Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach to Map Visibility in the Built Environment From Airborne LiDAR Point Clouds

Sustainable development can only be achieved with an innovative improvement from the way we currently analyze, design, build and manage our urban spaces. Current digital analysis and design methods for cities, such as visibility analysis, deeply rely on mapping and modeling techniques. However, most methods fall short of depicting the real visual landscape in the urban realm and this could bring a significant error in visibility calculations which may lead to an improper decision for urban spaces. The technical development of light detection and ranging(LiDAR) technology introduces new approaches for urban study. LiDAR utilizes point clouds including thousands or even millions of georeferenced points, and thus can support 3-D digital representation of urban landscape with detailed information and high resolution. Besides the superiority in representing urban landscape, LiDAR point clouds also has a clear advantage in quantitative analysis and provides better visibility than traditional models. In this paper, we first introduced a novel approach to map visibility in the urban built environment involving vegetation data directly using airborne LiDAR point clouds. This approach calculates neighborhood statistics for occlusion detection. Then we presented 2 case with different scenarios showing how our approach can be used to obtain a precise visibility in an urban area in the Netherlands. At last, we discussed how point clouds based visibility models can be further explored and can better assist urban design.
Ultrafast and Energy-Efficient Ferrimagnetic XNOR Logic Gates for Binary Neural Networks

Ultrafast current-driven domain wall (DW) motions have been realized in ferrimagnetic (FiM) nanowires. However, the FiM dynamics can be significantly affected by the Joule-heating. In this work, we propose a highly efficient XNOR logic gate by properly leveraging the thermal effect on the FiM DW motions. Its functionality and advantageous performance have been confirmed by the micromagnetic simulations. Moreover, majority logic and full adder functions can also be reconfigured based on the proposed scheme. Lastly, a fully FiM DW based binary neural network (BNN) is built, which provides low energy consumption, short delay and excellent accuracy.
Generative Design in Weight Optimization of Reconfigurable Continuous Track Robot

Rapid prototyping of mechatronic devices is becoming more and more popular nowadays. The rapid development of additive manufacturing technologies and a wide range of available open-source microcontrollers allows realizing ideas on both hobby and university project levels. Current work presents a weight optimization of the reconfigurable continuous track robot, developed as a student theses project at Tallinn University of Technology. The paper describes prototype development stages and post-development optimization methodology for optimal motor-drive selection based on the requirements of traction forces. As one of the trending methods for improving mechanical construction, the generative design technique was selected to further development of the prototype, and to reduce the rated power of the motor-drive system required for developed platform. Moreover, the time needed for the printing of the prototype was reduced.
Thermoelectromagnetic Lumped-Parameter Model of High Temperature Superconductor Generators for Transient Stability Analysis

A refined dynamic lumped-parameter model of a REBCO field coil synchronous generator including details of the thermoelectromagnetic phenomenology of the superconductor has been developed in the classic dq0 frame of reference. The idea is to build a generic modelling framework that can be used in power systems engineering studies to infer trends on the behavior of HTS field coil machines in power grids. In the present work, the transient stability of a hybrid High Temperature Superconducting (HTS) generator is analyzed under the most severe operating condition applying a three-phase short circuit at its terminals. For the study, we chose the parameters of a wind generator at 1 MW. Two cases are considered: 1) no full transition of the HTS to the normal-resistive state under stable condition, 2) full transition of the HTS under unstable condition. The response of the HTS machine is compared against that of a conventional machine.
Joint Minimization of Spectrum and Power in Impairment-Aware Elastic Optical Networks

Flexible-grid elastic optical networks (EONs) are widely considered as the future of next generation optical transport networks. Depleting spectral resources and increasing power consumption are two critical bottlenecks of EONs. In this paper, we propose a novel impairment-aware resource allocation scheme that jointly minimizes bandwidth waste and power consumption of EONs. The proposed scheme is presented in the form of an analytical optimization for small-sized networks. A low-complexity heuristic is then proposed for large networks. Numerical results show that by appropriately assigning the route, modulation format, and carrier frequency to each connection request, the proposed scheme significantly increases spectral efficiency and reduces power consumption in networks that handle varying traffic volumes.
Vision-based Assistive Navigation Algorithm for Blind and Visually Impaired People Using Monocular Camera

