Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey on Indoor Map Standards and Formats

With the adoption of indoor positioning solutions, which enable for a variety of location-based spatial services, a number of indoor map standards and formats have been proposed in the last decade. As each of these indoor map standard has its own purpose, the strengths and weaknesses are necessary to be understood and analyzed before selecting one of them for a given application. The Indoor Map Subcommittee has been established under IPIN/ISC in 2017. Among others, the goal of this working group is to compare available indoor map standards, provide a guideline for their application and advise on changes to their standardization development organizations if necessary. In this paper we present a survey of indoor map standards as an achievement of the subcommittee. The scope of the survey covers official standards such as IFC of BuildingSmart, IndoorGML and CityGML of OGC, and Indoor OpenStreetMap. We present several use-cases to show and discuss how to build indoor maps.
A Probabilistic Tensor Factorization Approach to Detect Anomalies in Spatiotemporal Traffic Activities

Besides outliers generated from the data collection stage, anomalies in traffic data can also be valid data characterizing unusual traffic activities. Detecting these anomalies in spatiotemporal traffic activities can provide practical insights for traffic monitoring and operation. In this paper, we focus on detecting anomalies in spatiotemporal traffic demand data. We first apply a probabilistic tensor factorization framework to approximate the expected/predicted probability of each trip, and then quantify the degree of anomalous by using the log ratio of observed frequency over the expected probability. In this framework, each trip is considered a sample from an underlying multivariate categorical distribution of time of day, origin zone, destination zone, and day of week. We approximate this distribution using a probabilistic Tucker decomposition model and introduce an efficient expectation maximization (EM) algorithm for model inference. To test this framework, we design and implement two synthetic experiments in the traffic simulation software SUMO. The results show that the proposed framework can effectively detect anomalous activities in multivariate spatiotemporal traffic demand data.
What Lies Beneath: A Note on the Explainability of Black-box Machine Learning Models for Road Traffic Forecasting

Traffic flow forecasting is widely regarded as an essential gear in the complex machinery underneath Intelligent Transport Systems, being a critical component of avant-garde Automated Traffic Management Systems. Research in this area has stimulated a vibrant activity, yielding a plethora of new forecasting methods contributed to the community on a yearly basis. Efforts in this domain are mainly oriented to the development of prediction models featuring with ever-growing levels of performances and/or computational efficiency. After the swerve towards Artificial Intelligence that gradually took place in the modeling sphere of traffic forecasting, predictive schemes have ever since adopted all the benefits of applied machine learning, but have also incurred some caveats. The adoption of highly complex, black-box models has subtracted comprehensibility to forecasts: even though they perform better, they are more obscure to ITS practitioners, which hinders their practicality. In this paper we propose the adoption of explainable Artificial Intelligence (xAI) tools that are currently being used in other domains, in order to extract further knowledge from black-box traffic forecasting models. In particular we showcase the utility of xAI to unveil the knowledge extracted by Random Forests and Recurrent Neural Networks when predicting real traffic. The obtained results are insightful and suggest that the traffic forecasting model should be analyzed from more points of view beyond that of prediction accuracy or any other regression score alike, due to the treatment each algorithm gives to input variables: even with the same nominal score value, some methods can take advantage of inner knowledge that others instead disregard.
A Model Predictive Approach for Energy Management in Smart Buildings

Effective and efficient energy management is crucial in smart electrical systems like nanogrids. The application of model predictive control (MPC) for the energy management of electrical energy storage in nanogrids for smart buildings is considered in this paper. An MPC strategy is devised in order to improve the nanogrid operation at the points of connection with the utility grid, integrating in the control information about future power generation and load consumption. The strategy is compared with a heuristic approach that exploits the energy storage only on an instantaneous basis, without any future predictions. It is shown that the MPC approach can provide convenient references to the power converters interfacing energy resources with the grid and significant long-term advantages as compared to the heuristic strategy.
A Single-Phase Hybrid Six-Leg AC-DC-AC Multilevel Converter

This paper presents a single-phase hybrid six-leg AC-DC-AC multilevel converter. The configuration is composed of a three-leg neutral point clamped (NPC) converter connected in series with a three-leg two-level converter. The proposed converter provides a sinusoidal input current with unitary input power factor and it feeds the load with a stable AC voltage. The system model, operation principles and a control strategy are presented and discussed. Additionally, a space-vector PWM technique is developed to control the dc-link capacitors, produce optimal voltage waveforms and to reduce the switching losses. The space-vector strategy guarantees nine-level output voltage waveforms at both input and output converter sides. Experimental and simulation results are presented to validate the theoretical studies.
Comparative Power Loss Analysis of DCM Flyback Transformer Based on FEA, Numeric Simulation, Calculation and Measurements

