Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of a High Speed High Inertia Outrunner Reluctance Synchronous Machine Using Trivariate Polynomial Approximation of Flux Surfaces

Achieving a good performance of a field oriented encoderless control of a synchronous machine requires an accurate model of the electromechanical behavior of the machine. Modern reluctance synchronous machines show strongly self- and cross-saturation dependent nonlinearities of the flux linkages related to the d- and q-currents. Reluctance machine presented in this paper is used as a motor-generator unit in a flywheel energy storage system and is integrated in its rotor. Because of an outrunner design and a high inertia and rotational speed of the machine, a high centrifugal force acts on the rotor. This force causes the rotational speed dependent air gap variation between stator and rotor. This leads to an additional nonlinear effect on flux linkage surfaces. This paper presents a model of the reluctance synchronous machine with rotational speed dependent flux linkage and inductance surfaces. These surfaces are therefore trivariate functions and depend both on direct- and quadrature-axis currents and on a rotational speed. A simple trivariate polynomial approximation of the flux linkages and inductances is proposed, which can be efficiently implemented on a FPGA or DSP.
A Novel 3D Wideband Geometry-Based Channel Model for Massive MIMO V2V Communications in Riverside Urban Road Environments

In this paper, a novel three-dimensional (3D) wide-band geometry-based stochastic model (GBSM) is proposed for the massive multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) channels in riverside urban road environments. The proposed model consists of two semispheres, multi-confocal quarter ellipsoids and semi-ellipsoid cylinders, which is the first work to model the scattering environment of the riverside V2V channel. Specifically, we devise an effectively approach to describe the dynamic characteristics of river flow and fluctuation by treating the river surface as dynamic scatterers. For the wideband massive MIMO of interest, we first derive the channel impulse responses (CIRs) and generate the simulation model based on finite scatterers. Then, the effects of vehicular traffic density (VTD), dynamic river, and moving ships on the statistical properties are investigated in detail. Numerical results demonstrate a good agreement with theoretical analyses, which indicates that our proposed model is able to exactly capture the characteristics of V2V channels in riverside urban road environments, thus revealing the effectiveness of our proposed model.
Image-Based Optimization of Electrical Machines Using Generative Adversarial Networks

The generation of high-quality simulation data for electrical machines is very time-consuming due to the finite element analysis involved. Overall optimizations using existing data is often not possible due to different geometry descriptions. In this paper an image-based optimization of electrical machines is developed to enable future data-based geometry optimization. An overall optimization concept consisting of image generation using a generative adversarial network and image evaluation using a deep neural network is shown. The basic function is confirmed with two single objective optimization problems. The results show that the image-based approach can compete with conventional optimization and at the same time offers the advantage of a uniform description of the cross-section geometry.
A Novel MEMS Double-Layer Spiral Inductor with High Current-Carrying Capacity

MEMS spiral inductors have a widely potential application in thin-film electronics and consumer electronics due to their advantages of miniaturization, very thin, low loss and excellent uniformity. However, the existing MEMS inductors have a huge challenge of low current-carrying capacity, which limits their applications. In this paper, a novel high current-carrying micro-inductor based on MEMS spiral coils is proposed, and is analyzed by finite element method (FEM). The simulation results demonstrated that compared with the traditional SL (single-layer spiral inductor) under the same coil size, the maximum current of the DL-P (double-layer spiral inductor with parallel connection) can be improved with 27.53% (DC) and 21.61% (AC at 1GHz) respectively. This finding is useful for the design of micro inductors with high current-carrying capacity in theory.
Investigation on Axial-Flux Switched Reluctance Machine with Different Winding Configurations

This paper investigates the impact of winding configurations on the electromagnetic performance of 3-phase, 12-slot/8-pole axial-flux switched reluctance machines (AFSRMs) with different winding configurations such as double and single layers as well as conventional and mutually coupled windings. Fulfilling the maximum torque design requirement, various square-wave current supplies have been considered, e.g., unipolar 120 degree and bipolar 240 degree. 2D finite-element analysis (FEA) has been applied to evaluate the machine performance in terms of phase inductance, on-load torque, axial force, and losses. On the other hand, to investigate the 3D effect of the AFSRMs, 3D FEA has also been carried out. The machine dynamic performances, such as torque-speed characteristics and efficiency map, between different winding configurations have been compared.
Joint Design of Power Allocation and Beamforming for Energy Efficiency Optimization in D2D-Enabled Distributed MIMO System

