Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Artifical Intelligence for Automating Pavement Condition Assessment

The financial burden due to pavement damage on road networks is a major handicap to the economic development of a country. According to an ASCE report, this issue may cost as much as $67 billion per year. Regularly planned condition assessments and repairs of pavement can mitigate any derived costs and increase traffic safety. However, due to the large extent of civil infrastructure networks, required periodic inspections and assessments can be expensive and time-consuming. Further compounding the issue is that the majority of damage assessment mechanisms rely on human visual analysis, which can be prone to potential user bias and errors. In this study, we present a framework to automate roadway assessment by implementing a Convolutional Neural Network (CNN) that classifies various types of cracks in pavements. CNNs are a special type of deep artificial neural networks that demonstrate high accuracy and efficiency in image-based machine learning tasks. One of the main advantages of CNNs is that they can automatically learn the salient features of an image dataset without any prior knowledge or pre-processing by the user. Thus, the need for feature engineering is obviated and thereby eases the deployment of our assessment framework. Our framework was developed and tested on a balanced dataset containing 400 color images and consisting of four types of pavement damage: (1) longitudinal, (2) transverse, (3) alligator, and (4) pothole cracks. We apply image augmentation using a bundle of transformations to improve the crack classification accuracy of our CNN. The classification accuracy of the four types of cracks was found to be 76.2%. Demonstrating that the proposed CNN model can predict crack types without any user intervention at a good level of accuracy. To improve the robustness and accuracy of our assessment framework, we will analyze more types of cracks, using a larger dataset size in future studies.
Precision oncology: lessons learned and challenges for the future

Abstract The decreasing cost of and increasing capacity of DNA sequencing has led to vastly increased opportunities for population-level genomic studies to discover novel genomic alterations associated with both Mendelian and complex phenotypes. To translate genomic findings clinically, a number of health care institutions have worked collaboratively or individually to initiate precision medicine programs. These precision medicine programs involve designing patient enrollment systems, tracking electronic health records, building biobank repositories, and returning results with actionable matched therapies. As cancer is a paradigm for genetic diseases and new therapies are increasingly tailored to attack genetic susceptibilities in tumors, these precision medicine programs are largely driven by the urgent need to perform genetic profiling on cancer patients in real time. Here, we review the current landscape of precision oncology and highlight challenges to be overcome and examples of benefits to patients. Furthermore, we make suggestions to optimize future precision oncology programs based upon the lessons learned from these “first generation” early adopters.
TIMTAM: Tunnel-Image Texturally Accorded Mosaic for Location Refinement of Underground Vehicles With a Single Camera

Many mine-site processes such as vehicle operation require localisation systems that are reliable, robust and work in a range of environmental conditions. In underground operations, GPS is not available: solutions instead rely on static infrastructure or expensive, laser-based solutions with limited operational capability. In this letter we present a new vision-based technique, Tunnel-IMage Texturally-Accorded Mosaic (TIMTAM), for sub-metre, infrastructure-free localisation in underground mining environments using a single camera. Our approach stitches upward-facing camera images to form planar mosaic maps, using locations generated by the coarse mapping engine based on a small number of manually anchored locations. Localisation is achieved by refining coarse location estimations with a best fit pixel location for the query image within a search neighbourhood in the mosaic map. Our direct pixel-based method is more robust to the challenging illumination and surface-texture environments encountered in underground mine operations than feature-based techniques. Localisation refinement is only triggered when a confidence threshold for the estimate is exceeded. The system is evaluated in a real world mine tunnel, with results showing that the confidence threshold approach is predictive of the quality of the location estimate refinement, and achieves a reduction in mean localisation metric error of up to $\sim$66% from simulated coarse results.
Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market

One of the challenges in the transition towards a zero-emission power system in Europe will be to achieve an efficient and reliable operation with a high share of intermittent generation. The objective of this paper is to analyse the role that Demand Response (DR) potentially can play in a cost-efficient development until 2050. The benefits of DR consist of integrating renewable source generation and reducing peak load consumption, leading to a reduction in generation, transmission, and storage capacity investments. The capabilities of DR are implemented in the European Model for Power Investments with high shares of Renewable Energy (EMPIRE), which is an electricity sector model for long-term capacity and transmission expansion. The model uses a multi-horizon stochastic approach including operational uncertainty with hourly resolution and multiple investment periods in the long-term. DR is modelled through several classes of shiftable and curtailable loads in residential, commercial, and industrial sectors, including flexibility periods, operational costs, losses, and endogenous DR investments, for 31 European countries. Results of the case study shows that DR capacity partially substitutes flexible supply-side capacity from peak gas plants and battery storage, through enabling more solar PV generation. A European DR capacity at 91 GW in 2050 reduces the peak plant capacities by 11% and storage capacity by 86%.
Decomposition and Synthesis of High-Order Compensated Inductive Power Transfer Systems for Improved Output Controllability

