Miracle-dz/UniFAR-FTUs huggingface dataset:
A Rolling Bearing Fault Diagnosis Method based on improved MFE and WOA-PNN

Considering the uncertainty of entropy value estimation in multi-scale fuzzy entropy (MFE), and the difficulty of determining the smoothing parameter of probabilistic neural network (PNN). The rolling element bearing fault diagnostics method on the basis of improved MFE and improved PNN is proposed. Firstly, variational mode decomposition (VMD) is employed to decompose rolling bearing vibration signal into several intrinsic mode functions (IMF). Secondly, aiming at the uncertainty problem of entropy estimation in MFE, refined composite multi-scale fuzzy entropy (RCMFE) method is proposed to obtain characteristic from the selected IMF. Finally, smoothing factor of PNN are determined by whale optimization algorithm (WOA), and the extracted features are input into the WOA-PNN model to achieve condition identification. Experimental results illustrate that the identification accuracy is 98.75%, which indicates its high superiority.
A community discovery algorithm based on local extension of high-order triangle

Community structure is an important feature of complex networks. Compared with global community discovery methods, local community discovery methods can discover communities efficiently without complete information about the network structure. Most locally extended algorithms rely on core nodes to discover communities, where the core nodes are based on local density without considering the topological distribution among nodes, and it is difficult to determine the communities to which nodes that are far away and nodes that are in between communities have low similarity to different cores. Therefore, a community discovery algorithm based on local extension of higher-order triangles is proposed at LEHT. The algorithm firstly identifies all three closely linked nodes based on the local topological distribution of nodes in the network using the higher-order triangle structure, and then calculates the most core higher-order triangle of each node using the link strength to form the initial community. Secondly only the core higher-order triangles need to be merged in the topological dimension, and the problem of low similarity of nodes further away from the core is eliminated by extending the direct neighbourhood information between the core higher-order triangles. Finally independent nodes that are not within the core triangles are joined to the community where it is close to a neighbouring node with large centrality. The LEHT algorithm is analysed in comparison with five representative classical algorithms on real and synthetic networks, and the experimental results show that the effectiveness of the LEHT algorithm performs better.
Differential Privacy Machine Learning Based on Attention Residual Networks

To address the problems that the differential privacy stochastic gradient descent algorithm DP-SGD does not apply to deep network models with more parameters and noise addition affects model performance, a differential privacy machine learning model based on attention residual networks is proposed, which solves the problem of differential privacy training on larger models by embedding the attention module, replacing the batch normalization with group normalization, and replacing the activation function with more efficient Experimental results on MNIST and CIFAR-10 datasets show that the classification accuracy of the model is improved by 3.99% and 2.43%, respectively, compared with the original model before the improvement, with the same privacy budget $\varepsilon$.
A Direct Current MPPT Control with Eliminated Outer Loop For Grid Interfaced Solar Inverter

The maximum power extraction of solar PV panels is influenced by not only solar irradiance but also climatic conditions like ambient temperature, humidity, and cloud cover. Thus to extract the maximum power from the solar PV array irrespective of dynamic climatic conditions, the MPP algorithm is an essential need of grid-connected inverters for efficient energy harvesting. Nevertheless, various maximum power point tracking algorithms are available based on the voltage reference generation corresponding to maximum power. Since the solar panels primarily exhibit the current source characteristics and the inverter operates in current injection mode, the current reference generation instead of voltage reference can eliminate the outer voltage loop of the grid-connected inverter. It makes the control loop more robust and efficient in MPP tracking. Considering this fact, in this work, a novel Current MPPT technique for PV energy extraction is proposed. The proposed algorithm generates the current reference for the inverter corresponding to available active power from the sensed PV voltage and current. The superiority of the proposed algorithm in terms of maximum power tracking compared to the conventional MPPT technique is demonstrated through MATLAB simulations.
Design of a Novel Four DOF Active Magnetic Bearing for Space Applications

This paper describes the details of a configuration study carried out for different types of magnetic bearings employed in spacecraft actuators. A suitable magnetic bearing configuration is selected based on the axial and radial stiffness offered by the system. Distributed double-stacked Active Magnetic Bearing is proposed for the momentum-biased inertial wheels. Detailed design and analysis of the Active Magnetic Bearing that includes the permanent magnet assembly, which establishes the passive stiffness and the electromagnetic circuit required for the control of the system. Optimization of the magnetic bearing parameters has been done to get the required stiffness in both axial and radial directions. This Hybrid Active Magnetic Bearing has 4DOF, i.e., two radial translations and rotation with passive control in axial translational motion, and is best suited for space applications where low jitter and long life are preferred.
A Lightweight Transfer Learning-Based Model for Building Classification in Aerial Imagery

