bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ultra-wideband and high-performance microwave vortex beam generator based on single-layer Pancharatnam–Berry metasurface with stacked-arc-shaped meta-atoms
Authors: Dong Hexin , Yang Xiaoqing , Su Piqiang , Gu Dezhen , Long Shanshan , Li Meng , Zhai Xiaoyu 
Published: July 2022

Abstract
Vortex beam carrying orbital angular momentum (OAM) reveals essential application potential in regimes ranging from communication to high-resolution imaging due to the capability to expand channel capacity. However, many current methods for generating vortex beams suffer from narrow bandwidth, low efficiency, and high profile, which undoubtedly will limit their practical application. In this paper, a microwave ultra-wideband single-layer Pancharatnam–Berry metasurface is proposed to convert incident plane waves into vortex waves. The proposed novel stacked-arc-shaped meta-atoms of the metasurface can realize high-performance spin angular momentum-OAM conversion with the efficiency of more than 83% within a considerable bandwidth from 5.85 to 19.56 GHz (fractal bandwidth of 108%), which achieves high-efficiency operation within a wider bandwidth compared to some other wideband metasurface-based vortex beam generator. Three metasurface prototypes for generating vortex beams with the topological charges of l = ±2, −1, −3 respectively are designed. In addition, a metasurface for generating a vortex beam with the topological charge of l = ±2 is fabricated as an example to demonstrate the feasibility and high performance of our vortex beam generator. The generated vortex beams with the mode order of ±2, −1, −3 both in the near-field and the far-field zone are simulated theoretically and the generated vortex beam with the mode order of l = −2 is measured experimentally. The results of the simulation and experiment coincide with each other, which verified the proposed vortex beam generation method successfully. The proposed vortex beam generator is valuable and user-friendly in practical application because of the advantages of low profile, ultra-wide bandwidth, high efficiency, and easy fabrication.
Evaluation of the blackbody radiation shift of an Yb optical lattice clock at KRISS
Authors: Heo Myoung-Sun , Kim Huidong , Yu Dai-Hyuk , Lee Won-Kyu , Park Chang Yong 
Published: July 2022

Abstract
As optical clocks are improved to reach the frequency uncertainty below the 10−17 level, the frequency shift due to the blackbody radiation (BBR) has been one of the major systematic effects hindering further improvement. To evaluate the BBR shift of an Yb optical lattice clock at KRISS, we installed an in-vacuum BBR shield and made radiation thermometry using a black-coated-sphere thermal probe. After we quantitatively measured the conduction loss of the thermal probe and the effects of all the external radiation sources, we determined the temperature at the atom trap site with an uncertainty of 13 mK, which corresponds to an uncertainty of 0.22 mHz in the clock frequency (a fractional frequency of 4.2 × 10−19). The total uncertainty of the BBR shift including the atomic response is 9.5 × 10−19.
New impedance bridge at LNE for the two terminal-pair AC resistor calibration up to 20 kHz
Authors: Ouameur Mohamed , Vasconcellos Renata , Agazar Mohamed , Thévenot Olivier , Patois Emmanuel 
Published: July 2022

Abstract
This paper presents the evolution of LNE’s impedance bridge (Wheatstone bridge) used to calibrate AC resistors defined in the two terminal-pair configuration. A new automated Wheatstone bridge, based on four resistances, was developed to operate in the full complex plane for resistances ranging from 400 Ω to 2 MΩ and for frequencies up to 20 kHz. A commercial dual precision arbitrary waveform generator supplies the bridge and PXI modules achieve the balance. Phase and frequency synchronization solutions between the arbitrary waveform generator source and the PXI modules were used. The choice of these instruments allows the bridge to be easily automated and to benefit from recent progress in the development of digital-analog converters, in particular those with high frequency sampling rates and resolution. The bridge is mainly dedicated to realize the periodic calibration of LNE’s standard resistors, used for routine customer calibrations. The traceability of LNE’s standard resistors is ensured, firstly, by the calibration of a 1 kΩ resistor by comparison to a calculable resistor up to 20 kHz. Then, this resistor is used as a reference element to ensure the traceability of all LNE’s standard resistors using the new bridge. The validation was done by comparing the results obtained, for some resistors with very low drift, with those obtained during calibrations performed in 1983, 1995 and 2009. For resistors of 1 kΩ in a 1:1 ratio, the expanded uncertainty of series resistance variation and argument are, respectively, less than 1.3 µΩ Ω−1 (k = 1) and 1.2 µrad (k = 1), up to 20 kHz.
Flexible coding scheme for robotic arm control driven by motor imagery decoding
Authors: Ai Qingsong , Zhao Mengyuan , Chen Kun , Zhao Xuefei , Ma Li , Liu Quan 
Published: July 2022

