Miracle-dz/UniFAR-FTUs huggingface dataset:
MV Propulsion Drive using Solid State Transformer (SST) Technology

High frequency (50-100 kHz) power electronic converters enable attractive technology for various motor drive applications where the weight and size are largely constrained, like marine propulsion systems. SiC-based converters are the most suitable choice for high-frequency applications because of their lower switching losses than Si-IGBT based converters. However, the SiC-based MV multilevel converter solution for high-power applications is very costly in the current scenario. This paper presents a three stage (LV/DC-DC/MV) Solid State Transformer(SST) fed field oriented controlled (FOC) induction motor drive. In order to minimise cost and electrical losses, Si-IGBT and SiC devices are adopted, and a novel hybrid modulation technique is proposed for the MV stage. The MV stage of the SST-fed drive is realised with an Si-IGBT based Cascaded H-Bridge (CHB) inverter connected in series with a SiC based 2L-VSI. In the proposed control, the CHB inverter is switched at the stator frequency of the drive to supply the fundamental component of demanded voltage from the drive. As a result, it reduces switching losses to a great extent. The 2L-VSI is switched at a high frequency to supply the harmonic content, resulting in a higher effective switching frequency of the drive. The proposed technique is validated on a 300 V(LV)/1.65kV (MV), CHB based SST-fed FOC induction motor drive with different speeds and loading conditions in MATLAB Simulink environment.
Counting Homotopy Types of Gauge Groups

For K a connected finite complex and G a compact connected Lie group, a finiteness result is proved for gauge groups G(P) of principal G‐bundles P over K: as P ranges over all principal G‐bundles with base K, the number of homotopy types of G(P) is finite; indeed this remains true when these gauge groups are classified by H‐equivalence, that is, homotopy equivalences which respect multiplication up to homotopy. A case study is given for K = S4, G = SU(2): there are eighteen H‐equivalence classes of gauge group in this case. These questions are studied via fibre homotopy theory of bundles of groups; the calculations in the case study involve K‐theories and e‐invariants. 1991 Mathematics Subject Classification: 54C35, 55P15, 55R10.
Adaptive Compensation for Downlink Broadcast Signaling in the Narrow-beam Communication

Narrow-beam communication has been widely applied in many fields which provides a long transmission distance. However, the downlink broadcast signaling before communication limits the coverage expansion since the transmission distance of broadcast is much shorter. In this paper, we analyze the performance differences of narrow-beam unicast and circular-beam broadcast and investigate the reason for these differences. We divide the data channel with narrow-beam unicast and the control channel with circular-beam broadcast. Then we propose an adaptive compensation method to eliminate the differences on the transmission distance. We adopt the two-step adjustment based on the received power spectral density which is fed back from user terminals. The spread spectrum coding is first used on the control channel to increase its transmission antenna gains. Then we reallocate the power for channels to make sure that user terminals can obtain the same received power spectral density on two kind of channels. In this case, we can achieve the same transmission distance for narrow-beam unicast and circular-beam broadcast which means the system downlink transmission distance reach the optimum. The numerical results show that the proposed optimization method is feasible and can eliminate the performance differences.
FPGA based Design and Simulation of basic Routing Protocols

Complex Software Routing algorithms are efficient in Routing and in resolving conflicts, contentions and congestions in any multiuser data environments. But this whole Routing process leads to data blocking i.e. increased latency and packet/data loss in real time applications. This paper is a serious attempt to develop one such robust Hardware Router Architecture based on Round Robin, Handshake and Priority based Routing basic protocols.
Stability Enhancement of an Autonomous Microgrid using an Internal Model-based Controller

The microgrid concept is gaining momentum to meet growing energy needs in autonomous mode. In autonomous mode, due to interaction through power electronics-based converter and low inertia of the DERs, microgrid stability is crucial. In literature, different control strategies are proposed with a passive load. The majority of which either vary the droop controller or droop constant parameters. However, the enhancement in microgrid transient response and stability is inadequate. The microgrids can be subjected to active, passive, or dynamic load. The active load consists of a rectifier whose dynamics are similar to those of an inverter control loop. Hence, the stability of an autonomous microgrid with active load is enhanced using voltage and current controllers based on an internal model-control approach (IMC) in this paper. The efficacy of an IMC-based controller is demonstrated through time-domain simulation and eigenvalue analysis, and the obtained results are compared with a traditional approach-based PI controller. The eigenvalue analysis and time-domain results show that IMC-based controllers significantly improve the transient response and damping of the critical modes.
Modeling of Series Resonant Inverter for Induction Heating Application

