bluuebunny/crossref_metadata_2025_split huggingface dataset:
About Rational Using of the Controlling Longitudinal and Transverse Magnetic Fields at arc Welding and Surfacing
Authors: Razmyshlyaev Alexander D , Ahieieva Marina V , Lavrova Elena V 
Published: October 2019

Abstract
The use of the controlling longitudinal magnetic fields (LMF) and transverse magnetic fields (TMF) at submerged arc welding and surfacing by a wire is shown. Application both LMF and TMF at the induction optimal values of the magnetic fields allows increasing the electrode wire melting productivity (melting coefficient) at arc welding and surfacing on 20…30 %. However there are no publications comparing the efficiency of the controlling LMF and TMF use at submerged arc welding and surfacing by a wire. A depth and area of parent metal penetration zone considerably decrease at the use of both ferromagnetic and non-magnetic basic and welding materials at submerged arc welding and surfacing by a wire with act of LMF and TMF. The methods of induction components calculation that generate input devices of LMF and TMF in the welding bath zone are briefly described. This comparative analysis allows establishing the areas of rational use of controlling LMF and TMF at arc welding and surfacing, taking into account the magnetic properties of basic and welding materials.
Calibration Method and Experiment of in-Situ Radioactivity Measurement in Seawater Based on Monte Carlo Simulation
Authors: Zhang Y Y , Wu B W , Feng X D , Liu D Y , Zhang Y 
Published: October 2019

Abstract
Some research on the in-situ radioactivity measurement in seawater and development of underwater spectrometer using NaI(Tl) scintillation crystal are being carried out for the automatic and continuous monitoring in the marine environment. The gamma spectrometer must be efficiency calibrated for the detection of radionuclides in the seawater. For the problem of traditional experiments, a simulation model of in-situ radioactivity measurement in the seawater was established by using Monte Carlo statistical method. The model simulates the in-situ measurement environment of seawater and the characteristics of the underwater spectrometer developed by ourselves. By calculating the interaction between gamma-ray photons emitted by radionuclides in seawater and various atoms in seawater and the internal structures of the spectrometer, the marine detection efficiency of the underwater spectrometer developed was calculated and verified by the field experiments. The results show that this kind of non-experimental calibration method is effective and feasible for the underwater spectrometer and further research and experiments will continue.
A Small Traffic Sign Detection Algorithm Based on Modified SSD
Authors: Shan Hongxin , Zhu Wenxing 
Published: October 2019

Abstract
Traffic sign detection is an important part of many systems such as autonomous driving, driver safety and assistance. In this paper, the detection capability of SSD for small targets is analyzed and improved based on ssd_300 model. CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB) dataset is used to train the model for Chinese road traffic conditions. The improved model was compared with ssd_300 model. The experimental results show that the mAP of the improved model on the test dataset achieves 0.85, which is 0.13 higher than ssd_300, and the algorithm can reach real-time detection. The improved model can effectively detect three categories of Chinese traffic signs and has strong robustness against various disturbances.
Artificial Intelligence’s Turn of Philosophy
Authors: Feng Tao 
Published: October 2019

Abstract
Artificial Intelligence’s turn of philosophy emerges because of the new crisis in philosophy. Philosophy should attach importance to technical thinking to supplement the deficiency of philosophical thinking. Philosophy’s “language turn” and “AI turn” are based on language. The paradigm shift is also that we can define “intelligence”, “consciousness” and “subjectivity” in the way of conventionalism. With the rapid development of AI technology, we also need to guide the development of technology with “Aesthetic reason” and promote the harmony between science and technology and art, human and nature.
Design and Implementation of Software Simulation System for Dental Orthodontic Robot
Authors: Zhou Chuanhong , Pan Shiyu , Zhou Tao 
Published: October 2019

Abstract
With the development of society and the improvement of living standards, people’s pursuit of their own beautiful appearance is getting higher and higher, especially the passion for beautiful features. Therefore, the demand for dental orthodontic surgery is growing in daily life. However, due to the limitations of oral physiological structure, the orthodontic brackets designed in traditional orthodontic surgery are often not the optimal dental arch shape for patients. Therefore, a dental orthodontic software system that can interactively simulate a patient’s oral model is particularly important. In this paper, a software system of dental orthodontic robot which can simulate dental orthodontics was developed by using Blender secondary development technology combined with orthodontics and tooth extraction algorithm.
Fault Diagnosis Method of Photovoltaic Array Based on BP Neural Network
Authors: Wang Junjie , Wang Shan , Liu Haixiong , Hong Jianbo , Gao Dedong 
Published: October 2019