The system aims to develop an algorithm, to be implemented in an open-source platform, that facilitate the navigation, and early door/window detection process, for people with visual difficulties in closed environments. The proposed algorithm relies in a three stage image process consisting of the removal of all unwanted information through grayscale conversion and edge detection (image pre-processing), followed by the second stage formed by the search of lines or patterns on the detected edges, with the help of a first order Hough Transform, in order to locate a region where the majority of these lines intersect with each other, or vanishing point, which is the pivot point of the entire algorithm. Finally, the last stage consist on the estimation of the main regions of the aisle (walls, floor and ceiling) in order to perform an early detection of special regions in the walls, or candidates, that could lead to the detection of doors and windows. The main idea is that, after the image processing, the algorithm will return instructions to the subject, through a physical medium to make the corrections on his trajectory. It was found that the estimation of the vanishing point through the use of Hough transformation and the second-order edge detection method using open source software tools is a viable method for navigation in closed environments and even its possible to use the same image processing tools to estimate regions that could be easily consider as the main regions of the aisle. It’s been demonstrated that perform the early detection of candidates for doors is also possible using the same techniques.
Regulation of App users’ privacy security based on three-party evolutionary game model

Evolutionary game is a method to explain the strategy-choices of decision-makers through calculating the replication dynamic equations. In the study, three-party evolutionary game and prospect theory are used to analyze the dynamics strategy changes of the government, the App store, and the App software under the government privacy regulation. MATLAB is used to do the simulation analysis. According to the results, the best evolutionary stability point will be changed when considering prospect theory. The sensitivity of government’s perceived value and loss avoidance are the key impact factors. The conclusions provide suggestions for further exploration of privacy regulation.
A Comparative Study of Four Merging Approaches for Regional Precipitation Estimation

To identify suitable merging methods to improve regional precipitation estimates using multiple sources of precipitation data, this study applied four different approaches (multiple linear regression (MLR), feedforward neural network (FNN), random forest (RF) and long short-term memory network (LSTM)) to merge four satellite precipitation products and one reanalysis data in the Jiangsu, Zhejiang and Shanghai of China. The pros and cons of the merging approaches are analyzed comprehensively, using correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) as evaluation indexes. Our results show that: (1) All merging approaches can improve the accuracy of precipitation estimations, but only RF and LSTM can improve the daily precipitation event detection capacity. These approaches can significantly reduce errors in moderate precipitation scenarios, but do not effectively improve accuracy in light and heavy precipitation scenarios. (2) MLR was the least expensive computing cost method in our study and performed better than the other three methods when gauge density was low. However, MLR had the worst daily precipitation event detection capacity (CSI = 0.67). (3) FNN performed moderately in most experiments (CC = 0.87, RMSE = 4.65 mm/day, RB = 1.19 %, POD = 0.94, FAR = 0.29, CSI = 0.70). (4) The merged data generated by RF was the most accurate and had the best daily precipitation event detection capacity (CC = 0.87, RMSE = 4.61 mm/day, RB = − 0.33 %, POD = 0.97, FAR = 0.20, CSI = 0.78). RF performed best in moderate precipitation scenarios. However, it performed worse than other methods when gauge density was low. (5) LSTM was the most robust methods and performed best in light precipitation scenarios. The FAR of the LSTM-generated data was the smallest (0.15) among four fusion methods. However, LSTM had the most expensive computing cost and the worst accuracy of the merged data (CC = 0.86, RMSE = 4.68 mm/day, RB = − 9.36 %).
Interleaved PWM for Parallel Converters with Single DC Link for Zero Circulating Current

The work proposes an interleaved modulation method for two three-level converters connected in parallel for grid applications such as high-power active front end rectifiers, STATCOMs, and so on. The parallel converter system is controlled using a centralised predictive controller and 60° discontinuous pulse width modulation, with phase-shifted/interleaved carriers. The proposed PWM utilises the switching state redundancy of ‘0’ state of the flying capacitor topology such that instantaneous pole voltage difference between the converters is zero. The proposed method eliminates the circulating current flow between the converters, and shifts the dominant grid current harmonics to twice the switching frequency. The method also ensures the balancing of flying capacitor voltages along with a reduction in common mode voltage.
Experimental Investigation of As Preamorphization Implant on Electrical Property of Ti-Based Silicide Contacts

The impact of As preamorphization implant (PAI) on specific contact resistivity (<inline-formula> <tex-math notation="LaTeX">$\rho _{c}{)}$ </tex-math></inline-formula> is investigated for Ti-based Ohmic contacts on n<sup>+</sup>-Si in this article. The <inline-formula> <tex-math notation="LaTeX">$\rho _{c}$ </tex-math></inline-formula> value of TiSi<sub><italic>x</italic></sub>/n<sup>+</sup> −Si contacts is greatly improved with low-dose As PAI whereas this improvement is impaired with increased dose of As PAI. The <inline-formula> <tex-math notation="LaTeX">$\rho _{c}$ </tex-math></inline-formula> results from the joint effort of the reduction of effective Schottky barrier height (SBH) to electrons (<inline-formula> <tex-math notation="LaTeX">$\phi _{\text {bn,eff}}{)}$ </tex-math></inline-formula>, retardation of Ti silicidation, and annihilation of end-of-range (EOR) defects. Besides, the interfacial microstructures of TiSi<sub><italic>x</italic></sub>/n<sup>+</sup> −Si affect <inline-formula> <tex-math notation="LaTeX">$\rho _{c}$ </tex-math></inline-formula>, correlated with the post metal anneal (PMA).
Learning and Growth Perspective in Balanced Scorecard

