Miracle-dz/UniFAR-FTUs huggingface dataset:
Disturbance observer based model following controller for four wheel steering vehicles

In this paper, performance limitations of the conventional front wheel steering (FWS) system is analyzed in order to design the controller that can improve the vehicle handling and stability. By adding steering input at the rear wheels, four wheel steering model under the influence of external crosswind disturbance acting on the vehicle is obtained. The disturbance observer (DO) based model following sliding mode controller is designed for modified steering model to achieve ideal vehicle handling. A simulation is carried out to prove the effectiveness of the proposed controller in tracking the dynamics of the system to the desired reference model. Robustness of the proposed controller is also verified against external disturbances and in tracking different steering inputs.
Multi-View Hierarchical Fusion Network for 3D Object Retrieval and Classification

The rapid development of 3D technique has led to the dramatic increase in 3D data. The scalable and effective 3D object retrieval and classification algorithms become mandatory for large-scale 3D object management. One critical problem of view-based 3D object retrieval and classification is how to exploit the relevance and discrimination among multiple views. In this paper, we propose a multi-view hierarchical fusion network (MVHFN) for these two tasks. This method mainly contains two key modules. First, the module of visual feature learning applies the 2D CNNs to extract the visual feature of multiple views rendered around the specific 3D object. Then, the multi-view hierarchical fusion module we proposed is employed to fuse the multiple view features into a compact descriptor. This module can not only fully exploit the relevance among multiple views by intra-cluster multi-view fusion mechanism, but also discover the content discrimination by inter-cluster multi-view fusion mechanism. Experimental results on two public datasets, i.e., ModelNet40 and ShapeNetCore55, show that our proposed MVHFN outperforms the current state-of-the-art methods in both the 3D object retrieval and classification tasks.
Detection and Classification of Brain tumor tissues from Noisy MR Images using hybrid ACO-SA based LLRBFNN model and modified FLIFCM algorithm

This paper presents a hybrid ACO-SA (Ant colony optimization- Simulated Annealing) based LLRBFNN (Local Linear Radial Basis Function Neural Network) model for detection and classification of brain tumor tissues from noisy images. Denoise of rician affected noisy images becomes a complex and difficult task. To denoise the MR (Magnetic Resonance) images the FLICM (Fuzzy Local Information C-Means) segmentation algorithm has been considered initially and modified the cost function of the algorithm for better segmentation result. The images are segmented using Modified FLICM algorithm and the features are extracted by using GLCM (Gray Level Co-occurrence Matrix) feature extraction technique which are fed as input to the proposed ACO-SA based LLRBFNN model for the purpose of classification of malignant and benign tumors from the MR (magnetic resonance) images. The proposed model has been compared with (particle swarm optimization) PSO-LLRBFNN, (Adaptive Particle Swarm Optimization) APSO-LLRBFNN models and the comparison results are presented. It is found that the proposed ACO-SA based LLRBFNN algorithm shows better results than the conventional methods.
Exploring Demand Patterns of a Ride-Sourcing Service using Spatial and Temporal Clustering

On-demand transport has become a common mode of transport with ride-sourcing companies like Uber, Lyft and Didi transforming the mobility market. Recurrent patterns in prevailing demand patterns can be used by service providers to better anticipate future demand distribution and thus support demand-anticipatory fleet management strategies. To this end, we propose three steps for extracting such demand patterns from travel requests: (1) constructing the origin-destination zones by spatial clustering, (2) composing the hourly and daily origin-destination matrix, and; (3) temporal clustering to extract the dynamic demand patterns. We demonstrate the three step approach on the open-source Didi ride-sourcing data. The data consists of travel requests data for November 2016 from Chengdu, China amounting to approximately 6 million rides. The analysis reveals pronounced and recurrent and thus predictable daily and weekly patterns with distinct spatial properties pertaining to ride-sourcing production and attraction characteristics.
Modelling of PSPP Control System by Using Vector Control Principle and VSI

This study models and simulates 180 MW units of pumped storage power plant (PSPP) with Variable speed machines. In this approach, instead of using traditional cyclo-converters, voltage source inverters (VSI) are used. Purpose of this study is to control the voltage source inverters and maintain DC voltage constant. In order to change and control frequency and voltage, AC/ DC/ AC converter is used in system configuration. DC/AC converter is a single phase inverter including 12 IGBTs. AC / DC converter is a three-phase rectifier bridge and the output includes constant voltage designed from inverters output. First, the generator and the transformer control sections are discussed and their block diagram is represented. Then, equations of each part and the general control structure of the plant are given. Finally, the simulated results of our method are presented.
Boundary Detection-Based Density Peaks Clustering

