Miracle-dz/UniFAR-FTUs huggingface dataset:
Speed Dependency of the Induction Machine Phase Current Spectrum Under Fault Conditions

Induction machines are used in a wide range of industrial applications due to their simplicity, low costs and robustness. However, several fault mechanisms exist which can lead to machine failures. Many of these faults induce specific frequency components in the motor current spectrum and can thus be used for fault detection and classification.This work investigates the speed dependency of the phase current spectrum of an induction machine. Bearing faults and broken rotor bars are failure mechanisms that induce speed-dependent frequency components. For inverter-fed variable-speed induction machine drives, a constant operating point in terms of phase voltage and frequency can be maintained for different motor speeds. Thus, speed-dependent frequency components can be identified and examined.This technique is applied to steady-state measurements of an inverter-fed 400 V induction machine and it is shown that the identified speed-dependent frequency components can be attributed to different fault mechanisms in the machine. The classical MCSA (motor current signature analysis) approach is applied to the fifth and the seventh harmonic and their respective sidebands. This leads to a significant improvement in fault detection capability, especially for low slip frequencies.
Towards Generating Transformation Rules without Examples for Android API Replacement

Deprecation of APIs in software libraries is common when library maintainers make changes to a library and will no longer support certain APIs in the future. When deprecation occurs, developers whose programs depend on the APIs need to replace the usages of the deprecated APIs sooner or later. Often times, software documentation specifies which new APIs the developers should use in place of a deprecated API. However, replacing the usages of a deprecated API remains a challenge since developers may not know exactly how to use the new APIs. The developers would first need to understand the API changes before they can replace the deprecated API correctly. Previous work has proposed an approach to assist developers in deprecatedAndroid API replacement by learning from examples. In this work, we also focus on Android APIs and propose an approach named No Example API Transformation (NEAT) to generate transformation rules that can assist developers in deprecatedAPI replacement even when code examples are not available (e.g., when the deprecation has happened recently). We have validated the effectiveness of NEATon generating transformation rules for deprecated Android APIs. Using NEAT, we can generate 37 transformation rules for 37 out of a selection of 100 deprecatedAPIs and have validated these rules to be correct.
Object Dynamics from Video Clips using YOLO Framework

In this paper we present a real-time object detection in the warehouse, to predict collision arising from moving forklifts andraising alarm when necessary. There are many research using YOLO for real-time object detection like detecting person or cars for Advanced Driver Assistance System(ADAS). There are many cargoes in the warehouse. Employees need to collect them and deliver to other place. When the employees are driving the forklift with many cargoes or big shipments that may block there vision to see objects in front of them. The employee driving the forklift might not see cargoes stored at the corner while turning, causing accident. The system will identify objects in real-time, received through surveillance camera set at a height from where it can clearly capture required frames to predict collision. If the camera predict that there will be an imminent collision, it will sound the alarm.
A Selective Modular Neural Network Framework

We propose a simple yet effective modular neural network framework for multi-class classification. The proposed framework significantly reduces the number of parameters while maintaining the accuracy comparable to more complex deep neural networks such as AlexNet and ResNet. The framework primarily consists of two major parts, namely a routing module and a set of expert modules. Each of the expert module is a binary classifier trained in Round Robin fashion. The novelty in this literature is on how we leverage the routing module to select only a small set of expert modules for each input datum during the testing phase. The selection of the expert module is carried out based on the routing module’s soft-max scores for top-2 classes. In addition, each of the modules in the framework is very minimalist by design. The framework was evaluated on three real-world public data-sets. Empirical results show that with only a budget of 0.35M parameters we can achieve 99.56% accuracy on MNIST (0.2% improvement over AlexNet). For the Fashion-Mnist dataset, with the same budget and network architecture as MNIST we achieved 91.00% accuracy. For the UCI-HAR signal data, with a budget of only 2.5M, we can achieve an accuracy of 96.00% which is comparable to AlexNet (96.30%).
Maximum Lifetime Optimized Collaboration of Data and Energy Routing in VANET

