bluuebunny/crossref_metadata_2025 huggingface dataset:
Controllable photonic spin hall effect of bilayer graphene
Authors: Qi Song , Da Haixia
Published: April 2022

Bilayer graphene, composed of two layers of monolayer graphene in AB stacking order, has emerged as an alternative platform for atomically thin plasmonic and optoelectronic devices. However, its behavior of photonic spin hall effect remains largely unexplored. In this work, we have theoretically observed that bilayer graphene has two obvious discontinuities but monolayer graphene only has a single step in the spectra of the spin shifts as a function of wavelength at the Brewster angle over the midinfrared frequency range, which enables a possible route of distinguishing monolayer graphene and bilayer graphene. Additionally, the magnitudes and positions of the peak and valley values in the spectrum of spin shifts of bilayer graphene can be tuned by its Fermi energy. We also achieved the enhanced out-of-pane spin shift of the glass-AB stacking bilayer graphene-air structure at both the Brewster angle (

33.55
°

) and the critical angle (

41.31
°

) with the aid of the high order of Laguerre–Gaussian beam. The realization of large and controlled spin shift in bilayer graphene indicates its promising applications in precision measurements and refractive index sensors at the midinfrared frequency region.
External bias dependent dynamic terahertz propagation through BiFeO3 film
Authors: Jana Arun , Rane Shreeya , Roy Choudhury Palash , Roy Chowdhury Dibakar
Published: April 2022

Interactions of terahertz radiations with matter can lead to the realization of functional devices related to sensing, high-speed communications, non-destructive testing, spectroscopy, etc In spite of the versatile applications that THz can offer, progress in this field is still suffering due to the dearth of suitable responsive materials. In this context, we have experimentally investigated emerging multiferroic BiFeO3 film (∼200 nm) employing terahertz time-domain spectroscopy (THz-TDS) under vertically applied (THz propagation in the same direction) electric fields. Our experiments reveal dynamic modulation of THz amplitude (up to about 7% within 0.2–1 THz frequency range) because of the variation in electric field from 0 to 600 kV cm−1. Further, we have captured signatures of the hysteretic nature of polarization switching in BiFeO3 film through non-contact THz-TDS technique, similar trends are observed in switching spectroscopy piezoresponse force microscope measurements. We postulate the modulation of THz transmissions to the alignment/switching of ferroelectric polarization domains (under applied electric fields) leading to the reduced THz scattering losses (hence, reduced refractive index) experienced in the BiFeO3 film. This work indicates ample opportunities in integrating nanoscale multiferroic material systems with THz photonics in order to incorporate dynamic functionalities to realize futuristic THz devices.
Simultaneous transverse loading and axial strain for REBCO cable tests in the SULTAN facility
Authors: Fry Vincent , Estrada Jose , Michael Philip C , Salazar Erica E , Vieira Rui F , Hartwig Zachary S
Published: April 2022

We present the design and first results of an assembly that enables rare earth barium copper oxide (REBCO) superconducting cables—the VIPER cable in this work—to be tested in the SULTAN facility under the simultaneous application of transverse electromechanical loading and axial mechanical strain. The objective is to emulate the loads that a REBCO cable would experience in a three-dimensional coil but in shorter and simpler straight cables, reducing the cost, schedule, and complexity of high-fidelity conductor qualification. The assembly uses two methods for inducing axial strain in the cables. First, hydraulic jacks stretch the assembly and inserted Invar shims lock in up to ∼0.3% axial cable strain on the benchtop at room temperature. Second, the different coefficients of thermal expansion between Invar clamps and the copper cable are exploited to induce an additional ∼0.3% axial strain on the cable when the sample is cooled in the SULTAN test well from room temperature to below 50 K. Finite element analysis modeling shows that the soldered matrix of the VIPER cable transfers approximately 50%–60% of the external cable strain into the REBCO stack. The assembly was successfully employed at SULTAN, enabling two VIPER cables to be cycled 500 times at 382 kN m−1 transverse electromechanical loads with ∼0.5% mechanical strain on the cable (corresponding to ∼0.3% mechanical strain in the REBCO stacks) demonstrating critical current degradation stabilizing after 30 cycles at less than 5% and providing confidence in VIPER cables under realistic high-field magnet conditions.
On the maximum volume of
collapsing structures
Authors: Ostrowski Jan J. , Delgado Gaspar Ismael
Published: April 2022

In many cosmological models, including the ΛCDM concordance model, there exist theoretical upper bounds on the size of collapsing structures. The most common formulations in the literature refer to a turnaround radius in spherical symmetry or a turnaround surface, defined as the zero-expansion boundary separating the outer Hubble flow from the inner flow of a collapsing fluid. In order to access a generic scenario, we propose an improvement of this cosmological test in terms of the maximum volume of the cosmological structures, which is equivalent to a zero-averaged expansion — instead of the zero-local expansion. By combining the Lagrangian perturbations method and the scalar averaging of Einstein's equations, we obtain a maximum volume for a collapse model without any restricting symmetries.
We compare this result with some exact, inhomogeneous solutions and discuss further potential developments.
Variability in quasar light curves: using quasars as standard candles
Authors: Solomon R. , Stojkovic D.
Published: April 2022

