Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Model Predictive Control Method for Two Induction Motor Fed by Five-Leg Inverter System

This paper proposes an improved model predictive control (MPC) method for a five-leg voltage source inverter (FLVSI) that drives the dual three-phase induction motor system. The conventional MPC method using the full-set voltage vectors (FSMPC) has high computational burden. To reduce the computational burden, the other conventional MPC method considering only the adjacent voltage vectors (A-MPC) stored in the lookup table (LUT) is introduced but increases the current ripple causing degradation of the control performance compared to FSMPC. Therefore, in this paper, the improved MPC is proposed to reduce the computational complexity and improve the control performance compared to A-MPC. Additionally, the proposed method is compared with the conventional methods (FSMPC, A-MPC) and its effectiveness is verified by simulation results.
Analysis of China's Urban Network Structure from the Perspective of “Streaming”

The urban network is an extension and improvement of the study of the traditional urban and regional spatial structure. It is one of the hot topics in the study of urban geography. Based on the Tencent LBSN data to build a city network, this paper uses the centrality and influence indicators to rank the importance and influence of the nodes of the Chinese urban network according to the complex network theory, and then integrates the methods of community detection and other methods to analyze the characteristics of the urban network in China during the transition period. The research shows that: $\bigcirc\! \! \! \! 1$. The eigenvector centrality and PageRank take into account the number and quality of neighbor nodes at the same time. It can identify the important city nodes more effectively, and the identified urban network accords with Pareto law. $\bigcirc\! \! \! \! \! 2$. The four major urban agglomerations of the Beijing-Tianjin-Hebei region, the Yangtze River Delta, the Pearl River Delta and Chengdu-Chongqing have a clear network structure. The national urbanization development strategy and important transportation facilities have a directional effect on the urban network. $\bigcirc\! \! \! \! \! 3$. The urban network structure conforms to the “Hu Huanyong Line” law. However, the networks of the Guanzhong city group, the Lanzhou-Xining city group, the Hubao Egu city group, and the urban slopes of the northern slope of the Tianshan Mountains centered on U rumqi began to appear. Important infrastructure construction such as the “Lanxin Line” and the “Belt and Road” strategy have become potential forces for guiding the urban system to break through the “Hu Huanyong Line”. 4Central cities in Central China, Fujian Province, and Northeast China have a low level of network development and do not match the regional functions undertaken. Central cities in Central China, Fujian Province, and Northeast China have a low level of network development and do not match the regional functions undertaken. $\bigcirc\! \! \! \! \! 5$. The phenomenon of excessive monopoly in the Pearl River Delta urban agglomeration should be given attention, and the links between key node cities and surrounding cities should be strengthened to ease the pressure on the core cities. Overall, LBSN data can effectively reveal the characteristics of China's urban network structure during the transition period, and improve effective case support for regional coordinated development, transportation system planning and construction.
Optimization Design of Cogging Torque and Back EMF Waveform for PMSM

CNC equipments require the motor to output smooth torque, while cogging torque is one of the main factors that affect the motor torque fluctuation. In this paper, the reason of cogging torque in PMSM was analyzed first, and the effects of notch width and closed slot structure on cogging torque were studied by FEM. Then, the height of bridge arc was designed by use of optimal design method to improve the sin degree of the back EMF waveform. Finally, the rationality of the design was verified by the no-load test of the prototype.
Feature matching and ArUco markers application in mobile eye tracking studies

This paper presents eye tracking glasses data analysis automation techniques, utilizing image processing. Two separate techniques will be described. One method is used to automate mapping of point-of-regard to a static reference image using a feature matching algorithm AKAZE. The second method utilizes ArUco markers for mapping of point-of-regard to a screencast from a mobile device. The described methods are used to aggregate experiment statistical data for future analysis and presentation in forms like heatmaps or gaze plots. Algorithms are implemented in Python 3.6 and OpenCV library.
Real-Time Network Traffic Classification Based on CDH Pattern Matching

In recent years, with the rapid development of the Internet, the data scale of application behavior and application traffic have exploded. How to classify the real-time traffic of network becomes a big challenge. How to balance the accuracy and real-time of traffic classification is a difficult problem in technology. Therefore, this paper proposes a pattern matching real-time traffic classification method named PM, which first uses jpcap to accept network traffic data in real time, and then uses pattern matching to perform real-time matching traffic characteristics to achieve traffic classification. Among them, the use of the distributed message system kafka and the parallel computing framework Spark significantly improve the execution efficiency of the program. The experimental results show that PM has good performance in terms of accuracy.
Deep Learning for Natural Language Processing and Language Modelling

