bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification, cost evaluation, and prioritization of urban traffic congestions and their origin
Authors: Serok Nimrod , Havlin Shlomo , Blumenfeld Lieberthal Efrat 
Published: July 2022

AbstractThe increasing urbanization in the last decades results in significant growth in urban traffic congestion around the world. This leads to enormous time people spent on roads and thus significant money waste and air pollution. Here, we present a novel methodology for identification, cost evaluation, and thus, prioritization of congestion origins, i.e., their bottlenecks. The presented work is based on network analysis of the entire road network from a global point of view. We identify and prioritize traffic bottlenecks based on big data of traffic speed retrieved in near-real-time. Our approach highlights the bottlenecks that have the most significant effect on the global urban traffic flow. We follow the evolution of every traffic congestion in the entire urban network and rank all the congestions, based on the cost they cause (in Vehicle Hours units). We show that the macro-stability that represents the seeming regularity of traffic load both in time and space, overshadows the existence of meso-dynamics, where the bottlenecks that create these congestions usually do not reappear on different days or hours. Thus, our method enables to identify in near-real-time both recurrent and nonrecurrent congestions and their sources.
Machine learning for real-time aggregated prediction of hospital admission for emergency patients
Authors: King Zella , Farrington Joseph , Utley Martin , Kung Enoch , Elkhodair Samer , Harris Steve , Sekula Richard , Gillham Jonathan , Li Kezhi , Crowe Sonya 
Published: July 2022

AbstractMachine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital’s emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68–0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.
Nanoscale physical unclonable function labels based on block copolymer self-assembly
Authors: Kim Jang Hwan , Jeon Suwan , In Jae Hyun , Nam Seonho , Jin Hyeong Min , Han Kyu Hyo , Yang Geon Gug , Choi Hee Jae , Kim Kyung Min , Shin Jonghwa , Son Seung-Woo , Kwon Seok Joon , Kim Bong Hoon , Kim Sang Ouk 
Published: July 2022

AbstractHardware-based cryptography that exploits physical unclonable functions is required for the secure identification and authentication of devices in the Internet of Things. However, physical unclonable functions are typically based on anticounterfeit identifiers created from randomized microscale patterns or non-predictable fluctuations of electrical response in semiconductor devices, and the validation of an encrypted signature relies on a single-purpose method such as microscopy or electrical measurement. Here we report nanoscale physical unclonable function labels that exploit non-deterministic molecular self-assembly. The labels are created from the multilayer superpositions of metallic nanopatterns replicated from self-assembled block copolymer nanotemplates. Due to the nanoscale dimensions and diverse material options of the system, physical unclonable functions are intrinsically difficult to replicate, robust for authentication and resistant to external disturbance. Multiple, independently operating keys—which use electrical resistance, optical dichroism or Raman signals—can be generated from a single physical unclonable function, offering millisecond-level validation speeds. We also show that our physical unclonable function labels can be used on a range of different surfaces including dollar bills, human hair and microscopic bacteria.
A dither‐less bit weight digital background calibration for bridge capacitor digital‐to‐analog converter successive approximation register analog‐to‐digital converters
Authors: Lee Soohoon , Kim Jintae 
Published: July 2022

AbstractThe authors present a dither‐less background digital bit weights calibration method for split‐capacitor digital‐to‐analog converter (CDAC) successive approximation register (SAR) analog‐to‐digital converters (ADCs) to improve non‐linearities caused by capacitor mismatch and bridge‐capacitor inaccuracy errors in a split‐CDAC. By using a digital pseudo‐random number (PN) generator and paired comparators with opposite offsets, a dither‐less background calibration quickly reaches the target signal‐to‐noise‐and‐distortion ratio (SNDR) within 5 × 104 samples for various process uncertainties. The simulated performance using a behavioural 6‐bit + 6‐bit ADC with 1‐bit redundancy for various random offset and capacitor mismatches shows that the SNDR increases from 45.7 to 65.7 dB after the calibration.
Supply-side drivers of phosphorus emissions from phosphorus supply chains in China
Authors: Zhang Jing , Zhong Qiumeng , Yu Yadong , Jetashree  , Yang Xuechun , Feng Cuiyang , Li Hui , Liang Sai 
Published: July 2022

