bluuebunny/crossref_metadata_2025 huggingface dataset:
An evaluation of the present status of quality assurance program implementation in digital mammography facilities in a developing country
Authors: Gonzalez-Ruiz Abraham , Sánchez Mendoza Helen Irais , Santos Cuevas Clara Leticia , Isidro-Ortega Frank J , Estrada Jaime Flores , Domínguez-García Ma Victoria , Flores-Merino Miriam V
Published: November 2022

Quality assurance program (QAP) implementation in mammography is a key aspect to guarantee the acquisition of -quality images and, thus, an opportune diagnosis of breast cancer. A QAP involves planned and systematic actions, such as quality control (QC) tests that will produce consistently high-quality images, while considering low radiation exposure for stakeholders. Currently, there is poor evidence of the current QAP implementation status in developing countries, particularly in Latin America. In this work, 47 Mexican digital mammography facilities were evaluated in terms of their QAP implementation’s current status during the period October 2021 to April 2022. A survey of three key indicators of compliance/non-compliance with QAPs was carried out. Also, 17 QC test parameters were assessed in all mammography units according to an international framework. The Kruskal–Wallis test, McNemar test, and Spearman correlation were used to identify the significant correlation between the QC tests and QAP indicators. Statistical analysis showed a significant positive correlation between the QAP indicators and QC parameters that were evaluated, especially associated with an increase in image quality. Almost 90% of the facilities evaluated failed in some QAP indicators, and only 44.68% of the facilities had an updated and current QAP implemented. Successful QAP implementation directly impacted on the QC mammography test results, particularly in terms of image quality, thus conditioning a successful mammography acquisition chain. The results obtained in this work suggest that better regulations need to be implemented to guarantee QAP surveillance, which could mean an increase in opportune diagnosis and management of breast cancer, with improved radiological safety for patients and occupational exposure personnel.
Estimation of state of charge integrating spatial and temporal characteristics with transfer learning optimization
Authors: Zhang Yiwei , Liu Kexin , Chuang Yutong , Zhang Jiusi
Published: November 2022

State of charge (SOC) estimation of lithium-ion batteries is of vital significance for the control strategy in battery management systems. To integrate the spatial and temporal characteristics of the data and to accomplish the transfer of knowledge, a novel convolutional neural network-bidirectional long short-term memory network based on transfer learning optimization (CNN-BiLSTM-TF) is proposed to estimate the SOC. Specifically, the spatial and temporal features hidden in the data are learned through CNN and BiLSTM, respectively. Furthermore, the CNN-BiLSTM network is established under one working condition and transferred to other working conditions through transfer learning, from which the SOC can be estimated online. A lithium-ion battery data set is applied to verify the proposed SOC estimation approach. The results of a case study demonstrate that the estimation performance of CNN-BiLSTM-TF is better than some existing approaches.
Design and performance investigation of a novel 3DOF compact MR damper
Authors: Suryadi Tan Aditya , Rabel Fabian , Sattel Thomas , Lee Sill Yannick , Goldasz Janusz
Published: November 2022

Magnetorheological (MR) fluid based dampers have been established as an alternative to classical hydraulic dampers with proportional electromagnetic valves under vibration processes which demand adaptive damping forces. Almost all MR-dampers are spatially 1-degree-of-freedom (DOF) dampers, having only one axis or direction of damping force generation. In many technical applications there exist movements in more than one spatial DOF, eventually necessitating more than one damper. Because of this, the damping is required not only in one but in more spatial directions, yet adjustable. In this work, a new design of a spatial 3DOF MR damper is proposed to allow damping in three directions within one damping device. The underlying motivation is to spatially integrate three damping directions in one device to potentially reduce installation space compared to three separate 1DOF dampers. The basic idea of the construction is to use one fluid chamber with several spatially distributed control elements at different positions of the fluid chamber. The control elements are electromagnets, generating the magnetic field in the fluid at different positions so that in total three spatial DOFs can be damped individually. Experiments and investigation are made, where the damper’s behavior are analyzed not only in one single DOF but also in more than one DOF. It is shown, that the damping concept can generate damping in all three spatial DOFs, both individually or together. Moreover, the damping can be generated to be dominant in one specific direction, meanwhile minimum in the other direction orthogonal to it.
Probing anisotropies of the Stochastic Gravitational Wave Background with LISA
Authors: Bartolo Nicola , Bertacca Daniele , Caldwell Robert , Contaldi Carlo R. , Cusin Giulia , De Luca Valerio , Dimastrogiovanni Emanuela , Fasiello Matteo , Figueroa Daniel G. , Franciolini Gabriele , Jenkins Alexander C. , Peloso Marco , Pieroni Mauro , Renzini Arianna , Ricciardone Angelo , Riotto Antonio , Sakellariadou Mairi , Sorbo Lorenzo , Tasinato Gianmassimo , Torrado Jesús , Clesse Sebastien , Kuroyanagi Sachiko
Published: November 2022

