Miracle-dz/UniFAR-FTUs huggingface dataset:
A Realisation of Synchronous Buck Power Converter for Energy Harvesting from Vibrations

The paper presents the implementation of a synchronous “buck” power converter that is used to energy harvesting from vibrating structures that oscillate in the 1Hz-1kHz range. This range is the most common in real cases. In addition to the frequency, a very important parameter is also the amplitude of the vibrations. Usually, low frequencies (< 10Hz) are associated with larger oscillatory masses and larger oscillation amplitudes, while higher frequencies (1kHz >f > 100Hz) refer to larger oscillatory masses and smaller oscillation amplitudes. The realized power converter is based on the integrated circuit LT3588-1, in which it integrates a low-loss full-wave bridge rectifier with a high efficiency synchronous “buck” converter to form a complete energy harvesting solution optimized for high output impedance of mechanic/electric converter such as piezoelectric lead zirconate titanate (PZT) ceramics or polymer based on polyvinylidene fluoride (PVDF), or other composite. The paper presents experimental results obtained on the vibration platform as vibration source, in order to testing of the proposed power converter in laboratory conditions.
Comparative Analysis of E-Core Stator Dual-Permanent Magnet Flux-Switching Machine with Different PM Arrangements

The E-core flux-switching permanent magnet (FSPM) machine has gained significant attention recently because of its inherent flux-focusing stator PM topology, robust rotor structure, and improved fault-tolerance capability. However, the torque is restricted as the utilization ratio of the stator iron core is low. Therefore, this analysis proposes two stator dual-PM FS(SDPM-FS) machines to address this issue. Extra PMs are introduced on the stator teeth to enhance the torque production ability. PMs with radial magnetization direction are placed on either the main or fault-tolerance stator teeth. The generation mechanism of the back electromotive force (EMF) is analyzed using the flux modulation effect, which reveals the improvement of working harmonics compared to an E-core FSPM machine. Furthermore, this paper provides a comparative study between the SDPM-FS machines and the conventional FSPM machines. The results demonstrate that the SDPM machines achieve notable enhancements in output torque and efficiency.
Torque Enhancement of an Axial Flux Permanent Magnet Synchronous Machine with Asymmetrical Rotor Configuration

An asymmetric axial flux permanent magnet synchronous machine (AAF-PMSM) with shunted magnetic bridges (SMBs) between adjacent rotor permanent magnets is proposed in this paper. The proposed asymmetric rotor structure can effectively increase the maximum output torque without increasing the magnet usage, which is achieved by the magnetic-field-shifting (MFS) effect and the increase of reluctance torque. The comparison between the AAF-PMSM and the conventional axial flux permanent magnet synchronous machine (AF-PMSM) verifies that the proposed design can obtain a torque enhancement as large as 10%.
Comparing Incremental Learning Methods for Human Activity Recognition

In this paper, we present a comparative analysis of incremental learning methods for neural networks in the context of human physical activity recognition. The study delves into the concept of incremental learning and its fundamental strategies. We provide a brief overview of these strategies to better understand their applicability in real-world scenarios. The primary focus of this investigation is the examination of three incremental learning methods: iCaRL, LwF, and FT. These methods are evaluated for their ability to adapt to new data while preserving knowledge of previously encountered classes in the context of human activity recognition. Through experiments conducted on three distinct human activity recognition datasets, we assess the performance of these methods. The evaluation is based on the Micro and Macro F1-Scores, which offer comprehensive insights into the models' adaptation capabilities and their handling of class imbalances. Our findings reveal significant variations in the performance of these methods. These results underscore the importance of method selection in incremental learning and emphasize the need for tailored approaches in different contexts.
Assessment of Upper-Body Movement Quality in the Cartesian-Space is Feasible in the Harmony Exoskeleton