Device-to-Device (D2D) communication is a promising technique to enhance the energy efficiency (EE) of green communication networks. In this paper, the energy-efficient joint power allocation (PA) and beamforming design algorithm for D2D-enabled distributed multiple-input multiple-output (D-MIMO) system is proposed. The objective is to maximize EE of the cellular user while ensuring the power and beamforming constraints and D2D user's minimum rate requirement. In order to make the non-convex and non-linear optimization problem solved, the original problem is decomposed into two subproblems using the block coordinate descent algorithm. The developed joint algorithm can compute closed-form solutions for users’ PA and beamforming. Compared with the benchmark scheme based on exhaustive search method, the proposed joint algorithm can achieve almost the same EE performance but yield lower complexity. Besides, with the increase of the receive antennas number, the EE will increase. Moreover, the EE performance will become better than that without D2D communication, as expected. Numerical simulations confirm the effectiveness of the joint algorithm in improving EE gain and reducing complexity.
The Application of Common-Mode Feedback in Operational Amplifiers

This paper mainly studies the operational amplifier based on folded-cascade and telescope cascade operation amplifiers. The telescopic cascade cannot be widely used because of its limited voltage swing. The gain of the folded-cascade is relatively small and its frequency is small. After adding common mode feedback (CFMB), when Vout1 and Vout2 rise, make sure that the current of M5 and M6 increases to make Vout, CM tend to Vref. Then the simulation software CooLSPICE is introduced, which is suitable for low temperature conditions and the advantages of using this software to simulate. After simulation, it is found that the collapsed cascade consumes more power than the retractable cascade, and the gain of the retractable cascade is higher than that of the folded cascade, which is 45dB and 37dB, respectively. The phase margins are 117 deg and 25 deg, respectively. Finally, common mode feedback can be used to effectively promote the static balance of the circuit.
A V2P Warning System on the Basis of LoRa Wireless Network

Vehicle-to-Everything (V2X) communication is a groundbreaking technology that enables interconnected services in the realm of smart transportation. Among the various V2X applications, Vehicle-to-Pedestrian (V2P) communication plays a crucial role in enhancing road traffic efficiency and safety by facilitating the exchange of information between vehicles and pedestrians. However, the existing V2P warning systems neglect the inherent uncertainty associated with pedestrian trajectories, leading to suboptimal accuracy in detecting collision risks between vehicles and pedestrians. Consequently, the potential for improving road safety is limited. To address this issue, we propose an advanced pedestrian-vehicle anti-collision model. This model takes into account the uncertain nature of pedestrian movement and leverages the Long Range (LoRa) wireless network to establish a V2P warning system. Specifically, we employ the long short-term memory artificial neural network (LSTM) to accurately predict pedestrian trajectories. By combining the pedestrian’s trajectory with a multi-dimensional normal distribution function, we obtain the probability density function that characterizes the pedestrian’s movement. Subsequently, we deduce the preliminary collision area between pedestrians and vehicles. Finally, we utilize a confidence probability metric to determine whether a warning should be issued to both pedestrians and vehicles. Simulation results demonstrate the effectiveness of our system in accurately warning pedestrians and vehicles, even under varying speeds and Global Positioning System (GPS) positioning errors. The experimental evaluation of our proposed method further validates its superior performance and efficacy.
Containerization of the Security Application on Electric Power IoT Terminal

The development of Internet of Things (IoT) technology brings convenience to the development of the electric power business but also brings security risks. Moreover, owing to the massive heterogeneity of IoT terminals, traditional security protection measures are restricted in terms of deployment, adaptation, operation, maintenance, and other aspects. Docker containers take up fewer resources and are easy to transplant; therefore, they are suitable for deploying secure ap-plications in the IoT computing environment with limited resources and power. This study fo-cuses on the container virtualization technology of the security applications on electric power IoT terminals. We propose a general process for application containerization to standardize the work done in each stage of containerization. The union file system is used to build a multilayer container architecture to reduce resource usage and improve deployment speed. Thus, the secu-rity applications can be run in containers to realize their real-time deployment and real-time op-eration in the heterogeneous intelligent electric power IoT terminals to achieve the security ca-pabilities of on-demand orchestration and timely response of electric power IoT terminals.
Contact-aware Multiple-Layer CHORD for routing in Large-scale Satellite Networks

