Miracle-dz/UniFAR-FTUs huggingface dataset:
Object Classification and Semantic Mapping

Rescue forces require quick and essential information about the unknown situation in order to achieve an efficient mission planning. For this purpose, micro air vehicles can provide this kind of information without risking harm of the task force members. Hence, the objective of this paper is to provide semantic object information of the explored area. In addition, it is of interest to know where the object is positioned. The paper deals with various challenges occurring in this application: Different observations of the same object during flight, close objects of the same and different classes, and no prior knowledge about the number of objects. The output of a fine-tuned region-based convolution neural network (R-CNN), i.e. detected object class, score and bounding box, is used for tracking over subsequent images. The corresponding features along with the navigation solution are input of a multi-view triangulation step. The resulting point cloud is further processed so that clusters are identified. Then, the bounding boxes are associated with the qualifying cluster. Based on that, the sizes of the found objects are estimated by introducing an outlier rejection strategy. The holistic approach is evaluated by obtaining real data in a typical office environment. The results show a comprehensive semantic map that includes multi-class objects of different sizes and shapes.
Robotic Process Automation Through Advance Process Analysis Model

Robotics Process Automation is an advanced technology that builds an intelligent software robot that can emulate human interactions with a business process. RPA is an efficient automated method where software agents interact through a graphical user interface in a human-like manner. RPA has various applications in most industries like banking and finance, human resources, healthcare, etc. In this paper, we have proposed our RPA process analysis model and have compared it with a traditional model using various comparison parameters like frequency of change, degree of complexity, time is taken, screen usage and volume of transactions and found out that proposed method gave efficient results over the traditional method.
Certificateless Provable Data Possession Protocol for the Multiple Copies and Clouds Case

For user’s extremely important data, storing multiple copies on cloud(s) may be a good option because even if the integrity of one or more copies is broken, it can still recover data from other intact ones, which increases the availability and durability of the outsourced data. Some provable data possession (PDP) protocols guaranteeing the integrity of multi-copies had been proposed in the past years. But almost all of them considered storing multi-copies to single cloud, and the necessary management of certificates as well as the dependence on PKI greatly decrease their efficiencies. Therefore, in recent work, Li et al. proposed an identity-based PDP protocol, which not only avoids the tedious certificates and PKI, but also supports muiti-copies stored on multi-clouds. However, it is well-known that identity-based protocols suffer from the key-escrow attack. In this paper, we consider the certificateless multi-copy-multi-cloud protocol. Specifically, we first present its security model and then construct a concrete protocol, whose security can be proven under the classical CDH assumption. Finally, the performance analysis demonstrates that our protocol yields better efficiency and hence is practical.
K-means Clustering and Linear Regression Based Protection Scheme for Transmission Line

In this paper, shunt fault is detected, classified and located in power transmission line. The synchronized Current signals at both the bus of the transmission line are used for obtaining approximate coefficients using wavelet transform. The proposed algorithm is used K-means clustering to detect and classify the faults. Fault location is estimated using linear regression method. The approximate wavelet coefficients of the both the buses are added to get resultant approximate coefficients. K-means clustering is applied on these resultant approximate coefficients to computes two centroid in a half cycle. The centroid difference (C.D) is computed, and the basis of the centroid difference the fault is detected and classified. Various case studies such as vary fault location, fault incidence angle and fault impedance to verify the robustness of the algorithm.
Blockchain Service Provider Selection Based on an Integrated BWM-Entropy-TOPSIS Method Under an Intuitionistic Fuzzy Environment

The emergence of blockchain technology has significantly changed the underlying infrastructure of existing information technology and will fundamentally affect the production modes of enterprises. However, because the application of blockchain is still in its infancy, it is difficult for an enterprise to develop a comprehensive assessment of various types of blockchain service providers in the market. Hence, enterprises need scientific decision tools to estimate which blockchain service provider is appropriate. However, few studies have focused on this phenomenon. Therefore, to address this challenge, this investigation proposes a novel integrated multi-attribute group decision-making (MAGDM) method to help enterprises estimate which blockchain vendor is more appropriate by considering more comprehensive influence factors. The proposed method is defined in an intuitionistic fuzzy environment and integrates entropy and the best-worst method (BWM) for comprehensive weighting of decision makers (DMs), subjective criteria and objective criteria in the decision-making process to make the decision results more reliable and reasonable. A numerical example and comparison are provided to illustrate the practicability and usefulness of the method. This study enriches the theory and methodology of blockchain technology and MAGDM analysis.
A Real-Time Application with Fully Predictable Task Timing

