bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Control Algorithm of Automatic Emergency Braking System (AEBS) for Commercial Vehicle Based on Identification of Driving Condition
Authors: Guo Jianhua , Wang Yinhang , Yin Xingji , Liu Peng , Hou Zhuoran , Zhao Di
Published: October 2022

Automatic emergency braking systems (AEBS) significantly improve the active safety performance of commercial vehicles, but their effectiveness is affected by the vehicle’s driving conditions, which mainly include the vehicle load and road conditions. In order to improve the adaptability of the AEBS, an AEBS control strategy with adaptive driving conditions was proposed and validated using a simulation and experimentation. This AEBS control strategy was designed based on an estimation of the vehicle mass, the center of gravity position, road grade, and the tire-road friction coefficient. In the simulation and experimental verification, the braking deceleration and braking distance under different driving conditions were compared. The results show that the AEBS control strategy proposed in this paper can avoid collisions in all test scenarios and maintain a parking spacing of approximately 5 m. In an extreme test scenario with a full load and low tire–road friction, as compared with the fixed threshold control strategy, the warning can be issued 0.2 s earlier and the maximum intensity braking can be carried out 0.5 s earlier.
Testing Method for Intelligent Loading of Mining Emulsion Pump Based on Digital Relief Valve and BP Neural Network Control Algorithm
Authors: Tian Jie , Liu Wenchao , Wang Hongyao
Published: October 2022

The mining emulsion pump is mainly used on a fully mechanized coal mining face, but it is rarely used on other occasions, so research on its loading test method is relatively limited. This paper proposes the application of a digital relief valve to the emulsion pump loading test. In addition, the small number of plungers in the emulsion pump will lead to large flow pulsation and pressure pulsation, and the nominal flow of different types of emulsion pumps varies greatly. These factors lead to the deficiency of a traditional PID control algorithm in control accuracy and efficiency. In order to improve control accuracy and efficiency, firstly, the influence of the flow rate of the tested pump and extension of the linear stepping motor shaft on the working pressure is studied. A backpropagation (BP) artificial neural network (ANN) model is used to fit a functional relationship between the three parameters. The flow rate of the tested pump and target pressure were provided as inputs to predict the extension of the linear stepping motor shaft, thereby realizing the remote intelligent control of the system pressure. Next, a BP ANN model is constructed, and its reliability is verified; the BP neural network algorithm and proportional-integral-derivative (PID) algorithm are compared through simulation. The simulation results show that the BP neural network algorithm has high control accuracy and small overshoot. Finally, two pumps with different flows are tested in a self-developed digital relief valve and test platform. The test results show that the proposed loading test method is intelligent and efficient, and it has high accuracy.
Response Surface Method for Optimization of Synchronous Reluctance Motor Rotor
Authors: Orlova Svetlana , Auzins Janis , Pugachov Vladislav , Rassõlkin Anton , Vaimann Toomas
Published: October 2022

In order to define the best design structure of the synchronous reluctance motor (SynRM) rotor, optimization must be carried out, implying the selection of the best alternative for each specific criterion. The optimization of an electrical machine is a complicated work involving meeting different criteria requirements while dealing with a range of constraints. In order to implement the optimization, it is necessary to process a huge number of options, changing the combinations of the factors affecting criteria and restrictions, which is a time-consuming process. This research presents the optimization technique that gives a mathematically proven solution of the optimal rotor design of a synchronous reluctance machine obtained by using metamodels in the form of local polynomial approximations. Analysis of the results of numerical modeling and experimental investigation has been performed in order to validate the developed technique and recommendations. SynRM rotor was manufactured, with the stator to be taken from the 1.1 kW W21 WEG induction motor, which makes possible the relevant experimental study. The performance analysis of the developed SynRM is shown in the paper.
Formation Control of Mobile Robots Based on Pin Control of Complex Networks
Authors: Rios Jorge D. , Ríos-Rivera Daniel , Hernandez-Barragan Jesus , Pérez-Cisneros Marco , Alanis Alma Y.
Published: October 2022

Robot formation control has several advantages that make it interesting for research. Multiple works have been published in the literature using different control approaches. This work presents the control of different groups of robots to achieve a desired formation based on pinning control of complex networks and coordinate translation. The implemented control law comprises complex network bounding, proportional, and collision avoidance terms. The tests for this proposal were performed via simulation and experimental tests, considering different networks of differential robots. The selected robots are Turtlebot3® Waffle Pi robots. The Turtlebot3® Waffle Pi is a differential mobile robot with the Robot Operating System (ROS). It has a light detection and ranging (LiDAR) sensor used to compute the collision avoidance control law term. Tests show favorable results on different formations testing on various groups of robots, each composed of a different number of robots. From this work, implementation on other devices can be derived, as well as trajectory tracking once in formation, among other applications.
Horizontal Bi-Stable Vibration Energy Harvesting Using Electromagnetic Induction and Power Generation Efficiency Improvement via Stochastic Resonance
Authors: Guo Linshi , Zhao Wei , Guan Jingchao , Gomi Nobuyuki , Zhao Xilu
Published: October 2022

