and determine the disease diagnosis. After analyzing the hyper-parameters, the integral technique provides 96.16% accuracy. This research of tomato diseases diagnosis demonstrates the context capsule network model is efficient and establishes a foundation for tomato fruits determination in natural growing environment.
Graph Neural Network Social Recommendation Algorithm Integrating Static and Dynamic Features
Authors: Qi Wei , Huang Zhenzhen , Zhu Dongqing , Yu Jiaxu
Published: June 2022

In recent years, the study of social-based recommender systems has become an active research topic. We incorporate a combination of static and dynamic interest characteristics to predict users’ real-time dynamic interests, which has rarely been considered in previous studies. In this paper, we propose a graph neural network social recommendation model that integrates static and dynamic feature relationships (FSDFR-GNNSR). The model uses a graph embedding algorithm to extract static features of users and movies, and takes the static features as input to gated recurrent unit (GRU), so that the model can take static features into consideration while modeling user dynamic behavior. Finally, we use graph attention networks to represent the dynamic influence of friends, simplify the update strategy of second-order neighbor nodes. We apply graph pooling operations to improve the generalization ability of the algorithm. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches.
A Control Strategy for Unmanned Surface Vehicles Flocking
Authors: Li Yan , Zhang Jianqiang , Wang Hongbin , Li Yi , Sui Bowen
Published: June 2022

Decentralized control allows each unmanned surface vehicle (USV) in a flock to be autonomous to some extent. In this paper, the flocking algorithm put forth by Olfati-Saber was improved and applied in the multi-leader scenario based on its original flocking movement. Repulsion was also taken into account in the formation. Subsequently, the concept of neighborhood in finite-range communications was borrowed to control the communication distance of each USV. In this way, USVs could satisfy the restriction of a given communication distance, and avoid collision in motion. USVs might have collaborative tracking of a virtual leader and obtain the distributed cooperative control strategy. Based on the simulation results, the improved flocking control algorithm allowed every USV to accurately track the speed of the virtual leader they followed, respectively. The test results also demonstrated the effectiveness of the algorithm in safe flock navigation, and proved that the proposed control algorithm must be an effective way to ensure the safe flocking of multiple USVs.
On the uniqueness of Sarkar-Seed-Szabo construction
Authors: Paul Pravakar
Published: June 2022

In an attempt to consolidate Szabo’s geometric chain complex and Bar Natan’s chain complex, Sarkar–Seed–Szabo defined a total complex [Formula: see text] over [Formula: see text] by adding some extra terms [Formula: see text] in the differential for a link diagram [Formula: see text] of [Formula: see text] in [Formula: see text]. In this paper, we prove the uniqueness of [Formula: see text] upto some properties. This in turn implies that the total complex [Formula: see text] is unique.
Upgrade of the ALICE experiment beyond LHC Run 3
Authors: Colella Domenico
Published: June 2022

The ALICE Collaboration completed the upgrade of the detector and is now commissioning for the beginning of the data taking during LHC Run 3. In parallel, R&D activities and simulation studies are being performed to define the future of the experiment beyond LHC Run 3. Two detector upgrades are foreseen for the next long shutdown (LS3). The first is the replacement of the three layers of the inner tracking system closest to the beam with a novel vertex detector consisting of curved wafer-scale ultra-thin silicon sensors arranged in perfectly cylindrical layers to improve impact parameter resolution and significantly extend the physics capability for the study of the heavy-flavor production and the low-mass dielectrons. The second upgrade for the LS3 is the addition of a Forward Calorimeter detector at large rapidity consisting of a Si-W electromagnetic calorimeter with pad and pixel readout, that will equip the experiment with unique capabilities to measure small-x gluon distributions via prompt photon production. A proposal of a next-generation heavy-ion experiment for LHC Run 5 is also in preparation and will be discussed. The aim is to perform novel measurements of the electromagnetic and hadronic probes of the QGP, such as the production of multiply-charmed baryons, which have so far been inaccessible, both because of detector performance and luminosity. The concept of the new apparatus foresees an extensive usage of thin silicon sensors for tracking and a modern particle identification system, combining a silicon-based time of flight detector, a RICH detector, an electromagnetic calorimeter and a muon system.
ANALYSIS OF THE COUPLING BETWEEN THE BRAIN AND FACIAL MUSCLE RESPONSES TO AUDITORY STIMULATION
Authors: SOUNDIRARAJAN MIRRA , KREJCAR ONDREJ , NAMAZI HAMIDREZA
Published: June 2022