Clustering algorithms have a very wide range of applications on data analysis, such as machine learning, data mining. However, data sets often have problems with unbalanced and non-spherical distribution. Clustering by fast search and find of density peaks (DPC) is a density-based clustering algorithm which could identify clusters with non-spherical data. In real applications, this algorithm and its variants are not very effective for the division of unevenly distributed clusters, because they only use one indicator (the distance of neighbor points) to handle inner points and boundary points at the same time. To this end, we introduce a new indicator named asymmetry measure which enhances the ability of finding boundary points. Then we propose a boundary detection-based density peaks clustering (BDDPC) algorthm that combines the above two indicators, so that different clusters are separated from each other accurately and the purpose of improving the clustering effect is achieved. The BDDPC algorithm can not only cluster uniformly distributed data, but also cluster unevenly distributed data. In real life, the distribution of high-dimensional data sets are always unbalanced, so this algorithm has very important applications. Experimental results with synthetic and real-world data sets illustrate the effectiveness of our algorithm.
Comparison Method in Indonesian Emotion Speech Classification

Emotion speech recognition aims to study the formation and change in emotional status based on human speech signals. This study aims to solve the problem of recognizing emotional speech, classifying it through sound processing to recognize sound patterns based on the classification of emotions and comparing the level of accuracy of some of the classifiers used. Stages the method starts from collecting or obtaining voice data, pre-processing, feature extraction, classification, results and evaluation. The preprocessing stage is carried out to clean the data first before processing. Extraction features used are MFCC and Classifier used are Support Vector Machine, Random Forest, Naïve Bayes and Neural Network. The system that is built can recognize 4 types of emotions namely anger, pleasure, sadness and disgust. The database used is the sound from audio recordings. From the results of observation, the highest accuracy rates are as follows: SVM of 100%, NN 99.7%, RF 97% and NB 94.1%. Evaluation stages using Confusion Matrix with 10 k-fold.
Additively Manufactured Perforated Superstrate to Improve Directive Radiation Characteristics of Electromagnetic Source

Additively manufactured perforated superstrate (AMPS) is presented to realize directive radio frequency (RF) front-end antennas. The superstrate comprises spatially distributed dielectric unit-cell elements with square perforations, which creates a pre-defined transmission phase delay pattern in the propagating electric field. The proposed square perforation has superior transmission phase characteristics compared to traditionally machined circular perforations and full-wave simulations based parametric analysis has been performed to highlight this supremacy. The AMPS is used with a classical electromagnetic-bandgap resonator antenna (ERA) to improve its directive radiation characteristics. A prototype is developed using the most common, low-cost and easily accessible Acrylonitrile Butadiene Styrene (ABS) filament. The prototype was rapidly fabricated in less than five hours and weighs 139.3 g. , which corresponds to the material cost of only 2.1 USD. The AMPS has remarkably improved the radiation performance of ERA by increasing its far-field directivity from 12.67 dB to 21.12 dB and reducing side-lobe level from −7.3 dB to −17.2 dB.
Small Signal Stability Analysis Toolbox: A MATLAB based GUI

A MATLAB® based Graphical User Interface(GUI) which can evaluate the small signal stability of a power system and also give various information related to types of system oscillations present in the system under study is described in this paper. Detailed modeling of system components and eigenvalue analysis along with participation factors and normalized angular participation factors are used to evaluate the system's small-signal stability and oscillations. The software takes power system data as input via data(*.DAT) files. The system stability of IEEE 14 bus modified test system is evaluated. Detailed modeling of components like generator, transmission lines, AVR and exciter provided by the software developed here facilitate user-friendly data presentation and help in their analysis. Plot of poles and zeroes of the system under study are also presented for better understanding of the results. This specific case shows the eigenvalues corresponding to different system states, their location and oscillation.
An application of convolutional neural network to derive vessel movement patterns

The rising need for highly effective maritime transportation, the demand for a higher safety level for protecting maritime environment and the intelligent ships’ development have been the driving forces towards Maritime Autonomous Surface ships (MASSs). Navigational devices such as the Automatic Identification System (AIS), Long Range Ship Radar (LRSR), high precision camera and Electronic Charts make it possible for MASSs to derive comprehensive maritime Situational Awareness (SA) by using the information collected by these devices. The vessel movement pattern extraction from these devices is one of the important parts for SA. In this paper, our research proposes one method called vessel movement pattern extraction from trajectory images using AIS data to classify the vessel movement patterns (static, cruise and manoeuvring). Firstly, we rebuild the vessel’s trajectories with historical AIS data and label the AIS data. In order for Convolutional Neural Network (CNN) to easily handle the raw AIS trajectories, our method converts raw AIS trajectories into an image data structure while keeping the vessel movement pattern information. And then, the CNN method is used to extract the vessel motion patterns. Finally, we demonstrate the effectiveness of the proposed method through several experiments using AIS historical dataset.
The LED spectral power distribution modelled by different functions - how spectral matching quality affected computed LED color parameters

