bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monte Carlo simulations of energy deposition and DNA damage using TOPAS-nBio
Authors: Wu Jianan , Xie Yaoqin , Wang Luhua , Wang Yuenan 
Published: September 2020

Abstract

Purpose. Monte Carlo (MC) track structure codes are commonly used for predicting energy deposition and radiation-induced DNA damage at the nanometer scale. Various simulation parameters such as physics model, DNA model, and direct damage threshold have been developed. The differences in adopted parameters lead to disparity in calculation results, which requires quantitative evaluation. Methods. Three simulation configurations were implemented in TOPAS-nBio MC toolkit to investigate the impact of physics models, DNA model, and direct damage threshold on the prediction of energy deposition and DNA damage. Dose point kernels (DPKs) of electrons and nanometer-sized volumes irradiated with electrons, protons, and alpha particles were utilized to evaluate the impact of physics models on energy deposition. Proton irradiation of plasmid DNA was used to investigate the disparity in single-strand break and double-strand break (DSB) yields caused by differences in physics models, DNA models, and direct damage thresholds. Results. Electron DPKs obtained with different physics models show similar trends but different diffusiveness and maximums. Energy deposition distributions in nanometer-sized volumes irradiated with electrons, protons, and alpha particles calculated using different physics models have the same trend although discrepancies can be observed at the lowest and highest energy deposits. Strand breaks from incident protons in DNA plasmids vary with adopted parameters. For the configurations in this study, changing physics model, DNA model, and direct damage threshold can cause differences of up to 57%, 69%, and 15% in DSB yields, respectively. All these simulation results are essentially in agreement with previously published simulation or experimental studies. Conclusion. All the physics models, DNA models, and direct damage thresholds investigated in this study are applicable to predict energy deposition and DNA damage. Although the choice of parameters can lead to disparity in simulation results, which serves as a reference for future studies.
2D simulation of solar/lamp two-chamber photoelectric converter with different sodium–noble gas mixtures
Authors: Mandour M M , Astashkevich S A , Kudryavtsev A A 
Published: September 2020

Abstract
This work is devoted to the numerical analysis of the opportunity to obtain a steady generation of electromotive force (EMF) in a two-chamber gas cell exposed to concentrated solar/lamp radiation. For this purpose, we have carried out 2D simulations of the low-pressure photoplasma in the mixtures of sodium atom vapor and different noble gases and their pressures at different values of photoexcitation rate of resonance levels of Na. Herewith plasma chemistry and transfer of charges and radiation in the volume have been taken into account. It has been established that argon is a better candidate to be used as a buffer gas for photoplasma electric converter based on the obtained plasma parameters and commercial aspects. A parametric study has been carried out for the Na–Ar mixture to investigate the effect of increasing homogeneous photoexcitation rate in the small chamber on plasma parameters and obtained EMF. Another parametric study has been conducted to calculate the output current and electric power at a loaded chain. The results show that EMF could be obtained using the photovoltaic effect in photoplasma for the two-chamber cell. The present paper gives a key to the understanding of the effect of different processes (radiation, chemical, and electric) in the cell with these gas mixtures. The obtained results can be used in projecting a solar photoelectric converter based on a two-chamber device with a mixture of sodium and noble gases.
Dual-frequency on–off modulation and slow light analysis based on dual plasmon-induced transparency in terahertz patterned graphene metamaterial
Authors: Li Min , Li Hongjian , Xu Hui , Xiong Cuixiu , Zhao Mingzhuo , Liu Chao , Ruan Banxian , Zhang Baihui , Wu Kuan 
Published: September 2020