Real-time control systems must adequately react to changes in system state within a time limit determined by the dynamics of the controlled system, otherwise their stability might be compromised. It is therefore essential to determine the Worst-Case Execution Time (WCET) of the control algorithms to verify that they will always be able to satisfy that constraint. However, WCET analysis is a complex process, requiring manual intervention, and the computed bounds are often grossly overestimated. We avoid the difficulties of WCET estimation by making use of the single-path paradigm for the implementation of the control algorithm for a quadcopter. By using single-path code we do not only replace the cumbersome WCET analysis with a single measurement, we simultaneously avoid the detrimental effects of response-time variability. We show that single-path code can be applied to real-time controllers and that it is practical for dramatically simplifying WCET analysis as well as eliminating variability in the execution time. Our results show that despite its apparent inefficiency, the WCET of single-path code on a time-predictable soft microprocessor rivals that of a state-of-the-art superscalar processor.
Machine Learning Approach to Classify Birds on the Basis of Their Sound

Bird Classifier is a system which will use machine learning approach and allows users to record bird sounds and identify them. The System will help bird species to be identified by taking input only the sound of that bird.The framework also offers tools for translating such annotations into datasets that can be used to train a computer to identify a species ’ presence or absence. Dataset recorded are preprocessed to remove unwanted noise and divide useful sounds in frames so that it can act as an input to classifier. The system was tested through different algorithms and the algorithm that gave best results was chosen for implementation.System uses audio features like MFCC,Mel- Spectra etc. This system will use different algorithms such as KNN, Random Forest, Multi layer perceptron, Bayes in classification of birds species.
Artificial Intelligence System to Determine Electrical Safety Level of Power Generation Facilities

The article presents a system of artificial intelligence, made for expert assessment of electrical safety level at power generation facilities. This information system, based on fuzzy logic methods, can more precisely calculate electrical resistivity of the soil in specific conditions. The researchers describe the technique of forming a database and a knowledge base of the expert system including a set of models and algorithms. The article provides a detailed description of fuzzy models of electrical resistivity $\rho$ for different types of soil depending on its humidity, salinity and temperature (both in positive and negative ranges). It offers fuzzy climatic models of soil in a specific area, taking into account changes of soil $\rho$ deep into the ground throughout a year. The created artificial intelligence system evaluates the security level of electric power facilities on the basis of different algorithms, used to calculate grounding, touch and step voltages; it can more precisely determine soil $\rho$ deep into the ground during a year. The researchers add an option of graphic depiction of modeling results for the validation of the solutions, offered by the expert system.
Neural Network Analyzer of Processes in Automatic Control System

The analyzer of the operating modes of an automatic system, based on the neural network, is considered. The processes in the system are investigated by the movement of the depicting point in the state space of three variables of the regulating device: system error, time derivative of the error signal and time derivative of the control signal. To classify the state of the system, the space of possible trajectories of the depicting point is divided into areas corresponding to normal operating modes and processes with various deviations. To analyze the trajectory of the depicting point of the regulating device in the state space and the formation of training sequences (learning patterns) of the neural network, the authors take not the controlled variables themselves, but logical functions defining their belonging to the specified ranges. In this case, the trajectory of the depicting point is represented as a vector. The artificial neural network is trained in such a way as to assign to the input vector the values from the state space a certain class in the set of normal operating modes of the regulating device as well as modes with different deviations from the normal process. For this purpose, multilayer perceptron is used. The number of input and output elements of the neural network is determined by the problem conditions. In this case, the input layer contains 12 neurons (by the number of components of the input vector), and the output layer - k neurons, by the number of diagnosed faults. The case of k=2 corresponds to the recognition of 2 abnormal modes: oscillations in the system and system operation with a mismatch, caused the actuator limited power.
Skin Detection in Images based on Pattern Matching Algorithms - A Review

Detection of skin in images is a very important clue in many image processing applications such as biometric system, medical imaging, face recognition and many more. An eye’s perception of human skin depends on sensing the color, texture, feel and many more factors. This paper is a survey paper. Some popular approaches that are used to identify human skin are endowed. To distinguish skin from the given image based on colors, various methods are discussed that are based on different color models. Based on the survey, a comparison of various methods is provided.
Extended Luenberger Observer for ABS Braking System

