Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based Image Cognition Platform for IoT Applications

With more and more Internet of things(IoT) applications are developed over the website, image retrieval is widely used in Web applications. Usually for image retrieval, the queries are needed to be presented in literal language and the retrieved images are captioned in advance. Deep learning is a recent solution to image retrieval, which could calculate the similarity of two images by extracting their features without captions. In this paper, an image cognition platform based on deep neural network is proposed to address the problem of image retrieval as well as image caption. The platform combines the convolutional neural network, recurrent neural network and hash technology together to provide interfaces for users to query with both images and natural language. A case study is given to verify our platform. The result shows that our platform can help managers to efficiently browse similar services provided by other auto 4S stores through image retrieval.
Complete Small-Signal Model of Three-Phase Photovoltaic Inverter Considering the Source and Load Effects

The amount of photovoltaic inverters connected to the electrical grid is increasing. In order to control the power fed to the grid, the inverter must be controlled, and many different approaches for small-signal modeling have been proposed to facilitate the controller design. However, a solar panel cannot be modeled by an ideal current source and the photovoltaic inverter is not connected to an ideal grid on the load side. This paper proposes a generalized method to include the load and source effects to the dynamic model of a photovoltaic inverter. The method can be used to include the source impedance of the photovoltaic generator and impedance of the distribution line in the small-signal model of the photovoltaic inverter.
Demonstration of Liquid Pressure Fiber Sensing Based on Distributed Polarization Crosstalk Analysis

The feasibility of a liquid pressure fiber sensing system based on the polarization crosstalk analysis technique through the water depth measurement is experimentally demonstrated for the first time to the best of the authors’ knowledge. A specialty fiber sensing tape which contains the polarization maintaining fiber (PMF) sensing medium and a high sensitive sensing unit is designed. The sensing unit can convert the liquid pressure into the crosstalk intensity which can be measured by the polarization crosstalk analyzer precisely. A good linear relationship is obtained in a water depth measuring range of 180 cm through linearly fitting the experimental data, and the goodness of fit is as high as 0.99278. The sensitivity of the proposed fiber sensing system is 0.05374 dB/cm. Future work should be focused on the realization of large-depth sensing with multiple sensing units as well as performing the liquid stratification measurement.
Analysis of an Application of the Extended Electromotive Force Model Based Position Sensorless Control on the Wound-Field Synchronous Motor with Dual-Three Phases in Standstill/Low Speed Region

Sensorless control has become a popular research area for ac drives. However, most of the works are attributed to conventional three-phase drives. Given that applications of multiphase machines keep spreading, studies of position sensorless for them are necessary. For the object in this paper, some studies for sensorless control in middle/high speed region are reported. To accomplish a full-speed range sensorless control, a study about the standstill/low speed sensorless control of the target motor is proposed in this paper. Particularly, conventional signal injection methods utilized in three-phase ac drives meet a new issue caused by the inner magnetic couplings of the system when applying to the object in this paper. It may lead to failure of sensorless control and torque ripple enlargement. Therefore, analyses to solve the issue become the main interest of this paper, simulation results are demonstrated in the last section.
A Navigation Framework for Mobile Robots with 3D LiDAR and Monocular Camera

This paper presents a navigation framework for mobile robots. In order to traverse in different environments, mobile robots have to avoid obstacles it encountered and make a suitable decision with the consideration of global destination. On the basis of grid map provided by the 3D LiDAR, this framework is related to the SLAMs with motion planning methods, where different SLAMs and different planning solutions can be combined to achieve the navigation in dynamic environments. Concretely, the ORB SLAM2 with a slightly improved VFH+ provides a solution, and a navigation method based on Bezier curve with Cartographer SLAM is presented, specifically, we consider the influence area caused by the dynamic obstacle with probabilistic descriptions. The effectiveness is verified by the experiments.
Is the Agile Development Method the Way to Go for Small to Medium Enterprises (SMEs) In Saudi Arabia?