To determine the most effective interventions for poststroke patients, it is imperative to monitor the recovery process. Robotic exoskeletons' built-in sensing capabilities enable accurate kinematic measurement with no additional setup time. Although position sensors used in exoskeletons are accurate, a mismatch between the robot's and the human's joints can lead to inaccurate measurements. In addition, the robot's residual dynamics can interfere with human's natural movements and the kinematic metrics assessed in the robot would not be representative of the human's movement in free-motion. So far, the accuracy of robotic exoskeletons in assessing upper-body kinematics has not been verified. The bilateral upper-body Harmony exoskeleton has features favorable to minimize joint misalignments and the robot's residual dynamics. In this study, we examined Harmony's ability to accurately assess Cartesian-space kinematic parameters associated with the wearer's movement quality. We analyzed data collected from eight healthy participants that executed point-to-point movements with and without the presence of the robot and at fast and slow speeds. Ground truth was acquired with an optical motion capture, and we extracted the kinematic parameters from the measured data. The results suggest that Harmony can accurately measure kinematic parameters associated with movement quality, and these parameters could appropriately reflect wearer's natural movements at a slow speed. Therefore, Harmony could aid the evaluation of the effectiveness of different interventions, which is more sensitive and efficient than currently adopted clinical outcomes. This allows for individualization of a treatment plan and a detailed follow-up.
An Innovative Method Based on Dilated CNN for Enhancing Image Classification Accuracy

In the domain of image classification, traditional convolution models are limited in their ability to accurately capture features across different scales within an image. Likewise, dilated convolution models suffer from the issue of generating uniform responses for features across all scales, leading to suboptimal performance in certain image classification tasks. To address these challenges, this paper presents a novel hybrid convolution model, referred to as i-HDC, which integrates traditional convolution models with dilated convolutions. The architecture of i-HDC is introduced and subsequently evaluated on the GTSRB data set. Comparative assessments are conducted against both traditional convolution models and dilated convolution models, while maintaining consistent experimental conditions. The results demonstrate that i-HDC exhibits enhanced convergence speed and superior generalization capabilities compared to its counterparts. Notably, it achieves a remarkable classification accuracy of 99.86% with a reduced training period, thereby significantly elevating the accuracy of classification tasks.
A Novel Honeynet Framework Based on Container: Design and Implementation

Honeynet organizes honeypots and collects attack information. With the development of virtualization and container, honeynet technology has made some progress. However, the scale and depth of the existing honeynet is limited, making it hard to capture complicated and deep attack information. Existing honeynet frameworks also have low-level simulation of protected network. To address these issues, we design and implement a novel honeynet framework that consists of five modules. Preliminary experiments show that the honeynet framework can be implemented and performs better than existing research.
LuGre Model Based Linear Active Disturbance Rejection Controller for High Precision Airborne Optoelectronic Stabilization Platform

To improve the tracking accuracy of the airborne optoelectronic stabilization platform, this paper proposes a linear active disturbance rejection controller (LADRC) based on the LuGre friction model. Firstly, the mathematical model of the controlled system is established. Secondly, a LuGre friction model based LADRC is designed and its stability is analyzed. Lastly, the designed controller was validated through simulation and experiment. The experimental results show that the proposed controller can improve position tracking accuracy by 87.5%, by comparing with traditional PI.
Research on MW-Level Half/Full Direct-Drive Doubly-Fed Induction Generators for Fractional Frequency Transmission System

Doubly-fed induction generators (DFIG) are widely used in wind power generation systems due to their low cost and high power quality. However, traditional DFIGs are designed with high-speed rotors and require the use of multi-stage gearboxes to increase the speed, which decreases the reliability of the system as well as increases the cost. This paper studies the feasibility of half/full direct-drive DFIG to improve the performance of generators for the Fractional Frequency Transmission System (FFTS). By comparing with traditional DFIGs, the results show that half-direct-drive and direct-drive DFIGs are larger and heavier while having higher copper losses and lower efficiencies. In addition, direct-drive DFIGs have a higher apparent rotor power, which means that a larger capacity converter is required. As can be seen, although the multi-stage gearbox can be reduced or omitted when the speed of the DFIG is lowered, it also poses some new challenges.
High-speed Interior Permanent-magnet Rotor Made of Dual-phase Magnetic Materials

