bluuebunny/crossref_metadata_2025_split huggingface dataset:
Causer and causee as two higher-ranked thematic roles
Authors: Li Chao 
Published: May 2020

AbstractOn the basis of the argument realization of Mandarin resultative verb compounds, this paper argues that the Proto-Agent properties as well as the Proto-Patient properties proposed byDowty (1991)are not equal in status. Specifically, the Proto-Agent property corresponding to the Causer and the Proto-Patient property corresponding to the Causee are two higher-ranked properties. In a non-prototype approach to thematic roles, this means that the Causer and the Causee are two higher-ranked thematic roles that are immediately relevant to the argument realization of monotransitive causative predicates. The paper shows that, compared with Dowty’s equal-weight approach, the alternative approach recognizing the Causer and the Causee as two higher-ranked properties or roles can give a simpler, more effective, and more straightforward account of the argument realization associated with monotransitive causative predicates, including lexical causatives, morphological causatives, and resultatives. This study has implications for research in the argument realization of causatives involving three (or more) arguments as well. Meanwhile, it has implications for any theory utilizing thematic hierarchy because (i) none of the thematic hierarchies proposed in the literature includes both the Causer and the Causee and (ii) a complete theory of thematic roles needs to take these two higher-ranked roles into consideration.
Glucose limitation andpka1deletion rescue aberrant mitotic spindle formation induced by Mal3 overexpression inSchizosaccharomyces pombe
Authors: Tanabe Takuma , Kawamukai Makoto , Matsuo Yasuhiro 
Published: May 2020

AbstractThe cAMP-dependent protein kinase Pka1 is known as a regulator of glycogenesis, transition into meiosis, proper chromosome segregation, and stress responses in Schizosaccharomyces pombe. We demonstrated that both the cAMP/PKA pathway and glucose limitation play roles in appropriate spindle formation. Overexpression of Mal3 (1–308), an EB1 family protein, caused growth defects, increased 4C DNA content, and induced monopolar spindle formation. Overproduction of a high-affinity microtubule binding mutant (Q89R) and a recombinant protein possessing the CH and EB1 domains (1–241) both resulted in more severe phenotypes than Mal3 (1–308). Loss of functional Pka1 and glucose limitation rescued the phenotypes of Mal3-overexpressing cells, whereas deletion of Tor1 or Ssp2 did not. Growth defects and monopolar spindle formation in a kinesin-5 mutant, cut7-446, was partially rescued by pka1 deletion or glucose limitation. These findings suggest that Pka1 and glucose limitation regulate proper spindle formation in Mal3-overexpressing cells and the cut7-446 mutant.
Characteristics and techniques of GaN-based micro-LEDs for application in next-generation display
Authors: Wang Zhou , Shan Xinyi , Cui Xugao , Tian Pengfei 
Published: May 2020

Abstract
Due to the excellent optoelectronic properties, fast response time, outstanding power efficiency and high stability, micro-LED plays an increasingly important role in the new generation of display technology compared with LCD and OLED display. This paper mainly introduces the preparation methods of the GaN-based micro-LED array, the optoelectronic characteristics, and several key technologies to achieve full-color display, such as transfer printing, color conversion by quantum dot and local strain engineering.
Designer substrates and devices for mechanobiology study
Authors: Xi Wang , Delacour Delphine , Ladoux Benoit 
Published: May 2020

Abstract
Both biological and engineering approaches have contributed significantly to the recent advance in the field of mechanobiology. Collaborating with biologists, bio-engineers and materials scientists have employed the techniques stemming from the conventional semiconductor industry to rebuild cellular milieus that mimic critical aspects of in vivo conditions and elicit cell/tissue responses in vitro. Such reductionist approaches have help to unveil important mechanosensing mechanism in both cellular and tissue level, including stem cell differentiation and proliferation, tissue expansion, wound healing, and cancer metastasis. In this mini-review, we discuss various microfabrication methods that have been applied to generate specific properties and functions of designer substrates/devices, which disclose cell-microenvironment interactions and the underlying biological mechanisms. In brief, we emphasize on the studies of cell/tissue mechanical responses to substrate adhesiveness, stiffness, topography, and shear flow. Moreover, we comment on the new concepts of measurement and paradigms for investigations of biological mechanotransductions that are yet to emerge due to on-going interdisciplinary efforts in the fields of mechanobiology and microengineering.
Growth of aligned SnS nanowire arrays for near infrared photodetectors
Authors: Shen Guozhen , Chen Haoran , Lou Zheng 
Published: May 2020

