bluuebunny/crossref_metadata_2025 huggingface dataset:
All-MBE grown InAs/GaAs quantum dot lasers with thin Ge buffer layer on Si substrates
Authors: Yang Junjie , Liu Zizhuo , Jurczak Pamela , Tang Mingchu , Li Keshuang , Pan Shujie , Sanchez Ana , Beanland Richard , Zhang Jin-Chuan , Wang Huan , Liu Fengqi , Li Zhibo , Shutts Samuel , Smowton Peter , Chen Siming , Seeds Alwyn , Liu Huiyun
Published: September 2020

A high-performance III–V quantum-dot (QD) laser monolithically grown on Si is one of the most promising candidates for commercially viable Si-based lasers. Great efforts have been made to overcome the challenges due to the heteroepitaxial growth, including threading dislocations and anti-phase boundaries, by growing a more than 2 µm thick III–V buffer layer. However, this relatively thick III–V buffer layer causes the formation of thermal cracks in III–V epi-layers, and hence a low yield of Si-based optoelectronic devices. In this paper, we demonstrate a usage of thin Ge buffer layer to replace the initial part of GaAs buffer layer on Si to reduce the overall thickness of the structure, while maintaining a low density of defects in III–V layers and hence the performance of the InAs/GaAs QD laser. A very high operating temperature of 130 °C has been demonstrated for an InAs/GaAs QD laser by this approach.
Probing the martensite transition and thermoelectric properties of Co
x
TaZ (Z
= Si, Ge, Sn and x
= 1, 2): a study based on density functional theory
Authors: Dutt Rajeev , Pandey Dhanshree , Chakrabarti Aparna
Published: September 2020

In this work, using density functional theory based electronic structure calculations, we carry out a comparative study of geometric, mechanical, electronic, magnetic, and thermoelectric properties of Co
x
TaZ alloys, where Z = Si, Ge and Sn and x = 1 and 2. In the present study, a systematic approach has been taken to perform calculations to probe the possibility of existence of a tetragonal (martensite) phase in these alloys and also to perform a comparative study of various physical properties of the six systems, mentioned above, in the cubic and possible tetragonal phases. From our calculations, a tetragonal phase has been found to be stable up to about 400 K in case of Co2TaSi and Co2TaGe alloys, and up to about 115 K for Co2TaSn, indicating the presence of room temperature cubic phase in the latter alloy unlike the former two. Further, the results based on the energetics and electronic structure have been found to corroborate well with the elastic properties. All the above-mentioned full Heusler alloys (FHAs) show magnetic behavior with metallicity in both the phases. However, their half Heusler counterparts exhibit non-magnetic semi-conducting behavior in the cubic phase. We calculate and compare the thermoelectric properties, in detail, of all the materials in the cubic and possible tetragonal phases. In the cubic phase, the half Heusler alloys exhibit improved thermoelectric properties compared to the respective FHAs. Furthermore, it is observed that the FHAs exhibit higher (by about an order of magnitude) values of Seebeck coefficients in their cubic phases, compared to those in the tetragonal phases (which are of the order of only a few micro-volts/Kelvin). The observed behaviors of the transport properties of the probed materials have been analyzed using the topology of the Fermi surface.
Elliptocyte detection technology based on super-resolution algorithms for a lensless imaging system
Authors: Li Jianwei , Dai Li , Yu Ningmei , Li Zhengpeng , Li Shuaijun
Published: September 2020

As typical poikilocytes, elliptocytes can indicate underlying diseases and are an important additional diagnostic parameter. The current method is to use a microscope for manual analysis of blood smears, which is non-automatic, time-consuming and requires skilled technicians. Lab-on-a-chip technology using a lensless imaging system provides a better solution. However, diffraction effects and low resolution are the two main problems faced by lensless imaging systems. In this paper, Fresnel diffraction at a straight edge is used to analyse the relationship between the red blood cell edge and diffraction rings. First, the diffraction characteristics of red blood cell edges are given. Next, the measurement accuracy is improved. The super-resolution algorithm proposed in this paper can make the measurement accuracy reach at least 1% of the pixel size, which can meet the needs of elliptocyte detection. Then, the complete elliptocyte detection algorithm is given. Finally, by analysing the diffraction characteristics of a typical elliptocyte in detail, a very good match with the elliptocyte images is achieved under the microscope. This proves that the detection technology is effective.
PV-Tower solar cell for small footprint photovoltaic energy harvesting for the internet of things application
Authors: Prakoso Ari Bimo , Rusli  , Wang Jianxiong , Lu Chenjin , Wang Hong
Published: September 2020

