Miracle-dz/UniFAR-FTUs huggingface dataset:
Adoption of the Synchronous Reluctance Motor in Electric Vehicles: A Focus on the Flux Weakening Capability

This article presents the definition of the performance that the electric motor should have in order to satisfy the requirements of the electric vehicle in terms of acceleration time and maximum speed, starting from a simplified vehicle model, considering the motor torque minimization and the input power minimization. In order to verify the effectiveness of the synchronous reluctance motor in traction applications, it has been evaluated in two case studies: a high-performance vehicle and a city car, where different flux weakening capabilities from the motor in function of the minimization strategies can be pursued to satisfy the requirements. If the car requires high performance in terms of acceleration time and maximum speed, the poor flux weakening capabilities of the synchronous reluctance motor induces a machine oversizing in terms of peak power; by assuming this, it is, however, possible to satisfy the requirements considering the maximum available encumbrance due to an advanced and innovative optimized design procedure. The motor of the city car is obtained only by modifying the stack length and the windings (scaled version), and on this last solution, a prototype has been realized and fully tested; the experimental tests confirm the results of the simulations.
Improved Diffuse Anisotropic Shading

Cloth, hair, brushed metal, and other surfaces with small, regular surface features exhibit anisotropic reflectance properties for which common isotropic shading methods are not suited. Shading of such materials is often implemented by computing the normal giving the maximum light contribution instead of solving the integral that is the sum of all reflected light. In this paper we show that this integral can be simplified if the direction to the viewer and fibre geometry is not taken into account. Still, this will give a more accurate result than the very rough simplification of using the maximum contribution. This computation is simple for diffuse light. However, the specular light still needs some more elaboration to work.
Prescribed-Time Extremum Seeking with Chirpy Probing for PDEs—Part II: Heat PDE

We introduce a prescribed–time extremum seeking (PT-ES) design for a PDE-ODE cascade of a heat PDE feeding into an integrator, which in turn feeds into an unknown map. Leveraging the integrator in the PDE-ODE plant, and employing “chirpy” probing and demodulation signals designed by PDE motion planning methods, we achieve convergence to the extremum in a user-prescribed time independent of the distance of the initial estimate from the optimizer. Although this PDE-ODE cascade is defined on a fixed spatial domain, it is inspired by free boundary models such as the Stefan model of phase change dynamics. The design is based on the time-varying backstepping approach, which transforms the PDE-ODE cascade into a suitable prescribed-time stable target system, and the averaging-based estimations of the gradient as well as the Hessian of the map. By means of Lyapunov method, it is shown that the average closed-loop dynamics are prescribed-time stable. This Part II paper is companion to a Part I paper which introduces PT-ES for two problems that are less challenging than here: a static map and a map with an input delay.
A Novel Oxidative Phosphorylation-Associated Gene Signature for Prognosis Prediction in Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common type of malignant tumor with high morbidity and mortality. The oxidative phosphorylation (OXPHOS) metabolic pathway produces adenosine triphosphate (ATP) by delivering electrons to transmembrane protein complexes in the mitochondria. This research was dedicated to identifying an OXPHOS-associated signature for the assessment of prognosis of HCC patients. A total of 371 HCC patients from the Cancer Genome Atlas (TCGA) and 231 HCC patients from the International Cancer Genome Consortium (ICGC) with RNA expression data and clinical data were employed as construction and validation cohorts, respectively. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to establish a multigene signature in the TCGA cohort, and the ICGC cohort was used for validation. The prognostic value of the risk signature was evaluated using univariate and multivariate Cox regression, Kaplan–Meier curves, and receiver operating characteristic (ROC) curves. The potential enrichment of biological functions was investigated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Meanwhile, we analyzed the correlation between the risk score and the tumor microenvironment (TME). A five-gene signature including ATP6V0B, ATP6V1C1, ATP6V1E1, TIMM9, and UQCRH was identified by LASSO Cox regression to classify patients into low- and high-risk groups. ROC curve analysis indicated that the five-gene signature is a prospective prognostic factor in HCC patients. Univariate and multivariate Cox regression analyses demonstrated that the risk score was an independent prognostic factor for overall survival (OS). Functional analysis showed that differentially expressed genes (DEGs) between the low- and high-risk groups were enriched in mitosis and the cell cycle pathway. In addition, the five-gene signature was associated with innate immune cell infiltration, immune subtypes, and tumor stemness. A novel OXPHOS-associated gene signature can be used for prognostic prediction for patients with HCC.
Vibration Control of an Overhead Crane with Hoisting Motion using Input Shaping Technique

