Miracle-dz/UniFAR-FTUs huggingface dataset:
Correction of Finite-Element Calculated Efficiency Map using Experimental Measurements

This paper examines correction of the finite-element (FE) calculated efficiency maps based on simple experimental tests. The error between calculated and measured efficiency maps significantly affects the energy consumption in variable-speed applications like electric vehicles. The flux-linkage correction requires the measured saturation flux-linkage obtained from stationary testing and the iron loss correction requires open-circuit or no-load test results. These tests need minimum effort and equipment and in the case of open-circuit tests, no inverter is required.This paper also provides a detailed loss analysis by using fitting functions in the form of Tmωn for the losses in the constant torque region and in the form of Pmωn for the losses in the constant power region for both FE and measured results. The results indicated that superposition of the speed dependent coefficients obtained from the no-load test and coefficients obtained from the FE calculation provides a good estimation of the measured loss. Comparing the original FE and modified losses provides loss scaling factors for the iron losses.
Autonomous Lane Keeping Control System Based on Road Lane Model Using Deep Convolutional Neural Networks *

In this paper, we propose a novel autonomous lane keeping system (LKS) based on a road lane model using a deep convolutional neural network (DCNN). The DCNN was trained by a dataset which consists of reliable road coefficients from the vision system mounted on the test vehicle driven by a human, and images captured by another camera. Then the proposed system was validated with a dataset which was not used for training the DCNN. We confirmed that there were good agreements between the steering wheel angles by the human driver and those given by the proposed LKS. Furthermore, we observed that the proposed system can provide the road coefficients necessary for implementing the LKS even either when there is no lane marker and/or when the vehicle is maneuvered for turning at an intersection.
Automatic Creation of Path Information on Digital Map

Digital maps are data that numerically represent map information necessary for autonomous driving. The digital map is used for estimating vehicle positions, surrounding environments, and determining moving paths to a destination. Because it includes important information such as surrounding environments and road signs. Path information is also included for safe autonomous driving by following traffic rules. However, such path data is annotated manually and it is costly. Therefore, we aim to create path-planning data automatically on digital maps. Then, we propose a path-planning approach for vehicle movement. Our approach defines a cost function based on semantic scene labels and creates a minimum and optimal path. To estimate such a path, we use optimal rapidly-exploring random trees. Thus, it is possible to estimate an optimal path efficiently. Optimal cost is determined by learning with vehicle moving-path data. We also made a dataset for creating paths at intersections for quantitative evaluation. The results indicate that our proposed approach can create a optimal moving path.
Active-Reset for the N+P Single-Ended SPAD Used in the NIR LiDAR Receivers

A monolithic resistorless circuit has been designed for active reset of the N<sup>+</sup>P single-ended (common anode) single-photon avalanche diode (SPAD). This type of SPAD has enhanced the photon detection efficiency in the near-infrared (NIR) region relatively to the P<sup>+</sup>N (two-ended) SPAD. Due to this fact, the N<sup>+</sup>P SPAD is a better candidate for the light detection and range (LiDAR) working in the NIR region. The circuit is fabricated in a standard 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS image sensor process. The dead time after each photon detection is adjustable; the minimum measured value is 4 ns. The maximum photon count rate corresponding to this dead time is <inline-formula> <tex-math notation="LaTeX">${2.5}\cdot {10}^{{8}}$ </tex-math></inline-formula> photons/s. This circuit guarantees precise repeatable response, short and well-controlled dead time, and after-pulsing effect reduction. All these are essential for many SPAD applications and crucial for increasing the saturation level of the silicon photo multiplier (SiPM)-based LiDAR systems working in strong background light conditions on sunny day.
Emission-Aware Microgrid Cluster Energy Management Scheme: A Distributed Trading Approach

