Miracle-dz/UniFAR-FTUs huggingface dataset:
Polar Coordinate Decoupling Power Flow Control for Triple Active Bridge Converter

A triple active bridge converter can play a role of energy management in dc grids and help to construct them including distributed power systems and loads. However, the control system of a triple active bridge converter is a 2-input 2-output system. Therefore, a decoupling control is necessary. In a conventional control method, the decoupling matrix is designed as an inverse of the small signal transfer function matrix. However, the transfer function matrix is developed by approximation, causing the errors in the decoupling matrix. There has a little interference by using this conventional decoupling control method. This paper proposes a novel decoupling power flow control method for triple active bridge converters. The polar coordinate directly determines the phase shifts between the ports by utilizing the amplitude and the phase angle represented by the sum and the difference of transmission powers. This method is expected to get faster responses by using feedforward control and to achieve precise decoupling. The experimental results validate these expectations.
Fault Tolerant Control by Asymmetric Operation of Double Three-Phase PMSMs with Inter-Turn Faults

Inter-turn faults are most likely the initial stator winding fault in variable speed drive systems with Voltage Source Inverters. The machine must stay within its thermal requirements locally at the fault to prevent its propagation. In this paper, we present a fault tolerant control scheme for a double three-phase Permanent Magnet Synchronous Machine which minimizes the propagation of the fault by adapting the operating points of both systems independently. We estimate the generated heat by the inter-turn fault with a machine learning regression. If the estimated heat of the faulty system is greater than a definite value, the operating point of this system is adapted until the estimated heat is below the limit. The healthy system is used to compensate the torque reduction of the faulty system. We validated the concept of the control scheme with a Finite Element Analysis simulation. The presented double three-phase machine can compensate 13.9 W at nominal torque, which is an improvement of 74% compared to the equivalent single three-phase machine with the same heat limitation strategy. This heat reduction equals a temperature reduction of 111.2 K at fault locally.
Alternative Sphere Decoding Algorithm for Long-horizon Model Predictive Control of Multi-level Inverters

The computational burden associated with long-horizon finite control set Model Predictive Control remains a challenging problem in power electronic systems with short sampling intervals. In an attempt to address this problem and enable real-time applications, we introduce an alternative Sphere Decoding Algorithm which utilizes matrix block processing and offline preprocessing to support the online optimization process. The expected performance of the algorithm is presented here in the form of MATLAB© simulations together with real-time simulations, run on an OPAL5600© real-time simulator.
First estimations of stator dimensions for permanent magnet synchronous machines with tooth-coil windings and direct liquid cooling

In case of very high current density (direct liquid cooling allows reaching $100\frac{\mathrm{A}}{\mathrm{mm}^{2}}$), the well-known rules for achieving an initial design for the stator geometry of an electric machine are not valid any more. Therefore, this paper presents a way to find first estimates for the stator dimensions of permanent magnet synchronous machines (PMSM) with tooth-coil windings and very high current densities. Certain criteria for beneficial choices of dimensions are presented. The problem of slot leakage is addressed and considered, as well as loss and torque production. The dimensions are determined partially by a magnetic equivalent circuit (MEC). The difference between geometric and self slot-reluctances as well as the calculation of the reluctances of the stator and air gap are discussed. The limits and usability of the chosen model will be presented and reviewed.
Biodegradation and decolourization of biomethanated distillery spent wash

A bioremediation method was optimized to degrade and discolour the biomethanated distillery effluent. This phytoremediaion involved a dual stage microbial treatment. During primary treatment, fungal consortium was employed using fluidized film aerobic system (FFAS) and during secondary treatment, algal biomass either in free state (powder form) or in immobilized condition (alginate beads) was employed. The analyzed effluent at the end of FFAS treatment showed a reduction of ~70% in BOD and ~63% in COD without causing any color change. However, at the end of the secondary treatment with algal biomass resulted in a reduction of ~80% in COD and effected 75% decolourization. The optimized conditions for discolouration in the packed bed column were 1.5mm size of immobilized beads, 3.5cm height of packing, 300ml/l spent wash flow rate, 20 H/D ratio of column for immobilized algae and 4cm packing height, 400ml/l spent wash flow rate and 20 H/D ratio of column for algae without immobilization. The efficiency of discolourization by algal biomass remained unaffected by immobilization. An approach of this study could be used to develop a cost effective, ecofriendly biotechnology tool for the bioremediation of spent wash.
III/V-on-bulk-Si technology for commercially viable photonics-integrated VLSI