High-order compensation provides more design freedom for inductive power transfer systems and can help improve output voltage/current controllability. This paper develops a simplified decomposition and synthesis method for high-order-compensated inductive power transfer systems. It can achieve load-independent (LI) output under coupling variation, and easily fulfill the various charging requirements, such as constant voltage or constant current. The coupling independent resonance is ensured by using the induced source model, and the whole resonant tank is effectively decomposed as three parts. The power transfer characteristics are discussed for each part, and the requirements for LI output and zero phase angle (ZPA) operation are combined to generate the compensation candidates for both sides. Two families of topologies are synthesized for four kinds of conversions, i.e., voltage to voltage, voltage to current, current to voltage, and current to current. Meanwhile, the proposed method dramatically simplifies the evaluation for the influence of the coil equivalent series resistors on the transfer function and efficiency. These resistor-caused effects are quite different for two families of topologies. Finally, a 6.78-MHz system with 10-W output is designed to verify the difference between two-family topologies.
The evolution of compact binaries

However, the evolution of all these objects is driven by a common mechanism: angular momentum loss. This process controls the change of the orbital period as well as, in the phases of interaction, the mass transfer rate. This means that the basic physics behind all these objects is the same, and, by comparing the results found for one class, we might be able to understand similar problems in another. Some of the open questions that are common to all classes of compact binaries are, for example: – the impact of the common envelope phase; – the role of rotation in the evolution of the secondary; – the role of magnetic fields in binary evolution; – possible braking mechanisms and their efficiency; – physical mechanisms to increase the mass of the compact component to finally reach a point of ignition.
1A1-D09 Multiple Persons Tracking by Multiple Cameras and Laser Range Finders in Indoor Environment

Successful multi-target tracking requires locating the targets and labeling their identities. For the tracking systems, the latter is more challenging. In this paper, we propose a method for multiple persons tracking using multiple cameras and laser range finders. Our method estimates 3D positions of human body and head, and labels their identities. The method is composed of multiple particle filters, and each particle filter tracks each person correctly by integrating information from laser range finders and the target-specific information from multiple cameras. Integration of these two types of sensors enables complement of each weak point and the correct tracking of the target. Moreover, we develop a new particle filter framework that tracks the human head by using the estimated human body position simultaneously. Our experimental results demonstrate the effectiveness and robustness of the method in multiple persons tracking.
On the impact of relay circuit losses in four‐coil wireless power transfer systems

In wireless power transfer (WPT) systems with more than two coils, the intermediary or relay circuits are used to extend the link distance. Thus, to achieve this extension efficiently in terms of power transfer, these relay circuits must have low losses. However, there are several instances in which there are restrictions in reducing the ohmic losses in all the relay circuits of the system. This is the case of biomedical applications where commonly there are size and access restrictions since one of the circuits can be implanted and also in applications using high‐temperature superconductor (HTS) coils due to the difficulty in implementing the necessary cooling system for all the coils of the system. Therefore, in these situations, the designer need to choose which relay circuit will be optimized. In this work, it is presented an analysis on the impact that losses in individual relay circuits have on efficiency, and power transfer, of typical four‐coil wireless power transfer systems consisting of circuit 1 (transmitter), relay circuits 2 and 3, and circuit 4 (load). It is shown that the losses on relay circuit 2 have greater impact on efficiency, while the losses of relay circuit 3 have a greater impact on power transfer for a given condition. Practical experiments confirm the developed analysis.
A Framework for Automatic Initialization of Multi-Agent Production Systems Using Semantic Web Technologies