The flyback transformer is a crucial part of the flyback converter, which has the highest portion of total power losses for most applications. Accurately prediction of the power losses due to flyback transformer early in the design stage provides an advantage about revealing reliable prototypes. In this paper, several loss prediction methods are applied for a 40W universal input and single output DCM flyback transformer as; analytical calculations via well-known Steinmetz and Dowell's formulas, a simulation including numerical solutions, and a finite element analysis (FEA) based investigation. It is shown that the comprehensiveness of the methods accounting for hysteresis losses, eddy current losses, DC bias, and fringing effect based losses determine the accuracy of the results. In the paper, it is shown that the more the comprehensiveness, the closer the results to the values of electrical and thermal measurements. The power loss results obtained from theory and measurements with five different methods are compared and discussed in the detail.
Voltage and Power Balancing in Solar and Energy Storage Converters

With recent growth in the rapid adoption of solar photovoltaic (PV) power conversion, the integration of energy storage systems (ESS) is also on the rise. Successful ESS integration depends on the balance between system cost and performance. In this paper we focus on system performance, enabled by the proposed architecture which includes voltage and power balancing. The ESS becomes a valuable asset that allows flexibility in three-level neutral-point clamped converters (3L-NPC) to expand and develop new functionality. A bidirectional three-level buck-boost (3L-BB) converter plays an instrumental role in the proposed PV-ESS architecture. The 3L-BB is designed to perform dc-split bus neutral-point voltage balancing and damping through a 3ω-harmonic controller. This new feature leads to an immediate benefit of expanding the dc bus voltage utilization and eliminating the dissipative components for voltage balancing of any 3L-NPC operating mode. The bidirectional feature of the 3L-BB allows PV-ESS power balancing with reduced grid disturbance under dynamic events (e.g., transient clouds) of solar power production. The proposed PV-ESS system architecture was evaluated using a 100kW three-phase 3L-NPC converter and a 10kW 3L-BB converter. The simulation results indicate that the dc bus utilization can be increased by 4% with the proposed strategy. The evaluation results demonstrate the architecture’s performance with voltage and power balancing.
A Survey on Path Prediction Techniques for Vulnerable Road Users: From Traditional to Deep-Learning Approaches

Behavior analysis of Vulnerable Road Users (VRU)s has become a crucial topic in the computer vision research area. In recent decades, numerous papers have extensively addressed the problem of VRU path prediction, which has a wide range of applications such as video surveillance and autonomous driving. The behavioral complexity of VRUs has forced researchers to employ various techniques in order to develop more comprehensive models that potentially respond better to VRUs movement patterns. This indeed has led to development of a large variety of models and approaches in the literature. The aim of this paper is to a) provide a comprehensive review of developed path prediction methods, b) individuate and classify the proposed methods from multiple viewpoints, and c) present a framework for better understanding of various aspects in VRUs path prediction problems.
Cascaded Multilevel Rectifiers with Reduced Number of Controlled Switches for Open-End Winding PMSM

This paper proposes three configurations of unidirectional open-end winding (OEW) cascaded multilevel rectifiers with reduced number of controlled switches. They are based on Vienna rectifiers and are composed of a series combination of two three-phase converters and three H-bridges employing floating capacitors (per phase). These configurations make the ac-dc conversion power, supplied by a permanent magnet synchronous machine (PMSM), which is connected to the rectifier by an OEW configuration. Compared to the conventional, proposed systems synthesize the same or higher number of voltage levels using fewer number of controlled switches and is still possible to obtain lower converter current harmonic distortion. Furthermore, they can operate with lower switch blocking voltage ratio. These features make them attractive options to the industry due to their low costs, low control complexity and size, besides they are more reliable and less prone to failure. Therefore, proposed configurations have a better cost-benefit than conventional one. The system model, PWM technique, capacitor voltage balancing and control strategy are given. Simulation and experimental results are provided in order to validate their feasibility.
Cooperative Game Radio Resource Management Scheme for Small Cell Network

Evolution of 5G leads to unprecedented demands in terms of data traffic volume, data rate, low latency and user quality of service requirements. These requirements are extremely challenging to meet due to inherent architecture of 5G which is hyper dense and heterogeneous. The utilization of available resource is extremely crucial and an intelligent and adaptive radio resource management (RRM) schemes is essential for future generation communication. Heterogeneous network suffers with co-tier and cross-tier interference due to hyper dense deployment of small cells in 5G environment. Interference management is one of the major issues of dense communication environment which affects the user quality of experience (QoE) and RRM technique must handle this problem efficiently. Cooperative network and game theory can be used for designing RRM scheme in hyper dense heterogeneous network. This paper addresses the RRM issues by introducing coalition game approach for weak users to solve these problems. A dense small cell simulation environment is developed for analyzing the proposed scheme and findings of work show improved results in terms of throughput and QoE for weak users present in dense small cell network.
VehicleChain: Blockchain-based Vehicular Data Transmission Scheme for Smart City