Development of Internet-of-Things requires the deployment of self-powered wireless sensor nodes (WSN). Towards this end, photovoltaic cells are often used to harvest ambient light energy. On the other hand, it is desirable to reduce the size of the WSN circuits as much as possible. However, WSN miniaturization will lead to less area available for solar cells and consequently, insufficient energy harvested to power the WSN. In this work, we develop a PV-Tower solar cell concept that offers a large active surface area for light absorption, while occupying only a small footprint. We demonstrate silicon-based PV-Tower solar cells with footprint areas of 0.2 mm2, 0.4 mm2, and 0.6 mm2, which can generate ≈80 times more power than conventional solar cells over the same footprint. We have also fabricated a small footprint PV-Tower module prototype that comprises 10 PV-Tower solar cells connected in series, which exhibits an open circuit voltage of 5.1 V. At the maximum power point, the module can deliver a voltage of 3.6 V and a power of ≈66.7 mW, which translates to an output power per footprint of ≈78.5 mW cm−2. By optimizing the module design, we estimate that there is room to significantly reduce its footprint and increase the output power per footprint by ≈10 times to 785 mW cm−2. The results show that the PV-Tower solar cell can serve as a compact small footprint photovoltaic energy harvesting device that can address the physical size constraints posed by miniaturization of the WSN circuit.
A study on the surface correction of large format infrared detectors
Authors: Zhang Wei-ting , Chen Xing , Ye Zhen-hua
Published: September 2020

Large format hybrid infrared focal array detectors, which require a wide range and high resolution, are of significant technical importance for earth observation systems and scientific space programmes. A typical large hybrid infrared focal plane array (IRFPA) detector formed by a detector chip is directly hybridized to a silicon readout integrated circuit (Si-ROIC) via indium bumps. However, for a large format IRFPA detector, the connection rate of the indium bumps strongly depends on the warpage of the Si-ROIC and the detector chip, which usually results from technological processes. In this paper, a surface correction structure is proposed to optimize the warpage of the Si-ROIC and the detector chip before flip-chip. The warpage of the Si-ROIC is analysed by the finite element method. The simulation results are in good agreement with the experiment. After optimization, the warpage of a 2048 × 2048 Si-ROIC decreased from 13 μm to less than 3 μm. Thus, it is beneficial to improve the IRFPA bump connection rate. Compared with traditional growth stress films, this method is more flexible and has easy accessibility. Eventually, it could enhance the stability of the large format IRFPA detector.
Double-valued strong-coupling corrections to Bardeen–Cooper–Schrieffer ratios
Authors: Talantsev E F
Published: September 2020

The experimental discovery of near-room-temperature (NRT) superconductivity in highlycompressed H3S, LaH10 and YH6 has restored fundamental interest in the electron–phonon pairing mechanism in superconductors. One of the prerequisites of phonon-mediated NRT superconductivity in highly compressed hydrides is strong electron–phonon interactions, which can be quantified by dimensionless ratios of the Bardeen–Cooper–Schrieffer (BCS) theory vs

k
B

T
c

/

ℏ

ω

l
n

variable, where Tc
is the critical temperature and

ω

l
n

is the logarithmic phonon frequency (Mitrovic et al 1984 Phys. Rev. B 29 184). However, all known strong-coupling correction functions for the BCS ratios are applicable for

k
B

T
c

/

ℏ

ω

l
n

< 0.20, which is not a high enough

k
B

T
c

/

ℏ

ω

l
n

range for NRT superconductors, because the latter exhibit variable values of 0.13 <

k
B

T
c

/

ℏ

ω

l
n

< 0.32. In this paper, we reanalyze the full experimental dataset (including data for highly compressed H3S) and find that the strong-coupling correction functions for the gap-to-critical-temperature ratio and for the specific-heat-jump ratio are double-valued nearly linear functions of

k
B

T
c

/

ℏ

ω

l
n

.
On the long-term stability of space-time crystals
Authors: Smits J , Stoof H T C , van der Straten P
Published: September 2020

We investigate a space-time crystal in a superfluid Bose gas. Using a well-controlled periodic drive we excite only one crystalline mode in the system, which can be accurately modeled in the rotating frame of the drive. Using holographic imaging we observe the stability of the crystal over an extended period of time and show the robustness of its structure in both space and time. By introducing a fourth-order term in the Hamiltonian we show that the crystal stabilizes at a fixed number of quanta. The results of the model are compared to the experimental data and show good agreement, with a small number of free parameters. The results yield insights in the long-term stability of the crystal, which can only be obtained by the combination of the extended control in the experiment and the nearly ab initio character of the model. From the model we derive a phase diagram of the system, which can be exploited in the future to study the phase transitions for this new state of matter in even more detail.
Near 100% spectral-purity photons from reconfigurable micro-rings*
Authors: Zhu Pingyu , Liu Yingwen , Wu Chao , Xue Shichuan , Yu Xinyao , Zheng Qilin , Wang Yang , Qiang Xiaogang , Wu Junjie , Xu Ping
Published: September 2020