In this paper, an extended Luenberger observer for estimating the vehicle linear velocity is proposed for substituting physical sensors such as the accelerometer or the GPS sensor. This estimation is necessary for the development of control laws for braking systems, like the ABS (Anti-lock Braking System). After presenting the non-linear model of the braking system, the modeling of wheel-road friction forces is discussed. In order to be able to use an observer for linear velocity estimation, the observability of the braking model is established by the rank method. The synthesis of the extended Luenberger observer is developed, and the stability of the observer is proved based on the Lyapunov’s tools. A validation by simulation using Matlab/Simulink shows the good performances of the proposed observer.
Suspension Effect in Tip-over Stability and Steerability of Robots Moving on Sloping Terrains

This article describes a model for a skid steer robot that quantifies the reaction forces between the wheels and the ground, which includes the effect of the passive suspension when the robot moves on flat and inclined terrains. In addition to the internal parameters of the suspension, this model included external parameters related to other components of robot and the robot-terrain relationship. With this model, which was validated through simulation, it was studied the effect that suspension produces in tip-over stability and steerability of robot when it moves on different terrain inclinations, specifically when the parameters of suspension system and wheel-ground contact are varied.
Floristic affinities of North Carolina inshore benthic marine algae

Abstract Comparative phenological studies and cross-gradient light-temperature experiments on selected Ceramiales from North Carolina demonstrated the presence of three biogeographic components in this taxon: eurythermal species of cool temperate, tropical, and warm temperate affinity. Results of these studies were extrapolated to support biogeographical conclusions postulated for the entire flora. Accuracy of these conclusions was tested by comparing the local inshore flora with other North Atlantic floras.
Framework for application of machine learning algorithms in telecommunications

Machine learning is showing a lot of promise in many different fields such as image recognition. But notably, in telecommunications, machine learning has not been productized yet, even though improvements using machine learning approaches have been already obtained when compared to conventional approaches. This lack of productization can be due to many reasons, one of which is the fact that applying machine learning requires the user to go through the machine learning workflow. The commonly used machine learning workflows are manual and iterative and in addition, implementing them requires machine learning knowledge that some telecommunications engineers might not have. To take full advantage of machine learning within the telecommunications domain, machine learning should be made more accessible. One of the ways to do this is by improving the current workflow and making it easier. In this thesis, I aim to improve the machine learning workflow that developers use to train and evaluate machine learning models by developing a framework to simplify and streamline the machine learning workflow, with a focus on the telecommunications domain. This improved workflow will be as automated as possible, reducing the need for manual, iterative work. With the improved workflow, the adoption of machine learning will be easier and more accessible to telecommunications engineers not specialized in machine learning. As a demonstration of the framework’s ability to produce good results, I study the performance of machine learning in a 5G NR UCI decoding use case and show that the performance of the machine learning receiver implemented using the framework is better than that of the conventional receiver with respect to the bit error rate and missed detection probability. However, the false alarm rate for the machine learning receiver is worse than for the conventional receiver. I also evaluate the usability of the framework and conclude that the workflow offered by the framework has improved usability compared to the conventional manual machine learning workflow. These results prove that machine learning can offer a solution for the 5G NR UCI decoding use case, and that the framework is able to produce well-performing machine learning models. Regardless, further research, for example related to the complexity of the machine learning models, is required to confirm that these models could indeed be successfully implemented in real-world communications systems.
Research on Position Servo System Based on Fractional-Order Extended State Observer

The control quality of servo system requires not only stability, speed, and accuracy, but also strong anti-jamming ability. The permanent magnet synchronous motor (PMSM) is a multivariable and strongly coupled time-varying nonlinear system. It will be subject to different degrees of external interference. According to the advantages and limitations of fractional-order PID (FOPID) controller and active disturbance rejection control (ADRC), the fractional-order calculus concept is integrated into ADRC. In this paper, a control strategy of position servo system based on fractional-order extended state observer (FOESO) is proposed. The FOESO and fractional-order control law are designed to form a fractional-order active disturbance rejection control (FOADRC). The FOADRC controller is applied to the position loop control of the servo system. Through the experiment and performance analysis, it is proved that the FOESO can well observe the internal and external disturbances of the system, and the FOADRC has a good control effect in the position servo system.
Computational Model for High-Pressurized Heat Recovery Steam Generator Heat Transfer Study

