Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Efficient UAV-Based Last-Mile Delivery: A Tactical-Operational Model With Shared Depots and Non-Linear Energy Consumption

This paper studies the drone-aided last-mile delivery problem with shared depot resources. Our research motivation comes from E-commerce logistics, where big companies such as Amazon, are already filing up patents for the development of drone-friendly fulfillment centers towers that could serve as both charging hubs and convenient pit stops for delivery drones to pick up and drop off packages efficiently. We mainly focus on the tactical decisions about the selection of shared fulfillment centers used as the drone launch and retrieve stations and the fleet size plans. The operational drone route decisions are also incorporated into a unified framework to account for the mutual impact between tactical and operational plans. Moreover, we consider explicitly the non-linear and load-dependent nature of the energy consumption function for drone batteries. The problem is formulated as a mixed integer program with linear constraints, developed in the realm of layered networks, where the non-linear nature of energy consumption and its load dependency are incorporated and efficiently handled without the need of approximating non-linear terms. The proposed model is tested on an extensive set of instances with up to 75 customers, showing its computational efficiency. Insights about the route costs and spatial configuration of depots are also discussed.
Enhanced Frequency-Constrained Unit Commitment Considering Variable-Droop Frequency Control From Converter-Based Generator

To improve the frequency stability of power systems with a high penetration of converter-based generators, this paper proposes an Enhanced Frequency-Constrained Unit Commitment (E-FCUC) considering variable-droop-controlled Frequency Control (FC) from converter-based generators. Unlike previous studies emphasizing conventional and fix-droop converter-based generators participating in primary frequency response, the impact of converter-based generators with variable-droop FC (VD-FC) is investigated, modeled, and then incorporated into the UC problems. Firstly, the transfer function of variable-droop-controlled converter-based generators is modeled; the available primary power reserves bounding provided by converter-based generators is analyzed. Secondly, the frequency dynamic of power systems considering joint frequency control from conventional generators and variable-droop-controlled converter-based generators is derived based on an analytical aggregated System Frequency Response (SFR) model. Since the nonlinear non-smooth feature of obtained frequency dynamic function, the “max-affine” Piece-Wise-Linearization method (PWL) is adopted here to fit the calculated frequency function. The fitting function is subsequently reformulated into mix-integer linearized constraints participating in UC studies. Case studies based on a modified IEEE 6 bus test system and a modified IEEE 118 bus test system are carried out to verify the effectiveness of the proposed E-FCUC through comparisons of results with existing empirical models.
Experimental Investigation of Non-contact 3D Sensors for Marine-growth Cleaning Operations

Marine growth on submerged structures causes additional mechanical loads from drag and mass increases. In order to ensure structural integrity, regular inspection and cleaning procedures are carried out on the submerged structures, most commonly using remote-operated vehicles (ROVs). Often, the measurement methodology in these inspections is spot-checks using simple mechanical gauges, which yield a rough estimate of marine growth thickness. Expanding on this method, in order to optimize these inspection and cleaning procedures, modern methods for 3D surface measurement can be applied to increase inspection quality and ensure that superfluous cleaning is not carried out. This work investigates three state-of-the-art sensor technologies: a Time-of-Flight depth camera based on modulated visible blue laser illumination, a commercial stereo-vision solution based on visible-light sensors, and high-frequency imaging sonar. The sensors' performance has been compared in a laboratory environment to assess their suitability for use as a measurement device for marine-growth measurement in terms of accuracy, resolution, and noise/artifacts. It is concluded that the measurement fidelity of all evaluated sensors shows promise for the application, pending future evaluation in a real-world test.
Hierarchical AGC Dispatch With Detailed Modeling of Energy Storage System Behaviors

The key challenge for automatic generation control (AGC) dispatch lies in the contradiction between the detailed modeling required for optimal dispatch and the tight calculation time. The current method includes (1) the heuristics method that allocates real-time commands based on certain rules (fast but nonoptimal) and (2) the proactive dispatch method with a detailed AGC dispatch model (relatively optimal but the forecast error exists). The development of renewables and the prevalence of energy storage systems (ESSs) with costly degradation calls for combining the advantages of heuristics and proactive methods in AGC dispatch. With this in mind, a hierarchical AGC dispatch method is proposed. A proactive AGC dispatch model embedding state of charge (SOC) recovery is established to make the best use of regulating resources while avoiding the degradation of ESSs. A uniform SOC model considering comprehensive ramping circumstances of ESSs is proposed with low model complexity. A theoretical analysis demonstrates the desired modeling accuracy. To compensate for the forecast error, real-time AGC dispatch is considered. The dead band and forced SOC recovery of ESSs are used to alleviate their degradation. Time-domain simulations based on practical data demonstrate that the proposed method can improve the system frequency performance while obviously reducing the degradation of ESSs.
Developing an Extremely High Flow Rate Pneumatic Peristaltic Micropump for Blood Plasma Separation with Inertial Particle Focusing Technique from Fingertip Blood with Lancets