A relation between the variational slope, sF
, and the mean absolute magnitude, 〈M〉, in the light curves of 58 spectroscopically confirmed quasars is measured with a dispersion of 0.15dex.
Assuming it holds for quasars in general, not only does this relation add to our working knowledge of quasar variability but it also shows great promise at accurately measuring luminosity distance to a quasar in a model independent way.
An accurate, model independent measure of the luminosity distance would allow quasars to be added to the cosmic distance ladder, easily extending the ladder out far beyond the redshifts accessible to type Ia supernovae where cosmological parameters can be better constrained.
Multi-outlet single photon quantum router between optics and microwave based on a hybrid optomechanical system
Authors: Wang Qiong , Xu Lan
Published: April 2022

A Multi-outlet single photon quantum router between microwave and optical frequency domain is investigated in a system consisting of a superconducting microwave cavity and an optical nanomechanical cavity coupled to a commom nanomechanical resonator (NAMR). It is demonstration that the single-photon of optical frequency can be routed into three different output ports by adjusting microwave power. More importantly, the two output signals can be selected according to microwave power. Meanwhile, we also show that thermal noise will be insignificant for the optical performance of the single-photon router at temperature of the order of 20 mK. Our proposal may have paved a new avenue towards multi-channel router and quantum network.
Classification of salivary gland tumors in optical coherence tomography images based on deep learning
Authors: Wu Guangyi , Yang Zihan , Yuan Zhuoqun , Shang Jianwei , Zhang Jun , Liang Yanmei
Published: April 2022

The diagnosis of oral diseases mainly relies on visual examination by doctors with clinical experience. Histopathological examination is still the gold standard of oral disease diagnosis, but it is invasive and time-consuming. In recent years, optical coherence tomography (OCT) has played an important role in the field of biomedicine with its unique advantages of non-invasiveness, high resolution, real-time and three-dimensional imaging, which can be well applied to the imaging of oral lesions. In this paper, four deep learning (DL) models including LeNet-9, VGG-16, ResNet-18 and ResNet-50 were used to classify oral tumors including two benign and two malignant salivary gland tumors (SGTs), which were imaged by our home-made swept-source OCT. The results indicated that ResNet-18 has the best classification performance, with accuracy, precision, recall (sensitivity), F1 score and specificity all above 98%. Then, we analyzed the visualization process of DL and explored how the DL model extracts features. It is demonstrated that the DL model has a good clinical auxiliary role in the classification of SGTs.
Q-switched fiber laser using a polysulfone membrane enhanced with biosynthesized zinc oxide and titanium dioxide nanoparticles for use as saturable absorber
Authors: Muhammad N A M , Awang N A , Zalkepali N U H H , Mahmud N N H E N , Basri H
Published: April 2022

We presented the polysulfone membrane enhanced with zinc oxide and titanium dioxide (Psf membrane ZnO TiO2) saturable absorber (SA) to induce passively Q-switched erbium-doped fiber laser (EDFL). The Psf membrane ZnO TiO2 was synthesized by the phase inversion technique. To obtain the Psf membrane ZnO TiO2 as a SA, the SA was deposited on the fiber ferrule through a simple exfoliation technique. The modulation depth of the Psf membrane ZnO TiO2 SA was 1.06%, with a saturation intensity of 0.0006 MW cm−2. Stable Q-switched pulses were generated at 1572 nm with a threshold pump power of 59.80 mW after inserting the prepared Psf membrane ZnO TiO2 into the EDFL ring cavity. As the pump power ranges from 59.80 mW to 165.50 mW, the repetition rate increases from 13.05 to 22.61 kHz, while the pulse duration decreases from 76.60 to 44.23 µs. When the pump power reaches a maximum power of 165.50 mW, the corresponding pulse energy and optical signal-to-noise ratio are 19.00 nJ and 61.27 dB, respectively. To our best knowledge, this is the first research utilizing Psf membrane ZnO TiO2 as SA to generate Q-switched pulses. Our research work addresses a new reference for the generation of pulsed laser using Psf membrane ZnO TiO2 and discovers that it has numerous applications in nonlinear optics and ultrafast laser technology, which significantly broadens the barrier of materials for the ultrafast laser techniques.
Toroidal modeling of energetic passing particle drift kinetic effects on tearing mode stability
Authors: Bai Xue , Liu Yueqiang , Hao Guangzhou , Zhang Neng
Published: April 2022