The Balanced Scorecard (BSC) is a structural tool used to measure the effectiveness of activities against the strategic plans of a company. The Balanced Scorecard is frequently used during strategic planning to stable the company’s efforts with overall strategy and vision. Standard Balanced Scorecard considers initiatives of a company from four different perspectives: Financial, Customer, Learning and Growth, and Business Processes. These activities are recorded in adequate buckets with agreed measures, targets, and objectives for data collection and analyzing. Learning and growth perspective examines the company’s vitality in terms of training employees on rapidly changing technologies and teaching employees how to grow their productivity. This paper discussing the position of Learning and Growth perspective in the BSC model applied to an airport. Employees’ skills and knowledge are values that move an airport ahead as a prospective, active and strong player on the market. Also, the paper deals with the significance of employees’ trainings and their further development.
Measurement of Economic Growth Contribution of Technological Progress in China’s First-tier Cities Based on Solow Residual Method

This study uses the Solow Residual method which is a tool to measure the productivity growth other than capital investment and labor input, and this productivity growth which can represent technological progress in long-term to construct a panel regression model for the four China’s first-tier cities in Beijing, Shanghai, Guangzhou and Shenzhen from 1992 to 2017 to measure the contribution rate of technological progress to economic growth, and conclude that the economic growth of first-tier cities mainly depends on technological progress.
Deep Convolutional Neural Network Using Transfer Learning for Fault Diagnosis

Fault diagnosis is critical in industrial systems since early detection of problems can not only save valuable time but also reduce maintenance costs. The feature extraction process of traditional fault diagnosis is time-consuming and laborious work. Recently, with the rapid development of the deep learning (DL) method, it has shown its superiority with an end-to-end process and has been applied to classification and other fields. To a certain extent, it solves the disadvantages of manual feature extraction in the traditional fault diagnosis method. However, the available training data is often limited, and it will degrade the performance of DL methods. A new DL method that combines deep convolutional neural network (DCNN) and transfers learning (TL) for fault diagnosis is proposed in this paper to handle different fault types. A signal processing method that converts one-dimensional time-series signals into grayscale images is firstly applied, and it can eliminate the effect of handcrafted features. Secondly, an optimal DCNN is designed and trained with the ImageNet datasets, which can extract the high-level features of massive images. Finally, TL is further developed to apply the knowledge learned in the source data distribution to the target data distribution, which greatly reduces the dependence on training data and improves the generalization performance of DCNN. Three well-known datasets, including the bearing vibration dataset from the Case Western Reserve University (CWRU), self-priming centrifugal pump dataset (SPCP), and bearing force dataset from the University of Paderborn, are utilized to the performance of the proposed method. Some popular classification methods are also added to the comparison. Results show that the proposed method can precisely identify different fault types and have the highest classification accuracy among other methods.
Detecting Various Road Damage Types in Global Countries Utilizing Faster R-CNN

Road damages are of great interest for federal road authorities and their infrastructure management as well as the automated driving task and thus safety and comfort of vehicle occupants. Therefore, we are investigating the automatic detection of different types of road damages by images from a front-facing camera in the vehicle. The data basis of our work is provided by the ’IEEE BigData Cup Challenge’ and its dataset ’RDD-2020’ with a large number of labelled images from Japan, India and the Czech Republic. Our Deep Learning approach utilizes the pre-trained Faster Region Based Convolutional Neural Networks (R-CNN). In the first step, we classify the destination of the image followed by expert networks for each region. Between the explanation of our applied Deep Learning methodology, some remaining sources of errors are discussed and further, partly failed approaches during our development period are displayed, which could be of interest for future work. Our results are convincing and we are able to achieve an F1 score of 0.487 across all regions for longitudinal and lateral cracks, alligator cracks and potholes.
Design and Analysis of Closed-Loop Control for Full-wave Rectifier by using IMC Controller

Power factor corrector (PFCs) aims to improve the power quality of the supplies as switching power supply tends to draw high current magnitudes in a short period, resulting in distorted current outputs. Active front-end rectifiers also called PFC, where we don't require regeneration or input reactive power. Also, it reduces input side current THD and thereby improving the power factor. Here, Full wave rectifier topology is used as an active front-end rectifier. This paper utilizes the d-q reference frame decoupled control theory to deliver rectified output using an internal model controller. The IMC structure is used as a conventional feedback controller, and it is relatively easy to tune. Furthermore, to validate the outcomes of the proposed controller, the fractional-order PI controller and the traditional PI controller are also analyzed and results have been verified by using MATLAB as testing platform.
Improved High-Performance Solution Processed In₂O₃ Thin Film Transistor Fabricated by Femtosecond Laser Pre-Annealing Process

