Miracle-dz/UniFAR-FTUs huggingface dataset:
Proof-of-principle validation of a novel intraluminal optical sensor for dynamic monitoring of intestinal anastomosis: An in vivo animal model case study

Intestinal resections are commonly performed to treat different colorectal conditions, including colorectal cancer. A successful primary anastomosis is the desired optimal outcome after intestinal resection. Maintaining adequate blood flow across the anastomosis is paramount for reducing anastomotic failure. Currently, there are no clinical devices capable of continuously assessing blood flow and blood perfusion at an anastomosis during and after surgery. The aim of this study was to develop an indwelling optical sensor for the monitoring of perfusion biomarkers using photoplethysmography and near-infrared spectroscopy principles. In an animal in-vivo proof-of-principle study, it was found that the developed sensor performed appropriately for the assessment of blood flow and perfusion in an anastomosis, showing changes in the assessed parameters after gradual devascularization of the transected bowel.
Customer Segmentation using K-Means Clustering

As the population rapidly increases Customer Segmentation is becoming critically important nowadays for many businesses. It is widely used to target the best customers for their product and provide service. But for improving its efficiency we need to apply different tactics from time to time. The K means clustering algorithm is used to analyze large and complex datasets. It groups similar types of items and forms clusters. Also, it helps to confirm business assumptions. In this paper, we discussed steps on how we can visualize the clusters of customers by plotting the graph and studying about the data using the k-means Clustering Algorithm. All the programming is done by using Python and its Libraries like pandas, numpy, seaborn, matplotlib and sklearn. The final data-driven decision-making by looking at the final clusters help business to get some valuable insights
A New Multisource Inverter Topology for Electrical Vehicle Applications Controlled by Model Predictive

A Multisource Inverter (MSI) comprises several DC sources in the input that can be combined together with varying voltage levels to operate at different loads to reduce the battery size of electric vehicles. Different multisource inverter configurations have been presented recently in the literature. These structures use two DC sources to operate at different demand loads by using a low battery size. The weakness of these multisource inverters is that they use a high number of power switches. In addition, these structures cannot connect two DC used sources together in series to operate under a heavy load, which increases the battery’s size, increases high power losses, and reduces efficiency due to a high number of switches. This paper proposes a new topology for multisource inverters that reduces the power electronics switches and the battery size due to generating four combinations between the two used DC sources in the proposed technique. The proposed multisource topology is controlled by the model predictive due to its popular advantages. The performance of the proposal is verified through simulation results in Matlab. The results show that the MPC is a good alternative for such applications due to its simplicity, high performance, and low harmonic content.
Self-sensing Piezoelectric Micro-lens Actuator

The fast micro-lens actuator used in portable micro-optics devices needs an integrated sensing mechanism for feedback control. This paper presents the design and implementation of a readout circuit using a charge amplifier for the piezoelectric micro-lens actuator. The designed circuit captures the small vibration of the piezoelectric actuator by measuring the charge generated due to the mechanical motion of the piezoelectric beam. The performance of the designed readout circuit is evaluated by comparing the measured readout circuit output with an optical sensor. The obtained open-loop settling time and signal-to-noise ratio for the piezoelectric micro-lens actuator are 120 ms and 30 dB, respectively, similar to the optical sensors. Experimentally measured results demonstrate that the on-chip self-sensing piezoelectric layer can replace the off-chip bulky optical sensor to provide a feedback signal for the closed-loop control of the piezoelectric micro-lens actuator, which is necessary for compact, handheld, and portable devices.
Model predictive control energy management strategy of fuel cell hybrid electric vehicle*