The roadside units (RSUs) based on traditional grid has high carbon footprint and high deployment cost in vehicle dedicated network. An ideal green alternative has become an important issue in Vehicular Ad-Hoc Networks (VANETs). In this paper, we propose a novel framework of energy harvesting and cooperation to maximize the life cycle in VENETs. In this VANET scenario, the RSU node can harvest energy from the nature and also receive down steam vehicle energy dispatch to maintain the energy needed for data transmission, may transfers a portion of obtained energies by RSU nodes to the neighboring RSU nodes through energy cooperation. Each vehicle can access the RSU in two ways: 1) direct delivery when the vehicle is within the transmission range of the RSU; 2) multi-hop relay when the vehicle is not within the transmission range of the RSU. By analysing the data rate, transmission power and energy transmission, we solve the problem of maximum network life cycle in the conservation law of energy and traffic. We consider involving energy constraints and data transmission joint problem, we optimize each RUS nodes energy management over vehicular outgoing data links and its energy transfers to neighboring RSU nodes. For this case, with no energy cooperation, we show that each RSU node should allocate more power to links with more noise and/or more data flow. In addition, when there is energy cooperation, our numerical results indicate that the energy is routed from nodes with higher energy to nodes with lower energy. Simulation results show that energy and data routing cooperation in VANET strategy can effectively improve the network life cycle.
Design and Analysis of a New Consequent-pole Permanent-magnet Servo Motor based on Field Modulation

This paper proposed a new consequent-pole permanent-magnet servo motor (CPMSM) based on field modulation, which has a surface-inset PM (SIPM) rotor with an arch on PMs. To increase the torque density under low speed, split teeth on main stator teeth are adopted. The fault-tolerant teeth and single-layer concentrated windings are employed to achieve phase decoupling and improve reliability. By using finite element analysis (FEA), the electromagnetic characteristics of the proposed motor are analyzed. The influence of main parameters, such as pole-arc coefficient, PM thickness and the height of the arch on PMs on back EMF, average torque and torque ripple are investigated. Furthermore, optimal design is developed to improve the performance of the proposed motor. Simulation results verified that the proposed motor is suitable for applications requiring low speed and high torque, such as robotic joints.
AvTA Based Assessment of Dependability Considering Recovery After Failures and Attacks on Vulnerabilities

The paper describes modification of the ATA (Attack Tree Analysis) technique for assessment of instrumentation and control systems (ICS) dependability (reliability, availability and cyber security) called AvTA (Availability Tree Analysis). The techniques FMEA, FMECA and IMECA applied to carry out preliminary semi-formal and criticality oriented analysis before AvTA based assessment are described. AvTA models combine reliability and cyber security subtrees considering probabilities of ICS recovery in case of hardware (physical) and software (design) failures and attacks on components casing failures. Successful recovery events (SREs) avoid corresponding failures in tree using OR gates if probabilities of SRE for assumed time are more than required. Case for dependability AvTA based assessment (model, availability function and technology of decision-making for choice of component and system parameters) for smart building ICS (Building Automation Systems, BAS) is discussed.
Research on Control Strategy for Multi-port Power Electronics Transformer Based on Interphase Coupling MMC Topology

In recent years, with the development of the power electronics technology, the power electronics transformer (PET) has been widely used in the hybrid AC/DC power grid. As the key equipment, the power electronics transformer needs to have multi ports to satisfy the requirement for different sources and loads. In this paper, a novel multi-port power electronics transformer was proposed. It includes medium voltage interphase coupling MMC power stage, multiple active bridge (MAB) DC/DC power stage and low voltage inverter power stage. With the proposed control strategy, it can better control the power distribution and power flow between the three phases of the power electronics transformer. Finally, the simulation results validate the effectiveness and feasibility of the proposed multi-port power electronics transformer and the control strategy.
Compensation of Current Harmonics for Switching-Table-Based Direct Torque Control of Dual Three-Phase PMSM Drive

Current harmonics are ubiquitous in dual three phase (DTP) permanent magnet synchronous machines (PMSM) drives. Due to inverter non-linearity and the back electromotive force (EMF) distortion and, significant current harmonics are generated in DTP system. The influence of back EMF harmonics is investigated in this paper and a novel switching-table-based direct torque control (ST-DTC) method to compensate the back EMF harmonics has proposed, thereby reducing the current harmonics. By changing voltage vector active time durations, voltage harmonic injection could be achieved so that the back EMF harmonics could be compensated, as verified by experiments.
Flexible bioimpedance spectroscopy system for wound care monitoring

This paper presents a system for measuring the bioimpedance of a wound, to give an indication of its healing progress without the need to remove the wound dressing. A 16-channel electrode array is printed on a composite nanocellulose–polyurethane substrate that is stretchable, breathable and biocompatible. An electronic readout system consists of an analogue multiplexor, analogue front end interrogated with an integrated impedance measurement device, and an on-board calibration circuit. The readout electronics is implemented on a polyimide flexible substrate, including additional sensing functions, wireless powering and radio communications easily integrated in various wound dressing scenarios. The electrode array is detachable from the readout allowing numerous application specific electrode patch configurations, and reuse of electronics with disposable, single-use electrode patches. The system is characterized measuring a range of known impedances, for measurements on a gelatine phantom and on a healthy human arm.
Efficient Updates of Battery Status for BLE Beacon Network