In this study, a vibration energy-harvesting system is developed by first proposing a horizontal bi-stable vibration model comprising an elastic spring and a mass block and then applying an electromagnetic induction power generation device composed of a magnet and a coil. Subsequently, based on a weight function that considers the mutual positional relationship between the magnet and conducting coil, a set of simultaneous governing equations that consider the elastic force of the elastic spring and the Lorentz force of electromagnetic induction is derived. Additionally, a numerical analysis method employing the Runge–Kutta method is utilized to obtain a numerical solution for the vibration response displacement and vibration power generation voltage simultaneously. Experiments are performed to verify the results yielded by the proposed bi-stable vibration energy-harvesting system. The results shows that the measured vibration response displacement and the vibration power generation voltage are consistent with the analytical results. Moreover, issues including the identification of damping coefficients that consider the mutual effects of normal kinetic friction and electromagnetic induction damping forces, as well as the effects of electromagnetic induction damping on the vibration response displacement, are discussed comprehensively. Simultaneously adding random and periodic signals to the bi-stable vibration model results in stochastic resonance and improves both the vibration amplification effect and vibration power generation.
Optimizing Manufacturing Parameters of DLP Machine by Taguchi Method and Desirability Approach
Authors: Yang Cheng-Jung
Published: October 2022

With less manufacturing time and less photosensitive resin consumed, Digital Light Processing of the bottom-up process is more forward-looking than the top-down one in terms of 3D printing technology. In view of differences in the optimization of machine printing parameters for objectives with different quality requirements, this paper discusses how the control factors consisting of machine printing parameters can obtain objects with a high quality through an experimental design with full considerations of the separation force and dimensional tolerance. By taking the titling separation mechanism as an example, the importance, contribution, and best calculation results of the control factors in forming a separation force and four dimensional tolerances are obtained using the Taguchi method and an analysis of variance, and then the optimal solution for multiple objectives is obtained via the desirability approach. It was found from this study that the rising speed of the forming platform is the most important parameter affecting the dimensional tolerance and separation force. The slice layer thickness has an inverse characteristic for optimizing the dimensional tolerance of the aperture and the face width. Regarding multi-objective optimization, it will not change the optimal printing parameters considering the minimum separation force and overall dimensional tolerance, regardless of the weight of the separation force.
Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram Images
Authors: Tayyab Syed Muhammad , Chatterton Steven , Pennacchi Paolo
Published: October 2022

Due to the excellent image recognition characteristics of convolutional neural networks (CNN), they have gained significant attention among researchers for image-processing-based defect diagnosis tasks. The use of deep CNN models for rolling element bearings’ (REBs’) defect diagnosis may be computationally expensive, and therefore may not be suitable for some applications where hardware and resources limitations exist. However, instead of using CNN models as end-to-end image classifiers, they can also be used to extract the deep features from images and those features can further be used as input to machine learning (ML) models for defect diagnosis tasks. In addition to extracting deep features using CNN models, there are also other methods for feature extraction from vibration characteristic images, such as the extraction of handcrafted features using the histogram of oriented gradients (HOG) and local binary pattern (LBP) descriptors. These features can also be used as input to classical ML models for image classification tasks. In this study, a performance comparison between all these image-processing-based defect diagnosis techniques was carried out in terms of fault detection accuracy and computational expense. Moreover, based upon the detailed comparison, a hybrid-ensemble method involving decision-level fusion is proposed, which is far less computationally expensive compared to CNN models while using them as end-to-end classifiers. The performance of all these models is also compared in the case of minimal training data availability and for diagnosis under slightly different operating conditions to ascertain their generalizability and ability to correctly diagnose despite the minimal availability of training data. The performance of the proposed hybrid-ensemble method remained outstanding for the REBs’ defect diagnosis despite the minimal of availability training data as well as the slight variation under operating conditions.
Design of Gas Turbine Cooling System Based on Improved Jumping Spider Optimization Algorithm
Authors: Li Tianyi , Liu Yanmei , Chen Zhen
Published: October 2022

The gas turbine cooling system is a complex MIMO system with a strong coupling, nonlinear, time-varying and large disturbance amplitude. In order to automatically control the target flow, target temperature and pipeline pressure, in this paper, the decoupler and regulator of a gas turbine cooling system are designed. Firstly, the working principle of a gas turbine cooling system and the coupling between the controlled variables of the system are analyzed. The decoupler of the system is designed by using the diagonal matrix decoupling method. The transfer function models of the coupling system are built through system identification, and the decoupling matrix of the system is calculated according to the diagonal matrix decoupling method and transfer function models. Then, the engine cooling control system simulation model is constructed and an improved jumping spider optimization algorithm is proposed. The parameters of the controller are optimized by the improved jumping spider optimization algorithm. Finally, the control system simulation is done and compared with the jumping spider optimization algorithm and the particle swarm optimization algorithm. The simulation results show that the improved jumping spider optimization algorithm is more suitable for the multivariable strong coupling nonlinear engine cooling system. For the flow and pressure control, the transient time and overshoot are reduced, and the steady-state error is less than 1%. For the temperature control, the result of the improved jumping spider optimization algorithm is more smooth, without overshoot, and almost does not exceed the set inlet water temperature. The overshoot, steady-state errors and transient time of the system have been improved, which proves the feasibility and significance of the improved jumping spider optimization algorithm by comparing the control performance and optimization time.
Grinding Performance of Laser Cladding WC/Fe Coatings by Different Adding Methods of WC Particles
Authors: Dai Qiulian , Liu Lianhong , You Fangyi , Luo Canbin
Published: October 2022