The article presents an example of practical application of deep learning methods for language processing and modelling. Development of statistical language models helps to predict a sequence of recognized words and phonemes, and can be used for improving speech processing and speech recognition. However, currently the field of language modelling is shifting from statistical language modelling methods to neural networks and deep learning methods. Therefore, one of the methods of effective language modelling with the use of deep learning techniques is presented in this paper. Presented results concerns the modelling of the Polish language but the achieved research results and conclusions can also be applied to language modelling application for other languages.
Capacity Improvement for Full Duplex Device-to-Device Communications Underlaying Cellular Networks

In this paper, the capacity improvement of full-duplex (FD) device-to-device (D2D) underlaying cellular networks (CNs) is analyzed. A single-cell environment is considered with one half-duplex (HD) base station, multiple HD cellular user equipments (CUEs), and one pair of FD D2D user equipments (DUEs). The BS is equipped with <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> multiple antennas, and one pair of FD DUEs is permitted to share the same resource with <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> out of <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> uplink HD CUEs, which will lead to a more complex interference environment. First, a novel interference-limited area-based (ILA-based) scheme is proposed to manage the co-channel interference from CN CUEs to FD DUEs. The ILA-based scheme does not allow the coexistence of HD CUEs and FD DUEs if the HD CUEs are located in any region of the two ILAs. Next, a senior ILA-based (SILA-based) scheme is proposed to reduce the complexity of the ILA-based scheme, in which only a common ILA is designed for both FD DUEs instead of the two ILAs in the ILA-based scheme. Then, the lower bounds of the ergodic capacity for FD D2D communications with imperfect self-interference cancellation (SIC) are derived in closed-forms with the ILA-based scheme and the SILA-based scheme. Furthermore, the capacity gain of the FD D2D communications is analyzed, and the numerical results show that the capacity improvement of the FD D2D communications is much greater than the traditional HD D2D communication if the sufficient SIC is achieved.
Draft Genome Sequences of New Isolates and the Known Species of the Family Microbacteriaceae Associated with Plants

Draft genome sequences of 11 bacteria belonging to the family Microbacteriaceae were obtained using Illumina technology. The genomes of these strains have sizes from 3.14 to 4.30 Mb with their genomic DNA characterized as having high G+C contents (above 65%). ABSTRACT Draft genome sequences of 11 bacteria belonging to the family Microbacteriaceae were obtained using Illumina technology. The genomes of these strains have sizes from 3.14 to 4.30 Mb with their genomic DNA characterized as having high G+C contents (above 65%). These genomic data will be useful for natural taxonomy and comparative genomic studies of bacterial strains of the family Microbacteriaceae.
Dynamic Keyword Search With Hierarchical Attributes in Cloud Computing

A growing number of organizations and individuals outsource their data to the cloud. As the cloud service provider is potentially untrustworthy, cloud user needs to encrypt their sensitive data before outsourcing. One challenge of the cloud service is how to provide an effective search on encrypted data while preserving user privacy. The existing attribute-based keyword search (ABKS) scheme has addressed this issue and enabled a data owner to conduct the search over the outsourced data utilizing an access policy. However, the encryption cost in ABKS linearly increases with the number of attributes in the access policy. In this paper, we proposed a dynamic hierarchical ABKS (DHABKS) scheme to efficiently achieve fine-grained search authorization. We introduced the attribute hierarchy into access policy to improve the encryption performance of the scheme and devised a dynamic updating access policy to largely reduce the workload of the data owner. The efficiency and effectiveness of the DHABKS scheme are validated by extensive experiments using real data sets.
A Robust Acoustic-Based Communication Principle for the Navigation of an Underwater Robot Swarm

This paper presents a robust acoustic-based communication principle for the localization and navigation of an underwater robot swarm in featureless environments. With the help of acoustic modems and two robots at the surface with available GPS positions, a number of various submerged autonomous underwater vehicles (AUVs) can be navigated to fulfill different tasks in underwater inspection and monitoring in a V-shaped formation. The principle was implemented using Monsun underwater robots and tested under real conditions in a shallow waterbody.
A Mathematical Model of Mitigating Memory Randomization Weakness via Moving Target Defense

The address space randomization technique was proposed to make determining the address of a shared library more difficult since each instance of the program is loaded into a random base address. However, when address space randomization layout (ASLR) is implemented on a 32-bit system, an attacker can use a brute force attack to guess the address of the shared library. The main goal of the research described in this paper is to study the use of a dispatching algorithm and multiple back-end servers as a moving target defense technique to mitigate ASLR weaknesses. First, we present a brute force attack when the number of servers is known. Second, we present a brute force attack when the number of servers is unknown. Last, we present the probability of the attacker's success on both of the attacks.
A Low RCS Microstrip Antenna Based on Broadband AMC Structures