Bluetooth low energy (BLE) beacon network is one of the most favored IoT infrastructures due to its flexibility and scalability. Monitoring and updating the battery statuses of the on-site BLE beacons is an essential task for reliable operation and timely maintenance of the infrastructure. However, unregulated frequent updates of the battery statuses result in stressing the beacon network management platform, possibly threatening the reliable operation of the infrastructure. Whereas too infrequent updates degrade the freshness and reliability of the updated information. Without a reliable estimation on battery status, management and timely battery replacement operation would be difficult. To address this issue, this paper presents an efficient update method of battery status for BLE beacon network that minimizes the stress on the management platform server. The proposed approach leverages the correlation in battery status information between certain beacons to reduce the number of necessary updates while retaining high accuracy. Necessary reference data estimation, reference data reliability checking, and error correction on the estimation are the three major components in the solution. An estimation model allows accurate estimation in the cold-start stage. Moreover, an error-correction model allows to check the reliability of reference data and make a correction on the estimated value.
Boosting Image Steganalysis Under Universal Deep Learning Architecture Incorporating Ensemble Classification Strategy

Image steganalysis based on convolutional neural networks (CNNs) has achieved remarkable performance. However, all existing CNN-based steganalysis methods form an ensemble by merging the outputs of independently trained networks with the same architecture. In this letter, we propose a universal CNN architecture incorporating ensemble classification strategy. Any CNN-based steganalysis with the proposed architecture needs to train only one model to form an ensemble and can boost detection accuracy in both spatial and JPEG steganography. In particular, we propose a new method to construct subspaces for training well-designed base learners. In addition, a novel voting fusion structure automatically optimized with the training process is proposed. Experimental results on the public dataset demonstrate that the proposed architecture can further improve the performance of CNN-based steganalysis. Source code is available via GitHub (https://github.com/Ante-Su/CAECS).
Mobility Management Optimization via Inference of Roaming Behavior

This paper contributes to the topic of mobility management anticipation via the exploitation of preferred network ranking utility functions. The work proposes, implements, and evaluates a set of ranking functions based on Quality of Service and Quality of Experience indicators. Such indicators are obtained via wireless overhearing, or via active network probing. The functions also take into consideration recommendations from neighboring nodes. The implementation of the functions has been integrated into open-source software. The proposed implementation is evaluated via experiments carried out on a wireless testbed in terms of handover time to completion, total handovers successfully achieved, and rejected handovers. Results obtained show that overhearing is relevant to estimate mobility, as it brings in more sensitivity and lowers the cost of active probing.
Rotor Design to Improve Torque Capability in Synchronous Reluctance Motor

This paper designs a new rotor for synchronous reluctance motors (SynRMs) to improve the torque performance. A U-type two-layer flux-barrier rotor is designed by parametric modeling method. A single-parameter variable method is firstly used to optimize from the simple one-layer flux barrier to complex two-layer flux barrier. It is verified that the optimized parameters of the first layer can be utilized to design the second layer. To validate the proposed design, an optimized two-layer rotor is compared with the original two-layer flux barrier structure based on a 2-D finite element analysis. The simulation results show that the efficiency of the optimized model can meet the operational requirements.
Coupling Method for Analyzing Temperature Rise in Disc-type Oil-Immersed Transformer Winding

Considering the materials properties for winding and transformer oil in disc-type oil-immersed transformer are all changed with the temperature, typically the disc-type winding operates in strong coupling of electromagnetic field, temperature field and fluid field. This paper proposed a coupling simulation method in which single-turn winding is used as data mapping element for iterative computation, realizing the strong coupling among electromagnetic field, temperature field and fluid field. In addition, the means of convection and viscous flow types of transformer oil, the influence of turn-to-turn insulation in heat transfer process and the mesh of calculation are all identified based on analytical theories and numerical method. Finally, the results of the coupling calculation are compared and analyzed with those of the other two different extent coupling simulation methods.
Reduction of Cogging Torque by Notching Groove on Magnets in SMPMSM

Surface mount permanent magnet synchronous motor (SMPMSM) is widely used in industrial fields. In order to improve its working accuracy, the cogging torque needs to be reduced. This paper studies the effect of notching groove on magnets on cogging torque in SMPMSM. The general formula of cogging torque under arbitrary groove on permanent magnets is derived by the energy method. Based on the equivalent structure of rectangular grooves and Fourier expansion, the formulae of the torque under single rectangular groove and double rectangular groove are derived. To get an optimal solution, the cogging torque of SMPMSM under notching single and double groove with different sizes is analyzed by using Finite Element Method. The research shows that the cogging torque of SMPMSM can be effectively reduced by notching auxiliary groove on permanent magnet.
Urban Blue-Green Factor Estimation In Fredrikstad, Norway From Hyperspectral And Lidar Remote Sensing Data Fusion - A Concept Study

