Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Structural Tolerance Influences on Thrust Ripple of U-Shaped Ironless Permanent Magnet Linear Motor

U-shaped ironless permanent magnet linear motors (IL-PMLMs) are widely implemented in precision manufacturing equipment with the advantages of small thrust and high positioning accuracy. However, the structural parameter variations of the motor caused by manufacturing and assembly errors will increase the thrust ripple of the motor and thus cause vibration and reduce the operating accuracy. This paper uses Monte Carlo simulation to analyze the manufacturing tolerances of the motor's primary and secondary structural parameters, including the spacing and position of the winding, the height and width of the permanent magnets, and the thickness of the back iron. Finally, various combinations of parameter tolerances are used for analysis and validation. Assuming all tolerances obey normal distribution, the influence of motor structural parameter tolerances on thrust and thrust ripple is obtained through finite element analysis.
Effects of Plant Diversity on Soil Microbes

Ecosystem processes are strongly affected by biodiversity. Changes in ecosystem processes may themselves lead to a de-cline in biodiversity and thus to further reductions in ecosystem function. Plant species richness and plant functional diversity have a positive influence on overall catabolic activity and catabolic diversity of soil bacterial community. Catabolic activity and catabolic diversity of soil bacteria increased linearly with the logarithm of plant species number and with the number of plant functional groups. These effects may have been caused by an increased diversity and quantity of material and energy flows to the soil. They may also have been mediated by increased diversity of soil microhabitats via a stimulation of the soil fauna. A reduction in plant biomass caused by a loss in plant diversity is expected to have strong effects on the decomposer community: microbial biomass is likely to decrease, because organic carbon sources limit soil microbial activity in most terrestrial ecosystems.
Adaptive Inertia and Frequency Damping Control of Photovoltaic Systems Based on Sparrow Search Algorithm

The increasing penetration of renewable energy leads to a decrease in inertia and frequency damping of power grid, which challenges the stability and security of grid frequency. In this context, frequency control needs to be developed for grid-connected photovoltaic (PV) systems. Among various methods, a simple and application-friendly approach stands out, which modifies the frequency response characteristics of PV systems to mimic the inertia and frequency damping responses of synchronous generator's rotor motion. Aiming to improve the efficiency of such a frequency control, this paper proposes an adaptive inertia and frequency damping control (A-IFDC) of PV systems based on sparrow search algorithm (SSA). Firstly, the power reserve of PV system is realized using the maximum power point estimation based power reserve control (MPPE-PRC) method. Then, the A-IFDC of PV systems is developed, where the SSA is employed to adaptively adjust the real-time virtual inertia and frequency damping control parameters with the objective of reducing frequency deviation and rate of change of frequency (RoCoF). Simulation is conducted in MATLAB/Simulink, of which the results show that the proposed control method improves the PV resource utilization and effectively supports the grid frequency, contributing to the stability and security of power grid.
Learning-Based Distributed Approach to Energy-Optimized Speed Trajectory for Electric Vehicles at Multiple Signalized Intersections

Range extension via speed trajectory optimization is an important issue for battery electric vehicles (EVs), To this end, this paper contributes a practical approach to globally minimize the energy consumption of EVs passing through multiple signalized intersections. The energy consumption is estimated using a traffic flow Gaussian mixture model (GMM) obtained from floating car data. Then, the optimal speed trajectory is generated using dynamic programming (DP). The algorithm is solved distributedly for each segment between two adjacent intersections, thereby, alleviating the computational burden. Furthermore, the proposal does not require vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Experimental results demonstrate that the proposed algorithm can successfully reduce energy consumption. The reductions are respectively 11.7% and 8.0% compared to the simple strategy with the constant speed and acceleration/deceleration, and the conventional method which independently minimizes the energy consumption in each road segment between two adjacent intersections.
A Novel Hybrid Feature Importance and Feature Interaction Detection Framework for Predictive Optimization in Industry 4.0 Applications