Induction heating is preferred over other heating methods because of efficiency, rapid heating, cleanliness and safety. Series Resonant Inverter (SRI) is a simple and cost-effective topology when it comes to induction heating. Several hybrid control schemes have been proposed in literature for optimal control of resonant inverters for which modeling be-comes imperative. Compared to PWM converters, the method of averaging fails in resonant converters/inverters. A simple and straight forward modeling method for series resonant inverter is proposed in this work. The proposed model captures the small signal and large signal dynamics of the inverter. The analytical expression for small signal control to output power is derived. With the proposed model, one can control the state variable, namely the quadrature component of resonant current so as to improve the performance characteristics of the inverter. The small signal inverter transfer functions are derived facilitating hybrid control.
Electron emission theory and its application: Fowler–Nordheim equation and beyond

In this article, we examine the Fowler–Nordheim (FN) equation for field emission using pedagogical models to introduce and illuminate its origins, limitations, extensions, and application to multidimensional structures. The analyses of modern electron sources generally invoke either the FN equation or the Richardson–Laue–Dushman equation (thermionic emission) to interpret experimental data. These equations have ranges of validity that are increasingly challenged by operating conditions. The present article shall therefore have several aims. An introduction to and review of the FN equation shall be presented. Extensions to account for many body and other effects, shall be motivated by accessible models, and a generalized thermal-field emission methodology developed to account for low work function, high fields, photoexcitation, and other conditions in which the incident electron energy is near the barrier maximum. An account of effects such as resonance, which are not generally part of the standard emissio.
Undergraduate Research on Adding Relay Models and Generator Capability Curves to Synthetic Electric Grids

Synthetic electric power systems are important models that allow researchers to conduct and publish their work without using nonpublic data about the real grid. These synthetic grids are often missing models that are important to certain studies, such as, fault analysis, cascading failure, or geomagnetically induced currents (GICs). Furthermore, these cases often lack the data to build these models because the data is nonpublic, or the data is synthetic. Because the data is synthetic, it is generally within an acceptable range, but it might not necessarily be precise enough for certain models such as generator capability curves. Using the synthetic data to build the generator capability curves, will often lead to unrealistic results. The generator capability curves can instead be estimated using data from the existing data set. Line distance relay and time-overcurrent relay models can also be added to the case, using known data. With these models, a synthetic case can be made much more realistic without the need to obtain or protect nonpublic, real grid data.
Simulation of Sensor Spoofing Attacks on Unmanned Aerial Vehicles using the Gazebo Simulator

Conducting safety simulations in various simulators, such as the Gazebo simulator, became a very popular means of testing vehicles against potential safety risks (i.e. crashes). However, this was not the case with security testing. Performing security testing in a simulator is very difficult because security attacks are performed on a different abstraction level. In addition, the attacks themselves are becoming more sophisticated, which directly contributes to the difficulty of executing them in a simulator. In this paper, we attempt to tackle the aforementioned gap by investigating possible attacks that can be simulated, and then performing their simulations. The presented approach shows that attacks targeting the LiDAR and GPS components of unmanned aerial vehicles can be simulated. This is achieved by exploiting vulnerabilities of the ROS and MAVLink protocol and injecting malicious processes into an application. As a result, messages with arbitrary values can be spoofed to the corresponding topics, which allows attackers to update relevant parameters and cause a potential crash of a vehicle. This was tested in multiple scenarios, thereby proving that it is indeed possible to simulate certain attack types, such as spoofing and jamming.
Neuronal Morphology Classification based on Improved Residual Network

To address the problem that traditional convolutional neural networks have difficulty in fully extracting important neuronal morphological features when classifying neuron types, resulting in low accuracy of neuron classification. We propose a neuron morphology classification method based on the optimization of the ResNet18 network model. First, ResNet18 is used as the base network and initialized using the weights trained by ImageNet. Second, at the back-end of the base network, we use a feature reconstruction module to suppress edge feature loss and the drawbacks associated with the padding strategy. Finally, experiments are conducted on the NeuroMorpho-rat dataset to verify the effectiveness of the proposed method. The experimental results showed that the accuracy of 2 classification on Img_raw, Img_resample and Img_XYalign datasets reached 94.32%, 85.37% and 86.74%, respectively, and the accuracy of 12 classification reached 94.15%, 85.47% and 85.81%, respectively, which effectively improved the neuron compared to the original residual network the accuracy of morphological classification was effectively improved compared to the original residual network.
Question Answering Algorithm for Grid Fault Diagnosis based on Graph Neural Network