As an aggregated group of interconnected loads and resources, microgrid is playing a more and more important role in integrating distributed renewable generation and improving energy efficiency of industrial and commercial power systems. Considering the geographic distribution characteristics, a group of microgrid can form a cluster and coordinate with each other to achieve better performance. Different with conventional optimal control approach which required a centralized coordinating agent, a distributed trading based energy management scheme is proposed in this paper to achieve the maximal social welfare for the entire cluster. To accurately reflect the utility preference of each microgrid during the energy management process, an emission-aware trading preference coefficient is introduced to model the incremental utility or cost associated with trading low-emission energy. The proposed energy management scheme is solved by a distributed trading method based on alternating direction method of multipliers. Numerical case study is provided to demonstrate the effectiveness of the proposed emission-aware microgrid cluster energy management scheme.
Design Considerations for Magnet Configurations in IPM Rotor for High Speed Traction Applications

This paper presents the design considerations for magnet configurations in IPM rotors for high speed traction applications. The study investigates V, double-V and U shaped magnet configurations designed with the same stator. The rotor in each configuration is optimized under the constraint of same power at max. speed, same low speed torque and electrical input. The objective from a mechanical perspective was a similar stress to attain the same max. speed. The configurations are then compared with respect to their no-load characteristics, short circuit characteristic, demagnetization, torque, efficiency, loss distribution, thermal performance and magnet volume. The study indicates that V shaped rotor configuration has the lowest magnet usage and best performance but has a lower corner speed due to higher no-load voltage. The double-V design has higher losses but the corner speed is higher for double V designs. The U shaped design exhibits performance characteristics which is a trade-off between the other options but has other advantages.
An Optimized Partial Rollback Co-simulation Approach for Heterogeneous FMUs

Cyber-physical systems (CPS) are generally defined as systems with integrated physical components and computational components. To simulate heterogeneous components of CPS, the Functional Mock-up Interface (FMI) standard provides the co-simulation technology to generate simulation traces. It play significant roles in analyzing and verifying behaviors of CPS. However, the FMI-based co-simulation algorithm called Master Algorithm with Step Revision (SRMA) is inefficient in some common scenarios. To improve the efficiency of SRMA, we propose an optimized Partial Rollback Co-simulation approach, which decreases the number of the rollback operations effectively. The novelty of our approach has two aspects. First, the Key FMUs Extractor and the Input/Output dependencies classification rules are proposed. They help to determine the minimum set of FMUs which are used to rollback for correcting the simulation error. Second, an optimized Master Algorithm with Partial Step Revision (PSRMA) is also proposed. To implement our approach, we also propose an extension for the FMI standard to check whether an FMU implements the function of the threshold crossing detector. The formal definition of the Zero Crossing Detector (ZCD) is presented to guide the construction of ZCD FMUs and evaluate the simulation error of the whole system. To illustrate the feasibility of our approach, two case studies are also discussed.
A Numerical Method for Calculating the Output Spectrum of an H-Bridge Inverter with Dead-time Based on Switching Mode Analysis

The distortion of an H-bridge inverter originates from many nonlinear factors, of which the most dominant one is typically the dead-time. Different analytical and numerical methods have been proposed to calculate the distortion caused by the dead-time. However, all the existing methods are neglecting the effects of soft-switching and discontinuous conduction mode, resulting in a deviation with the practice. In this paper, the characteristics and the corresponding effects of each individual switching mode is analysed. Current constraint functions are proposed in order to determine the time position of different switching modes. A numerical method for calculating the output spectrum of the H-bridge inverter based on the switching mode analysis is introduced. The method has an improved accuracy compared to the previous analytical method especially when the inductor current ripple is high. The results are verified by simulation and an experimental prototype.
Interleaved inverter with kinetic energy storage for increasing short circuit current