Mass customization and global competition require Cyber-Physical Systems of Systems (CPSoS) to become increasingly flexible. Modern CPSoS have to be able to create a wide and versatile variety of products, which takes centralized approaches to their limits. In addition, they have to produce these products as quickly as possible. Hence, they must be able to react promptly if problems arise, such as the failure of a single machine. Modern agent-based production systems provide the flexibility required to cope with these challenges. While resource agents (RAs) represent the available resources, i.e., machines, such as robots, individual customer orders can be represented by so-called product agents (PAs). However, a challenge in the design of agent-based production systems is still the amount of communication and computation that is necessary online. The PAs have to communicate their requests and the RAs their capabilities and capacities. On this basis, PAs must compute the appropriate production sequence. We propose to automatically initialize every agent with a knowledge base (KB) created a priori using semantic web technologies (SWT). On the one hand, the KBs of RAs describe the RAs’ capabilities in terms of product features and production processes. Every KB of a PA, on the other hand, expresses all possible production sequences based on the customer specification and the CPSoS in question. This allows consistency checks regarding the specification as well as more purposeful communication that focuses on aspects that actually need to be determined at runtime, such as the resources’ current capacities or failures. The framework presented aims to reduce both the communication and computational load necessary at runtime for agent-based CPSoS.
TOOLS FOR INQUIRY-BASED LEARNING IN PRIMARY SCHOOL

Information technology and information literacy are becoming increasingly important in today's knowledge society. Many studies have shown that students from elementary school to graduate school do not have enough information literacy and skills in IT, therefore, there is a need for effective pedagogical approach that will develop these skills. One of the important step in this direction is the possibility of combining a collaborative teaching approach with inquiry-based learning to develop information literacy of elementary school students and IT skills. Based on existing theoretical framework, we believe that modern teaching technology should be used in early childhood. Children of primary school age explore and learn about the environment by using queries, and computer technologies offer available way to extend the domain and range of this request. A great number of interactive games and educational software packages have been implemented over the last decade for primary schools. However, a large number of software packages are not focused on inquiry-based learning. Examples of existing and hypothetical apps to research in early childhood presented in this article. Problems and future research directions are also identified and discussed. DOI: https://doi.org/10.28925/2414-0325.2017. 3.4354
Calibrating Trust in Automation Through Familiarity With the Autoparking Feature of a Tesla Model X

Because one of the largest influences on trust in automation is the familiarity with the system, we sought to examine the effects of familiarity on driver interventions while using the autoparking feature of a Tesla Model X. Participants were either told or shown how the autoparking feature worked. Results showed a significantly higher initial driver intervention rate when the participants were only told how to employ the autoparking feature, than when shown. However, the intervention rate quickly leveled off, and differences between conditions disappeared. The number of interventions and the distances from the parking anchoring point (a trashcan) were used to create a new measure of distrust in autonomy. Eyetracking measures revealed that participants disengaged from monitoring the center display as the experiment progressed, which could be a further indication of a lowering of distrust in the system. Combined, these results have important implications for development and design of explainable artificial intelligence and autonomous systems. Finally, we detail the substantial hurdles encountered while trying to evaluate “autonomy in the wild. ” Our research highlights the need to re-evaluate trust concepts in high-risk, high-consequence environments.
Magnetic Sensor Design for a Permanent Magnet Linear Motor Considering Edge-Effect

This article focuses on the design of a magnetic sensor (MS) in position detection of a permanent magnet linear motor (PMLM). The MS is composed of a hall-effect-integrated-circuit and magnetic materials, and it is well known as a contactless sensor with a simple structure, and low cost. As a result, it is preferred for precise linear positioning applications. The work presented in this article consists of the following three steps to achieve high accuracy and economy in the mover position detection for a PMLM. First, the initial design of the MS is described which has been optimized using response surface methodology to obtain very low total harmonic distortion value of the sensing signal. Second, the leakage flux of PMLM is analytically investigated, from which it is determined that shielding the leakage flux is most appropriate to ensure the accuracy of the MS. Finally, in an attempt to reduce manufacturing cost, three MS prototypes using different materials (silicon steel laminated core, steel solid core, and ferrite solid core) are fabricated and tested. To verify the analytical results, several experiments are conducted. The experimental data prove that the accuracy of the MS is sufficient to use for mover position detection for the PMLM.
Electromagnetic Vector Antenna Array-Based Multi-Dimensional Parameter Estimation for Radio Propagation Measurement

A multi-dimensional parameter estimation method for radio propagation measurements is investigated. A distributed electromagnetic vector antenna (EMVA) array-based multiple signal classification (MUSIC) method is proposed to estimate the delay, the angle of arrival (AoA), and the cross polarization ratio (XPR) of every ray. The delay is estimated using the channel response matrix. After the polarization smoothing, the AoA is estimated. Applying the spatial smoothing method, the XPR is then obtained. Simulation results reveal that using the proposed multi-dimensional parameter estimation method, more rays with higher accuracy can be estimated than the ones using the conventional method.
NbO2 as a Noble Zero-Strain Material for Li-Ion Batteries: Electrochemical Redox Behavior in a Nonaqueous Solution