Laser cladding coatings generally need a follow-up grinding process to acquire the desired dimensional accuracy and surface roughness. In this paper, grinding experimental studies were set up to investigate the grinding performance of iron-based matrix composite coatings (WC/Fe with in situ and ex situ WC particles), using two types of grinding wheels. The influence of grinding parameters such as the cut depth, wheel speed and type of grinding wheel on grinding forces, force ratio, specific energy, surface roughness and the surface appearance of the two coatings was studied. The forming mechanism of the subsurface damages on the two ground coatings was discussed. Experimental results revealed that there were obvious differences between the two coatings in grinding forces, surface roughness and morphology, especially in the subsurface damages. These were attributed to their difference in size and the distribution of the WC particles, as well as their different mechanical properties.
Comparison of Two Sinusoidal Magnetization Modes of Bonded Magnetic Rings
Authors: Zhang Ze , Wang Zilong , Yu Juntao , Yu Dunbo , Luo Yang , Yan Wenlong , Yang Yuanfei , Hu Tengfei , Wang Li
Published: October 2022

This paper compares the application of two different sinusoidal magnetization methods of a bonded magnetic ring in a permanent magnet motor. Sinusoidal magnetization of a bonded magnetic ring can be realized by eccentric pole cutting or magnetization with eccentric fixture. Firstly, the parametric optimization of the two methods is carried out by finite−element simulation software, with the goal of minimizing the total harmonic distortion (THD) of Air−gap flux density. Then, when the THD of Air−gap flux density is roughly the same, the cogging torque, output torque, back−EMF, magnet dosage, and other parameters of the two magnets in the application of permanent magnet motor are compared. The results show that the performance of the two methods is similar, and the two methods have their own advantages. Finally, an eccentric bonded magnetic ring and eccentric magnetizing fixture are made, respectively, for experimental comparison. The experimental results are consistent with the simulation results. This paper can provide some reference value for the selection of sinusoidal magnetization mode of a bonded magnetic ring.
Dynamic Bloch Chirality and Enhanced Velocities from Spin-Orbit Torque Driven Domain Wall Motion in Thick Magnetic Films
Authors: Staggers Trae Lawrence , Pollard Shawn David
Published: October 2022

Spin-orbit torque (SOT) driven domain wall motion has attracted significant attention as the basis for a variety of spintronic devices due to its potential use as a high speed, low power means to manipulate the magnetic state of an object. While most previous attention has focused on ultrathin films wherein the material thickness is significantly less than the magnetic exchange length, recent reports have suggested unique dynamics may be achieved in intermediate and high thickness films. We used micromagnetic modelling to explore the role of the vertically non-uniform spin textures associated with the domain wall in nanowires of varying thickness on SOT driven domain wall motion. We found large velocity asymmetries between Bloch chiralities near the current density required for reversal of the Bloch component of the magnetization and linked these asymmetries to a gradual reorientation of the domain wall structure which drives a non-negligible, chiral Néel component of the domain wall. We further explored the influence of saturation magnetization, film thickness, the Dzyaloshinskii-Moriya interaction, and in-plane fields on domain wall dynamics. These results provide a framework for the development of SOT based devices based on domain wall motion in nanowires beyond the ultrathin film limit.
Structural and Photocatalytic Studies on Oxygen Hyperstoichiometric Titanium-Substituted Strontium Ferrite Nanoparticles
Authors: Grewal Jaspreet Kaur , Kaur Manpreet , Sharma Rajeev K. , Oliveira Aderbal C. , Garg Vijayendra Kumar , Sharma Virender K.
Published: October 2022