In this paper, an input shaping-based control law is designed for an overhead crane system with time-varying cable length. The proposed method employs a zero vibration (ZV) input shaper. It is well-known that the ZV input shaper is not robust to modeling errors causing by the time-varying dynamics, which leads to the fact that a direct application of ZV input shaper on time-varying systems often results in poor vibration suppression performance. Therefore, we first apply the standard ZV input shaper on an undamped linear time-invariant (LTI) second-order system. This step provides us a reference oscillation function that is exactly zero after the trolley stops. The real control input is then devised to perfectly match the real vibration of the crane system with the reference oscillation, and hence a vibration-free transportation can be achieved. The unshaped command is parameterized and its coefficients are designed to deal with control objectives other than the vibration elimination. The simulation results are provided to validate the efficacy of the proposed approach.
Web scraping Instagram Pre and During Covid-19: Examining customer engagement on Australian SMEs accounts

Instagram has gained the attention of hundreds of millions of users and evolved quickly into a critical customer engagement tool for businesses worldwide, more so during Covid-19. Impacts of Covid-19 have fundamentally changed the market, and therefore, this paper explores the relationship between Instagram practices and the engagement of 20 Australian SMEs (Small medium enterprises) pre and during Covid-19. This study aims to answer the following questions: (1) How should user-generated content (UGC) and call to act content (CTA) be included as Instagram posts? (2) How to use #Hashtags and @Tagging in Instagram posts to keep a campaign going? (3) How Instagram can be utilised to mitigate the effect of Covid-19? Findings revealed a statistically significant relationship between the number of UGCs to Instagram engagement, while CTA content performance recorded a mixed result. However, both UGCs and CTA positively affect the engagement when used to build a virtual community and engage with followers rather than redirecting customers to online selling locations. Also, diversity in @Tagging and #Hashtag uses are found to be effective drivers of engagement. The results imply that addressing the Covid-19 related concerns of followers while showing genuine brand social responsibility can be rewarded by extra engagement.
MFGAN: A Novel CycleGAN-Based Network for Masked Face Generation

—In the post-epidemic era, Masked Face Recognition (MFR) is of great significance to our daily life, but it confronts a severe challenge of lacking real-world large-scale masked face datasets with identity labels. Moreover, mask enhances the diversity of face images and further improves the requirements for datasets. To address the above problem, we propose a novel CycleGAN-based masked face generation method MaskedFace-GAN (MFGAN), which is able to generate correct, authentic-looking and type-diverse masked face while ensuring the invariance of facial features. We design a three-stage training pipeline for MFGAN, which corresponds to three modules, respectively. Specifically, a facial feature detector is adopted to guide the model to generate the correct mask in the correct position. Then, by utilizing a mask binary segmentation module, the authenticity of generated images can be guaranteed. Lastly, with mask style encoder, the model can be optimized towards generating type-diverse masked faces. Finally, comparing with advanced masked face synthesis and generation methods comprehensively, our MFGAN achieves the best results. Then we apply the generated masked face datasets to MFR model training, which further proves the feasibility of training MFR models on generated datasets and the effectiveness and advancement of MFGAN compared with other state-of-the-art methods.
Embeddedness of Economic Actions in the Social Network: Study among of Local Genuine Entrepreneurship in Cirebon, West Java

The purpose of this study was to analyze the attachment of economic activity in social networks among entrepreneurs in Cirebon, West Java, Indonesia. This research was conducted in Cirebon Regency, for a period of 6 months, from May to October 2019. This research method uses a qualitative approach. The results of the study found that: first, local entrepreneurs (genuine) in the rattan business sector have been running for a very long time. Second, as a result of historical formation, there is a wide network both between entrepreneurs and between entrepreneurs with certain institutions and community structures. Thrid, the map of economic network attachment in social networks shows the diversity of attachments between economic networks and social networks.
An Asynchronous Soft Macro for Ultra-Low Power Communication in Neuromorphic Computing

Asynchronous networks-on-chip (NoCs) playa fundamental role to materialize energy efficiency and scalability of spiking neural network-based neuromorphic systems. An unmistakable trend in this field consists of using bundled-data encoding for NoC design, showing promise in overall cost metrics while incorporating moderate timing constraints. To date, no framework exists for cost-effective and flexible NoC design with this approach. This paper aims at bridging this gap, by making bundled-data NoCs available to neuromorphic system designers through a highly-configurable and technology-independent soft macro. To our knowledge, this is the first paper to prove synchronous-equivalent design flexibility for an asynchronous NoC.
Hardware-Friendly Logarithmic Quantization with Mixed-Precision for MobileNetV2