We investigate the sensitivity of the Laser Interferometer Space Antenna (LISA) to the anisotropies of the Stochastic Gravitational Wave Background (SGWB). We first discuss
the main astrophysical and cosmological sources of SGWB which are characterized by anisotropies in the GW energy density, and we build a Signal-to-Noise estimator to quantify the sensitivity of LISA to different multipoles. We then perform a Fisher matrix analysis of the prospects of detectability of anisotropic features with LISA for individual multipoles, focusing on a SGWB with a power-law frequency profile. We compute the noise angular spectrum taking into account the specific scan strategy of the LISA detector. We analyze the case of the kinematic dipole and quadrupole generated by Doppler boosting an isotropic SGWB. We find that β ΩGW ∼ 2 × 10-11 is required to observe a dipolar signal with LISA. The detector response to the quadrupole has a factor ∼ 103
β relative to that of the dipole. The characterization of the anisotropies, both from a theoretical perspective and from a map-making point of view, allows us to extract information that can be used to understand the origin of the SGWB, and to discriminate among distinct superimposed SGWB sources.
Multi-solutions with specific geometrical wave structures to a nonlinear evolution equation in the presence of the linear superposition principle
Authors: Ismael Hajar F , Sulaiman Tukur Abdulkadir , Osman M S
Published: November 2022

Lump solutions are one of the most common solutions for nonlinear evolution equations. This study aspires to investigate the generalized Hietarintatype equation. We auspiciously provide multiple M-lump waves. On the other hand, collision phenomena to multiple M-lump waves with soliton wave solutions are also provided. During the collision, the amplitude of the lump will change significantly over the processes, whereas the amplitude of the soliton will just minimally alter. As it is of paramount importance, we use suitable values of parameter to put out the physical features of the reported results through three dimensional and contour graphics. The results presented express physical features of lump and lump interaction phenomena of different kinds of nonlinear physical processes. Further, this study serves to enrich nonlinear dynamics and provide insight into how nonlinear waves propagate.
A laser pistonphone designed for absolute calibration of infrasound sensors from 10 mHz up to 20 Hz
Authors: Rodrigues D , Vincent P , Barham R , Larsonnier F , Durand S
Published: November 2022

There has been an increased demand for traceable calibrations at infrasonic frequencies in support of geophysical monitoring applications, an example being the Comprehensive Nuclear-Test-Ban Treaty Organization, which provides global international coverage for a nuclear testing ban, and requires the International Monitoring System. In this paper, a new laser pistonphone design is presented with the objective of establishing primary standards for sound pressure at very low frequencies down to 10 mHz. The piston is a modified accessorized loudspeaker driver whose diameter is equal to the diameter of the front pistonphone cavity. The volume velocity of the piston is measured through a laser interferometer and the current version was designed to have an upper frequency limit of 20 Hz, to overlap with the closed coupler reciprocity method of calibration. Particular attention has been given to the sealing to avoid pressure leakage loss. The dimensions of the front cavity were designed to allow the calibration of a large variety of sensors, including microphones, barometers, manometers and microbarometers. Examples of calibrations for several sensors are presented and also an uncertainty budget for the Brüel & Kjær type 4160 laboratory standard microphones, commonly used for primary calibrations. Finally, the metrological performance of the laser pistonphone is demonstrated by comparing the calibration results with those obtained with alternative methods.
Experimental study of the core instability before and after internal transport barrier formation in EAST
Authors: Wu Mingfu , Liu Zixi , Li Gongshun , Han Xiang , Zhang Tao , Li Yingying , Zhou Tianfu , Chao Yan , Wang Shouxin , Wu Xiaohe , Geng Kangning , Xiang Haoming , Zhong Fubin , Ye Kaixuan , Huang Jia , Zhou Zhen , Yang Shuqi , Wen Fei , Wang Yumin , Zhang Shoubiao , Zhuang Ge , Gao Xiang , EAST Team the
Published: November 2022

In a reversed shear discharge in the Experimental Advanced Superconducting Tokamak (EAST), an internal transport barrier (ITB) was formed. For the first time, the turbulence in the plasma core before and after the formation of the ITB in EAST was investigated by microwave reflectometry. It was found that during the formation of the ITB, the turbulence inside the barrier was not completely suppressed. The density fluctuation level decreased in the early phase of ITB and significantly increased later. It was found that the density fluctuation level increased with the density gradient after the appearance of reversed shear Alfvén eigenmodes (RSAEs). The change in turbulence, measurable by the reflectometer, did not affect the rate of increase in core density. Through the measurement of RSAEs, it was found that the formation of this ITB may be related to the minimum q (q
min). q
min was close to 2.
Electric energy substitution technology suitable for Tibet
Authors: Li Yu , YiXi CuoMu , GaMa DanZeng , Xia Qiang , DanZeng BaSang , Wang Lei , Ma Ziben
Published: November 2022

