Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment for INS/GNSS/Odometer/Barometer Integration in Loosely-Coupled and Tightly-Coupled Scheme in a GNSS-Degraded Environment

With the increasing demands for seamless landvehicle navigation, systems with robust performance are required in highly urbanized areas. The traditional INS/GNSS integration is widely applied to solve this issue. However, the system still suffers from bad GNSS signal reception and INS time accumulated errors that seamlessness and stability are difficult to maintain. In this study, the performance of the low cost INS/GNSS with aiding sensors, such as an odometer and barometer, was evaluated for both the loosely-coupled (LC) scheme and tightly-coupled (TC) scheme. Moreover, considering barometric error accumulation, a vehicle-behavior based drift control method has been proposed. An experiment was conducted under harsh GNSS-degraded scenarios to assess the characteristics and performance for different sensor combinations, using single constellation (GPS) with single-frequency (L1 band) measurement. Overall, the TC scheme without additional strategies in detecting abnormal measurement encounters more challenges to achieve stable performance. In an INS/GNSS/barometer system with the proposed drift control method, error accumulation under unpredictable environmental changes was successfully mitigated in both schemes. The proposed method can maintain a height accuracy of 2-meter level root mean square even after a long term operation. In an INS/GNSS/odometer combination, improvements were observed in the horizontal and vertical direction for both schemes. According to statistical analysis, an INS/GNSS/odometer/barometercombination shows 16.57% and 6.11% in the horizontal, and 30.71% and 71.28% in the vertical for the LC scheme and TC scheme, compared with an INS/GNSS combination.
Human Action Recognition Using Deep Learning Methods on Limited Sensory Data

In recent years, due to the widespread usage of various sensors action recognition is becoming more popular in many fields such as person surveillance, human-robot interaction etc. In this study, we aimed to develop an action recognition system by using only limited accelerometer and gyroscope data. Several deep learning methods like Convolutional Neural Network(CNN), Long-Short Term Memory (LSTM) with classical machine learning algorithms and their combinations were implemented and a performance analysis was carried out. Data balancing and data augmentation methods were applied and accuracy rates were increased noticeably. We achieved new state-of-the-art result on the UCI HAR dataset by 97.4% accuracy rate with using 3 layer LSTM model. Also, we implemented same model on collected dataset (ETEXWELD) and 99.0% accuracy rate was obtained which means a solid contribution. Moreover, the performance analysis is not only based on accuracy results, but also includes precision, recall and f1-score metrics. Additionally, a real-time application was developed by using 3 layer LSTM network for evaluating how the best model classifies activities robustly.
Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections

This paper presents a surround vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). The motion predictor is trained using the states of subject and surrounding vehicles, which are collected by sensors mounted on an autonomous vehicle. Data on 484 vehicle trajectories were collected from real traffic situations at multi-lane turn intersections. 11,662 and 4,998 samples acquired from the vehicle trajectories were used to train and evaluate the networks, respectively. A motion planner based on Model Predictive Control (MPC) is designed to determine the longitudinal acceleration command based on the predicted states of surrounding vehicles. The future states of the subject vehicle derived by MPC is used as an input feature to reflect the interaction of subject and target vehicles in LSTM-RNN based motion predictor. The proposed algorithm was evaluated in terms of its accuracy and its effects on the motion planning algorithm based on the driving data sets. The improved prediction accuracy substantially increased safety by bounding the prediction error within the safety margin. The application results of the proposed predictor demonstrate the improved recognition timing of the preceding vehicle and the similarity of longitudinal acceleration with drivers.
Porous Microneedle Integrated in Paper based Glucose Sensor for Fluid Channel Interface

Porous microneedles (MNs) are considered as promising methods to collect biosamples for painless and minimal invasive diagnosis of diseases such as diabetes. MNs have microscale size and interconnected pores and thus they can penetrate through epidermis without pain and collect interstitial fluid. These properties can be utilized for a new method for glucose sensing for diagnosis of diabetes. We thus focus on an integrated porous polydimethylsiloxane (PDMS)-based MNs (PDMS MN) with paper-based glucose sensor to realize the device for colorimetric diagnosis of diabetes. Disc-shaped PDMS MN layer consisting of 21 microneedles within an area of 1.7 cm2 was prepared. The microneedle showed a length of 800 μm and a porosity of 70 %. In this integrated system, porous media connects needles and sensor via a microfluidic channel. We successfully demonstrated the qualitative andquantitative analysis of glucose in the sample solution collected by porous MNs.
CT Instrumentation Channel Error Correction Using Dynamic State Estimation