Doping of ferrites is an important domain of research for their application as photocatalysts. In the present work, the effect of Ti4+ substitution on the structural and photocatalytic properties of strontium ferrite nanoparticles (NPs) is studied. Ternary doped Sr1−xTixFe2O4+δ ferrite NPs (x = 0.0–1.0) were synthesized by sol–gel methodology. Tetravalent Ti4+ ions caused oxygen hyperstoichiometry and enhancement in the surface area from 44.3 m2/g for SrFe2O4 NPs to 77.6 m2/g for Sr0.4Ti0.6Fe2O4+δ NPs. The average diameter of NPs ranged between 25–35 nm as revealed by TEM analysis. The presence of two sextets in the Mössbauer spectrum of pristine SrFe2O4 and Ti4+-substituted ferrite NPs and a paramagnetic doublet in the TiFe2O5 confirmed their phase purity. The photocatalytic potential of pure and Ti4+-substituted ferrite NPs was studied using nitroaromatic compounds, viz. pendimethalin, p-nitrophenol and Martius yellow, as model pollutants. Doped ferrite NPs with a composition of Sr0.4Ti0.6Fe2O4+δ NPs showed the highest degradation efficiency ranging from 87.2% to 94.4%. The increased photocatalytic potential was ascribed to the lowering of band gap (Eg) from 2.45 eV to 2.18 eV, a fourfold decrease in photoluminescence intensity, increased charge carrier concentration (4.90 × 1015 cm−3 to 6.96 × 1015 cm−3), and decreased barrier height from 1.20 to 1.02 eV. O2●− radicals appeared to be the main reactive oxygen species involved in photodegradation. The apparent rate constant values using the Langmuir–Hinshelwood kinetic model were 1.9 × 10−2 min−1, 2.3 × 10−2 min−1 and 1.3 × 10−2 min−1 for p-nitrophenol, pendimethalin and Martius yellow, respectively. Thus, tuning the Ti4+ content in strontium ferrite NPs proved to be an effective strategy in improving their photocatalytic potential for the degradation of nitroaromatic pollutants.
Magnetic, Electric and Optical Properties of Ion Doped CuCr2O4 Nanoparticles
Authors: Apostolov Angel Todorov , Apostolova Iliana Naumova , Wesselinova Jilia Mihailowa
Published: October 2022

The magnetic, electric and optical properties of pure and ion doped CuCr2O4 - bulk and nanoparticles are investigated theoretically. The magnetization Ms and the band gap Eg decrease with increasing particle size. By Co ion doping Ms and the polarization P show a maximum whereas by Pr ion doping they decrease with increasing the doping concentration. The dielectric constant decreases with enhancing Pr dopants. It is shown that the difference between the doping and host ions radii leads to appearing of a compressive or tensile strain and to different exchange interaction constants in the doped state. Eg decreases by Co doping, whereas it increases by Pr doping.
How Do Deep-Learning Framework Versions Affect the Reproducibility of Neural Network Models?
Authors: Shahriari Mostafa , Ramler Rudolf , Fischer Lukas
Published: October 2022

In the last decade, industry’s demand for deep learning (DL) has increased due to its high performance in complex scenarios. Due to the DL method’s complexity, experts and non-experts rely on blackbox software packages such as Tensorflow and Pytorch. The frameworks are constantly improving, and new versions are released frequently. As a natural process in software development, the released versions contain improvements/changes in the methods and their implementation. Moreover, versions may be bug-polluted, leading to the model performance decreasing or stopping the model from working. The aforementioned changes in implementation can lead to variance in obtained results. This work investigates the effect of implementation changes in different major releases of these frameworks on the model performance. We perform our study using a variety of standard datasets. Our study shows that users should consider that changing the framework version can affect the model performance. Moreover, they should consider the possibility of a bug-polluted version before starting to debug source code that had an excellent performance before a version change. This also shows the importance of using virtual environments, such as Docker, when delivering a software product to clients.
Almost Optimal Searching of Maximal Subrepetitions in a Word
Authors: Kolpakov Roman
Published: October 2022

For some fixed δ such that 0<δ<1, a δ-subrepetition in a word is a factor whose exponent is less than 2 but is not less than 1+δ (the exponent of the factor is the ratio of the factor length to its minimal period). The δ-subrepetition is maximal if it cannot be extended to the left or to the right by at least one letter while preserving its minimal period. In the paper, we propose an algorithm for searching all maximal δ-subrepetitions in a word of length n in O(nδlog1δ) time (the lower bound for this time is Ω(nδ)). It improves the previous known time complexity bounds for solving this problem.
Numerical Study of a Confined Vesicle in Shear Flow at Finite Temperature
Authors: Lamura Antonio
Published: October 2022

The dynamics and rheology of a vesicle confined in a channel under shear flow are studied at finite temperature. The effect of finite temperature on vesicle motion and system viscosity is investigated. A two-dimensional numerical model, which includes thermal fluctuations and is based on a combination of molecular dynamics and mesoscopic hydrodynamics, is used to perform a detailed analysis in a wide range of the Peclet numbers (the ratio of the shear rate to the rotational diffusion coefficient). The suspension viscosity is found to be a monotonous increasing function of the viscosity contrast (the ratio of the viscosity of the encapsulated fluid to that of the surrounding fluid) both in the tank-treading and the tumbling regime due to the interplay of different temperature-depending mechanisms. Thermal effects induce shape and inclination fluctuations of the vesicle which also experiences Brownian diffusion across the channel increasing the viscosity. These effects reduce when increasing the Peclet number.
A Novel Approach of Fuzzy Control Chart with Fuzzy Process Capability Indices Using Alpha Cut Triangular Fuzzy Number
Authors: Ahmad Mohammad , Cheng Weihu
Published: October 2022