Lithium-ion batteries are widely available commercially and attempts to extend the lifetime of these batteries remain necessary. The energy storage characteristics of NbO2 with a rutile structure as a material for the negative electrode of lithium-ion batteries were investigated. When negative potential was applied to the NbO2 electrode during application of a constant current in a nonaqueous solution containing lithium ions, these ions were inserted into the NbO2. Conversely, upon application of positive potential, the inserted lithium ions were extracted from the NbO2. In situ X-ray diffraction results revealed that the variation in the volume of NbO2 accompanying the insertion and extraction of lithium was 0.14%, suggesting that NbO2 is a zero-strain (usually defined by a volume change ratio of 1% or less) active material for lithium-ion batteries. Moreover, the highly stable structure of NbO2 allows the corresponding electrode to exhibit excellent cycling performance and coulombic efficiency.
Experimental Validation of a Novel Method for Harmonic Mitigation for a Three-Phase Five-Level Cascaded H-Bridges Inverter

In modern high-power electrical drives, the efficiency of the system is a crucial constraint. Moreover, the efficiency of power converters plays a fundamental role in modern applications requiring also a limited weight, such as the electric vehicles and novel more electric aircraft. The reduction of losses pushes for systems with a dc bus and a high number of dc/ac converters, widespread in the vehicle, not burdened by a too expensive data processing system. The purpose of this article is to concur to reduce losses by proposing an innovative selective harmonic mitigation method based on the identification of the working areas where the reference harmonics present lower amplitudes. In particular, the main objective is to find a new way to calculate the control angles in real-time operation without solving nonlinear equations, whose resolution would require expensive controllers. Through a very simple approach, the polynomial equations, which drive the control angles, were detected for a three-phase five-level cascaded H-bridge inverter and implemented in a digital system to real-time operation with a low computational cost. As a result, a comparison between the simulation and experimental behavior is presented. In the last part of this article, a real electric machine is driven by considering the appropriate working areas and current harmonics are also evaluated.
Development and commissioning of test device for bearings used in wind turbine gearbox high speed shaft

Paattakainen, Oula Development and Commissioning of Test Device for Bearings Used in Wind Turbine Gearbox High Speed Shaft Master's thesis Department of Physics, University of Jyväskylä, 28.5. 2019, 86 pages. Wind energy market pressure towards lower levelized cost of energy has led to size optimizations for wind turbine gearboxes which are one of the most important turbine subcomponents in converting the slow rotor speed to high speeds suitable for the electricity grid connected generator. Gearbox size optimization decreases material costs but in turn introduces challenges for example for gearbox lubrication solutions. The so called high speed shaft (HSS) and its bearings are often the most critical components for lubrication. Because the HSS is connected to the generator its rotational speed is the highest and therefore it usually also experiences the highest temperatures. Based on these considerations a component level test device for high speed shaft bearings had been designed. Process variable measurement system, automation control logic and simulation model were developed for the test device in the practical part of this thesis. Practical work also included the commissioning of test device and execution of reference tests. Main goal of the measurements was to study the lubrication oil's temperature change from bearing inlet to outlet. It was observed that previously used estimation of increase of 10 ◦C is applicable only for part of the tested bearings. Analysis of test results was limited by the notion that the measured torque losses for the test device were signi cantly higher than expected based on theoretical values. However, the practical implementation of measurement and control system was deemed successful. Increased torque losses were hypothesized to be due to load independent oil drag losses and therefore future test with HSS bearing tester should focus on decreasing the drag losses closer to a level corresponding to theory.
An Improved Torque Control Strategy of PMSM Drive Considering On-Line MTPA Operation

An improved direct torque control with space-vector modulation (DTC-SVM) scheme is presented in this paper. In the conventional DTC-SVM scheme, torque control performance is affected by the load conditions, due to the inappropriate linearization of the relationship between the flux angle and electromagnetic torque. Different from the conventional method, a torque controller with load angle estimation (TC-LAE) is proposed and the change rate of torque is regulated according to the variation of the load conditions, which could ensure the rapidity and consistency of torque performance at different load conditions. Meanwhile, an online permanent magnet synchronous motor and maximum torque per ampere (PMSM-MTPA) operation strategy based on the fitting solving method is proposed instead of the traditional two-dimensional look-up table, and the reference value of flux amplitude is calculated online to meet the MTPA requirement with the proposed method. The improved strategy is applied on a 6 kW PMSM, and the simulation and experimental results verified the effectiveness and the feasibility of the proposed strategy.