Due to the existence of uncertain factors such as the power grid system itself, natural climate change and human factors, various faults will still occur in the power grid system. If the fault alarm is not responded to in time, it is likely to cause grid instability or even collapse, resulting in inestimable losses. By building a knowledge graph for massive power grid operation and maintenance information, we can achieve fast and accurate fault information reasoning and traceability, and retrieve reasonable fault resolution measures. Use artificial intelligence technology and big data to assist power grid systems to achieve more efficient operation and maintenance. Realizing the intelligent fault diagnosis of power grid is an urgent problem to be solved at present. With the rapid development and application of artificial intelligence technology, if artificial intelligence and big data technology can be applied to the fault diagnosis and analysis of power grids, this situation of relying on manual analysis will be broken, and the efficient processing of massive operation and maintenance data will be realized.
Restricted interleaving phenomenon in Interleaved Boost PFC with leg sharing APF circuit

The paper proposes an interleaved Power factor correction (PFC) Rectifier integrated active power filter (APF). The interleaving helps to reduce individual switch stress and improves input current by reducing the ripple. While integrating the interleaved structure with APF, an inherent problem of the structure becomes prominent and creates disturbance in both PFC and APF. The problem is discussed thoroughly and remedy proposed by introducing the diode leg.
Deployment for Balanced and Efficient 5G Slice Based on VIKOR

The Fifth-Generation (5G) mobile network is expected to meet the diverse requirements of verticals by providing a promising platform for innovative applications. Network slicing is recognized as a key technology to realize the provision of services on demand for 5G broadcasting by slicing an infrastructure physical network into a dedicated sub-physical logical network. However, as the dimension of physical network resources and the number of users increase, how to efficiently and balanced deploy the network slices is the main issue in a multi-domain network since the network slices share limited resources. In this paper, a heuristic and dynamic algorithm is proposed based on the VIKOR method which shortens the time of NS deployment by orchestrating nodes and links simultaneously. In the nodes provisioning stage, according to the topology information based on Complex Network theory, the importance of nodes is sorted, which increases the acceptance ratio. In the path-selecting stage, a strategy is put forward that decreases resource usage and balances the physical resource distribution. Extensive simulations have been conducted which show that our algorithm performs better in terms of resource balancing and achieves a higher acceptance ratio both in a static and dynamic environment.
Generic Predictive Control Algorithm to Eliminate the Common-Mode Voltage in Multilevel Three-Phase Inverters

Medium-voltage industrial motor drives have been facing the problem of common-mode voltage(CMV) for the past few decades. Multilevel inverters provide not only better inverter performance but also greater electrical motor utilization. Conventional control of inverters with SVPWM has to limit constraints to accommodate the non-linearity and operational flexibility. The predictive controller can build the cost function to accommodate all possible improvement functions that are preferred over these conventional inverter-driven machines. This paper describes a generic approach to developing a predictive control algorithm for two-, three-, and five-level diode-clamped inverters. This algorithm provided the choice of optimal voltage vector selection in the elimination of the common-mode-voltages in three-phase inverters. The effectiveness of these switching voltage vectors also contributes to the improvement of inverter efficiency and DC-bus utilization. The implementation of this generic algorithm has been verified through simulation and laboratory prototypes.
A Power Saving Scheme for Smart Phone GPS Tracker Using Kalman Filtering

A power consumption saving scheme of a smart phone by increasing the update interval of the GPS tracking signal is proposed. To compensate its degradation of GPS accuracy due to the longer update time interval, a Kalman filter is applied with signal processing technology at the server side to improve the accuracy at the same time without consuming the power of the mobile phone. The battery life duration can be extended by choosing proper parameter for continuously GPS tracking process with acceptable error.
Integrated Transmission and Distribution Optimal Power Flow Simulation Using Linear Decision Approach

The existing solutions to the simulations of a transmission network(TN) and distribution network(DN) involve solving each of the networks separately, however with grid modernization DN are becoming active and depending on the penetration level can have a significant effect on TN as a result of reverse power flow from DN interconnected devices. In this paper, an optimal power flow framework was used to solve transmission and distribution systems together using an integrated optimization engine (IOE) with a linear decision rule (LDR) to enable the integrated system to adjust itself when fast changes are occurring in the network due to distributed generation. This enables the architecture to operate optimally and beneficially which improves economic operation and minimizes voltage rise. Our proposed methodology was implemented on an integrated T&D network using the IEEE 9 bus & 13 bus interconnection.
Performance Analysis of Distributed Compressed Sensing Schemes for Data Aggregation in Low-Power Wireless Sensor Networks

