Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite-time Coordinated Path-following Control along Convex Loops of Multi-agents with Input Saturation

This article addresses coordinated path-following problem along convex loops of multi-agents with first-order integrator dynamics. Unlike the simple coordinated path-following problem, a finite time bounded algorithm for first-order multi-agent system is proposed, considering both finite time control and bounded control. In this article, the cooperative path tracking system is decoupled into the tracking subsystem and the cooperative formation subsystem. For the tracking subsystem, combining the redesign of potential energy function and the technique of inequality expansion and contraction, the path tracking of agents is realized by the bounded speed of agents along the path method. For the formation subsystem, the path tracking of agents is designed based on the bounded finite time consistency algorithm of the first-order integrator The velocity of radial tangent upward is used to realize cooperative formation.
Improvements in the Voltage Stability of a Microgrid due to Smart FACTS – an Approach from Resilience

Flexibility along with other characteristics of electrical microgrids (MGs) have allowed, from the point of view of supply, a greater presence of distributed generation (DG) systems through Distributed Energy Resources (DERs). In the context of demand, this permits the connection of different types of static and dynamic loads. However, the increase of dynamic loads can give rise to voltage instability problems; this resulting in a high reactive power compensation requirement. Furthermore, the MG transition from the grid-connected operating mode to the islanding one significantly hinders control actions of DERs. This can lead to a difficult transition and in extreme cases to the MG voltage collapse, which seriously compromise its operational resilience. This paper proposes incorporating smart FACTS (Flexible AC Transmission System) devices, particularly DSTATCOMs (Distribution Static Synchronous Compensators), as an effective solution to substantially improve the MG voltage stability. It aims at dealing with a great amount of dynamic loads especially during the transition to isolated operation of the MG. To this aim, several case studies were considered and analyzed using the CIGRE microgrid test system in the DIgSILENT Power Factory software. The Results obtained showed comparative improvements with the incorporation of smart DSTATCOM devices as regards the margin and behavior of MG voltage stability, as well as improvements in terms of operational resilience.
Experimental Analyses of an Efficient Aggregated Robot Processing with Cache-Control for Multi-Robot System

This study aims to develop a network architecture for low-cost multi-robot systems, in which robots operate with limited hardware capability and on-board computational power. Low-speed data processing and high latency time in communication are unavoidable issues in such a multi-robot system. To address those problems, this study proposes Aggregated Robot Processing – Robot Operating System (ARP-ROS) as a network architecture for low-cost multi-robot systems. This network architecture arranges a computing environment dedicated to data processing in a multi-robot system. In the proposed network architecture, most of sensor data obtained by each robot are transmitted to the computing environment via the communication network and processed there, and then control commands are sent back to the robots. For the data exchange in the network architecture, ROS2 node communication is employed because it uses Data Distribution Service (DDS) suitable for real-time distributed embedded systems. In addition, Cache-Control Algorithm (CCA) is introduced to the communication network for its performance improvement. This paper presents experimental analyses of the ARP-ROS with CCA in terms of latency time and data process failures.
Impact of Saturation and Scaling on the Field Weakening Performance of an Interior PM Machine

If magnetic saturation is neglected, powerful visual tools exist for understanding field weakening in interior permanent magnet machines (IPMs). However, saturation can severely reduce field weakening performance. This is of concern in electric vehicle propulsion, where compact machines operate short-term at high power over a wide speed range. This paper shows how finite element results, including saturation, can be interpreted in established visual tools. This aids deeper understanding of field weakening in IPMs with significant saturation. Further, scaling laws for finite element results are shown to preserve field weakening performance. This allows analysis of machine peak performance over a range of maximum currents, with varying saturation severity, while scaling ensures precise fulfilment of torque and power requirements. Ultimately, this combination of current variation and scaling can be used to identify the trade-off between inverter rating and machine volume, with finite element analysis accuracy, for any specific application and IPM geometry.
Voltage Multiplier Topologies Comparison for UHF RFID Applications

Due to the limited power that can be obtained from the RF field, UHF RFID systems need to be able to operate with few dozens of µW. Therefore, an efficient voltage multiplier is a key element to obtain tags able to operate at long ranges and with advanced functionalities. The objective of this work, is obtaining a topology able to provide the required operative voltage with the minimum RF input power. The highest possible sensitivity and efficiency will provide the longest range, which is a critical parameter in UHF RFID applications. Factors such as transistor and diode size and number of stages have been analyzed in order to obtain the maximum sensitivity and power conversion efficiency (PCE). In this paper, four voltage multipliers are developed and analysed, following the same methodology using a 180µm process. Sensitivities as high as -12dBm and PCEs up to 16% with a load of 7µA are obtained in the Schottky multiplier, during simulation of an input power range from - 15dBm to -8dBm.
Experimental Study on the Impact of MMF Spatial Harmonics in the Mechanical Vibration of a Three-Phase Induction Motor