Advanced machine learning algorithms are increasingly utilized to provide data-based prediction and decision-making support in Industry 4.0. However, the prediction accuracy achieved by the existing models is insufficient to warrant practical implementation in real-world applications. This is because not all features present in real-world datasets possess a direct relevance to the predictive analysis being conducted. Consequently, the careful incorporation of select features has the potential to yield a substantial positive impact on the outcome. To address the research gap, this paper proposes a novel hybrid framework that combines the feature importance detector - local interpretable model-agnostic explanations (LIME) and the feature interaction detector - neural interaction detection (NID), to improve prediction accuracy. By applying the proposed framework, unnecessary features can be eliminated, and interactions are encoded to generate a more conducive dataset for predictive purposes. Subsequently, the proposed model is deployed to refine the prediction of electricity consumption in foundry processing. The experimental outcomes reveal an augmentation of up to 9.56% in the R2 score, and a diminution of up to 24.05% in the root mean square error.
Single Stage Boosting Inverter with Multi-Winding Equalizer for Photovoltaic Applications

Mismatch faults of photovoltaic (PV) modules causing by partial shading in series PV system will lead to a serious drop in power generation. Unless the full power processing converters handling all the generated power, the differential power processing converters only deals with the mismatched power between PV modules, thus, differential power processing (DPP) converters usually have low power loss, high efficiency and relatively low cost. Nevertheless, a DPP converter is usually an additional device to the existing PV system and will introduce extra power losses and expense. In this paper, a single stage boosting inverter integrated with a multi-winding type DPP converter is proposed. The functions of voltage step-up, inversion and power equalization are achieved in one power stage. The operating principle and control method of the proposed topology is described in detail. The feasibility of the topology is verified by simulation with a 17.13% and 66.99% power increment under two different partial shading conditions and good quality sinusoidal output.
A Novel Dual-Active-Bridge Converter for Electric Vehicle Charging Station

This paper presents a comprehensive analysis from a theoretical perspective, showcasing the development of a Multiphysics simulation that incorporates both electrical and magnetic components to investigate the energy conversion of a bidirectional DAB converter. Various phase shift modulation control methods are proposed and thoroughly validated through simulations. Notably, the extended phase shift modulation scheme successfully minimizes the inductor peak current and reactive power of the DAB converter. Beyond electrical circuit simulations, the study delves into the design and simulation of an 800V high-frequency transformer using COMSOL for the DAB circuit. In-depth testing, including open circuit and short circuit tests, is conducted to derive the equivalent circuit. Switching and conduction losses are also estimated based on datasheet specifications. Ultimately, the research culminates in the simulation and comparative analysis of converter efficiency across different modulation strategies. The findings shed light on the most effective approaches for optimizing the DAB converter's performance and energy conversion capabilities.
Dynamic Event-Triggered Formation Control for Unmanned Aerial Vehicles

This paper presents a dynamic event-triggered communication mechanism to mitigate limited communication in multi-UAV systems. This mechanism aims to reduce resource consumption within these systems by enabling UAVs to communicate only when specific triggering conditions are met. Unlike static event-triggered communication mechanisms, our approach incorporates a dynamically adjusted triggering threshold through a carefully designed dynamic rule. As a result, the system performance is enhanced while communication resource utilization in multi-UAV systems is reduced. Building upon this dynamic event-triggered communication mechanism, we propose a distributed formation control strategy. Furthermore, we outline a criteria for designing the relevant control parameters. To validate the proposed approach, numerical simulation is conducted.
Process-Oriented Design Paradigm for Automatic Code Generation in Manufacturing

Industry 4.0 brings new features to the manufacturing industry, including informatization, intelligence, and higher integration. Complex interrelationships among components within the industrial Cyber-Physical System (iCPS) further increase the automation system design and development difficulty. Therefore, integrated design models for contemporary industrial systems should ensure flexibility and interoperability to accommodate highly integrated systems and rapidly changing requirements. This paper proposes a generic process modeling method based on Process-Oriented Models (POM) for automatic code generation. Process-Oriented Models can be regarded as a semantic set of operations encapsulating the process and corresponding attributes in the manufacturing process. Each operation is executable. The execution results can be used for model optimization to achieve the optimization process. These models with parameters can be further converted into modular code automatically according to pre-defined mapping rules. A process manufacturing case study proves the proposed method can achieve complete and correct process modeling of automation systems. Industrial software development based on Process-Oriented Models can significantly increase the efficiency and accuracy of software development of industrial Cyber-Physical Systems.
Study on Robustness to Optical Diffusion on Undersea Optical CDMA System Using 2D Optical ZCZ Sequences