Abstract
Photovoltaic arrays are prone to various failures due to long-term work. In order to quickly and accurately diagnose the type of failure of the PV array and implement online monitoring of the PV array, this paper proposes the BP neural network for PV array fault diagnosis, and proposes a network search method when training BP neural network. And the K-cross-validation method is used to select the number of hidden layer nodes. The BP neural network fault diagnosis model designed and trained by this method is proved to have high precision.
Divide-and-Conquer CBS Design for Multi-Agent Path Finding in Large-scale Scenario
Authors: Chen Mengxia , Liu Yiming , Huang Hejiao 
Published: October 2019

AbstractMulti-Agent Path Finding(MAPF) problem is intensively studied in theoretical computer science, robotics and so on. The key for MAPF problem is to plan a conflict-free path for each agent with different start and goal positions, and to minimize the cost of the paths.Conflict-Based Search(CBS) is one of the optimal algorithms, which can ensure that the optimal solution is obtained, in the case of small-scale maps and low number of agents. However, in many real-world multi-agent systems, the scale of the map is very large and the number of agents is more. In most cases, CBS is not applicable any more. Therefore, we develop Divide and Conquer CBS, called DC-CBS algorithm which divides large-scale map and the original problem into several smaller ones. For each subproblem, we use the CBS to get the optimal solution. The experimental results show that for the large-scale scenario, the DC-CBS algorithm can get the result more quickly and plan the paths for agents successfully. As for small-scale situation, the performance of DC-CBS is almost as good as that for CBS.
Document Segmentation Method Based on Style Feature Fusion
Authors: Liu Gang , Wang Kai , Liu Wangyang , Cheng Xu , Li Tao 
Published: October 2019

Abstract
Style crack refers to the position where the author’s identity changes in the article completed by multiple authors. This paper summarizes the current situation and theory of related fields at home and abroad, and proposes a multi-feature based document segmentation method for plagiarism detection. Seven text style features are used for style crack recognition. Through the result of feature extraction, the combination of multi-feature fusion and unsupervised machine learning algorithm is used to classify the features based on extraction, and the clustering algorithm is used to cluster the style features so as to find the location of style cracks. Experiments show that the method is effective and scientific, and achieves good results.
Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices
Authors: Xia Tiancheng , Jiang Richard , Fu Yong Qing , Jin Nanlin 
Published: October 2019

Abstract
Automated in-vitro cell detection and counting have been a key theme for artificial and intelligent biological analysis such as biopsy, drug analysis and decease diagnosis. Along with the rapid development of microfluidics and lab-on-chip technologies, in-vitro live cell analysis has been one of the critical tasks for both research and industry communities. However, it is a great challenge to obtain and then predict the precise information of live cells from numerous microscopic videos and images. In this paper, we investigated in-vitro detection of white blood cells using deep neural networks, and discussed how state-of-the-art machine learning techniques could fulfil the needs of medical diagnosis. The approach we used in this study was based on Faster Region-based Convolutional Neural Networks (Faster RCNNs), and a transfer learning process was applied to apply this technique to the microscopic detection of blood cells. Our experimental results demonstrated that fast and efficient analysis of blood cells via automated microscopic imaging can achieve much better accuracy and faster speed than the conventionally applied methods, implying a promising future of this technology to be applied to the microfluidic point-of-care medical devices.
Fusion of Deep Feature and Hand-Crafted Features for Terrain Recognition
Authors: Li Xue , Ma Xin , Song Peng 
Published: October 2019

Abstract
Terrain recognition is one of the key problems of mobile robots. It can help the robots understand the surrounding environment. With terrain prediction, the robots could realize autonomous navigation and path planning. This paper focuses on image feature selection for terrain recognition with visions. For terrain recognition tasks, feature is used to represent image information. Traditional visual features can be targeted to express the low-level information like color or texture. The deep feature is extracted by self-learning of neural network, containing richer semantic information than low-level features. There is a complementary relationship between the two. The efficiency and accuracy of terrain recognition is remarkably raised by the fusion of two above features. In the course of algorithm, combination of the off-line training model and on-line recognition model is used to identify the terrain type of the sample, which is to ensure the real-time performance. The corresponding terrain dataset--SDUterrain is established. The algorithm achieves 96% or higher classification accuracy in the experiments based on the SDUterrain Dataset, which is much higher than the single feature classification algorithm.
Visual System for Tracking Specific Human Body Extraction of 3D Skeletal Points Based on Monocular
Authors: Xu Baopeng , Zhao Shuying 
Published: October 2019