Over the past decade, there has been a growing interest in the potential of artificial intelligence and computerr vision in tackling challenges related to disaster resilience in urban communities. Unmanned aerial imagery has been the focal of a number of initiatives targeting urban planning and aftermath disaster assessment. Within this context, this presents a novel lightweight transfer learning-based model for assessment of building conditions from aerial images. The proposed method is suitable for EDGE-based operations in resource-limited settings. Experiments were conducted to identify post-flooding building conditions in Zanzibar city in Tanzania. Considerable gains in terms of memory and computation time have been achieved while maintaining accuracies that are in line with state-of-art approaches.
A game theoretic approach to group centrality

This paper is centered in the valuation of the centrality of groups following aproblem-specific approach (Friedkin, 1991). Assuming a TU-game that reflects theinterests which motivate the interactions among individuals in a network, we extend thegame theoretic centrality measure of Gomez et al. (2003) to the case of groups, anddefine the game theoretic group centrality of a group as the variation of its value orpower due to their social relations. We rely on the Shapley group value (Flores et al.,2014) for measuring the value of a group in a game without any restriction, and weintroduce the Myerson group value in order to measure the value when the socialstructure is considered.
Design and Control of PV and Battery Connected Standalone Refrigeration System

This paper presents a standalone refrigeration system including a permanent magnet synchronous motor (PMSM) driving a compressor unit, a Photo-Voltaic (PV) array system, and a battery storage element. The source PV is interfaced to a common DC bus, shared with the three-phase Voltage Source Inverter (VSI), with an intermediate power conversion stage of the boost converter. A battery-connected DC-DC half-bridge bi-directional converter is used to regulate the DC link voltage. The speed of the PMSM is controlled using a vector control scheme in a $dq0$ rotor reference frame (RRF). The compressor load is coupled to the PMSM to investigate the energy management in view of refrigeration application. The system's performance is tested under various operating conditions and simulated in MATLAB/Simulink.
Dynamic Configuration of slot per pole per phase of an Induction Motor

In this work, the impact of slot per pole per phase on the performance of an Induction Motor (IM) is analyzed. The significance of magnetising and leakage reactance with respect to the slot per pole per phase is understood. An alternate way of providing excitations to the stator windings and there by altering the number of slots per pole per phase is proposed. FEA simulation is carried out on a 36-slot 18-terminal IM, excitation to the windings are provided by an 18-leg inverter. The performance parameters and characteristics are extracted and studied to determine the change in performance of the motor by altering the number of slot per pole per phase. It was proved that increase in number of slot per pole per phase positively impacts performance and efficiency of the IM motor under study.
Integration of Battery Energy Storage with a Transformer-less Solar PV Micro-inverter

The conventional solar PV micro-inverters are designed to be operated in grid connected mode and do not possess any provision for energy storage. In this paper a battery integrated transformer-less solar PV micro-inverter with two different configurations are proposed. One of the configurations is suitable for 12/24 V battery integration, while the other configuration is suitable for 48 V battery integration. The proposed micro-inverter controls the battery current along with the current drawn from the PV module depending on the solar irradiance level and the state of charge of the battery. Though it is suitable for interfacing with a 220 V ac grid, it can operate in standalone mode as well in the absence of the grid. The leakage current remains to be zero as the micro-inverter is doubly grounded, and it is having inherent power decoupling capability. The detailed analyses of the proposed scheme is carried out. The proposed battery integrated micro-inverter under a suitable control strategy is simulated on MATLAB/Simulink platform to validate the scheme.
Dynamic Task Allocation for Heterogeneous Multi-Robot System under Communication Constraints

Multi-robot system has important application potential in disaster rescue and other dangerous scenarios, in which task allocation is the basis for multi-robot cooperation to complete tasks. Due to the limited ability of individual robots, many complex scenarios requires coordination of different types of robot, e.g. robots for search, communication, rescue and so on. At the same time, communication is often limited in a wide range of scenarios. This brings two challenges in the realistic environment: heterogeneous multi-robot cooperation problem and communication constraint problem. This paper proposes a task allocation method based on hierarchical auction and dynamic grouping. By introducing a dynamic grouping algorithm to the general allocation procedure, we simultaneously optimize the number of robots in each group and the communication distance between robots. Then the communication loss rate can be reduced to improve task execution efficiency. Simulations of a communication constrained search and rescue scenario are conducted to verify the proposed algorithm, which are compared with the vanilla auction algorithm and a hierarchical approach on static grouping. The results show that our method surpasses the baseline on both task achievement and communication performance.
Design of predictive controller for Networked Control Systems