High-speed is a research hotspot to increase the power density thereby reducing the size and weight of the motor. The key problem limiting the speed is the high centrifugal force, which might lead to rotor cracking. In order to strengthen the rotor, the flux bridge area of an interior permanent-magnet rotor is usually enlarged, which leads to high flux leakage and reduced torque. In this paper, a dual-phase magnetic material consisting of non-magnetic and magnetic regions is used and a high-speed interior permanent magnet synchronous motor (IPMSM) is designed. The performance of two rotor structures made of the dual-phase magnetic material are compared. The effect of rotor design on the performance of the high-speed IPMSM is investigated.
A Novel Flux Switching Permanent Magnet Machine Based on Quantitative Air-Gap Construction Method

For ship propulsion, wind generation, and other applications, there are the urgent demands for the high torque density machine. Benefited from flux modulation effects and easy thermal management, FSPM machines can be a promising candidate. To further improve torque density, this paper proposes a novel FSPM machine with non-uniform auxiliary tooth by quantitative air-gap construction method. Endowed with the special stator structure, the additional stator permeance harmonics are introduced, which contribute the production of higher working flux density harmonic and torque density. Especially, under the same magnet usage and geometric dimension, both the no-load and on-load electromagnetic performances of the proposed FSPM are compared with the conventional FSPM by finite element analysis (FEA), including no-load magnetic field, phase back electromotive force (EMF), electromagnetic torque, loss and efficiency. It indicates that comparing to the conventional FSPM machine, the torque density of proposed FSPM machine can be enhanced from 19.2 Nm/L to 23.4 Nm/L at the electric loading of 160 A/cm, which verifies the superiorities of the proposed FSPM machine.
Investigation and Optimization of a Magnetic Geared Permanent-Magnet Machine for Torque Density Improvement

Magnetic geared permanent-magnet machine (MGPM) is considered ideal for high-torque direct drive applications. In this paper, a new topology of MGPM for torque density improvement is proposed and optimized. First, based on the analytical model of MGPM, the analytical expression of transmission torque is derived, and the dominant components are identified, which in turn are used to increase the transmission torque by maximizing the working flux density harmonics through topology innovation. Then, a multilevel objective-layered optimization method is introduced to reduce the complexity and increase the efficiency of the optimization process. Finally, simulation results are presented to demonstrate that the proposed MGPM can operate at high output torque density over 130 Nm/L.
Electromagnetic Vibrations of Interior Permanent Magnet Synchronous Motor With Different Pole and Slot Combinations

The vibration and noise problems of permanent magnet motor has attracted more and more attention because of their bad effects on human. This paper investigates the effect of pole and slot combination on the vibrations in an interior permanent magnet synchronous motor (IPMSM). First, the expression of electromagnetic force and unbalanced magnetic pull(UMP) under no load is derived. Then the derivation is verified by two-dimensional Fourier decomposition. Finally, a joint simulation is performed to demonstrate the vibration magnitude of the motor with four different pole-slot fits, and it is concluded that the lower the non-zero lowest spatial order, the higher the vibration.
Maximum Efficiency Per Ampere Control of Synchronous Reluctance Motor Sensorless Drives

The maximum efficiency per ampere (MEPA) control strategy stands out as a highly effective means of improving the efficiency of a synchronous reluctance motor (SynRM) drive. This paper introduces an improved approach to optimizing efficiency that treats the motor and inverter as a unified system, ensuring maximum efficiency throughout operation. Diverging from conventional $i_{d}=i_{q}$ or maximum torque per ampere control methods, this approach accounts for both iron losses and inverter losses, while also addressing cross-coupling effects. This is achieved through real-time virtual signal injection, which extracts optimal operation points, and an accurate analytical approach to assess iron losses. Experimental results convincingly validate the effectiveness of this novel methodology.
Modular Permanent Magnet Vernier Machine Design and Optimization for Forging Servo Application