AbstractIn this paper, we introduce the Extracting specific human 3D skeleton point system based on monocular tracking. The system mainly consists of two parts. The first part is the detection and tracking of specific human body. This article uses simple online and real time tracking with a deep association metric (DEEP SORT)[1] algorithm, which is simple but effective, and meets system requirements in terms of efficiency and real-time. The second part is to extract the 3D bone points for the specific target of the tracking. We refer to Xingyi Zhou’s research work[2] in this area. Utilizing the correlation between 2D pose and depth estimation subtasks, the training is end-to-end, and the algorithm introduces 3D geometric constraints to normalize 3D pose prediction, which is effective without ground truth value depth labels. In this paper, the two methods are combined by improvement, and the Extracting specific human 3D bone point system based on monocular tracking is designed. It can realize the tracking of 3D skeletal points of specific targets. The system has high practical value in human-computer interaction, virtual reality and motion recognition.
Human Motion Trajectory Prediction in Human-Robot Collaborative Tasks
Authors: Li Shiqi , Wang Haipeng , Zhang Shuai , Wang Shuze , Han Ke 
Published: October 2019

Abstract
A method is introduced to predict human motion trajectory in the process of human-robot collaboration (HRC). In the method, the human-robot distances are assumed to be a Gaussian Process (GP). To achieve this, a human-robot handover task is conducted by a human and a collaborative robot, while the positions of the human hand and the robot end-effector are recorded. Some of the recorded data are used for the Gaussian Process Regression (GPR), a GP and a 95% confidence convince about the GP are obtained by the GPR. Experimental results show that about 80% of the testing data are included in the 95% confidence convince. The method and results here are useful to other human-robot collaborative tasks where existing human-robot relative motions, especially, the method is able to predict the human motion trajectory with varying initial position of the human hand and varying locations of the robot end-effector.
Vibration Characteristics of Elastic Metamaterial Rod
Authors: Zheng Ziyue , Shu Haisheng , Shuowei A , Mu Di , Zhao Lei , Su Yuqiang 
Published: October 2019

Abstract
This paper investigates the free vibration and steady forced vibration of elastic metamaterial rods. Firstly, the elastic metamaterial rod is equivalent to the effective medium rod based on the effective medium theory. Then the free vibration and steady forced vibration method of the effective medium rod are established from the classical theory, and with this method we can explored the difference characteristics between the elastic metamaterial rod and the natural media rods. The results show that the effective medium rod has some strange phenomenon such as the same shape of different natural frequencies, the accumulation of natural frequencies near the lower band and the loss of natural frequency. This work is expected to help the vibration control of the elastic metamaterial for finite structure.
Prismatic Species of Snelson’s Tensegrity Structure
Authors: Vumiliya Angelo , Luo Ani , Liu Heping , González Andrés 
Published: October 2019

Abstract
Snelson’s prisms are regular minimal tensegrity structures. They are symmetric according to the rotation axis and have two prisms in two parallel planes. These planes, separated by a certain height, have a finite number of vertices related to the number of bars. Evidently, by changing the position of the upper nodes of bars results in a new stable configuration. This paper demonstrates the possibility to obtain different species through one set and identify the structures’ class according to their species.
Hydrodynamic Analysis and Structural Optimization of an Underwater Robot
Authors: Liu Zhimin , Zhou Xiaozhong 
Published: October 2019

Abstract
BYSQ-2 is an amphibious spherical exploring robot which can move flexibly in water and on land, it has good water pressure resistance and can perform a rotational motion with a zero degree turn radius. But the actual experiment results show the shortcoming of the robot is the low maximum surge speed. The main objective in this paper is to improve the maximum surge speed and alleviate the heavy pendulum’s oscillation. Firstly, the hydrodynamic coupling under different structural parameters and propeller working states is analyzed by using CFD method, the influence of structure parameters and propeller rotation speed on thrust and water resistance are summarized, based on the optimal structural parameters, the new generation robot with better performance is designed. The simulation and experiment results show the validity of the new design scheme.
The Numerical Analysis of Hydrodynamic Characteristics of Ducted Propeller by using SST k-ω Model
Authors: Yue Wang , Wanlong Ren , Gang Liu , Yuanming Zhang , Zongrui Hao 
Published: October 2019

Abstract
Hydrodynamic characteristics of the four-blade ducted propeller are numerically simulated by CFD method. The hydrodynamic performance of the propeller is analyzed by the SST k-ω Model, including the external characteristic parameters such as thrust and torque. The forward velocity at the propeller surface of the ducted propeller, the distribution of the induced velocity along the propeller surface, and the relationship between the propeller thrust and the surrounding velocity field are analyzed. The pressure distribution on the inner wall of the conduct is complicated. The pressure in the front half of the inner wall is small and large in the rear half. Axial, radial and tangential velocity distributions of sections at different distances from the paddle surface are calculated. The velocities on the dorsal side of the propellers are significantly higher than that on the foliar side and lower near the conduit.
Numerical Simulation on Oil Spill at Different Positions on the Back Surface of a Blunt Body
Authors: Xu Juan , Hao Zongrui , Wang Yue , Liu Jin , Liu Gang , Zhang Yuanming 
Published: October 2019