We propose an on-chip reconfigurable micro-ring to engineer the spectral-purity of photons. The micro-ring resonator is designed to be coupled by one or two asymmetric Mach–Zehnder interferometers and the coupling coefficients hence the quality-factors of the pump and the converted photons can be dynamically changed by the interferometer’s internal phase-shifter. We calculate the joint-spectrum function and obtain the spectral-purity of photons and Schmidt number under different phases. We show that it is a dynamical method to adjust the spectral-purity and can optimize the spectral-purity of photons up to near 100%. The condition for high-spectral-purity photons is ensured by the micro-ring itself, so it overcomes the trade-off between spectral purity and brightness in the traditional post-filtering method. This scheme is robust to fabrication variations and can be successfully applied in different fabrication labs and different materials. Such high-spectral-purity photons will be beneficial for quantum information processing like Boson sampling and other quantum algorithms.
Semi-supervised Deep Learning in Motor Imagery-Based Brain-Computer Interfaces with Stacked Variational Autoencoder
Authors: Chen Junjian , Yu Zhuliang , Gu Zhenghui
Published: September 2020

Recently, deep learning methods have contributed to the development of motor imagery (MI) based brain-computer interface (BCI) research. However, these methods typically focused on supervised deep learning with the labelled data and failed to learn from the unlabelled data, where additional information may be critical for performance improvement in MI decoding. To address this problem, we propose a semi-supervised deep learning method based on the stacked variational autoencoder (SVAE) for MI decoding, where the input to the network is an envelope representation of EEG signal. Under the framework of SVAE, the labelled training data and unlabelled test data can be trained collaboratively. Experimental evaluation on the BCI IV 2a dataset reveals that SVAE outperforms competing methods and it also yields state-of-the-art performance in decoding MI tasks. Hence, the proposed method is a promising tool in the research of the MI-based BCI system.
Robust Learning Control for a Class of Uncertain Linear Motor Systems
Authors: Shou Qisheng , Xia Huijie , Yan Qiuzhen , Cai Jianping , Ma Yan , Bao Zhiyan
Published: September 2020

The tracking problem for a class of linear motor systems with unknown input deadzone is addressed in this paper. Alignment condition is adopted to solve the initial position problem for iterative learning control systems, with the control law and learning laws being developed according to Lyapunov method. Iterative learning control technique and robust control technique are together adopted to compensate uncertainties and disturbances. The position state and velocity state can accurately track the desired trajectories over the whole interval, as the iteration number increases. In the end, a simulation example is presented to demonstrate the effectiveness of the designed control scheme.
Automatic Modeling Method Based on Bayesian Network
Authors: Han Lu , Shi Xianjun , Wang Taoyu
Published: September 2020

In fact, the relationship between fault and test is uncertain, and the traditional testability model cannot deal with uncertain information. Bayesian network model with uncertain information reasoning ability and self-learning ability has become a research hotspot. Aiming at the difficulty and high cost of Bayesian network testability model, a automatic modeling method based on Bayesian network was proposed. This method uses the circuit functional structure relationship characterized by EDA to provide the required conditions for the K2 algorithm, and verifies the results of the structure and parameter learning. Through this method, the Bayesian network model can continuously update the model structure and parameters by combining expert knowledge, historical experience and later use data, making the model more and more accurate. After simulation and comparison, the algorithm can effectively build a model and reduce the workload of related personnel.
Research on Harmonic Power Sharing Method Based on Intelligent Matching Virtual Harmonic Impedance
Authors: Zhu Jiaxiong , Qiang Jiang , Xiao Zhigang , Feng Chang
Published: September 2020

The mismatch between the feeder impedance of each parallel inverter connected to the public AC bus in the microgrid and its rated capacity will cause harmonic circulation in the system. In order to effectively suppress the harmonic circulating current and balance the power distribution among the inverter units, this paper introduces an adaptive virtual harmonic impedance controller in the harmonic domain to reshape the impedance of the system to match the feeder impedance, Dynamically adjust the harmonic current distribution, obtain the modulated wave through the measuring current and voltage double-loop control, and then drive the inverter to operate through the SPWM modulation circuit. MATLAB/Simulink simulation, the results show that the harmonic power distribution method based on virtual intelligent matching harmonic impedance raised in this paper can significantly suppress the harmonic circulating current between parallel inverter systems, and improve the quality of output voltage of parallel inverter systems. The accurate power distribution of the inverter is realized, and performance of inverter in parallel system with a nonlinear load is improved.
Consistent-Resolution Network for 3D Hand Shape Estimation from a Single RGB Image
Authors: Wu Qi , Chen Joya , Yao Zhiming , Zhou Xu , Wang Jianguo , Wang Shaonan , Yang Xianjun
Published: September 2020