With the increase of population and the development of technology, energy use and pollution emissions continue to increase. While focusing on the development of new energy and renewable energy, electricity will continue to rise in future user energy supply ratios, and electrical alternative technology has become an important way for environmental protection and economic development. This paper takes the Tibetan region as an example to study the electrical alternative technology applicable to the Tibetan region. This paper selects the heat pump technology in the electric energy substitution technology, focuses on the analysis of the principle and unit structure of the heat pump technology, evaluates the performance of the heat pump with the performance coefficient and energy efficiency ratio, and analyses the performance of the heat pump through the T-S diagram and lgp-h diagram. It is concluded that with the improvement of the heating and cooling capacity of the single machine, its efficiency is higher and higher, and its energy-saving effect is also better.
Application of CEEMD and permutation entropy in noise elimination of hydropower unit swing signal
Authors: Li Gang , Ma Shengheng , Cai Yuanfei , Wang Hao , Jiang Peidong , An Xueli
Published: November 2022

A noise elimination algorithm of hydropower unit swing signals by using complete ensemble EMD (CEEMD) and permutation entropy is presented. The CEEMD is selected to decompose hydropower unit swing signals into several intrinsic mode functions (IMFs). Each IMF’s permutation entropy is computed. These IMFs (whose permutation entropy is lower than preset threshold) are reconstructed to denoise these abnormal noises of hydropower unit swing signals. Wavelet transform and CEEMD are used to process noise of hydropower unit swing signals respectively. Noise reduction performance of two methods is compared. The results can be seen that the CEEMD method has better adaptability and denoising capacity. It’s perfect for online applications for de-noising hydropower unit swing signals.
Optimization method of distribution network energy storage and capacity planning considering uncertainty of new energy sources
Authors: Zhou Junyu , Luo Guoming , Chen Zhangguo
Published: November 2022

The large-scale connection of the renewable energy with random and fluctuating output of the grid has a serious impact on the safe and stable operation of the grid. Considering the high cost of energy storage and the fluctuation of load, in this study, an optimization approach for designing the distribution network’s energy storage capacity is presented. This paper analyzes the uncertainty of new energy, and constructs a single distribution network energy storage station model based on the analysis results. In this paper, the typical daily total network loss is taken as the objective function for site selection and capacity planning. In this paper, particle swarm optimization algorithm is used to optimize the energy storage and capacity planning of distribution network. The experimental results show that this method can reduce the operating cost of distribution network and restrain the system load fluctuation. This method optimizes the economy and reliability of distribution network system.
Optical Design and Performance Analysis of the Medium Temperature Parabolic Trough Collector by Simulation Method
Authors: Zhang Siyuan , Niu Dongsheng , He Zhibao , Zhou Zhi , Duan Yanglong , Chen Pengfei
Published: November 2022

The parabolic trough heat collection technology was mainly used in the field of high-temperature power generation, and there was less research on the application of medium temperature heat utilization. In view of the huge heat demand in the medium temperature range (120°C-240°C) in the industrial field, trough solar thermal technology for the medium temperature heat energy recovery have unique technical advantages and wide perspective applications. In this study, based on the meteorological resources of Ejina Banner (Inner Mongolia Autonomous Region) and the heat demand, we propose a design for small sized parabolic trough collector, aperture width is 2.55m, focal length is 0.721m, and rim angle is 83°. The optical efficiency of this collector is 77.05% calculated by Monte Carlo ray tracing (MCRT) method, and the heat flux intensity distribution of the collector is simulated by the Soltrace. Finally, the results can provide some support and technical references for the medium temperature application.
A machine learning-based fault identification method for microgrids with distributed generations
Authors: Liu Yang , Zhang Shidong , Li Lisheng , Wang Shaorui , Lu Tianguang , Yu Haidong , Liu Wenbin
Published: November 2022

The development of renewable energy sources such as solar and wind based on distributed generators are growing rapidly in the face of the global energy crisis. As a connection between distributed generation and the main grid, microgrids are also growing rapidly. However, due to the randomness and uncertainty of the output of the solar and wind power, as well as the bidirectional characteristic of current flow, the faults in microgrids are difficult to identify using the traditional fault detection methods. To address this problem, this paper proposes a machine learning-based fault identification method for microgrids. First, the modified K-means algorithm is implemented to cluster the voltage data. Then, FP-growth algorithm is using to extract the association rules. Third, the mini-batch gradient descent (MBGD) algorithm is using to train the fault identification model based on machine learning theory. To verify the validity of this method, a case study considering single-phase short-circuit fault and two-phase phase short-circuit fault is simulated. The method presented in this work is with a high accuracy according to the simulation results.
A Distributed Power Consumption Metering Assessment Method for the Energy Internet
Authors: Li Guocheng , Fan Jianjun , Hu Guorui , Li Chao , Lu Tianguang , Che Lixuan , Lu Zeguang
Published: November 2022