This work considered the analysis and design of networked control systems (NCSs) with the communication delay and data loss, which are responsible for degradation of the control performance. Predictive control strategy is applied to compensate the communication delay and data loss in the NCSs. The stability and the system performance of the close-loop networked control system are analyzed.
Multi-domain Network Intrusion Detection Based on Attention-based Bidirectional LSTM

Different types of network traffic can be treated as data originated from different domains with the same objectives of problem solving. Most previous work utilizing multi-domain machine learning simply assumes that data in different domains have the same distribution, which can neither address the domain offset problem effectively, nor achieve excellent performance in every domain. This study proposes an attention-based bidirectional LSTM (Bi-LSTM) model to detect different types of coordinated network attacks (i.e., malware detection, VPN encapsulation recognition, and Trojan horse classification). First, the HTTP traffic is modeled as a series of natural language sequence, and each request follows strict structural standards and language logic. Second, the model is designed in the frame of multi-domain machine learning technologies to rec-ognize anomalies of network attacks from different domains. Experiments on real HTTP traffic data sets demonstrate that the model proposed in this study has good performance on detection of abnormal network traffic and generalization ability and can effectively detect different network attacks at the same time.
Machine Learning Based Demand Supply System of an Airport to Minimize Operational Cost

Innovation in Electric Vehicle (EV) Battery and technological boost in charging infrastructure has proven the potential use of EV as Energy Storage Device (ESD) in Vehicle to Grid (V2G) mode to smooth the load curve. The Energy demand-supply system of a critical load i.e. an Airport industry has been analysed in this paper. This paper proposes a methodology to supply the demand combining three different energy sources - Solar, Hydro and EV in V2G mode as ESD and to decrease dependency on conventional supply i.e. state electricity board (SEB) and thereby focusing on reduction of overall operational cost as well as carbon emission. This work considers the revenue from carbon market as well. Day-ahead Load and solar energy forecasting using ANN with correlation analysis and State of Charge (SOC) prediction of EV using XGBoost has been used for proper planning.
Markov chain Monte Carlo for continuous-time discrete-state systems

A variety of phenomena are best described using dynamical models which operate on a discrete state space and in continuous time. Examples include Markov (and semi-Markov) jump processes, continuous-time Bayesian networks, renewal processes and other point processes. These continuous-time, discrete-state models are ideal building blocks for Bayesian models in fields such as systems biology, genetics, chemistry, computing networks, human-computer interactions etc. However, a challenge towards their more widespread use is the computational burden of posterior inference; this typically involves approximations like time discretization and can be computationally intensive. In this thesis, we describe a new class of Markov chain Monte Carlo methods that allow efficient computation while still being exact. The core idea is an auxiliary variable Gibbs sampler that alternately resamples a random discretization of time given the state-trajectory of the system, and then samples a new trajectory given this discretization. We introduce this idea by relating it to a classical idea called uniformization, and use it to develop algorithms that outperform the state-of-the-art for models based on the Markov jump process. We then extend the scope of these samplers to a wider class of models such as nonstationary renewal processes, and semi-Markov jump processes. By developing a more general framework beyond uniformization, we remedy various limitations of the original algorithms, allowing us to develop MCMC samplers for systems with infinite state spaces, unbounded rates, as well as systems indexed by more general continuous spaces than time.
A Multi-Layer Fault Triggering Framework based on Evolutionary Strategy Guided Symbolic Execution for Automated Test Case Generation

The powerful technique, symbolic execution, has become a promising approach for analyzing deep complex software failure modes recently. However, as the software scale grows rapidly in intelligent automatic control system, these methods unavoidably suffer the curse of path explosion and low global coverage. To solve the problem, an evolutionary strategy guided symbolic execution framework is proposed for triggering hard-to-excite input-relevant faults. A novel alternate asynchronous search strategy is adopted to enhance the breadth-search capability of symbol execution. Furthermore, by combining ANGR, a popular symbolic execution engine, and genetic algorithm, this method synchronously triggers the potentially hidden hybrid fault modes at different levels in the software architecture. Case studies on the SIR test suite demonstrate that the GA-enhanced symbolic execution greatly improves coverage and accelerates test convergence. Among them, the coverage rate has increased by up to 23.7%. With a baseline of 95% line coverage, the proposed method can reduce the number of iterations by at least 43.3%.
Detection of Olea europaea subsp. cuspidata and Juniperus procera in the dry Afromontane forest of northern Ethiopia using subpixel analysis of Landsat imagery