The paper is describing different alternatives for mathematical modelling of LED’s spectral power distribution. Gaussians, logistic power peak and asymmetric double sigmoidal functions are tested with different shape and peak wavelength LEDs. Article shows how SPD accuracy modeling is affecting color quality parameters and luminous efficiency of LED. Consequences of applying always single SPD model for different LED emitters are shown for chosen parameters.
Towards a Smartwatch Application to Assist Students with Disabilities in an IoT-enabled Campus

Limited access to built environments such as buildings and classrooms is one of main barriers faced by students with disabilities (SWDs) in higher education institutions (HEIs) that prevents them from fully participating in campus life. Based on the increasing interest in the integration of the Internet of Things (IoT) into buildings, our ongoing research explores how IoT and wearable technology can benefit SWDs in an IoT-enabled campus and improve the quality of their college experiences. In this paper, we present our first prototype application that helps SWDs achieve daily life tasks inside a building such as monitoring environmental conditions and controlling in-building devices. We then report the initial results from a preliminary test with two students with motor impairments and discuss the opportunities and challenges for further improvements.
Analysis and Prediction of Disruptions in Metro Networks

Public transport disruptions can result in major impacts for passengers and operator. Our study objective is to predict disruption exposure at different stations, incorporating their location-specific characteristics. Based on a 13-month incident database for the Washington metro network, we successfully develop a supervised learning model to predict the expected number of disruptions, per type, station and time of day. This supports public transport authorities and operators to prioritize what type of disruptions at what location to focus on, to potentially achieve the largest reduction in disruption exposure. Our clustering results show that start/terminal and transfer stations are most susceptible to disruptions, mainly due to operations- and vehicle-related disruptions.
Impact and Detection of Facial Beautification in Face Recognition: An Overview

Facial beautification induced by plastic surgery, cosmetics or retouching has the ability to substantially alter the appearance of face images. Such types of beautification can negatively affect the accuracy of face recognition systems. In this work, a conceptual categorisation of beautification is presented, relevant scenarios with respect to face recognition are discussed, and related publications are revisited. Additionally, technical considerations and trade-offs of the surveyed methods are summarized along with open issues and challenges in the field. This survey is targeted to provide a comprehensive point of reference for biometric researchers and practitioners working in the field of face recognition, who aim at tackling challenges caused by facial beautification.
Classification of Ship Trajectories by Using Naive Bayesian algorithm

In order to automatically classify the ship’s historical trajectory and predict the class of a ship’s trajectory, a ship’s track classification algorithm based on naive Bayesian method is proposed. Using the Automatic Identification System (AIS) data of the Yangtze River in Wuhan section, the AIS data is first preprocessed to extract valid trajectory data. Then the trajectory data is analyzed and the characteristics of average speed, average heading, maximum heading, minimum heading, heading variance and maximum turning rate are extracted. The Naive Bayes classifier is trained and verified. The results show that the accuracy of classification is as high as about 98.59%. It takes only 0.165s to extract features from 709 ship trajectories. The Naive Bayesian classification method can effectively classify inland ship trajectories.
Hardware Implementation of an Asymmetrical 11-Level Inverter with Automatic Boost Charge Control in PV Applications

The increasing electricity demands for residential purposes results in the development of renewable photovoltaic standalone systems. With power converter technology as the core of the design; an undesirable harmonic distortion which affects the durability of electric equipment emerges from the construction that is above recommended practice standards. For decades, continuous research for the means to reduce harmonics in the power inverter unit of the system leads to multilevel technology. Nevertheless, the conventional topology of inverters would often deal with volume building and analysis complexity as the number of level increases. This paper discusses a further hardware implementation of the unique asymmetrical design of an 11-level inverter with an efficient number of reduced switches to achieve a higher level demand. This inverter is designed for single phase off-grid photovoltaic system application. The first verification of this research is a simulation design of the proposed topology. Finally, the hardware implementation which is done in a laboratory test environment, the THDi test value is recorded 3.43% for the fundamental current of 4.72A at a frequency of 50Hz measured with the HIOKI 3286–20 power meter THD record function.
Adaptive Randomized Scheduling for Concurrency Bug Detection

Multi-threaded programs often exhibit erroneous behaviours due to unintended interactions among threads. Those bugs are often difficult to find because they typically manifest under very specific thread schedules. The traditional randomized algorithms increase the probability of exploring infrequent interleavings using randomized scheduling and improve the chances of detecting concurrency defects. However, they may generate many redundant trials, especially for those hard-to-detect defects, and thus their performance is often not stable. In this work, we propose an adaptive randomized scheduling algorithm~(ARS), which adaptively explores the search space and detects concurrency bugs more efficiently with less efforts. We compare ARS with random searching and the state-of-the-art maximal causality reduction method on 27 concurrent Java programs. The evaluation results show that ARS shows a more stable performance in terms of effectiveness in detecting multi-threaded bugs. Particularly, ARS shows a good potential in detecting hard-to-expose bugs.
Structural analysis of superficial cracks on structural elements