Agile software development is based on iterative and incremental delivery of software to customers. It is mainly designed to serve dynamic and small team sizes, who are usually collocated in one place. These features make agile methodologies suitable to be applied in managing small and medium-sized enterprises (SMEs), where the number of their members is limited, and the nature of their projects is usually dynamic and flexible. This research tries to study the current state of SMEs' project management in Saudi Arabia and to what extent they are applying agile methodologies. A survey of twenty-six enterprises with different sizes reveals that most of the SMEs are following the core concepts of agile methodology, despite that more than half of them are not aware of the agile concept and its best practices. This result leads that agile methodologies are suitable to be applied to SMEs in Saudi Arabia. However, there is a need to spread awareness and deliver solutions to these enterprises that help them in adapting the best available agile practices and tools. Moreover, following these well-known practices would help to raise productivity and efficiency of their work.
Corona Discharge Characteristics for Conductors of HVDC Transmission Lines with Different Heights to Groud

The corona discharge characteristic of the conductor is one of the key factors affecting the electromagnetic environment of the project. In order to obtain corona discharge characteristics of conductors with different heights to ground and correction methods for inception corona voltage of conductors with different heights to ground, corona test were carried out in Ultra-high Voltage Test Base in Wuhan with ultraviolet image apparatus. the corona test of conductors of JL/G1A-1250/100 and JL/G lA-1250/70 at different heights to ground were carried out. , one single conductor. , 6-bundle conductor with bundle space 500mm and 8-bundle conductor with bundle space 550mm were tested separately. The results show that Under DC positive voltage. , the corona inception voltage for conductors increased with the rise of the height to ground. , different bundle distances. , different bundle conductors and one single conductor corona discharge characteristics have some differences; The corona inception voltage between single conductor and different bundle distances conductors have certain functional relationship. By establishing the functional relationship between them. , the corona inception voltage of bundle conductors can be calculated by the corona inception voltage value of a single conductor. , the value of correction errors can be controlled at less than 13%.
Emoji-Based Sentiment Analysis of Arabic Microblogs Using Machine Learning

Nowadays, there is an explosive growth of social data analytics to measure human reactions to various events. However, the application of natural language processing for text analysis and mining of microblogs is a daunting task with an excessive complexity. In this paper, we explore the idea of adopting new non-verbal features for sentiment analysis of microblogs. We considered 969 emojis and prepared a dataset of 2091 instances written in multidialectal Arabic and each contains at least one emoji. Several machine learning algorithms are evaluated on the suggested features. The experimental results demonstrated that emoji-based features alone can be a very effective means for detecting sentiment polarity with high performance. For instance, when using a multinomial naive Bayes classifier, an $F_{1}$ score of 80.30% and AUC of 87.30% were achieved using the 250 most relevant emojis.
An Intelligent Inspection Robot of Power Distribution Network Based on Image Automatic Recognition System

It has an important meaning for promoting development of intelligent electric power supply. In the development process of artificial intelligence, electric power system absorbs relevant technology continually. In this paper, we add an image automatic recognition system based on an artificial neural network algorithm into traditional inspection robot of power distribution network so that inspection robot can discover the malfunction automatically. Experimental data and analysis prove that inspection robot becomes more intelligent by adding image automatic recognition system. It will promote intelligent electric power supply level and timeliness of malfunction repair compared with traditional inspection robot.
Distance Metric Learnt Kernel based SVMs for Semi-Supervised Pattern Classification

The performance of kernel methods based pattern analysis tasks depends on learning a suitable kernel function that captures the similarities between the data examples. Many methods have been proposed for learning a suitable kernel matrix for a given labeled training set. In this work we discuss a novel approach for learning a kernel gram matrix using distance metric learning (DML) techniques from a sparingly labeled training set. We formulate the semi-supervised DML (SS-DML) problem in which the label information is available for a limited number of training examples only. This iterative method uses the DML technique to learn a kernel gram matrix by using pair wise similar/dissimilar constraints deduced out of the available limited number of labeled training examples. The label information for all the unlabeled examples is incrementally determined using an iterative self training strategy that uses an SVM whose output values are converted into posterior probabilities. This SVM uses a confidence measure in the decision making process. The SS-DML method learns an optimal kernel gram matrix from the available sparingly labeled training data by claiming both the benefits of DML and the superiority of SVM as a classifier. The SS-DML method can be effectively applied in tasks that are used in pattern analysis applications.
Formal Ontology Driven Model Refactoring

