Miracle-dz/UniFAR-FTUs huggingface dataset:
Using fluctuation statistics to infer random forces impinging on a nanosensor

Cantilever-based nanosensors are increasingly being used as gas and chemical sensors. We mechanistically model an important source of stochasticity arising in these systems - the random collisions between the sensor and the surrounding molecules. The sensor dynamics is modeled as an underdamped second-order system with molecular collisions occurring at random times, and each strike adding noise to the sensor velocity. The time between two molecular strikes is drawn from an arbitrary distribution. Our analysis of the steady-state variance in the sensor displacement and velocity reveals two insights: firstly, the variance cannot discriminate between frequent low-impact molecular collisions versus rare high-impact collisions. Secondly, the variance only depends on the mean inter-strike time and is completely independent of other properties of the inter-strike time distribution. We next derive higher-order statistical moments (such as kurtosis) of the sensor displacement/velocity and show that these can be harnessed to discriminate between frequent low-impact strikes and rare high-impact strikes. As expected, these moments converge to Gaussian statistics in the limit of frequent molecular strikes. In summary, our work generalizes Brownian noise-driven models and quantifies deviations from Gaussian statistics that arise from mechanistic modeling of physical collisions. Furthermore, higher-order statistics can be exploited to infer random forces impinging on these nanosystems that ultimately determine the sensor signal-to-noise ratio.
Frequency Predistortion Strategy Based Digital Phase Locked Loop for PFC Converter

In the high frequency boost power factor correction (PFC) converter, it is necessary to obtain the phase of the grid side voltage. However, in some cost-sensitive applications, there is only a sensor to detect the rectified voltage. This paper proposes a digital phase locked loop (PLL) scheme with a frequency predistortion digital bandpass filter to obtain the rectified voltage phase. Based on analyzing the frequency domain response characteristics of the digital bandpass filter, the continuous bandpass filter is digitized by bilinear transformation with frequency predistortion. On this basis, the rectified voltage is filtered to obtain its fundamental component as the PLL input. Then the grid side voltage phase is obtained at the output side, so the in-phase PFC reference current signal can be generated. Experiments have been carried out on the platform of the 70kHz PFC converter to verify the effectiveness of the strategy.
A Simple Structure Dual-Band Dual-Circularly Polarized Antenna With Controlled Frequency Ratio

This paper proposes a dual-band dual-circularly polarized (CP) aperture-coupled patch antenna with controlled frequency ratio. By simply etching a modified S-shaped slot at the central of a circular patch, dual-band dual-CP operation is realized. In addition, the frequency ratio of the two CP bands can be controlled by optimizing the length parameters of the S-shaped slot arms. As a result, the frequency ratio can be tuned from 1.12 to 1.46 according to different requirements. For demonstration, the circularly polarized patch antenna operating at 3.42 and 3.85 GHz is manufactured and tested. The measured 3-dB axial-ratio (AR) bandwidth is 1.1% and 2.3% for the right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP), respectively. The measured gains at the lower and upper bands are 9.4 and 9.8 dBic, respectively.
Design and Implementation of IoT-Based Automatic Oxygen Flow Control in Response to the Covid-19 Crisis

Internet of Things (IoT) technology has brought a revolution in several ways to a common person's life by making everything smart and intelligent. During the Covid-19 crisis, health workers around the world needed to monitor patients' health and needed to provide sufficient oxygen, when necessary, as Covid-19 was responsible for many respiratory cases. Health workers were at high risk of being contaminated while treating Covid-19 patients. The study of this paper is to propose an IoT-based automatic oxygen flow control in response to the Covid-19 crisis. The proposed approach helped to real-time monitoring of SpO2, heartbeat, oxygen quantity of oxygen cylinder, and control of the flow of oxygen based on SpO2 value. A health worker can monitor a patient's health-related parameters and control the flow of oxygen without any physical contact with it. Also, provides an alarm to the health worker when SpO2 is below the threshold and re-measuring oxygen quantity of oxygen cylinder with the help of our developed android app. Implementation of IoT-based low-cost pulse oximeter and IoT-based pressure gauge helps to monitor and control different health parameters. The IoT-based system may potentially be valuable during the Covid-19 pandemic for accurate oxygen flow distribution and for saving people's lives.
Study Of High-Tech Factory Digital Twin Virtual Simulation Model Based On Inherently Safer Design Strategy