ABSTRACT
Human activities interfere with natural Phosphorus (P) cycles by introducing increased levels of P emissions to air, land, and water. A supply-side analysis of P supply chains and associated P emissions can provide insights into underlying economic activities and transitions responsible for human-induced P emissions. Taking the mainland China as the case, this study constructs time-series physical input-output tables to describe P supply chains during 1949–2012. Subsequently, it identifies critical products and influencing factors of P supply chains enabling P emissions to the environment (including air, land, and water) from the supply perspective. The results show that phosphate rock, an important initial supplier of P from natural environment to China’s P supply chain, was responsible for 86% of P emissions in 2012. Moreover, food crops and livestock are important initial suppliers of P from soil to China’s P supply chain, through cultivation and pasturing, respectively. From 1949 to 2012, the change in primary input level was the largest driver of P emission increments, followed by changes in population, emission intensity, and primary input structure. On the contrary, changes in production structure reduced P emissions. These findings could support supply-side policy decisions on P emission control.
Simulation study on an atmospheric pressure plasma jet interacting with a single fiber: effects of the fiber’s permittivity
Authors: Kong Xianghao , Xue Shuang , Li Haoyi , Yang Weimin , Martynovich E F , Ning Wenjun , Wang Ruixue 
Published: July 2022

Abstract
Polymer fiber surface modification by low temperature plasma has received much attention in recent years. The plasma kinetic behavior and reactive species distribution can be totally different with the existence of fiber. In this work, a 2D axisymmetric fluid model is established to study the interaction between an atmospheric pressure plasma jet and a single fiber with different relative permittivity (ε
r = 1.5 and 80, respectively). Division of the ionization wave is observed when approaching the fiber, followed by full wrapping of the fiber surface. Afterward, the ionization wave travels across the fiber and continues to propagate forward. Significant effects induced by the variation of the fiber’s permittivity are observed. For the fiber with ε
r = 1.5 the high electric field (EF) region shifts from the south pole (SP; closer to the jet tube) to the north pole (NP; opposite side to the SP), but the high EF region remains at the SP for the fiber with ε
r = 80. This is due to the large potential difference on the surface of the fiber with ε
r = 1.5. Furthermore, the time-averaged surface fluxes of the main ions (He+, N2
+, and O2
+) and excited species (Hes, N, and O) are analyzed. It is found that the surface fluxes of the fiber with ε
r = 80 at the NP are significantly higher than that of the fiber with ε
r = 1.5. Therefore, increasing the relative dielectric constant of fiber would increase the main ions and excited species surface fluxes at the NP.
Second harmonic generation of high power Cosh-Gaussian beam in cold collisionless plasma
Authors: Walia Keshav , Singh Kulkaran , Tripathi Deepak 
Published: July 2022

Abstract
The purpose of this study is to explore the second harmonic generation (SHG) of a high power Cosh-Gaussian beam in cold collisionless plasma. The ponderomotive force causes carrier redistribution from high field to low field region in presence of a Cosh-Gaussian beam thereby producing density gradients in the transverse direction. The density gradients so produced the results in electron plasma wave (EPW) generation at the frequency of the input beam. The EPW interacts with the input beam resulting in the production of 2nd harmonics. WKB and paraxial approximations are employed for obtaining the 2nd order differential equation describing the behavior of the beam’s spot size against normalized distance. The impact of well-established laser-plasma parameters on the behavior of the beam’s spot size and SHG yield are also analyzed. The focusing behavior of the beam and SHG yield is enhanced with an increase in the density of plasma, the radius of the beam and the decentred parameter, and with a decrease in the intensity of the beam. The results of the current problem are really helpful for complete information of laser-plasma interaction physics.
Phase-matching quantum key distribution based on orbital angular momentum under atmospheric turbulence
Authors: Shen Zhigang , Chen Gang , Wang Le , Li Wei , Mao Qianping , Zhao Shengmei 
Published: July 2022

Abstract
We propose a phase-matching quantum key distribution (PM-QKD) protocol based on orbital angular momentum (OAM) to exceed the linear key-rate bound without quantum repeater in the paper, named OAM-PM-QKD protocol, where the coherent state consisting of OAM with opposite topological charges is used as the information carrier. With the equivalent entanglement distillation model, we analyze its security and then derive its key generation rate. The numerical simulation results show that the proposed OAM-PM-QKD protocol is able to exceed the linear key-rate bound when the transmission distance exceeds 230 km, and the maximum secure transmission distance is also longer than those of original PM-QKD protocol due to no misalignment in the basis. Furthermore, the influence of the atmospheric turbulence (AT) on the key generation rate is also discussed. The stronger the strength of AT is, the shorter the transmission distance is.
EV-VINS: An Efficient and Versatile Binocular Visual-Inertial SLAM Algorithm
Authors: Gao Junjie , Yang Chengkun , Lv Fu 
Published: July 2022