Drift kinetic effects of the neutral beam injection induced passing energetic particles (EPs) on the linear stability of the n = 1 tearing mode (TM) (with the dominant poloidal harmonic of m = 2) are numerically investigated utilizing the MARS-K code (Liu et al 2008 Phys. Plasmas
15 112503), in a tokamak plasma with finite equilibrium pressure and anisotropic thermal transport. In the low plasma pressure regime, it is found that co- (counter-) passing EPs stabilize (destabilize) the TM, agreeing with previous studies. However, as the plasma pressure increases beyond a critical value, it is found that co-passing EPs also destabilize the mode. An in-depth analysis reveals that the net effect of co-passing EPs is a result of competition between the stabilizing contribution from the non-adiabatic drift kinetic terms and the destabilizing contribution associated with adiabatic terms, with the latter becoming more dominant at higher equilibrium pressure. Non-perturbative magnetohydrodynamic-kinetic hybrid modeling also finds that co- and counter-passing EPs modify the TM eigenfunction differently, with the counter-passing EPs enhancing the sideband harmonics. Furthermore, effects of the plasma resistivity and toroidal rotation, as well as that of the equilibrium distribution of EPs in the particle pitch angle space, are also investigated, showing asymmetric results on the TM stability between the co- and counter-passing EPs. The first order finite orbit width correction is found to be stabilizing with co-passing EPs and destabilizing with counter-passing particles. Finally, drift resonances between passing EPs and the TM induce finite frequency to the mode and generate finite net torques inside the plasma, due to the neoclassical toroidal viscosity and the Reynolds stress associated with 3D perturbations.
Traffic classification algorithm using CNN and multi-head attention mechanism for representation learning
Authors: HaoLi
Published: April 2022

With the development of the times, the demand for privacy and security by people, enterprises and government units has become stronger and stronger, driven by this, encrypted traffic has shown a blowout growth, and the security of traffic has become an issue that cannot be ignored. To solve the traffic classification problem, this paper proposes a new traffic classification algorithm based on convolutional neural network and multi-head attention mechanism. In addition, this paper uses a feature engineering method based on representation learning and proposes a discard threshold to improve the quality of data sets obtained by feature engineering. The experimental results show that the algorithm model and the optimized feature engineering method proposed in this paper have good performance on the traffic classification task.
Design of real-time target location algorithm for industrial scene
Authors: Wang Rui , Li Di
Published: April 2022

In order to meet the needs of real-time visual inspection of workpiece targets in industrial environment. In this paper an algorithm for locating workpiece based on edge feature is designed by using image information. At the same time, a model description method based on generalized Hough model and an acceleration strategy based on pyramid search are proposed to solve the problem of insufficient real-time performance of the algorithm. The algorithm is mainly divided into two stages. First, in the offline stage matching model is created. In the online stage, according to the model created in advance, the pyramid based accelerated search strategy is used to match, and the parallel computing method is adopted to calculate the similarity. Experimental results show that the algorithm has strong anti-occlusion and anti-interference ability, and can achieve real-time target positioning under any Angle and coordinates. At the same time, it is proved that the proposed acceleration strategy has better acceleration performance than the existing matching speed method.
Research on hybrid collision detection algorithm based on separation distance
Authors: Hu Anlin , He Yongyi
Published: April 2022

In order to improve the safety of digital subtraction angiography (DSA) equipment in the treatment of cardiovascular diseases, a hybrid collision detection algorithm based on separation distance is proposed by combining oriented bounding box (OBB) algorithm with GJK algorithm. Firstly, according to the structure of DSA equipment, the real-time position of DSA equipment is simulated in robot operating system (ROS); Then, the OBB is used to envelope the moving parts, and then the generated OBB model is applied to GJK algorithm to calculate the separation distance between the moving parts and quickly eliminate the detection pairs outside the threshold; Finally, for the detection pairs within the threshold, the real model is applied with GJK algorithm to calculate the separation distance between the real models and judge whether the models collide. The experimental results show that the average detection time of the hybrid collision detection algorithm is 15ms. The application of the algorithm in practical equipment has achieved good anti-collision effect.
Optimization of flow shop scheduling based on genetic algorithm with reinforcement learning
Authors: Gao Xiaoyu , Yang Shipin , Li Lijuan
Published: April 2022

Genetic algorithm, as a kind of evolutionary algorithm, has the characteristics of easy operation and global search, but its stochasticity is relatively strong and highly susceptible to parameters. When facing a large-scale scheduling problem, a strategy is needed to improve the parameter adaptability to make its solution more effective. Reinforcement learning, as an optimization method, has a strong autonomous learning capability. Therefore, this paper proposes a genetic algorithm based on reinforcement learning, which uses Q-learning to self-learning the crossover probability and improve the generalization ability of genetic algorithm, so as to achieve the solution of large-scale replacement flow shop scheduling problem.
Layered Convolutive Nonnegative Matrix Factorization for Speech Separation
Authors: Yao Wang , Lv Danjv , Huang Xin , Zi Jiali , Gao Mingyuan , Xi Rui , Zhang Yan
Published: April 2022