For Taiwan’s semiconductor industry supply chain, the upstream is IP design and IC design industry, the midstream is IC manufacturing, wafer manufacturing, related production process testing equipment, photolithography, chemicals and other industries, and the downstream is IC packaging testing and related production process testing. Taiwan’s IC manufacturing sector, the epidemic has not slowed down. In order to avoid chain disconnection and market share, downstream manufacturers have successively increased inventories and even urgent orders, making Taiwan’s foundry industry still perform well in the second half of 2021. This research first conceives the control system requirements, and uses PLC to build the system, then uses MATLAB to build the corresponding simulation model, and uses the configuration software to design the important safety human-machine interface to complete the communication function between the human-machine interfaces based on two models. Two models are studied for testing. Under the same input conditions, the physical model and the simulation model can achieve the same input and output conditions, and the possibility of using the simulation model for the electrical control safety design of ISD of new recruits in high-tech factories is verified. This is an important contribution to this research.
Visualization of Streamer Channels and Shock Waves Generated by Positive Pulsed Corona Discharge Using Laser Schlieren Method

Streamer channels generated by a positive pulsed corona discharge are visualized using the laser schlieren method. The discharge occurs between a point-to-plane gap at atmospheric pressure with a pulse duration of less than 1 µs. In order to enhance the intensity of the schlieren image, water vapor is added to ambient gas. The schlieren visualizes heated gas in a streamer filament of 0.4 mm diameter. A temporal variation of the schlieren image after the discharge pulse shows that the heated gas moves outward from the streamer channel due to the diffusion. The diameter of the heated filament, in which the heated gas exists, increases from 0.4 mm to 1.1 mm within 1 ms following the discharge pulse. The schlieren image also shows shock waves generated by the discharge: a spherical shock wave generated at the tip of the point electrode and a plane shock wave generated at the surface of the plane electrode.
Implementation of Neural Network and Bagging Technique for Predicting Electricity Consumption

Electrical energy is a basic human need. With the increase in vulnerable populations and technological developments, the demand for electrical energy is also increasing. Forecasting or predicting electricity usage obtain a necessity for planning future energy resources. Electricity usage accurately forecasting is imperative and challenging. However, electricity usage data in society contains significant, nonlinear, and complex variances. The neural networks effectively overcome complex and nonlinear data problems, while bagging can reduce substantial conflicts. Therefore, this study combines a neural network with bagging to improve the prediction performance of electricity consumption. The results show that both data sets significantly reduce RMSE values between neural network applications using the neural networks and bagging techniques. The comparison between the neural network and bagging shows that the RMSE value is smaller than that of the neural network. Based on these results, the combination of neural networks and bagging performs better than a particular neural network.
Miniaturized Wet-Wet Differential Pressure Sensor

We report a miniaturized wet-wet differential pressure sensor with applications in pressure and flow sensing in water networks and other harsh environments. The device is similar in concept to a conventional wet-wet differential pressure sensor in that the sensing element is protected from the external environment by oil-filled cavities closed off by corrugated diaphragms. However, with a package envelope of 11.0 x 4.8 x 3.4 mm3, corresponding to a volume of only 0.18 cm3, the device is considerably smaller than commercially available wet-wet differential pressure sensors. A high degree of miniaturization has been achieved by using micromachining to fabricate the corrugated diaphragms. Preliminary experimental results are presented showing operation of the device as a delta-pressure flow speed sensor in a water flow test rig.
Machine Learning-Based Cell-Free Support in the O-RAN Architecture: An Innovative Converged Optical-Wireless Solution Toward 6G Networks

One of the most crucial challenges for the global socio-technical domain nowadays is the support of unprecedented use cases for a variety of applications such as augmented reality, 3D holographic telepresence, Industry 4.0, robotics, e-health, and pervasive connectivity. In this environment, beyond 5G intelligent networks that are able to provide enhanced flexibility through the dynamic allocation of the system's resources, while realizing the perceived zero-latency vision, are envisioned to ensure a smooth transition toward 6G. The proposed innovative converged optical-wireless network configuration targets to enable such a transformation, through the development of novel radio access networking solutions. These advanced radio-edge architectures are designed by utilizing the distributed cell-free concept and the serial fronthaul approach, while contributing innovative functionalities to the O-RAN project. At the same time, the proposed approach facilitates integrated connectivity of both mobile and fixed services, which share the same edge and midhaul infrastructures, while both are served by a core. The proposed network architecture can be envisioned as the key technology enabler to satisfy the requirements of future 6G networks through the modifications of the O-RAN interfaces toward a cell-free-enabled solution, and through the incorporation of novel Machine Learning techniques at the Radio Edge.
A Symbiosis with Panicle-SEG Based CNN for Count the Number of Wheat Ears