Abstract
A dual-frequency on–off modulator with considerable modulation depth (MD) and relatively low insertion loss (IL) is performed with patterned monolayer graphene metamaterial. Destructive interference in this structure gives rise to the dual plasmon-induced transparency (DPIT) phenomenon. The coupled mode theory, confirmed by simulated values, is comprehensively introduced to expound the physical mechanism of the DPIT effect. In addition, the influences of the Fermi level on the DPIT transmission spectrum and the carrier mobility of graphene on the on–off modulation are researched. It is found that the dual-frequency on–off modulator exhibits remarkable modulation performance on both switches and is easier to fabricate in operation than other multi-layer graphene-based modulators. In the ‘on1/off1’ state, the MD and IL are 93%, 0.32 dB, respectively. In the ‘on2/off2’ state, the MD and IL are 85%, 0.25 dB, separately. Moreover, the property of slow light reflected by the group index is analyzed. It exhibits that the group index of the proposed structure with multi-channel can reach 358. Thus, the proposed structure stretches the versatile applications in multi-function modulators and multi-channel slow light devices at the terahertz band.
Solving the all-FLR ICRH integro-differential wave equation as a high-order differential equation for studying combined ICRH-NBI heating
Authors: Van Eester Dirk , Lerche E A 
Published: September 2020

Abstract
A fast and intuitive method is proposed to solve the integro-differential wave equation relevant for modelling ion cyclotron resonance heating (ICRH) at any cyclotron harmonic, for arbitrary ion distributions and retaining all finite Larmor radius effects. The method is inspired on recent work of Budé who proposed to fit the dielectric tensor in k-space by a higher order polynomial, allowing to derive a suitable differential operator that conveniently mimics the integro-differential operator in a sufficiently large

k
⃗

-range to describe all physically relevant wave modes in

k
⃗

-space. Eliminating a drawback of the Budé method by going back to first principles yields a wave equation dielectric response operator that guarantees a positive definite power absorption for populations in thermal equilibrium—as physically expected—and that can directly be used in the Fokker–Planck equation. The method is exploited for 1D application but retaining the toroidal curvature. Analytical expressions of the components of the dielectric tensor are available for Maxwellian distributions as well as bi-Maxwellians with a finite parallel drift. Numerical integration of the velocity space integrals allows to account for arbitrary distributions. A number of examples relevant for high performance tokamak plasma operation and exploiting simultaneous ICRH and neutral beam injection heating are given to illustrate the method’s potential.
Multi-category Classification Problem Oriented Subsampling-Based Active Learning Method
Authors: Shi Wei , Feng Yanghe , Cheng Guangquan , Liu Shixuan , Liu Zhong 
Published: September 2020

Abstract
Traditional active learning methods have achieved gratifying results in the classification tasks of less categories such as binary classification, the application research of active learning in the field of big data problems still faces enormous challenges. Since many active learning query strategies need to perform matrix inversion, the amount of calculation increases exponentially with the increase of the scale of the problem, it is difficult to apply these active learning methods in large scale multi-category data classification task. In order to solve this problem, this paper designed a subsampling-based active learning model, and integrate unsupervised clustering algorithm with traditional active learning method, then conducted experiments on Binary Alphadigits and OMNIGLOT data sets. This paper compares the performance of five traditional active learning algorithms using this subsampling method, namely random sampling, uncertainty sampling, query-by-committee, density weighting and learning-based active learning. Through comparative experiments, the feasibility of active learning based on subsampling for solving the multi-category classification problem is verified, and it is found that the subsampling-based method can break the limitations of traditional active learning methods that cannot deal with large-scale data classification.
Real-Time Multi-object Grasp Based on Convolutional Neural Network
Authors: Yu Hui , Xu Yulin 
Published: September 2020

Abstract
The grasping of the robotic arm is the basic ability of the robot to perform a variety of complex tasks. For the robotic arm, visual-based grasping tasks are still a challenging problem. This paper presents a system to implement dynamic grasping of the robot arm. The neural network is used to detect objects in multiple scenes, and then the robot arm is controlled to achieve closed-loop grasp of the target object. In the process of grasping the target object, if the position of the target object moves, the robot arm will immediately stop and go to the new target position for grasping. The extensive experiments demonstrate that the proposed method enables the robotic arm to achieve dynamic grasping in real world.
3D Pose Registration of Antenna Board Based on Genetic Algorithm for AR Application
Authors: Zhu Teng , Su Yunzhong , He Junyong , Zhang Po 
Published: September 2020