Continuous land development and urbanization highlights the importance of improved surface water management to avoid flooding and delayed drainage. Thus, several cities around the world have used variations of the blue-green factor (BGF), a policy instrument to attain desired levels of vegetation and water surfaces in new property developments. However, estimating BGF for existing infrastructure is equally important in order to ensure proper water management of the entire city. The presented work shows a possible workflow to estimate BGF semi-automatically from remote sensing hyperspectral and lidar data in Norway. A set of urban features and areas were detected and analyzed for individual properties affecting the BGF, along with corresponding estimation accuracies. The results demonstrate the potential to extract information and calculate BGF automatically. Lastly, it is pointed out that the models used for BGF estimation need to be further investigated and developed to enhance the estimation accuracy.
Neural Networks for Pathological Gait Classification Using Wearable Motion Sensors

Gait, as an essential feature reflecting human health status, has attracted extensive attention in research. Automatic pathological gait identification can contribute to diseases diagnosis and intervention. In this paper, an unobtrusive sensing technology with deep learning methods to discriminate healthy and pathological gaits is proposed. Two accelerometers are mounted on the left and right lower limbs to acquire the motion signals. Based on these signals, three Neural Networks, namely, BPNN (Back Propagation Neural Network), LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks) are proposed for classifying the gaits. Experimental results exhibit that the accuracy of the proposed method can reach 86%, 81%, and 93% on a database of 15 participants while using BPNN, LSTM, CNN, respectively. With the strong ability of spatial-temporal signal analysis, CNN outperforms the other two neural networks and provides a favorable result. The proposed method can be extended to an automated gait classification tool, which can be used in the diagnosis and identification of pathological gaits.
Analysis of Output Distortion in Asymmetric Sinusoidal Inverter

In this digest, asymmetric sinusoidal inverter (ASI) output distortion is analyzed. Based on the spectral analysis of the asymmetric sinusoidal waveform, the influence of the output filter and digital control of the asymmetric sinusoidal inverter is analyzed. Through the method of double Fourier transform, the accurate spectral component expression of dead time waveform is obtained, and the influence of dead time waveform on output distortion is analyzed. The results provide a theoretical basis for suppressing the output voltage waveform distortion of this kind of power supply. Finally, the validity of the analysis is verified by simulation and experiment.
Multi-time Scale Energy-Management Strategy Based on Rule Reasoning for Stand-alone Microgrid

This paper is focused on energy management studies in the context of remote stand-alone microgrid. Big challenges in power quality, reliability, efficiency and control could be brought in the system, when the penetration of renewable energy is considerable high. A multi-time scale energy-management strategy based on rule reasoning is proposed to ensure a reliable power supply at the minimum cost of the system operation. Based on different time scales, the proposed energy-management strategy consists of economic scheduling and real-time scheduling. The economic scheduling is to achieve economy optimal status, while the real-time scheduling maintains the stability of the system. Experimental results are presented to demonstrate the performance of the proposed strategy in both stability and economy of the system.
A LSTM Based Wind Power Forecasting Method Considering Wind Frequency Components and the Wind Turbine States

The existing ultra-short-term wind power prediction methods do not fully consider the influence of wind speed frequency components and wind turbine states. This leads the prediction accuracy is difficult to improve. An ultrashort-term wind power prediction method is proposed. This method is based on long short-term memory (LSTM) network with wind speed components and wind turbines states. Based on the filtering characteristics of LSTM, the wind speed components with different resolutions are screened automatically. These components were generated by wavelet packet decomposition (WPD) and ensemble empirical mode decomposition (EEMD). The forecasting results show that wind speed components and wind turbines states be used as the network input. The prediction accuracy can be improved. The error index will be smaller when wind turbine states was considered. Considering yaw error and rotor speed of wind turbine states, the mean absolute percentage error at the 5 s time interval is 2.72%.
Fault Characteristic Analysis of a Permanent Magnet Synchronous Generator-based Wind Farm

There is a need for a better understanding of the factors affecting the behavior of a wind farm (WF) under fault conditions, so it is essential to analyze the fault characteristic of a WF. This paper firstly describes a detailed modeling of a permanent magnet synchronous generator (PMSG)-based WF, and analyzes the symmetrical fault characteristic of the WF considering different wind speeds, fault positions and reactive power control strategies of the grid side converter. The relay protection and the related grid code are also taken into account. The results show a symmetrical fault characteristic analysis of a PMSG-based WF and the conclusions can be used to develop new control and/or protection methods for a PMSG-based WF.