A comprehensive topology performance diagram of the permanent magnet vernier machine is constructed in this paper. The changes in the air gap harmonic magnetic field and the contribution of each harmonic to the average torque of the permanent magnet vernier machine (PWVM) with integer slot distribution winding and fractional slot concentrated winding are studied. Additionally, in the key electromagnetic performance indicators, such as copper loss, iron loss, efficiency, and power factor of different machines are analyzed and summarized. After comprehensive consideration, the optimal design scheme is identified, and the geometric parameters of the machine are optimized using a global optimization method. Subsequently, a prototype is manufactured employing a modular stator structure, and bench experiments are conducted. The experimental results demonstrate a close agreement with the finite element analysis results, providing crucial technical reference for the design of forging servo machines. It is noteworthy that the machine topology has been accepted by Estun Ltd for potential future industry applications.
Scalp Acupuncture and Mental Disorders

While conventional treatments for many mental problems remain problematic and unsatisfactory in therapeutic outcomes, there is great demand for an effective yet economical treatment method that can alleviate the suffering of psychiatric patients. In traditional Chinese medicine, acupuncture on the scalp has been used for centuries. Not until recent decades did acupuncturists and clinicians develop new understandings and theories on the effect of scalp acupuncture. Upon elabora-tion on the therapeutic value of scalp acupuncture, it shows great potential in treating mental health disorders including depression, anxiety disorders, schizophrenia, and insomnia. Its profound treatment outcome in clinical use has caught clinicians’ attention in recent years. However, controlled studies and investigations on its effect on psychiatric problems remain relatively small in number, and determina-tive evidence has yet to be found. In order to provide conclusive evidence on the use of scalp acupuncture to these disorders, more data from high-quality controlled trials are urgently needed. Since scalp acupuncture has advantages over the use of traditional acupuncture or body acupuncture in clinical and investigation settings, we are expecting a shift of attention from individualized acupuncture to a standard-ized universal scalp acupuncture treatment in clinical practice and academia.
A Survey on Security Attacks on Blockchain Using Deep Learning

Securing decentralized networks becomes crucial as blockchain technology grows more popular. This study suggests a system for intrusion detection that combines blockchain features with deep learning. Blockchain's tamper-resistance and deep learning's aptitude for pattern identification offer better threat detection and adaptive defenses. Empirical tests demonstrate this strategy's potential to strengthen developing blockchain security, which would be a huge advancement in cybersecurity.
Self-healing Scheme of Dual-Redundancy Permanent Magnet Synchronous Motor Based on Voltage Disturbance Observer

Dual-redundancy permanent magnet synchronous motor (DR-PMSM) has been widely used in the aviation electrical actuation system, which shows excellent reliability for its redundancy structure. When one set of winding or inverter fails, the other set can work normally. However, traditional fault tolerant method needs to locate the faulty winding firstly, and then remove the fault winding, which leads to torque and speed disturbance during the process. Besides, there is strong coupling between the two sets of windings to realize fault location. The paper proposed the self-healing scheme of DR-PMSM, which solved the coupling influence on fault diagnosis and eliminated the diagnosis delay for fault tolerant control of DR-PMSM. The proposed scheme largely reduced the torque and speed drop during fault tolerant control.
Sensorless Control of Synchronous Reluctance Machines Based on Improved Kalman Filter-Phase Locked Loop