Refactoring, successfully used in the field of programming, can be used in maintenance and restructuring of the large and complex models. In this paper, we present a novel approach for model refactoring and a set of modelling patterns that are applicable for refinement-based formal development. In order to carry out this study, we investigate the previously developed large and complex model and required ontology to develop a domain model and a refactored system model. Further, we use the Rodin tools to check the internal consistency with respect to the desired functional behaviour and the required safety properties. Our main contributions are: to develop a refactoring technique related to the correct by construction approach; to use the domain specific knowledge in a system model explicitly; to define a set of modelling patterns; and to define a restructuring mechanism in the formal development. Finally, this proposed approach is evaluated through a complex medical case study: ECG clinical assessment protocol.
Nonlinear Optimal Control of the UAV and Suspended Payload System

A nonlinear optimal control approach is developed for the UAV and suspended load system. The dynamic model of the UAV and payload system undergoes approximate linearization. This makes use of Taylor series expansion around a temporary operating point which recomputed at each iteration of the control method. The linearization procedure relies on the computation of the Jacobian matrices of the state-space model of the system. Next, an H-infinity feedback controller is designed for the approximately linearized model. The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the UAV and payload system, under model uncertainties and external perturbations. To compute the controller's feedback gains an algebraic Riccati equation is solved at each time-step of the control algorithm. The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the UAV and payload system, under moderate variations of the control inputs. Finally, Lyapunov analysis is used to prove the global stability properties of the control scheme.
NARMA-L2 Neuro controller for speed regulation of an intelligent vehicle based on image processing techniques

In order to be deployed in driving environments, intelligent transport system (ITS) must be able to recognize and respond to exceptional road conditions such as traffic signs, highway work zones and imminent road works automatically. This research presents a new vehicle speed control approach based on image processing technique. It aims to firstly recognize the road sign image and then, send the recognized speed as reference to the control unit. Obviously, recognition of traffic sign is playing a vital role in the intelligent transport system, it enhances traffic safety by providing drivers with safety and precaution information about road hazards. To recognize the traffic sign, an image processing unit ensured 3 steps: traffic board detection, feature extraction and sign classification. The detection phase is based on morphological operations, thresholding and contrast analysis. Steerable filters based technique was adopted to extract features from the segmented sign images. Finally, traffic signs classification is done by an intelligent Bayesian Regularization Neural Network (BRNN). It achieves a classification accuracy up to 97%. Once recognized, the resulting speed is taken as reference of the NARMA-L2 based control unit to regulate the speed of a DC motor. The simulation results show that our speed vehicle is controlled successfully with different speed references
Kinship Determination in Mobile Social Networks

With the progress of mobile communication technology, mobile social networks that can reflect the real situation of human social activities in real life have gradually formed. An even challenging problem whether it is possible to determine the kinship merely based on mobile social networks is raised during research on mobile social networks. This enlightens us the following research. First, we take the mobile phone contacts, telephone network and SMS network as the object of social network research. Second, we tackle the challenge of kinship mining using novel feature extraction and selection methods. Third, after selecting the most discriminative data features, we use a variety of algorithms to compare the study of the kinship in this paper. Finally, we demonstrate a classification accuracy of 81.04% on a test set using XGBoost.
Simulation design of fiber Bragg grating resonant sensor for train bearing damage detection

We propose a resonance and temperature composite fiber Bragg grating resonant sensor (FBG-RS) basing on resonance detection principle. The sensor’s detection procedure is that the micro- impact caused by early damage of train bearings periodically stimulates resonance structure for axial vibration sensing. When the signal frequency reaches the natural frequency of the sensor, the weak damage shock is amplified dramatically. The resonance structure includes bolt, mass and the elastomer. One certain pre-stretched FBG is embedded and solidified into the bolt by the epoxy resin for measuring strain. Another FBG is mounted in sensor base for monitoring the bearings’ temperature. We determined the materials and geometrical dimensions of the resonant structure components through ANSYS software simulation, the natural frequency of resonance sensor is 18.8kHz. The frequency is within the range of the 10kHz-20kHz of damage impact’s vibration component. The results of this simulation have guiding significance for the manufacture of train bearings structure health monitoring sensor.
Review of Photonic and Hybrid On Chip Interconnects for MPSoCs in IoT Paradigm