Large-scale satellite network attracts wide attentions of research community within the coming developments of next-generation networks. Considering the large expansion of the number of nodes in satellite networks, storing and updating global routing tables is almost impossible. Besides, the link state among nodes is more complex with a dynamic topology. To address above issues, we study the distributed routing problem in a large-scale satellite network. In this paper, we propose a routing mechanism in large-scale satellite networks based on Chord, which consists of a well-designed multiple-layer Chord architecture with a set of identification mapping schemes, a contact-based multi-layer finger table, an efficient path lookup algorithm and a routing table update algorithm, respectively. Via simulation results, it could be verified that under different satellite network scales, the proposed routing mechanism in this paper can achieve better end-to-end latency and update overhead.
Research on the Positioning of Industrial Detection Robot Based on RGB-D Visual Slam

In the context of the positioning of industrial detection robots, the problem is that it is difficult for detection robots to accurately and independently complete real-time positioning in an unknown and complex environment or in occlusion scenes such as inside the building structure, the GPS positioning is inaccurate, or the signal is lost. Based on visual SLAM technology, this paper uses RGB-D camera as a sensor to extract the original ORB feature points and uses the PNP algorithm to estimate the position of the industrial robot and uses the RANSAC algorithm to remove the wrong features in case of mismatch, aiming to optimize the positioning of industrial detection robots. In the process, feature point extraction and matching strategy can solve the autonomous positioning problem of GPS signal loss. In this paper, the TUM RGB-D data set is selected to test the algorithm model, and the positioning results are compared with the ground real value of the data set, which verifies the feasibility and effectiveness of the algorithm. The positioning accuracy error is controllable, and the positioning is highly robust.
Local Heat Transfer Coefficient Measurements on Shaft Spray Cooled End Windings

High power density electrical machines are mainly limited due to their cooling system. Oil spray cooled end windings are one possibility to significantly increase the power density. However, foundations for the estimation of the cooling capability are rather scarce in the design process. Different approaches to estimate the global heat transfer coefficients for spray cooled end windings exist in literature. However, local heat transfer coefficients, which are essential for the prediction of hot spots, can not be estimated. In this contribution, we present local heat transfer coefficient measurements for shaft spray cooled end windings. It is shown that the existence of a sump remarkably lowers the heat transfer coefficient, whereas it is almost constant over the remaining circumference. The rotational speed has a minor influence on the heat transfer. The latter is only increasing for very high speeds. A modeling approach for the Nusselt number is developed to apply it to different coolants. The achieved mean absolute percentage error between experimental and modeled Nusselt is 10.8%.
Prediction-Assisted Compressed Spectrum Sensing

In compressed spectrum sensing (CSS), true spectrum support is often assumed to be entirely unknown, as is the case in real-world applications. This unknown spectrum support, however, renders the design of the sensing matrix a complex and arduous task. In this paper, we demonstrate that it is possible to obtain certain prior knowledge on the spectrum support, which can then be employed to efficiently design a high-quality sensing matrix. We propose prediction-assisted CSS schemes that incorporate integrated spectrum support prediction methods or rely on external cooperative decisions to update the sensing matrix in accordance with spectrum dynamics. Numerical experiments reveal that the proposed schemes yield significant performance improvements in both single-node and cooperative scenarios.
Damping of Oscillation in Permanent Magnet Torque Limiter

Permanent magnet (PM) based torque limiting devices are a viable choice to limit torque and prevent damage in a drive train. It offers contactless, maintenance-free operation, but also suffers from oscillation. In this work, oscillation damping technique for PM torque limiter is explored. The damping is achieved through eddy currents induced in conducting bodies. Three different configuration of conducting elements are studied using static and transient electromagnetic simulation. Torque capability, speed error and settling time is reported in each case. The trade-off between pull-out torque and damping is highlighted. Damping initially improves then degrades as amount of conductive material is increased. It is shown that conductive elements placed in air-gap is most effective to damp out oscillations, while copper bars embedded in iron have the least change in pull-out torque.
Capillary-Based Structural Long-Period Gratings for High-Sensitivity Curvature and Strain Measurement

A structural long-period gratings built with cascaded-sections of capillaries for high-sensitive curvature and strain measurement is proposed and experimentally demonstrated, and the correspondent sensitivities of 29.58 dB/m-1 and -18.9 pm/$\mu \varepsilon$ could be achieved, respectively.
Research on Evaluation Method of Ship EMC Based on Multi-layer Coupling Network