This paper proposes an on-chip for blood plasma separation based on a pneumatic peristaltic micropump (PPM) linked to a spiral channel with a trapezoidal cross-section, which is used as an inertial particle focusing technique. The concept was based on the excellent bonding strength of PMMA/PDMS/PMMA and developed the PPM capable of delivering an extremely high flow rate of 3,500 μL/min. The device rapidly extracts blood plasma from diluted blood within 3 minutes for four rounds, with an efficiency of 97%. The total volume needed for each extraction process was only 4μL whole blood drawn from a fingertip with a lancet.
IoT-Based Road Accident Rescue System Implementation for Smart City Applications

Road accidents are one of the biggest problems in the world, in which many precious lives have been lost. This work proposes a road vehicle accident detection system with location alerts to rescue accident victims. The main hardware modules include the MPU9250, a 9 degree-of-freedom (9-DoF) micro electro mechanical system (MEMS) based inertial measurement unit (IMU), Arduino nano with ESP8266 microcontroller and GPS. The IMU’s three axis accelerometer, gyroscope and magnetometer data are programmed to determine the orientation and position of the vehicle. A Wi-Fi-based communication is established using ESP8266 to send data received from sensor units to Google Firebase cloud servers in real-time. The performance of the developed device has been evaluated using a laboratory setup and also in real-time driving scenarios. The developed sensor module performs well on accident detection and emergency alert generation, which can be used in vehicles to save many lives in the event of an accident through its automatic alert service.
Injection of a viscoplastic material inside a tube or between two parallel disks: Conditions for wall detachment of the advancing front

The injection of a viscoplastic material, driven by a constant pressure drop, inside a pipe or between two parallel coaxial disks under creeping flow conditions is examined. The transient nature of both flow arrangements requires solving a time-dependent problem and fully accounting for the advancing liquid/air interface. Material viscoplasticity is described by the Papanastasiou constitutive equation. A quasi-elliptic grid generation scheme is employed for the construction of the mesh, combined with local mesh refinement near the material front and, periodically, full mesh reconstruction. All equations are solved using the mixed finite element/Galerkin formulation coupled with the implicit Euler method. For a viscoplastic fluid, the flow field changes qualitatively from that of a Newtonian fluid because the material gets detached from the walls. For small Bingham numbers, the contact line moves in the flow direction, so that initially the flow resembles that of a Newtonian fluid, but even in that case de.
Study on Optical Fiber Gas-Holdup Meter Signal Denoising Using Improved Threshold Wavelet Transform

The optical fiber probe gas-holdup meter is a new logging tool, which can be used to measure the gas-holdup of oil-gas-water three-phase flow during production logging. In this paper, the wavelet transform is used to denoise the measured gas-holdup signal of the optical fiber probe. For the wavelet transform threshold de-noising method, the existing hard and soft threshold functions have certain defects. So we propose a wavelet transform denoising method with an improved threshold function, and use this method to denoise the measured gas-holdup signal of the optical fiber probe. The signal-to-noise ratio, root mean square error, smoothness, and correlation coefficient are used as the evaluation indexes of signal denoising effect. The improved threshold function is between the hard threshold function and the soft threshold function. It has the advantages of both hard and soft threshold functions, can effectively overcome the discontinuity of the hard threshold function and the constant deviation of the soft threshold function, and improves the denoising efficiency of the gas-holdup signal. The proposed method is applied to the synthetic data and the real gas-holdup signal of the optical fiber probe respectively. Compared with the traditional soft and hard threshold functions, the experiment results show that the improved threshold wavelet denoising method is feasible. And it is effective in the gas-holdup signal denoising, improving the signal-to-noise ratio, reducing root mean square error, and increasing the correlation coefficient. The proposed method preserves well the original features of the measured gas-holdup signal, which provides a reference for the signal processing and dynamic monitoring analysis of the subsequent oil-well downhole development.
Performance Enhanced Thermal Flow Sensor with Novel Dual-Heater Structure Using CMOS Compatible Fabrication Process

For the first time, a CMOS compatible calorimetric flow sensor with a dual-heater (DH) structure is proposed instead of using the traditional single-heater (SH) design. CFD simulation results demonstrated that the DH design can enhance the performance of the device with high sensitivity. Thereof, the DH sensor was fabricated with four suspended bridges and tested under three configuration modes: parallel-heater (PH) mode, full-bridge (FB) mode, and half-bridge (HB) mode. Experimental results demonstrated that the PH mode can achieve a high sensitivity of 403.25 mV/(m/s)/W, which is over 4 times that of the HB mode (83.7mV/(m/s)/W). This DH flow sensor under its PH mode can also provide an ultra large measurement range (±40 m/s) and fast response time (1.8 ms @16.7 m/s). The good performance of this novel flow sensor demonstrated its potential applications in respiration monitoring in medical areas and airflow control in HVAC systems for smart buildings.
A Performance Enhancement Method for Thermopile Sensors Using a Chip Probe Test System

