bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on water meter reading recognition based on deep learning
Authors: Liang Yue , Liao Yiqi , Li Shaobo , Wu Wenjuan , Qiu Taorong , Zhang Weiping
Published: July 2022

At present, there are still many old-fashioned water meters in the society, and the water department needs to send staff to read the water meter after arriving at the scene with a handheld all-in-one machine. However, there are many problems in this manual meter reading method. First, a large number of meter reading work leads to low efficiency of the entire water department, consuming a lot of time and energy, and high labor costs; second, the water meters in natural scenes have problems such as serious dial contamination and other environmental factors that interfere with the meter reading staff, and the results of the meter reader cannot be verified later. In response to these problems, this paper studies a deep learning method for automatic detection and recognition of water meter readings. This paper first introduces the existing in-depth learning models, such as Faster R-CNN, SSD, and YOLOv3. Then two datasets are sorted out, one is the original water table picture dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Then two plans are proposed, one is the original water table image dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Finally, by comparing the three models from different angles, it is determined that YOLOv3 in the second solution has the best recognition effect, and the accuracy rate reaches 90.61%, which can greatly improve work efficiency, save labor costs, and assist auditors in reviewing the read water meter readings.
Estimation of nutrient loads with the use of mass-balance and modelling approaches on the Wełna River catchment example (central Poland)
Authors: Bojanowski Damian , Orlińska-Woźniak Paulina , Wilk Paweł , Szalińska Ewa
Published: July 2022

Nitrogen and phosphorus budgeting is considered to be a key tool for policy makers and stakeholders when dealing with nutrient contamination issues, however no unified method has been employed in countries affected by this eutrophication problem. The current study offers a detailed insight into the estimations of nutrient loads and their distribution between different sources for a middle-sized agricultural catchment, with the use of two approaches: mass balance (static) and modelling (dynamic). Both methods revealed similar contributions of analysed nutrient sources, although the final estimates in the chosen calculation profile were divergent due to the various reasons related to the methods’ specificity. The advantages and disadvantages of both approaches have been specified in our study, and a hybrid solution on a local and country wide scale has been proposed.
Deep learning-based defects detection of certain aero-engine blades and vanes with DDSC-YOLOv5s
Authors: Li Xubo , Wang Wenqing , Sun Lihua , Hu Bin , Zhu Liang , Zhang Jincheng
Published: July 2022

When performed by a person, aero-engine borescope inspection is easily influenced by individual experience and human factors that can lead to incorrect maintenance decisions, potentially resulting in serious disasters, as well as low efficiency. To address the absolute requirements of flight safety and improve efficiency to decrease maintenance costs, it is imperative to realize the intelligent detection of common aero-engine defects. YOLOv5 enables real-time detection of aero-engine defects with a high degree of accuracy. However, the performance of YOLOv5 is not optimal when detecting the same defects with multiple shapes. In this work, we introduce a deformable convolutional network into the structure of YOLOv5s to optimize its performance, overcome the disadvantage of the poor geometric transformability of convolutional neural networks, and enhance the adaptability of feature maps with large differences in the shape features. We also use a depth-wise separable convolution to improve the efficiency of multichannel convolution in extracting feature information from each channel at the same spatial position while reducing the increased computational effort due to the introduction of deformable convolution networks and use k-means clustering to optimize the size of anchor boxes. In the test results, mAP50 reached 83.8%. The detection accuracy of YOLOv5s for common aero-engine defects was effectively improved with only a 7.9% increase in calculation volume. Compared with the metrics of the original YOLOv5s, mAP@50 was improved by 1.9%, and mAP@50:95 was improved by 1.2%. This study highlights the wide application potential of depth science methods in achieving intelligent detection of aero-engine defects. In addition, this study emphasizes the integration of DDSC-YOLOv5s into borescope platforms for scaled-up engine defect detection, which should also be enhanced in the future.
A theoretical investigation of the effect of Ga alloying on thermodynamic stability, electronic-structure, and oxidation resistance of Ti2AlC MAX phase
Authors: Sauceda Daniel , Singh Prashant , Arroyave Raymundo
Published: July 2022