In a variety of computer vision applications, convolutional neural networks (CNNs) have achieved excellent accuracy. However, in order for a CNN to operate on embedded platforms such as mobile devices, hardware resources and power consumption must be reduced. Accordingly, research involving the application of low-precision quantization to lightweight networks, such as MobileNet, has attracted considerable attention. In particular, compared to linear quantization, logarithmic quantization can significantly reduce hardware resources by processing multiplication operations as addition operations when implementing a hardware accelerator. In this study, we propose a novel logarithmic weight quantization considering the characteristics of MobileNetV2, which is known to be notoriously difficult to quantize, and a mixed-precision quantization that minimizes accuracy loss by training the distribution range using the trainable parameter $\alpha$, Experimental results show that the proposed method achieves accuracies greater than 1.47% and 2% on the CIFAR-10 and Tiny-ImageNet datasets, respectively, compared to the general log-scale quantization methods. As a result, the proposed method achieves a significant hardware resource reduction with only a slight degradation in performance when compared to the full precision (i.e., FP32), and achieves an additional power reduction effect of about 48% compared to linear scale quantization at the same precision.
Representativeness and certainty of sea surface temperature from MODIS in semi-enclosed bays

ABSTRACT The sea surface temperature estimated by satellites (SSTskin) may facilitate the understanding of processes that affect water quality in semi-enclosed bays. However, the representativeness and certainty of SSTskin in semi-enclosed bays have not been fully investigated. We investigated the SSTskin from MODIS in Tokyo Bay and Ise Bay to test the hypothesis that water-mass structure and proximity to land would affect SSTskin. Results showed that the SSTskin from MODIS in two semi-enclosed bays can be as representative and certain as the data from previous studies in the open ocean. We found that horizontal gradients of water temperature had little effect on the certainty of SSTskin. However, we found that both the representativeness and certainty of satellite-based estimates of SSTskin in semi-enclosed bays were reduced by seasonal and location characteristics. The representativeness and certainty of SSTskin near the mouth of Ise Bay were compromised by the complex vertical structure of water temperature in summer. Because SSTskin may greatly enhance understanding of the processes that affect water quality in semi-enclosed bays, our results indicate that prior validation of SSTskin by comparison with in-situ sea surface temperature (SSTdep) is important.
Adjusting and Logging Rheo Knee Parameter Values Using a Smartphone Application

A smartphone application, including its architecture, is presented which can establish a Bluetooth connection with a Rheo Knee. The Rheo Knee is a prosthetic knee produced by Össur which is designed for use by transfemoral amputees. The smartphone application includes functionality which allows it to transfer parameter data from the Rheo Knee to a Google sheet database, as well as adjust parameter values within the Rheo Knee. The adjustment of these values results in different levels of damping in the prosthetic knee joint. These damping magnitudes are used to control the amount of flexion in the knee during stance, as well as the amount of impact experienced as the knee reaches full extension in swing. The smartphone application can be used to select different parameter values by adjusting the position of a marker within an interactive two-dimensional graph. The initial scheme that was used to map the marker’s position to particular parameter values is evaluated. Test results are analyzed and used as a benchmark for a revised mapping scheme. Also discussed is the efficiency of the application in transferring parameter data from the knee to the Google sheet database. Aspects of the application ́s software architecture can be used as an aid in the development of commercial software which serves a similar purpose. This can either be by direct refinement and integration of programing code, or through the utilization of similar approaches as discussed in this paper.
A New Augmented L1 Adaptive Control for Wheel-Legged Robots: Design and Experiments

This paper proposes the augmentation of an 1 adaptive controller with a feedback Linear Quadratic Regulator (LQR) to control a wheel-legged biped robot. The performance of linearized model-based controllers, such as LQR, depends on the accurate knowledge of model parameters, a priori information about input and output disturbances, and other unforeseen conditions. We propose a hybrid scheme where an ${\mathcal{L}_1}$ adaptive controller is combined with LQR to compensate for matched uncertainties and other disturbances related to the environment change such as friction conditions of the floor. The proposed control scheme is able to keep the robot stable under model uncertainties and external disturbances through a series of validation scenarios including simulations and real-time experiments.
Shape Estimation of Soft Manipulators using Piecewise Continuous Pythagorean-Hodograph Curves

