Miracle-dz/UniFAR-FTUs huggingface dataset:
Research and design of intelligent guidance system based on deep learning

At present in Chinese hospitals, patients need to choose their own medical departments for medical treatment, often resulting in wrong registration, causing patients to travel back and forth, delay the condition and easy to cause doctor-patient disputes. In order to help patients correctly select appropriate departments and doctors, and successfully register for medical treatment, this paper analyses and designs an intelligent guidance system based on deep learning. This paper designs the work flow and overall structure of the system, and studies the key technologies adopted by the system. The intelligent guidance system can make use of the deep learning algorithm to triage departments according to the self-reported symptoms of patients, and assist to make an appointment, so as to provide efficient medical services for patients, reduce the workload of medical staff, improve the satisfaction of patients have positive significance.
Health Monitoring of IGBTs Using Machine Learning Techniques

Insulated-Gate Bipolar Transistors (IGBTs) are widely used in medium to high-power applications like switched-mode power supplies, traction motor inverters and induction heating. Their reliability is an essential requirement for safe operation of any PE application. To determine the condition of an IGBT, its state of health (SOH) must be evaluated. Since it is challenging to measure the SOH directly, prediction methods can be used to provide an estimate of this variable. The IGBTs were subjected to accelerated ageing experiments, resulting with gathered data of various parameters including on-state voltage drop, as well as minimum and maximum junction temperature for each power cycle. The thermo-mechanical stresses induced during this process, lead to irreversible damage of the modules and causes permanent failure of the device. In this paper, a data-driven approach is taken. Low quality data leads to inaccurate predictions, and, therefore, processing the data prior to feeding it to the model is necessary. Concepts like input shape of data and window size are introduced, to define the input shape of the data. The purpose of the model is to output the SOH of the IGBT device, when voltage drop and maximum junction temperature are inserted at input. When the prediction of the model is 0% or close to 0%, maintenance or replacement of the device is required. Prediction of the SOH of the power modules close to ‘real time’ is also presented. The aim of the model is to determine when the device is no longer compliant for usage by giving an idea of the SOH of the device in question. This prediction gives an insgiht of how the model behaves prior to, at, and after end of life of the tested discrete device. As a conclusion, the developed model can be used for evaluation of SOH and gives an insight to the condition of the power electronic device. The objective of this paper is to contribute to health monitoring of power semiconductor devices.
End-of-Life Comparison of Full-Bridge and Half-Bridge DC/DC Converter Switches Used for EV Charging

EV fast chargers are essential in addressing the concern of limited driving range for E-mobility applications. However, the load profile of a converter for fast charging involves a high-current pulse that can last for a few minutes to efficiently replenish the EV battery, which is followed by a cooldown period after the charging process is finished. This results in thermal cycles that can lead to thermo-mechanical fatigue and degradation of power electronic components, thereby impacting device lifetime. This paper presents a comparative study on the reliability of power devices in isolated half-bridge and full-bridge DC-DC converters in EV fast chargers. The study focuses on the differences in thermal stresses that Si switches experience in each converter during charging cycles and how it impacts the end-of-life of each device. This study provides valuable insights for selecting reliable power converters for EV fast charging applications.
Assessing the Impact of EV Charging and Discharging Profiles on T-Type Active Front End Charger Lifetime

This paper aims to assess the lifetime of a T-type active front end (AFE) converter in the context of Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) applications providing various grid services. To achieve this goal, the research paper presents two G2V and three V2G profiles and measures the junction temperature oscillations for the entire mission profile. Then, the temperature profiles are imported into a reliability assessment model to determine each mission profile's impact on the system's reliability.
experiencias dendroclimáticas con árboles de ecosistemas contrastantes de c Experiências dendroclimáticas com árvores de ecossistemas contrastantes da Colômbia Dendroclimatic Experiences with Trees from Contrasting Ecosystems in Colombia