Our facial muscles react due to exposure to various stimuli. For example, when we sniff an odor, our facial muscles react. Since the brain controls the activities of muscles, therefore, a coupling should exist between their activities. This paper studies the coupling between the activations of the brain and facial muscles in auditory stimulation by the complexity-based analysis of Electroencephalogram (EEG) and Electromyogram (EMG) signals. Three pieces of music were chosen according to the differences in their complexities. We calculated the fractal dimension and sample entropy of EEG and EMG signals for 13 subjects in rest and response to different music. The results showed strong couplings among the alterations of the complexities of these signals and music, which indicate the correlation between brain and facial muscle activities. This complexity-based analysis could be further extended to the case of other physiological signals to decode the correlation between the responses of different organs and the brain.
Associated Consistency and the Aumann–Drèze Value
Authors: Navarro Florian
Published: June 2022

This paper presents an axiomatic characterization of the Aumann–Drèze value [Aumann, R. J. and Drèze, J. H. [1974] Cooperative games with coalition structures, Int. J. Game Theory 3, 217–237] for cooperative games with coalition structures. We build an associated game that extends the original associated game presented by Hamiache [[2001] Associated consistency and Shapley value, Int. J. Game Theory 30, 279–289] to cooperative games with coalition structures. We use a similar approach to the one used in Hamiache and Navarro [[2020] Associated consistency, value and graphs, Int. J. Game Theory 49, 227–249]. This new associated game is expressed through a matrix form. We show that the series of successive associated games is convergent and that its limit is an inessential game. This allows us to propose a characterization of the Aumann–Drèze value that relies on associated consistency, inessential game and continuity axioms. Hence, this paper strengthens the results of Hamiache [[2001] Associated consistency and Shapley value, Int. J. Game Theory 30, 279–289] and Hamiache and Navarro [[2020] Associated consistency, value and graphs, Int. J. Game Theory 49, 227–249] considering that if these axioms are viewed as desirable, we are now able to provide a unique value for three different types of problems: the Shapley value on standard games [Hamiache, G. [2001] Associated consistency and Shapley value, Int. J. Game Theory 30, 279–289], the Hamiache–Navarro value on games with graphs [Hamiache, G. and Navarro, F. [2020] Associated consistency, value and graphs, Int. J. Game Theory 49, 227–249] and the Aumann–Drèze value for games with coalition structures.
A Semi-Analytical Solution of Liquid Response in a Two-Dimensional Rectangular Tank with a Vertical Rigid Baffle Under Pitching Excitation
Authors: Cao Zhanxue , Wang Jiadong
Published: June 2022

The present work uses a semi-analytic mathematical model to study the dynamic response of the partially liquid-filled rectangular tank equipped with a vertical rigid baffle at the bottom subjected to pitching excitation. The velocity potential consists of the rigid container velocity potential and the disturbance potential, which contains a Stokes–Joukowski potential. The liquid domain is divided into four simple sub-domains. The formal solution of the Stokes–Joukowski potential in each sub-domain is solved using the superposition principle and the separation of variables method. The dynamic response equation is established by using the free liquid surface equation. The ADINA solution is compared with the present method solution, which verifies the correctness of the proposed method. Finally, the effect of baffle parameters on liquid sloshing for a baffled tank subjected to harmonic and seismic pitching excitations is discussed in detail.
Mantari’s Higher-Order Shear Deformation Theory of Sandwich Beam with CNTRC Face Layers with Porous Core Under Thermal Loading
Authors: Eghbali Mohammadreza , Hosseini Seyed Amirhosein , Hamidi Babak Alizadeh
Published: June 2022