Low-power wireless sensor networks (WSNs) consist of a large number of battery-operated sensors deployed over an area for objectives such as monitoring environments, tracking objects, and controlling various industrial operations. The aggregation of the huge amount of data generated from the explosively growing number of sensors is critical and challenging. Optimizing the energy spending and improving the overall lifetime of the network through efficient data aggregation and compression approaches are open problems in low-power WSNs. A major portion of the energy consumption of sensor nodes is contributed by data transmission. Hence, efficient data aggregation schemes are to be deployed to improve the lifetime of the WSNs. This paper evaluates the efficient data aggregation frameworks for low-power WSNs.
Bus Clamped PWM Based Vector Control of PMSM Drive

This paper deals with the analysis of the vector controlled Permanent Magnet Synchronous Motor (PMSM) drive operated with different types of space vector pulse width modulation (SVPWM) techniques. Different control techniques are available for the speed control of the PMSM drive. Vector control is the most significant control method. It gives better performance, since it is possible to control the flux and torque separately. Vector control is known as decoupled control or independent Control. The harmonic profile of the machine is affected by the type of Pulse width technique used. This paper evaluates the vector control strategy of PMSM drive based on conventional space vector pulse width modulation (CSVPWM) fed inverter and Bus clamped pulse width modulation (BCPWM) fed inverter.
Observer Based Current Sensorless Control for DAB Converter with Improved Dynamic Performance

This article proposes a current sensorless sliding mode control strategy of dual active bridge converter (DAB). Here, the load current is estimated by using nonlinear disturbance observer. Then this estimated load current is used as a feedforward signal in sliding mode control. The proposed control strategy can regulate the output DC voltage with good disturbance rejection capability, fast dynamic response and accurate reference voltage tracking without using current sensor. The complete design procedure with mathematical analysis is presented. Moreover, the performance of the proposed controller has been compared with other conventional controllers. The proposed controller is simulated in MATLAB and real time OPAL-RT platform used to verify its performance. Typical results are presented
P2P Energy Exchanges for Lowering the Hydrogen Production Cost Using Realistic Electrolyzer Model

Motivated by the net-zero emission policy targets, this paper aims at exploiting the benefits of peer-to-peer (P2P) energy exchanges in lowering the hydrogen (H2) cost in the active distribution networks. This paper also develops a detailed model for grid-integrated electrolyzer based on polarization curves, which addresses the non-linear conversion efficiency issues of this asset to provide realistic results. Simulation results demonstrate that 1) electrolyzer efficiency changes under different operating conditions which significantly depends on actual stack operation (actual cell voltage and current); 2) H2 could be produced at lower costs due to P2P energy exchanges by reducing the peers’ electricity demand, providing more available capacity of renewables at the customer side, and as a consequence reducing the market clearing price and produced H2 cost.
A Method of Predicting Continuous Blood Pressure by PPG Signal Based on Neural Network

The relationship between blood pressure obtained from photoplethysmography (PPG) signals and pulse duration, is not always linear. To estimate blood pressure more accurately from PPG signals, this paper presents PPGLAB, a convolutional neural network that can predict the continuous ABP waveform of the human body from PPG Signals. In this work, we use the MIMIC-III data set. All signals are sampled at 125Hz and recorded with 8-bit accuracy. By model measurements, the comparison of estimated blood pressure to reference values shows good accuracy, meeting the Class A criteria for BHS on DBP and ABP, and also meeting the national standards of the American Medical Device Association.
Adaptive Recognition of Aircraft Cable Brackets Based on Improved Mask R-CNN and Synthetic Dataset

In the field of aircraft assembly, the realization of automatic assembly inspection of aircraft parts is essential to improve aircraft assembly efficiency. However, how to identify each part accurately in a complex assembly scene is still a thorny issue. What's more, deep learning algorithms require large training datasets to converge. In this paper, the problems that are met in the recognition of aircraft cable brackets are analyzed in detail, and a region-based convolutional neural network is optimized by comparing different feature extractors and improving Feature Pyramid Network for small brackets. In addition, a simulation platform is developed based on the Unreal Engine to automatically generate synthetic realistic training datasets. Finally, take the task of inspecting cable brackets in the C919 aircraft as an example, experimental results show that the mAP@50 of the optimized model trained with the synthetic dataset is up to 96.3%.
Routh type approximation for discrete system

A method of reduction for a discrete system is presented. The denominator of the reduced model is directly obtained in z-domain from the denominator of the higher-order system. The denominator of the reduced model is directly obtained from a Routh-type table. The numerator of the reduced model is obtained either by matching the discrete time moments or by minimizing the step response error constructed in discrete time domain and computed in z-domain. The stability of the reduced model is guaranteed if the higher-order system is stable. A numerical example is examined which involves the determination of a second-order reduced model for a fourth-order digital filter. <<ETX>>