The article presents the results of different dendroclimatic research projects carried out in three contrasting ecosystems in Colombia. Dendrochronology techniques and densitograms were used to obtain the growth and density chronologies, respectively. Findings showed significant relations between chronologies, local climatic variables, and ENSO, for example: the density of the growth rings of Rhizophora mangle to precipitation; the width of the growth rings of Parkinsonia praecox and Capparis odoratissima to precipitation, air temperature, and sea surface temperature; and the width of the growth rings of Prioria copaifera to the limnimetric elevation of the Atrato River. These results demonstrate the applicability and potential of dendroclimatology in tropical regions.
Leveraging Machine Learning for Predictive Analysis in Incomplete Data Scenarios

The workplace conditions, such as optimal CO2level and humidity are essential factors to ensure the safety of employees. The work safety concerns established practices to protect employees' health and ensure wellbeing. Studies show that various human and environmental factors impact infections spreading across the office space. By monitoring these factors a less infectious workplace can be created. To reach this goal, an IoT enabled solution DROVIDS with continuous environment monitoring capabilities is proposed. The solution works with near real-time data and integrates predictive analysis, allowing to gain a comprehensive understanding of the environment and detect risks precisely. The solution enables near real-time response and provides a straightforward and immediate operational setup. The DROVIDS analytical service offers adjustability, it can be used without the missing data prediction component if there is no intention of calculating risk and receiving possible mitigations for future planned events. This paper presents DROVIDS architecture, design and operational principles.
Feed-Forward Technique to Emulate Natural Sampling Method for Cascaded H-Bridge Converters

Phase-shifted pulse width modulation (PS-PWM) is one of the preferred modulation strategies for cascaded H-bridge (CHB) converters due to its excellent harmonic performance. Nevertheless, this modulation strategy needs a fast sampling frequency, which can increase with the number of H-bridge sub-modules (SMs). Moreover, the advantages related to the harmonic content of the output voltages are usually lost for applications that require unbalanced operation conditions regarding SM power or voltage references, such as solar PV or battery energy storage systems. This work proposes a feed-forward compensation technique that reduces the sampling frequency to two times the carrier frequency while maintaining the good harmonic performance of the CHB output voltages. Simulation results are carried out to compare and demonstrate the superior performance of the proposed technique.
Keeping the Local Traffic Local – The NaissIX Internet Exchange Point Success Story

With the increased demand for additional bandwidth, when it comes to the Metropolitan Area Networks, there is also a growing need to keep the local traffic local. Preventing the traffic of local origin from "spilling" over the MAN borders, or maybe even country borders become one of the very important tasks. This is where local Internet Exchange Points can help. This paper illustrates possibilities local IXP implementation has to offer (city of Nis, Serbia in this case) and improvements that can come out of it.
Laser-Induced Graphene for Wearable Respiratory Monitoring

Laser-induced graphene (LIG) shows promise as an active sensing material for a wearable respiratory monitoring system. This work describes the integration of a graphene-based sensor and an electronic system for real-time respiratory monitoring. The sensor transduces body movements during breathing into resistance changes after which the resistance is converted into a digital signal using the developed electronic system. An adjustable current source provides optimal biasing based on microcontroller calibration. Measurements on a supine subject exhibit good waveforms corresponding to breathing cycles. While signal filtering is needed to improve the signal-to-noise ratio, the capability of laser-induced graphene for respiratory monitoring is demonstrated. With optimization of material synthesis and electronics, the laser-induced graphene sensing concept could enable non-invasive respiration monitoring for advanced healthcare applications. The further development of wireless connectivity, data storage, user interfaces, and algorithmic processing could allow the practical deployment of this technology for diagnostic applications.
Barrier Function-based Adaptive Super Twisting Active Fault Tolerant Control for Robotic Manipulators with Actuator Faults