We have demonstrated a complete set of on-chip Si-photonic components using our III/V-on-bulk-silicon (Si) technology. Our bulk-Si-based technology shows unrivaled CMOS compatibility and much improved thermal dissipation, leading to superior performance over the III/V-on-SOI technology at a fraction of cost. The components demonstrated on the bulk-Si platform include single & tunable wavelength laser diodes, semiconductor optical amplifiers, Ge & III/V photodiodes, modulators, waveguides, and couplers. Fabrication of Si-photonics components are all carried out on the same bulk wafers that will host CMOS ICs, and all the fabricated components show robust performance in experimental characterizations. Using these bulk-Si components, a solid-state beam scanner for the light detection and ranging (LiDAR) system is currently under development. The III/V-on-bulk-Si technology will open a new path for silicon photonics to share and enjoy the colossal infrastructure and remarkable commercial success of the CMOS technology in coming years.
Capacitor Current Feedback Active Damping with Lagged Compensator for DFIG Wind Turbines with LCL Filter

Several active damping strategies have been proposed in the literature for grid-connected converters with LCL filter but there are not specific strategies for DFIG wind turbines. In this system, there is an interaction between the two converters of the back-to-back conversion structure, which must be properly modeled in order to design effective damping strategies for the LCL filter resonant poles. This paper proposes a robust active damping strategy for DFIG wind turbines with LCL filter that considers the special features of this system. In this technique the filter capacitor current is fed back through a lag compensator that adjusts the delay of the feedback loop to emulate a virtual impedance that has dominant resistive behavior in the range of possible resonance frequencies. It is shown that a similar damping of the LCL filter resonance is achieved when the strategy is implemented in either of the two converters.
A Novel Semi-Supervised Probabilistic Model of Fisher Discriminant Analysis for Data-Driven Fault Classification and Detection

Fault classification and detection is an important and challenging work to ensure the efficiency and safety of modern industrial processes. Data are being generated rapidly, but most of them are fetched in normal condition, and labeling abnormal data is a time consuming and costly job; therefore, semi-supervised learning is attracting more attention. Fisher discriminant analysis (FDA) is a prevalent supervised classification method, and there is an inherent connection between FDA and the well-known Gaussian mixture model. Motivated by such a connection, we proposed a new semi-supervised classification method based on FDA. In virtue of the expectation maximization algorithm, one can obtain projection directions, means and covariance matrices of all classes, and the predicted labels of unlabeled data simultaneously. Then these information can be utilized for fault analysis, classification and online detection. The method is guaranteed to converge with an acceptable computational cost. A numerical example and Tennessee Eastman case studies are carried out to demonstrate potential advantages of the proposed method.
Bearing-only Nonlinear Formation Control for Nonholonomic Unicycles*

This paper considers the nonlinear formation control problem of nonholonomic systems, where the control of each robot merely relies on the relative bearings between the communicating robots. Although this problem has received increasing research attention recently, it is still unsolved for the nonlinear model. The main contribution of this paper is to propose a bearing-only formation control law for the nonholonomic unicycles, then the nonlinear theory and non-smooth theory are introduced to prove the global stability of the close-loop systems. Finally, the effectiveness of the proposed control strategy is verified by simulation examples.
Study on the Application of NOMA Techniques for Heterogeneous Satellite Terminals

This paper addresses the application of nonorthogonal multiple-access techniques (NOMA) to those satellite relayed communications for which a significant imbalance in the link quality of user terminals can be expected. The Signal-to-Interference and Noise Ratio (SINR) imbalance could be caused by the coexistence of different types of terminals, possibly with different antenna sizes, and offering different classes of service. This link SINR asymmetry can be exploited to outperform orthogonal access schemes under different rate metrics, paying special attention to fairness in the service provision. Both forward and asynchronous return link are addressed, with minimum signaling information and emphasis on some relevant implementation issues such as framing and synchronization.
Autosub 2000 Under Ice: Design of a New Work Class AUV for Under Ice Exploration

The ice covered portions of the world’s seas and oceans are some of the most inaccessible areas on the planet, yet hold crucial clues to the behaviour of the world’s climate. Largely inaccessible by manned vessels, robotic platforms provide our best opportunities to explore and learn about these regions. Autosub2000 Under Ice (Autosub2KUI) will become the newest iteration of the Autosub family of Autonomous Underwater Vehicles which have played a significant role in under ice exploration over the last two decades. Autosub AUVs have conducted over 30 missions under sea ice and tidewater glaciers in both the Arctic and Antarctic. This paper outlines the design of Autosub2KUI with a focus on the adaptations required for operation under ice.
NOMA based Efficient Spectrum Sharing for Underwater UAV System with Multi-agent Reinforcement Learning

