Miracle-dz/UniFAR-FTUs huggingface dataset:
An Enhanced Hybrid Feature Selection Technique Using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification

Sentiment classification is increasingly used to automatically identify a positive or negative sentiment in a text review. In classification, feature selection had always been a critical and challenging problem. Most of the related feature selection for sentiment classification techniques unable to overcome problems of evaluating the significant features that will reduce the classification performance. This paper proposes an enhanced hybrid feature selection technique to improve the sentiment classification based on machine learning approaches. First, two customer review datasets namely Sentiment Labelled and large IMDB are retrieved and pre-processed. Next, the proposed feature selection technique which is the hybridization of Term Frequency-Inverse Document Frequency (TF-IDF) and Supports Vector Machine (SVM-RFE) is developed and tested on these two datasets. TF-IDF aims to measure features importance. The SVM-RFE iteratively evaluates and ranks the features. For sentiment classification, only the ${k}$ top features from the ranked features will be used. Finally, the Support Vector Machine (SVM) classifier is deployed to observe the performance of the proposed technique. The performance is measured using accuracy, precision, recall, and F-measure. The experimental results show promising performances with 84.54% to 89.56% in the measurements especially from the large IMDB dataset. The results also outperformed other related techniques in certain datasets. Consequently, the proposed technique able to reduce from 19.25% to 70.5% of the features to be classified. This reduction rate is significant in optimally utilizing the computational resources while maintaining the efficiency of the classification performance.
An Adaptive Network-Constrained Clustering (ANCC) Model for Fine- Scale Urban Functional Zones

Urban functional zones are considered significant components for understanding urban landscape patterns in the socioeconomic environment. Although the spatial configuration of road networks contributes to urban function delineation at the block level, the morphological uncertainties caused by the road network structure in fine-scale urban function retrieval are ignored. This paper proposes an adaptive network-constrained clustering (ANCC) model to map urban function distributions at a finer level. By utilizing points of interest (POIs) to indicate independent functional places, the adaptive road configuration with a multilevel bandwidth selection strategy is proposed. On this basis, a term frequency–inverse document frequency-weighted latent Dirichlet allocation (TW-LDA) topic model is designed to delineate urban functions from semantic information. Taking Futian District, Shenzhen, as a case study, the results show an overall accuracy of approximately 77.10% in urban function mapping. A comparison of a block-level mapping model, a non-adaptive network-based model and the ANCC model reveals accuracies of 53.10%, 59.20% and 77.10%, respectively, indicating the advantages of the ANCC model for improving urban function mapping accuracy. The proposed ANCC model shows potential application prospects in monitoring urban land use for sustainable city planning.
A New Approach to Achieve a Trade-Off Between Direction-of-Arrival Estimation Performance and Computational Complexity

The multiple signal classification (MUSIC) algorithm is a promising method for the plethora of problems related to the direction-of-arrival (DOA) estimation. Conventionally, this approach uses the whole sensor array observations to obtain the signal or noise subspace, which consequently leads to a huge computational burden. In this letter, to circumvent this problem, we make a significant modification to the traditional MUSIC algorithm. First, we compute only two sub-matrices of the sample covariance matrix (SCM) exploiting the Nystrom method avoiding its complete calculation. These matrices can be used to construct an accurate noise subspace without calculating the SCM and its eigenvalue decomposition (EVD). Furthermore, to have a uniform DOA estimation, we modify the classical ULA by displacing two antenna elements from both the ends of the array to a top and a bottom of the array axis. This unique structure improves the estimation of DOAs near and at the array end fires. Several numerical results are included to confirm the efficacy of the new method.
Underwater Long Baseline Localization based on Doppler Measurements for a Fast-Moving Underwater Vehicle

Underwater navigation that relies solely on dead reckoning suffers from unbounded position error growth. In traditional positioning method based on TDOA, usual interference from multipath reflections and background noise leads to the choice of some fault correlation peaks under low SNR condition, especially in LBL. In the paper, the combination of LBL and Doppler shift estimation of an AUV will be expected to enable survey accuracy at any water depth where the position of an underwater acoustic transmitter equipped on the moving platform can be determined. And based on pattern time delay shift coding pulse, the positioning solution method about frequency estimation of Doppler shift in LBL could be described with Doppler effects from AUV moving. And a computation simulation, including three beacons in LBL, was performed to verify the performance of the proposed algorithm.
Application of Machine Learning to Create a Recommendation in Social Communication Based on Data Analysis