Statistical Quality Control is a valuable strategy that applies to the statistical technique for monitoring a manufacturing system under particular situations. On the other hand, the fuzzy set theory is an ideal instrument to cope with an unclear situation. The existing studies are restricted, and there is still mystery behind the unclear data. This paper deals with technique: namely, the fuzzy control chart based on fuzzy process capability indices (FCPI) using triangular fuzzy numbers (TFNs). Alpha cut theory is applied in statistical quality control for fuzzy process control industrial application. This is a five-phase study that deals with the control chart using capability indices. The numerical example is also performed using the proposed technique. This paper would help to better assess/understand the manufacturing system data and would explore the application of the fuzzy control techniques.
Mathematical Analysis of Unsteady Stagnation Point Flow of Radiative Casson Hybrid Nanofluid Flow over a Vertical Riga Sheet
Authors: Shatnawi Taqi A. M. , Abbas Nadeem , Shatanawi Wasfi
Published: October 2022

Heat and mass transfer study of hybrid nanomaterial Casson fluid with time-dependent flow over a vertical Riga sheet was deliberated under the stagnation region. In the presence of the Riga sheet in fluid flow models, this formulation was utilized to introduce Lorentz forces into the system. We considered the three models of hybrid nanomaterial fluid flow: namely, Yamada Ota, Tiwari Das, and Xue models. Two different nanoparticles, namely, SWCNT and MWCNT under base fluid (water) were studied. Under the flow suppositions, a mathematical model was settled using boundary layer approximations in terms of PDEs (partial differential equations). The system of PDEs (partial differential equations) was reduced into ODEs (ordinary differential equations) after applying suitable transformations. The reduced system, in terms of ODEs (ordinary differential equations), was solved by a numerical scheme, namely, the bvp4c method. The inspiration of the physical parameters is presented through graphs and tables. The curves of the velocity function deteriorated due to higher values of M. The Hartmann number is a ratio of electric force to viscous force. The electric forces increased due to higher values of the modified Hartmann number, ultimately declining the velocity function. The skin friction was reduced due to an incremental in ϖ, while the Nusselt number raised with higher values of ϖ. Physically, the Eckert number increased, which improved kinetic energy and, as a result, skin friction declined. The heat transfer rate increased as kinetic energy increased, and the Eckert number increased. The skin friction reduced due to physical enhancement of β1, the shear thinning was enhanced which reduced the skin friction.
An Adaptive Control Algorithm Based on Q-Learning for UHF Passive RFID Robots in Dynamic Scenarios
Authors: Wang Honggang , Yu Ruixue , Pan Ruoyu , Pei Peidong , Han Zhao , Zhang Nanfeng , Yang Jingfeng
Published: October 2022

The Identification State (IS) of Radio Frequency Identification (RFID) robot systems changes continuously with the environment, so improving the identification efficiency of RFID robot systems requires adaptive control of system parameters through real-time evaluation of the IS. This paper first expounds on the important roles of the real-time evaluation of the IS and adaptive control of parameters in the RFID robot systems. Secondly, a method for real-time evaluation of the IS of UHF passive RFID robot systems in dynamic scenarios based on principal component analysis (PCA)-K-Nearest Neighbor (KNN) is proposed and establishes an experimental scene to complete algorithm verification. The results show that the accuracy of the real-time evaluation method of IS based on PCA-KNN is 92.4%, and the running time of a single data is 0.258 ms, compared with other algorithms. The proposed evaluation method has higher accuracy and shorter running time. Finally, this paper proposes a Q-learning-based adaptive control algorithm for RFID robot systems. This method dynamically controls the reader’s transmission power and the robot’s moving speed according to the IS fed back by the system; compared with the default parameters, the adaptive control algorithm effectively improves the identification rate of the system, the power consumption under the adaptive parameters is reduced by 36.4%, and the time spent decreases by 29.7%.
Analysis of a Queueing Model with MAP Arrivals and Heterogeneous Phase-Type Group Services
Authors: Chakravarthy Srinivas R.
Published: October 2022

Queueing models have proven to be very useful in real-life applications to enable the practitioners to optimize the limited resources to conduct their businesses as well as offer services efficiently. In general, we can group such applications into two sectors: manufacturing and service. These two sectors cover everything we deal with on a day-to-day basis. Queues in which the services are offered in blocks (or groups or batches) are well established in the literature and have a wide variety of applications in practice. In this paper, we look at one such queueing model in which the arrivals occur according to a Markovian arrival process and the services are offered in batches of varying sizes from 1 to a finite pre-determined constant, say, b. The service times are assumed to be of phase type with representation depending on the size of the group. Thus, the distributions considered are heterogeneous from both the representation and rate points of view. The model can be studied as a GI/M/1-type queue or as a QBD-model. The model is analyzed in steady state by establishing results including on the rate matrix and the waiting time distribution and providing a number of illustrative examples.
Further Inequalities for the Weighted Numerical Radius of Operators
Authors: Altwaijry Najla , Feki Kais , Minculete Nicuşor
Published: October 2022