Abstract

Objective. Brain computer interface (BCI) technology is an innovative way of information exchange, which can effectively convert physiological signals into control instructions of machines. Due to its spontaneity and device independence, the motor imagery (MI) electroencephalography (EEG) signal is used as a common BCI signal source to achieve direct control of external devices. Several online MI EEG-based systems have shown potential for rehabilitation. However, the generalization ability of the current classification model of MI tasks is still limited and the real-time prototype is far from widespread in practice. Approach. To solve these problems, this paper proposes an optimized neural network architecture based on our previous work. Firstly, the artifact components in the MI-EEG signal are removed by using the threshold and threshold function related to the artifact removal evaluation index, and then the data is augmented by the empirical mode decomposition (EMD) algorithm. Furthermore, the ensemble learning (EL) method and fine-tuning strategy in transfer learning (TL) are used to optimize the classification model. Finally, combined with the flexible binary encoding strategy, the EEG signal recognition results are mapped to the control commands of the robotic arm, which realizes multiple degrees of freedom control of the robotic arm. Main results. The results show that EMD has an obvious data amount enhancement effect on a small dataset, and the EL and TL can improve intra-subject and inter-subject model evaluation performance, respectively. The use of a binary coding method realizes the expansion of control instructions, i.e. four kinds of MI-EEG signals are used to complete the control of 7 degrees of freedom of the robotic arm. Significance. Our work not only improves the classification accuracy of the subject and the generality of the classification model while also extending the BCI control instruction set.
ICoRD: iterative correlation-based ROI detection method for the extraction of neural signals in calcium imaging
Authors: Kang Seongtak , Park Jiho , Kim Kyungsoo , Lim Sung-Ho , Kim Samhwan , Choi Joon Ho , Rah Jong-Cheol , Choi Ji-Woong 
Published: July 2022

Abstract

Objective. In vivo calcium imaging is a standard neuroimaging technique that allows selective observation of target neuronal activities. In calcium imaging, neuron activation signals provide key information for the investigation of neural circuits. For efficient extraction of the calcium signals of neurons, selective detection of the region of interest (ROI) pixels corresponding to the active subcellular region of the target neuron is essential. However, current ROI detection methods for calcium imaging data exhibit a relatively low signal extraction performance from neurons with a low signal-to-noise power ratio (SNR). This is problematic because a low SNR is unavoidable in many biological experiments. Approach. Therefore, we propose an iterative correlation-based ROI detection (ICoRD) method that robustly extracts the calcium signal of the target neuron from a calcium imaging series with severe noise. Main results. ICoRD extracts calcium signals closer to the ground-truth calcium signal than the conventional method from simulated calcium imaging data in all low SNR ranges. Additionally, this study confirmed that ICoRD robustly extracts activation signals against noise, even within in vivo environments. Significance. ICoRD showed reliable detection from neurons with a low SNR and sparse activation, which were not detected by conventional methods. ICoRD will facilitate our understanding of neural circuit activity by providing significantly improved ROI detection in noisy images.
Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer’s disease
Authors: Cao Jun , Zhao Yifan , Shan Xiaocai , Blackburn Daniel , Wei Jize , Erkoyuncu John Ahmet , Chen Liangyu , Sarrigiannis Ptolemaios G 
Published: July 2022

Abstract

Objective. This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram, a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer’s disease (AD). Approach. The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a revised Hilbert–Huang transformation (RHHT) cross-spectrum as a biomarker, the support vector machine classifier is used to distinguish AD from healthy controls (HCs). Main results. With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD. Significance. Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.
Resource Operation Intelligent Scheduling Algorithm and System Based on Integrated 5G Public Private Network Communication Network
Authors: Lu Huiying , Yan Xin 
Published: July 2022

Abstract
With the development of the economy, users’ demands for communication systems continue to increase. At the same time, due to the development of technologies such as the Internet of Things, big data, and cloud computing, Internet users’ requirements for communication systems are also increasing. Network application scenarios such as in-vehicle networking, transmission of ultra-high-definition video, and real-time data transmission are emerging one after another. The breakthrough development of 5G communication technology has opened a chapter in the 5G era and has become the key to solving the problem of cellular communication technology. However, in the architecture of advanced networks, it is still a big problem to solve the problem of load balancing and realize the reasonable scheduling of network resources. At the same time, it has also become a hot spot of current research. This paper studies the resource allocation problem in the 5G communication network. Based on the analysis of its communication principle, the communication simulation system in the corresponding scenario is built and improved. At the same time, in order to solve the resource allocation under the new SDN architecture in the core network To solve the problem, a slice resource allocation scheme based on reinforcement learning is designed, and a slice resource allocation algorithm based on DQN (Deep Q-Network) is proposed. By testing the system functions, the superiority of the proposed algorithm in relevant scenarios is verified.
Classification Model of 3D Point Cloud Based on Linked Adaptive Graph Convolution
Authors: Yu Ying , Zhang Jun 
Published: July 2022