Nonnegative matrix factorization (NMF) has attracted significant attention for its good performance in single-channel speech separation. The improved algorithms of NMF have become research hotspots. Layered NMF (LNMF), an improved algorithm, can express the source signal more accurately for its multilayer structure. However, LNMF sometimes performs poorly because it ignores the short-term correlation of speech signals. Based on LNMF and the advantages of Convolutive NMF (CNMF), we proposed a Layered Convolutive NMF(LCNMF) algorithm for single-channel speech separation. The LCNMF corporates the multilayer structure into the NMF and expands the convolution of the top-level NMF model. During the training, NMF is used to learn the non-top-level basis matrices, and CNMF is used to learn the top-level basis matrix, then combined with each single-layer of basis matrix. During the prediction, CNMF is used to separate mixed signals. The results on the dataset MIK-1K showed that LCNMF outperformed NMF and LNMF for separating the mixture of single-channel speech signals. LCNMF improved by 0.019, 1.049dB, 1.305dB, and 0.851dB on average compared with NMF, and improved by 0.007, 0.172dB, 0.090dB, and 0.366dB on average compared with LNMF in sort-term objective intelligibility (STOI), Source to Distortion Ratio (SDR), Source to Interference Ratio (SIR) and Source to Artifacts Ratio (SAR)
The empirical analysis of the two types of change points estimation algorithms based on MOSUM
Authors: Zhao Dan , Zi Xuemin
Published: April 2022

In order to make big data play a greater value in financial markets, exploring a new data processing method to improve the ability to detect structural mutations is necessary for the diagnosis and monitoring of financial data and investment direction. This article applies financial time series data, based on the moving sum (MOSUM) statistics, empirically analyzes changes of stock data in Kweichow Moutai in the past ten years. Detecting the feasibility of two multiscale MOSUM algorithms of bottom-up merging and local pruning and visualizing the results by numerical simulation, combined with the fact of the time point, we proved the consistent advantages of the two algorithms in detecting the accuracy and precision of variable points.
Distribution analysis method of key targets based on big data analysis engine for maritime space-time trajectory
Authors: Xu Yue , Yang Zhaozhe , Zhang Xinyu
Published: April 2022

Now a large amount of maritime space-time trajectory data has been collected, but its deep value is unable to be tapped, and the analysis of the distribution of key targets has also encountered bottlenecks. The emergence of big data technology provides us with a solution. This paper proposes a big data analysis engine for maritime space-time trajectory, which makes full use of the data characteristics and improves the overall computing performance of the engine. Based on this engine, a dispersal analysis algorithm for key military targets at sea is proposed to achieve rapid retrieval and dispersal analysis of key marine targets. It has been tested and verified that this engine has excellent performance and can become an efficient query and analysis engine for maritime space-time trajectory data. The key target distribution analysis algorithm has certain research significance and practical value.
Research on flow distribution optimization method of fluid loop system based on generalized Lagrange multiplier method
Authors: Wang Lu , Du Changshuai , He Yufeng
Published: April 2022

With the development of space science in China, the scale of space application payloads continues to increase, and the demand for heat dissipation is also gradually increasing. The fluid loop thermal control is a more efficient way of heat dissipation. In order to optimize the heat dissipation effect of the fluid loop system, this paper establishes a thermal model of the fluid loop system and converts it into a constrained optimization problem. The generalized Lagrange multiplier method is used to calculate the optimal flow distribution strategy, and the influence of the weight is analyzed, which provides a reference for the setting of the weight in practical applications.
Research on Vessel Behavior Modeling Based on MAXSim Simulation Platform
Authors: Guan Weidong , Wang Yumei
Published: April 2022

In a combat simulation system with the ocean as the main environment and vessels as the main platform, the accuracy of the behavior model of the computer-generated force will directly affect the confidence of the simulation system and the efficiency of simulation exercises. As a combat simulation application and development platform, the MAXSim simulation platform based on the Generic Black-Board structure has good secondary development. In order to research the behavior model of vessels and provide general modeling ideas and methods for the maritime combat simulation system, the architecture of the MAXSim simulation platform and the specific functions of the modules are firstly researched. Then, the common methods of behavior modeling by using MAXSim are researched from the level of weapons and equipment. Finally, starting from the system-level development of behavior models, the development methods of actions and conditions of periodic-driven and event-driven behavior models are researched, which improves the accuracy of Computer Generated Forces and supports a good basis for further improving the overall performance of the simulation system.
Comprehensive evaluation about the quality of steel strip based on combination weighting
Authors: Wang DongXu , Cui GuiMei , Pi LiXiang , Liu Wei
Published: April 2022

In order to solve the problem that the quality of steel strip products is rough by manual evaluation, this paper will combine expert knowledge with quality data and establish four evaluation grades by taking surface quality, mechanical properties and dimensional accuracy as evaluation indicators. Analytic hierarchy process (AHP) and entropy weight method are used to confirm the weight of each evaluation indicator. The mechanical properties and dimensional accuracy indicators are confirmed by statistical method to find out the optimal range of indicator parameters, and the indicator deterioration degree is calculated. The deterioration degree of surface quality indicator is confirmed by fuzzy language quantization. The membership degree of each indicator parameter is calculated according to the membership degree function of ridge distribution, and then the fuzzy comprehensive evaluation result matrix of strip quality is obtained by synthesizing each indicator parameter step by step. Finally, the comprehensive score of strip steel quality is obtained by quantizing and summing the evaluation result matrix. The experimental results show that the evaluation result of the model is consistent with the actual data, so it has certain practicability.
A network security situation assessment model based on BP neural network optimized by D-S evidence theory
Authors: Yin Kun , Yang Yu , Yang Jinwei , Yao Chengpeng
Published: April 2022