This paper deals with the so-called A-numerical radius associated with a positive (semi-definite) bounded linear operator A acting on a complex Hilbert space H. Several new inequalities involving this concept are established. In particular, we prove several estimates for 2×2 operator matrices whose entries are A-bounded operators. Some of the obtained results cover and extend well-known recent results due to Bani-Domi and Kittaneh. In addition, several improvements of the generalized Kittaneh estimates are obtained. The inequalities given by Feki in his work represent a generalization of the inequalities given by Kittaneh. Some refinements of the inequalities due to Feki are also presented.
Using Domain Adaptation for Incremental SVM Classification of Drift Data
Authors: Tang Junya , Lin Kuo-Yi , Li Li
Published: October 2022

A common assumption in machine learning is that training data is complete, and the data distribution is fixed. However, in many practical applications, this assumption does not hold. Incremental learning was proposed to compensate for this problem. Common approaches include retraining models and incremental learning to compensate for the shortage of training data. Retraining models is time-consuming and computationally expensive, while incremental learning can save time and computational costs. However, the concept drift may affect the performance. Two crucial issues should be considered to address concept drift in incremental learning: gaining new knowledge without forgetting previously acquired knowledge and forgetting obsolete information without corrupting valid information. This paper proposes an incremental support vector machine learning approach with domain adaptation, considering both crucial issues. Firstly, a small amount of new data is used to fine-tune the previous model to generate a model that is sensitive to the new data but retains the previous data information by transferring parameters. Secondly, an ensemble and model selection mechanism based on Bayesian theory is proposed to keep the valid information. The computational experiments indicate that the performance of the proposed model improved as new data was acquired. In addition, the influence of the degree of data drift on the algorithm is also explored. A gain in performance on four out of five industrial datasets and four synthetic datasets has been demonstrated over the support vector machine and incremental support vector machine algorithms.
Estimating the Performance of Computing Clusters without Accelerators Based on TOP500 Results
Authors: Rybintsev Vladimir O.
Published: October 2022

Based on an analysis of TOP500 results, a functional dependence of the performance of clusters without accelerators according to the Linpack benchmark on their parameters was determined. The comparison of calculated and tested results showed that the estimation error does not exceed 2% for processors of different generations and manufacturers (Intel, AMD, Fujitsu) with different technologies of a system interconnect. The achieved accuracy of the calculation allows successful prediction of the performance of a cluster when its parameters (node performance, number of nodes, number of network interfaces, network technology, remote direct memory access, or remote direct memory access over converged Ethernet mode) are changed without resorting to a complex procedure of real testing.
A Direct Prediction of the Shape Parameter in the Collocation Method of Solving Poisson Equation
Authors: Luh Lin-Tian
Published: October 2022

In this paper, we totally discard the traditional trial-and-error algorithms of choosing the acceptable shape parameter c in the multiquadrics −c2+∥x∥2 when dealing with differential equations, for example, the Poisson equation, with the RBF collocation method. Instead, we choose c directly by the MN-curve theory and hence avoid the time-consuming steps of solving a linear system required by each trial of the c value in the traditional methods. The quality of the c value thus obtained is supported by the newly born choice theory of the shape parameter. Experiments demonstrate that the approximation error of the approximate solution to the differential equation is very close to the best approximation error among all possible choices of c.
A Super-Twisting Extended State Observer for Nonlinear Systems
Authors: Li Yi , Tan Panlong , Liu Junjie , Chen Zengqiang
Published: October 2022

Disturbances and uncertainties are the main concerns in control systems. To obtain this information in real time, the extended state observer is proposed as the core of the active disturbance rejection control. However, the estimation errors of extended state observer cannot converge to zero in the presents of unknown but bounded disturbances, which will bring unexpected tracking errors to the closed-loop system. By taking advantage of the linear extended state observer and the super-twisting algorithm, the super-twisting extended state observer is proposed to deal with the non-diminishing second-order derivable disturbances in this paper. The asymptotic convergence of the super-twisting extended state observer and the controller are proved by the Lyapunov method. Moreover, the effectiveness and robustness of the super-twisting extended state observer are verified by simulation analysis. Simulation results show that the proposed super-twisting extended state observer maintains the minimized estimation error with less settling time compared the with linear extended state observer. The tracking performance of the controller with the proposed observer is greatly improved.
Residual-Prototype Generating Network for Generalized Zero-Shot Learning
Authors: Zhang Zeqing , Li Xiaofan , Ma Tai , Gao Zuodong , Li Cuihua , Lin Weiwei
Published: October 2022