We propose a novel method for 3D hand shape estimation from a single RGB image. Most exiting methods leverage a deep network to extract a low-resolution representation to estimate 3D coordinates, which always leads to the loss of spatial information. In contrast, we present a Consistent-Resolution Network (CRNet) to extract the same resolution representation as the original image, thus preserve more details about spatial information. Specifically, we introduce the recent high-resolution network (HRNet) to generate high-resolution feature maps, which can attain high-resolution representation of the original image. Then, we design a deconvolution module to recover this map to the size of the original image. Therefore, we can directly leverage this feature to learn the precise 2D shape and the depth map, and transfer them into 3D coordinates in the camera space. Through extensive experiments on a large real-world dataset FreiHAND, we show that our proposed method can predict precise and suitable 3D hand shape from a monocular view.
Design of Tire Damage Image Recognition System Based on Deep Learning
Authors: Zhang S Z , Wu Y H , Chang J
Published: September 2020

In view of the problems of low efficiency, prone to misjudgment, and complicated identification levels in the current market, the use of manual judgment methods for the cause of tire damage. A method of tire damage image recognition based on convolutional neural network algorithm is proposed, and an intelligent tire damage image recognition system is designed. Through image processing, feature extraction, classification and identification of the input tire damage image, the computer can automatically output the cause of the damage. The experimental results show the feasibility of this method and also improve the shortcomings of traditional manual judgment of the cause of tire damage.
Research of Face Detection Based on Skin Color Feature
Authors: Li Pei , Wang Hongjuan , Li Yeli , You Fucheng , Liu Mengyang
Published: September 2020

Face image contains many pattern features and this paper studies a face detection algorithm based on skin color features. Through the analysis of the face detection method based on skin color features, the calculation process and principle are understood, and the algorithm and detection techniques such as color space, preprocessing face image detection technology. Building a corresponding skin color model based on face characteristics are analyzed in depth. Because the skin color only occupies a specific range in the primary color of color space, the probability likelihood graph of skin color is obtained by calculating skin color similarity. Then the binary image of the possible face region is obtained by smooth filtering, threshold segmentation, and other operations. Then the area center and height ratio are used to calculate the center of mass and deflection angle, and the correlation coefficient is matched with the face module. Finally, the face area is detected and marked. Test results show that the algorithm can achieve face detection well.
Training and Operation of Multi-layer Convolutional Neural Network Using Electronic Synapses
Authors: Ding Yi , Li Penglong , Liu Jiaqi , Luo Ding , Li Xiaolong , Li Zhenghao
Published: September 2020

For the reason that electrotonic-based memristive devices have been developing rapidly, memristive synapses show a strong superiority in being exploited to construct the neural network system. Nanoscale of memristive devices provides wide prospects for making the hardware implementation of neuromorphic networks. The primary neural network can be satisfactorily implemented on the memristor, which means that memristors can be applied to simple machine learning tasks. However, training and operation of the peculiar neural network with multilayer special processing functions on memristors is still a challenging problem. In this paper, we introduce the experimental implementation of transistor-free metal-oxide memristive crossbars, with device variability sufficiently low to allow operation of integrated neural network, in a multilayer convolutional neural network. Our network consists of multiple 3×3 memristive crossbar arrays both on the convolutional layers and the last layer, which reduces the challenge for the practical implementation of the deep networks. To perform the perfect recognition of the shape in the 27×27 pixel binary images, we bring forward a new coarse-grain variety of the gradient descent algorithm to train the proposed network. Finally, our trained network achieves desirable accuracy.
Prediction of LEA Plant Proteins Based on Machine Learning
Authors: Hu Siquan , Zhao Meidi , Shi Zhiguo , Zhang Min
Published: September 2020

Plants’ ability to grow smoothly in harsh environments is restricted by various factors. Late embryogenesis abundant proteins play a vital role in plants’ resistance to environmental stresses. Therefore, identifying lea proteins has become a valuable task. Although biological experiments are very important in such task, machine learning methods could narrow the searching scale with a certain accuracy and the increased speed. At present, there is a lack of such work in this area. This paper proposes a set of machine learning methods for predicting late embryogenesis abundant proteins and compares the performance of SVM and random forest (RF) methods with CNN method. Samples are collected from UniProt and NCBI with experts’ confirmation. The results show that the RF model is better than other models. The accuracy of the RF model is higher than 89% on the training set and is higher than 91% on different independent samples. The work provides some new ideas and methods for the identification of LEA proteins.
Study of Underwater Target Recognition Using Feature Extraction Based on Wavelet Technology
Authors: Guo Lihua , Wang Hongling
Published: September 2020

The characteristic information in target echo’s that using geometry and elastic information is possibly the only way to recognize the underwater target. The findings indicate that if the material of the elastic target is different, the vertical wave velocity and the horizontal wave velocity’s relative value is different. Even if the shape is the same, its scattering difference in frequency characteristic is also very obvious, therefore the elastic information may be the only way to recognize the target of the same geometrical shape but material to be different. This paper studies three feature extraction methods based on wavelet technology to obtain the elastic information from the target echo. It can obtain the essential eigenvector and the characteristic dimension is lower than the conventional way. Using the three methods to analyse the target echoes of two different material targets in pond, the recognition ratio shows that the feature extraction method is effective.
A Novel Sleep Scheduling Strategy on RISC-V Processor
Authors: Zhou Weixin , Wu Dehua , Xiao Wan’ang , Gao Shan , Gao Wanlin
Published: September 2020