The number of wheat ears per unit area counting helps to measure the wheat grain quality. The manual counting process is a high laborious and time-consuming process. For better counting of wheat ears, a Panicle segmentation-based CNN approach has been used to estimate the number of wheat ears in the wheat plant. A total of 236 wheat ears have been taken from the wheat ears detection dataset [1]. The manually labeled and superpixels data have been integrated to form segmented data. The segmented data have been used for the classification of wheat ear classes. As the 1056 segmented panicles have been extracted. The segmented panicles were constructed into three classes: one ear, two ears, and three ears and these panicles have been used for training and validation purposes in CNN. The panicle segmentation-based CNN approach achieves a high F1 score (98.3%) and Qseg(0.82) in one ears classification. Classification of segmented images, unlike target recognition, not only reduces the labour of manual annotation but also greatly improves the efficiency and accuracy of wheat ear counting, thereby meeting the requirements of wheat yield estimation in the field environment.
Optimal Value Determining Method of Parameters Compensating Devices of Bulk Electric Networks

The given method of optimizing the modes of main electric networks allows obtaining a universal approach for solving such tasks as minimizing the total losses of active power and compensating excess flows of reactive power. The methodology for selecting the parameters of compensating devices of controlled ultra-high voltage power transmissions with intermediate systems to ensure the balance of reactive power has been substantiated. An analytical solution to the problem of selecting the parameters of the compensating ultra-high voltage power transmission line with one intermediate system was obtained. An approach to solving the problem of optimization of the main electrical network is presented, taking into account the provision of reactive power balance. The objective function and its optimization criteria are described. The method of optimizing the mode of the electrical network according to voltage and reactive power was developed with an assessment of the effectiveness of the use of controlled compensating compensation devices, and the possibility of applying this approach was revealed. Recommendations are given on the use of reactive power compensators to improve the reliability and quality of power supply. The technique has been developed for evaluating the efficiency of controlled nodes based on optimizing the mode of electrical networks containing such nodes. In the work, two problems are solved when choosing installation sites and values of parameters of compensating devices: balancing reactive power and optimizing the operating modes of the main electrical network according to the criterion of minimum active power losses. The most efficient hardware based on static devices of the FACTS technological platform is proposed.
A Reference Governor for Control of Bistable Structures with Polynomial Constraints

Bistable structures are a type of morphing structure which have the ability to obtain two stable shapes which do not require any energy to be maintained. In this work, a reference governor control scheme is implemented to control the shape change of a bistable structure while satisfying actuator saturation constraints. The structure is modelled as the nonlinear Duffing-Holmes oscillator and the actuation force becomes a polynomial of the states once the system is linearized. By extending an augmented version of the state, the computation of the maximal output admissible invariant set in the case of polynomial actuator constraints is achieved. The reference governor is implemented by solving a nonlinear optimization problem at initialization and then uses a bisection algorithm at subsequent time steps. This methodology is shown to be an effective means by which a bistable structure may be controlled despite the polynomial constraints associated with actuator saturation. The proposed methodology is then compared to the most current method for controlling bistable structure shape change.
Population trapping and rotational revival of N2 molecules during filamentation of a femtosecond laser pulse in air

We study the fluorescence emitted from filaments in air using a pump–probe scheme with a femtosecond Ti–sapphire laser. The fluorescence intensities from the first negative band (B2Σ+u → X2Σ+g) and the second positive band (C3Πu→B3Πg) show enhancement and change periodically as a function of the pump–probe time delay. We attribute this phenomenon to the universal yet probably forgotten phenomenon of population trapping of nitrogen molecules in highly excited states together with field-induced alignment of nitrogen molecules followed by revivals of the rotational wavepackets. Theoretical calculation of the alignment dynamics of nitrogen molecules is consistent with the experimental data.
Automated Diagnostic Radiographs of COVID-19 Based on Deep Learning Method

The COVID-19 outbreak has had a serious impact on Taiwan’s health care system. Deep Learning is an effective technology to help doctors make the most appropriate medical decisions for every patient in this crisis. In this study, we select four state-of-the-art Deep Learning–Xception, MobileNetv2, DenseNet169, and DenseNet201. Additionally, Transfer Learning is used for pre-training them before four models individually classify normal and positive COVID-19 chest X-ray images. Lastly, the best results reached 98.58% accuracy, 98.58% precision, and 98.42% recall.
Foldy–Lax approximation on multiple scattering by many small scatterers

The multiple scattering of time harmonic wave emitted by a localized source through a medium with many scatterers can be approximated by an Foldy–Lax self-consistent system when the relative radius of each scatterer is small and the distribution of scatterers is sparse. The scattering amplitude in the Foldy–Lax self-consistent system will be specified in terms of scatterer volume and scattering strength. By neglecting the self-interaction effect, the difference from the exciting field in the Foldy–Lax formula to the analytic wave field given implicitly by the Lippmann–Schwinger integral equation is compared. An upper bound of the difference is obtained in terms of scaled radius and sparsity of the distribution of the scatterers.
MilliWear — A Short Range InSAR Approach for Surface Wear Inspection using mm-Wave Radar