This paper proposes a distributed power supply consumption metering assessment index system and method for rural energy internet, which can be used in the dispatch and design departments of power companies. The method can solve the problems that exist in the existing distribution system, such as the lack of basis for siting and capacity determination and access of large-scale distributed power units, and the single and one-sided assessment index of distributed power consumption capacity. Firstly, analysing the characteristics of the new rural power grid, considering the impact of the uncertainty of distributed power supply output, organically combine the influencing factors of power quality, power supply reliability and operational safety and establish a multi-dimensional index evaluation system to measure the ability of distributed power sources to consume. Finally, the improved fuzzy estimation is used to determine the consumption capacity of the rural grid. The data of a rural grid operation is analysed by case study to accurately assess the consumption capacity of distributed power sources in the area.
A hybrid full-bridge three-level DC-DC converter with crossing auxiliary capacitors
Authors: Liang Chonggan , Guo Xinran , Wang Hongxing , Xu Qi , Liu Shi , Yang Yi , Liu Zhigang
Published: November 2022

This paper presents a high-power medium-frequency hybrid full-bridge (FB) three-level (TL) DC-DC converter with crossing auxiliary capacitors. In this converter, two auxiliary capacitors are deployed between phases a and b, replacing the flying capacitor. The converter’s three-level leg switches are with low voltage stress, while the two-level leg switches are with high voltage stress. The topology and modulation method, as well as the switching characteristics of the hybrid FBTL DC-DC converter with crossing auxiliary capacitors are described. The feasibility and performance of the above-mentioned converter is validated by a laboratory prototype. Based on the mechanism analysis and experiments, the zero-voltage switching (ZVS) and zero-switching loss (ZSL) of power switches, self-balance of the clamped capacitors voltages, and the voltage self-balance of the switches in the three-level leg can be achieved.
Theoretical research on the discontinuity of temperature gradient under interfacial heat transfer
Authors: Zhang Debin , Cheng Xiaofang , Wang Peiqi
Published: November 2022

The interfacial thermal conductivity has always been undetermined, and the discontinuity of temperature gradient at the interface will also cause problems in the study of the driving force of heat transfer. Based on Fourier’s law, the generalized Fourier’s law is proposed by assigning a subscript to the thermal conductivity, which can describe the unobstructed and blocked state of the heat transfer at the interface. By comparing the phenomena of the heat transfer and waterflow at the interface, it is deduced that the interfacial thermal conductivity always takes the smaller value among the thermal conductivities on both sides of the interface. Through adjusting the thermal conductivity, the temperature gradient is continuous at the interface, which guarantees the driving force of the interfacial heat transfer.
Simulation and optimization design of hydrogen ejector in proton exchange membrane fuel cell system
Authors: Fan Ai-min , Fan De-xin , Xiao Wen-ping , Ye Chang-liu
Published: November 2022

The optimization of structural parameters in ejector design is a problem of optimization of multivariate nonlinear unsteady flow field, in this paper, the numerical model of the ejector is established by using the computational fluid dynamics (CFD) method. The CFD model is used to study the influence law of the structural parameters of the mixing section, diffuser section and fluid inlet of the ejector, optimize the structure of the ejector, and find out the best scheme for the structural optimization design of the hydrogen ejector, so as to broaden the available load range of the ejector.
Research on Massive multi-dimensional Sensor Layout Strategy used for edge computing in distribution of Internet of Things
Authors: Wei Yong , Pang Jiefeng , Wu Bingxin , Yong Mingchao , Jiang Ruizhi
Published: November 2022

There are a large number of multi-dimensional different types sensors in DIoT (Distribution Internet of Things). Based on edge computing function of smart power DIoT terminals, local access and logical operations are realized. Data aggregation will reduce network traffic overhead and improve the real-time performance of edge computing. This paper proposes an asynchronous distributed aggregation layout construction algorithm based on hierarchical clustering. In this algorithm, all nodes are first aggregated according to the distance to construct a collection tree according to hierarchical clustering. And then the optimal number of groups is calculated according to the number of groups. Then the best aggregation node is selected according to the asynchronous distributed strategy. Simulation experiments show that the algorithm can quickly find a data aggregation method with minimal overhead, and improve the perception of power DIoT and edge computing efficiency.
Simulation of In-cylinder Mixture Formation in SIDI Diesel Engine
Authors: Wang Shangxue , Liu Changzhen , Chen Jinbing , Zhao Chengwei , Yang Guichun
Published: November 2022