We present a systematic investigation of thermodynamic stability, phase-reaction, and chemical activity of Al containing disordered Ti2(Al-Ga)C MAX phases using machine-learning driven high-throughput framework to understand the oxidation resistance behavior with increasing temperature and exposure to static oxygen. The A-site (at Al) disordering in  Ti2AlC MAX (M=Ti, A=Al, X=C) with Ga shows significant change in the chemical activity of Al with increasing temperature and exposure to static oxygen, which is expected to enable surface segregation of Al, thereby, the formation of Al2O3 and improved oxidation resistance. We performed in-depth convex hull analysis of ternary Ti–Al–C, Ti–Ga–C, and Ti–Al–Ga–C based MAX phase, and provide detailed contribution arising from electronic, chemical and vibrational entropies. The thermodynamic analysis shows change in the Gibbs formation enthalpy (ΔGform) at higher temperatures, which implies an interplay of temperature-dependent enthalpy and entropic contributions in oxidation resistance Ga doped Ti2AlC MAX phases. A detailed electronic structure and chemical bonding analysis using crystal orbital Hamilton population method reveal the origin of change in phases stability and in oxidation resistance in disorder Ti2(Al1−xGax)C MAX phases. Our electronic structure analysis correlate well with the change in oxidation resistance of Ga doped MAX phases. We believe our study provides a useful guideline to understand to role of alloying on electronic, thermodynamic, and oxidation related mechanisms of bulk MAX phases, which can work as a precursor to understand oxidation behavior of two-dimensional MAX phases, i.e., MXenes (transition metal carbides, carbonitrides and nitrides).
Identification, cost evaluation, and prioritization of urban traffic congestions and their origin
Authors: Serok Nimrod , Havlin Shlomo , Blumenfeld Lieberthal Efrat
Published: July 2022

The 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

Machine 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

Hardware-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.
Exploring the coating of 3D-printed insulating substrates with conductive composites: a simple, cheap and versatile strategy to prepare customized high-performance electrochemical sensors
Authors: de Oliveira Fabiano Mendonça , Mendonça Maria Zizi Martins , de Moraes Natália Canhete , Petroni Jacqueline Marques , Neves Matheus Meneguel , de Melo Edmar Isaias , Lucca Bruno Gabriel , Bezerra da Silva Rodrigo Amorim
Published: July 2022

This work reports the coating of insulating 3D-printed substrates in conductive inks for the electrode preparation, usable in BIA, μFIA and stationary cells. The electrodes exhibited performance like usual carbon electrodes, including 3D-printed.
A new hexanuclear Fe(iii) nanocluster: synthesis, structure, magnetic properties, and efficient activity as a precatalyst in water oxidation
Authors: Akhtar Muhammad Nadeem , Bikas Rahman , AlDamen Murad A. , Shaghaghi Zohreh , Shahid M. , Sokolov Andrei
Published: July 2022

The oxo-bridged hexanuclear iron coordination cluster and its magnetic and electrocatalytic activity for water oxidation are presented. Experiments confirm that the electrode modified by this cluster is active for water oxidation.
Hydroxytyrosol improves strenuous exercise-associated cardiac pathological changesviamodulation of mitochondrial homeostasis
Authors: Xiong Yue , Xu Jie , Cao Wenli , Zhang Jiawei , Feng Zhihui , Cao Ke , Liu Jiankang
Published: July 2022

Hydroxytyrosol (HT) from olive oil exerts profitable effects on strenuous exercise-induced cardiac pathological changesviamodulation of mitochondrial homeostasis.
Current challenges and future applications of antibacterial nanomaterials and chitosan hydrogel in burn wound healing
Authors: Bai Que , Zheng Caiyun , Chen Wenting , Sun Na , Gao Qian , Liu Jinxi , Hu Fangfang , Pimpi SaHu , Yan Xintao , Zhang Yanni , Lu Tingli
Published: July 2022

Application of antimicrobial agents and antimicrobial hydrogels in wound dressings.
An atomistic perspective on the diffusion and permeation of hydrogen and isotopes through an engineered nanoporous silica membrane using molecular dynamics simulations
Authors: Sahu Pooja , Ali Sk. Musharaf
Published: July 2022

Silica membrane for the storage and transportation of H2 and its isotopes.
Self-strengthening tape junctions inspired by recluse spider webs
Authors: Skopic Ben H. , Koebley Sean R. , Schniepp Hannes C.
Published: July 2022

Studying the webs of the Chilean recluse spider made from the spider's peculiar ribbon silk, we discovered a self-strengthening mechanism in junctions of two adhesive tapes that eliminates the weakness inherent in tape peeling.
Photocatalytic and antibacterial activity of graphene oxide/cellulose-doped TiO2 quantum dots: in silico molecular docking studies
Authors: Ikram Muhammad , Rasheed Fahad , Haider Ali , Naz Sadia , Ul-Hamid Anwar , Shahzadi Anum , Haider Junaid , Shahzadi Iram , Hayat Shaukat , Ali Salamat
Published: July 2022