In the dynamic study of sandwich structures, the analysis of forced vibrations of these structures is particularly important. Also, no exact solution can be found from the forced vibrations of sandwich beams, and mainly by numerical methods, the dynamic response of sandwich beams has been obtained. Also, there is no coupling solution for this type of structure with an exact solution. Therefore, the present work aims to present a method by which an accurate solution to the dynamic response of sandwich beams can be obtained to eliminate the computational error in numerical methods. Hence, the model is a five-layer sandwich beam with a constant moving load. Carbon nanotubes (CNTs) are used as functionally graded (FG) distributions as reinforcements for the core. Mantari’s higher-order shear deformation theory is also used for displacement fields. The governing equations were derived using the Hamilton principle. The Laplace method is used to obtain the exact solution of the dynamic response of the sandwich beam in both longitudinal and transverse directions. For validation, the natural frequency is compared with previous research. In the following, parameters such as voltage, thickness ratio, the volume fraction of CNTs, and velocity of moving load on the dynamic response of piezoelectric sandwich beams in transverse and axial displacement are investigated.
Dynamic Analysis of Wind–Vehicle–Bridge Systems: An Advanced Hybrid Method
Authors: Zhu Siyu , Li Yongle , Xu Xinyu
Published: June 2022

To investigate the dynamics of the wind–vehicle–bridge (WVB) system in the multi-body dynamics framework, which avoids the large computation cost of programming the motion equation of complex vehicle models and improves the calculation efficiency, an advanced hybrid method is proposed to optimize the WVB system coupling vibration analysis model. The dummy body coupling (DBC) method is integrated to build the connection between the MBS and the FE model, which cannot change the mechanical characteristics of the MBS vehicle model and the bridge FE model. The proposed method makes full use of the high efficiency of the established structure modeling, the powerful wheel–rail analysis function, and vehicle modeling in the multi-body dynamics framework. The complex bridge is modeled by a number of elements to reflect the actual dynamic characteristics of structure, which cannot satisfy the requirement of calculation in the multi-body dynamics framework. Thus, to avoid forming the wheel–rail relationship function, the bridge modeling as a finite element model would be transferred into multi-body dynamics coupling model of the WVB system as a flexible body. The verification of the relationship among the sub-systems of the multi-body model of the WVB system is investigated by analysis of the wind–vehicle, wind–bridge, and vehicle–bridge sub-systems. Finally, a dynamic analysis of the WVB system based on the proposed method is carried out for a double-track railway continuous bridge, in which the effects of different vehicle speeds and the incoming wind directions are studied. The simulation of the WVB system by the hybrid method has a high computational efficiency and strong practicability.
Human-Induced Vibration Annoyance Rate Model of Pedestrian Bridge Based on Stevens’ Law
Authors: Lu Pengzhen , Shi Qingtian , Zhou Yutao , Wu Ying , Wang Jiahao
Published: June 2022

To more realistically describe the psychological feeling of pedestrians walking on the pedestrian, and better evaluate the vibration comfort of pedestrians, this paper adopted Stevens’ law. Based on statistical and psychophysical principles, Stevens’ membership function in discrete and continuous distributions was obtained which established a new annoyance rate model. Moreover, an approximate solution to the annoyance obtained using the fitting curve is proposed. Furthermore, calculating the annoyance curves and the coefficient of variation of human-induced vibration for a pedestrian bridge, to verify the accuracy and reliability of the improved pedestrian bridge annoyance rate, field tests are performed on an actual pedestrian bridge. The results show that Stevens’ annoyance rate model can more accurately describe the uncertainty of subjective responses. The changing regular of the fitting value of the approximate curve is consistent with the measured value. Thus, Stevens’ annoyance rate model can provide a new basis for the evaluation of walking vibration comfort of pedestrian bridges.
Analysis of the Fluid–Structure Coupling Characteristics of a High-Speed Train Passing through a Tunnel
Authors: Ji Zhanling , Liu Wen , Guo Dilong , Yang Guowei , Mao Jun
Published: June 2022