In the fast-growing world, a significant increase in the number of vehicles has opened an opportunity to transform vehicles from a mechanical device to an intelligent system. The vehicular ad-hoc network (VANET) is designed to exchange messages with nearby devices. However, it is tough to transmit information from a sender to a receiver in a public environment due to malicious activities (by an adversary), wireless connection, mobility, and high-cost requirement. In this paper, we propose a state-of-the-art protocol, names as “VehicleChain” for smart transportation to ensure secure and cost-effective vehicle-to-vehicle and vehicle-to-infrastructure communications. The VehicleChain combines the robustness of a blockchain with elliptic-curve cryptography (ECC) to improve the system security level without increasing the computational cost. The VehicleChain is protected against different attacks, i.e., insider, server spoofing, modification, man-in-the-middle, plaintext, replay, and impersonation. Further, the proposed scheme comparatively achieves better results for different performance measures.
Pedestrian Trajectory Prediction at Un-Signalized Intersection Using Probabilistic Reasoning and Sequence Learning

Autonomous vehicles are expected to predict the near future trajectories of the pedestrian who might wait or continue crossing the road at the un-signalized intersection. This study presents a new framework for pedestrian trajectory predictions, which integrates Dynamic Bayesian Networks (DBN) and sequence to sequence (Seq2Seq) learning through an adaptive weighting strategy. DBN anticipates the pedestrian’s future trajectories using a probabilistic graphical model that combines environmental clues and kinematic information. Seq2Seq predicts the future trajectories with a well-trained learning model based on the observed trajectories up to the current instant. The adaptive weighting strategy can allocate weights online for DBN and Seq2Seq using the stopping probability and prediction errors. A real-world pedestrian dataset is employed for evaluations and results show that the proposed model outperforms DBN and Seq2Seq. The mean errors and final destination errors of the proposed model when predicting one second ahead are 0.04m, 0.10m in crossing scenarios, and 0.06m, 0.17m in stopping scenarios. This study expects to provide active pedestrian protections for autonomous vehicles on urban roads.
Application of State Feedback Controller with Feedforward for velocity ripples reduction of PMSM drive at low speed operation

This paper proposes a relatively simple and effective method for velocity ripples reduction of a Permanent Magnet Synchronous Motor (PMSM) using a State Feedback Controller (SFC) with feedforward path coupled with the estimated load torque. A linearized state-space mathematical model of the plant was introduced using a classical decoupling method. To ensure good dynamical properties of the system an a posteriori Model Predictive Approach Constraints (MPAC) method for drive current limitation was introduced. The designed compensation path is based on a disturbance observer based on extended Kalman filter algorithm. The estimated disturbance signal contains an periodic component caused by the presence of cogging effect phenomenon, pulsating torque and current measurement errors. A detailed harmonic analysis was performed for the measured and estimated variables i.e. angular velocity, estimated disturbance, $q$ -axis voltage and current components. Experimental results show efficient reduction of velocity ripple at low speed operation.
Design and Implementation of Three-Level Half-Bridge Bidirectional CL3C Resonant DC Converter

A three-level half bridge bidirectional resonant converter for the energy transferring between the DC bus and the battery of the electric vehicle is designed and implemented in this thesis. The three-level half-bridge CLLLC has the symmetrical characteristics and can achieve soft switching for reducing the switching losses. In addition, the voltage stress of power switches can be reduced with the three-level topology for improving the system efficiency. The operating principles of three-level half bridge bidirectional resonant converter are discussed in detail. Then, the steady-state equivalent models and the transfer functions of the resonant tank are also derived. Finally, a laboratory prototype with rated power 2 kW, DC bus voltage 400 V and battery voltage 250~400 V is implemented to validate the theoretical analysis. The highest efficiency in the charging and discharging stages are 94.8% at 800 W and 95.1% at 1 kW, respectively.
A Dynamic Feature Fusion Strategy for Magnetic Field and Wi-Fi Based Indoor Positioning

The paper presents a dynamic feature fusion strategy to provide more accurate and reliable location estimates in areas of weak discernibility for indoor magnetic signals. Experimental evaluations were carried out in two different scenarios to confirm improved performance of the proposed strategy. The results achieved is more than a 25% and 45% improvement in the average error distance compared to conventional magnetic-field/Wi-Fi localisation system and single magnetic localisation system respectively.
Modeling and Real-Time Simulation of a DC Shipboard Microgrid