Traditional networks rely heavily on the distribution of expert experience when assessing complex network security situations, resulting in low assessment accuracy, which has been unable to adapt to the current network security needs of the big data era, and has unavoidable problems such as low efficiency and poor flexibility. In response to these problems, this paper proposes a network security situation assessment method based on D-S evidence theory to optimize neural networks. First establish the CS-BP neural network model, enhance the local search ability of the cuckoo algorithm through conjugate gradient calculation, and then introduce it into the BP neural network to improve the training convergence speed and overcome the local minimum problem; finally, in order to reduce the basic probability distribution (BPA) subjective impact, using DS evidence theory to optimize the CS-BP neural network, determine the degree of impact of each attack, and evaluate the value of the network security situation. The experimental results show that the network situation assessment model of CS-BP neural network optimized based on D-S evidence theory can effectively assess the network security situation in the environment of trusted equipment.
Maritime search and rescue probability of containment model design and simulation
Authors: Zhang Jun , Zhu Yu-zhu
Published: April 2022

In maritime search and rescue operations, in order to improve the success rate of search and rescue, it is necessary to quickly determine the search area after target drift according to the existing accident and Marine environment information, and establish the probability of containment model, to provide information support for subsequent search and rescue planning. AP98 wind pressure drift model is used to model the drowning person at sea, and Monte Carlo simulation is used to simulate the floating situation of the drowning person. A large number of initial hypothetical points were arranged, the factors affecting the drift trajectory were quantitatively analyzed, and the probability density of the hypothetical points in the search area after a certain period of time was counted to form the inclusive probability model. Matlab software is used to test the model in different scenarios to prove the effectiveness of the proposed design model.
Transfer learning with CNN-LSTM model for capacity prediction of lithium-ion batteries under small sample
Authors: Zou Gang , Yan Zhen , Zhang Chengying , Song Lei
Published: April 2022

Nowadays, the application of lithium-ion batteries in various industries including our daily life is increasing, so the evaluation of the safety and reliability of lithium-ion batteries is essential. Assessing the performance of lithium-ion batteries accurately and in real-time has become a more critical research direction. However, in most application scenarios, the problem of small sample is often hidden in performance evaluation tasks of lithium-ion batteries. A model-based transfer learning framework to solve this problem is presented in this paper. Firstly, a CNN-LSTM fusion model is proposed by combining the solid spatial feature extraction capability of convolutional neural network (CNN) and the solid temporal dimensional feature extraction capability of long short-term memory network (LSTM); further, a model-based transfer learning approach is introduced to accomplish the performance evaluation task by pre-training and re-training. The experiments are conducted on NASA’s public dataset of lithium-ion batteries, and multiple sets of comparison experiments are set up. The results show that the method can better solve the small sample problem encountered in the lithium-ion batteries performance evaluation task.
Document-level Stance Detection Method for Specific Targets Based on XLNet
Authors: Fang Yuwei , Zhang Lei , Yang Fei , Zhao Xiaoming , Song Xueqiang , Li Dong , Li Hong , Wang Haizhou
Published: April 2022

Social media and slef-media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. As far as we know, most of the current research focuses on short texts. However, there are relatively few studies on how to collect and extract people’s opinions and stances from long texts such as review articles. This paper proposed a target-specific hierarchical attention model based on the XLNet pre-training model, named XLNet-THAN. The attention mechanism and topic embedding are utilized to obtain the semantic representation of the document. After contrast experiments with other popular text classification models, the result shows that the model proposed in this paper can process document-level text well, and achieves great performance in the task of document-level stance detection.
Time series prediction of cabin temperature based on mechanism model
Authors: Zhang Tanke , Zhang Kanjian , Yao Xu , Wei Haikun , Fang Shixiong , Wang Tao
Published: April 2022

Since the Antarctic scientific research device has been operating in Antarctica for a long time, the power generation cabin needs to continuously provide heat so that its equipment can work within a suitable temperature range. In view of the complexity of the environment and the high cost of equipment, it is difficult to test and evaluate, which can be well solved by numerical simulation test. The thermodynamic mechanism model of the Antarctic redundant cabin is established to simulate the temperature distribution of the Antarctic scientific research power cabin. The temperature data of the mechanism model is combined with the time series algorithm to predict the future high temperature accumulation in the engine room, thereby providing theoretical and data support for the decision-making of the temperature control system of the scientific research device.
Research on the application of OpenPose in escalator safety systems
Authors: Zhao Xuehan
Published: April 2022