This paper presents the novel method of increasing short circuit current by means of interleaved power converter combined with kinetic energy storage. The proposed method provides a way to increase short circuit current delivered to circuit breakers without increasing overall cost of the power inverter. Use of interleaved topology allows for smooth connecting and disconnecting of kinetic energy storage to the system, which is done by temporarily disconnecting one of the inverters from the grid and using it to start spinning and synchronizing the flywheel of the energy storage to the island. It also reduces overall distortions generated by inverter during normal operation. This paper proposes a control algorithm and a method which allow to increase safety of the end users by increasing current flowing through circuit breakers during short circuits under island conditions. The proposed algorithm was tested in laboratory and results show that this solution has great potential in microgrid applications.
Stationary Detector for Monocular Visual-Inertial SLAM

Monocular Visual-Inertial SLAM (VISLAM) algorithms are very popular solutions for accurate indoor localization. However, they may suffer from speed divergence when the system is at rest as illustrated on Figure 1. In this paper we propose to tackle this issue. For that we investigate the detection of time epochs when a visual-inertial sensor rig is stationary. Two kind of stops are deduced from raw sensor data. SoftStop when the system is at rest with a slight movement noise (e.g a human at rest holding the system) and HardStop when the system is perfectly at rest (e.g a robot at rest holding the system). We propose an inertial detector and a visual detector to decide if the system is on move, on SoftStop or HardStop and describe how to take advantage of this additional information in a VISLAM. A significant accuracy gain and better robustness against divergence is demonstrated on our datasets.
Exploring structures and processes of medicines management in elderly hospitalised patients in the United Arab Emirates

Given the complexity of medicines use in elderly patients, structures and processes of medicines management are key to deriving best outcomes. This research was conducted in hospitals in the United Arab Emirates (UAE) and focused on the patient journey from admission to discharge. The overall aim was to explore the structures and processes of medicines management in elderly hospitalised patients in the UAE, conducted in three phases. Phase 1. Following a review of systematic reviews of aspects of medicines management (e.g. reconciliation), this phase focused on a specific, emerging tool (the Drug Burden Index (DBI)) relating to anticholinergic/sedative agents, which are problematic in the elderly. The aim was to critically appraise, synthesize and present evidence of DBI use. The review protocol was registered with the Joanna Briggs Institute and conducted according to best accepted practice. The key finding was the lack of evidence of DBI use prospectively to identify potentially inappropriate prescribing. Phase 2. Phase 2 employed a qualitative phenomenological design to explore health professionals views and experiences of medicines management. Semi-structured interviews were conducted with 27 professionals and analysed using Normalization Process Theory (NPT) and the Theoretical Domains Framework (TDF). Findings revealed little evidence of coherence, cognitive participation, collective action and reflexive monitoring (NPT). TDF domains dominant were: professional role, identity; beliefs about capabilities; beliefs about consequences; environmental context, resources; and knowledge. Phase 3. The Delphi technique in phase 3 aimed to determine consensus around medicines management using an expert panel of policy makers, educators and lead health professionals. Phase 1 and 2 findings were used in construction of validated statements. A high level of consensus (â‰¥70% strongly agree/agree) was obtained for statements other than those for targeting medicines management (rather than all elderly admissions) and tasks linked to professions (rather than trained staff). Overall, this research has generated original findings focused on the entire inpatient hospital journey, particularly the need to more clearly define, refine and agree on healthcare structures and processes across the entire patient journey from admission to discharge. The use of the NPT and TDF has highlighted those individual practitioners and organisational issues which require consideration.
Semi-supervised Variational Autoencoder for WiFi Indoor Localization

We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction model from a small set of annotated WiFi observations and a massive set of non-annotated ones. Our method is based on the variational au-toencoder deep network. We complement the network with an additional component of structural projection able to further improve the localization accuracy in a complex, multi-building and multi-floor environment. We consider several different network compositions which combine the classification and regression sub-tasks to achieve optimal performance. We evaluate our method on the public UJI-IndoorLoc dataset and show that the proposed method allows to maintain the state of the art localization accuracy with a very limited amount of annotated data.
Spin Crossover Phenomena in $\mathrm{SmCo}_{0.5}\mathrm{Ga}_{0.5}\mathrm{O}_{3}$