Model predictive control (MPC) based energy management strategies (EMS) are promising to achieve high-efficiency power conversion for different hybrid electric vehicles. In this work, we investigate the impact of velocity prediction on the performance of EMS. For this, MPC controllers are designed respectively for fuel cell hybrid electric vehicles (FCHEV) using four prediction settings: Prescient MPC, Frozen time MPC, exponentially decreasing MPC, and MPC with Markov chain model. The comparison of the results using different driving cycles is performed to study the effects of prediction horizon and prediction accuracy on the performance of EMS, in terms of hydrogen consumption and battery charge sustainability. Simulation results show that the performance of MPC-based EMS is highly dependent on the prediction accuracy and the control horizon length. With proper velocity prediction methods and horizon length configurations, low hydrogen consumption and sustainable battery charge can be achieved. Moreover, the necessity of co-designing the prediction model and the horizon length by specifying the driving condition is highlighted.
Pneumonia Detection from Chest X-ray Images using Transfer Learning

Pneumonia is a disease that an individual can acquire at any stage of the life. As per a report from Cleveland clinic, America, pneumonia is responsible for about 18% of all contagious diseases. In the phases that follow, this condition may lead to death. Chest radiographs have been found to as constant value by field professionals in clinical practice in order to identify pneumonia. In this work, chest X-ray scans. , which are accessible for the identification and diagnosis of pneumonia are utilized. For feature extraction from the images, a VGG16 transfer learning model is used. In this paper, various pre-existing models for pneumonia detection have been reviewed along with the identification of their performance results. A framework of VGG16 used for the study has been explained with a variety of applications of deep learning in disease diagnosis. The dataset for the research is collected from kaggle containing 5. , 856 images. , which have been further divided into training and testing datasets. Further, the results have been presented in the form of accuracy, precision, Fl-score and recall as 90.8%, 0.9102, and 0.935, 0.9615, respectively.
Anisotropy and magnetotransport in ordered magnetic antidot arrays

Magnetic films containing ordered arrays of holes (“antidots”) with period ∼200nm have been prepared using porous anodic alumina substrates with square and hexagonal symmetries. Large area (∼cm2) single-layer CoFe ordered antidot arrays show well-defined in-plane magnetic anisotropy related to the symmetry of the arrays, and the anisotropic magnetoresistance is smaller than that of a continuous film. For NiFe∕Cu∕CoFe antidot arrays, the giant magnetoresistance ratio of the patterned films is of similar magnitude to that of the unpatterned film, and shares the symmetry of the substrate. This behavior is attributed to the geometry of the antidots, which confine the magnetization of each layer parallel to the current flow.
Rigidity and persistence for ensuring shape maintenance of multi‐agent meta‐formations

This paper treats the problem of merging formations, where the underlying model of a formation is graphical. We first analyze the rigidity and persistence of meta‐formations, which are formations obtained by connecting several rigid or persistent formations. Persistence is a generalization to directed graphs of the undirected notion of rigidity. In the context of moving autonomous agent formations, persistence characterizes the efficacy of a directed structure of unilateral distance constraints seeking to preserve a formation shape. We derive then, for agents evolving in a two‐ or three‐dimensional space, the conditions under which a set of persistent formations can be merged into a persistent meta‐formation, and give the minimal number of interconnections needed for such a merging. We also give conditions for a meta‐formation obtained by merging several persistent formations to be persistent. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
Photonic Crystal Band Structure Prediction Using Artificial Neural Network

We aim to develop an artificial neural network which can predict the band structure of two dimensional photonic crystals. We calculated two kinds of photonic crystals, square lattice and triangular lattice, using the wave vectors in reduced Brillouin zone, band number and r/a ratio to predict the eigen-frequencies of photonic modes
Small-Signal Model and Controller Design of Interleaved Isolated Boost Converter for PV Application