With the development of the Internet of Things, low-power technology has gradually become a primary factor in processor design. Processor sleeping mode is considered as an effective low-power technique. However, operation errors may occur if the long cycle instruction has not been completed during sleeping mode switch. To solve this problem, a novel sleep scheduling strategy based on RISC-V instruction set architecture is proposed. In this paper, the structure of RISC-V processor with task dispatching mechanism is described firstly. Then, WFI instruction and gating clock technology are adopted to realize the sleep scheduling strategy. Finally, hardware simulation is executed to demonstrate the feasibility of the novel sleep scheduling strategy.
Fast Retrieval Method of Image Data Based on Learning to Hash
Authors: Fang Shiyuan , Wang Jianfeng , Yang Cheng , Tong Pengpeng
Published: September 2020

Hash is a method that is widely used in nearest neighbor retrieval, and its goal is to convert high-dimensional image data into low-dimensional representations or into a set of ordered binary code. As one of the more efficient methods of data storage and retrieval, the hash method is widely used in the nearest neighbor retrieval of large-scale image data. The traditional hashing method generates a hash code by manually extracting features, so that the feature and the hash code do not have the best fit, so that the generated hash code is suboptimal. The rapid development of deep learning makes the computer have a good effect on image visual feature recognition. Combining the learning of hash function makes the performance better than the traditional hash method. In this paper, a deep hash method based on triple constraint is proposed to extract the similarity features of the same category and distinguish the features between different categories. Further learning the hash code makes the similarity of the image preserved. Experiments show that the Hash-based learning method has better performance on CIFAR-10 and NUS-WIDE than other methods.
Modal Parameters Identification Based on Equalization Principal Component Analysis
Authors: Zhang Xuan , Zhang Jingjing , Xu Zequan , Luo Jinglin
Published: September 2020

Aiming at the magnitude influence of different discrete points in modal parameters identifications with traditional principal component analysis (PCA), a method of modal parameters identifications by equalization PCA is proposed in this paper. The basic idea is using the mean of time domain response data before identifying modal parameters with PCA. On the premise of not losing the main information of the original data, the influence of magnitude on the data is weakened. Taking a doubly-clamped beam as an example, modal parameters of the beam are identified by traditional PCA and equalization PCA with the same constraints of principal component cumulative contribution rate. The results show that compared with the traditional PCA, equalization PCA can effectively identify more orders of modal parameters. The errors of vibration modal shapes and natural frequencies identified by equalization PCA are allowable.
Virtual Simulation of Fruit Picking Robot Based on Unity3D
Authors: Wang Chenglin , Liu Suchwen , Zhao Lijun , Luo Tianhong
Published: September 2020

With the development of the fruit farming industry, there have been breakthroughs in both scale and harvesting requirements, and the resulting problem is that the demand of picking robot is more and more high, the function, cost and quality of the harvest and picking efficiency compared with the traditional manual operation mode with strong competitiveness for market, that is to say, in terms of cost cheaper, picking at a faster rate, and can avoid damage on the fruit in the process of picking. Therefore, in terms of mechanical structure design, the transmission accuracy and efficiency of the picking robot should be improved. At the same time, the structural design of the picking robot should simplify the structure as much as possible, reduce the manufacturing cost, and ensure the feasibility of the picking robot’s functions. Picking robot combined with automatic walking system, automatic detection system and control system-centered intelligent system, can achieve accurate and efficient work in various conditions. Realize picking robot toward automation, intelligence, scientific development. The design of this topic is mainly aimed at the development of the picking robot and the use of analysis, determine the feasibility of the picking robot design and virtual simulation.
Chinese Sentiment Analysis of Online Education and Internet Buzzwords Based on BERT
Authors: Zheng Jibin , Wang Jinhai , Ren Yafeng , Yang Zeming
Published: September 2020

In recent years, sentiment analysis has made a great breakthrough in the field of natural language processing. Many researchers attempt to build a user comment analysis model of e-commerce products to infer the quality of goods. However, the application of sentiment analysis technology in online education platform and intelligent feedback to students and managers is almost irrelevant. In this paper, we collected a large number of online education platform web resource reviews to build a large corpus to pre-train the Word2vec model for comparative experiments. At the same time, the BERT+BiGRU model is designed for the Feedback System Structure (FSS), which achieves the effect of intelligent interaction between students and teachers and the improvement of the curriculum. On the self-built data set, the BERT+BiGRU model has the best prediction effect, with an accuracy of 98.82%. Compared with traditional machine learning methods such as Naive Bayes, the classification accuracy rate is improved by 21.54%. The experiment result shows that we use BERT+BiGRU on the FSS system to achieve the effect of teacher-student interaction and intelligent feedback of academic intelligence. In addition, we also carry out sentiment analysis on the text with Chinese Internet buzzwords. The experimental results show that the sentiment analysis model of our intelligent feedback system (FSS) has strong analytical capability.
Transmission Line Scene Classification Based on Light-VGGNet
Authors: Wang Hongxing , Huang Zheng , Liu Bin , Huang Xiang , Han Wei , Li Hongchen
Published: September 2020