This paper describes the application of the OpenPose based human pose detection model in escalator safety systems. The YOLO target detection is used to obtain the position of the human body in the image, and then the human limb information is extracted by OpenPose, and the human joint connection algorithm connects the identified human limb information to obtain the human pose information in the image. The experimental results show that the algorithm can effectively identify the human body posture and can provide timely alarms for dangerous postures, thus improving the safety of escalator riders.
Non-feedback control of Lorenz chaotic system
Authors: Zhao Yang , Sun Shiyan , Wei Ping
Published: April 2022

Non-feedback chaos control of Lorenz chaotic system is realized with non-resonant parametric drive. The control input is a periodic signal whose frequency is far higher than the system characteristic frequency. The feasibility of this scheme is proved by averaging method and Lyapunov method, and the condition of control parameter is obtained. Numerical researches show that the controlled system can reach stable state rapidly even when the control signal is noisy.
Architecture design of battery energy storage coordinated control system based on Multi-Agent mechanism
Authors: Qiu Xuan , Tang Chun-sheng , Dong Wen-hao
Published: April 2022

Energy storage can realize rapid voltage, frequency support and energy adjustment, and is an important guarantee for promoting the efficient consumption of renewable energy and the safe and stable operation of the power system. The flexible adjustment of energy storage is not only affected by its own characteristics, but also closely related to the operation control mode of energy storage. Based on the PQ constant power and virtual synchronization control strategy of the battery energy storage system, this paper constructs the operation architecture of the battery energy storage system based on the Multi-Agent cooperative mechanism, and further gives the energy storage system Multi-Agent cooperative control system’s application scenarios in active frequency modulation and reactive voltage regulation. Research shows that this architecture helps to fully tap the potential of aggregation and regulation of multi-point decentralized energy storage facilities and meet the flexible regulation needs of the power system under the access of high-permeability intermittent renewable energy.
Research on robust control method of permanent magnet synchronous motor
Authors: Wang Zenghui , Zhang Yingfeng , Sun Pan , Sun Jun , Zhang Junhong
Published: April 2022

During the operation of permanent magnet synchronous motor, the traditional vector control method does not match the regulator parameters with the actual parameters due to the disturbance factors such as motor parameter perturbation and load change, resulting in the decline of control performance. The research object of robust control is uncertain system, which has natural advantages for improving the robustness of control system. Firstly, this paper introduces the basic principle and implementation process of robust control, simulates and analyzes the influence of motor parameters on control performance, investigates and summarizes the improvement methods of traditional vector control, and points out the advantages and disadvantages of each scheme; Secondly, the similarities and differences of the improved methods are analyzed and compared; Finally, the development trend of permanent magnet synchronous motor control technology based on robust control is prospected.
Model-based Fault Detection of Rotor Position Sensor Fault in Permanent Magnet Synchronous Motor for Electric Vehicle
Authors: Zhou Nana , Kong Lingchao , Fan Jie , Wang Zhenpo
Published: April 2022

Permanent magnet synchronous motors (PMSMs) are widely used in drive system of electric vehicles (EVs) because of their high efficiency, wild torque range, high power density, etc. However, rotor position, as a very critical parameter for its control strategy, often faces inaccuracies in operation. In this paper, the problem of position sensor fault detection in drive system of EV was studied. The mathematical model of EV with a specific drive system was introduced in discrete form and built in Matlab/Simulink. Based on the field oriented control strategy model, a novel fault detection scheme based on the parity space for the drive system was proposed. The proposed method considered the influence of the closed-loop control to the fault detection result. A residual generator and a residual evaluator were included. The simulation results demonstrated the effectiveness and applicability of the proposed method.
Discriminator Based Resilient Multi-agent Deep Deterministic Policy Gradient Under Uncertain Faulty Agents
Authors: Wang Jing , Huang Hongyun , Hou Jian
Published: April 2022

In recent years, Multi-agent reinforcement learning (MARL) is widely applied in various of fields, to achieve a global goal in a centralized or distributed manner. However, during its application it is crucial to be fault-tolerance as some agents behave abnormal. In this paper, we propose a Resilient Multi-gent Deep Deterministic Policy Gradient (RMADDPG) algorithm to achieve a cooperative task in the presence of faulty agents via centralized training decentralized execution. At training stage, each normal agent observes and records information only from other normal ones, without access to the faulty ones. Meanwhile, a discriminator is generated based on the well-trained actor network to identify each faulty agent via supervised learning. Followed by executing stage, each normal agent selects its action based on local observation according to its actor network and its discriminator, so as to achieve certain system goal. Specifically, RMADDPG offers a scheme to train agents for improved resilience against arbitrary number of faulty agents. Finally, a cooperative navigation experiment is provided to validate the effectiveness of the proposed algorithm.
Optimized realization of Quantum Fourier Transform for domestic DCU accelerator
Authors: Ma Ke , Han Lin , Shang Jian-dong , Xie Jing-ming , Zhang Hao
Published: April 2022