This article discusses the solution to the problem of social interactions caused by the transition of all communication processes to the digital space. The social network Tinder was chosen as the main platform for research, the work with the API of which is discussed in the article. The paper investigates methods for assessing profiles based on user preferences and message generation. These software functions are aimed at helping the user in the process of finding an interlocutor. In the study, convolutional neural network architectures are used to analyze user photos by several parameters, the results of which, together with the semantic analysis of the profile text information, form the basis for generating the text of the first message. As a result of the research, software was obtained that can simplify the user's interaction with the social network Tinder.
Development and Implementation of the H.264-Codec Deblocking Filter Based on the MIPS SIMD Architecture

The paper presents the approach to a virtual architecture system profiling within a QEMU emulator. The aim of the research is to demonstrate virtual architecture MIPS64 Release 6 profiling within QEMU emulator taking as an example implementation of the H.264 video codec element, namely, a deblocking filter. The described approach is characterized by the ability to test any guest models of vector processors. We present the model of vector processor based on the MIPS SIMD Architecture (MSA) and the recommendations on vectorization optimization based on metrics for vectorization performance estimation that are suggested in the paper. The approach and special program tool for metrics estimation were developed by the authors. It is shown through emulation that the model based on the MSA will give better performance for image and video processing tasks like elements of the H.264 video codec.
Design of a Control System for a Modular Multi-Level Converter of a Single-Phase Solar Power System Based on a Proportional-Resonant Controller

Modern solar power stations traditionally use lumped elements to convert electrical energy. This approach is simple and affordable for small power plants; however, the cost of such controllers increases significantly for power of several kilowatts. It is also necessary to place other lumped conversion elements such as MPPT (maximum power point tracking) controller, inverter and the sine filter. Presented control system allows to combine all these devices into one distributed system per moving to a modular multilevel conversion topology. In order to ensure the flexibility and the ability to operate in any network configurations, the proposed article also considers the use of a proportional-resonant regulator to abandon the use of phase-locked loop frequency and, as a consequence, simplify the control system. The presented article will consider a model of a 10 kW power station for a standard mains voltage of 220 V.
A Novel Method of Rapid Pitch of Cownose-ray AUV

Bionic Autonomous Underwater Vehicles (BAUV) have the advantages of high efficiency, high mobility and high stealth, which have attracted more and more attention in ocean investigation and military reconnaissance. Fast pitch mobility is important for BAUV to launch from the bottom of the sea or avoid obstacles quickly. The paper aims to imitate the cownose ray to make a Cownose-ray AUV to accomplish rapid pitch motions using central pattern generator (CPG) algorithm. The experiment results indicate that the Cownose-ray AUV can realize fast pitch from the bottom of the water tank at zero forward speed and change the pitch angle from 0° to 75° during 10s.
Visual Docking Control of an AUV with a Moving Station Using a Cross-Tracking Approach with Unscented Kalman Filtering

In this paper, we propose an unscented Kalman filterer (UKF)-based visual docking control system for an un-deractuated autonomous underwater vehicle (AUV) by using a position-based visual servoing approach. At first, the underwater vehicle's equations of motion and the visual system model are derived. Based on the system models, the relative position and orientation of the underwater vehicle with respect to a moving docking station are estimated by a UKF with the visual measurements of point features on the station. Then, adopting the cross-tracking methodology, the desired yaw and pitch angles are designed with the estimated pose to drive the underactuated underwater vehicle to move along the docking path, which is defined by the straight line along the center of the docking station. Finally, the attitude tracking of underwater vehicles is proposed by using non-singular terminal sliding mode control to overcome the system uncertainties in the rotational dynamic model. The stability of the attitude tracking system is briefly analyzed. Simulation experiments are provided to demonstrate the performances of the proposed visual docking controller.
RANSAC Compressive Sensing Reconstruction in Noisy Wideband Underwater Sonar Imaging

The reconstruction of noisy wideband underwater sonar images using the improved techniques of compressive sensing is analyzed in this paper. The received signal is considered with different kinds of additive noise, with some of the samples being affected by a high-impulsive noise. The sonar images are assumed to be sparse, which makes the compressive sensing (CS) theory suitable for their processing and reconstruction. The goal is to localize and reconstruct targets using a combination of the CS technique and a sonar signal sample selection method (RANSAC). Numerical examples confirm the robustness of the proposed method.
Trawler Fishing Track Interpolation using LSTM for Satellite-based VMS Traces