Abstract
In view of the requirement of high geometric consistency and robustness of the Augmented Reality technology in the field of industrial auxiliary assembly applications, a theoretical model is proposed which can detection and tracking target pose and position in short period time by using the point features on the surface of base station antenna. Based on the Genetic Algorithm, the PnP matching problem can be transformed into gene evolution in the feature space. Comparing with the Hough transform method, the experimental result shows that the antenna pose detection method proposed in this paper can effectively adapt to the actual working scenario.
Remote Sensing Image Building Extraction Method Based on Deep Learning
Authors: Wang Minshui , Wang Mingchang , Yang Guodong , Liu Ziwei 
Published: September 2020

Abstract
Using random patches and deeplabV3+ network can effectively improve the building extraction accuracy and ensure the integrity of building. First, acquisiting the image of a 5000×5000 pixel one, and using the random Patch Extraction Datastore function to create a number of random patches with the size of 224×224 pixels as network input images. Second, creating a convolutional neural network based on resnet50 by using the deeplabv3plusLayers function, and then projecting the learned discrimination features with lower resolution to the pixel space with higher resolution, to realise the automatic extraction of the building. Third, two images were input to verify the extraction accuracy of the trained network. The results showed that the Pixel accuracy of image 1 and image 2 reached 97.98% and 92.59%. Compared with other building extraction algorithms, this method has higher extraction accuracy. This method has strong expansibility and It can be used for automatic extraction of other feature types.
A Ship Ranging and Positioning System Based on Binocular Vision Technology
Authors: Zheng Yabo , Wang Lixia , Zhou Ming , Wang Yu 
Published: September 2020

Abstract
Relative ranging and positioning is the basis to ensure the navigation safety of ships, and it has a broad application prospect in various maritime activities, especially in the aspects of docking and undocking, sailing in crowded areas, towing, convoy sailing, and warship replenishment, etc. In order to improve the autonomy and automation of ship ranging and positioning and reduce the cost, a novel type of ship ranging and positioning system based on binocular vision technology is proposed. The proposed system includes seven modules, i.e. video information acquisition module, ship attitude acquisition module, camera parameter calibration module, target recognition and segmentation module, image matching module, ranging and positioning module, and information display module. After acquiring the target image sequences by using two cameras, the proposed system identifies and segments maritime targets quickly by using Canny algorithm in the artificial interactive mode, realizes reduced-space search by using epipolar constraint method, achieve fast target features matching to obtain the vision disparity of the feature points by using the SURF algorithm, and calculates the distance of the target according to the binocular vision ranging principle, so as to realize interactive visual ranging and positioning. The proposed system has the advantages of low cost, simple structure and convenient operation. It can realize relative ranging and positioning with only one computer and two cameras. Experiments demonstrate that the proposed system, with fast running speed and high accuracy, can satisfy the relative ranging and positioning needs of ships.
Named Entity Recognition for the Horticultural Domain
Authors: Liu Ziyu , Luo Meng , Yang Hehe , Liu Xingwang 
Published: September 2020

Abstract
Named Entity Recognition (NER) is one of the fundamental tasks in natural language processing and knowledge engineering, as well as a prerequisite step of many downstream applications. Horticulture, a major branch of agricultural science, means the cultivation, processing, and sale of fruit, nuts, vegetables, and ornamental plants as well as numerous additional services. NER for the horticulture domain means to find key biological traits and state-of-art experimental methods for the horticulturists, new cultivation methods and useful tools for farmers, as well as other important information for planners and policy makers to trigger decision-making procedures. In this paper we designed an NER tagging-set of 7 fine-grained types, and since there is no publicly shared annotated corpus available in horticulture domain, we constructed training and testing corpora manually. Thus, we realized Horticulture NER in scientific literature abstracts with CRF method. Results showed that our system’s accuracy and precision were satisfactory but still have room for improvement.
The Enlightenment of the Theory Model of Audio-Visual Integration to Human-Computer Interaction Technology
Authors: Lu Hong , Pan Chaochao 
Published: September 2020