In this paper, an experimental study on the influence of the air-gap magnetomotive force spatial harmonic content on an induction motor, is presented. In the experimental tests, a 4-kW, 4-pole, 36-slot, 400-V, 50-Hz, three-phase, squirrel-cage, induction motor with reconfigurable double-layer windings was used, allowing changing the coil pitch. Three different coil pitches were tested, namely, 9/9, 8/9, and 7/9, and the motor air-gap flux and radial vibration were measured. A Fast-Fourier Transformer was then used to build the flux and vibration spectra to be analyzed for harmonics. The coil pitch of 7/9 (77.8% short pitch) led to the best results in reducing the harmonic distortion and mechanical vibration. The conducted experiments and results are thus presented on this paper.
Outdoor Monocular Visual Odometry Enhancement Using Depth Map and Semantic Segmentation

An outdoor environment is challenging for the localization of a mobile robot. For robust visual odometry, accurate feature matching and triangulation are essential. The features extracted from the windows of buildings and car surfaces lead to wrong triangulation results due to reflective features. The landmarks at short-distances affect the feature matching performance and the landmarks at long-distances cause triangulation errors. Inaccurate feature matching and triangulation error lead to the localization error of the robot pose. In this paper, an outdoor monocular visual odometry using the pre-trained depth estimation network and semantic segmentation network is proposed. By using the pre-trained semantic segmentation network, a semantic label is predicted for every pixel. Also, by using the pre-trained depth map estimation network, the depth of every pixel is predicted. Using semantic constraints for feature matching and depth constraint for triangulation, the accuracy of these procedures is enhanced. Additionally, pose graph optimization is performed on every estimated robot pose and landmark position. The performance of the proposed method is evaluated using dataset-based experiments. The experiments showed that the proposed algorithm is more accurate than the visual odometry algorithm that uses Oriented FAST and rotated BRIEF (ORB) features.
Tail of Distribution GAN (TailGAN): GenerativeAdversarial-Network-Based Boundary Formation

Generative Adversarial Networks (GANs) are a powerful methodology and can be used for unsupervised anomaly detection, where current techniques have limitations such as the accurate and robust detection of anomalies near the tail of a distribution. GANs generally do not guarantee the existence of a probability density and are susceptible to mode collapse, while few GANs use likelihood to reduce mode collapse. In this paper, we create a GAN-based tail formation model for anomaly detection, the Tail of distribution GAN (TailGAN), to generate samples on the tail of the data distribution and detect anomalies near the support boundary. Using TailGAN, we use maximum entropy regularization and leverage GANs for anomaly detection. Using GANs that learn the probability of the underlying distribution has advantages in improving the anomaly detection methodology by allowing us to devise a generator for boundary samples, and use this model to characterize anomalies. TailGAN addresses supports with disjoint components and achieves competitive performance on images. We evaluate TailGAN for identifying Out-of-Distribution (OoD) data and its performance evaluated on MNIST, CIFAR-10, Baggage X-Ray, and OoD data shows competitiveness compared to methods from the literature.
Evaluating the Cloud-RAN architecture: functional splitting and switched Ethernet Xhaul

The Cloud-RAN architecture is a key enabler to building future mobile networks in a flexible and cost-efficient way. For instance, switched Ethernet is a prime candidate for mobile transport networks (Xhaul), due to its flexibility, ubiquity, and cost-effectiveness. Understanding its performance under different network configurations would allow concluding about its appeal for Cloud-RAN. On the other hand, evaluating resource sharing mechanisms is relevant to put in place best solutions to host multiple virtual Base Band Units (vBBUs) into the same compute infrastructure. This paper assesses the feasibility of using a switched Ethernet Xhaul, by instantiating two vBBUs using different functional splits. Moreover, this paper evaluates two mechanisms for sharing network interface cards (NIC) in a general purpose server (GPS) hosting vBBUs. Our results point to a marginal performance degradation caused by the switched Ethernet Xhaul and the NIC sharing mechanisms. Such deviations could be seen from the increase in average and maximum Jitter and RTT results.
Application of Genetic Algorithms for Construction of a Model of PLC in ICS Network Based on Observation

It is often necessary to be able to build a model of the system or its components to conduct a behavioral analysis of a system. Methods using knowledge of the features or constructiveness of the system are not universal and cannot be transferred to an arbitrary system. Therefore, a universal approach for building a system model based on observing its behavior is required. It is proposed to use genetic algorithms for system components that can be represented as finite state automata. The paper considers the choice of representing an automaton as an individual of a genetic algorithm, as well as the influence of various parameters of genetic algorithms on the evolution process and the model‵s ability to predict system behavior. The main stages of genetic algorithms with respect to finite automata are described and implemented: creation of an initial population, crossover, mutations, selection and fitness function calculation. Six types of mutations are proposed. Their probabilities can be managed at the beginning of evolutionary process. The practical results of investigation are presented on the basis of both synthetic binary data and data from real network traffic.
Design of 4th Order Resonance Filter for 5.4 W 20 MHz Buck Converter with PCB Integrated Inductor