Protective relays depend on the fidelity of Protection-Class current transformer (CT) instrumentation channel outputs to perform reliably. Such channel errors as caused by CT saturation thus pose serious protection reliability challenges. This paper presents a new method for estimating the CT primary current in real-time through a Dynamic State Estimation (DSE) process. Specifically, the method involves obtaining discrete measurements of the CT secondary burden voltage through a continuous monitoring process. The CT primary current is then continuously estimated by the DSE process, based on fitting the continuously obtained measurement samples to the detailed and non-ideal CT channel model. The continuously estimated primary current can then be made readily available for protection functions. The CT primary current is one of multiple state variables dynamically estimated using this method. The paper also presents a simple algorithm that can detect CT saturation nearly instantaneously, based on using the dynamically estimated flux linkage.
Analysis and Modeling of Spill Back Effect in High Illumination CMOS Image Sensors

To improve charge transfer efficiency (<italic>CTE</italic>) and eliminate image lag, the impact of spill back effect on image lag is studied in CMOS image sensors (CISs), particularly in high illumination condition. By establishing a mathematical model based on the thermionic emission and drift-diffusion theory, the physical mechanism of spill back effect is described. This model shows that a lower transfer gate (TG) operating voltage and a higher reset voltage of Floating Diffusion (FD) node would mitigate spill back effect. In a <inline-formula> <tex-math notation="LaTeX">$0.18\mu \text{m}$ </tex-math></inline-formula> CMOS process, by setting that the gate voltage of transfer transistor and the reset voltage of FD is 2.8 V and 3.8 V respectively, <italic>CTE</italic> of the proposed pixel is increased to 100%. The theoretical analysis and TCAD simulation results can explain spill back effect and offer a reference for designing a high <italic>CTE</italic> pixel in high illumination CISs.
Fractional Order Controller for DVR to Mitigate Torque Pulsations of an Induction Motor during Voltage Sags

In this study to enhance dynamic performance of DVR, a novel and an emerging control methodology ‘fractional order PI (FO-PI) controller’ with multi-loop control scheme is proposed to counteract the ill effects of torque peak/ pulsations of voltage sags on an Induction Motor. It is seen that the proposed controller, which tunes five (Kp,Ki,Kd, λand μ) independent parameters, provides a powerful and efficient framework to mitigate the effects of voltage variations on induction motor. For fine tuning gain and order of the FO-PI controllers a MATLAB toolbox FOMCON is used here. The simulation results show that the reliability and robustness of the DVR systems to mitigate the torque peaks/pulsations at the fault and recovery instants of an induction motor is greatly improved using fractional order controller. The control energy necessary to alleviate the effects of voltage sags on induction motor for the suggested FO-PI controller is found to be lesser, ensuring energy efficiency of the controller scheme. With FO-PI controller a reduction in time performance error indices viz. IAE, ISE, and per-cent THD is observed as compared to the present day PI controllers.
Domain Adaptation for Ageing State Recognition of Cables used in Power Systems

As power cable systems play a critical role with respect to the reliability of power grids, it is necessary to adopt predictive maintenance techniques to prevent their damage. Especially for very expensive equipment used in power generation or smart grids, failure must be avoided. Partial Discharge (PD) phenomenon is considered as one of the main cause of deterioration of power cables insulation and reduce the cable lifetime. Thus, PD detection is an effective way to evaluate the degradation state of cable insulation. In this study, PD data are extracted from two different environments : a low voltage and high voltage cable using two different experimental devices. Original feature extraction, ranking and selection methods are implemented to design optimized Support Vector Machines classifiers (SVMs) that attribute an ageing state to the cable insulation for both cable types. Given the great interest of transferring a cable diagnosis model from one cable to any other cable type a domain adaptation technique is implemented. Recognition rate exceeding 99% are achieved when a classifier built in one environment is used to predict the ageing state of a new cable type based on PD data acquired in different conditions. The methodology presented in this work could be applied in realistic settings, for the monitoring of different cable types used in power grid system based on PD measurements.
Spatio-Temporal Seismic Data Analysis for Predicting Earthquake: Bangladesh Perspective