The underwater communications have been challenged by the long propagation delay as well as the limited utilizable bandwidth. As a result, although the underwater UAV system can benefit many applications, the communication performance still works as a barrier for its wide adoption. This paper proposes the utilization of non-orthogonal frequency division multiple access to increase the efficiency of underwater UAV networks, which will ideally avoid the negative effect from long propagation time, but rise multiuser interference problem. To solve this problem, this paper model the multi-user interference as a non-cooperative game, and resort the multiagent reinforcement learning to approach the Nash Equilibrium. This method can avoid the a priori constraint of scenario information, which usually cannot be obtained in the underwater UAV communications. A simple but descriptive simulation has validated the feasibility of proposed method.
Modeling and Control of a Dual Three-Phase Permanent Magnet Machine Accounting for Asymmetry between Two Winding Sets

In this paper, a dual three-phase machine is modeled with a general inductance definition considering machine saliency and asymmetry between two sets of three-phase windings. It is well known the αβ variables of a dual three-phase machine will be further transformed to the dq counterparts as a conventional three-phase machine, however, as for the xy and O1O2 subspaces, the suitable rotational reference frames have not been studied adequately in the machine modeling level, which will be investigated from the view of resulting in a constant transformed inductance matrix. It shows the anti-synchronous reference frame would be the most proper choice in xy subspace for the isolated-neutral-points configuration. Quantitative relation among phase inductances and transformed inductances is deduced. When accounting for the asymmetry between two sets, additional coupling arises between αβ and xy subspaces, which may degrade the current control performance. The proposed model is examined by a dual three-phase flux switching permanent magnet machine, and then a current controller using the internal model control method is designed aiming at canceling the coupling due to asymmetry winding sets. Simulation verifies the effectiveness of the proposed mothed.
R2-D2: Filter Rule set Decomposition and Distribution in Software Defined Networks

Software Defined Networks administrators can specify and smoothly deploy abstract network-wide policies. The rule sets of these policies are deployed in the forwarding tables of the available switches. In this paper, we propose a technique, named R2-D2, for decomposing and distributing a rule set on network switches of limited flow tables size, while preserving the network policy semantics. Through experiments on several rule sets with single dimension, we evaluate and analyse the performance of our rule decomposition techniques. Our results show that our technique is efficient in practice compared to existing techniques.
Design of a Novel High Power Density Single Sided Axial Flux Motor Using SMC Materials

In this paper a novel construction of a single sided axial flux permanent magnet motor with a hybrid stator using Soft Magnetic Composite and laminated soft magnetic iron for hand-held power tool applications is presented. The required motor characteristic, the design process of the motor as well as the construction of the prototype are shown. The required motor characteristic is derived based on dynamic measurements of screwing sequences with different screw sizes. Based on the requirements and the geometry constraints, a high power density single sided axial flux motor is designed and optimized using 3D-FEA simulations. Challenges in the construction of single sided axial flux motors are identified and handled. Measurements of the motor characteristic validate the design process and show the advantages of the axial flux motor concept.
New method to measure the influence of Twitter users

Influence is one of the most studied concepts in social network analysis. Through influence marketing on social networks, we can measure influence by calculating the number of followers of an influencer, then the number of retweets made by these followers. In this work, we propose a hierarchical framework to identify sentiment influencers, and we present a new method to measure the influence of Twitter users based on Retweet. Finally, we use several comparisons to other ranking systems to show highly influential and recognizable users.
Modification of DSDV and Secure Routing using Blockchain Technology

Wireless networking have increased considerably, and the network deployed on ad hoc basis called MANET is the need for todays’ communication activities. Also, the security issues of a MANET and its routing are equally important, otherwise, the benefits and easy usability would be seriously hampered. This paper proposes a modification of a well-known proactive routing protocol called DSDV and provides security protection using blockchain technology. In our protocol, instead of allowing all nodes to prepare their routing tables as in DSDV, a miner-node, who creates blocks and maintains blockchain, generates a common routing table containing all-pair shortest paths using Floyd-Warshall algorithm. This routing table is then floated to all nodes publicly for their uses. A group of trusted nodes are used for miners, however, one such node acts as a miner at a time for managing of power consumption. The security and performance analysis of our protocol are provided, and it found that a MANET with modified DSDV and blockchain is well protected, however, some extra computation cost for running the Floyd-Warshall algorithm is required.
Generator Loss Analysis and Comparison for 5MW Wind Turbine System

