Miracle-dz/UniFAR-FTUs huggingface dataset:
A Semantic Space-Time Event Representation Model in Production Equipment Monitoring of Technical State and Utilization Condition

Production equipment monitoring is implemented in emerging applications of Industrial Internet of Things (IIoT). The goal of monitoring is detection of technical state and utilization condition. The collected data form the basis of so called “digital twin”. In this paper, we introduce a semantic space-time event representation model, which is used for data fusion of multiple events on technical state and utilization condition of production equipment. The model describes monitoring objects and their surrounding context. The model considers a hierarchy of production equipment nodes and implements rules for identifying and composing events in the production process. The model is oriented to a graph database for collecting and linking the evens in space and time dimensions. As a result, efficient search queries can be used to construct diagnostic and predictive services on top of the database.
Dynamics Modeling and Optimization for the Tilting System of Unmanned Aerial Vehicles

The tilt-rotor Unmanned Aerial Vehicles (UAVs) possess not only the ability of long-duration and high-speed cruising of fixed-wing aircraft, but also the advantages of flexible taking off, landing and hovering of rotor aircraft. The tilting system is what makes the tilt-rotor UAV convert between multi-rotor mode and fixed-wing mode. This paper establishes a multi-body dynamics model for the tilting system through Newton-Euler approach, based on which the system is optimized. Numerical examples are given to show the effectiveness of the proposed model and optimization method.
Drone Micro-Doppler Identification with Radar Calibration

Discriminating drones from each other and from other similarly sized objects such as birds is an ongoing problem. This paper presents a methodology for calibrating radars with corner reflectors to empirically verify the range-bin to range conversion. Radar returns of drones are analyzed using time-frequency methods. Using a drone that mimics a bird, the ability to differentiate a drone from the bird is demonstrated using helicopter rotor modulation (HERM) lines.
AI-Driven Intelligent Vehicle Behavior Decision in Software Defined Internet of Vehicle

Intelligent driving technology plays a key role in reducing road traffic accidents and ensuring driving, where the vehicle behavior decision making capability largely determines the driving performance of intelligent vehicles. At this point, more research is focused on enhancing vehicle environment identification and vehicle control capabilities, with decision-making systems receiving less attention. In order to improve the accuracy of intelligent vehicle behavior decision making and ensure the active safety of path planning, this paper firstly establishes an edge intelligence based software-defined Internet of Vehicles (ESIOV) architecture. Then, a POMDP-based intelligent vehicle behavior decision model is designed using the time-series iterative property of partially observable Markov decision process (POMDP). Finally, a reinforcement learning vehicle behaviour decision (ERVBD) algorithm based on edge intelligence is proposed to ensure the accuracy of intelligent vehicle behavior decision results and improve the decision speed. The simulation results show that ERVBD can successfully implement vehicle behavioural decision making, enabling intelligent vehicles to anticipate collision risks and adopt reasonable avoidance strategies in real time.
Pole-Slot Combination Design and Investigation of Spoke-Type In-Wheel Motor Considering Flux Modulation

In this paper, a Spoke-type flux-modulated permanent magnet in-wheel (SFMPM-IW) motor based on flux modulation effect is proposed. The key is to derive the relationship between the pole-slot combination and the power factor and torque based on the principle of flux modulation. And, the best pole-slot combination is determined by comparing the performance of the motor under different pole-slot combinations. By selecting torque and power-factor as targets, the performance advantages of the motor are deeply explored. Finally, the optimal pole-slot combination of the SFMPM-IW motor is determined and its comprehensive performance is analyzed. Both simulation results and theoretical analysis verify the validity of the method to analyze the pole-slot combination design and investigation of the SFMPM-IW motor considering power factor and torque.
An Investigation of a Power Module for Multiple Series-Connected Si-MOSFETs Realizing Voltage Balance by a Fully Digital Active Gate Control

This paper presents a concept of a power module with multiple series-connected power devices controlled by fully digital active gate drivers. The performance of series-connected power devices with lower voltage ratings is expected to be higher than the case of using only one device with higher voltage ratings. The required voltage balance of each power device to prevent an overvoltage issue can be achieved by a fine adjustment of the switching speed using the active gate control. As a first design example, a prototype module configured mainly by two series Si-MOSFETs, active gate drive circuits, and a fully digital controller was designed and developed. It was experimentally verified that suitable active gate control can realize the voltage balance in two series-connected MOSFETs under continuous operation.
Operation of Single-Delta Bridge-Cell Converter With Single-Phase Medium-Frequency Transformer Under Low Magnetizing Inductance