This paper investigates the issue of active fault tolerant control (AFTC) for manipulator with actuator faults. First, a higher-order sliding mode (HOSM) observer for fault diagnosis is constructed to estimate unmeasured states and compensate for the actuator faults simultaneously. Then, a feedback fault tolerant control (FTC) strategy is proposed to guarantee system's convergence in finite time and to reduce chattering phenomena based on the nonsingular fast terminal sliding mode (NFTSM) and barrier function-based adaptive super twisting (BFAST) algorithm. The finite time stability of the proposed FTC scheme is demonstrated by Lyapunov theory. Finally, simulations are performed to demonstrate the effectiveness of the proposed control method.
Implications of Forest Management in Response to the 1970's Mountain Pine Beetle Infestations on Grizzly Bears and Moose in the Flathead Drainage

3 Introduction . 5 Study Area . 6 History of mountain pine beetle in the Flathead Valley.6 CHAPTER 1. Implications of 1970s Salvage Logging of Mountain Pine Beetle Trees and Gas Exploration on the Population of Grizzly Bears, by Bruce McLellan . 12 Introduction . 12 Study Area . 13 Methods . 14 Results . 16 Discussion.22 Acknowledgements.28 Literature Cited . 28 CHAPTER 2. Grizzly Bear Population Distribution and Habitat Selection in Response to Influence From Mountain Pine Beetle Infestation and Management in the Flathead Valley. by Clayton Apps.31 SECTION 1: Grizzly Bear Population Distribution Response . 31 1.1 Objective . 31 1.2 Methods . 31 1.3 Results . 33 SECTION 2: Grizzly Bear Habitat Selection Response . 35 2.1 Objective . 35 2.2 Methods . 35 2.3 Results . 37 SECTION 3: Factors Influencing Human-Caused Grizzly Bear Mortality . 38 3.1 Objective . 38 3.2 Methods . 38 3.3 Results . 39 Literature Cited . 42 CHAPTER 3. Microsite habitat selection of stand characteristics and food resources for grizzly bears and moose in mature pine, young pine, and recent clearcuts in the Flathead River drainage of British Columbia. by Robin Munro.44 Objectives . 44 Methods . 44 Results . 51 Discussion.63 Literature Cited . 64
Seven Levels Inverter Using Current Model Predictive Control for Household V2G, V2H and G2V

In this paper a micro-grid configuration is proposed to enhance battery charger of electric vehicle for residential household, V2G, V2V and V2H. The micro-grid configuration consists of a single phase seven level PUC, a single phase Dual Active H Bridge (DAB) and PV solar to support fast charging operation of EV batteries. A single phase seven levels acting as interface between different energy sources and acting as compensator for current harmonics and reactive power. A current Model Predictive control is adopted to take advantages of low switching function, easy to regulate both DC voltages through the cost function. A lower DC bus voltage is a great advantage which represents (2/3) of the Vdc compared to the maximum grid voltage. A Boost converter acts as solar controller to extract maximum power from the PV solar. A non-linear control approach is proposed for single-phase dual active bridge (DAB) to control the power through the phase shift angle to charge and discharge the battery of electric vehicle using constant current mode and constant voltage mode (CC/CV) to provide protection for the electric vehicle (EV) battery.
Analysis of Self-Oscillation Frequency in an Inductive Power Transfer System

Inductive power transfer (IPT) systems conventionally operate at the natural resonant frequency for efficient power transmission, but their output power is sensitive to mutual inductance changes. Recent studies have shown that parity-time-symmetric IPT systems have two splitting frequencies in the strongly coupled range, enabling robust wireless power transfer with constant output power and high efficiency. Self-excited oscillation circuits can automatically track these optimal frequencies without measuring circuit parameters. However, practical self-oscillating systems contain voltage harmonics and feedback delays, which are often ignored in the analysis of steady-state performance. In this paper, the effects of high-order harmonics and time delay on steady-state frequencies are systematically analyzed. First, the formula of ideal bifurcation frequency is derived based on the zero-phase-angle condition. Then, steady-state conditions under square wave voltage and feedback delays are obtained using the Fourier series and superposition theorem. Finally, numerical examples demonstrate that exceptional points shift towards stronger coupling due to harmonics, whereas time delay could eliminate frequency bifurcation.
Predicting and Identifying Antimicrobial Resistance in the Marine Environment Using AI & Machine Learning Algorithms