Abstract
The visual and auditory systems are the most important information processing systems in the brain. We usually sense external stimuli in the way of audio-visual integration. In this process, “visual” and “auditory” nervous form two functional systems which are relatively independent and interactive. It is of great significance for the fusion of multi-sensor data and human-computer interaction technology to understand the theoretical principles of integrating visual and auditory cues and the neural mechanism of audio-visual integration. Here, we try to explore new methods of audio-visual integration and develop new potential of human-computer interface. Previous studies have shown that the principles of integrating visual and auditory cues mainly include spatio-temporal rule, inverse-effectiveness rule and reliability rule. On this basis, researchers have gradually developed a theoretical model of audio-visual integration—causal inference model. By clarifying the mechanism of audio-visual integration, it provides a theoretical basis for the multimodal interaction technology of human-computer interaction, so as to improve the naturalness and efficiency of human-computer interaction and overcome the shortcomings of traditional human-computer interaction.
Implementation of License Plate Recognition System of Electric Bicycles in Nantong Area
Authors: Lv Xingqin , Wang Yan 
Published: September 2020

Abstract
Electric bicycle is a very important means of transportation for Chinese people. The regularization management of electric bicycles is an important means to ensure the orderly operation of traffic. This article will adopt LabVIEW software to identify the number of the new national standard electric bicycle license plate in Nantong area through image pre-processing, image correction, threshold processing, character recognition and other methods. Then the system will determine its place of ownership automatically and save the recognition results to the designated access database of the license plate number, which is convenient for the traffic management department to manage the corresponding violations, and also can effectively inhibit the occurrence of theft or hit-and-run of electric bicycles.
Effect of State Loneliness on Robot Anthropomorphism: Potential Edge of Social Robots Compared to Common Nonhumans
Authors: Li Sijia , Yu Feng , Peng Kaiping 
Published: September 2020

Abstract
Previous studies have widely demonstrated that loneliness will increase people’s anthropomorphic tendency on nonhuman agents. This research extends the effect of loneliness to social robots and differentiates them and common nonhuman agents (e.g., gadgets, animals). Remarkably divergent effects have been verified: experimentally induced loneliness has a positive effect on anthropomorphism of a social robot but not a gadget clocky or an animal. Practical implications are: (1) could prime consumers’ state loneliness when marketing domestic robot products; (2) emphasize humanlike warmth when designing social robots for companion. The findings have found social robots’ edge in providing companionship compared with common nonhuman agents.
A Deep Learning Approach to Reduce False Alarms for Optical Smoke Detectors
Authors: Liu Ming , Zhou Hongbin , Ren Yupeng , Lu Wei 
Published: September 2020

Abstract
Optical smoke detectors (OSDs) are fire-fighting equipment used to detect fire by detecting smoke with scattering phenomenon. From the principle of OSDs we can see that they are vulnerable to false alarms caused by dust or water steam. To reduce false alarms and to make OSDs more reliable, we present a deep learning approach to train a classifier to distinguish fire event from non-fire ones based on time series data. The classifier is modelled with a 1-Dimension convolutional neural network, and generative adversarial network is used to augment and balance training data. Experiment shows that our classifier can reduce more than 50% false alarms caused by water steam while maintaining sensitivity for fire events.
Explore Deep Learning for Chinese Essay Automated Scoring
Authors: Li Haojin , Dai Tao 
Published: September 2020