A single-delta bridge-cell (SDBC) converter with a single-phase transformer operating at 150 Hz can exchange the active power of 37% of the rated reactive power with three-phase ac mains without increasing voltage and current ratings of the SDBC converter. Meanwhile, it is difficult to achieve negative-sequence reactive power control because of high magnetizing inductance of the transformer, which is required under grid fault conditions. To achieve the negative-sequence reactive power control, this article applies a single-phase transformer with low magnetizing inductance to the converter at the cost of exchangeable active and reactive power. The relationship of magnetizing inductance, active power, and reactive power is revealed in this article, and the operation of the converter under low magnetizing inductance is verified experimentally using a downscaled 110-V, 7.0-kVA model.
Stochastic Learning Control Framework in the Integrated Frequency and Position Domain

This paper proposes stochastic learning control with disturbance observer and Gaussian process to achieve the robust-ness against disturbance. Disturbance observer is designed from a frequency domain perspective to effectively remove disturbances within a desired range. The Gaussian process stochastically models disturbances that may have high frequency components in the time domain but have a definite period in the position domain. As a result, the synergy between DOB and Gaussian model improves control performance. In addition, the stability of the Gaussian model is theoretically verified and the proposed method is compared under various conditions.
Dried Fish Classification Using Deep Learning

Dried fish is a great procedure for fish reservation all over the world. Dried fish is evaluated as a choice food on the menu for a large number of Bangladeshi people. Dried fish is also considered as a proper origin of vitamins, minerals, and protein in the meal of people in numerous portion of the world along with Europe and Asia. Bangladesh is now the Asia’s fifth source of inland water fish, after only China and India (2020-2021). Dried fish is mostly produced from saltwater fishes captured by the fisherman and put up for sale all over the country by many steps of trading to arrive the customer. Thus lots of fresher men and businessmen engaging in the trading business of dried fish. It is very crucial for the fresher man, businessman, and others people who want to involve this business to observe that; which type of fish drying will be profitable according to the market value and easier and low-cost drying method. The paper can assist fishermen, businesspeople, and common citizens in identifying the many types of dried fish. This set of data contains locally cognized dried fish like Bashpata, Chanda, Chapila, Chingri, Chouka, Dhela, Fesha, Ilish, Kachki, Loitta, Maya, Puti, Shundori, and Tengra. Some pictures of this dataset are collected by us then we have segmented and then augmented this dataset, this model is a trained Deep Learning and Convolutional Neural Network (CNN) based model for classifying dried fish. The present model achieved an accuracy of 97.72%.
Analysis and Winning Prediction in T20 Cricket using Machine Learning

It was exceedingly hard to forecast the winner of cricket match before the game started in the earlier statistical analysis of sports. Cricket is a popular sport in many nations throughout the world, and it is India’s most popular sport. Cricket predictions have always caught the interest of fans. Individual players, teams, games, and seasons in sports games provide a variety of statistical data. We all always try to predict the game of result by ourself but Machine learning has benefited in enhancing the prediction power. The objective of this paper is comparative analysis of various Machine learning algorithms for predicting the winning team. To achieve our goal, various supervised classification algorithms are used, like logistic regression, random forest regression, k-Nearest Neighbor and Support Vector Machine, Naïve Bayes and Decision Tree.
Comparative Evaluation of MVAC-LVDC SST and Hybrid Transformer Concepts for Future Datacenters

A transition from state-of-the-art 400 V AC to 690 V AC or 800 V DC (±400 V DC) distribution improves the overall power conversion efficiency of datacenter power supply systems. The latter requires megawatt-level isolated medium-voltage (MV) AC to low-voltage (LV) DC conversion stages that operate with an efficiency of at least 98 %. Whereas today's most advanced industrial MVAC-LVDC solid-state transformer (SST) prototypes do achieve this, they are highly complex and do not show any clear advantages in terms of efficiency and power density over alternative approaches that retain the low-frequency transformer (LFT) to provide isolation and decoupling from harsh MV grid realities. We thus comparatively evaluate MVAC-LVDC SSTs against such alternative concepts, i.e., LFTs with LV-side SiC PFC rectifiers, or 12-pulse rectifier systems hybridized with active filters that just provide sufficient power processing capability to achieve unity power factor operation.
BLE Fingerprinting Automatic Calibration using an Ultrasonic IPS

Radio-frequency Received Signal Strength (RSS) based positioning systems have become the most important alter-natives for regions where satellite measurements are unavailable. However, the calibration stage of these systems still presents several drawbacks due to the technical limitations in accurate measurement solutions. In this work, we propose an automatic RSS calibration system for fingerprinting positioning in the context of Bluetooth Low Energy (BLE), using the advantages of ultrasound time of flight estimations. The proposed system allows using the complete RSS distributions, improving positioning results in more than 0.3 m in static positioning and robustness in dynamic positioning. However, the most significant contribution of this work is the reduction of the calibration work by automatizing reference measurements without human presence, allowing for longer calibration times and greater numbers of measurements.
A Stress Emulation Method for Concurrent Testing of AC and DC Capacitors