Conventional zero-shot learning aims to train a classifier on a training set (seen classes) to recognize instances of novel classes (unseen classes) by class-level semantic attributes. In generalized zero-shot learning (GZSL), the classifier needs to recognize both seen and unseen classes, which is a problem of extreme data imbalance. To solve this problem, feature generative methods have been proposed to make up for the lack of unseen classes. Current generative methods use class semantic attributes as the cues for synthetic visual features, which can be considered mapping of the semantic attribute to visual features. However, this mapping cannot effectively transfer knowledge learned from seen classes to unseen classes because the information in the semantic attributes and the information in visual features are asymmetric: semantic attributes contain key category description information, while visual features consist of visual information that cannot be represented by semantics. To this end, we propose a residual-prototype-generating network (RPGN) for GZSL that extracts the residual visual features from original visual features by an encoder–decoder and synthesizes the prototype visual features associated with semantic attributes by a disentangle regressor. Experimental results show that the proposed method achieves competitive results on four GZSL benchmark datasets with significant gains.
Orthogonal Frames in Krein Spaces
Authors: Ferrer Osmin , Sierra Arley , Polo Osvaldo
Published: October 2022

In this paper, we introduce the concept of orthogonal frames in Krein spaces, prove the independence of the choice of the fundamental symmetry, and from this, we obtain a number of interesting properties that they satisfy. We show that there is no distinction between orthogonal frames in a Krein space and orthogonal frames in its associated Hilbert. Furthermore, we characterize frames dual to a given frame, which is a useful tool for constructing examples.
Studying the Efficiency of Parallelization in Optimal Control of Multistage Chemical Reactions
Authors: Sakharov Maxim , Koledina Kamila , Gubaydullin Irek , Karpenko Anatoly
Published: October 2022

In this paper, we investigate the problem of optimal control of complex multistage chemical reactions, which is considered a nonlinear global constrained optimization problem. This class of problems is computationally expensive due to the inclusion of multiple parameters and requires parallel computing systems and algorithms to obtain a solution within a reasonable time. However, the efficiency of parallel algorithms can differ depending on the architecture of the computing system. One available approach to deal with this is the development of specialized optimization algorithms that consider not only problem-specific features but also peculiarities of a computing system in which the algorithms are launched. In this work, we developed a novel parallel population algorithm based on the mind evolutionary computation method. This algorithm is designed for desktop girds and works in synchronous and asynchronous modes. The algorithm and its software implementation were used to solve the problem of the catalytic reforming of gasoline and to study the parallelization efficiency. Results of the numerical experiments are presented in this paper.
Two Interval Upper-Bound Q-Function Approximations with Applications
Authors: Perić Zoran , Marković Aleksandar , Kontrec Nataša , Nikolić Jelena , Petković Marko , Jovanović Aleksandra
Published: October 2022

The Gaussian Q-function has considerable applications in numerous areas of science and engineering. However, the fact that a closed-form expression for this function does not exist encourages finding approximations or bounds of the Q-function. In this paper, we determine analytically two novel interval upper bound Q-function approximations and show that they could be used efficiently not only for the symbol error probability (SEP) estimation of transmission over Nakagami-m fading channels, but also for the average symbol error probability (ASEP) evaluation for two modulation formats. Specifically, we determine analytically the composition of the upper bound Q-function approximations specified at disjoint intervals of the input argument values so as to provide the highest accuracy within the intervals, by utilizing the selected one of two upper bound Q-function approximations. We show that a further increase of the accuracy, achieved in the case with two upper-bound approximations composing the interval approximation, can be obtained by forming a composite interval approximation of the Q-function that assumes another extra interval and by specifying the third form for the upper-bound Q-function approximation. The proposed analytical approach can be considered universal and widely applicable. The results presented in the paper indicate that the proposed Q-function approximations outperform in terms of accuracy other well-known approximations carefully chosen for comparison purposes. This approximation can be used in numerous theoretical communication problems based on the Q-function calculation. In this paper, we apply it to estimate the average bit error rate (ABER), when the transmission in a Nakagami-m fading channel is observed for the assumed binary phase-shift keying (BPSK) and differentially encoded quadrature phase-shift keying (DE-QPSK) modulation formats, as well as to design scalar quantization with equiprobable cells for variables from a Gaussian source.
Effects of Seat Belts and Shock Absorbers on the Safety of Racing Car Drivers
Authors: Itu Calin , Toderita Ana , Melnic Lucia-Violeta , Vlase Sorin
Published: October 2022

This paper aimed to study the behavior of a body (dummy) that was in a race car in the event of a frontal collision with a wall in order to see what loads were acting on the dummy. Based on a complex car model, equipped with two safety system seat belts and a shock absorption system, the behavior of the dummy was obtained following frontal collision of the car–dummy assembly. The accelerations were obtained at different points of the dummy’s body and the force that appeared on the seat belts were determined. The Gibbs–Appell method was used to assess the response of the system based on the equations of motion in a problem involving shocks. This paper demonstrates that the revisited old principle of mechanics can offer an interesting and convenient means to obtain results in a short time. FEM and Altair Hyperworks software II was used to model the system. It can be used to determine whether a seat belt is able to work if it has defects during use, such as scratches, cigarette burns or animal bites.
The Power Fractional Calculus: First Definitions and Properties with Applications to Power Fractional Differential Equations
Authors: Lotfi El Mehdi , Zine Houssine , Torres Delfim F. M. , Yousfi Noura
Published: October 2022