This paper addresses the structural dynamics of models of cracked Euler-Bernoulli beams by using finite element methods and modal analysis techniques. The natural frequency is also characterized and analyzed in order to present the decrease in natural frequencies due to the presence of a superficial crack. Some numerical and experimental results are provided to illustrate the effects of a crack on aluminum beams.
Parametric Lighting Design

Lighting a space serves not only purely functional purposes like making objects visible but also purposes which have to do more with impression and aesthetics. In most cases, the procedure of lighting design for achieving certain visual effects is still carried out empirically. Procedures or algorithms that could offer a “reverse engineering” way of lighting design i.e. algorithms that take specific visual objectives as input and give optimum lighting design solutions as output could help a lot the lighting designers to do their job more easily and efficiently.This text includes a literature review of the research on lighting design parameters and how they can be methodically controlled to attain specific visual objectives, and a presentation of a proposal for the parameterization of specific visual effects and linking it with corresponding parameters of lighting design, with the goal of arriving at more automated lighting design techniques.
JSSA: Joint Sidelobe Suppression Approach for Collaborative Beamforming in Wireless Sensor Networks

Conventional collaborative beamforming (CB) with virtual node antenna array often results in high maximum sidelobe level (SLL) due to the unexpected positions of the nodes. In this paper, a sidelobe suppression approach (JSSA) to suppress the maximum SLL by jointly optimizing the locations and amplitude weights of the nodes is proposed. JSSA organizes the node positions according to the concentric circular antenna array for location optimization. Then, a novel algorithm called variation particle chicken swarm optimization (VPCSO) is proposed to further optimize the amplitude weights of the selected CB nodes. The proposed VPCSO introduces the variation mechanism and location learning mechanism to enhance the performance of the conventional chicken swarm optimization algorithm. Simulations are conducted and the results show that the proposed location optimization approach is effective, and the maximum SLLs of beam patterns obtained by VPCSO are lower than that of other algorithms. Moreover, the energy consumption can be saved by VPCSO. In addition, electromagnetic (EM) simulations are conducted to verify the performance of the proposed JSSA in EM environments.
Denoising and Features Extraction of ECG Signals in State Space Using Unbiased FIR Smoothing

The electrocardiogram (ECG) signals bear fundamental information for making decisions about different kinds of heart diseases. Therefore, many efforts were made during decades to extract features of heartbeats via ECG records with high accuracy and efficiency using different strategies and methods. In this paper, we solve the problem in discrete-time state-space using a novel $q$ -lag unbiased finite impulse response (UFIR) smoother, which we adapt to the ECG signal shape via the time-varying optimal averaging horizon. It is shown that the adaptive UFIR smoother performs better in applications to ECG signals than the standard techniques such as the Savitsky-Golay, wavelet-based, low-pass, band-pass, notch, and median filters. Applications are given for the PhysioBank data benchmark, which contains several records taken from different databases such as the MIT-BIH Arrhythmia (MITDB). A complete statistical analysis is provided via normalized histograms and statistical classifiers. It is shown in a comparison with other methods that the adaptive UFIR smoother has a higher accuracy in denoising, features extraction, and features classification for ECG records with normal rhythm and atrial fibrillation (AF).
SRPRID: Pedestrian Re-Identification Based on Super-Resolution Images

Intelligent security expects to avoid the occurrence of robbery, theft, and other undesirable situations through video surveillance. In video surveillance, images of human faces undimmed are not easily available, so pedestrian re-identification (person ReID) is an alternative technique which attracts a mount of researchers attention. Person ReID is a technique used to match pedestrian images across cameras. Due to the interference of shooting angle and camera quality, it is difficult to obtain the images of high resolution, no obstructions, simple backgrounds and similar posture, which brings great challenges to the research of person ReID. Most existing methods of pedestrian re-identification ignore the inconsistency of resolution, and they are based on the assumption that all images have similar and high enough resolution by default. In this paper, we propose a hybrid framework, Super-Recognition of Pedestrian Re-Identification (SRPRID), in order to strengthen pedestrian re-identification based on multi–resolutions images captured by disparate cameras. Particularly, residual dense block (RDB) and Integrated Attention (InnAttn) block are merged to SRPRID. It is worth mentioning that the rank_1 accuracy of our method outperforms the state-of-art method by 17.2 points (86.9% - 69.7%) on CUKH03 dataset of extremely challenging.