This paper proposes a capacitor testing method that can concurrently emulate the electrical stresses of both AC and DC capacitors for high-power converter applications. It preserves the advantages of a recently reported method with a minimum required power sup-plies and is robust to testing sample degradation, which, however, limits to test either AC or DC capacitors. The circuit architecture and application examples are presented. Experimental results verify the feasibility and effectiveness of the proposed testing method.
POS-Trends Dynamic-Aware Model for Video Caption

Video caption aims to generate descriptive sentences about the video, and the most critical problem is how to achieve accurate word prediction with standardized and coherent syntax structure, which requires the model to thoroughly understand video content and precisely map them into corresponding sentence components. Many existing methods usually fuse different video features into a single visual feature for generating sentences. However, they ignore the word dataset prior information in the annotations (such as Part-Of-Speech) and they also ignore the association between sentence components and types of visual features. To solve these problems, we propose a POS-trends dynamic-aware model (PDA) to fully exploit the word dataset prior information in the captions to predict POS tag, so as to assist generating captions. We propose a POS feature extraction (PFE) module to use different filters to extract different POS-trends features, predict POS tags and fuse visual features. Furthermore, we propose a visual-dynamic-aware (VDA) module to dynamically adjust the mapping way of words and supplement the visual information into the local features. The fusion features provide directional visual information to generate correct words, and the predicted POS tags to guide the decoding process to generate a more standardized and coherent syntax structure. A large number of experiments based on MSVD, MSR-VTT and VATEX demonstrated that our method outperforms the state-of-the-art methods in BLEU-4, ROUGE-L, METEOR, CIDEr. Code can be available at: https://github.com/WangLanxiao/PDA-for-video-caption.
ADALINE Current Control for Single-Phase Active Power Filter

The use of electrical appliances of general households today is a single-phase electrical power system that often encounters problems while operating because of harmonics current. There are several researchers approached harmonic reduction techniques. However, those approaches are very complicated in controlling the compensated current. Therefore, this paper presented an ADALINE control technique for controlling the compensated current of single-phase active power filter to improve the electrical power quality. The proposed method in this paper is comparing with the famous techniques - PI and Hysteresis techniques. Two switching techniques, which are PWM and Pulse, are adopted in this research. The hardware in the loop is applied for the system performance verification in two different load intervals. After compensation, the proposed method performs better performance for controlling than PI and Hysteresis techniques in both load intervals. In the first interval, ADALINE gives 6.85 - 29% reduction of % THD comparing to PI technique, while in the second interval gives about 19% reduction of % THD comparing to the Hysteresis technique. The source current is also nearly sinusoidal and the electrical power system has a unity power factor.
What is Discussed About Software Engineering Ethics on Stack Exchange (Q&A) Websites? A Case Study

Software engineering ethics has been an interest of the software engineering community for over two decades, as evidenced by the creation of the joint ACM/IEEE-CS Soft-ware Engineering Code of Ethics. When reviewing the cur-rent software engineering literature, it was found that Stack Exchange question-and-answer (Q&A) websites are particularly good means of gaining a better understanding on a given subject: what topics are addressed within a subject area, the popularity of these topics, and the difficulty of these topics. As such, this paper utilizes Stack Exchange Q&A websites to review software engineering ethics and presents a case study that aims to provide a better understanding of the presence of software engineering ethics on three popular Stack Exchange Q&A websites, which are Stack Overflow (SO), Software Engineering (SE), and Project Management (PM). The study analyzes over 2,170 posts, using Latent Dirichlet Allocation (LDA), to better understand what ethic-related topics are discussed. Subsequently, the authors analyzed the popularity and difficulty of each identified topic. The study found that users discuss six ethic-related topics, which are as follows: web scraping, software quality, software security, open-source software usage, team and employers, and software billing. Moreover, the results revealed that software security is the most popular and the most difficult topic. This study highlights ethic-related challenges that are faced by Q&A website users. Software practitioners, researchers, and educators can utilize the results presented in this study to pursue new avenues when addressing pressing issues related to software engineering ethics.
Automatic Placental Distal Villous Hypoplasia Scoring using a Deep Convolutional Neural Network Regression Model

Distal villous hypoplasia (DVH) is a placental abnormality associated with fetal growth restriction, which can be assessed in histopathological images of the placenta. In this study, we use a convolutional neural network (CNN) regression model to score DVH in 724 placental images (grade 0 = no DVH; grade 1 = mild to moderate DVH; grade 2 = severe DVH). We applied a patch-wise approach to train the network and scored the whole image by averaging the patch scores. Image scores were compared to a ground truth score obtained by manual scoring by a pediatric pathologist. We obtained a mean squared error of 0.19 and a strong Pearson’s correlation (r = 0.82) between the image score by the CNN and ground truth. Results suggest that this deep learning approach is a promising approach for automated analysis of placental histopathology images.
Tunable Chipless RFID Pressure Sensor Utilizing Additive Manufacturing