In recent years, power departments have gradually utilized UAV to carry out regular inspections of transmission lines to ensure the safety and stability of power systems. However, a large number of images without useful information are usually captured during the UAV inspection. These images that contain no transmission line information are transmitted to the ground station with informative images, which leads to the surge of workload. To solve this problem, we propose an intelligent image filtering method based on the VGGNet, which is called Light-VGGNet. Firstly, this paper collects the aerial images captured by cameras during the UAV inspection and builds an aerial dataset of transmission line scenes. Then, the Light-VGGNet is proposed, which achieves much lower memory consumption and faster running speed than those of the VGGNet. We use the aerial image dataset to train the proposed network. Finally, the best weight is loaded and utilized to predict the label of the test dataset.
Prioritized Experience Replay in Multi-Actor-Attention-Critic for Reinforcement Learning
Authors: Fan Sheng , Song Guanghua , Yang Bowei , Jiang Xiaohong
Published: September 2020

Experience replay is a significant method of off-policy reinforcement learning (RL), which makes RL reuse the past experience and reduce the correlation between samples. Multi-Actor-Attention-Critic (MAAC) is a successful off-policy multi-agent reinforcement learning algorithm, due to its good scalability. To accelerate convergence, we use prioritized experience replay (PER) to optimize the experience selection in MAAC, and propose the PER-MAAC algorithm. In the PER-MAAC, the priority metric is based on the temporal-difference error during training. The algorithm is evaluated in the scenarios of Multi-UAV Cooperative Navigation and Rover-Tower. The experimental results show that PER-MAAC improves the speed of convergence.
Machine Fault Detection under the Background of Strong Interference Based on Line Learning Music Algorithm
Authors: Jiang Yi , Chen Eryang
Published: September 2020

First, this article introduces the importance of fault diagnosis in social production, and analyzes many existing machine fault detection methods. Considering the use conditions of the algorithm, these methods may be complicated and may not be suitable for online applications. Especially for the background of strong interference, this phenomenon occurs in the early stage of machine failure, and the traditional weak signal detection method loses its function. The characteristics of random noise modulation are given, and then an improved online learning evolutionary MUSIC algorithm is designed. First, it constructs a data array based on the sampling sequence, and then analyzes the signal spectrum under the condition that the number of signals is underestimated. Because traditional analysis methods are limited to current data, it is difficult to obtain stable analysis results under strong interference. Finally, some historical analysis data is weighted to obtain the true spectrum. The effectiveness of the algorithm can be seen through simulation. For weak fault signals under strong interference background, this method has better detection performance than FFT algorithm.
Design of the Segmentation Algorithm of HCC Based on the Improved U-net Network
Authors: Zhou S , Li W Y , Lin R
Published: September 2020

Computed tomography is an important method for clinical evaluation of the disease status and the therapeutic efficacy of hepatocellular carcinoma (HCC). In order to effectively reduce the workload of artificial segmentation of CT images and to minimize the obvious influence of subjective factors upon the result, a segmentation algorithm based on the improved U-net network and region growing was proposed. Median filtering and adaptive histogram equalization were performed firstly on abdominal CT images with HCC, secondly the CT images were trained by the improved U-net network in which residual structure and deep separable convolutions layer were used, and then region growing was used for capturing edges of CT images. 126 groups of target CT images were selected for analysis, which were divided into training set, verification set and test set according to the ratio of 8: 1: 1, of which the training set was expanded to 800 by image enhancement technology. By comparing the lesion regions extracted with handled consequences manually labelled by clinicians, the sensitivity reached 83.18%, the accuracy 85.25%, and the Dice coefficient 83.05%. The algorithm revealed a good result on the segmentation of CT image with HCC, and could provide support for the evaluation of the treatment efficacy of HCC.
CMS System Identification Based on Improved Algorithm of Apriori
Authors: Liu Zhonglin , Wang Yan
Published: September 2020

Web applications on the Internet are often attacked by various vulnerabilities. Once various vulnerabilities are published, it becomes more and more important to quickly and accurately locate the affected Web application or system from the list of website resources. In this paper, based on the data and information of similar CMS websites, the improved Apriori algorithm is used to extract the features of CMS websites and their association rules, which can be used to quickly identify whether unknown websites are CMS templates of this type. Through experimental tests, four feature information used to identify WordPress is obtained. According to these features and relevance, the system type website can be identified more accurately.
An Improved Bacterial Foraging Algorithm for Multi-modal Problems
Authors: Hu Jianjun , Jin Nianfu , Li Pu , Zhang Huimin
Published: September 2020