Vessel Monitoring System (VMS), based on China Beidou Satellite System, provides a high time resolution of 3 minutes for all trawlers in the East China Sea. However, the data loss is still a common phenomenon, hindering the research on spatial and temporal patterns of fishing activities. Most existing studies apply Cubic Hermite Spline (CHS) to interpolate loss samples. However, CHS interpolation is deficient in precision when the inserted trajectory is composed of turns. This paper proposes a hybrid interpolation scheme, combing Long-Short Term Memory (LSTM) and CHS for loss sample insertion. When the loss samples are at the fishing state and more than seven samples, we apply LSTM for interpolation; otherwise, we use CHS. The proposed hybrid interpolation scheme is evaluated with the VMS dataset of 1689 otter trawlers on the East China Sea during October 2016. The results show that the proposed hybrid interpolation scheme limits the interpolation error under 0.4km for continuous one-hour sample losses, 33% lower than CHS.
Research of Modern Routing Systems

This research is devoted to the study and analysis of modern methods of building routes. The article describes the data provided by the Open street map, google maps API and ways of working with them, as well as various routing algorithms used in Graphhopper, Yandex.Maps, and other routing services. In the course of the study, based on the material studied about working with maps, graphs, and routing services, a web application was created. It is aimed at solving the problem of optimizing the route for the needs of the user.
Improved Signal/Pause Segmentation Algorithm Based on the Probability Density Function of Background Noise and Empirical Mode Decomposition

Segmentation into informative regions is an important stage in pre-processing of speech. The quality of segmentation affects the performance of almost all known applications of speech technologies (speech recognition, speaker identification, speech-to-text conversion, etc.). The article presents an improved speech/pause segmentation algorithm. The original algorithm is based on the use of probability density function of background noise, and the analysis of one-dimensional Mahalanobis distance of discrete timing for the investigated speech signal. Modernization consists in the fragmentation of speech and the decomposition of fragments into empirical modes for subsequent analysis of one-dimensional Mahalanobis distance of discrete timing for each mode separately. A study of the modernized algorithm has been carried out in comparison with the original algorithm and the well-known segmentation methods based on the analysis of zero-crossing rate and short-time energy. In accordance with the obtained results of the study, it was concluded that the improved segmentation algorithm provides the best detection of the boundaries of the beginning and the end of informative speech sections with the first and second kind errors, being 4.5767 % and 1.421 %, respectively.
Fault Diagnosis of Rolling Bearings of Different Working Conditions Based on Multi-Feature Spatial Domain Adaptation

The running state of rolling bearings is complex in operation, and the data are generally collected under different working conditions. However, when single-source domain adaptation is used to model the heterogeneously distributed data obtained under different working conditions, the domain-invariant representations can hardly be used for representation, which directly affects the fault diagnosis rate. To this end, a method for the fault diagnosis of rolling bearings under different working conditions based on multi-feature spatial domain adaptation is proposed. Firstly, all the data from source and target domains are mapped into a feature space to learn the common representations of all domains. Secondly, the data for each pair of source and target domains are mapped into different feature spaces to get the fault feature representations under various working conditions. And the multi-domain adaptation network is used for the domain-specific distribution alignment to learn multiple domain-invariant representations. Thirdly, these representations are used to train multiple domain-specific classifiers, thus obtaining the recognition result for each domain-invariant representation. Finally, the domain-specific decision boundaries predicted by multiple classifiers are employed to align the classifiers’ output of target samples and thus to reduce the influence from different classifiers. The effectiveness and feasibility of this proposed method have been verified by diagnostic experiments conducted according to the rolling bearing data from Case Western Reserve University and Laboratory, respectively.
Hermitian Self-Dual GRS and Extended GRS Codes

Self-dual MDS codes over finite fields are linear codes with important cryptographic and combinatorial applications. In this correspondence, a different approach is proposed to obtain Hermitian self-dual MDS codes from (extended) generalized Reed-Solomon (abbreviation to GRS and EGRS) codes. The necessary and sufficient conditions of a Hermitian self-dual (extended) GRS code are presented. With the help of this new method, we revise and improve the results in Niu and Yue <italic>et al.</italic> (IEEE Communications Letters, 23(5): 781-784, 2019). Furthermore, we suppose that there exist Hermitian self-dual (extend) GRS codes over <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{q^{2}}$ </tex-math></inline-formula> if and only if their code length <inline-formula> <tex-math notation="LaTeX">$n\leq q+1$ </tex-math></inline-formula>.
Adaptive Control of a Manipulator with Closed Kinematic Chains and Linear Actuator