Each sensing application has its own set of unique requirements and constraints that must be satisfied by the sensor. These requirements can include being wireless, passive, non-contact, customizable, multi-functional, and/or minimally invasive, depending on the application. Chipless RFID tags are a type of microwave sensor that have the potential to meet many of these requirements, especially when additive manufacturing (AM) is employed to create customizable substrates that are sensitive to different ranges of environmental measurands. This work examines the efficacy of a proposed chipless RFID pressure sensor that utilizes an AM substrate. Complex permittivity and stress-strain measurements were conducted to develop a model that relates the resonant frequency of the chipless RFID sensor to pressure, which in turn allows for the design of pressure sensors with customized sensing ranges and sensitivities. Simulations and measurements of a fabricated tag were conducted to provide a proof-of-concept of the developed model and sensor design approach.
Impedance scanning with chirps for single-phase converters

Impedance scanning of single-phase AC-DC converters is challenging due to frequency couplings, which map one injection frequency to a multiple of output frequencies. Single-tone injections are often preferred over wideband signals, as spectral overlap negates the benefits provided by quick-to-implement wideband scans. In this paper we take advantage of the time-frequency properties of chirp excitation signals, and develop a model so that a single chirp sweep can provide the impedance frequency response - formulated as a Harmonic Transfer Matrix, which takes into account the frequency couplings. Though we concentrate on single-phase AC-DC converters, the general HTM formulation extends application to any linear time-periodic and linear time-invariant system. Numerical impedance measurements are presented for a single-phase grid-connected converter, operating in grid-forming mode with dispatchable Virtual Oscillator Control.
What is my heart rate right now? Comparing data from different devices

The extraordinary development of wearable devices and Internet of Things (IoT) technologies has substantially expanded the opportunities offered to remote healthcare systems, allowing to monitor physiological parameters out of clinical settings. One of the most important parameters is surely a subject’s heart rate (HR). A key point in the design of a reliable remote healthcare solution is the proper selection of the devices: here, the focus is on the accuracy of HR data collected by wearable devices, which is still debatable, since rigorous validations are rarely applied. This paper aims at comparing the HR values obtained by different devices, namely smartwatches, chest-strap, pulse-oximeter, and by different methods, like the oscillometric-based one and the palpatory one, using an electrocardiogram as the reference measurement instrument. Results show different accuracy and precision values included in the ranges [-1, 3] bpm and [4], [12] bpm, respectively, thus confirming the importance that manufacturers make these figures available, for a better device selection depending on the target application.
Signals Features Extraction in Radioisotope Liquid-Gas Flow Measurements using Autocorrelation Function

Knowledge of the two-phase flow structure is essential for the proper conduct of industrial processes. Description of liquid-gas flow regimes is possible by using of data analysis in time, frequency, or state-space domain. In this research studies, the autocorrelation function is applied for analysis of signals obtained for liquid-gas flow by use gamma-ray absorption. The experiments were carried out on the laboratory hydraulic installation fitted with Am-241 radioactive source and scintillation probe with NaI(Tl) crystal. Four types of flow regimes as plug, slug, bubble, and transitional plug - bubble were studied in this work. It was found that the selected amplitudes of the normalized autocorrelation function are helpful to recognize the flow regime.
An expert system for early glaucoma pathology detection

In medicine, the decision is considered to be the center of the medical act. Thus, many decision support systems have been developed in this area during the past decades. The purpose of these applications is to support medical staff in their decision-making. In this context, we propose to exploit an artificial intelligence tool in the detection of glaucoma. This is a disease that affects the optic nerve, resulting in its destruction as well as an irreversible loss of vision. Indeed, we describe in this paper the conception and development of a medical decision support system based on fuzzy logic classification. These parameters will be combined with other factors known as risk factors in order to obtain the best results. The developed system achieved an accuracy of 96%.
Electromagnetic Field and Energy Flux in Wireless Power Transfer System

Wireless Power Transfer (WPT) is proposed as a power supply method for electric vehicles. WPT has been currently being commercialized in the 85 kHz band, although it is considered for use in the 13.56 MHz band (ISM band) and other high frequencies where couplers can be made more compact in the future. Since there is a possibility of unexpected energy transmission in the high-frequency band, it is necessary to design couplers that consider it. Therefore, in this study, we evaluated the relation between circuit elements such as inductors, capacitors, resistors, and the electromagnetic field in space from the energy perspective and also elucidated the energy transmission mechanism. In addition, the effectiveness of the method was demonstrated by adapting it to the Inductive Power Transfer.