The low-temperature annealing process has a critical impact on the electrical performance of thin-film transistors (TFTs). This paper reports significant performance enhancements of TFTs using a femtosecond laser pre-annealing (FLA)-based preparation method. The solution-processed In<sub>2</sub>O<sub>3</sub> films were fabricated by FLA at various laser irradiation times and then annealed on a hot-plate at 230 °C. When the FLA time was set to 30 s, the device exhibited high saturation mobility of 10.03 ± 0.64 cm<sup>2</sup>/Vs, <inline-formula> <tex-math notation="LaTeX">$I_{\mathrm {on}}/I_{\mathrm {off}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$3.4\times 10^{5}$ </tex-math></inline-formula>, low <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {TH}}$ </tex-math></inline-formula> of 0.14 ± 0.64 V, and small SS of 1.44 ± 0.37 V/dec. The FLA process improved the formation of M-O lattices effectively, which led to an improvement in mobility. Furthermore, the gate-bias-stress stability and time-dependent environmental stability were improved considerably by the FLA process. These results show that high-performance In<sub>2</sub>O<sub>3</sub> TFTs can be prepared at low temperatures using FLA-centered annealing technology. This work suggests that the FLA preparation method has tremendous potential for the fabrication of low-cost, high performance, and flexible TFT devices.
Distribution Media-Independent Multidevice Cooperation System for Content Viewing

With the growing popularity of the Internet of Things (IoT)-enabled devices such as smartphones and connected TVs, users can now watch video content available on various media services through either broadcast or broadband networks. However, selecting an appropriate combination of distribution media, application, and devices is difficult. Therefore, we develop a multidevice cooperation system that facilitates content viewing with an appropriate combination of devices and applications, according to the device function and content distribution status. In this paper, the proposed system is described along with the implementation and effectiveness of the prototype. The experimental results show that this system can increase the possibility of playing content and decrease the time required to start them. We also confirm that the users can view content via the most appropriate viewing method using this system.
An Enhanced Thermoelectric Collaborative Cooling System With Thermoelectric Generator Serving as a Supplementary Power Source

Thermoelectric coolers (TECs) are widely used in state-of-the-art thermal management systems. Recently, there is a big trend to power TECs using thermoelectric generators (TEGs). Mainstream research efforts focus on attaining a higher figure of merit (ZT) of thermoelectric material, which now faces a great challenge. Alternatively, this article proposes a different approach to improve the performance of TEC, that is, integration of a TEG with a TEC. The TEG converts the collected heat energy into electric current, which reduces the power consumption and enhances the cooling capacity of the TEC. Using different methods of connecting the TEC and TEG, two thermoelectric collaborative cooling systems are proposed. Accurate SPICE models of the two cooling systems are established. The experimental results demonstrate that the discrepancy between the currents flowing through the TEC in the experiments and in the SPICE models is less than 4.8% on average. Based on the verified SPICE models, the proposed TEC-TEG collaborative cooling systems are assessed in terms of power consumption, cooling capacity, coefficient of performance, and cooling efficiency. Compared with a typical Peltier cooling system, the two collaborative cooling systems achieve significant performance improvements.
Charging Management of Chemical Lithium Nickel Cobalt Aluminum Oxide (LiNiCoAlO2) Batteries, Considering Battery Degradation Effects under Uncertain Operation in Multi-energy Systems

Chemical Lithium battery energy storage (BES) systems are considered as critical assets in providing flexibility and reliability for today's power and energy systems. However, BES degradation, which is due to the electrolyte reaction in lithium batteries, introduces some challenges to smart grid operators in terms of optimal charging/discharging management strategies. Although, considering degradation in BES operation can lead to long-term benefits, the associated uncertainties of renewable generations, load, and energy prices can pose a noticeable effect on BES operation optimality and accordingly affect the associated long-term benefits, if ignored. In fact, disregarding uncertainties in BES operation can lead to nonoptimal or even infeasible charging/discharging solutions, resulting in extra costs and even higher BES degradation rates. This paper presents an adaptive robust optimization approach to optimally characterize degradation in charging/discharging management of BES at the presence of uncertainties. The model is developed for a multi-energy system with electricity and natural gas as operating energy types. A Lithium Nickel Cobalt Aluminum Oxide (LiNiCoAlO2) battery is considered as the BES in the multi-energy system. Uncertainties of load, renewable generation, and energy prices are characterized with bounded interval through polyhedral uncertainty sets. The proposed model is solved through a column-and-constraint generation approach. A post-event analysis is conducted to evaluate the long-term performance of the proposed model. According to the obtained results, the proposed adaptive robust energy management model shows 18% growth in long-term benefit recovery and 15% reduction in loss of load, compare to deterministic model, considering battery degradation effects.