In this paper, we propose an undersea optical code-division multiple access (OCDMA) system that suppresses interference between neighboring light-emitting elements caused by beam spreading using a two-dimensional (2D) optical zero-correlation zone (ZCZ) sequence. In general, the OCDMA system allocated a 1D spreading sequence for one user requires transmission time corresponding to a sequence length of N i.e. N chip time to transmit one bit of information. On the other hand, the OCDMA system using a 2D optical ZCZ sequence instead of a 1D sequence requires transmission time corresponding to 1 chip time, therefore the system can increase the transmission speed by N times. In addition, because the 2D optical ZCZ sequence has ideal correlation properties around shift 0, where the autocorrelation output is impulse, and the cross-correlation output is zero, the system can be suppressed interference between the neighbors due to beam spreading which is the main cause of bit errors. As a result, we clarify that the interference can be suppressed by increasing the ZCZ size relative to the beam spread in various undersea areas by computer simulation.
Deep Reinforcement Learning Based Energy Management Strategy in Fuel Cell Range Extender Buses

To extend the range of battery electric buses, a fuel cell system is used as an additional onboard energy source in fuel cell range extender (FC-REX) buses. FC-REX vehicles are mainly powered by a battery, while the fuel cell serves as an auxiliary power supply. An energy management system (EMS) controls the power supply of both energy sources. The EMS distributes the load of the battery and the fuel cell efficiently, considering resources and operating states. The control problem can be formalized as a multidimensional optimization problem, where an optimal control path should be approximated online without previous knowledge of the route. For this purpose, a reinforcement learning (RL) EMS based on the twin delayed deep deterministic policy gradient (TD3) algorithm is applied to learn optimal power distribution. The RL agent is trained and deployed in a simulation environment. The results of the RL control approach are compared to a rule-based approach, where the fuel cell operates at a constant load. The dynamic adjustment of the fuel cell power supply takes advantage of the high efficiency characteristics of the battery and allows to reduce battery degradation caused by deep discharge.
Changes in inner retina thickness and macular sensitivity in diabetic 2 patients with moderate diabetic retinopathy

To analyze total retinal(RT) and inner retinal layers(IRL) thicknesses in type 2 diabetes mellitus (DM2) patients and retinal sensitivity, using swept source OCT(SS-OCT), and microperimetry. A total of 54 DM2 subjects with moderate diabetic retinopathy(DR) with no signs of diabetic macular edema(DME) and 73 age-matched healthy individuals were assessed by SS-OCT to quantify retinal thickness in the nine macular areas of the ETDRS grid. Retinal sensitivity was measured by microperimetry with MAIA. Mean ages were 64.06 ± 11.98 for the DM2 group and 60.79 ± 8.62 years for the control group. DM2 patients presented lower visual acuity (p < 0.001) and thicker RT (260.70 ± 19.22µm in the control group vs. 271.90 ± 37.61 µm in the DM2 group, p = 0.01). Retinal nerve �ber layer (RNFL) was signi�cantly lower in the outer nasal area (50.38 ± 8.20µm vs 45.17 ± 11.25µm, p = 0.005) as ganglion cells and inner plexiform layers(GCL+) in DM2. A positive correlation between LDL-C and RNFL and a negative correlation between HDL-C levels and inner temporal and central RNFL thickness were detected. The central and inner nasal areas presented a negative correlation between RNFL (p = 0.015) and MAIA (p = 0.008), while the outer inferior area showed a positive correlation(p = 0.025). Retinal sensitivity and macular RNFL thickness decrease in DM2
Comparison of Video Photoplethysmography, Video Motion Analysis, and Passive Infrared Thermography against Traditional Contact Methods for Acquiring Vital Signs in Emergency Department Populations