Quantum Fourier Transform (QFT) is a key part of quantum algorithms such as Shor algorithm, Grover algorithm, and quantum phase estimation algorithm. The “Songshan” supercomputer system adopts the heterogeneous architecture of domestic CPU+DCU. The simulation implementation of the quantum Fourier transform on the “Songshan” supercomputer can promote the research and optimization of related quantum algorithms. Based on the HIP heterogeneous programming model, this paper first maps the phase shift transformation R gate operation of the QFT transformation to the DCU accelerator, and initially constructs the heterogeneous implementation of QFT on the “Songshan” supercomputer; then, the Hadamard gate transformation operation originally implemented on the host side was implemented on the DCU, which greatly reduces the communication overhead caused by the quantum information data transmission between the CPU and the DCU device; finally, the quantum logic gate is analyzed, and based on the operation of the phase shift transformation R gate on the probability amplitude information in the simulation algorithm, an optimization scheme to improve the activity of the DCU thread is proposed. During the experimental verification process, the realized 28-qubit quantum Fourier transform optimization has achieved 13.440 speedup compared with the serially realized QFT simulation on the CPU. The basic quantum gate optimization method based on DCU acceleration equipment in this paper can provide a reference for the efficient simulation of other quantum algorithms on domestic heterogeneous platforms.
An Improved Method of Sub-band Peak Energy Detection
Authors: Lou Wanxiang , Fu Qiren , Feng Kai
Published: April 2022

In order to solve the problems that the quantity of false targets is huge and the ability of target detection is poor in the conventional method of sub-band peak energy detection(SPED), an improved method of SPED is proposed. The method screens peaks based on the levels of background noise in every sub-band and then eliminates the noise peaks. These measures reduce the quantity of false targets and improve the ability for target detection. Simulation result and experimental data analysis prove its validity and superiority.
Research on HPLC Experimental Platform Component Scheme
Authors: Liang Yutu , Li Bo , Li Xingnan , Deng Xiaozhi , Lu Jiangang , Shi Zhan
Published: April 2022

Power line communication is a kind of communication mode which uses power line as transmission medium and carries on data transmission and information exchange through carrier. Power line has wide distribution, high economy, high transmission rate and good reliability. However, as a communication mode without special transmission media, there is still a lack of power line communication experimental platform in the industrial field. The communication experiment platform is composed of power line carrier field test platform, protocol consistency test platform, interconnection test platform and courts simulation platform. In this paper, the design scheme of the experimental platform is proposed from the aspects of hardware and software respectively. In terms of technology, the requirements of reliability, efficiency, enforceability and practicability are fully considered.
Complete Coverage Path Planning of Underwater Wall-climbing Cleaning Robot
Authors: Wang Peng , Li Qian , Zhang Lei , Xu Yulong
Published: April 2022

Aiming at the special working environment of the underwater wall-climbing cleaning robot and the low efficiency and poor universality of the complete coverage path planning algorithm in this environment, a complete coverage path planning algorithm based on enclosing window is proposed. Firstly, the robot’s floating system is briefly introduced. Then, the grid map method is used to model the environment, and the traversal priority order of the single-layer enclosing window is determined according to different grid maps. Finally, when the robot falls into the dead corner, the diffusible water-ripple enclosing window search algorithm is used to find the escape grid, then the robot escapes from dead corner and continues to traverse until all traversable grids are traversed. The simulation results show that the method in this paper can not only achieve 100% full coverage and 0% repetition rate, but also reduce path length. It has higher planning efficiency in underwater environments, and has better applications.
Identification of voltage sag source based on Improved GRA
Authors: Li Ma , Dong Pengyuan
Published: April 2022

Accurate identification of fault source causing voltage sag is of great significance to the governance and further research of voltage sag. In this paper, a voltage sag source identification method based on improved grey correlation analysis (GRA) is proposed. In view of the deficiency that the traditional GRA only considers the similarity, the proposed method considers the geometric area and slope of sequence into the calculation of correlation coefficient, so that the improved GRA has attributes of both sequence similarity and data closeness. Eight features are extracted from six common types of sag waveforms to form standard and test sequence; The improved GRA is used to calculate the correlation degree between the test and standard sequences, then the identification of sag source can be realized. Simulation model is built in MATLAB. Results show that this proposed method can effectively identify the voltage sag source. Compared with the traditional GRA, this method has higher identification accuracy and stronger identification ability.
Research on Process Control Strategy of Asphalt Tank Inventory Based on Dynamic Programming Algorithm in Post Epidemic Era
Authors: Pang Dong
Published: April 2022

Based on the background of post epidemic era, starting with the traditional industrial inventory process control, this paper discusses the dynamic inventory process control strategy under the asphalt tank inventory mode, and analyzes the intelligent inventory mode under the trend of intelligent logistics through the dynamic programming method, so as to provide reference for the development direction of intelligent inventory of unconventional storage materials such as chemical industry and energy. At the same time, with the recurrence of short-term epidemic and the dilemma of low return to work and production rate, through the integration and interconnection of mobile communication, intelligent analysis, digital management and other technologies with all links, the strategies of inventory dynamic control, inventory risk control, improving inventory efficiency and optimizing inventory cost are put forward.
Rapid Reduction Dimensionality Transfer Alignment Method Based on CKF for SINS of Ship-borne Aircraft with GNSS Assistance
Authors: Wang Yongjun , Xu Jingshuo , Gao Fengjuan
Published: April 2022