In this work, a conventional chip probe test system is not only adopted to evaluate functionality of thermopile sensors, but is also utilized to integrate ink dots on the devices. Compared with a pristine sensor, such devices integrated with ink dots are able to increase output by >200% in a wavelength range of 635-1550 nm, and by >40% in a temperature range of 30-450 ℃. Moreover, the size of the ink dots can be well controlled in an 8-inch wafer, and the performance enhancement ability of this simple and low-cost approach has a satisfactory uniformity, thus it has great potential in mass preparation of thermopile sensors with high performance.
Chatter alleviation for a class of second-order under-actuated mechanical systems with matched and mismatched disturbances

The chattering phenomenon and a system with both matched and mismatched disturbances are the major difficulties in sliding mode control design. This paper presents an effective design procedure to alleviate these two difficulties for a class of second-order under-actuated mechanical systems. In the proposed design, new hierarchical sliding surfaces are designed and a modified disturbance observer is utilized to estimate the lumped disturbance which is a linear combination of the matched and mismatched disturbances. The chatter in control input is filtered out by an integrator, which acts as a low-pass filter. The asymptotic stabilities of the entire sliding surfaces are guaranteed. A design study considering lateral control of a vehicle with matched and mismatched disturbances demonstrates the effectiveness of the proposed design.
Stock Market Price Prediction through News Sentiment Analysis & Ensemble Learning

Stock market trends tend to vary according to real-life situations and market sentiments. The growing interest in the Financial-Technology sector has culminated in more research being carried out in this area. For sentiments, news headlines have proven to be a major source and can be used without the further context of the news article. Most of the research for stock market price prediction has been carried out based only on daily stock data or using standard machine learning algorithms. Hence, in this research project, we find the importance of using the sentiment data extracted from news headlines and daily stock data combined for the prediction of trends and stock prices. To carry out market trends prediction we propose the use of the ensemble technique XGBoost, whereas, for price prediction time series, LSTM (Long ShortTerm Memory) cells are used. Our results indicate that combining both data as input using ensemble techniques and time series models gave much better and more accurate output than the standard methods that have been employed before.
Optimal roughness for minimal adhesion

Roughness has a significant affect on adhesion. The authors used a single-asperity model to describe a smooth tip in contact with a rough surface and predicted that an optimal size of asperity yields a minimum of adhesion. Experimentally, adhesive forces on silicon wafers with varying roughness were measured using atomic-force-microscopy cantilevers with varying tip radii. It was found that minima do exist, and for all tip radii, the adhesion falls significantly for roughness greater than 1–2nm and drops at higher roughness for larger tips. This work should help minimize stiction in microelectromechanical systems and progress the understanding of nanoscale-contact mechanics.
An Improved Dynamic Programming-Based Decentralized Algorithm for AC-OPF of Radially Connected Multi-Level Networks

In this paper, an improved dynamic programming (IDP)-based decentralized algorithm is proposed for the AC-optimal power flow (AC-OPF) of radially connected transmission-distribution-microgrid networks (RCTDMNs). Most of the existing decentralized algorithms need to iterate among networks, which may lead to additional computational and information exchange burden. Different from these algorithms, we decompose the original AC-OPF problem into small-scale subproblems based on dynamic programming (DP) theory, and then solve them in a decentralized manner without iteration among networks. In order to improve the computational performance of DP, its value functions are directly obtained by solving Galerkin projection equations (GPEs) instead of traversing the whole state space. Moreover, a parallel technique is proposed to further accelerate the proposed IDP algorithm when dealing with the AC-OPF problem of RCTDMNs. Case studies on various test systems demonstrate the effectiveness of the proposed method.
EmoGo: A Smart Wearable IoT System for Human Emotion Detection

The emotional well-being influences the thoughts and behavior of people. A person can experience different types of emotions throughout the day. These emotions significantly compel the decisions that affect their lives. Therefore, emotional awareness allows people to recognize the role and effects of their emotions. In addition, negative emotions that are repressed can harm the human body in multiple ways like chronic stress and damaging the immune system. Depression can cause feelings of sadness and a loss of enjoyment throughout the daily life. While these negative emotions can also decrease the human ability to function; in contrast, positive emotions boost a persons morale and increase their cognitive processing. These good emotions affect cognitive processing areas such as attention and learning. Therefore, classifying emotions in different spectrum is important to help people with mental health struggles. Our solution proposes an innovative wearable device that tracks and classifies different types of emotions. Our research aims to investigate physiological changes associated with relieved experiences of happiness, sadness, anger, fear, and disgust. Tracking these changes allows for an accurate representation of peoples emotions. In the psychiatric field, patients tend to exaggerate or downplay their emotions; this creates issues during therapy.
Cardio-Vascular Risk Detection System using different Machine Learning Techniques