In this paper, a 5MW wind system, with a PMaSynRG as a generator, was analyzed. This paper analyzes the efficiency when using a 2level converter and 3level converter for a PMa-SynRG. According to the analysis results, it was found that when the 3level converter NPC type was used, the iron loss was found to be lower, in addition to the fact that the converter losses were lower.
Recent Trends of Left and Right Ventricle Segmentation in Cardiac MRI Using Deep Learning

Clinical indications of heart disease are shown from left ventricle (LV) or right ventricle (RV) volume measurements of cardiac MRI images. LV and RV segmentation of cardiac MRI images can detect and measure image volume. Public dataset MICCAI, ACDC, Kaggle, and SCD provide data on MRI images of cardiac that have been widely used by researchers. The deep learning method approach can optimally solve problems in analyzing heart disease from cardiac MRI images. The aim of this paper is to determine the availability of public datasets that are appropriate for the research objectives. It can support the optimization of the segmentation method for LV and RV images of cardiac as the contribution of this paper. The results of the study are that the public dataset (MICCAI, ACDC, Kaggle, and SCD) provides sufficient data for the identification, classification, and measurement of LV and RV volumes. Furthermore, a deep learning approach with convolutional neural networks can detect and classify heart diseases with high accuracy.
Energy Efficiency Maximization in Downlink Multi-Cell Multi-Carrier NOMA Networks With Hardware Impairments

In this paper, we investigate energy efficiency (EE) maximization in multicell multicarrier non-orthogonal multiple access (MCMC-NOMA) networks with hardware impairments (HIs). We formulate the optimization problem as a mixed integer nonlinear NP-hard problem, which is difficult to solve efficiently. To solve this problem, we decompose it into two subproblems. The first subproblem is the user and base station (BS) association and subchannel assignment problem, where binary whale optimization algorithm (BWOA) is proposed to handle it. For the second subproblem of the non-convex power allocation problem, successive pseudo-convex approximation (SPCA) is employed to establish the problem’s pseudo-convexity. The approximate problem is separable into a sequence of equivalent problems that can be easier to solve. Each problem of the obtained sequence has a stationary point solution and is guaranteed to converge. The simulation results demonstrate that the proposed algorithm achieves a performance comparable to that of the successive lower bound maximization (SLBM) algorithm and outperforms both fractional transmit power allocation (FTPA) benchmark for NOMA and the conventional orthogonal multiple access (OMA).
A Feature Compression Technique for Anomaly Detection Using Convolutional Neural Networks

Anomaly detection classification technology based on deep learning is one of the crucial technologies supporting network security. However, as the data increasing, this traditional model cannot guarantee that the false alarm rate is minimized while meeting the high detection rate. Additionally, distribution of imbalanced abnormal samples will lead to an increase in the error rate of the classification results. In this work, since CNN is effective in network intrusion classification, we embed a compressed feature layer in CNN (Convolutional Neural Networks). The purpose is to improve the efficiency of network intrusion detection. After our model was trained for 55 epochs and we set the learning rate of the model to 0.01, the detection rate reaches over 98%.
A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport’s networks.
Posture based Assessment of Plogging Activity

Plogging is a combination of jogging with picking up litter. It is an eco-friendly fitness craze which started off as an organized activity following the increased concern of plastic pollution. People involved in plogging are exposed to repetitive bending. Plogging activity can be carried out in stoop, squat, semi-squat or lunge posture. The present research aims to identify which posture is more suitable for the activity. Posture analysis was conducted in thirty-six healthy subjects during plogging using Rapid Entire Body Assessment (REBA) to check the risk level of the postures. Kruskal-Wallis test is performed in order to determine whether there are any statistically significant differences between the risk level of different postures as well as post-hoc test is conducted in order to find out the interaction between risk level and postures for significance. Squatting posture and lunge posture are found out to be more suitable during the plogging activity when compared to the other two postures such as stoop and semi-squat due to the less lumbar loading. Ergonomic interventions can play a vital role in reducing posture-related injuries by introducing a user-friendly hand tool for picking plastic wastes.