Aiming at the problem of rapid transfer alignment of inertial navigation system in shipborne environment with large azimuth misalignment angle, a dimension reduction fast transfer alignment scheme of shipborne aircraft inertial navigation system based on CKF algorithm is designed. Firstly, the nonlinear attitude error equation, velocity error equation and position error equation of the Global Navigation Satellite System (GNSS) assisted strapdown inertial navigation system in the case of large azimuth misalignment are analyzed. Secondly, the nonlinear dimensionality reduction rapid transfer alignment state equation and measurement equation are derived. Finally, the CKF algorithm is simulated and verified, and compared with EKF algorithm and UKF algorithm. The simulation results show that the reduced dimension fast transfer alignment method of shipborne aircraft inertial navigation system based on CKF algorithm can complete the estimation of attitude error angle in 80s, with horizontal estimation error of 1’ and azimuth error of 4’, and the estimation accuracy is equivalent to EKF and UKF, with less calculation and better rapidity.
SMX and front-end board tester for CBM readout chain
Authors: Zabołotny W.M. , Emschermann D. , Gumiński M. , Kruszewski M. , Lehnert J. , Miedzik P. , Poźniak K. , Romaniuk R. , Schmidt C.J.
Published: April 2022

The STS-MUCH-XYTER (SMX) chip is a front-end ASIC dedicated to the readout of Silicon Tracking System (STS) and Muon Chamber (MUCH) detectors in the Compressed Baryonic Matter (CBM) experiment. The production of the ASIC and the front-end boards based on it is just being started and requires thorough testing to assure quality. The paper describes the SMX tester based on a standard commercial Artix-7 FPGA module with an additional simple baseboard. In the standalone configuration, the tester is controlled via IPbus and enables full functional testing of connected SMX, front-end board (FEB), or a full detector module. The software written in Python may easily be integrated with higher-level testing software.
BLEMAB European project: muon imaging technique applied to blast furnaces
Authors: Bonechi L. , Ambrosino F. , Andreetto P. , Bonomi G. , Borselli D. , Bottai S. , Buhles T. , Calliari I. , Checchia P. , Chiarotti U. , Cialdai C. , Ciaranfi R. , Cimmino L. , Ciulli V. , D’Alessandro R. , D’Errico M. , Ferretti R. , Finke F. , Franzen A. , Glaser B. , Gonzi S. , Liu Y. , Lorenzon A. , Masone V. , Nechyporuk O. , Pezzato L. , Rangavittal B.V. , Ressegotti D. , Saracino G. , Sauerwald J. , Starodubtsev O. , Viliani L.
Published: April 2022

The BLEMAB European project (BLast furnace stack density Estimation through on-line Muon ABsorption measurements), evolution of the previous Mu-Blast European project, is designed to investigate in detail the capability of muon radiography techniques applied to the imaging of a blast furnace’s inner zone. In particular, the geometry and size of the so called “cohesive zone”, i.e. the spatial zone where the slowly downward moving material begins to soften and melt, that plays an important role in the performance of the blast furnace itself. Thanks to the high penetration power of the natural cosmic ray muon radiation, muon transmission radiography represents an appropriate non-invasive methodology for imaging large high-density structures such as blast furnaces, whose linear size can be up to a few tens of meters. A state-of-the-art muon tracking system, whose design profits from the long experience of our collaboration in this field, is currently under development and will be installed in 2022 at a blast furnace on the ArcelorMittal site in Bremen (Germany) for many months. Collected data will be exploited to monitor temporal variations of the average density distribution inside the furnace. Muon radiography results will also be compared with measurements obtained through an enhanced multipoint probe and standard blast furnace models.
Performance of the ALICE upgraded inner tracking system
Authors: Liu J.
Published: April 2022

Major upgrades of the ALICE (A Large Ion Collider Experiment) detector are underway and will be completed during the LHC long shutdown 2 in order to start operation in 2022 for LHC run 3. One key part of this upgrade is the new Inner Tracking System (ITS2), a full silicon-pixel detector constructed entirely with CMOS monolithic active pixel sensors. The upgraded ITS2 detector consists of three inner layers (50 μm thick sensors) and four outer layers (100 μm thick sensors) covering 10 m2 and containing 12.5 billion pixels with a pixel pitch of 27 μm × 29 μm. Compared with the silicon tracking system used during the LHC run 1 and run 2, the increased granularity, the very low material budget (0.35% X
0/layer in the inner barrel) as well as a smaller beam pipe radius, will result in a significant improvement of impact-parameter resolution and tracking efficiency. The assembly of the detector and services finished in December 2019, and the detector was fully installed in the ALICE experiment in May 2021. A comprehensive commissioning phase (on the surface) was completed in December 2020 to validate the detector performance. In this paper, an overview of the design and construction, as well as the performance of the ITS2 studied from the on-surface commissioning will be discussed in detail.