Antimicrobial resistance (AMR) is an increasingly critical public health issue necessitating precise and efficient methodologies to achieve prompt results. The accurate and early detection of AMR is crucial, as its absence can pose life-threatening risks to diverse ecosystems, including the marine environment. The spread of AMR among microorganisms in the marine environment can have significant consequences, potentially impacting human life directly. This study focuses on evaluating the diameters of the disc diffusion zone and employs artificial intelligence and machine learning techniques such as image segmentation, data augmentation, and deep learning methods to enhance accuracy and predict microbial resistance.
Markov Chain Based Explainable Pattern Forecasting

The explosive penetration of artificial intelligence (AI) and machine learning (ML) based technologies are dramatically transforming the traditional decision support systems. We consider the pattern recognition and forecasting for demand timeseries in a business-to-business supply chain where demand exhibits high volatilities, non-stationarities, and skewness. We develop a pattern forecasting system by developing a data driven, feature dependent Markov chain-based framework. This may include any arbitrary user defined pattern qualitative in nature, such as plummet or recovery from plummet. To increase adoption of AI based techniques among the various stakeholders (e.g., sales, marketing, procurement, production planning) the inherent modeling and forecasting of different patterns needs to be explained in terms of domain knowledge the user is more familiar with. We therefore define two metrices to evaluate explainability to enable cross scenario comparison as the notion of explainability lacks mathematical precision. These are, namely, relevance and informativeness. Relevance is measured by direct scoring from the user whereas informativeness is inspired by the fundamental concept of measuring differences or discrepancies between distributions and for the sake of simplicity in our case measured by the variance in the main attribute of explainability. Moreover, our dataset is high dimensional where number of columns are much higher than number of rows and therefore the method for selecting features for fitting the Markov chain is extremely critical. To provide guidelines on selecting different attributes of our pipeline, we compare between feature selection methods from two families, one advanced and one traditional. We perform extensive evaluation with real dataset obtained from a business division belonging to the Panasonic Industry Co. Ltd and observe a sparsity promoting feature selection method performs better in terms of accuracy and explainability.
Power and Thermal Cycling Testbed for End of Life Assessment of Semiconductor Devices

The reliability of semiconductor power devices can be studied by performing a thermal and power cycling test. In order to create the desired temperature cycles, there are four free variables to select during the power cycling test, namely the heating current, heating time, cooling time, and heatsink temperature. In this paper, the relation between the selected variables and the minimum and maximum junction temperature is extensively tested for the silicon IGBT with serial number IKP06N60T. Furthermore, the thermal model is discussed and verified and a rough estimate of the electrical resistance, thermal time constant, thermal resistance, and thermal capacitance are calculated.
Fractional Power Processing Architecture for Ultra-Fast Charging of Electric Vehicles

This paper proposes a novel fractional power processing (FPP) based solution for developing efficient ultra-fast charging (UFC) infrastructure for electric vehicles. The proposed strategy allows only a fraction of power to be processed through the converter, while rest of the power is bypassed through a series path from source to load. This improves the overall power conversion efficiency and reduces the current and voltage stress on the devices. In addition, the proposed FPP based architecture utilises the battery discharge voltage itself for charging of the battery, thus reducing the required DC voltage at the source. The operation of the converter is discussed and its advantages are mathematically established. A laboratory prototype of the proposed solution with current-fed resonant dual active bridge converter is developed and its steady-state behaviour is verified. Further, the performance of the proposed architecture is compared with the full rated converter through a 48 V battery charging, and the experimental results are presented.
Substrate Integrated Coaxial Line Millimeterwave Components Manufactured in Standard PCB