In order to improve the performance of BFO (bacterial foraging optimization) algorithm in multi-modal problems, the algorithm is optimized and improved in this paper. In the chemotaxis operation, the gradient accumulation variable swimming step size is realized, and the convergence speed of the algorithm is accelerated. In the reproduction operation, a new population competition mechanism is added, and a double-index sorting is realized, which improves the ability of the algorithm to jump out of the local optimum and the diversity of the population. In the migration operation, the vitality index of bacteria is added to dynamically affect the migration probability, ensure the survival probability of elite individuals, and further improve the global optimization ability of the algorithm. Finally, a multi-modal function is selected to test. Compared with the genetic algorithm, the improved BFO algorithm is better than the genetic algorithm in speed and quality.
Research on the Influence of Improved K-shell Algorithm on Commodity Profit
Authors: Chen Jing , Huang Jincheng
Published: September 2020

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN (Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities.
The Chaotic Slime Mould Algorithm with Chebyshev Map
Authors: Zhao Juan , Gao Zheng-Ming
Published: September 2020

In this paper, we proposed an improvement for the newly raised swarm-based algorithm called the slime mould algorithm (SMA) with chaos. The so-called chaotic SMA introduced the specific Chebyshev mapping, which had already been verified to perform better in optimization. Three types of simulation experiments were carried out with the unimodal, multi-modal benchmark functions and those which have basins/valleys in their profiles. In order to reduce the influence of randomness involved in the algorithms, 100 Monte Carlo experiments were carried out and the final results were their averages. Results confirmed the capability of the improvements and demonstrated that the chaotic SMA with Chebyshev map would perform better, steadier, and faster than the original one in optimization. Discussions on the capability in optimization of the chaotic SMA together with the original SMA were made, and the chaotic SMA was recommended in applications for real engineering problems.
Accurate ECG Data Generation with a Simple Generative Adversarial Network
Authors: Wang Haiyan , Ge Zhaoyang , Wang Zongmin
Published: September 2020

Since ECG data is highly sensitive medical data, the acquisition of ECG data is highly restricted. However, with the increasing demand for ECG big data research, the improvement of computer computing capabilities, and the development of deep learning, the direction of ECG intelligent analysis is facing a serious lack of standard clinical data. In order to generate more precise ECG data, this paper proposes a GAN architecture for generating ECG heartbeat data. The network structure is simple and does not require any domain knowledge. In this paper, the MIT-BIH Arrhythmia database is selected, from which all left bundle branch block heartbeats are selected to form a training dataset. The training process shows that the proposed GAN structure is effective and accurate, and the generated results show that not only the generated simulated ECG heartbeat data is diverse, but also highly similar to the real data.
A Comparison of Four Algorithms for Land-Use Classification Based on Landsat 8 OLI Image
Authors: Zhang Yanghua , Zhao Hu , Ni Jianbo
Published: September 2020

Accurate mapping and monitoring of land-use is essential for reasonable land management and planning. To extract land-use classes based on remote sensing images, many classification algorithms have been proposed. However, the comparison between some main supervised classification algorithms is rarely researched. This study selected the eastern fringe area of Jinan as the study area and the Landsat 8 OLI image of 2019 as data to compare the performance of four supervised classification algorithms that are MLC, SVM, ANN and RF especially. The results shown that the overall accuracy and kappa of RF is 86.2% and 0.8, and the overall accuracy and kappa of SVM is 83.2% and 0.75, and the overall accuracy and kappa of ANN is 81% and 0.72. The overall accuracy and kappa of MLC is 73.6% and 0.63. These denote that the RF can achieve the best classification result in four algorithms, followed by SVM, ANN and MLC.
Real-Time Recognition Algorithm of Vehicle External Specific Sounds Based on Spectrum Characteristics
Authors: Lei Hongbin , Wang Botao
Published: September 2020

As the development of vehicle sound insulation technology, drivers ignore some external sound information regularly. It is particularly important for the recognition of external sounds when the vehicle is driving. The real-time detection of the fundamental frequency of the external sound is not accurate due to the complexity of the external environment and the noise interference. In this paper, a recognition algorithm of vehicle external specific sounds is presented. The spectrum information of the external sound is converted into an image form, and the analysis result is obtained after processing the spectrum image. We choose five different types of sounds and the total number of 1000 samples for testing. The experiment data shows that the overall accuracy of the algorithm’s processing data can reach more than 90%, and the recognition rate can reach more than 90%. This algorithm has the characteristics of high accuracy and strong robustness, and it can easily be realized in real time.
A 3-D Meshless Method for Solving Maxwell’s Equations by Using the Steger-Warming Flux Vector Splitting Approach
Authors: Gao Yukun , Chen Hongquan , Xu Shengguan , Zhang Jiale , Cao Cheng , Gao Huanqin
Published: September 2020