In this paper, the in-cylinder mixture formation process of a two-stroke spark-ignition direct-injection(SIDI) diesel engine is simulated and analyzed. The effects of the shape of the combustion chamber, the injection time and the initial temperature of the engine on the mixing process and the performance of the SIDI diesel engine are analyzed. The simulation results show that the offset combustor can obviously improve the fuel economy of the SIDI diesel engine. Under the premise of constant fuel injection quantity, there is an optimal fuel injection time to make the power performance and economy of the SIDI diesel engine optimal. Increasing the initial temperature is beneficial to the atomization of diesel oil and the formation of diesel mixture in cylinder. The results will guide the engineering design of SIDI diesel engine.
Error Analysis of Digital Electric Energy Metering Based on FPGA
Authors: Zheng Ke , Zhao Yan , Luo Huiyong
Published: November 2022

With the emergence and development of smart grid, digital meters have been widely used in substations, so the research on digital energy metering error has become a research hotspot. The purpose of this paper is to analyze the error of digital power measurement based on FPGA, mainly take the analog input fusion module and digital power meter in the digital power measurement system as the research object, and analyze the source of measurement error. An FPGA-based energy metering error detection platform is built to analyze the energy metering error when there are harmonics in the full energy metering method currently used by most digital energy meter manufacturers. The comparative analysis of numerical integration algorithms shows that the asymmetric distortion rates of the simulated waveforms are approximate. The results of measurement error analysis under harmonic conditions show that when the number of sampling points per cycle is 60, the power measurement errors of the 2nd to 4th harmonics are equivalent. The magnitude of the measurement error is still consistent with other low-order harmonics, and the simulation results are consistent with the theoretical analysis results.
APInf: Adaptive Policy Inference Based on Hierarchical Framework
Authors: Zhang Hongjie , Wang Shuo , Li Jing
Published: November 2022

Deep reinforcement learning has made adequate progress in addressing complex visual tasks. However, policy inference in relatively large neural networks is costly. Inspired by the fact that some states in a specific task can easily make decisions, we propose an APInf framework that can achieve low policy inference costs with quality guarantees. Two-stage training is proposed: First, to accelerate inference at easy states, sub-policy networks ordered by increasing capacity are generated; Second, to keep the quality of policy, master-policy is designed to choose a suitable sub-policy network subjected to state difficulty dynamically. We also improve the efficiency of master-policy inference by sharing the convolution layer between sub-policy networks and master-policy and then train master-policy under an extended MDP. Extensive experiments conducted in gym show that the adaptive inference framework reduces the FLOPs by 41.2% while maintaining similar quality. Furthermore, APInf outperforms the existing dynamic network structure in terms of policy quality and inference cost.
Global Mean Square Error Separation Loss
Authors: Hou Yun
Published: November 2022

Class imbalance problem greatly affects the accuracy of deep-learning-based object detectors. To weaken its influence, this paper proposes a global mean square error separation loss (GMSES Loss). GMSES Loss assigns a factor to each example, and then enhances the learning of hard-to-learn examples by reducing the factors of easy-to-learn examples. Meanwhile, the global mean square error separation method is introduced to enhance the learning of foreground classes. To verify the effectiveness of our algorithm, experiments are conducted with YOLOv4 and RetinaNet as the baselines, respectively. The results have shown that our loss function can improve the performance of one-stage object detectors without adding any extra hyperparameters.
Design of Unmanned Ship System with Two Engines and Two Propellers
Authors: Zhang Yongliang , Yu Juanhui , Dai Cheng , Fu Tianshuang
Published: November 2022

Unmanned ship is a kind of surface robot with integrated technology, which is composed of multiple disciplines, including intelligent remote control, wireless communication, autonomous navigation and obstacle avoidance algorithm.When the unmanned ship is sailing in the water, it is difficult to control its dynamic performance due to the influence of sea conditions. Therefore, the research on the unmanned ship technology has extremely important practical value. In order to improve its maneuverability and control accuracy, an unmanned ship based on two engines and two propellers is proposed. This paper designs an unmanned ship bottom system with two engines and two propellers. The system is mainly composed of two main engines, including two generators and two rudders and propellers. Two of them adopt Controller Area Network communication protocol and are controlled by two Controller Area Network communication interfaces respectively; The two generators adopt RS485 serial port communication protocol and are controlled by two RS485 serial ports respectively; The two-rudder propeller equipment adopt RS485 serial port communication protocol and are controlled by the other two RS485 serial ports respectively.Through the experiment, it is found that the unmanned ship equipped with two engines and two propellers can better control the navigation accuracy of the ship and have high maneuverability.
A Deep Facial BRDF Estimation Method Based on Image Translation
Authors: Feng Lulu , Weng Dongdong , Liang Bin
Published: November 2022

The reconstruction of photorealistic 3D face geometry, textures and reflectance (BRDF) is one of the most popular fields in computer vision, graphics and machine learning. However, the acquisition of facial reflectance remains a challenge. In this article, we propose a method for estimating the facial reflection properties of a single portrait image based on image translation. From a RGB face image, we obtain the BRDF with a large amount of detail. To achieve it, we perform a reverse engineer, which renders face images with the obtained texture map to form training data pairs based on the Blinn-Phong illumination model. We also apply random rotate-and-crop and sliding-window-crop to augment the data and optimize the network weights by minimizing the generated adversarial loss and reconstruction loss. As demonstrated in a chain of quantitative and qualitative experiments, our method achieves superior performance compared to the state-of-the-art methods.
Target Detection Network for Underwater Image Based on Adaptive Anchor Frame and Re-parameterization
Authors: Xu Huipu , Wang Yiteng
Published: November 2022