After increasing the train speed, the wheel-rail coupling effect is intensified, as is the coupling effect between the train and the surrounding air, which causes the severe vibration of the train. Considering this problem and taking the high-speed train as the object of this study, a method for the coupling of aerodynamics and vehicle dynamics is proposed. Its validity is then demonstrated by the results of field tests and moving model experiments. On this basis, the fluid–structure coupling characteristics of a high-speed train passing through a tunnel are studied, as well as the effects of different coupling methods and track irregularities. The obtained results demonstrate that the interaction between the tail car and the surrounding air is significant. In the tunnel and at its exit, the lift and the car body acceleration significantly change, especially when the coupling of aerodynamics and vehicle dynamics, as well as the vertical and lateral track irregularities, are simultaneously imposed. When the head car travels out of the tunnel, the lift of the middle and tail cars both dramatically will change. It is deduced that the fluid–structure interaction has a significant effect on the swing phenomenon of the tail car. This phenomenon is the result of the combined effects of aerodynamics and track irregularities. The different frequencies of the lateral displacement for each car body, except the frequencies of the car bodies themselves, are mainly determined by vertical track irregularities.
Electromagnetic Vibration Absorber with Tunable Negative Stiffness for Variable Frequency Vibration Control
Authors: Wang Dida , Wang Xi , Zhang Yang , Wang Tao , Fu Guoqiang , Lu Caijiang
Published: June 2022

Vibration absorber usually aims to control the vibration in engineering, but few efforts focus on suppressing the low and variable frequency vibration by tuning the natural frequency. To obtain the optimal tooth layer parameters of the electromagnetic vibration absorber in this paper, the working bandwidth and corresponding linearity are reasonably optimized by establishing a nonlinear multiobjective programming function, and the results indicate that the working performances are significantly improved. The simulations illustrate that the optimized vibration absorber is within the effective stroke of the negative stiffness characteristic, and the reduction of its natural frequency can realize an apparent control effect in a wide low-frequency band for variable frequency vibration by adjusting the coil current. The experimental results demonstrate that the natural frequency of the vibration absorber is tuned to a reasonable value by adjusting the current, and a notable vibration suppression effect is achieved under the variable frequency. As the current increases from 0 to 550[Formula: see text]mA, the natural frequency of the vibration absorber can be tuned from 9[Formula: see text]Hz further to 7.2[Formula: see text]Hz for vibration control, and the vibration suppression effect can reach 62.1%.
Nonlinear Vibration and Stability Analysis of Flexible Rotor Supported on SFD by IHB Method
Authors: Ri Kwangchol , Kim Kumchol , Yun Cholil , Kim Kwangchol , Choe Tongil
Published: June 2022

In this paper, the nonlinear vibration of a flexible rotor supported on squeeze-film dampers (SFDs) with centering springs is analyzed using the incremental harmonic balance (IHB) method, and bifurcation phenomena appeared in the resonance region are investigated. Complex nonlinear phenomena occur in this system due to the interaction of the fluid-film forces and the unbalance forces of the rotor in the SFD. Systems with these complex nonlinearities cannot be solved using the classical IHB methods. To overcome this problem, the classical IHB method and the alternating frequency/time (AFT) method are combined. The processing of linear matrices is performed in the same way as the classical IHB method, and only the processing of nonlinear force matrix caused by fluid–structure interaction is modified (application of transformation matrix). To prove the validity of the proposed method, the results calculated using the proposed method are compared with the results calculated using the Runge–Kutta method and the results presented in reference. Then, frequency response curves according to changes in bearing parameter [Formula: see text], gravity parameter [Formula: see text], stiffness ratio [Formula: see text], mass ratio [Formula: see text], and unbalance parameter [Formula: see text] are constructed. Stability and bifurcation analyses of the calculated solution are performed using the Floquet theory. The proposed method can be effectively applied to the nonlinear vibration analysis of rotor systems supported on fluid-film bearings.
Analytical Method to Estimate the Lateral Restraint for Unbraced Top Chords of Warren-Truss Bridges with Independent Cross-Beam Decks
Authors: Siekierski Wojciech
Published: June 2022