Size reduction of passive components in power converters especially magnetic components is essential to reduce the overall size of the converter and achieve higher power densities. For that purpose, this paper presents a detailed design procedure of a 4th order resonance output filter to show the advantages it provides in terms of passives size and loss reduction. A design study comparison is presented between 2nd order, 4th order and 4th order resonance filters in a 5.4 W, 20 MHz buck converter with PCB integrated solenoid inductors. The comparison considers the same circuit specifications and PCB manufacturing capabilities for the PCB inductors. It is found that there is potential for a total inductor size reduction of 38% with a corresponding increase in full load inductor efficiency of 2.1% and full load circuit efficiency of 1.3% provided by the 4th order resonance filter compared to a standard 2nd order filter in a 5.4 W buck converter switching at 20 MHz.
Simple Single Particle Model for Interpreting Fast Charge Results in Intercalation Batteries

A model based on a simplified single particle approximation for a battery cell is proposed. The global behavior of the cell is derived from the ion diffusion in a single representative particle by only adding a resistor in series. The objective of the model is the study and interpretation of charge transients at different rates. An adjustment method has been applied to an experimental result set in order to obtain the optimal parameters of the model with which the best agreement is achieved. It has been observed that the model is acceptable for the purpose for which it was developed, achieving a reasonable trade-off between complexity and accuracy. Once the model has been validated and its parameter values have been obtained, it can be used to evaluate and compare different fast charging algorithms.
Effect of Airgap Length on Electromagnetic Performance of Surface Mounted Permanent Magnet Vemier Machine

This paper investigates the effect of airgap length on the electromagnetic performance of 3kW surface mounted permanent magnet Vernier (SPM-V) machine. The performance is compared with a conventional surface mounted permanent magnet (SPM) machine with same airgap length using 2D Finite Element Analysis (2D FEA). For each airgap length, the slot/pole number combination for the SPM-V machine is investigated to achieve the optimal performance compared to the conventional SPM machine. The results show that the SPMV machine can achieve much higher torque capability than the conventional SPM machine at smaller airgap length. However, there is an optimal airgap length beyond which the torque performance of SPM-V machines drops below the conventional SPM counterparts. Moreover, unlike the conventional SPM machines, the power factor of SPM-V machines drops significantly with increase in airgap length.
Automatic Classification of Respiratory Sounds Considering Time Series Information Based on VGG16 with LSTM

According to the 2016 World Health Organization (WHO) survey, respiratory diseases are serious diseases that account for four of the top ten causes of death in the world, accounting for more than 8 million deaths worldwide. Currently, the diagnosis of respiratory disease is made by auscultation, but in order to make an accurate diagnosis, a number of abnormal patterns of respiratory sounds need to be memorized, and the results of the diagnosis are dependent on the proficiency of the physician. Therefore, a computer aided diagnosis (CAD) system is needed to quantitatively classify the respiratory sounds and output the results as a "second opinion". In this paper, a short-time Fourier transformed spectrogram, a Constant-Q transformed logarithmic frequency spectrogram, and a continuous wavelet transformed scalogram are simultaneously input to VGG16 which is one of the network models of CNN(Convolutional Neural Network) and classified by LSTM (Long short-term memory). The proposed method is applied to 26 respiratory sounds, and the 0.90 of accuracy, sensitivity of 0.97, and specificity of 0.90 is obtained.
An Efficient Content Distribution Network Architecture Using Heterogeneous Channels

With the popularization and development of the IoT(Internet of Things), more and more data needs to be transmitted over the Internet, which leads to the deterioration of network quality. The CDN (Content Distribution Network) technology is an important theoretical model to ensure network QoS (Quality of Service). To improve the QoS, we extend current CDN system to the hierarchical CDN system, that traditional CDN system is just a special case of hierarchical CDN system. In the traditional CDN system and the hierarchical CDN system, by analyzing the bandwidth between subsystems, we found the inter-system bandwidth is the bottleneck that impedes CDN system expansion. To address this problem, a new kind of distributed system architecture is proposed in this paper. This new architecture uses broadcast channels to distribute broadcast type data and still using bidirection uni-cast channels for other type of data, so we call the new architecture as CHCDN (Channel Heterogeneous CDN) in this paper. The new architecture is analyzed and compared with the traditional CDN system architecture and hierarchical CDN system architecture. Moreover, the experimental simulation result has shown that the CHCDN system features better in real-time cache updating and features with higher data transmission efficiency than the hierarchical CDN system, which indicating that the new architecture has great potential for being widely used in distributed computing.
IoT security with Deep Learning-based Intrusion Detection Systems: A systematic literature review