Graphene oxide (GO) and cellulose nanocrystal (CNC)-doped TiO2 quantum dots (QDs) were effectively synthesized by employing the co-precipitation method for the degradation of dyes and antimicrobial applications.
GaAs/GaP superlattice nanowires: growth, vibrational and optical properties
Authors: Arif Omer , Zannier Valentina , Rossi Francesca , De Matteis Diego , Kress Katharina , De Luca Marta , Zardo Ilaria , Sorba Lucia
Published: July 2022

A HAADF-STEM image of a portion of a GaAs/GaP SL NW with 100 repetitions of 3 nm GaAs and 3 nm GaP alternating layers, and the corresponding μ-Raman (top) and μ-photoluminescence (bottom) spectra.
Polymer-complexed SnO2 electron transport layer for high-efficiency n-i-p perovskite solar cells
Authors: Xu Zhenhua , Ng Chi Huey , Zhou Xinming , Li Xiaohui , Zhang Putao , Teo Siow Hwa
Published: July 2022

The binding interaction between the functional polymer and the commercial SnO2 colloidal solution.
The structure–self-assembly relationship in PDMAEMA/polyester miktoarm stars
Authors: Mielańczyk Anna , Kupczak Maria , Klymenko Olesya , Mielańczyk Łukasz , Arabasz Sebastian , Madej Krystian , Neugebauer Dorota
Published: July 2022

Amphiphilic PDMAEMA/polyester miktoarm star-shaped polymers showed a structure–property correlation indicating their potential application as stimuli responsive solubilizing agents.
Electrochemical enantioselective dihydroxylation reaction of N-alkenyl nucleobases for the construction of chiral acyclic nucleosides
Authors: Zhang Qi-Ying , Lu Pei-Xian , Wang Song-Lin , Li Lu-Xin , Qu Gui-Rong , Guo Hai-Ming
Published: July 2022

A simple and sustainable KI-mediated electrochemical enantioselective dihydroxylation reaction for the synthesis of chiral acyclic nucleosides was developed for the first time with only a 0.1 mol% Os catalyst.
A combination therapy strategy for treating antibiotic resistant biofilm infection using a guanidinium derivative and nanoparticulate Ag(0) derived hybrid gel conjugate
Authors: Dey Ananta , Yadav Manisha , Kumar Deepak , Dey Anik Kumar , Samal Sweety , Tanwar Subhash , Sarkar Debrupa , Pramanik Sumit Kumar , Chaudhuri Susmita , Das Amitava
Published: July 2022

Dispersion of biofilms that protect bacteria and its subsequent killing in the planktonic state are effectively achieved by a guanidinium–Ag(0) nanocomposite.
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

The 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.
Risk of severe COVID-19 infection among adults with prior exposure to children
Authors: Solomon Matthew D. , Escobar Gabriel J. , Lu Yun , Schlessinger David , Steinman Jonathan B. , Steinman Lawrence , Lee Catherine , Liu Vincent X.
Published: July 2022

Susceptibility and severity of COVID-19 infection vary widely. Prior exposure to endemic coronaviruses, common in young children, may protect against SARS-CoV-2. We evaluated risk of severe COVID-19 among adults with and without exposure to young children in a large, integrated healthcare system. Adults with children 0–5 years were matched 1:1 to adults with children 6–11 years, 12–18 years, and those without children based upon a COVID-19 propensity score and risk factors for severe COVID-19. COVID-19 infections, hospitalizations, and need for intensive care unit (ICU) were assessed in 3,126,427 adults, of whom 24% (N = 743,814) had children 18 years or younger, and 8.8% (N = 274,316) had a youngest child 0–5 years. After 1:1 matching, propensity for COVID-19 infection and risk factors for severe COVID-19 were well balanced between groups. Rates of COVID-19 infection were slightly higher for adults with exposure to older children (incident risk ratio, 1.09, 95% confidence interval, [1.05–1.12] and IRR 1.09 [1.05–1.13] for adults with children 6–11 and 12–18, respectively), compared to those with children 0–5 years, although no difference in rates of COVID-19 illness requiring hospitalization or ICU admission was observed. However, adults without exposure to children had lower rates of COVID-19 infection (IRR 0.85, [0.83–0.87]) but significantly higher rates of COVID-19 hospitalization (IRR 1.49, [1.29–1.73]) and hospitalization requiring ICU admission (IRR 1.76, [1.19–2.58]) compared to those with children aged 0–5. In a large, real-world population, exposure to young children was associated with less severe COVID-19 illness. Endemic coronavirus cross-immunity may play a role in protection against severe COVID-19.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Battery storage to improve the cutter negative effect transient stability control strategy
Authors: Xu Jiaqi , Li Peiqiang , Zhang Lan , Fan Xiuhua
Published: July 2022