Emergency Department (ED) patients’ vital signs (VS) were obtained concurrently with contact cardiorespiratory monitors (CM) and experimental methods for comparison: HR was measured with CM and video photoplethysmography (vPPG); RR with CM and video motion analysis (vMA); core and surface temperatures with rectal or temporal artery (TA) thermometry and passive infrared thermography (pIR) camera, respectively. Continuous 25-minute research video recordings were made with each subject alternating between focusing on the vPPG-vMA camera for 1 minute then resting for 5 minutes. 237 of 375 study-eligible ED patients were enrolled, 4 subjects withdrew and 13 subjects were withdrawn due to technical problems. Video data were collected from 220 subjects of whom 42.6% were female; mean ESI was 3±0.5 (range 2–4) and mean Fitzpatrick score was 2.0±1.4 (1–6). Of 204 subjects with usable video data (92.7% of collected sample), 198 had all video windows and 164 had full study data available; video data were able to be processed for 787 (80.0%) of 990 total video windows. CM datastreams successfully captured 701 (89.1%) HRCM and 554 (70.4%) RRCM for the 787 video samples; vPPG and vMA algorithms were able to extract 661 (84.0%) HRvPPG and 569 (72.3%) RRvMA measurements. Median HRCM was 75.6bpm (IQR1-3: 66.5–87.0bpm), median HRvPPG was 74.5bpm (66.7–84.4bpm), R = 0.86; median HR difference was 0.1bpm (-0.2–0.5bpm). Median RRCM was 18.5bpm (IQR1-3: 15.4–22.5bpm), median RRvMA was 18.5bpm (15.7–22.0bpm), R = 0.92; median RR difference was 0.0bpm (-0.5–0.4bpm). HRvPPG and RRvMA measurements would have resulted in ESI VS groupings matching those based on CM measurements for 591 (97.8%) of 604 patients and 444 (96.1%) of 462 patients with ESI data available, respectively. Median TCM was 97.8°F (IQR1-3: 97.3–98.3°F), median TpIR was 95.3°F (93.7–96.4°F), R = 0.47; median temperature difference was -2.6°F (-3.8 – -1.4°F). vPPG, MA, and pIR thermography successfully measured select vital signs concurrently with promising results in a live ED patient cohort study.
Response of RADFETs to Photon Ionizing Radiation and Subsequent Recovery at Room and Elevated Temperature

The paper presents the experimental results of the response of RADFETs with an oxide thickness of 1 μm during irradiation and subsequent recovery at room and elevate temperatures. First, the results of the influence of energy of photons ionizing radiation on the threshold voltage shift for the absorbed radiation dose interval from 1 to 5 Gy are shown. It is shown that with a decrease in the energy of ionizing radiation, the value of threshold voltage shift increases and that this increase is a consequence of the increase in traps where positive charge in the oxide captured. Recovery of previously irradiated RADFETs by gamma radiation up to 5 Gy at room and elevated temperatures was also investigated. It was shown that there is no loss of dosimetric information during 20 h recovery at room temperature. Recovery at a temperature 150 °C for 500 h leads to an almost complete loss of dosimetric information, which manifests itself as the value of the threshold voltage shift being approximately equal zero. It was also shown that pre-irradiation of RADFETs up to an absorbed radiation dose of 10 mGy and recovery at room temperature leads to a significant increase in the sensitivity of these components to ionizing radiation.
Design of an Animal Types and Movement Based Pest Animal Repellent System

In Japan, it is a need for countermeasures against animal damage caused by pests animals, such as deer and boars. Therefore, there are pest animals repellent by stimulation of sound containing ultrasonic frequency ranges. However, the problem is that if pest animals are listening to repellent sounds continuously, the effect is lower than before. On the other hand, it is possible to prevent pest animals from becoming resistant to repellent sounds by listening to sound stimuli only to the pest movement. In this paper, we develop a pest animal avoidance system that adapts to the pest animal situation and generates vermin avoidance sounds by classifying vermin based on object recognition and estimating their eating behavior based on posture estimation.
SAPD: Secure Authentication Protocol Development for Smart Healthcare Management Using IoT