In order to solve Maxwell’s equations so as to study the electromagnetic stealth characteristics of targets, a 3-D meshless method is developed. According to the meshless method, the spatial derivatives on clouds of points are computed by using the weighted least square approach. After that, the Steger-Warming flux vector splitting approach is used to calculate the physical flux of Maxwell’s equations. By using the developed method, the calculated bistatic radar cross sections (RCS) of a 3-D sphere are obtained, which are agree with the series solutions. Finally, the electromagnetic scattering characteristics for a 3-D stealth aircraft model with different situations is given, which shows the developed method has the ability to accommodate complicated 3-D configurations with multi-element.
A Scientometrics Analysis on Brain-Inspired Intelligence
Authors: Liang Jianghai , Yang Xiao , Liu Shulei , Wu Ji
Published: September 2020

In recent years, brain-inspired intelligence has attracted increasing attention from both academia and industry. Although there are several literature reviews of brain-inspired intelligence research, they appear to be a lack of systematic quantitative and visual investigation. The purpose of this paper is to make insights into certain characteristics of brain-inspired intelligence research, and consequently to reveal the global trends as well as support researchers to determine future research directions. The data were retrieved from Science Citation Index Expanded (SCI-E) of core databases in Web of Science (WoS) and the global patent database of incoPat. Based on the aforementioned data and the scientometric techniques, we show the general characteristics of R&D (R&D) output, identify the research hotspots and the knowledge structure of brain-inspired intelligence and evaluate the R&D capability of countries, institutions. Finally, we conclude that (1) China, the United States, UK, Germany and South Korea are the main R&D forces in brain-inspired intelligence domain. (2) The researchers focus mainly on brain-computer interface, neuromorphic device, neuromorphic computing, and brain operation mechanism, and the R&D forces concentrate on the directions of G06N, G06F, A61B, G06K, H01L. (3) Major countries/regions pay more attention on academic cooperation and exchanges in brain-inspired intelligence. (4) America is leading in the basic research of brain-inspired intelligence. (5) China still has a big gap with the European and American countries in terms of industrial application.
Construction and Application of Big Data System in Automobile Intelligent Factory
Authors: Yang Yixin , Gao Jianjun , Pi Zhibo , Guo Konghui
Published: September 2020

Under the background of intelligent manufacturing, the application of industrial big data is the key to improve the utilization efficiency of manufacturing resources. Starting from the purpose of big data application and how to replace the efficiency of enterprise resource utilization, aiming at the construction of intelligent and transparent big data system in automobile factory, this paper combed the concept of big data system construction, discussed the construction ideas of big data system, constructed the big data system framework suitable for discrete manufacturing enterprises, and gave the methods of data acquisition, transmission, storage, processing and Analysis of the specific program. For big data applications, taking the enterprise intelligent operation cockpit, intelligent electronic process system and enterprise epidemic prevention and health cloud system as examples, the scene and application effect of big data application are demonstrated. The construction and application of big data system in intelligent and transparent automobile factory provides a reference model for further promoting intelligent manufacturing driven by big data.
Prediction of Length of Stay on the Intensive Care Unit Based on Bayesian Neural Network
Authors: Fang Jiansheng , Zhu Junlin , Zhang Xiaoqing
Published: September 2020

Predicting length of stay (LoS) accurately in the intensive care unit (ICU) is important to improve care quality and resource utilization. However, for LoS prediction, existing methods are facing main challenges, including uncertain prediction, generalization, interpretability, etc. In this paper, we utilize Bayesian neural network (BNN) to alleviate the above main challenges. The BNN introduces prior knowledge on the weights of neural networks and estimates outcomes from the predictive distribution after calculating the posterior distribution of weights. Extensive experiments on the eICU collaborative research database (eICU-CRD) show that the proposed method is competitive and more capable of anti-overfitting.
Research of Big Data Analysis Platform Based on Positioning Information
Authors: Li Xiong , Wang Shiyun
Published: September 2020

With the wide spread of intelligent terminals, a variety of location-based business applications continue to emerge. We began to think about the analysis and application of location-based information big data, using big data technology to transform the existing industry, so as to better deal with epidemic disasters and other issues. For the novel Coronavirus, if artificial influenza virus infection or infection is detected by artificial means, it is easy to cause a series of problems, such as low efficiency, potential infection, and deliberately concealing travel history and contact history, which will have certain impact on epidemic prevention and control. Therefore, with the huge amount of data and the rapid growth of service demand, it is very urgent and necessary to isolate and confirm suspected cases based on big data analysis of location information. The big data analysis platform of location information can analyse the user’s location information, form the user’s trace, and provide more comprehensive information for epidemic control.