Abstract
Aligned SnS nanowires arrays were grown via a simple chemical vapor deposition method. As-synthesized SnS nanowires are single crystals grown along the [111] direction. The single SnS nanowire based device showed excellent response to near infrared lights with good responsivity of 267.9 A/W, high external quantum efficiency of 3.12 × 104 % and fast response time. Photodetectors were built on the aligned SnS nanowire arrays, exhibiting a light on/off ratio of 3.6, and the response and decay time of 4.5 and 0.7 s, respectively, to 1064 nm light illumination.
Dynamic Analysis of Rotor-Seal System Considering the Radial Growth Effect of the Seal
Authors: Shet Vivek V , Sekhar A.S. , Prasad B. V. S. S. S. 
Published: May 2020

Abstract
Effect of the increase in operating speed, temperature and pressure ratio on turbomachine components must be studied and understood seriously. One of the major challenges is to choose a right initial clearance for seals considering the seal growth as it may affect rotor system dynamic characteristics and leakage performance. In the present study the dynamic response of the rotor system has been calculated considering the seal growth effect at each operating speed. It is been analyzed by the response plots and the orbital plots. The results show that the dynamic response of the system is influenced by radial growth of seal at lower pressure ratio and higher rotational speed. At higher pressure ratio the radial growth of seal has insignificant effect on rotor stability. In addition to these, the present study also emphasizes on the effect of the seal on the overall rotor system by comparing two different rotor-seal system.
Research of Using Inflatable Braking Devices in the Orbital Service System Application
Authors: Koryanov V V , Chen Danhe , Wei Xiaodie 
Published: May 2020

Abstract
In this scientific work are considered questions regarding the use of inflatable braking devices to deorbit spacecrafts, thus avoiding the formation of new space debris. The principle of this technology is to increase the ballistic coefficient by creating a larger cross-sectional area of the space vehicle (compared to the original one) due to the deployment of braking devices. The final form of the disclosed braking device may be different. In this case, the spacecraft can initially be equipped with brake devices during its design and assembly on Earth. Another option is when the braking device is installed on the spacecraft after it has been classified as space debris. In this case, we need to capture the space debris and install the device. But then, it requires space debris to have a standardized interface for docking. In this scientific work, several variants of the form, arrangement of types and types of inflatable braking devices are considered. Next, a comparison of these devices according to various criteria is made and a conclusion is made about the advantages and disadvantages of using each of them.
Performance Comparison of Load Frequency Control for Power System by ADRC Approach and PID Controller
Authors: Abdalla Mohammed. A. , Yang Guotian , Huang Congzhi 
Published: May 2020

Abstract
Load Frequency Control (LFC) is the main problem of power networks which outcomes because of mismatch between real power generation and consumption as the power consumption changes every period. therefore, the frequency of the power system may not be at rated value resulting in power quality problems.
Hence, the aim of LFC is to reduce the transient changes of area frequency and tie-line power transfer maintaining them at steady state. Moreover, the external disturbances and uncertainty factors, as well as parameters of the power system, causes a big problem for controller design in LFC. Therefore, this research describes a comparative consideration for Active Disturbance Rejection Control (ADRC) based LFC as an operational control strategy, which requires few information from the plant model and being robust against disturbances and uncertainties. Two intelligent controllers Particle Swarm Optimization (PSOA) Algorithm, Bacterial Foraging Optimization (BFO) mechanism are used for tuning Proportional Integral Derivative (PID) controller based LFC to acquire optimal values, taking into consideration single-area power system. The simulation results exhibit that the ADRC can obtain better performance than PID tuned by BFO and PSO in terms of frequency deviation and settling time.
An On-line Diagnosis Method of Broken Rotor Bars Fault in Motor of Aerospace Surveying Vessel via Salient Characteristic Residual Signal from Dynamic Model
Authors: Luo Q Y , Zi Y Y , Chen J L 
Published: May 2020