Abstract
Automated Essay Scoring (AES) has gained increasing attention in recent years. In this paper, we propose a novel automated Chinese essay scoring model, called BLA (BERT and Bi-LSTM with Attention), by using neural network. The model uses a BERT network to obtain the sentence vectors for an essay, and then uses a Bi-LSTM network with two layers to extract the essay vector. We also consider topic instruction as a long sentence and obtain its vector representation by BERT. The obtained topic instruction vector is then used as attention information to further help obtaining more effective essay vector. Moreover, we also present a new dataset that contains topic instruction and related essays collected from Chinese high school. The detailed experimental results show that our proposed model outperforms other baseline methods.
Ferroelectric Memristors Based Hardware of Brain Functions for Future Artificial Intelligence
Authors: Shi Mingmin , Zhang Miaocheng , Yao Suhao , Qin Qi , Wang Min , Wang Yu , He Nan , Zhu Jiale , Liu Xiaoyan , Hu Ertao , Xu Jianguang , Tong Yi 
Published: September 2020

Abstract
Brain-inspired neuromorphic computing systems have long attracted significant interests to replace the conventional Von-Neumann systems because the development of big data analysis and artificial intelligence has put forward higher requirements for computing speed and energy consumptions. Memristive devices are known as one of the most significant candidates to implement brain-inspired neuromorphic computing systems due to their special properties to emulate biological synapses of human brains. Ferroelectric material is a breakthrough for the resistive-switching layer of memristors due to their continuously tunable resistive switching behaviors. However, the application of ferroelectric memristors is limited by unstable performance and the low switching ratio. In this work, ferroelectric memristors have been fabricated and two-dimensional (2D) material MXene (Ti3C2) has been firstly introduced to the Cu/PZT/Pt devices. The insertion of MXene can enhance the performance of the devices by boosting the formation of conductive filaments of oxygen vacancies. The Cu/MXene/PZT/Pt devices exhibit stable resistive switching phenomena and have a high switching ratio about 106. The resistance of Cu/MXene/PZT/Pt memristors can be regulated under continuous voltage pulses. More interestingly, some synaptic functions have been successfully mimicked using Cu/MXene/PZT/Pt memristors. This work will pave the way for the development of the implementation of brain-inspired computing systems.
An Innovative Method for Locating the Welded Circular Seam on the Inner Surface of Cylinder Pipeline to Inspector Robot
Authors: Dong Xiang , Feng Qiaosheng , Xia Junfei , Zhang Yaping 
Published: September 2020

Abstract
Locating the welded seam on the inner surface of a pipeline from the frame image captured by the monocular optical camera is a key and challenging problem in intelligentizing the defect inspection robot. Due to the lack of feature points and feature straight lines on the image of the welded seam, the existing visual odometry method cannot be used to locate the welded seam relative to the inspector robot. In this paper, an innovative method is proposed for locating the welded circular seam on the inner surface cylinder pipeline to inspector robot. In this method, the whole shape of the welded seam is retrieved from the frame captured by the monocular camera of the robot and is used as the feature for the location. Through combining the retried shape features and the moving velocity of the robot, the distance from the robot to the welded seam can be calculated quickly based on the optical imaging principle. Experiments show that our method can effectively locate the welded seam. Our method can used in intelligent pipeline inspector robot to locate the welded seam to defect test.
Auxiliary Decoder and Classifier for Imbalanced Skin Disease Diagnosis
Authors: Xu Zili , Zhuang Jiaxin , Zhang Rong , Wang Ruixuan , Guo Xuemei , Zheng Wei-Shi 
Published: September 2020

Abstract
To date, deep learning has been widely adopted in medical diagnosis systems and made great achievements in real-world applications. However, in medical image-based intelligent diagnosis, the phenomenon of class imbalance often appears due to the substantially smaller available training data for rare diseases compared to common diseases, which usually degrade the performance of classification dramatically. In this paper, we propose a novel learning framework to effectively alleviate the impact of class imbalance, adding an auxiliary decoder to reconstruct original images and reusing the original CNN classifier to help the classifier more likely extract disease-relevant features for both rare and common diseases. Throughout experiments on two skin disease datasets support that the proposed framework outperforms strong baselines with a very explicit margin. The proposed method is also independent of network architecture so that it can be flexibly combined with different model structures. Besides, applying our proposed method with existing training strategies designed for class imbalance can still improve the classification performance.