In the recent years, Internet of things (IoT) is rising increasingly to become a big research topic due to the billions of devices dispatched around the world. These devices are connected to the Internet and communicate directly with each other without human intervention. However, this creates new security challenges, which are increasing more and more and becoming relevant research issues. Our study in this paper focuses on the state-of-the-art of IoT security threats and vulnerabilities by conducting a classification of some wellknown security threats according to Cisco IoT reference model architecture. We also make a review of existing works in the area of IoT security targeting more particularly the Intrusion Detection Systems based on Deep Learning (DL) techniques, which are rising as emerging techniques in various fields including cybersecurity. This state-of-the-art and its findings can serve as a potential basis for future research directions.
Deceptive Opinion Spam based On Deep Learning

The revolution of web technologies and e-commerce platforms such as Amazon and eBay have promoted the businesses and have become essential in our daily life. Despite that these tools have helped the ease of purchases, there is a lot of scams in these kinds of technologies. Unfortunately, several companies use fake opinions to influence the customers about buying a product or to demote the competitors’ one. The detection of deceptive opinion spam is a hard task because of the way it is written. The majority of existing deceptive opinion detection models focus on machine learning with hand-engineered feature extraction. Unfortunately, these architectures do not provide the semantic information of the reviews, which is the key to the detection of deceptive opinions. In this paper, we address the comparison between the different neural network architectures and their effectiveness in the detection of deceptive opinion spam. The results show that Convolutional Neural Networks perform better compared to other models.
Research on Tree Classification Algorithm Based on Morphology and Leaf

For a long time, forestry workers carry out forestry field investigation work, when they do not know the tree species, they can only search the relevant literature such as “tree annals”. The query procedure is tedious and the accuracy is not high, which seriously affects the efficiency of forestry production. In this paper, after data augmentation of tree images, GAP (Global Average Pooling) was used to fine-tune Inception-V3, ResNet50 and Xception for transfer learning. Feature vectors of the model were extracted and merged, and two feature vectors of tree overall shape and leaves were obtained through training. After feature fusion, SoftMax was used for classification, and the result of tree species determination was obtained. The practice has proved that the tree classification method proposed in this paper has a great advantage and the accuracy is higher.
Automatic Derivation of State-Space Model from Linear Electrical Circuits with Dependent Variables using Modified Nodal Analysis

The increasing complexity of power electronic circuits calls for the use of advanced control algorithms, which in turns rely on extensive and accurate models. This paper lays out the rigorous mathematical framework for an elegant and automatable state space modelling of a linear electrical circuit. The steps consist in extracting key information of a netlist and de-riving the state space model after identifying independent states. The netlist can either be written by the designer or a SPICE software. The resulting Multi-Input Multi-Output (MIMO) state space model can then be fed into classic control design methods, e.g. linear quadratic gaussian control (LQG) or model predictive control (MPC), possibly coupled with robust observing methods, e.g. Kalman filter, to create performant regulators. The proposed automatised derivation strategy is verified by a simple case of electrical circuit and a case of modular multilevel converter (MMC).
A Method For Short-Term Traffic Flow Forecasting Based On GCN-LSTM

The prediction about short-term traffic flow is a crucial research field in Intelligent Transportation Systems (ITS), which focuses on forecasting traffic data to provide data for optimal traffic scheduling to reduce congestion. This paper presents a new deep learning network based on the graph convolution network (GCN) and long short-term memory (LSTM) unit. The model was trained and tested by using local traffic data from the city of Mianyang. After introducing the new deep learning network, a data reduction method is also established in this paper which was select the relevant road links as model’s input based on time correlation. Finally, the proposed algorithm is compared with other popular methods, and better results can be obtained in the conventional 5-minute prediction test. At the same time, the performance of this GCN-LSTM model can be maintained in different time range from 5 minutes to 125 minutes by multi-step prediction, which is much better than other models.
Cloud Extraction from INSAT-3D Satellite Image by K-Means and Fuzzy C-Means Clustering Algorithms

This paper presents algorithms for extraction of clouds from INSAT-3D satellite image over the Indian region. The K-Means and Fuzzy C-Means clustering algorithms are applied on INSAT-3D satellite images on some specific dates and time in the year 2017, when the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite crosses the Indian region. Prior to this, the number of cluster segments k is selected from the MODIS Aqua sensor's cloud product. The result of segmentation algorithms is validated by comparing with the cloud optical thickness of the MODIS data. The comparison shows that INSAT-3D cloud segmentation matches well with the cloud optical thickness of the MODIS data.