The assessment of the lateral buckling for the unbraced top chord of a truss bridge (a pony truss bridge) requires a finite element analysis of a three-dimensional model of the whole structure or a method of the chord analysis based on the reliable estimation of the lateral restraint for the chord nodes. The latter alternative becomes crucial if rapid assessment is necessary. Majority of the existing analytical methods of assessment of the stability of top chords of pony truss bridges rely on the stiffness of [Formula: see text]-frames that consist of cross-beams and truss bracing members. The available analytical methods of setting the [Formula: see text]-frame stiffness seem to neglect the existence of independent cross-beam decks that, together with Warren-trusses, constitute contemporary truss bridges. Such structures are analyzed in the paper. The stiffness of the lateral restraint provided to the truss top chord by the cross-beams, the diagonals and the bottom chord are computed. The influence of the cross-beams is reflected, for the first time, as an equivalent torsional stiffness of the truss bottom chord. The analytical method is applied against the method included in the DIN design code to the pony Warren-truss bridges of various height-to-span ratios and various arrangements of their member cross-sections. Then the Timoshenko and Gere model of a compressed bar is adapted to find the critical load for the top chords of the aforementioned truss bridges. The results are compared to those obtained from the finite element method (FEM) analyses. It is concluded that the analytical method provides lower estimate for the critical load obtained from the FEM analysis provided the smallest values of cross-sectional properties, within the respective groups, are taken as representative for the computations.
Strong solutions and asymptotic behavior of bidomain equations with random noise
Authors: Kapustyan Oleksiy , Misiats Oleksandr , Stanzhytskyi Oleksandr
Published: June 2022

In this paper, we study the conditions for the existence of strong solutions (both local and global) for stochastic bidomain equations. To this end, we use a priori energy estimates and Serrin-type theorems. We further address the asymptotic behavior of the solutions, which includes the analysis of small stochastic perturbations and large deviations. In a separate section we specify the support of the invariant measure, whose existence was established in [M. Hieber, O. Misiats and O. Stanzhytskyi, On the bidomain equations driven by stochastic forces, Discrete Contin. Dyn. Syst. 40 (11) (2020) 6159–6177].
Regularization of differential equations by two fractional noises
Authors: Nualart David , Sönmez Ercan
Published: June 2022

In this paper, we show the existence and uniqueness of a solution for a stochastic differential equation driven by an additive noise which is the sum of two fractional Brownian motions with different Hurst parameters. The proofs are based on the techniques of fractional calculus and Girsanov theorem. In particular, we show that the regularization effect of the fractional Brownian motion with the smaller Hurst index dominates. A key challenge in this paper is to extend and apply the Girsanov theorem for two noises given by the sum of two (dependent) fractional Brownian motions by using profound techniques of fractional operator theory.
Convergence problem of reduced Ostrovsky equation in Fourier–Lebesgue spaces with rough data and random data
Authors: Yan Xiangqian , Yan Wei , Zhao Yajuan , Yang Meihua
Published: June 2022

This paper is devoted to studying the convergence problem of free reduced Ostrovsky equation in Fourier–Lebesgue spaces with rough data and the stochastic continuity of free reduced Ostrovsky equation in Fourier–Lebesgue spaces with random data. On the one hand, we establish the pointwise convergence related to the free reduced Ostrovsky equation in Fourier–Lebesgue spaces [Formula: see text] with rough data. In particular, we show that [Formula: see text] is the necessary condition for the maximal function estimate in [Formula: see text], which means that [Formula: see text] is optimal for rough data. On the other hand, we present the stochastic continuity of free reduced Ostrovsky equation at [Formula: see text] in Fourier–Lebesgue spaces [Formula: see text] with random data.
Volterra equations driven by rough signals 2: Higher-order expansions
Authors: Harang Fabian A. , Tindel Samy , Wang Xiaohua
Published: June 2022