A method of adaptive control of a specialized manipulator designed to work as part of a robotic complex for servicing vertical surfaces is proposed and studied. The robotic complex consists of a wheeled platform, a wheeled vertical movement robot with a vacuum contact device and a manipulator. The article describes the operation of the manipulator at the stage of bringing the robot to work. The suggested controller contains a computed torque controller to eliminate the nonlinearity in the model, in addition to linear quadratic regulator to achieve an optimal tracking error. Finally, the adaptive mechanism is added to the controller to process the uncertainties in the model due to the loading and unloading operations.
Single Exposure Optical Image Watermarking Using a cGAN Network

A single exposure optical image watermarking framework based on deep learning (DL) is proposed in this paper, and original watermark image information can be reconstructed from only single-frame watermarked hologram by using an end-to-end network with high-quality. First, the single exposure watermarked hologram is acquired with our presented phase-shifted interferometry based optical image watermarking (PSOIW) frame, and then all holograms and corresponding watermark images are constructed to the train datasets for the learning of an end-to-end conditional generative adversarial network (cGAN), finally retrieved the watermark image well with the trained cGAN network using only one hologram. This DL-based method greatly reduces the recording or transmitting data burden by 1/4 compared with our presented PSOIW technique, and may provide a new way for the real-time 3D image/video security applications. The feasibility and security of the proposed method are demonstrated by the optical experiment results.
Biometric Identification Method using Image Key and P300: A Pilot Study

In this study, we proposed a novel framework for the personal identification technique using the event-related potential called P300. To evoke P300, we utilized the attractive actress image, and each person selects one of which is based on one's interest to create the image key. To test our framework, we recruit 10 male subjects, and the EEG is recorded when the subjects watching 10 images the including image key selected proceeding with the experiment. As the result, we confirmed that the P300 for the image key is observed for all subjects. Also, the peak amplitude for the P300 is different among the subjects who have the same image key. This implies the possibility that our framework is one of the useful techniques for EEG based biometric identification.
Dynamic Manipulation of Deformable Objects With Implicit Integration

Due to their complex dynamics and high-dimensional configuration spaces, non-rigid objects such as cables, garments, bedding and various food items remain notoriously challenging for robots to manipulate effectively. In this letter, we therefore develop, validate and analyze model-based optimal control techniques for dynamic manipulation of deformable objects. We study, in particular, the application of both the batch Newton method and the stagewise Differential Dynamic Programming (DDP) approach to this challenging problem domain. On a technical level, we derive analytic formulations for all necessary derivatives, noting that numerically stable simulation of deformable objects demands implicit integration schemes, which do not have closed form solutions. While both DDP and Newton's method converge quadratically, our experiments and analysis show that the relative overall performance of these two approaches depends heavily on the dimensions of the control problems being solved. We demonstrate the efficacy of our trajectory optimization formulations through a variety of simulation and real-world experiments.
Overview of Reactive Power and Voltage Control of Offshore Wind Farms

With the concern of global warming, the amount of renewable energy integration is increasing recently. A high penetration of wind power generation causes not only frequency instability but also voltage instability. In order to maintain the voltage in an acceptable range, supporting reactive power by wind turbines play a vital role. This paper provides a complete introduction to the reactive power and voltage control of offshore wind farms. Three conventional control modes of wind turbines include power factor control mode, voltage control mode and reactive power control mode. Typically, system operators request that the power factor of the point of connection (POC) of a wind farm is adjusted within a certain range; e.g., from 0.95 lagging to 0.95 leading. If the wind farm has insufficient capability to maintain an appropriate voltage range, the system needs other appropriate measures, such as the adaptive Q-V scheme or other methods. Additionally, the efficiency of High Voltage Direct Current (HVDC) transmission system has been improved significantly. A HVDC system is competitive for a long distance, and it controls the active power and reactive power independently. Thus, this paper also discusses about the reactive power and voltage control by voltage-source-converter based HVDC systems.
Mechanical Vibration Analysis of a Gantry 3D Printer

The accuracy and quality of 3D printing with FDM technology depends on many factors: type of material, environment, software and 3D printer mechanics. An important role is given to the mechanical design of a 3D printer, since low rigidity of the entire system increases influence of mechanical vibrations on printing accuracy and limits its capabilities. In this work we present a methodology for assessing the quality of a gantry 3D printer exemplified by Anycubic i3 Mega. The main evaluation criteria are frequency and amplitude of vibrations resulting from the resonance on the natural frequencies of the printer. Modal analysis was performed using Fusion 360 software to obtain the natural frequencies and associated displacements of the 3D printer. The results indicate that the natural frequencies of a 3D printer have a strong impact on high-speed printing accuracy. Also, the model in Fusion 360 was incapable of predicting some low-frequency high-amplitude vibrations.