Abstract
Diagnosis of broken rotor bar fault in induction motors is a hot research focus in recent years. The most common method of diagnosis of this fault is to analyse the spectral characteristics of stator current. However the characteristic frequencies are too close to both sides of power frequency and usually covered by it in early failure. To overcome this limitation, this paper presents a method by introducing a dynamic model based on field-oriented control (FOC), which was widely applied in control. The FOC technique is modified to generate an estimated stator current signal in a little computation. The residual signal of stator current is produced by comparing the measured one and the estimated one as the motor running. The result of simulation shows that power frequency component is greatly reduced and the feature of fault is more significant than the measured current signal.
Research status and key technologies of all-day star sensor
Authors: Zheng Xunjiang , Huang Yongqin , Mao Xiaonan , He Feng , Ye Zhilong 
Published: May 2020

Abstract
The all-day star sensor can complete the star detection throughout the day to solve the problem that the sky signal is overwhelmed due to excessive sky background radiation during the daytime. In this paper, a comprehensive analysis of the status quo of the all-day star sensor is presented, and the technical indicators of the typical all-day star sensor at home and abroad are introduced. In order to improve the detection ability of daytime stars, based on the factors affecting the detection performance of star sensors throughout the day, short-wave infrared technology is used to detect weak star signals. The small field of view lens design is adopted, and the hood in the optical system is optimized to achieve an extinction ratio of 8.65×10−6. Low-contrast star map processing is performed using spectral filtering methods. The target of daytime stargazing is achieved.
An Eight-parallel Hierarchical Constant Modulus Algorithm for Sixty-four Quadrature Amplitude Modulation
Authors: Chao GAO , Xiaojie WU , Libin JIAO , Xiaofeng LI 
Published: May 2020

Abstract
One derivative type of the constant modulus algorithm (CMA) termed hierarchical CMA (HCMA) is investigated for its parallel implementation with 8 lanes. Taking the essence of CMA, HCMA is also the steepest descent under the least mean-square (LMS) criterion theoretically. To update the tap coefficients of the equalizer, HCMA utilizes adjustable modulus in accordance with the dynamical amplitude of the baseband signals. The above mechanism of HCMA can make the expectation of the error estimation converge to 0. The 8-parallel HCMA adopts the framework of the pipelined LMS adaptive filter. On the basis of the iterated short convolution and the relaxed look-ahead approximation, the implementation of 8-parallel HCMA can reduce much multiplication. The numerical simulation indicates that HCMA performs better than CMA with evident gain for the equalization of 64 quadrature amplitude modulation (QAM).
Experimental Study on Multi-Channel Positioning Technology of High Voltage Switchgear
Authors: Yu Jun , Li Ning , Zhao Jinlian , Reziya·Aierken  
Published: May 2020

Abstract
This paper introduces the basic principle of high voltage switchgear discharge, expounds several common discharge types, and studies the precise localization of partial discharge of high voltage switchgear based on hyperboloid positioning method and space grid search method. The simulation test of the diagnostic process is used to test the feasibility of the above method. On the basis of precise positioning of the power supply, the local discharge power supply is simplified to a Gaussian current source, and the pulse parameters are calculated by the combination of the simulated annealing algorithm and the fdtd method. Evaluation. Simulation results show that the method of using multiple sensor measurements can accurately evaluate the equivalent pulse width τ and equivalent current amplitude i0 of the simulated Gaussian pulse.
Research on Virtual Maintainability Verification Method of EMU Engine
Authors: Liu Yuan , Song Zuowei , Gao Peng , Liu Jian 
Published: May 2020