For low target detection rate and inaccurate localization of most target detection networks in special underwater environments, we propose an underwater target detection algorithm with adaptive anchor frames. The adaptive anchor frame selection strategy differs from the traditional methods of manually designing anchor frames and obtaining anchor frames using the K-means clustering algorithm. Our method is based on the K-means clustering algorithm and consists of a grouping strategy and a dynamic K-value strategy. It can also automatically calculate the appropriate numbers of anchor boxes and layers of network output based on the characteristics of label data. And performance of the network is guaranteed while avoiding the computation caused by redundant structures. The simple but powerful RepVGG classification network is used as the backbone network. Therefore, the feature extraction capability of the network is enhanced and the detection performance of the network on fuzzy targets is improved. The experimental results show that the algorithm in this paper achieves better detection results in the underwater target detection task compared to other algorithms.
UAV Formation Transformation Based on Two-Stage Path Planning Method
Authors: Song Bingyu , Zuo Yahui , Huang Shan , Liu Xiaolu , Chen Yingguo
Published: November 2022

With the maturity of unmanned aerial vehicle (UAV) technology, the application of UAV in various fields is more in-depth. A method to quickly and accurately solve the flight paths of a large number of UAVs from one formation to another is crucial for application scenarios that require UAVs to perform formation transformation tasks. In this paper, the path planning of UAV swarm formation transformation is divided into two stages: rough planning and fine planning. In the rough planning stage, a 0-1 variable planning model is established to match the start and end points of the UAV flight path, and the model is solved using a Strengthen Elitist GA Algorithm to quickly obtain high-quality feasible solutions. In the fine planning stage, four models including behavioral mode, obstacle avoidance criterion, optimal waypoint selection and target exchange are established. On this basis, a heuristic fixed-step space search algorithm is designed and implemented to plan the path finely, and the path planning for the UAV formation transformation in two scenarios is finally completed.
Calculation Method of Ship’s External Magnetic Field Based on Neural Network
Authors: Zuo Chao , Ma Mingyu , Li Maobin , Pan Yang , Yan Hui , Wang Jianxun , Geng Pan , Ouyang Jun
Published: November 2022

In view of the difficulties in the calculation of the external magnetic field in the closed-loop degaussing of the ship, this paper analyzes the relationship between the internal and external magnetic field of the ship, and builds a neural network model accordingly to realize the calculating model between internal and external magnetic field. Aiming at the problem of insufficient training set, a data augmentation method based on the linear nature of the induced magnetic field is proposed. By using this method to expand the training set, the training speed and accuracy of the model can be improved. Through experimental verification of the steel double-layer shell model, the proposed magnetic field estimation method can accurately predict the external magnetic anomaly when the induced magnetic field and the fixed magnetic field are changed. This method not only has the advantages of fast calculation speed and high real-time performance, but also solves the long-standing problem that the fixed magnetic field changes are difficult to predict.
Research on Intelligent Decision Method of Optimal Production Planning and AGV In-time Delivery in Mixed-Model Assembly Line
Authors: Jia Li , Shi Lei , Yao Jianfeng , Dai Xiang , Guo Gang
Published: November 2022

An automated guided vehicle (AGV) system is an indispensable part of a mixed-model assembly line (MMAL) for flexible and efficient production, which is utilized for the material delivery. With the urgent needs of industry digitalization and market customization, along with the trend of corporate smart decision management, MMAL has been widely adopted in the manufacturing process of modularized products. To address the parameter matching and energy consumption optimization of the AGV system in MMAL, the overall architecture of an intelligent MMAL (IMMAL) is firstly proposed. And the model of this problem is constructed. Then, the research methodology is proposed, which comprehensively adopts the dispatching rule, genetic algorithm, design of experiment, BP neural network, and simulated annealing. Eventually, a real-life case is used to validate the practicality of the proposed solutions. The results show that the optimal solutions of MMAL production and AGV system delivery at the minimum energy consumption are obtained successfully. Furthermore, three factors: the quantity, capacity, and velocity of AGVs greatly impact the energy consumption of the AGV system, where the quantity of AGVs has the most negligible significance, while the capacity of AGVs has the most significance.
Toward Efficient Geolocation in SAGIN: A Single-Satellite Geolocation Method Combined with Bandlimited Signal Extrapolation
Authors: Zhang Yusi , Sha Xuejun , Li Yong , Lin Xu
Published: November 2022