Abstract. Comprehensive and less costly forest inventory approaches are required to monitor the spatiotemporal dynamics of key species in forest ecosystems. Subpixel analysis using the earth resources data analysis system imagine subpixel classification procedure was tested to extract Olea europaea subsp. cuspidata and Juniperus procera canopies from Landsat 7 enhanced thematic mapper plus imagery. Control points with various canopy area fractions of the target species were collected to develop signatures for each of the species. With these signatures, the imagine subpixel classification procedure was run for each species independently. The subpixel process enabled the detection of O. europaea subsp. cuspidata and J. procera trees in pure and mixed pixels. Total of 100 pixels each were field verified for both species. An overall accuracy of 85% was achieved for O. europaea subsp. cuspidata and 89% for J. procera. A high overall accuracy level of detecting species at a natural forest was achieved, which encourages using the algorithm for future species monitoring activities. We recommend that the algorithm has to be validated in similar environment to enrich the knowledge on its capability to ensure its wider usage.
Improvement of the sensor system in magnetic flux leakage-type nondestructive testing (NDT)

In magnetic flux leakage (MFL)-type nondestructive testing (NDT) system, the sensitivity of MFL sensor depends on the change of the magnetic field, not just on the magnitude of the field. In this paper, the leakage parameter was defined to determine the operating point in saturation curves, and an optimum design method to determine the size of the magnet to maximize the MFL signals in NDT is described. For detecting the small depth defect, we also developed the back yoke sensor system. The sensitivity of the optimum sensor system is increased up to 200%. The computed MFL signals in the optimally designed system are verified by measurement using Hall sensors mounted on the six-legged PIG in an 8-in test pipe with defects. The rhombic defects could be successfully measured and identified from the weak defect signals.
A comprehensive general simulation of small twin screw displacement hull boats with validation

A comprehensive general simulation of small twin screw displacement hull boats is developed as a tool to estimate ship and actuator responses in support of developing and tuning of control systems. The general form of this model allows many small displacement hull vessels to be rapidly simulated by specifying a set of 17 vessel parameters. The response of the vessel in the surge, sway and yaw degrees of motion are estimated from parametric equations and data compiled from literature and, when not found in literature, inferred from sea trials. Model validation and tuning of the coefficients not found in literature is accomplished using manoeuvring and response data obtained in a series of sea trials. The parametric model proved to be accurate and, when compared with the sea trial data, model estimates have rms errors over the vessel operational range of 0.09 m/s and 0.02 m/s for surge velocity when moving in a straight line in forward and reverse, respectively. For rotating on a fixed point, the simulation overestimates the rotational velocity by 7.6% and has an rms error of 0.27°/s. Open loop circle tests show that with both propellers rotating at 580 rpm, the simulated vessel has a minimum turning radius of 24.4 m and can complete a circle in 44 s, which is 18% smaller and 8% faster than during sea trials. Simulated rotation with the engines in opposite gears at similar RPMs of 500 result in a complete revolution in 68 s, 3 s faster than during sea trials.
Variable parameter Muskingum-Cunge method for flood routing in a compound channel

The paper describes the use of a non-centered finite difference scheme that transforms the kinematic equation into a diffusion equation. The routing solution exhibits a numerical error that results in attenuation and lag of the outflow hydrograph. The trick is then to select appropriate values for the weighting coefficients in time and space in order to cause the numerical diffusion to match the physical phenomenon. This discussion concerns the conditions required for numerical stability and describes a method that automatically ensures selection of the correct increments in time and space for any desired values of the weighting coefficients. The authors' Fig. 1 shows an element of the computational grid in which the spatial weighting factor e and the temporal weighting factor 0 are defined. However, following eq. (4) the assumption is made that 9= 0.5 so that the finite difference terms are assumed to be centered in time throughout the remainder of the paper. This discussion suggests that there is some value in maintaining some generality with both temporal and spatial weighting factors. A more general depiction of the space-time module is shown in Figure 1 below, The weighting coefficients a and (3 are those used in Smith (1980) and correspond to the authors' e and 9 respectively with the difference that 9 = l~p. The size of the space-time element is Ax wide and At high. The diagonal with slope -1/c defines the maximum time-step Ate which is required to satisfy the Courant condition for numerical stability. Expressing the continuity equation as:
Room-temperature ferromagnetism and electrical properties of Cu2O/GaN heterostructures

Single phase and epitaxial Cu2O thin films were synthesized by thermal oxidation of Cu films deposited on GaN/sapphire substrates using electron beam evaporation. A transition of the conduction type was observed when the vacuum annealing temperature was varied from 400 to 500 °C. Current–voltage characterization confirmed the transition and indicated the successful formation of p-Cu2O/n-GaN as well as n-Cu2O/n-GaN heterostructures. Weak room-temperature ferromagnetism appeared in the Cu2O/GaN heterostructures, which may have originated from the defects of the Cu2O films.