Using the Laplace transform method and the convolution theorem, we introduce new and more general definitions for fractional operators with non-singular kernels, extending well-known concepts existing in the literature. The new operators are based on a generalization of the Mittag–Leffler function, characterized by the presence of a key parameter p. This power parameter p is important to enable researchers to choose an adequate notion of the derivative that properly represents the reality under study, to provide good mathematical models, and to predict future dynamic behaviors. The fundamental properties of the new operators are investigated and rigorously proved. As an application, we solve a Caputo and a Riemann–Liouville fractional differential equation.
A Family of Hybrid Stochastic Conjugate Gradient Algorithms for Local and Global Minimization Problems
Authors: Alnowibet Khalid Abdulaziz , Mahdi Salem , Alshamrani Ahmad M. , Sallam Karam M. , Mohamed Ali Wagdy
Published: October 2022

This paper contains two main parts, Part I and Part II, which discuss the local and global minimization problems, respectively. In Part I, a fresh conjugate gradient (CG) technique is suggested and then combined with a line-search technique to obtain a globally convergent algorithm. The finite difference approximations approach is used to compute the approximate values of the first derivative of the function f. The convergence analysis of the suggested method is established. The comparisons between the performance of the new CG method and the performance of four other CG methods demonstrate that the proposed CG method is promising and competitive for finding a local optimum point. In Part II, three formulas are designed by which a group of solutions are generated. This set of random formulas is hybridized with the globally convergent CG algorithm to obtain a hybrid stochastic conjugate gradient algorithm denoted by HSSZH. The HSSZH algorithm finds the approximate value of the global solution of a global optimization problem. Five combined stochastic conjugate gradient algorithms are constructed. The performance profiles are used to assess and compare the rendition of the family of hybrid stochastic conjugate gradient algorithms. The comparison results between our proposed HSSZH algorithm and four other hybrid stochastic conjugate gradient techniques demonstrate that the suggested HSSZH method is competitive with, and in all cases superior to, the four algorithms in terms of the efficiency, reliability and effectiveness to find the approximate solution of the global optimization problem that contains a non-convex function.
The Exact Solutions for Several Partial Differential-Difference Equations with Constant Coefficients
Authors: Xu Hongyan , Xu Ling , Srivastava Hari Mohan
Published: October 2022

This article is concerned with the description of the entire solutions of several Fermat type partial differential-difference equations (PDDEs) μf(z)+λfz1(z)2+[αf(z+c)−βf(z)]2=1, and μf(z)+λ1fz1(z)+λ2fz2(z)2+[αf(z+c)−βf(z)]2=1, where fz1(z)=∂f∂z1 and fz2(z)=∂f∂z2, c=(c1,c2)∈C2, α,β,μ,λ,λ1,λ2,c1,c2 are constants in C. Our theorems in this paper give some descriptions of the forms of transcendental entire solutions for the above equations, which are some extensions and improvement of the previous theorems given by Xu, Cao, Liu, and Yang. In particular, we exhibit a series of examples to explain that the existence conditions and the forms of transcendental entire solutions with a finite order of such equations are precise.
Approximation-Avoidance-Based Robust Quantitative Prescribed Performance Control of Unknown Strict-Feedback Systems
Authors: Feng Yin’an , Bu Xiangwei
Published: October 2022

In this article, we propose a robust quantitative prescribed performance control (PPC) strategy for unknown strict-feedback systems, capable of quantitatively designing convergence time and minimizing overshoot. Firstly, a new quantitative prescribed performance mechanism is proposed to impose boundary constraint on tracking errors. Then, back-stepping is used to exploit virtual controllers and actual controllers based on the Nussbaum function, without requiring any prior knowledge of system unknown dynamics. Compared with the existing methodologies, the main contribution of this paper is that it can guarantee predetermined convergence time and zero overshoot for tracking errors and meanwhile there is no need for any fuzzy/neural approximation. Finally, compared simulation results are given to validate the effectiveness and advantage.
Identification of Location and Camera Parameters for Public Live Streaming Web Cameras
Authors: Zatserkovnyy Aleksander , Nurminski Evgeni
Published: October 2022

Public live streaming web cameras are quite common now and widely used by drivers for qualitative analysis of traffic conditions. At the same time, they can be a valuable source of quantitative information on transport flows and speed for the development of urban traffic models. However, to obtain reliable data from raw video streams, it is necessary to preprocess them, considering the camera location and parameters without direct access to the camera. Here we suggest a procedure for estimating camera parameters, which allows us to determine pixel coordinates for a point cloud in the camera’s view field and transform them into metric data. They are used with advanced moving object detection and tracking for measurements.