The interleaved isolated boost converter (IIBC) with voltage-doubler provides voltage boosting, galvanic isolation, and low risk of transformer saturation issue in case of mismatched gate signals. These good features make the IIBC a good candidate as a first stage in microinverter architectures, where the photovoltaic (PV) module voltage needs to be boosted up at the input of a second stage dc-ac inverter. Although the topology is well-known, no research has been conducted on the dynamics and the closed-loop control of this converter. This paper presents the small-signal model and a control system design in the specific case of photovoltaics. Step-by-step small-signal modeling is reported, as well as the design of a finite dc-gain controller, which is discussed in detail. Both the open-loop control-to-output analytical model and the closed-loop results including the PV source have been validated with simulations.
Development of MEMS Flow Path for Miniature Waste Heat Utilization Generator

This paper suggests miniature silicon flow paths for a miniature organic Rankine cycle (ORC) generator that utilizes waste heat. Conventionally, most waste heat energy in an industrial plant is lower than 573 K. This waste heat energy can serve as a renewable power source for IoT devices by realizing a miniature organic Rankine cycle generator. To build such a miniature generator, miniature flow paths that connect the structural parts were developed. The developed flow paths were fabricated using a microelectromechanical systems (MEMS) process, and a plate type and a multilayer type were designed. The dimensions of the plate type flow path were 50 mm (length) by 50 mm (width), creating a flow path area 1.75 mm wide and 0.3 mm deep. The multilayer type was a three-dimensional structure measuring 10 mm (length) by 6.0 mm (width) by 3.7 mm (height), with a path width of 1.05 mm. A low-boiling-point working fluid (boiling point of 307 K) was injected into these structures to reuse waste heat energy. The plate type flow path showed displacement of the working fluid by a phase change when the heating temperature was 310 K and the cooling temperature was 282 K. In the multilayer type flow path, the working fluid was discharged by expanding the fluid at 308 K and 313 K. In addition, the outflow pressure (needed to embed this structure into a miniature ORC system in the future) was calculated. The developed structure achieved an outflow differential pressure of 2.2 kPa when the heating temperature was 313 K and the cooling temperature was 293 K. These results show that the developed MEMS flow paths realized the displacement and heat transport of the working fluid by phase change.
Direct Digital Frequency Synthesizer Modeling with a Re-configurable DAC Evaluation for Electrochemical Impedance Spectroscopy

A wide range Direct Digital frequency synthesizer (DDS) is considered as an important component that used in measuring Electrochemical Impedance Spectroscopy (EIS). This paper introduces a DDS complete system design using a Matlab model. In addition, It presents the usage of a re-configurable digital to analog converter (DAC) that can be used either as single bit delta sigma (Δ∑) DAC or a binary-weighted DAC, for both low and high frequency generation ranges respectively. The designed DDS generates an output frequency range from 762.933 Hz to 10 MHz by using a physical 8 bits DAC. It achieves a total harmonics distortion (THD) 0.02%.
Decision-making in Autonomous Driving by Reinforcement Learning Combined with Planning & control

In recent years, there are more and more studies on reinforcement learning (RL) and RL algorithms have achieved good performance in autonomous driving simulation experiments. But the simulation experiments are too idealistic, their safety cannot be guaranteed in practical application, with the action implementation error not being taken into account. To this end, a method combining the RL-based decision-making model with the planning & control module is proposed in this paper. In this paper, DDQN is selected as the algorithm for model-free deep reinforcement learning (DRL) decision-making model, A* is selected as the algorithm of path planner and PID is selected as the algorithm of controller, which is demonstrated in CARLA, to make accurate decisions while considering the real vehicle dynamics model and action implementation error, and improve the robustness and realistic transferability of the RL decision-making algorithm. Several improvement measures are proposed and experiment shows that the proposed GRSD-DDQN has better test performance with the free collision rate improved from 81.5% to 98.6% and the average speed improved from 45.5 (km/h) to 53.7 (km/h) compared with the Baseline-DDQN.
Study of the time response of 4H-SiC Schottky junctions for radiation high speed detection