The satellite network, as an essential part of space-air-ground integrated network (SAGIN) with high coverage and mobility, not only expects a promising development but faces increasing interference and tenser satellite spectrum resources. The geolocation function of single satellite is widely used in the low-orbit satellite network but limited to the long measuring duration, which cannot adapt the flexiblity in SAGIN scenario. In this paper, an efficient single satellite geolocation method is proposed combined with bandlimited signal extrapolation in order to save more operation time and ensure the positioning accuracy. Due to the bandlimited characteristic of the radiated signal, information received from the satellite can be further extrapolated so that more doppler frequency values are obtained. The estimated accuracy can be improved with the extra information input to grid search method. Besides, short-term single-satellite geolocation can be realized to meet the higher efficiency requirement of SAGIN. Simulation results demonstrate the superiority of the proposed method in terms of the positioning accuracy compared to the geolocation method without extrapolation.
A Deep Learning-Based Cascade Automatic Classification System for Malignant Thyroid Nodule Recognition in Ultrasound Image
Authors: Zhou Haoyuan , Wang Rui , Zhou Meng , Fu Peng , Bai Yang
Published: November 2022

As the most common malignancy in the endocrine system, thyroid cancer is often determined based on ultrasound images by professional radiologists based on ultrasound images. In this study, a cascade automatic classification system (CACS) is proposed to accurately achieve automatic segmentation and classification of thyroid nodules, reducing over-reliance on experienced doctors in the diagnosis. Firstly, the Res-UNet is used to segment the region of interest (ROI) of thyroid nodules in ultrasound images. Then, to overcome the limitations of the small sample size of medical training data as well as category imbalance, the modified Conditional Variational Auto-Encoder (CVAE), learning the probability distribution of the ultrasound images, is trained to produce new medical ultrasound images for ROI data augmentation. Finally, we perform the classification task by a three-branch ensemble network combined with clinical diagnostic features of thyroid nodule such as the aspect ratio, internal and external echoes, and edges. The proposed CACS can identify benign and malignant thyroid nodules with an accuracy of 0.892 and an AUC of 0.887, according to testing on real patient data.
3D CNN Hardware Circuit for Motion Recognition Based on FPGA
Authors: Lv Shidong , Long Tao , Hou Zhixian , Yan Liang , Li Zhenzhong
Published: November 2022

In recent years, three-dimensional convolutional neural network (3D CNN) has been widely used in the fields of action recognition and video analysis. The general purpose processors are difficult to achieve efficient and intensive computing, and the deployment of 3D CNN based on FPGA has the advantages of low power consumption, high energy efficiency, and customizability, and has gradually become a hot choice for deploying convolutional neural networks in many embedded scenarios. This paper designs a small 3D convolutional neural network based on the classic 3D convolutional neural network C3D, and uses the general matrix multiplication (GEMM) to map the 3D convolution calculation to the 2D matrix multiplication calculation. The matrix is divided into blocks and transmitted to the FPGA through the AXI bus, and the multiplication operation of the block matrix is realized through a two-dimensional multiply-accumulate array. A System on Chip (SoC) architecture is built on the PYNQ platform, using ARM Cortex-A9 as the process control core, and the calculation of the entire matrix is completed under reasonable block and scheduling on the ARM. The IP core of matrix calculation is designed using High Level Synthesis (HLS), and the corresponding parallel optimization scheme is given. Experiments have verified that the prototype design of the hardware acceleration circuit achieves low power consumption, high energy efficiency, and high precision motion recognition while using less hardware resources.
Spatial Relationship Extraction of Geographic Entities Based on BERT Model
Authors: Yang Jiannan , Jia Hong , Liu Hanbing
Published: November 2022

Geographic entity relationship extraction from text is an important way to acquire geographic knowledge. Entity relations in Chinese text are difficult to discover because of implicit representations of relations between entities in Chinese text. Therefore, using existing pattern matching and machine learning methods to extract entity relations from Chinese text often has problems such as insufficient artificial features, poor generality, inability to resolve word polysemy, and difficulty in making full use of contextual information. However, deep learning methods can better solve the above problems. This paper takes the spatial relation between geographic entities as the main research object to build a geographic entity spatial relation corpus, and propose a geographic entity relationship extraction method that combines BERT and attention mechanism. The method is trained and tested on the corpus. The results show that the F1 value of the BERT-BiGRU-Attention model proposed in this paper reaches 85.2% on the test set, which is a great improvement in relation extraction ability compared with other baseline models. The geographic entity relationship extraction method based on the BERT model can effectively learn the context information in Chinese text sentences, improve the accuracy of relationship extraction, and provide support for geographic knowledge graph construction and geographic entity information retrieval.
Force balance in thermal quantum many-body systems from Noether’s theorem
Authors: Hermann Sophie , Schmidt Matthias
Published: November 2022