Abstract
A three-dimensional point cloud classification model LAGCN based on adaptive graph convolution is proposed to solve the problem that PointNet only extracts feature information from isolated points but not from neighborhood structure. Firstly, the three-dimensional point cloud is transformed into undirected graph structure, which is used to obtain the neighborhood information of point cloud adaptively, and the classification accuracy is improved by incorporating residual structure. In the classification experiment, this paper trains and tests on ModelNet40 dataset, and compares the classification accuracy with VoxNet, PointNet and DGCNN models. The classification accuracy of this model is better than that of the above models.
Research on Maximum Power Tracking of Grey Wolf Optimization Algorithm Introducing Levy Flight
Authors: He Sijiang , Li Hao 
Published: July 2022

Abstract
In the case of partial shadow, the power-voltage characteristic curve of photovoltaic array output will show multi-peak phenomenon, the traditional maximum power point tracking method is easy to fall into the local maximum power point, resulting in the loss of output power. Aiming at the common problems of traditional swarm intelligence optimization algorithms, such as slow convergence speed, large oscillation amplitude, and easy to fall into local optimum, a control method of gray wolf optimization algorithm with Levy flight was proposed. The algorithm uses GWO’s excellent ability of fast convergence speed and high solution accuracy to quickly converge to the vicinity of the maximum power point, and then uses CSA to achieve a stable state near the maximum power point. The power fluctuation is smaller. The simulation statistics show that compared with the traditional The improved gray wolf optimization algorithm has higher solution accuracy, shorter tracking time, and improves the output efficiency of the photovoltaic array.
Research on Dimension Reduction Algorithm of Prediction Model Input Variables Based on Principal Component Analysis
Authors: Shi Lei 
Published: July 2022

Abstract
The selection of input variables of neural network prediction model is very important. If there are too many input variables, the input layer nodes of the network will increase and we need an exponential number of training samples which can easily lead to “disaster of dimension”. According to the characteristics of so many influencing factors of cervical spinal cord injury and intertrochanteric fracture, principal component analysis is used to screen the input variables of neural network prediction model in this paper. And in this method, the principal component factor with the greatest contribution is selected as the input node of the neural network. Then it not only comprehensively considers the various information contained in the input variables, but also compresses the number of input variables to the greatest extent, so as to simplify the topology of the neural network. At the same time, the training time is greatly shortened. Simulation results show that the method is effective and feasible.
Unknown network attack detection method based on reinforcement zero-shot learning
Authors: Wang Hui , Wang Yifeng , Guo Yuanbo 
Published: July 2022

Abstract
With the increasing growth of zero-day attacks, traditional machine learning-based network intrusion detection systems (NIDS) are difficult to cope with a large amount of unknown network attacks without labeled data. To this end, this paper proposes a new unknown network attack detection method, which combines zero-shot learning algorithm with reinforcement learning algorithm. First, the feature vector in traffic data and the semantic vector in threat intelligence are encoded in the hidden space by variational autoencoder, so that the two modalities are matched in the hidden space, and then the hidden features of known class and unknown class are used to input into the training classifier to get the predicted of the labeling results, and then use asynchronous advantage actor-critic based algorithm to learn and correct the attack results that are misclassified by the variational autoencoder to improve the accuracy of detection. Experiments prove that the proposed method in this paper achieves more than 95% accuracy on four unknown network attack classes that are difficult to identify, namely MAILBOMB, U2R, POD, and SNAPATTACK, which illustrates the effectiveness and feasibility of the method.
Optical Remote Sensing Image Object Detection Model Based On Improved YOLOv5s
Authors: Zhou Qikai , Zhang Wei , Li Ruizhi , Wang Jin , Zhen Shuhui , Niu Fu 
Published: July 2022

Abstract
Aiming at the problem of error and omission caused by the complex image background and the high similarity between the remote sensing objectt and the background in remote sensing image detection, an improved model of remote sensing image detection based on YOLOv5s is proposed. The dual attention mechanism of channel and space is added to the convolutional layer of the trunk feature extraction network and the feature fusion network, which enhances the features with high correlation of the objectt to be detected and suppresses the features with low correlation, and improves the ability of the model to extract the features of remote sensing objectts in a complex background, so as to improve the detection accuracy of the algorithm. In this study, a comparative experiment was conducted on the NWPU VHR-10 dataset, and the results showed that the average accuracy of the proposed model was 94% when the dataset was turned over and the ratio was 0.5, which was 2.3% higher than that of the original YOLov5s, which verified the effectiveness of the proposed method.
Bi-LSTM-attention Based on ACNN Model for Disfluency Detection
Authors: Tian Xin , Fang Bei , He Juhou , He Xiuqing 
Published: July 2022