A study of the time response of 4H-SiC radiation detectors by means of a custom simulator is presented. The detector response to X-rays has been studied under different bias conditions and interaction points within the detector active region. The contributions of electrons and holes to the current and voltage signals have been disentangled to determine their effects on the detector response speed. Signal rise-times between 340 ps and 710 ps and pulse widths of 0.93 ns and 1.12 ns Full Width at Half Maximum (FWHM) have been derived, in good agreement with experimental results.
Deep Learning with Recurrent Expansion for Electricity Theft Detection in Smart Grids

The increase in electricity theft has become one of the main concerns of power distribution networks. Indeed, electricity theft could not only lead to financial losses, but also leads to reputation damage by reducing the quality of supply. With advanced sensing technologies of metering infrastructures, data collection of electricity consumption enables data-driven methods to emerge in such non-technical loss detections as an alternative to traditional experience-based human-centric approaches. In this context, such fraud prediction problems are generally a thematic of missing patterns, class imbalance, and higher level of cardinality where there are many possibilities that a single feature can assume. Therefore, this article is introduced specifically to solve data representation problem and increase the sparseness between different data classes. As a result, deeper representations than deep learning networks are introduced to repeatedly merge the learning models themselves into a more complex architecture in a sort of recurrent expansion. To verify the effectiveness of the proposed recurrent expansion of deep learning (REDL) approach, a realistic dataset of electricity theft is involved. Consequently, REDL has achieved excellent data mapping results proven by both visualization and numerical metrics and shows the ability of separating different classes with higher performance. Another important REDL feature of outliers correction has been also discovered in this study. Finally, comparison to some recent works also proved superiority of REDL model.
Control Design via Bayesian Optimization with Safety Constraints

Bayesian Optimization is a powerful machine-learning tool enabling automated design of fixed-structure controllers. A sequence of closed-loop calibration experiments is performed, and the next configuration to be tested is selected by the optimization algorithm in order to minimize an objective function measured directly on the real system. While the approach has been shown to be effective, its applicability is limited in certain domains by safety considerations, as the algorithm may suggest controller configurations which lead to dangerous behaviours in some of the calibration experiments. In this paper, we modify the standard Bayesian Optimization algorithm by introducing explicit constraints for safe exploration of the controller configuration space. The constraints are derived based on a preliminary model of the process dynamics, which is assumed to be available. Aspects for efficient implementation of the proposed methodology are discussed. Simulation examples highlight the advantage of the proposed methodology for controller calibration over the plain Bayesian Optimization algorithm.
Anomaly based Intrusion Detection Model using Supervised Machine Learning Techniques

An intrusion detection system (IDS) is a software system that keeps track of network traffic and looks for anomalies. Abnormal or unusual network changes could be signs of fraud at any phase from the start of an attempt to a complete intrusion. Since data sharing primarily depends on the internet, it must be safe. For internet security, data encryption and authentication are insufficient and firewalls are unable to identify fraudulent packets that are fragmented. Moreover, attackers frequently vary their strategy, equipment, methods and tactics which can have disastrous results and effects such as lost productivity, financial loss, data loss etc. So, it become essential to put in place an effective intrusion detection system which is a very challenging task. The various supervised Machine Learning (ML) algorithms are applied in this paper, like J48, Random Forest, Random Tree, Hoeffding Tree and Logistic Model to predict the accuracy of an IDS system. The analysis was performed on the basis of three categories of data split and the algorithm that gives the best accuracy is suggested for future predictions. The various performance measures like accuracy, execution time, precision, F-measure and ROC curve are also analyzed. Random Forest exhibits best accuracy of 99.84% at a split ratio of 80:20 ratio as compared to other ML algorithms in all aspects. The execution time to build and test the model is less incase of Random Tree. As accuracy is the prime concern for an intrusion detection system (IDS); Random Forest is suggested to be the best solution as it provides highest accuracy.
Establish a Dynamic Detection System for Metal Bicycle Frame Defects Based on YOLO Object Detection