We extend the recently developed rough path theory for Volterra equations from [F. Harang and S. Tindel, Volterra equations driven by rough signals, Stoch. Process. Appl. 142 (2021) 34–78] to the case of more rough noise and/or more singular Volterra kernels. It was already observed in [F. Harang and S. Tindel, Volterra equations driven by rough signals, Stoch. Process. Appl. 142 (2021) 34–78] that the Volterra rough path introduced there did not satisfy any geometric relation, similar to that observed in classical rough path theory. Thus, an extension of the theory to more irregular driving signals requires a deeper understanding of the specific algebraic structure arising in the Volterra rough path. Inspired by the elements of “non-geometric rough paths” developed in [M. Gubinelli, Ramification of rough paths, J. Differential Equations 248 (2010) 693–721; M. Hairer and D. Kelly, Geometric versus non-geometric rough path, Ann. Inst. Henri Poincaré-Probab. Stat. 51 (2015) 207–251], we provide a simple description of the Volterra rough path and the controlled Volterra process in terms of rooted trees, and with this description we are able to solve rough Volterra equations driven by more irregular signals.
Cyclic polynomials arising from the functional equation for Dickson polynomials
Authors: Bayarmagnai Gombodorj , Ganbat Batmunkh
Published: June 2022

In this paper, we study algebraic properties of a family of certain polynomials arising from the functional equation for Dickson polynomials. We see that the roots and discriminants of those polynomials have very simple expressions, and each polynomial is cyclic. Further, we provide an irreducibility criterion analogous to the well-known criterion of Vahlen-Capelli. We finish the paper by showing that any cyclic extension of a certain field comes from a member of the family.
CS-Baer and dual CS-Baer objects in abelian categories
Authors: Crivei Septimiu , Tütüncü Derya Keskin , Radu Simona Maria , Tribak Rachid
Published: June 2022

We investigate relative CS-Baer objects in abelian categories in relationship with other relevant classes of objects such as relative Baer objects, extending objects, objects having certain summand intersection properties and relative CS-Rickart objects. Dual results are automatically obtained by applying the duality principle in abelian categories. We also study direct sums of relative CS-Baer objects, and we determine the complete structure of dual self-CS-Baer modules over Dedekind domains. Further applications are given to module categories.
On the algebraic structure of quasi-group codes
Authors: Borello Martino , Willems Wolfgang
Published: June 2022

In this paper, an intrinsic description of some families of linear codes with symmetries is given, showing that they can be described more generally as quasi-group codes, that is, as linear codes allowing a group of permutation automorphisms which acts freely on the set of coordinates. An algebraic description, including the concatenated structure, of such codes is presented. This allows to construct quasi-group codes from codes over rings, and vice versa. The last part of the paper is dedicated to the investigation of self-duality of quasi-group codes.
THE INFLUENCE OF FRACTIONAL ORDER AND MAGNETIC FIELD ON ELECTRO HYDRODYNAMIC FLOW OF JEFFREY FLUID ALONG WITH MAGNETIC NANOPARTICLES THROUGH ARTERIAL SEGMENT
Authors: ADAMU G. T. , ABDULHAMEED M. , YAKUBU D. G. , ADAMU M. Y.
Published: June 2022

The study reports the influence of Caputo–Fabrizio fractional derivative and magnetic field on blood flow as an electrically conducting non-Newtonian fluid along with magnetic nanoparticles through circular cylindrical arterial segment, by assuming blood as Jeffrey fluid. The main structure of the governing fractional nonlinear partial differential equations was obtained from non-Newtonian fluid model in which convective derivative is considered instead of the time derivative which is capable of describing the phenomena of relaxation time and retardation time.

The exact solutions of the governing fractional partial differential equations were obtained by the virtue of Laplace and Hankel transforms. Numerical simulations have been performed to analyze the behavior of the Jeffrey fluid flow using Mathcad software and the results are presented graphically for an explicit and detail discussion. We noticed from the graphical representation of results that the fractional derivative, particle concentration, electro-kinetic width, Jeffrey number and the applied magnetic field have profound influence on the magnitude of blood velocity along with magnetic nanoparticles. The effect of each of the influential parameters tremendously decelerates the fluid flow and thereby showing appreciable variation in the distribution of axial and radial velocities. Practically, increasing the applied magnetic field strength and the value of the Jeffrey number on the motion of fluid flow together with magnetic nanoparticles, vehemently decreases the velocities of blood along with magnetic nanoparticles, which helps to have effective control over the fluid networks. From the study, we conclude that the fractional model, Jeffrey number and the application of the external magnetic field on the fluid flow have extensive applications in the clinical and the medical sciences.
DESIGN AND RESEARCH OF A LOWER LIMB CYCLING REHABILITATION ROBOT
Authors: JIANG DAWEI , YANG HUIXIANG , ZHANG FAN
Published: June 2022