Abstract
Disfluencies are self-corrections in spontaneous speech, including filled pauses, repetitions, repairs and false starts. The task of disfluency detection is to identify these disfluencies phenomena in spoken speech and make them consistent with written text. Recent research has applied machine learning and deep learning approaches to disfluency recognition and classification. In addition, long distance dependence is one of the core issues in disfluency detection. In order to solve this problem, most existing approaches had combined plenty of hand-crafted features and words as input. However, hand-crafted model features need lots of time and energy. Some studies reduced the dependence on hand-crafted features, but they ignored the dependence between long sentences. In this article, we consider disfluency detection as a problem of sequence labeling, then apply Bi-LSTM and attention mechanisms to disfluency detection. In particular, on the basis of obtaining the dependency of rough copy by Auto-correlational neural network (ACNN), we improve the ACNN model to handle the long-term dependency, so that we can better capture dependency between words and words. In other words, our method can not only find the rough copy relationship between sentences without additional hand-crafted features, but also obtain the dependency relationship between long sentences. Experiments on commonly used English Switchboard test sets show that our approach achieves good performance compared to previous models that used texts only without other hand-crafted features.
Detection of road crack defects based on improved YOLOv5s model
Authors: Zhang Pingjun , Li Wenwen , Weng Yue 
Published: July 2022

Abstract
In order to meet the requirements of road crack defect detection accuracy and detection speed, a road crack defect detection algorithm based on the improved YOLOv5s network model is proposed. First, the Backbone area of the YOLOv5s model is improved, and the self-correcting convolution SCConv module is embedded in the Backbone area as a feature extraction network, which can better integrate multi-scale feature information; secondly, the Neck area of the YOLOv5s model is improved. SE-Net attention The module is added to the Neck area to improve the retention of defect feature information and strengthen the learning of feature information; finally, the Prediction area of the YOLOv5s model is improved to delete the 80×80 feature map branch suitable for detecting objects of smaller size, thereby reducing the complexity of the model and improving the Detection speed, the improved YOLOv5s network model is obtained. Using the improved YOLOv5s model to train and test on the road crack defect dataset, the experiments show that the improved YOLOv5s network models precision (P) is increased by 4.91%, the recall rate (R) is increased by 4.79%, and the average precision rate (mAP) is increased by 5.42%, the model size is reduced by 1.04MB, indicating that the improved YOLOv5s model has improved detection accuracy and detection speed.
Research on Rule Matching Model Based on Spark
Authors: Xiong Shiyong , Jiang Kaiwen , Wu Rongsen 
Published: July 2022

Abstract
Rule engines are widely used in engineering and academic fields because they can flexibly separate facts and rules from the rule matching process. Since the rule matching process is very time-consuming, and the traditional rule matching uses single-computer operation when many facts and rules exceed the computer’s memory and computational capacity limit, it will cause the application to crash and paralyze. To solve the above problems, this paper investigates the Spark framework and Rete algorithm to take advantage of Spark’s in-memory computation to alleviate the time-consuming problem of the traditional rule matching process. A high-performance distributed rule matching model is designed by combining the actual rule matching scenario and the development process. In addition, according to the form of rules and facts in the actual scenario, this paper effectively divides the matching process and improves the scalability of the rule matching model.
Discrete particle swarm algorithm with Q-Learning for solving flexible job shop scheduling problem with parallel batch processing machine
Authors: Song Libo , Liu Chang , Shi Haibo 
Published: July 2022

Abstract
For the flexible job shop scheduling problem with parallel batch processing machines (FJSP-PBPM), the mathematical model of the problem is defined, and an improved enhanced discrete particle swarm algorithm is proposed to solve this problem. In terms of global search, an improved discrete particle swarm algorithm is adopted, and effective particle velocity and position update formulas are designed; in the local search, in order to speed up the convergence speed and avoid local convergence, it is integrated into the q-learning method in reinforcement learning. The state space, action space, reward function, and Q-table update methods that meet the scheduling problem are designed, and the proposed hybrid algorithm achieves an effective balance between global search and local search capabilities. At the same time, according to the characteristics of FJSP-PBPM, effective process sequence scheduling rules and batch machine selection rules are designed respectively to improve the search ability of the algorithm. Through the standard FJSP benchmark problem and the production data of a transformer manufacturer that meets the characteristics of the FJSP-PBPM problem, the validity and feasibility of the proposed algorithm are verified.