Cardiovascular disease is a prominent cause of disability and early death worldwide; it significantly contributes to growing healthcare costs. Atherosclerosis is the underlying pathology, which develops over time and is usually advanced by the time symptoms appear, usually in middle life. Acute coronary and cerebrovascular crises occur suddenly, and patients might die before medical treatment arrives. Modifying risk factors have been shown to reduce mortality and morbidity in people with known or undiagnosed cardiovascular disease. Classification methods are used frequently in Medical Sciences. One of the major problems faced in Medical Science is Cardiovascular Risk Detection. Classification has been used intensively to group patients into Risky and Non-Risky. The current methodology performs extensive experimentation over specific models and achieves a recognition accuracy of 98% through Decision Tree Classifier. The comparison of accuracies was made for different predictions recorded through various algorithms. The research aims to provide a patient-level risk detection system so patients can know the risk beforehand and follow their prescribed prevention routines.
Fiber optic LDF to monitor vascular dynamics of urticarial dermographism in pressure-tested patients before and after treatment with antihistamines

The local microcirculatory dynamics underlying phenomenon of urticarial dermographism (UD) are not yet sufficiently elucidated in dermatological patients. A fiber optic laser Doppler flowmeter (LDF) was used to monitor skin blood flow (SBF) changes on the back of the patients with UD before and after application of the series of pressure stimuli (9.8×104, 14.7×104, 19.6×104 and 24.5×104 Pa). All patients acted as self-controls to assess their disease activity by means of SBF values based on response to pressure stimuli before and after treatment with antihistamines, when compared to baseline SBF. Throughout 30 minutes evaluation inter-subject SBF values at pressure-tested sites were noticeably distinguished as high, moderate and low. By LDF we could differentiate the highest development of vascular dynamics after 5 minutes, coming back to normal within about 30 minutes in one group of patients, and the vascular dynamics reaching its maximum in 15 minutes, but with no fade after 30 minutes, in another group of patients. All treatment regimens in both groups of patients by LDF produced a measurable reduction already during 1-2 days of therapy, accompanied by a reduction in SBF baseline values in patients with severe and moderate symptoms of UD.
Fully Integrated Back-Biased 3d Hall Sensor with Wafer-Level Integrated Permanent Micromagnets

This paper reports on a 3D Hall sensor back-biased by rare-earth micromagnets which are integrated directly into the silicon substrate using a wafer-level fabrication process, called PowderMEMS. For the first time, the technique is used to realize a fully integrated back-biased magnetic field sensor. The feasibility of the approach is proven by measurements of the motion of a rotating gear wheel which represents a typical back bias application. Additionally, the ability of the approach to create optimal magnetic field geometries for magnetically biased sensors is demonstrated.
A Pioneering Approach of Hyperspectral Image Classification Employing the Cooperative Efforts of 3D, 2D and Depthwise Separable-1D Convolutions

Due to its promising uses in diverse sectors including agriculture, food technology, land cover mapping, etc., hyperspectral image (HI) analysis plays an important role. HI analysis using deep learning (DL) has made enormous strides in recent years. The literature makes it clear that employing simply 2-D-CNN or 3-D-CNN included several limitations, such as inadequate channel association details or mandating very complex models, respectively. The most important justification is that HIs are volumetric data with several spectral bands. This paper proposes a synergetic effort by 3D, 2D, and Depthwise separable-1D Convolutional layers in the proposed Lightweight Deep Learning model for enhancing HI classification accuracy. The spectral dimension of the input HI is reduced by utilizing traditional Principal Component Analysis (PCA). The 3D CNN expedites the synthesis of simultaneous spatial-spectral characteristics out of a pile of spectral channels. The 2D CNN also picks up the higher-level spatial interpretation in addition to the 3D CNN. Finally, the Depthwise separable-1D Convolutional layer that works on depth and space is incorporated into the model. The introduction of the Depthwise separable-1D Convolutional layer, the quantity of the channels, and the kernel sizes resulted in a sharp reduction in computation. Strenuous classification trials are conducted over the Indian Pines, University of Pavia, and Salinas Scene HI data to assess the viability of this proposed approach. The findings are juxtaposed with cutting-edge hand-crafted and sophisticated deep learning-based methods. The suggested DL architecture for HI classification performs quite effectively with fewer training samples and less trainable parameters.