Abstract
To address the problem that the current optimization-based visual-inertial SLAM algorithms have difficulty in achieving high accuracy, high robustness and high efficiency at the same time, an efficient and versatile SLAM algorithm (EV-VINS) based on the improvement of VINS-Mono is proposed in this paper. EV-VINS tightly coupling the binocular and inertial measurement unit is divided into decoupled front-end, which detects GFTT corners and performs optical flow tracking, and back-end, which mainly performs initialization, nonlinear optimization and outlier rejection. The dense map module is added for subsequent navigation functions. In particular, a feature classification strategy is proposed based on the analysis of the existing problems in the initialization, which not only greatly reduces the number of outliers during initialization, but also makes the points used for back-end optimization have good initial values. Therefore, the computational efficiency and the stability of the back-end optimization are significantly improved while ensuring the accuracy. The simulation experiments based on the EuRoC datasets and actual test results show that EV-VINS has better real-time performance while having the basically same location accuracy compared to the classical algorithms.
Algorithm Design for On-ramp Traffic Flow Controlling of Expressway Based on Toll Gates
Authors: Liu Xiaojuan 
Published: July 2022

Abstract
On-ramp control is one of the most widely used and most effective methods for relieving highway congestion in foreign countries. In this paper, the principle and common algorithms of the on-ramp control are summarized. Combining the reality of China, an practical algorithm for on-ramp traffic flow controlling of expressway based on toll gates controlling is proposed. That is, the traffic flow model and queuing model between the toll gates and the traffic flow are established based on the demand-capacity control theory, so as to realize the flow control by controlling the toll gates. Finally, the algorithm is verified by taking Guangzhou Airport Express as an example.
Calculating the target location based on sliding fiducials
Authors: Wang Kezhi , Zhu Yuyu , Cao Like 
Published: July 2022

Abstract
The use of trilateration and triangulation methods to obtain location information is cumbersome and error-prone. Aiming to solve these problems, this paper designs a method of calculating target location, establishes an error model and improves the accuracy of the location calculation. It uses a laser to provide a linear relationship, sliding fiducials provide basic data, calculates the location of the target point. Experimental results show that within the measurement range of 10 meters, the average error of solving the east and west coordinates 30 times is 4.7 cm.
Gas Concentration Prediction Method Based on Denoising Deep Forest
Authors: Yang Chang 
Published: July 2022

Abstract
Response lag is an inevitable problem for most electrochemical gas sensors, while the information about gas concentration and trends should be obtained immediately in some specific fields. Therefore, focusing on the goal of improving the accuracy and time efficiency of gas concentration prediction, and adopting data-driven method, this paper proposes a novel regression prediction model—Denoising Deep Forest(DDF). Comparative experiment’s results show that Denoising Deep Forest’s MAPE value is 4.43% and R2 value is 0.991 on ethylene oxide gas leakage detection dataset, which outperforms other common models. Moreover, this paper designs a noise-influence experiment to further test the performance of the models. The results of noise-influence experiment indecate that compared with the common models, Denoising Deep Forest has stronger anti-noise ability and it can maintain the robust performance on data with various noises.
A clustered oversampling coupled random forest classification method
Authors: An Wei-Peng , Gao Feng , Shang Xin-Xin 
Published: July 2022

Abstract
To address the limitations of current classification prediction models, an algorithm DPC-ASMOTE-IRF for Clustering by fast search and find of density peaks (DPC) with adaptive SMOTE (ASMOTE) coupled with improved random forest (IRF) is proposed. The algorithm first introduces in the data processing stage DPC clustering algorithm to cluster the samples, and proposes ASMOTE oversampling technique to process a minority class of samples to get relatively balanced samples. In the algorithm stage, the model’s overall classification performance for unbalanced data is improved by assigning weights to each decision tree in the random forest through the classification accuracy. The experimental results show that the classification performance indexes of the proposed method are all improved compared with similar classification experimental models.
Research on Named Entity Recognition Based on Model Fusion
Authors: Shi Xueying , Zhu Bin , Li Guanyu 
Published: July 2022

Abstract
In view of the defects of using deep learning method to identify named entities, which can’t represent polysemy and doesn’t contain context information, this paper proposes a new fusion model, which organically combines the bidirectional gated recurring neural network (BiGRU) and the dynamic graph edge evolutionary network (DGCNN) which are improved with attention mechanism. The time sequence features of the vector are captured by the improved BiGRU-ATT, and the local spatial features of the vector are enriched by DGCNN. Finally, fused output results are corrected and output by the constraint rules of CRF model, and the global optimal sequence is obtained by calculating the probability. The experimental results demonstrate that the proposed model is improved compared with the single model, which further highlights the superiority of the proposed fusion model.
Research on reasoning method of multi-relational knowledge graph based on translation
Authors: Yuan P , Wang R J 
Published: July 2022