In-situ inspection of surface wear remains a daunting task for the industry. Detection of intermediate progressive dynamics of wear requires repeated examinations which is time-consuming, adding to mill downtime. Runtime inspection inside the mill necessitates the ability to sense through occluded media due to the presence of grinding dust. The primary objective of this work is to assess the feasibility of using short range interferometric synthetic aperture radar (InSAR) to detect and quantify wear or other tribological phenomena associated with metallic surfaces. A novel variable focusing algorithm has been used to generate SAR images and a unique automated co-registration scheme has been employed in the InSAR processing pipeline. The reconstructed SAR images and the generated interferogram reflect strong overlap with the ground truth of the sample under test with MSE of 0.0611.
Elastic Shape Analysis for Anomaly Detection in Textile Finishing Time-Series Data

Quality control represents a challenge in the industrial textile finishing field. The general unavailability of labelled data from real production plants and the imbalanced nature of the problem suggest tackling it with a semi-supervised approach that monitors the behaviour of the system and identifies whether shifts from the nominal conditions arise. In particular, we utilize techniques from Elastic Shape Analysis to study the time-series data collected from sensors installed in real production plants and to extract features used to define distance metrics that quantify the data variability. The results of this exploratory study support the efficacy of the proposed strategy.
Active Synchronization of Islanded Microgrid using Droop-controlled Grid-forming Inverters

For smooth transition from islanded mode to grid-tied mode, synchronization to the incoming grid is required for any islanded microgrid. This paper is proposing a novel active synchronization method for such islanded microgrids. In this method, the proposed synchronization controller compensates the phase angle and voltage magnitude difference between islanded microgrid side and the incoming grid side to zero by adjusting the droop laws of all grid-forming (GFM) inverter systems of the microgrid dynamically via secondary control layer of microgrid hierarchical control architecture. It is analytically shown that compensation on only phase angle and voltage magnitude is sufficient for synchronization as frequency is compensated by this proposed method indirectly by the phase angle compensation. A systematic approach for tuning the proposed synchronization controllers are provided. For validation, a controller hardware-in-the-loop-based real-time simulation is conducted using OP5700 RT-simulator manufactured by OPAL-RT on a 3-phase, 480V, 500kVA, 55-bus urban microgrid system with 6 GFM inverters where control of 2 GFM inverters are realized on low-cost Texas-Instruments TMS28379D Delfino controller boards. The proposed method is relatively fast and enables a smooth re-connection of microgrid with grid with less transients.
Lidar Point Cloud Object Detection and Semantic Segmentation Fusion Based on Bird's-Eye-View

Self-driving or assisted driving of self-driving cars are based on identifying and categorizing obstructions. This paper mainly intends to create a precise object detection frame based on radar or lidar to collect information and to detect and categorize obstructions. 3D objects in point cloud are often represented using a 3D box, which is similar to how objects are represented on top of a 2D image. But objects in the 3D world do not follow any particular orientation, additionally, box-based detectors can hardly enumerate all directions. So, this model use key point detector to detect the objective's center and return to other attributes, which include the size of 3D box and direction. In the meanwhile, we find that huge discrepancies exist in the 3D box's frontier of pure objective detection and actual objective's frontier. It is hard to directly detect the objective's frontier in 3D point clouds. As a result, this model use the point cloud semantic segmentation method based on Bird'View and polar coordinates, and use the results of semantic segmentation to optimize the boundary of the 3D box. The model in this paper achieves 65.7% and 66.7% mAP for vehicles and pedestrians at level2, with a speed of 19.38 FPS
Design and Realization of a Synchronous Reluctance Motor with Printed Rotor

This paper describes the design and realization of a Synchronous Reluctance (SynRel) motor with the maximum feasible magnetic saliency, both from a practical and industrial point of view. The performance of the SynRel motor is strictly related to the magnetic saliency which is limited by the iron saturation and influenced by the rotor flux barrier configuration. In the paper, the maximum achievable value for the magnetic saliency is first investigated. Then a specific rotor design which allows to obtain a saliency value as close as possible to the theoretical one is introduced. This design is achievable thanks to the adoption of Additive Manufacturing technology. In this way maximum flexibility in the rotor design can be exploited to maximize the motor performance increasing the magnetic saliency.
Optimal current setpoint computation for externally excited synchronous machines

Torque control strategies for externally excited synchronous machines are typically based on calculating suitable reference currents, whereby system limitations such as the maximum possible phase and excitation current as well as the maximum voltage of the inverters must be taken into account. Based on the static voltage equations of the dq-model, a nonlinear constrained optimization problem is derived in this paper, which aims at minimizing copper losses and finding the optimal reference currents for a desired torque subject to the aforementioned constraints. This problem is then solved by a combinatorial algorithm which allows the solution to be computed efficiently in real time. Simulations and a comparison with an approach from the literature show the suitability of the algorithm with respect to the quality of the achieved torque and loss minimization as well as the low computational load.