A cycling rehabilitation robot is developed to assist in rehabilitation training for stroke patients. The robot can realize flexion movements of the patient’s hip, knee and ankle. First, based on physiological principles, the range for flexion motion angles of the lower limbs is presented. The mathematical modeling of the lower limb rehabilitation motion angles is established. The angular motion characteristics of the lower limb hip and knee joints are analyzed, and the relationship of the motion between the lower limb hip and knee joints is presented. To verify the correctness of the theoretical model, the structure of the lower limb rehabilitation training robot is designed. The experiments show that the rehabilitation robot can achieve flexion rehabilitation training of the lower limbs effectively, which proves the correctness of the proposed theoretical model. The results provide scientific guidance for patients using cycling rehabilitation devices.
MEASUREMENT, PREDICTION AND VALIDATION OF HUMAN GAIT TORQUE FOR LOWER LIMB ASSISTIVE DEVICES
Authors: ROY GANESH , BHATIA DINESH , BHAUMIK SUBHASIS
Published: June 2022

The lower limb rehabilitation process requires external control inputs based on joint kinematics and kinetics to its actuator for generating the motion of a robotic assistive device. This paper describes the procedures for measuring, predicting and validating the joint kinematics and kinetics of human gait for the lower limb exoskeleton. The high-speed multi-camera-based video system associated with passive optical markers and a force plate sensor is used to measure the gait data. The lower limb joint torques have also been predicted using three different machine learning regression models, viz. Decision Tree (DT), Gaussian Process Regression (GPR) and Support Vector Machine (SVM). The analysis is further validated with the geometry-based analytical method. Three lower limb joint torques have been predicted by utilizing gait period, joint angle, joint velocity and joint acceleration as the predictors. The lowest average 10-fold cross-validation Root Mean Square Error (RMSE) using the DT model between measured and predicted torques have been recorded for the ankle, knee and hip joint as 1.9521, 1.5527 and 1.5684[Formula: see text]Nm, respectively. This study enhances the field of medical rehabilitation by providing the required knowledge of human gait kinematics/kinetics and predicting the joint torque profile without any force sensor.
Hierarchical unsupervised multi-view feature selection
Authors: Chen Tingjian , Yuan Haoliang , Yin Ming
Published: June 2022

Multi-view feature selection is an important research direction in the field of multi-view learning. To remove the negative effect of irrelevant and redundant information and select the important features from the multi-view data, in this paper, we propose a novel hierarchical unsupervised multi-view feature selection method, which integrates matrix decomposition and hierarchical regularization as a joint model. Specifically, to exploit the consistency information of multiple views for projection matrix, we project the multiple views into a latent basis space based on a matrix decomposition model. The hierarchical regularization containing the [Formula: see text]-norm, dependence and Frobenius-norm is imposed on projection matrix to, respectively, exploit the row-level, dependency-level and view-level feature selection. We also develop an efficient optimization algorithm to optimize our method. Extensive experimental results on six popular multi-view datasets show the effectiveness and superiority of our method by comparing with the state-of-the-art methods.
Adoption of Artificial Intelligence Along with Gesture Interactive Robot in Musical Perception Education Based on Deep Learning Method
Authors: Zhang Chao , Li Huizi
Published: June 2022