This paper illustrates the modelling of a Low Voltage DC ship electrical system, including batteries and variable speed generators, and its implementation on the Opal-RT real-time simulator. There is more and more necessity to represent the full dynamics of the component and relative control to have a proper knowledge of the behavior of the system. The use of digital twin is becoming more and more attractive in order to have a complete and reliable tool to assess the system behavior in all conditions. The coupling of variable speed generator and BESS (Battery Energy Storage Systems) is an efficient solution in order to improve energy efficiency on board of marine vessel and it is necessary in different SECA areas that have strong environmental requirements. Results show the effectiveness of the proposed simulation platform and the results obtained by the implementation of a droop-based power control strategy.
Analysis of PCB parasitic effects in a Vienna Rectifier for an EV battery charger by means of Electromagnetic Simulations

The aim of this work is to investigate the impact of the PCB (Printed Circuit Board) electrical parasitic parameters on the performances of a SiC Three-Level T-type rectifier used for electric vehicle battery chargers. The short switching times of the SiC devices enables high switching frequency at very high efficiency. On the other hand, as the time derivative of the current increases (as a rule of thumb we can set 1A/ns as a breaking point), PCB parasitics start to play a significant role and may cause the degradation of the performances or even a design failure, if not taken into account right from the first design stage. Industry standard EDA (Electronic Design Automation) tools can be used to extract a model for the PCB to evaluate all the unwanted effects introduced by the physical realization of the board layout. For the scope of the current study, the software Advanced Design System, ADS by Keysight Technologies has been used to extract an S-parameter based model of the PCB and co-simulate it with the circuit components. Results obtained with and without the PCB model are finally compared for two different design iterations.
Door Transition Detection for Long-Term Stability in Pedestrian Indoor Positioning

Many smartphone-based indoor positioning systems rely on pedestrian dead reckoning for fine-grained position tracking. In this paper, we show how one of its major shortcomings – the accumulation of errors over time – can be effectively overcome in a navigation setting by detecting door transitions along the route. Using only the sensors included in a handheld smartphone and different on-device machine learning techniques, door transitions can be classified correctly in up to 86% of all cases. The system runs in real-time on current smartphones and – when integrated into our baseline particle filter – improves overall positioning performance significantly, as the subsequent evaluation on a realistic navigation data set shows. A detailed analysis of several edge cases illustrates the concept and provides insights into the remaining challenges.
Evaluation of An Indoor Localization Engine

Pedestrian Indoor localization based on modalities available in modern smartphones have been widely studied in literature and many of the specific challenges have been addressed. However, very few approaches consider the whole problem and proposed solutions are very often evaluated under very limited scenarios. We propose a fusion engine for localization that makes use of various data provided by a smartphone (Inertial sensors, pressure sensors, Wi-Fi, BLE, GNSS, map etc.) to provide a fused localization that is robust under harsh conditions (poor RSS coverage, device position change etc.). Moreover, our solution has been evaluated for hardware integration and tested over a large database including more than 250 experiments representing different scenarios, showing feasibility of lightweight implementation and good results over various conditions.
On the equal substitution of MILP unit commitment subproblems with dynamic programming

By splitting unit commitment into a coupling problem and individual unit commitment subproblems, Lagrangian relaxation is very effective in decreasing the solving time of large scale problems. Consequently, unit commitment subproblems should be solved equally fast. If they have been originally formulated in mixed-integer linear programming, performance advantages can be expected by replacing it with dynamic programming. However, it has not been reported whether a one-to-one reformulation is feasible. We suggest approaches to choose states for dynamic programming that replicate equal solutions as well as measures to reduce memory requirements. Results of the two subproblem models differ only fractionally and for explicable reasons. Subproblem computation time has been reduced by up to 100 times at modest memory requirements.
Calibration of MEMS Based Inertial Measurement Unit Using Long Short-Term Memory Network

MEMS-based IMU performance is limited in some applications for its low accuracy, resulting from combine of many factors like orthogonal error, nonlinear scale factor, random drift, drift periodic error, etc. Calculating the main error matrix is one traditional solution, which can’t express full error model of IMU. In this paper, MEMS based IMU’s model has been developed, deep neural network was introduced to analyze the error model and the correction parameters were figured out. In the error model and calibration algorithm, output data of IMU are feed in Long Short-Term Memory Network(LSTM) with reference linear acceleration and angular rate, error character is delivered, and correction parameters are educed after training. Our experimental result on three-axle turntable have proved the effectiveness of our algorithm.