Abstract
The movement of oil spilled from the back surface of an underwater blunt body is influenced by the flow around the blunt body, so the trajectory of the oil movement changes with the vortex behind the blunt body. A numerical modelling using VOF model is established to simulate the oil spill process. Three typical spilling points at the top, middle, and bottom of the back surface of the blunt body, respectively, are simulated at different leaking rates. Their vorticity fields and the oil trajectories are analyzed. The results show that the spill from the top position generates the smallest oil distribution on the sea surface, while that from the bottom position leads to the largest one.
Seismic Effect Research of Seismic Measures for Beam Bridge under Different Earthquakes
Authors: Zhang Yu-min , Weng Guang-yuan , Shi Yun , Dai Jian-bo 
Published: October 2019

Abstract
Collision, collapse and expansion device damage are very likely to occur in bridge structures under large earthquakes. Earthquake waves with exceeding probability of 63.2%, 10%, and 2% in 100 years, as well as the Northridge earthquake and Kobe earthquake have been used to analyze the seismic response under earthquakes. The result shows that collision and collapse damage of bridge can be effectively controlled by the seismic measures. When the peak acceleration of earthquake is large and the control ability of the limit device to the excessive displacement of the structure is insufficient, the unseating prevention device can effectively make up the deficiency of the limit device to limit the excessive displacement of bridge.
Phase space trajectories generated under coupling between a dynamic system and a thermal reservoir
Authors: Sinha Dhiraj 
Published: October 2019

Abstract
We analyze the phase space trajectories generated under coupling between a dynamic system and a thermal reservoir which generates a fluctuating as well as dissipative force field. We argue that the phase space trajectory of particles associated with intermediate equilibrium states under heat transfer possess a symmetric form, while the corresponding trajectory is asymmetric in energy transfer as work. The new perspective can help in developing a closed form expression of heat and work at microscopic dimensions with a few degrees of freedom. We also present a novel mathematical model of thermal reservoir as a dynamic system described using a transfer function comprising of a set of zeros. It addresses the theoretical weaknesses of current models of a thermal reservoir comprising of a collection of harmonic oscillators.
Early warning tropical forest loss alerts in Peru using Landsat
Authors: Vargas Christian , Montalban Joselyn , Leon Andrés Alejandro 
Published: October 2019

Abstract
Since March 16, 2017, the National Forest Conservation Program for Climate Change Mitigation (PNCBMCC) of Peru’s Ministry of the Environment (MINAM) has been implementing a methodology to detect early warning alerts of humid tropical forest cover loss in Peru using data from the Landsat 7 and 8 satellites. The method uses Direct Spectral Unmixing (DSU) to detect forest loss as small as 25% of a pixel. Between March 16 and December 25 of 2017, 500 Landsat images have been used to detect 137,143 hectares of humid tropical forest cover loss, including deforestation for agricultural expansion and illegal or informal extractive activities, such as the opening of roads for selective logging. Natural forest loss was also detected, produced by windstorms and landslides in mountainous areas, among others. The results were verified with high-resolution satellite images and the accuracy was evaluated using a stratified random sample, showing a high level of both user’s and producer’s accuracy. The early warning alerts are distributed and available through the Geobosques platform (http://geobosques.minam.gob.pe).
Spatial and temporal variation of offshore wind power and its value along the Central California Coast
Authors: Wang Yi-Hui , Walter Ryan K , White Crow , Kehrli Matthew D , Hamilton Stephen F , Soper Patrick H , Ruttenberg Benjamin I 
Published: October 2019

Abstract
The analysis of the spatiotemporal variability of wind power remains limited during the planning stage of an offshore wind farm. This study provides a framework to investigate how offshore wind power varies along the Central California Coast over diurnal and seasonal time scales, which is critical for reliability and functionality of the grid system. We find that offshore wind power in this region peaks during evening hours across all seasons and maximizes in spring and summer. The timing of peak offshore wind power production better aligns with that of peak demand across California than solar and land-based wind power production, highlighting its potential to fill the supply gap when demand is high and power production from other renewable energy sources is low. We further assess the value of offshore wind power using demand-based and wholesale market metrics. Both metrics indicate high potential value of offshore wind power over most areas in this region. Finally, we show that the estimate of power production is significantly biased when using mean wind speeds that do not account for temporal variability, leading to potentially inaccurate predictions about locations that are expected to produce the most power. These results reiterate the importance in considering spatiotemporal variability in wind power for accurately calculating the value of offshore wind development.