This paper proposes the method based on multi-layer coupled networks to evaluate ship electromagnetic compatibility (EMC). Firstly, based on the theory of multi-layer coupled network systems, the equipment and their transmission link that mainly influence the ship EMC are defined as the nodes and edges of the evaluation model; Secondly, the network layers of the different domain of the evaluation model are located, as well as the coupling methods between the layers are provided; Then, according to the characteristics of the multi-layer network model, the evaluation method of the influence of the key nodes on the ship’s EMC is given; Finally, the effectiveness of the evaluation method of ship EMC based on multi-layer coupled network is verified by analyzing several frequency using equipment on the ship. This method has certain reference significance and development prospects in the field of the evaluation of ship EMC.
A Sensor-Based Smart Urban Flood Warning and Management System

In recent years, the rapid global climate change has resulted in frequent urban flooding disasters. The phenomenon of blocked drain covers in urban areas directly affects the passage of vehicles and pedestrians, causing inconvenience to citizens' travel. In severe cases, it can even submerge vehicles, leading to casualties and posing a serious threat to public safety and property. To address this issue, we propose a smart urban flood warning and management system. The system installs terminal devices on rainwater grates along the roadsides. When the water level on the road reaches a certain depth, pressure sensors are used to acquire the water level information, which is then uploaded to the server. The system provides realtime and accurate feedback on urban flooding conditions, demonstrating broad prospects for the prediction and control of urban flooding disasters.
Enhancing Water Pollutant Detection with few Training Samples using Feature Image and Acoustic Signals

Water pollution is a major global environmental issue with severe consequences, highlighting the importance of accurate pollutant detection. While traditional chemical analysis methods are commonly employed to detect water quality, which involves collecting water samples for analysis to detect water quality, they are time-consuming and resource-intensive. In recent years, computer-based detection methods have emerged, but those typically rely on the experience of researchers to manual feature extraction and can be limited in their ability to generalize. This paper proposes a novel approach to water pollutant detection using acoustic signals and Convolutional Neural Network (CNN) technology. By leveraging the microphone and speaker embedded in smartphones to collect and process acoustic data, this method extracts time-frequency domain features and converts them into images for analysis by the CNN. The experimental results demonstrate high accuracy about 98% even in scenarios with limited data, indicating that this method could be an effective and efficient tool for detecting water pollutants.
Halbgruppen und Automaten

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A Photonic Integrated Circuit-Based Erbium-Doped Waveguide Amplifier

The invention of erbium-doped fiber amplifiers (EDFAs) [1] evolutionized long-haul fiber-based optical communications. Erbium amplifiers have a number of unique properties highly suitable for optical communications, such as the broadband gain covering optical communication bands around 1550 nm, a long ms-lifetime of the parity forbidden intra-4-f shell transition that leads to slow gain dynamics and negligible inter-channel crosstalk, high temperature stability, and low noise figure. Rare-earth ion doping can equally provide a basis for compact erbium-doped waveguide amplifiers (EDWAs) [2]. Earlier efforts in the 1990s were made to implement EDWAs based on oxide glass waveguides, but these approaches were limited by large waveguide background losses, large device footprints and incompatibility with contemporary photonic integrated circuits. Recent demontrations of EDWAs using Erbium-doped Al2O3, TeO2 and erbium chloride silicate nanowire achieved output power of typically < 1 mW, far below the level demanded by many applications.
Influence of Tooth-tips on Dual 3-phase Modular Permanent Magnet Machines with Redundant Teeth

Three kinds of tooth-tips are analyzed to show their influence on the electromagnetic performances of dual 3-phase modular permanent magnet machines with redundant teeth. For symmetrical tooth-tips, each tooth is symmetrical to its own center line. However, the tooth-tips can also be accommodated in either the left or right side of each tooth. Since both the open-circuit and armature field distributions are affected due to the existence of these tooth-tips, the corresponding machines will show different performances, such as back-EMF, cogging torque, on-load torque, etc. It can be found that each kind of tooth-tip has its own advantage. Therefore, the adoption of tooth-tips needs to be determined according to the specific requirement.
Optimization of Induction Machine Design for Electric Vehicle Powertrain

This paper proposes a methodology to include induction machine (IM) in an optimization scheme for EV powertrains using Particle Swarm Optimization (PSO) as the optimization algorithm. The IM model is developed based on finite element analysis (FEA) and all the losses are estimated. The main objective is to generate a large database of base designs in a computationally efficient but accurate way. To perform the optimization, scaling methods are used. Detailed transmission and inverter models are also considered based on previous work to estimate the overall powertrain efficiency. The optimization case study shows how the optimizer evaluates a large number of alternatives and picks the optimal IM design and associated transmission and inverter. The proposed methodology can serve as a basis for future research on powertrain optimization for dual-motor driven vehicles.