We address the consequences of invariance properties of the free energy of spatially inhomogeneous quantum many-body systems. We consider a specific position-dependent transformation of the system that consists of a spatial deformation and a corresponding locally resolved change of momenta. This operator transformation is canonical and hence equivalent to a unitary transformation on the underlying Hilbert space of the system. As a consequence, the free energy is an invariant under the transformation. Noether’s theorem for invariant variations then allows to derive an exact sum rule, which we show to be the locally resolved equilibrium one-body force balance. For the special case of homogeneous shifting, the sum rule states that the average global external force vanishes in thermal equilibrium.
Intelligent COVID-19 screening platform based on breath analysis
Authors: Xue Cuili , Xu Xiaohong , Liu Zexi , Zhang Yuna , Xu Yuli , Niu Jiaqi , Jin Han , Xiong Wujun , Cui Daxiang
Published: November 2022

The spread of coronavirus disease 2019 (COVID-19) results in an increasing incidence and mortality. The typical diagnosis technique for severe acute respiratory syndrome coronavirus 2 infection is reverse transcription polymerase chain reaction, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic. Here we constructed an intelligent system based on the volatile organic compound (VOC) biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p < 0.001) between the COVID-19 and the control groups. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection. The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and an F1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network, k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model. The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.
Impact of the climate on fishing locations of fish Larvae in Palabuhanratu bay
Authors: Tirani N R , Supriatna S , Wibowo A
Published: November 2022

The phenomenon of climate change influences primary production, food chain interactions, and distribution of fisheries species. Sukabumi Regency, especially Palabuhanratu Bay, is a fishing ground for fish larvae. Fish larvae are an essential phase in supporting the existence of fish resources. The study aims to determine the spatial distribution of fish larvae locations in Palabuhanratu Bay and analyze the relationship between the phenomenon of climate change and fish larvae locations. The variables are rainfall, salinity, sea surface temperature, currents, and tides. The phenomenon of climate change using rainfall data years 2000-2019, Landsat 5 TM and Landsat 8 OLI/TIRS. Salinity estimation generated from Landsat 5 TM and Landsat 8 OLI/TIRS using the Cimandiri Algorithm. The Split-Window Algorithm with the sea surface temperature value. The results found fishing larvae located around the estuary and the beach towards the sea along the Palabuhanratu Bay. The biggest catches from these fish larvae are in Cimandiri estuary and Citepus estuary. This research concluded that rainfall affects the catches of fish larvae because it is directly related to the oceanographic condition of the bay waters.
The sensitivity of meteorological drought index towards El Nino-Southern Oscillation
Authors: Koem S , Lahay R J , Nasib S K
Published: November 2022

El Nino-Southern Oscillation (ENSO) contributes to the regional climates, such as precipitation and droughts. The objectives of the present work were to: (1) identify the severity index; (2) analyze the correlation of SPI and RDI, and; (3) identify the response of SPI and RDI towards ENSO. SPI and RDI were calculated for time scales (3, 6, and 12 months), and these represented the seasonal and annual drought. The identification of the responses of the drought severity index, based on ENSO, consisted of several thresholds, namely weak, moderate, and strong. The correlational value and RMSE only represented the performance of SPI and RDI on different time scales. The drought severity index would decline along with an increase in the time scales. The strong El Nino phase could be significant to the seasonal and annual drought. In other words, ENSO was impactful on the precipitation and dynamics of drought. Drought periods were due to the moderate and strong El Nino phase, while the weak phase led to a normal condition. For this reason, ENSO could be functioned as an indicator to predict drought.
The identification of flood susceptibility and its contributing factors in Sampang Regency
Authors: Irawan L Y , Sumarmi  , Panoto D , Pradana I H , Faizal R , Devy M M R , Putra D B P
Published: November 2022

Sampang Regency is susceptible to flood which caused by river overflow due to rainfall peak period and rising seawater. Kamoning watershed is one of an area that got annual overflow flood. While the coastal area of Sampang Regency faces rising-seawater flood/high-tide flooding. This research aimed to map the flood susceptibility class in Sampang Regency using a morphological approach. Remote sensing and GIS analysis was used to classify the area into three flood susceptibility classes: high, moderate, and low. After that, the map is verified using hydrostratigraphy analysis using the geoelectric Schlumberger method. Based on the identification, 6 villages in 9 subdistricts located alongside the Kamoning River have a high flood susceptibility owing to river overflow. On the other hand, rising seawater is causing floods in Sreseh Sub-District, Jrengik Sub-District, Pangarengan Sub-District, Sampang Sub-District and Camplong Sub-District in the southern part of Sampang Regency, whereas Banyuates Sub-District, Ketapang Sub-District and Sokobanah Sub-District in the northern part. Consequently, those areas are also classified as high flood susceptibility areas. The hydrostratigraphy analysis showed that the surface of Sampang Regency is mainly composed of clay. With low permeability level (0.0002 m/day), water pools and surface runoff could easily be generated when rain falls.