An improved Kalman filter-phase locked loop (KF-PLL) is proposed to be implemented in sensorless control of a synchronous reluctance machine (SynRM) in the full speed range. By applying the fixed gain vector in the updating the gain matrix of the KF method, the computation of the proposed KF-PLL is greatly reduced which only require two equations with multiplications and additions. The better dynamic performance and estimated accuracy can be achieved with the KF-PLL that is proved by the experimental results.
Comparison of Multi-Modal Federated Learning Framework and SPSS in the Evaluation of Lymph Node Metastasis Probability in Gynecological Malignancies

This study aimed to evaluate the diagnostic efficacy of CT, MRI, and PET/CT in detecting lymph node metastasis (LNM) in patients with gynecologic malignancies. A total of 567 patients diagnosed with cervical carcinoma (CC) and endometrial carcinoma (EC) were included in this study. The results of this study indicated that PET/CT demonstrated higher levels of sensitivity but lower levels of specificity compared to CT and MRI. Additionally, a deep learning-based multimodal federated learning model was implemented, which integrated non-image and MRI data, to improve diagnostic accuracy in the detection of LNM. The model exhibited heightened sensitivity and specificity in comparison with individual imaging modalities. Overall, the findings of this study underscore the potential of employing multimodal federated learning models to accurately detect LNM in patients with gynecological malignancies, which in turn emphasizes the necessity for further research and validation.
Improving Logistics of a Paraguayan Company Using an Ant Colony Algorithm

This paper describes a solution to the Vehicle Routing Problem (VRP) of a Paraguayan company, in a multi-objective context. The problem is formalized as the simultaneous minimization of three objective functions: (1) the total travel distance, (2) the total travel time, as well as (3) the number of routes used, while satisfying specific company constraints, such as: (a) serve the complete demand of each customer, (b) every route starts and ends on the same depot, which is one of the several depots of the company, and (c) workload limits for all fleet vehicles. To solve the problem, a specific algorithm based on MOACO (Multiobjective Ant Colony Optimization) was designed and implemented to obtain a set of Pareto solutions. The results were validated by comparing these Pareto solutions with the company's current planning, based on the experience of experts who plan the company's logistics. Experimental results confirm that in most of the studied cases, the proposed method manages to find solutions that dominate the one adopted by the company, i.e. they are better even considering all objective functions simultaneously, as shown in 2 case studies analyzed in this paper, one of which allows cost savings exceeding 40%.
A 200mV Operable On-Chip Temperature Sensor for IoT Devices Powered by Energy Harvesters with Ultra-Low Output Voltage

This paper describes a 24.8 μm2 on-chip CMOS digital temperature sensor core operable on 95 nW at the 200mV supply voltage. The target of this sensor is IoT devices powered by energy harvesters with ultra-low output voltage such as thermoelectric generators. Unlike the conventional ring-oscillator type temperature sensor, the proposed sensor directly monitors the time to store the electrical charge into the capacitance by temperature-dependent leakage current. The result of this “temperature-to-time” conversion is given to a counter to obtain a digital output. We designed and fabricated the proposed temperature sensor circuit in a 65 nm FDSOI technology. Measured results demonstrated that the proposed temperature sensor successfully operated at 0.2 V - 1.2 V for the temperature range from -20 °C to 80 °C. Excellent linearity in the relationship between the logarithm value of the counter output and the reciprocal of absolute temperature was demonstrated in an analytical model and confirmed in the measured results. This enables us to identify the temperature from the counter output by a 2-point calibration with good accuracy. Since all MOS transistors for the temperature-to-time conversion operate in the subthreshold region (i.e. at the off-state) in 0.2 V, the accuracy is significantly affected by the process variation. In the proposed temperature sensor, by tuning the body-bias voltage to pMOS $(V_{BS,p})$ and the reference voltage (VREF), the maximum error in the temperature across four fabricated sensors on a chip was reduced to 0.24X. By tuning $V_{BS,p}$ and VREF to the conversion temperature range [−20 °C, 80°C], the error was improved to +5.7 °C/-5.1 °C, while further improved to +1.2 °C/-2.4 °C by tuning them to the temperature range [0 °C, 80°C].