Single-phase samarium cobaltite-gallate $\mathrm{SmCo}_{0.5}\mathrm{Ga}_{0.5}\mathrm{O}_{3}$ with the orthorhombic Pbnm perovskite structure was synthesized for the first time by solid state reactions technique. No symmetry-related structural changes were observed in a broad temperature range of 310–1100 K by using in situ X-ray synchrotron powder diffraction. The temperature-dependent synchrotron powder diffraction measurements of $\mathrm{SmCo}0.5 \mathrm{Ga}_{0.5}\mathrm{O}_{3}$ revealed pronounced deviation from a “normal” lattice expansion, which are manifested in nonmonotonic temperature dependence of the unit cell dimensions and abnormal increase of thermal expansion coefficients with broad maxima near 780 K. These anomalies, as well as detectable deviations in the temperature dependencies of the intraoctahedral bond length and octahedra tilt angles are associated with the temperature induced spin state transitions of Co3+ and coupled insulator-metal transitions occurred in $\mathrm{SmCo}_{0.5}\ \mathrm{Ga}_{0.5}\ \mathrm{O}_{3}$ at the elevated temperatures.
Silhouette Image Classification ssssusing Bag of Local Features

Shape is an important feature to identify an object in the image. Classifying objects by using their shape has been an interesting and important area in computer vision. It has improved a lot in the last decades. In this paper, an approach is proposed for shape classification that uses both local and global image representation using Histogram of Oriented Gradient (HOG), Pyramid of Histogram of Orientation Gradients (PHOG) and CENsus Transform hISTogram(CENTRIST). These features are extracted from MPEG-7 Part-B silhouette dataset and bag of feature approach is used to classify the silhouette image. The performance of proposed method is compared with state-of-the-art methods and analyzed the performance of the descriptors in silhouette image classification. By evaluating these descriptors it can be concluded that the combined HOG, PHOG give better performance than CENTRIST in the context of silhouette image classification.
Haiku Generation using Word Associations COMP 550 Project

We formulate a method for automatic haiku generation based on the importance of association in creativity. Our model takes input words, generates an outline using a word association model and finally outputs a free form 3-line haiku through a word to sentence Recurrent Neural Network. Generated haikus show similar association score to human haikus based on our metric.
terminal-deoxynucleotidyl transferase dUTP nick end labeling assay were adopted to detect apoptosis of cells in the trans- planted tumors. Reverse transcription-quantitative polymerase chain reaction and western blotting were used to determine the mRNA and protein expression levels of target genes, 