We propose a packaging technique for the intricate routing networks in millimeterwave (mmWave) applications based on substrate integrated coaxial lines (SICLs), to eliminate spurious feed network radiation that may influence the antenna array's radiation pattern. Next to a thorough comparison with grounded co-planar waveguides (GCPWs), a coaxial via transition and a novel packaged hybrid coupler are designed, fabricated and measured in the 28 GHz 5G bands. The proposed shielded coaxial via transition from SICL to SICL exhibits a measured insertion loss smaller than 0.7 dB from 24 GHz to 30 GHz, while the measured amplitude imbalance of the hybrid coupler remains below 1 dB and the phase imbalance does not exceed 6.
Cybersecurity Challenges and Solutions in IoT-based Precision Farming Systems

Adaptation of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), and block chain in agriculture has revolutionized farming activities, offering several benefits such as increased efficiency, reduced costs, and improved crop yields. However, these advancements also have their fair share of security challenges, opening space for vulnerabilities that adversaries can exploit and compromising agricultural IoT networks, autonomous farming equipment, and vehicles. This may lead to compromised services and devices disrupting farming activities, causing losses to the farmers. This paper captures the state-of-the-art review of IoT-based precision farming systems, including the technological applications, cybersecurity challenges, and mitigation measures to secure the agricultural environment.
Developing the Australian mid-infrared spectroscopic database using data from the Australian Soil Resource Information System.

We developed and tested spectroscopic models derived by partial least squares regression (PLSR) using data from the Commonwealth Scientific and Industrial Research Organisation’s (CSIRO) national soil database (NatSoil) and soil samples from the Australian National Soil Archive. Models were constructed for 21 soil attributes, and their predictability assessed using the R2, ranged from 0.57 for bicarbonate extractable available phosphorus to 0.92 for the sum of the exchangeable bases. Investigating the spectral library coverage with a suite of 1484 unknown samples from a geochemical survey of Australian catchments, we found that the models could be used to predict many soil attributes in a geographically diverse set of samples.
Quasi-Minimum-Time Positioning Control of an Ultrasonic Motor Using Control Barrier Function

Ultrasonic motors have the potential to achieve high-precision servo control due to their high static torque. However, the motors have various nonlinearities because they are driven by the frictional force resulting from the piezoelectric conversion. In this paper, we aim to achieve fast and precious control for an ultrasonic motor servo system by designing a minimum time controller and a compensator to attenuate chattering phenomena. We first design a proportional-integral (PI) controller for a simple mathematical model proposed by a previous study. Then, we redesign the controller to a minimum time controller for fast control. To attenuate chattering phenomena caused by the discontinuity of the redesigned controller, we further improve the controller by borrowing the strategy of a control barrier function method. The usefulness of the proposed control design is confirmed by numerical simulation and experiments.
Robust Regulation of Oxygen Excess Ratio and Cathode Pressure for PEMFC Air Supply Systems with Centrifugal Compressor

This paper studies robust regulation problem of oxygen excess ratio (OER) and cathode pressure for proton exchange membrane fuel cell (PEMFC) air supply systems with centrifugal compressor. With the speed of the centrifugal compressor and the valve opening degree as control inputs, a robust output regulation problem is formulated for the PEMFC air supply system modelled as a two-input two-output nonlinear control system. To overcome the difficulties of nonlinearities, uncertainties and couplings between input-output channels, we introduce a change of coordinate to transform the original problem into a robust output stabilization problem of a two-input two-output nonlinear system. For such a problem, an “ideal” full-information controller is designed via feedback linearization, based on which an extended-state observer-based control method is proposed, robustly regulating the OER and cathode pressure arbitrarily close to desired values. Simulations are made to demonstrate the effectiveness of the proposed method.