To improve the transient stability of power grid, this paper proposes an emergency control strategy considering battery storage to participate in the transient stability of power grid. The phase plane trajectory convexity is used to identify the transient stability of the power grid. After the system is unstable, the dual-machine equivalence is carried out for the multi-machine power system regulated by the energy storage power supply, and the control quantity required by the energy storage power supply to restore the system stability in the single-machine system is calculated. The effectiveness of the control strategy was verified in the EPRI-7 system. The simulation results show that the transient stability emergency control strategy with energy storage power participating in the regulation can effectively improve the transient stability, reduce the emergency control failure, and inhibit the frequency and voltage reduction.
Motion Velocity Detection of Circuit Breaker Operating Mechanism
Authors: Sun Keke , Yang Ke , Ku Zhaoyu , Liu Yu , Dong Huajun
Published: July 2022

The movement speed of the operating mechanism is one of the main factors affecting the breaking performance of the circuit breaker. To accurately detect the movement speed, this paper proposes an image processing method based on LabVIEW virtual instrument and designs a set of software and hardware systems for real-time movement speed detection of the operating mechanism. On the basis of the movement images of the operating mechanism collected by the high-speed camera, it uses the image processing module in LabVIEW to pre-process the images. The paper adopts the Laplacian edge detection operators to detect the edges and extract the edge feature information for straight line fitting and data processing. The experimental results show that the detection system can effectively detect the motion state of the operating mechanism, realize the real-time detection of the motion state of the operating mechanism, and provide technical support for the detection of the motion characteristics of the circuit breaker.
Multi-scale based defect detection for automotive glass
Authors: Lv Hui , Huang Shihao , Xie Wenming , Chen Zhe , Guo Zhou , Zhang Xuanxuan
Published: July 2022

Small targets have low resolution and small size. Using the YOLOv3 model may not detect the target object. In view of this situation, this paper improves the YOLOv3 model and proposes a network structure with refined features. The channel feature refinement mechanism is introduced, and four detection scales are designed to prevent small objects from being submerged; and the spatial pyramid structure-Spp module is added to detect five defects of glass. The experimental results show that compared with the YOLOv3 model, the mAP value of the optimized model is 97.22%, and the mAP value of the original version is 92.31%, an increase of 4.91%. In the self-built data set, the accuracy of automotive glass defect recognition reached 97.66%, and the optimized model’s recognition effect on small targets was significantly improved. Compared with the traditional glass defect detection method, the method improves the detection accuracy and the recognition accuracy.
Analysis of Automatic Collision Avoidance Strategy for Unmanned Surface Vessel
Authors: Huang Liang , Zhou GuoQing , Luo Bing
Published: July 2022

Automatic collision avoidance is a key technology in the research of unmanned surface vessel(USV). By establishing the collision avoidance decision coordinate system of USV, this paper estimates the motion parameters of obstacles relative to USV under two different conditions. Based on azimuth measurement information and based on azimuth and distance measurement information at the same time, analyzes the influence of speed and heading change on DCPA and TCPA, then, according to several typical sea encounters, the calculation methods of different collision avoidance decisions are given.
Research on Baseline Extraction Method Based on NMR Spectrogram
Authors: Xiong ShiYong , Zhu Fuli , Xu Daoxu
Published: July 2022

This paper proposes a baseline extraction method based on a nuclear magnetic resonance atlas, which uses the DBSCAN algorithm to cluster the data extracted by the nuclear magnetic resonance spectrometer and determines the baseline of the atlas according to the clustering results. In this paper, the core parameters of the DBSCAN algorithm are improved, and the Eps parameter set is selected by combining k-dist and equal interval division. The optimal number of outliers is obtained based on this set, and the baseline value range is set. Finally, the clustering quality is tested by the silhouette coefficient. The final experiment shows that the accuracy and stability of the baseline set based on the improved DBSCAN clustering algorithm are better.
A DAG-free Method to Event Correlation for Revealing Abnormal Connections of Cloud Instances
Authors: Li Xiaojian , Hu Hangping , Jiang Yiyi , Zhang Zhen , Yang Haopeng , Liu Liqin
Published: July 2022

We proposes an event correlation method with rule evaluation(ECRE) to reveal abnormal connections from cloud Instances. We combine some characteristics of status-pair to categorize all abnormal status-connections as far as possible. Our method can classify abnormal connections of instances but doesn’t require the DAG of tasks. Those labels that unmatched the supervision rules are removed to remain more and better samples for training. The results that our method achieved show high accuracy, and a significant correlation between the accuracy and the usage count of supervision-rules.
A research on the control strategy of automatic charging robot for electric vehicles based on impedance control
Authors: Zhang Hu , Zhu Wenyang , Huang Yuancan
Published: July 2022