Abstract
In the knowledge reasoning filed, algorithms based on translation have achieved great results in recent years. However, when translation-based models fit multi-map attribute relations, different relations will compete for the same embedding vector. At the same time, many knowledge graphs can automatically extract new triples from the Internet now. This further reduces the frequency of occurrence of a single relation in the knowledge graph, which creates more difficulty for translation-based models. Thus, the TransE_rel model is proposed by relaxing the constraints on relation vectors in the low-dimensional space. This model focuses more on relation reasoning and, when combined with other models, can also be used for entity reasoning. We conduct relation prediction and entity prediction on benchmark dataset and one dataset extracted by ourselves. By comparing with recent work, experimental results show that our method has achieved better performance on two tasks especially on relation prediction.
Research on deep learning model design technology of mobile terminal
Authors: Liang Tuo , Shi Shengyuan , Tao Ye , Zhang Bowen 
Published: July 2022

Abstract
Nowadays, object recognition and detection are inseparable from our lives which are related to various aspects in life. However, with the development of society and technology, the model of object detection become more and more complicated which affects the running velocity to some extent. In order to solve this problem, the key is to compress the model. On that note, the most important thing is to improve the running velocity without influencing the success rate of detection. To prune the mode, quantization is a low-cost way. Firstly, the object will be identified and detected by original model. After condensing model by pruning technology, run on this model to the same objects. Finally, it turns out that the model runs faster after pruning with little effect on the accuracy of object’s detection. Pruning minimizes model complexity, reduces the space needed for model storage, and also focuses on accelerating model training and prediction. It Can be applied during or after training.
Apply Yolov4-Tiny on an FPGA-Based Accelerator of Convolutional Neural Network for Object Detection
Authors: Zhang Fengxi , Li Yuying , Ye Zhihao 
Published: July 2022

Abstract
With the continuous expansion of Neural Network technology in the artificial intelligence field, for example, image recognition and retrieval, object detection, pixel processing, automatic speech generation, etc., Convolutional Neural Networks (CNN) and Deep Learning of Neural Networks (DNN) have made apparent breakthroughs. To improve the inference speed of images, the combination of FPGA-based acceleration and multiple model quantization methods has become one of the most contemporary alternative methods. This paper designed an FPGA-Based acceleration scheme combining software and hardware and effectively applied it to the Yolov4-Tiny object detection model, realizing the accelerated detection inference process from the original 6-7mins to 383ms. First, it chose the static quantization method of fixed-point numbers, fixed the position of the decimal point, and then added Batch Norm between the convolutional layer and the activation function to form a connection structure. Second, it further improved inference speed on an FPGA with a version of ZYNQ-7020 by increasing the bandwidth cap and reducing bandwidth requirements, employing a massive pipeline design. Finally, in the test of the Coco dataset, the plan has completed a substantial acceleration of the average inference speed of the Yolov4-Tiny object detection model from 7.13mins/Picture to 498.89ms/Picture, which has a high application value in the field of object detection. It dramatically improves the inference speed as well as keeps the average accuracy above 0.95.
TensorRT acceleration based on deep learning OFDM channel compensation
Authors: Liu Zhongqian , Ding Dan 
Published: July 2022

Abstract
In order to accelerate the DNN module computing speed and significantly improve the operation efficiency of the miniaturized deep learning communication platform. TensorRT-based DNN computing acceleration technology is studied and implemented. By performing model preservation, model parsing, engine optimization and inference deployment on trained DNNs, the DNN network structure and quantized data volume are effectively optimized. The performance of the TensorRT-accelerated models is tested, and the results show that the TensorRT-based DNN computation acceleration technique has shorter computation time and higher computation throughput with high performance.
Robot intelligent grasping experimental platform combining Jetson NANO and machine vision
Authors: Bao Xiujuan , Li Lianhui , Ou Weiqiang , Zhou Lu 
Published: July 2022

Abstract
According to the requirements of the intelligent manufacturing experimental teaching project, this experimental platform develops a robot intelligent grasping experimental platform based on Jetson NANO and vision. The platform adopts the Jetson NANO controller based on Google open source machine learning framework TensorFlow + Keras, cooperates with the computer vision library OpenCV to develop the machine vision algorithm, and realizes the grasping and handling of materials based on a 6-DOF articulated robot, it can support open project-based experimental teaching. The experimental platform integrates the knowledge of robot control, machine vision, mechatronics and other aspects of courses, which can train students’ ability to solve complex engineering problems and stimulate students’ creative thinking. And the experimental platform has good openness. In the experimental teaching, it only needs to specify the objectives to be achieved without restricting the specific implementation methods. Students can independently design and test the algorithm program of each link in the visual positioning system through software, and deeply experience the relevant theoretical knowledge and practical methods through hands-on experiments.