Multi-Processors Systems-On-Chips (MPSoCs) are evolving as a vital technology for Internet-of-Things (IoT) domain integrating hundreds or thousands cores on single die. However, key challenge and vital limiting factor in overall performance of MPSoCs is the on-chip interconnect network among the cores. The evolution of optical and hybrid NoCs have emerged as a viable solution. This paper presents a comprehensive review of recently presented optical/hybrid NoC topologies and explores their key contributions. A detailed study has been carried out on the performance metrics i.e. latency, throughput and power/energy efficiency providing valuable insight of ONoC/HNoC architectures. The proposed work also explores wide summary of existing accomplishments in optical/hybrid interconnects. Based on the comparison of multiple on-chip interconnection schemes, the implications of future research endeavors can be comprehended.
Scalable and Real-Time Time Series Analytics: Telemedicine as Use Case

Real-time processing and data analytics of big data has become a main operation in different business, such as extracting manufacturing, healthcare, smart Cities, social and media network data, … etc. Also another concept has been appear that significant interest in building new system refers to high-speed real-time and near real-time data streams. Big data workloads in the wild show a strong temporal variability that not only poses the risk of slow responsiveness in data analysis, but also leads to a high risk of service outage. The recent development of batch streaming systems based on the MapReduce framework is shown effective on non-overloaded systems. However, little is known on how to enhance the performance of the batch streaming systems for bursty workloads. In this paper, we propose a latency-driven data controller, which aims to process as much data as possible, while processing these as fast as the application target latency and system capacity allow. In particular by implementing Spark Streaming as emerging and complex batch streaming system which include features that allow placing data in an augmented distributed memory, shedding out-of-date data, (improving the processing locality of Map tasks, and delaying data processing in transient overloads
The Study on Mechanism of the Influence from MMC-HVDC on Sub-synchronous Oscillation of the Units

As MMC-HVDC system is of a large number of applications, the mechanism of its influence on the SSO characteristics of the adjacent units needs to be studied. This article proposed the coupling loop of the voltage disturbance between HVDC system and the AC bus, and further established the response characteristics equations of MMC-HVDC control system between amplitude and phase perturbation of the AC bus voltage. Then, the damping characteristics based on response characteristic equations above were analyzed to reveal the phenomena and reason of the weak connection between MMC-HVDC and the units' SSO characteristics. Finally, the results are verified by electromagnetic transient simulations. The results show that MMC-HVDC system has weaker damping effect on the units' SSO characteristics.
Comparison of Model Predictive Control Strategies for Six-Phase Permanent Magnet Synchronous Machines

This paper presents a comparative study between different finite control set model predictive control (FCS-MPC) strategies for electric drives based on the six-phase permanent magnet synchronous machine (PMSM) with surface mounted permanent magnets (PMs). Special emphasis is given to predictive current control (PCC) and predictive torque control (PTC) strategies, where a proportional-integral (PI) controller is used to generate the reference value of the torque-producing current component or the reference torque. Simulation results obtained with the different control strategies are presented, their performance is compared, and their strengths and weaknesses are analyzed.
Discriminate Method of Power Frequency Ferroresonance in System with Non-Effectively Earthed Neutral of Three-Phase Enclosed GIS

Ferroresonance overvoltage is one of the most important factors to impact electric equipment inner insulation. In system with non-effectively earthed neutral, the equipment damage caused by ferroresonance overvoltage possesses great proportion. This paper derives the criterion of power frequency ferroresonance in system with non-effectively earthed neutral in detailed, in order to conveniently judge whether there is power frequency ferroresonance when system transferring working mode. This criterion derives the relation between equivalent circuit resistance and critical resistance using analytical method and can be used for determining the zero sequence resistance value in cascade with PT secondary side for eliminating ferroresonance. The method is verified by an example under two conditions. Finally, the effect of neutral connecting arc suppression coil on ferroresonance is analyzed.
On Effect of Electrical Double Layer Capacitor Storage Devices on Power Quality in Power System with Electrified Railways

Energy storage devices for electrified railway have acquired significant attention from both academia and industry due to its capability of energy saving and conservation. Therefore, models of electrified railways with energy storage devices (ESD) need to be built for investigation on the effect of ESD on traction systems and power networks. This paper assesses the effect of the electrical double layer capacitor (EDLC), one prototype of ESD, on the power quality of the network after installation of EDLC. An integrated electromagnetic transient model composed of the traction substation, the transmission line, the locomotives with control systems, and the energy storage devices with control systems is built in ADPSS based on the topology of the real network and data sheets of the selected EDLC from the manufacturer. The overall power quality at the grid side is analyzed using analytical hierarchy process and compared with that before installation of EDLC to assess the effect of EDLC on power quality. The overall power quality performance can be improved after installation of EDLC.