The most essential part of healthcare procedures is dealing with patients. The deployment of "Healthcare Internet of Things (IoT)" could improve the standard of care offered by healthcare personnel. The emergence of IoT technologies makes it more feasible for healthcare to move from in-person consultation to eHealth. However, the structure, security, and privacy for linked things should be considered in the IoT. It is essential to employ a systematic approach for security and privacy safeguards in all IoT devices and healthcare administration in the context of healthcare and remote health monitoring. This paper proposes a secure authentication protocol to protect the healthcare management from the security threat or attackers. The paper's Secure Authentication Protocol Development (SAPD) approach offers a foundation for securing IoT-based smart healthcare management. The protocol improves the security and privacy of healthcare systems by utilizing strong authentication processes, and encrypted communication processes, enabling dependable and trustworthy access to confidential patient information. To protect healthcare management from security threats and attackers, this study suggests a secure authentication protocol. We also investigate the numerous security risks associated with smart healthcare management.
Knowledge Distillation and Multi-task Feature Learning for Partial Discharge Recognition

To achieve accurate detection and recognition of partial discharge (PD) in switchgear, developing an intelligent PD diagnosis system has garnered significant attention in recent years. Due to inevitable noise interference and high similarity of different PD signals, detecting and identifying PDs using a portable PD detector poses significant challenges. In this study, we aim to transfer the knowledge acquired by the large-scale network to a lightweight network for precise PD recognition. To achieve this, we employ a k-means clustering model to effectively separate signals originating from different sources, thereby obtaining Phase Resolved Partial Discharge (PRPD) patterns. Then, we introduce knowledge distillation and a multi-task feature learning framework to extract discriminative features from PRPD patterns. We conduct experiments and compare the proposed method against some state-of-the-art methods on our constructed PD recognition dataset to evaluate the superiority of the proposed method.
NFC Implementation Methods for ESP32 Based IoT Systems

This paper presents research on methods for implementation of NFC technology into ESP32 based IoT systems. Two main methods are proposed, first with the commercial modules that incorporate built-in antenna and module that is universal transceiver and decoder, and second method, where all of the integral parts of the system are designed separately. Methods are analyzed and compared in terms of feasibility, speed, resources and cost, using specially developed tests. Tests consist of multiple writing and reading tasks from the NFC tag, ESP32 and the smartphone. Tests also examine the compatibility with the ESP32 for IoT applications and with NFC enabled smartphones. Results point out that first implementation method offers higher performance rate in terms of the most of criteria, while the second method gives broader versatility and wider compatibility range.
Current Injection-Based Self-Commissioning of Synchronous Reluctance Motor Considering Cross-Saturation Effect

The flux linkage characters of synchronous reluctance motors (SynRM) are usually tested offline due to a significant cross-saturation effect. In this paper, a self-commissioning method with current injection is proposed, in which the cross-saturation effect is considered. The reference equation for the injection current is derived in order to avoid potential rotor motion during the injection, and it ensures that sufficient valid data is distributed in the current plane after the injection. Considering the difficulty of PI tuning under unknown motor pa-rameters, a current controller based on general-integral extended state observer (GI-ESO) is applied to track the given current. Experiments are performed on a 3kW SynRM platform to verify the validity of the proposed method. The proposed method offers a promising solution for industrial applications of SynRM.
Research on Nonlinear Dynamic Equivalent Magnetic Network Models of Axial Flux Permanent Magnet Synchronous Machines

Axial flux machines are typically analyzed by the 3-D finite element method. To solve the time-consuming issue, this paper proposes a nonlinear dynamic equivalent magnetic network model. The proposed model is able to account for non-linear magnetic saturation and calculate the flux linkage and torque under diverse loads during rotation. After verification, the proposed model achieves satisfactory precision and a computation speed much faster than the typical one.