The purpose of this research is to develop a real-time bicycle frame's defect detection system using YOLO (You Only Look Once) and machine vision. Firstly, the defect locations are manually selected and a database is established. Next, a Darknet method is used to train the YOLO model. Its static detection accuracy rate is 92.6%, and then the static training model is combined with a robotic arm and an industrial camera to perform dynamic detection verification. The result shows that its detection rate reaches 87%. Finally, the above-mentioned defect detection technology is used with the detection machine to complete the development of the online defect detection system.
Brain Tumour Detection Using Convolutional Neural Networks

Processing of medical images pose the greatest challenge when it comes to finding brain tumors. However, the traditional techniques that are used in MRI brain tumor segmentation are extremely time-consuming, and the classification can only be performed at a few diagnostic centers. In this paper we are proposing the VGG-19, ResNet50 and DenseNet121 architectures for classification and detection. The proposed system aims to categorize and distinguish the brain tumor images to identify and categorize the MRI images as “TUMOUR DETECTED” or “TUMOUR NOT DETECTED”. This proposed model architectures performed well and achieved 92% accuracy using ResNet50, 90% of validation accuracy using DenseNet121 and 88% validation accuracy using VGG19 architectures. ResNet50 achieved highest among other two architectures. Also, in this experimentation we validated all the three architectures using the Accuracy, Recall, Precision and F1-scores.
Determination of Dielectric Constant and Dissipation Factor of Printed Circuit Board by Microstrip With Ring and Straight-Line Resonator Measurements

This paper presents the determination of dielectric constant (DK) and dissipation factor (DF) of a printed circuit board (PCB) by microstrip structure for its easy fabrication as a test coupon in manufacturing, as well as the mature empirical formulas to extract DK and DF from measured data of microstrip propagation constant $\gamma$. The ring and straight-line resonators (SLRs) are utilized to measure $\gamma$ and different results are observed. The explanations of the data deviations with the modified approaches are proposed in this paper to improve the consistency. With the measured $\gamma$, the DK value can be extracted by an iterative process where the propagation constant $\gamma$ is calculated based on an initial DK using the microstrip empirical formulas with dispersion effects, and the process is continued until reaching the acceptable error between calculated value and the measured $\gamma$. The DF is computed based on the solved DK with conductor loss. The proposed method is examined by the PCB material of RO3003 using probing technique up to the fifth order of resonant modes in the frequency range of 18 GHz to 90 GHz.
Electric Vehicle Physical Parameters Identification

Electric vehicle physical parameters highly influence the modeling of its different systems. Although a simulation using data acquired from field tests can have satisfactory results, the parameters inaccuracy, due to either insufficient information or wear, can prevent a better performance. In this paper, 10 electric vehicle physical parameters are adjusted/calibrated by three metaheuristic algorithms: Particle Swarm Optimization, Genetic Algorithms, and Simulated Annealing. Moreover, tests on real short and long distance data sets were used in order to validate the proposed framework to EV model calibration. The results achieved indicate that parameter calibration is effective in the reduction of the modeling error.
Simulation modeling of the dynamics of the process of charging the pumped hydro storage station from wind turbine installation

Mathematical and simulation models of the dynamics of the process of water flow rate by a multi-unit hydraulic pumping station powered by wind electric installation taking into account the stochastic change of the instantaneous value of wind speed have been developed. The simulation model implements the solution of differential nonlinear equations describing the dynamics of rotational motion of a two-mass electro-hydro-mechanical system. The mutual influence of one inertial component on another is carried out through the electrical connection between the wind turbine generator and the electric motor of the hydraulic turbine. The model takes into account the nonlinearity of the aerodynamic characteristics of the wind turbine from the speed and angle of rotation of the blades the nonlinearity of the hydromechanical characteristics of the hydraulic turbine from the speed. The results of modeling shown certify the reduction of energy efficiency of wind turbines during the accumulation of their energy at the pumped hydro storage station due to the presence of wind speed pulsations.