In this paper, we conducted a research about the automatic charging robot and develop a kinematic model for the UR5e robot based on the D-H method. And the simulation analysis of the load of the robot joint during the automatic charging gun insertion task was conducted, the robot’s ability to effectively complete the task when the exceeding payload was applied was demonstrated. The force control strategy based on the impedance control and the ARCC concept is proposed. Experiments in a laboratory environment and on a Tesla model 3 electric vehicle have shown that the method is effective in achieving a soft and smooth insertion of the charging gun.
Design of a multi-channel high-resolution TDC based on FPGA
Authors: Huang S. , He Z. , Wen X. , Li X. , Ma Q. , Zheng B. , Xu X. , Yin Z.
Published: July 2022

High-resolution multi-channel time-to-digital converters
(TDC) is an important tool in many physical experiments. In the
HL-2A tokamak device,a real-time double-ring neutron time-of-flight
(TOFII) spectrometer system used to measure the neutron energy
spectrum requires the TDC to have a measurement accuracy of 100 ps
and a number of 90 channels. In the measurement-device-independent
quantum key distribution (MDI-QKD) system, multi-channel and
high-precision TDC is also one of the key factors affecting the
final bit rate.

In this paper, we propose the field programmable gate array (FPGA)
method to develop the TDC. By using the coarse-fine combined
measurement method, we complete the TDC design through the tapped
delay line formed by the internal carry chain of the FPGA. Our
method achieves a time measurement of 90 channels and a time
resolution of 52 ps.
Biofabricated functionalized graphene quantum dots (fGQDs): unraveling its fluorescence sensing mechanism of human telomerase reverse transcriptase (hTERT) antigen and in vitro bioimaging application
Authors: Tade Rahul Shankar , Patil Pravin Onkar
Published: July 2022

Lung cancer (LC) is a deadly malignancy that is posing a serious threat to human health. Therefore, early detection of LC biomarkers is the key to reducing LC-related fatalities. Herein, we present the first fluorescent-based selective detection of LC biomarker human telomerase reverse transcriptase (hTERT) using polyethyleneimine (PEI) functionalized graphene quantum dots (fGQDs). One-pot in situ synthesis of amine-functionalized GQDs was accomplished by hydrothermal carbonization of biowaste-derived cellulose and PEI. Synthesized fGQDs were characterized by various analytical techniques. Synthesized fGQDs not only exhibited enhanced fluorescence life-time but also excellent stability in the different solvents compared to bare GQDs. The surface activation of hTERT-Ab by carbodiimide chemistry (EDC-NHS) resulted in stacking interactions with fGQDs, involving adsorption-desorption as well as competitive mechanisms. The higher inherent affinity of hTERT-Ag (hTERT antigen) for hTERT-Ab (hTERT antibody) resulted in complex formation and recovery of fGQD fluorescence. As a result, this fluorescence sensing demonstrated a greater linear detection range (0.01 ng ml−1–100 μg ml−1) as well as a notable low detection limit (36.3 pg ml−1). Furthermore, the fabricated immunosensor (Ab@fGQDs) has excellent stability and performance in real samples, with an average recovery of 97.32%. The results of cytotoxicity and cellular bioimaging study in A549 cells show that fGQDs can be used for additional nanotherapeutics and biological applications.
Tracing the influence of caffeine on the pharmacokinetic parameters of three headache relieving pharmaceuticals applying synchronous fluorescence spectroscopy
Authors: Wahba M E K , El Sherbiny D , El Enany N , Draz Mohammed E
Published: July 2022

A simple, sensitive, and selective first derivative synchronous fluorimetric method was developed and optimized to track the influence of caffeine content in beverages on the pharmacokinetic parameters of three pharmaceuticals used in relieving headache namely, aspirin (ASP), ibuprofen (IBU), and ergotamine tartrate (ERG). A full validation procedure was carried out to impart validity to the proposed method to apply it to biological fluids. The unique dissolving power of micellar solutions was utilized to avoid multiple extraction steps for both thein vitroandin vivoexperiments, aiming to obtain acceptable recoveries and to accomplish sustainability, where 0.1 M sodium dodecyl sulphate (SDS) was used for this purpose. Moreover, the developed bioanalytical method was subjected to full validation to avoid interferences emerging from biological matrices. The greenness of the proposed method was assessed according to the Analytical Eco-Scale and proved to be excellent green carrying a score of 98%.