A new method aimed to design wideband microstrip antennas with low radar cross section (RCS) is proposed in this paper. The reflection phase inverse points and the 0° phase reflection points of the AMC unit cells can be designed to obtain 180°(± 30°) effective phase difference over broadband frequency range. The antenna is fabricated on a chessboard configuration which is structured with two AMC unit cells. Both simulated and measured results show that the proposed antenna possesses more than 7 dB RCS reduction with 69.2% relative bandwidth for both polarizations, covering the working band of the original antenna. The maximum reduction is 31.4 dB. Meanwhile, the radiation properties of the antenna are almost not changed.
Sparse Fractional Fourier Transform and its Applications in Radar Moving Target Detection

Attention has been focused on the radar moving target detection under limited radar resources conditions and complex environment. First, some key technology of sparse Fourier transform (SFT) is introduced in this paper. Then the implementation of sparse fractional Fourier transform (SFRFT) is briefly described. Most importantly, the SFRFT algorithm is applied to radar target detection, and a novel fast and effective method is proposed to improve the detection and parameter estimation performance of maneuvering target. The effectiveness of the method is verified by simulation experiments.
Robust Local Learning and Discriminative Concept Factorization for Data Representation

Concept factorization (CF), as a matrix factorization method, has been applied widely in obtaining an optimal data representation and has yielded impressive results. However, some shortcomings exist in the existing CF method. 1) The standard concept factorization uses the squared loss function that is sensitive to outlier points and noises. 2) The graph generated by the original data does not reflect the real geometric structure of the data distribution. 3) The discriminant information is ignored. Herein, we propose a novel method, called robust local learning and discriminative concept factorization (RLLDCF) for data representation. Specifically, RLLDCF adopts the $\ell _{2,1}$ -norm-based loss function to improve its robustness against noises and outliers, and exploits the discriminative information by local linear regression constraints. In addition, the method obtains the topology structure of the data distribution during learning rather than known a priori and fixed. A new iterative multiplicative updating rule is derived to solve RLLDCF’s objective function. The convergence of the optimization algorithm is proved both theoretically and empirically. Numerous experiments on both synthetic and real-world datasets are conducted, and the results indicate that our proposed method is significantly better than all the comparison methods, thus validating the effectiveness and robustness of RLLDCF.
A Low Flicker Noise Automatic Amplitude Control ASIC for a Split-Mode MEMS Gyroscope

This paper presents a fully differential low flicker noise automatic-amplitude-control (AAC) ASIC for a split-mode MEMS gyroscope. The impact of flicker noise in AAC on the gyroscope’s bias instability is firstly analyzed. Chopper stabilized amplitude detector and error amplifier are employed to remove their flicker noise. A second-order loop filter attenuates the flicker noise from variable gain amplifier (VGA). The ASIC is fabricated in TSMC 0. 35μm CMOS process and consumes 1mW power with a 3.3V supply voltage. Experimental results indicate the flicker noise component in the gyroscope’s drive amplitude is greatly reduced.
Scalability and Key Tradeoffs of Variable Flux PM Machines for EV Traction Motor Systems

Methodologies for the design and control of variable flux permanent magnet synchronous machines (VF-PMSMs) to meet electric vehicle traction requirements with significantly reduced driving cycle losses have been proposed recently. The effectiveness of the proposed methods was demonstrated by a systematic design based on finite element analysis (FEA) and experimental driving cycle loss evaluation of a full scale VF-PMSM prototype. In this paper, simplified analytical models are developed to estimate the properties and key performance metrics of VF-PMSMs across a range of power and are verified using FEA. Fundamental tradeoffs between the normalized high-speed power capability and the range of magnetization state $(\boldsymbol{M}\boldsymbol{S})$ variation are identified. The relationship between $\boldsymbol{M}\boldsymbol{S}$ variation range and driving cycle loss reduction is evaluated quantitatively across the design space. A detailed analysis of the scalability of VF-PMSMs including loss reduction capability and system cost (including active materials in the machine, inverter power electronics, and battery cost) is presented.
Spectral–Spatial Feature Extraction for HSI Classification Based on Supervised Hypergraph and Sample Expanded CNN