Abstract
Aiming at the problem of insufficient maintainability design method of EMU engine, a method of using virtual simulation to verify maintainability design is proposed. This paper mainly studies the quantitative evaluation method of maintainability index, and provides the quantitative evaluation method of accessibility and operation space based on virtual maintenance, the quantitative evaluation method of maintenance human factors and the calculation method of maintenance time. Combining with the characteristics of EMU engine system, the scheme design of virtual maintainability design software system for EMU engine is carried out, and the specific process of maintainability verification is given. The efficiency and quality of engine maintainability design and evaluation are effectively improved, and the development cycle is shortened. It is an effective method to realize the parallel optimization design of engine maintainability for EMU.
Research on Human Action Recognition Algorithm Based on Sine Feature
Authors: Zhang Haiyun , Dou Huazhou , Li Bo 
Published: May 2020

Abstract
Recent studies have shown that exploring features of the skeleton data is vital for human action recognition. Nevertheless, how to effectively extract discriminative features is still a challenging work. In this paper, we propose a novel method that extracts a sine feature for human action recognition from skeleton data. Kinect is used to extract human skeleton information (3D coordinates of each joint point) firstly, then two joint points are connected to define a skeleton vector according to the principle of human body structure, and the sine feature of each skeleton vector is calculated as a new pose description feature, SVM is used to classify the obtained features for human action classification. Experimental results on Cornell Activity Database are provided, and the results demonstrate the effectiveness of our approach.
A Novel Image Super-Resolution Method Based on Attention Mechanism
Authors: Li Da , Wang Yan , Liu Dong , Li Ruifang 
Published: May 2020

Abstract
Image super-resolution processing technology is used to reconstruct high-resolution images from low-resolution images. With the development of deep convolution neural network, image super-resolution processing methods based on deep learning have become the main technology. A novel image super-resolution processing method which is based on attention mechanism is proposed in this paper. The network framework consists of two modules: one is an improved position feature extraction network based on residual network and dense network, and another is a channel feature extraction based on channel attention mechanism. Meanwhile, this paper extracts features directly from low-resolution images, and then amplifies them to reduce the computational complexity. From the experimental results, we can find that the proposed method can enlarge the image better and increase the PSNR on Set5, Set14 and B100.
Real Time Detection Algorithm of Parking Slot Based on Deep Learning and Fisheye Image
Authors: Xu Congcong , Hu Xizhi 
Published: May 2020

Abstract
Parking slot detection is the basic part of environment perception in automatic parking system. How to detect parking slot accurately and effectively is a key problem that has not been solved in automatic parking system. In order to make up for the defects of parking slot detection based on ultrasonic radar and solve the problems of low recognition rate, sensitivity to environmental changes and weak generalization ability brought by vision-based parking slot detection method. In this paper, a method based on the deep convolution neural network is proposed to detect parking slot. The algorithm takes the fisheye image collected by the four-way fisheye camera mounted on the car body as the input and adopts the improved YoloV3 network structure to detect parking slot directly in the fisheye image. The experimental results show that the recall ratio of the method is 98.72% and the accuracy is 99.14% on the self-made parking data set. The algorithm has achieved good results in real vehicle vehicle environment and can achieve real-time detection.
A Literature Review: Intrusion Detection Systems in Internet of Things
Authors: Chauhan Anamika , Singh Rajyavardhan , Jain Pratyush 
Published: May 2020

Abstract
Internet of Things (IoT) is a new and surging technological advancement where in which the Internet is connected to various day-to-day physical objects belonging to various domains and making them “smart” like that of various machine dependent processes, manufacturing processes and healthcare. It is a system of interrelated computing devices, machines and people. Internet-connected IoT devices brings several benefits in our daily life but are susceptible to security issues. These vulnerabilities to IoT systems create security threats for any smart environment formed around the aforementioned concept. Thus, there is a need for intrusion detection systems (IDSs) which are designed specifically for IoT systems in order to combat these security related threats and create a secure network for the smart environment. IoT devices tend to have limited computing and storage capabilities. Hence, conventional IDSs might not be suitable for such networks. Due to the specifications and protocols of IoT devices, intrusion detection in IoT systems proves to be a challenging task. Thus, the field of intrusion detection systems in IoT systems is one that requires research and considerable effort to address the key security issues and security threats. A literature review is presented on the IDS in IoT topic, with emphasis on the present-day research, challenges and future directions.
DNN Based Camera and Lidar Fusion Framework for 3D Object Recognition
Authors: Zhang K , Wang S J , Ji L , Wang C 
Published: May 2020