The present study aimed to investigate the effects of photothermal therapy with gold nanorods (AuNRs) or epidermal growth factor receptor monoclonal antibody-conjugated AuNRs (EGFRmAb-AuNRs) on hypopharyngeal carcinoma (HC) in nude mice. In addition, the associated signaling pathways were explored. Briefly, a subcutaneous transplantable hypopharyngeal tumor model was established in nude mice injected with FaDu human HC cells. A total of 70 nude mice were randomly divided into seven groups, each of which received a different treatment. Mice were treated with AuNRs, locally or through intravenous injection, whereas EGFRmAb or EGFRmAb-AuNRs were only administered locally. Near infrared spectroscopy (NIR) was also applied for plasmonic photothermal therapy (PPTT). The growth curve and the inhibitory rate for tumor growth were used to evaluate the effects of each treatment. Flow cytometry and the terminal-deoxynucleotidyl transferase dUTP nick end labeling assay were adopted to detect apoptosis of cells in the transplanted tumors. Reverse transcription-quantitative polymerase chain reaction and western blotting were used to determine the mRNA and protein expression levels of target genes, respectively. Local treatment with AuNRs + NIR or EGFRmAb significantly inhibited tumor growth, and EGFRmAb conjugation further increased the inhibitory effects. Furthermore, there was a significant increase in apoptosis of tumor cells in the AuNRs + NIR, EGFRmAb and EGFRmAb-AuNRs + NIR groups; treatment with EGFRmAb-AuNRs + NIR induced the highest apoptotic effect. Mechanistic studies indicated that EGFRmAb-AuNRs + NIR may inhibit tumors through the AKT serine/threonine kinase (Akt) and DNA damage signaling pathways. In the AKT pathway, the mRNA and protein expression levels of phosphatase and tensin homolog were increased, whereas the expression levels of Akt and glycogen synthase kinase 3β were decreased. In the DNA damage signaling pathway, the mRNA and protein expression levels of ATR serine/threonine kinase, checkpoint kinase 1 and p53 were enhanced, whereas phosphorylated-p53 protein expression was reduced. The present findings indicated that AuNRs + NIR inhibited HC tumor growth, and conjugating EGFRmAb to AuNRs further enhanced the inhibitory effects. EGFRmAb conjugation may increase the antitumor effects of AuNRs-induced PPTT by downregulating the phosphatidylinositol-3-kinase/Akt pathway and upregulating the DNA damage pathway. These findings may provide novel insights into tumor-targeting PPTT in vivo.
INNOVATING FOR MARKET ADOPTION IN THE FOURTH INDUSTRIAL REVOLUTION

Will the market adoption of innovative products and services in the fourth industrial revolution require an alternative reality? This question is investigated in a concept paper in which new product adoption patterns, alternative innovation regimes that include intelligent machines as innovation partners with humans, disruption of the producer, the fourth industrial revolution consumer, and a fundamental change in business models are considered. Thought models are proposed in which these four entities drive a new concept of ‘life-world’ products through which consumers innovate for their own personalisation and customisation, manufacturing plants for volume products become algorithm factories, and the linear value chain is destroyed and replaced by the value network. This happens because the consumer becomes part of the value chain, and overlaps of producer and consumer functions as we know them merge into a new production ecosystem driven by social commerce. Innovation by the consumer takes away the producer’s concerns about market adoption; they may now servitise innovation support. This paper is meant to stimulate academic debate and to initiate research that will validate the thought models it suggests.
NUMERICAL AND ANALYTICAL STUDY OF WIND TURBINE WAKES IN YAWED CONDITION

A new analytical wake model is proposed to predict the wake deflection, mean velocity, and turbulence intensity in the wake flow of yawed wind turbines. Firstly, two kinds of operating condition with different thrust coefficients under two types of inflow with different ambient turbulence intensity are simulated for two yaw angles by the Reynolds stress model. The wake deflection, mean velocity and turbulence intensity in the wakes are systematically investigated. A new wake deflection model is then proposed to analytically predict the wake center trajectory in the yawed condition. Finally, the proposed wake deflection model is incorporated in the Gaussian-based wake model. The wake deflection, velocity deficit and added turbulence intensity in the wake predicted by the proposed model show good agreement with the experimental data and numerical results.
Irregular Migration and the Unintended Consequences of Search and Rescue Operations in the Central Mediterranean Sea

The Central Mediterranean is the most dangerous crossing for irregular migrants in the world. At any given point in time, over half a million potential migrants wait in Libya to travel to Italy with the aid of human smugglers. In response to high proﬁle shipwrecks and mounting deaths, Europe intensiﬁed search and rescue operations beginning in 2013. We develop a model of irregular migration in order to identify the eﬀects of these operations on activity along this smuggling route. Leveraging plausibly exogenous variation from rapidly varying weather and sea conditions, we ﬁnd that smugglers responded to these operations by shifting from seaworthy wooden boats to ﬂimsy inﬂatable rafts. By doing so, these operations induced more crossings despite occurring during a period when overall crossings were on a downward trend. We show that this had the ultimate eﬀect of entirely oﬀsetting the intended safety beneﬁts of search and rescue operations, which were captured at least in part by smugglers.