This article shows the main stages of computational model development for CFD (Computational Fluid Dynamics) research dedicated to High-Pressurized Heat Recovery Steam Generator (HPHRSG). This project could solve the problem of quick decommissioning and replacement of energy equipment. It has different structure comparing existing units made especially for decreasing the costs of reconstruction.The model was created in ANSYS CFX software. Several approaches have chosen to validate results of the model. The model allows quick geometrical and physical properties change for studying different heat transfer effects. Great attention is paid to the correct choice of mathematical models for studying the flow motion and heat transfer processes. The most applicable way of solving engineering tasks is the RANS (Reynolds Averaged Navier-Stokes equations) method. Several three-dimensional geometry models and computational meshes are shown. Key material features and model parameters are presented. Initial mesh has shown poor results although the mesh quality was high because of inflation layers lack. It is essential in questions of convective heat transfer study to make inflation layers.The results of research have shown that current model’s calculation time reduced by 2 times comparing initial version and accuracy of the results amounts less than 2% comparing the existing approaches.
Analysis of Deep Learning Architecture for Non-Uniformly Illuminated Images

The use of deep learning to hone image processing techniques has become increasingly popular. Following the success of Convolutional Neural Networks (CNNs) for image classification, they have been tested for various applications. By training CNNs on a dataset with ground truth (light) images and the corresponding darkened version of the images, neural networks can be used for enhancement. This must account for the non-uniform illumination seen in night-time images. A novel method of training a neural network to enhance non-uniformly illuminated images is proposed. Further, the visualization of convolutional features extracted at each layer of the neural network is discussed, to understand which parts of an image helps the neural network identify the object, thereby enhancing its recognition power. The potential application of this system lies in detecting animals in the non-uniformly lit surveillance video, useful to settlements near forest regions, where wild animals pose a threat to the living areas.
Rapid Detection and Localization of Gray Failures in Data Centers via In-band Network Telemetry

Network reliability becomes increasingly important in modern data center networks (DCNs). The DCNs are expected to work sustainably under internal failures and assist network operators in troubleshooting them rapidly. However, some network failures will happen silently with packets discarded without producing any explicit notification before causing tremendous damage to the network. To troubleshoot these "gray failures", in this work, we present a rapid gray failure detection and localization mechanism based on the recently proposed In-band Network Telemetry (INT). Specifically, we leverage simplified INT probe packets to conduct network-wide telemetry to help the servers under ToR switches obtain all the feasible paths between sources and destinations. Once a network failure occurs, the affected thus unavailable paths will immediately be detected and flushed out of the path information table at each server by a timeout mechanism. Hence, servers can proactively perform source routing-based fast traffic reroute to avoid massive packet loss and retain uninterrupted quality of experience. At the meantime, all the aged path entries will be uploaded to a remote controller for centralized failure localization by identifying common path elements. To verify the feasibility of our design, we build a virtual network testbed with software P4 switches and a Redis database. Evaluation shows that our system can successfully detect network gray failures and reroute the affected traffic in no time while complete failure localization within only a few seconds.
An Improved Grid Synchronization Method of Grid-Interactive Power Converter System During Distorted Grid Conditions

In recent years, integration of renewable energy systems (RES) into the electrical grid has acquired the attention of researchers across the globe. Grid parameters such as frequency, voltage and phase angle must be continuously monitored to overcome the problem of synchronization, as gridside converters usually interact with the electrical grid. In this paper, an enhanced sliding Goertzel discrete Fourier transform (SGDFT) based phase locked loop (PLL) for grid synchronization of grid-interactive power converter system was proposed. With the proposed enhanced SGDFT PLL based frequency estimation, a robust frequency tracking capability with a very fast transient response under distorted grid conditions are achieved. The complete analytical model of Proposed PLL is analyzed and compared with existing PLLs such as Enhanced Prefilter Moving Average Filter Type 2 PLL (EPMAF Type 2 PLL), Harmonics Interharmonics DC Offset PLL (HIHDO PLL), SGDFT PLL. The proposed work has been analyzed in MATLAB/Simulink® environment and the obtained results are suitable for harmonic mitigation and DC offset rejection. Finally, the Hardware in Loop (HIL) simulation of different PLLs are investigated by dSPACE 1104 environment to verify the effectiveness of the proposed study.