Abstract
A 3-stages deep neural network (DNN) based camera and lidar fusion framework for 3D objects recognition is proposed in this paper. First, to leverage the high resolution of camera and 3D spatial information of Lidar, region proposal network (RPN) is trained to generate proposals from RGB image feature maps and bird-view (BV) feature maps, these proposals are then lifted into 3D proposals. Then, a segmentation network is used to extract object points directly from points inside these 3D proposals. At last, 3D object bounding box instances are extracted from the interested object points by an estimation network followed after a translation by a light-weight TNet, which is a special supervised spatial transformer network (STN). Experiment results show that this proposed 3d object recognition framework can produce considerable result as the other leading methods on KITTI 3D object detection datasets.
A Semantic Segmentation and Edge Detection Model Based on Edge Information Constraint Training
Authors: Wang Longlong , Liu Fuxiang , Xu Jingqing 
Published: May 2020

Abstract
The purpose of semantic segmentation is to classify the pixels within the target contour. Edge detection is another major basic vision task in machine vision. Today’s most effective semantic segmentation models and contour edge detection models are isolated networks. The edge of the output of the semantic segmentation model is coarse and cannot be directly used. And the output of the edge detection network cannot output the classification information of the pixels inside the contour. In view of the above shortcomings of the existing network, we propose a semantic segmentation model based on edge constraint optimization, so that the output of the semantic segmentation model has more delicate edge information, and the network directly outputs accurate contour edge graphs. The edge information output by the network can be directly used for tasks such as corner detection and center point detection. Experiments show that the mIOU statistics obtained by our model on the validation set of PASCAL VOC2012 can reach 83.9%. At the same time, more detailed edge details can be obtained. This algorithm has high engineering and theoretical research value.
Flsnet: Fast and Light Segmentation Network
Authors: Sun Qingwei , Chen Wei , Chao Jiangang , Zhang Hongbo 
Published: May 2020

Abstract
The use of deep learning for image segmentation has proven to be an efficient and accurate method, but with the complexity of the network structure, it takes up a lot of computing resources. The consumption of computing resources may be unacceptable during tasks. Aiming at this problem, a fast and light segmentation network (FLSNet) is proposed, which uses the Encoder-Decoder method to extract features. All convolutional layers use depthwise separable convolutions and the channel attention module is linked between Encoder and Decoder. Experiments are performed on the autonomous driving dataset CamVid. The results show that with a slight increase in segmentation accuracy, the model size becomes 8.65% of SegNet, the required computing resources are reduced by a dozen times, and the segmentation speed is increased by about 12%, which show that our network is efficient.
A Robust Monocular Depth Estimation Framework Based on Light-Weight ERF-Pspnet for Day-Night Driving Scenes
Authors: Zhou Keyang , Wang Kaiwei , Yang Kailun 
Published: May 2020

Abstract
With the development of deep learning, various fields of computer vision have made huge progress. Among them, depth estimation is an important part of scene perception, therefore receives much interest and is widely used in daily life with the assistance of GPUs. Besides, the ways to obtain depth maps have also been improved, from using multiple images to a single image to obtain depth, which is called monocular depth estimation task. In this paper, we design a convolutional neural network called ERF-PSPNet to perform the task. We prove that by using unsupervised training, monocular depth estimation’s result learned from large-scale dataset is close to the result of stereo matching. We also show that the monocular depth estimation model proposed in this paper can achieve a satisfying precision while maintaining a certain real-time frame rate for day-night driving scenes, which confirms the practical applicability of our design and result.