Earthquake prediction concerns specifying the earthquake's occurrence time, location, latitude, longitude, and intensity level. The determination of factors for the next earthquake happening in a region is very hard, almost impossible because earthquake occurrence depends on many things, such as changes in global warming, underground seismic wave, underground explosions, and underground rocks colliding, etc. But, nowadays, many types of research have been done around the world to build an earthquake warning system which upon detection of an earthquake, provides a real-time warning to the neighboring regions that might be affected. In this study, only the Spatio-temporal seismic data of Bangladesh is analyzed to propose an earthquake prediction model using the probabilistic assumption of the next earthquake happening in and around the Bangladesh region. The experimental dataset contains 100 years of a historical earthquake happening records in and around Bangladesh from the year 1918 to 2018 and this data is collected from Bangladesh Meteorological Department's (BMD) climate division. A comparative and comprehensive analysis is done to identify the best-suited model for earthquake prediction using some pioneer computationally intelligent and probabilistic machine learning algorithms, such as support vector machine, random forest, decision tree, naïve Bayes, and k-nearest neighbor.
Achieving Near-Optimal Traffic Engineering Using a Distributed Algorithm in Hybrid SDN

To empower advanced traffic engineering (TE) mechanism, while considering the infeasibility of one-step migration to software-defined networking (SDN), SDN nodes are incrementally deployed into legacy network, which gives rise to hybrid SDN. In hybrid SDN, redirecting flow of every source-destination pair through at least one SDN node, can enhance TE performance and obtain flow manageability, while on the other hand leading to increasing demands of TCAM resources in SDN nodes. In this paper, we make minimization of maximum link utilization as the TE objective, and comply with SDN waypoint enforcement and TCAM resource limitation. We first formulate the TE problem as an integer linear programming (ILP) model and solve it in a centralized manner, where SDN waypoint selection and splitting fractions for each flow are jointly determined. Then, based on a fact that the logically centralized control plane in hybrid SDN is composed of multiple physically decentralized controllers, each of which manages part of SDN nodes, as well as considering a real situation that a centralized solution is infeasible or too fragile for large-scale network, we develop a distributed algorithm deriving from Lagrangian decomposition theory to effectively solve the TE problem. The simulation results indicate that, when 30% of the SDN nodes are deployed, the proposed traffic engineering-aware distributed routing (TEDR) algorithm obtains maximum link utilization comparable to that of full SDN, and has a limited influence on the routing efficiency.
Filtering Power Amplifier With Up to 4th Harmonic Suppression

Unlike conventional conception that power amplifier and filter are regarded as two separate components and designed separately, this paper presents a high efficiency power amplifier with filtering capability, which is called filtering power amplifier. The proposed power amplifier integrates a bandpass filter as output matching network so that entire size is reduced to some extent. The filter will play the role of filtering and impedance matching simultaneously, and thus high-order harmonics are suppressed for output of the filtering power amplifier. For demonstration, an example of filtering power amplifier was designed, fabricated and measured. The measurement shows that it is featured by highly-selective bandpass response and good out-of-band performance. As high as 46.2 dB suppression can be achieved and extended over the forth-order harmonic. The example also retains the advantages of high efficiency and output power. The maximum output power can reach 40.5 dBm, and the maximum power-added efficiency is as high as 62%.
Performance of Methods for Testing the Ratio of Coefficients of Variation of Two Gamma Distributions

This paper presents two methods for testing the ratio of coefficients of variation of two gamma distributions. The proposed statistical tests were based on the methods of score and the Wald interval following the concept of the method of variance of estimate recovery (MOVER). To compare the performance of the proposed statistical tests, a simulation study was conducted under several shape parameter values in two gamma distributions. The performances of the statistical tests were compared based on the empirical size and the empirical power of test. The simulation results showed that the statistical test based on the method of score interval performed better than the statistical test based on the method of the Wald interval in terms of the nominal significance level attained and empirical power of the test, and is thus recommended for researchers. The performances of the proposed statistical tests also were examined through a numerical example.
Efficiency Improvement Evaluation and Loss Modeling of a Hybrid Switch-Based T-NPC Inverter

Si-IGBT and SiC-MOSFET based hybrid switch is an emerging technology which combines the advantages of the two types of devices and introduces SiC benefits at significantly lower cost compared to state-of-the-art SiC inverter technology. In this paper, a 1200V/50A Si/SiC hybrid switch is proposed. The switching characteristics and the steady-state current sharing model are analyzed. The analysis indicates that the switching loss of the hybrid switch can be reduced significantly due to the zero-voltage switching (ZVS) of Si-IGBT. Furthermore, a hybrid switch-based T-type neutral-point-clamped (T-NPC) inverter and its loss model are proposed. A 2.5kW hybrid switch-based T-NPC inverter prototype is designed and implemented to evaluate the efficiency improvement over a pure Si-IGBT-based inverter. The experimental results demonstrate that the inverter efficiency is improved by 4.75% at the switching frequency of 40kHz. The results also validate the proposed device loss model provides a good efficiency estimation of the hybrid devices-based T-NPC inverter.
Influence of Harmonics on the Interpolated DFT-based Frequency Estimation for the Control of Power in Renewable Energy Systems using Generalized Maximum Sidelobe Decay Windows