Hyperspectral image (HSI) classification remains a challenging problem due to unique characteristics of HSI data (such as numerous bands and strong correlations in the spectral and spatial domains) and small sample size. To address such concerns, we propose a novel spectral–spatial feature extraction method for HSI classification by employing graph embedding and deep learning (DL) models. Since the conventional graph cannot capture the complex manifold relationship of HSI data, and there exist the observations of within-class variation as well as the similarity between different classes in the spectral domain, we construct the supervised within-class/between-class hypergraph (SWBH) to extract the spectral features of HSI. Since it is difficult for DL models to learn representative features for HSI data when the labeled training samples are limited, we propose the random zero settings to newly generate a large amount of labeled HSI samples for the training of convolutional neural network (CNN). The designed sample expanded CNN (SECNN) is used to extract the HSI spatial features. Thus, the spectral–spatial features of HSI can be learned by integrating the features extracted from SWBH and SECNN, respectively. Experiments on three real HSI datasets demonstrate higher classification accuracy of the proposed SWBH–SECNN method.
Unsupervised Segmentation of Point Clouds From Buildings Using Hierarchical Clustering Based on Gestalt Principles

Segmentation is a fundamental step for parsing the point clouds of three-dimensional (3-D) scenes, which normally contain a wide variety of complex objects and structures with a large amount of points. In this paper, we propose a voxel-based point cloud segmentation method using Gestalt principles under a hierarchical clustering framework, allowing a completely automatic but parametric process for segmenting 3-D scenes of buildings. The voxel-based data structure can increase the efficiency and robustness of the segmentation process. By the use of Gestalt principles, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, which can be applied to general applications. The clustering of patches in our method is carried out on the basis of the local geometric information, which is modeled by the probabilistic formulation and solved by the graphical model. Experiments using terrestrial laser scanning dataset have demonstrated that our proposed method can achieve good results, especially for complex scenes and nonplanar surfaces of objects. The quantitative comparison between our method and other representative segmentation methods (i.e., region growing, voxel-based incremental segmentation, locally convex connected patches, etc.) also confirms the effectiveness and efficiency of our method, with overall $F_1$ measures better than 0.66 for our datasets under complex situations of urban scenes with irregular shaped buildings.
An Intelligent Human Behavior-Based Reasoning Model for Service Prediction in Smart Home

With the rapid development of technologies and improvement of living conditions, people are increasingly concerned about their life safety, convenience, and comfortableness. Household intelligence starts to play an increasingly important role in improving people living environment. This paper focuses on service prediction in smart home automatic control systems, and proposes an intelligent human behavior-based reasoning model on the basis of linear prediction and case-based reasoning. Hardware and software are designed after analysis of the embedded architecture. In addition, an embedded smart home platform is built up and implemented to validate the proposed human behavior-based reasoning algorithm.
An Item-Diversity-Based Collaborative Filtering Algorithm to Improve the Accuracy of Recommender System

Matrix factorization is a model-based collaborative filtering recommendation algorithm. From the performance of the recommender system of large websites such as Amazon and Facebook in recent years, it can be seen that collaborative filtering technology has achieved important results in theoretical and practical. Although personalized recommendation based on collaborative filtering has been widely used in the world, it still faces some challenges, such as data sparseness, cold start, recommendation accuracy, and novelty issues. This paper proposes an improved recommendation method based on item-diversity, adding bias and implicit feedback. Through research, we find that users prefer multiple choices. To achieve that, we add the term based on variance to the matrix factorization algorithm. It significantly improves the diversity of recommendations. And accuracy also be improved to some extent with diversity growing. By combining the bias and implicit feedback in the improved algorithm, we derive the list of recommendations that users want. The experiment used real data sets. The result show that our optimized model can effectively improve recommendation accuracy and quality.
An Automatic Visual Inspection Method based on Statistical Approach for Defect Detection of Ship Hull Surfaces

Robotized blasting of ship hull surfaces requires an accurate identification of defective regions of the hull to maximize the blasting efficiency. Accurate surface defect detection may not be achieved by current manual procedures, as its success is highly vulnerable to the human operators' experience and their subjective judgements. Therefore, there is a need for a more accurate and non-subjective method for defect detection. This paper proposes a computer vision based method for detection of ship hull defects. The method utilizes the histogram of hue and entropy data of the hue to identify the defects in two steps. Step 1 is an automatic circular thresholding based on the histogram of hue to distinguish the defects whose hue is different from the defect-free regions. A wrapped Gaussian mixture model is utilized to estimate the circular hue histograms, and maximum likelihood criterion is adopted to set the thresholds. Step 2 uses the probability distribution of the entropy for each segment identified in the first step to decide whether the segments are either defective, defect-free or a mixture of both. For the mixed regions, a Gaussian mixture model is fitted to the probability distribution of the entropy. The maximum likelihood criterion is utilized to segment these regions so as to discriminate their defective and defect-free parts. The high accuracy (F-measure=0.89) and short execution time (~3.5 s) of the proposed method show that it is a good starting point for an automatic defect detection for a fully autonomous ship hull blasting.