The present work aims to improve students’ interest in music teaching and promote modern teaching. A distributed application system of artificial intelligence gesture interactive robot is designed through deep learning technology and applied to music perception education. First, the user’s gesture instruction data is collected through the double channel convolution neural network (DCCNN). It uses the double-size convolution kernel to extract feature information in the image and collect the video frame’s gesture instruction. Secondly, a two-stream convolutional neural network (two-stream CNN) recognizes the collected gesture instruction data. The spatial and temporal information is extracted from RGB color mode (RGB) images and optical flow images and input into the two-stream CNN to fuse the prediction results of each network as the final detection result. Then, the distributed system used by the interactive robot is introduced. This structure can improve the stability of the interactive systems and reduce the requirements for local hardware performance. Finally, experiments are conducted to test the gesture command acquisition and recognition network, and the performance of the gesture interactive robot in practice. The results indicate that combining convolution kernels of [Formula: see text] and [Formula: see text] can increase the recognition accuracy of DCCNN to 98% and effectively collect gesture instruction data. The gesture recognition accuracy of two-stream CNN after training reaches 90%, higher than the mainstream dynamic gesture recognition algorithm trained with the same data set. Finally, the recognition test of gesture instructions is carried out on the gesture interactive robot reported here. The results show that the recognition accuracy of the gesture interactive robots is more than 90%, meeting the routine interaction needs. Therefore, the interactive gesture robot has good reliability and stability and is applicable to music perception teaching. The research reported here has guiding significance for establishing music teaching with multiple perception modes.
Exploring the Caricature Style Identification and Classification Using Convolutional Neural Network and Human–Machine Interaction Under Artificial Intelligence
Authors: Wang Li , Kim Jaewoong
Published: June 2022

The objectives are to explore the application effect of deep learning (DL), neural network (NN) and human–machine interaction (HMI) technology in caricature style recognition and classification in the environment of artificial intelligence (AI). It provides a realistic basis for the integration of caricature and AI technology. The convolutional neural network (CNN) model is optimized, and the ARtoolkit, JAVA-based processing and photoshop are applied, combined with an augmented reality (AR) editor to register the logo of caricature. The results indicate that the output value of the optimized CNN is the largest near the fourth neural node, and the output values of the remaining neural nodes are almost all close to zero. The label value of the cat image is the same as the output value, and the image label range is [0, 10]. The use of AR technology can make caricature images have animation functions, giving people the visual experience that the lotus can move, and the caricature style has changed. When the screen is not touched, the fish under the lotus is in a static state. When the screen is touched with the hand, the fish swims away quickly, indicating that the function of HMI is realized. Therefore, the CNN and HMI technology under AI can successfully perform caricature style recognition and classification, which can provide an experimental reference for the subsequent intelligent development of caricature.
The Establishment and Optimization of Public Emotion Network Communication Model using Deep Learning
Authors: Zhang Yating , Dai Bin , Zhong Yuan
Published: June 2022

This work aims to construct and optimize the public emotion network communication model under the artificial intelligence (AI) environment and provide a new research direction for exploring public emotion network communication with intelligent human–computer interaction (HCI). The main challenge of this research is the lack of interactivity of intelligent human–computer expression design, and the accuracy of emotion recognition is not high. First, the theoretical analysis of network public opinion is made, and the network public opinion calculation method and emotion recognition method based on deep learning are proposed. On this basis, the public emotion network communication model is established, and the model’s communication mechanism and rules are described in detail. Next, the emotion calculation of human–computer expression interaction and the emotion expression mode of the humanoid robot are proposed, which provides an experimental basis for HCI and public emotion network communication analysis. According to the data of email network and microblog network in the real network, the network degree distribution map is established. The statistical results show that email and microblog networks belong to power law distribution, and the aggregation coefficient is relatively high. Microblog network degree changes slowly, and more points are scattered in the middle area. In a practical sense, when the positive and negative emotions in the public emotion network communication model are input into the bionic robot, the robot can show happy and lost expressions. The results show that compared with the traditional convolutional neural network (CNN) model or recurrent neural network (RNN) model, the RNN–CNN structure model of RNN combined with CNN reduces the waiting time by about 20% and improves the algorithm accuracy by at least 3.1%. The public emotion network communication model based on deep learning and intelligent HCI can accurately reflect the public emotion state in the network, which provides a practical basis for the application of AI technology in network public opinion judgment.