Harmonic components in a multifrequency signal have a big influence on the frequency estimation accuracy and play a vital role in a power system performance. They emerge in the grid due to a large number of nonlinear loads. This paper presents a new estimation method based on Generalized MSD time windows used to analyze a grid signal distorted by harmonics in the control of grid-connected photovoltaic systems. The accuracy of the 3-point IpDFT method decreases in the presence of 3-rd and 5-th harmonics and up to two measured signal cycles there can be chosen windows for which the frequency error is at the level of 10^−4 or 10-3 Hz/Hz which is a good accuracy in real time systems. Obtained in various measurement conditions results are useful from a practical point of view as usually harmonic components are present in the grid signal.
LearnBlock: A Robot-Agnostic Educational Programming Tool

Education is evolving to prepare students for the current sociotechnical changes. An increasing effort to introduce programming and other STEM-related subjects into the core curriculum of primary and secondary education is taking place around the world. The use of robots stands out among STEM initiatives, since robots are proving to be an engaging tool for learning programming and other STEM-related contents. Block-based programming is the option chosen for most educational robotic platforms. However, many robotics kits include their own software tools, as well as their own set of programming blocks. LearnBlock, a new educational programming tool, is proposed here. Its major novelty is its loosely coupled software architecture which makes it, to the best of our knowledge, the first robot-agnostic educational tool. Robot-agnosticism is provided not only in block code, but also in generated code, unifying the translation from blocks to the final programming language. The set of blocks can be easily extended implementing additional Python functions, without modifying the core code of the tool. Moreover, LearnBlock provides an integrated educational programming environment that facilitates a progressive transition from a visual to a general-purpose programming language. To evaluate LearnBlock and demonstrate that it is platform-agnostic, several tests were conducted. Each of them consists of a program implementing a robot behaviour. The block code of each test can run on several educational robots without changes.
A Comparative Study of Deep Neural Network and Meta-Model Techniques in Behavior Learning of Microgrids

Behavior learning of microgrids (MGs) is a necessary and challenging task for multi-MGs cooperation and energy pricing of distribution energy market. With the increasing demand for user privacy, this problem becomes more severe because of much less limited access to device parameters and models behind the Point of Common Coupling (PCC), which hinders conventional model-based power management methods. In this paper, to address this problem, some novel model-free data-driven methods including Deep Neural Network (DNN) and Meta-model techniques, such as Radial Basis Function (RBF), Response Surface Methods (RSM), and Kriging methods are introduced. These methods can predict the behavior of MGs through continuous iterative learning by accessing merely the historical active power measurements at the PCCs as well as public electricity price and weather information behind the PCCs, without full system identification and no prior knowledge on the system. A comparative study has been fully carried out by comparing with the conventional model-based model to better understand their advantages, drawbacks and limitations. The validity and applicability of the proposed methods is verified by numerical experiments. This paper can provide some references for future MGs interactive operation under incomplete information.
Optimal Guidance for Integrated Waypoint Following and Obstacle Avoidance

This paper addresses the problem of energy-optimal waypoint-following guidance for an Unmanned Aerial Vehicle with the consideration of arbitrary number of obstacles. The proposed guidance law is derived as a solution of a linear quadratic optimal control problem in conjunction with convex parameter optimization. The algorithm developed integrates path following and obstacle avoidance into a single step and is able to be applied to a general waypoint-following mission. Several particular cases of the proposed guidance law are presented to provide better insights of the proposed algorithm. Nonlinear numerical simulations clearly demonstrate the effectiveness of the proposed formulations.
Multi-objective Shipment Allocation using Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III)

Dealing with logistical problems are common among companies where delivery of goods are involved. An item to be delivered is commonly known as a shipment and hence efficient shipment allocation is essential for such companies to maintain profits and company competitiveness. This work is concerned with shipment allocation of a large and worldwide delivery company, which has a distribution center in a country where this is still carried manually. Determination of an efficient shipment allocation becomes a multi-objective optimization. Three objectives are identified, these are minimizing number of vehicles used, maximizing vehicle utilization and maximizing operator's route familiarity. Three Multi-Objective Evolution Algorithms (MOEAs) are utilized in this work, these are the commonly known, Nondominated Sorting Genetic Algorithm-III (NSGA-II), Nondominated Sorting Genetic Algorithm-III (NSGA-III) and the recently developed Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). Hypervolume is used as the metric to measure the quality of solutions. The results from MOEAs reveal superiority over the existing manual solutions.