In recent years, there has been significant interest in use of soft and continuum manipulators in diverse fields including surgical and agricultural robotics. Consequently, researchers have designed open-loop and feedback control algorithms for such systems. Here, the knowledge of the manipulator shape is critical for effective control. The estimation of the manipulator shape is challenging due to their highly deformable and non-linear nature. Researchers have explored inductive, magnetic and optical sensing techniques to deduce the manipulator shape. However, they are intrusive and economically expensive. Alternate non-contact sensing approaches may involve use of vision or inertial measurement units (IMUs) that are placed at known intervals along the manipulator. Here, the camera provides position of the marker, while the slope (rotation matrix or direction cosines) can be determined using IMUs. In this paper, we mathematically model the manipulator shape using multiple piecewise continuous quintic Pythogorean-Hodograph (PH) curves. A PH-curve has continuous slope and is a convenient parametric model for curves with constant length. We investigate the use of multiple piecewise continuous-curvature PH curves to estimate the shape of a soft continuum manipulator. The curves model manipulator segments of constant lengths while the slopes at the knots are assumed to be known. For N curve segments with (4N + 8) unknowns, the shape estimation is formulated as a constrained optimization problem that minimizes the curve bending energy. The algorithm imposes (4N + 3) nonlinear constraints corresponding to continuity, slope and segment length. Unlike traditional cubic splines, the optimization problem is nonlinear and sensitive to initial guesses and has potential to provide incorrect estimates. We investigate the robustness of the algorithm by adding variation to the direction cosines, and compare the output shapes. The simulation results on a five-segment manipulator illustrate the robustness of the algorithm. While the experimental results on a soft tensegrity-spine manipulator validate the effectiveness of the approach. Here estimation error of the end-effector position normalized to the manipulator length are 6.53% and 6.2% for the two experimental poses.
Multi-Label Code Smell Detection with Hybrid Model based on Deep Learning

—Code smell is an indicator of potential problems in a software design that have a negative impact on readability and maintainability. Hence, it is essential for developers to make out the code smell to get tips on code maintenance in time. Fortunately, many approaches like metric-based, heuristic-based, machine-learning based and deep-learning based have been proposed to detect code smells. However, existing methods, using the simple code representation to describe different code smells unilaterally, cannot efficiently extract enough rich information from source code. What is more, one code snippet often has several code smells at the same time and there is a lack of multi-label code smell detection based on deep learning. In this paper, we propose a hybrid model with multi-level code representation to further optimize the code smell detection. First, we parse the code into the abstract syntax tree(AST) with control and data flow edges and the graph convolution network is applied to get the prediction at the syntactic and semantic level. Then we use the bidirectional long-short term memory network with attention mechanism to analyze the code tokens at the token-level in the meanwhile. Finally we get the fusion prediction result of the models. Experimental results show that our model can perform outstanding not only in single code smell detection but also in multi-label code smell detection.
Distance-Based Formation Control of Nonlinear Agents Over Planar Directed Graphs

This paper presents a distance-based formation control of nonlinear agents on a plane. Due to mathematical complexity, the distance-based formation is mainly studied for linear single- and double-integrator models. Here, we introduce a novel control scheme for a distance-based control of a set of nonlinear agents. The formation topology is modeled as directed graphs where just one incident agent controls the corresponding edge (distance constraint). The proposed method is based on state-dependent Riccati equation (SDRE) theory, which can effectively be applied to nonlinear systems. The asymptotic stability of the formation is rigorously proven. Moreover, using the SDRE method and signed area constraints, the proposed controller guarantees collision avoidance and prevents flip ambiguity of the formation. Simulation results are presented that support theoretical results.
Robust Industrial Control Systems: Optimal Design Approach for Polynomial Systems

Introduction to optimal robust control scalar LQG optimal control problem and solution scalar H infinity optimal control problem and solution multivariable LQG optimal control problems and solution multivariable H infinity optimal control problem and solution robust control design procedures H2 optimal filtering smoothing and prediction problems H infinity optimal filtering smoothing and prediction problems industrial applications of H infinity optimal control.
Notions and applications of algorithmic randomness

Algorithmic randomness uses computability theory to define notions of randomness for infinite objects such as infinite binary sequences. The different possible definitions lead to a hierarchy of randomness notions. In this thesis we study this hierarchy, focussing in particular on Martin-Lof randomness, computable randomness and related notions. Understanding the relative strength of the different notions is a main objective. We look at proving implications where they exists (Chapter 3), as well as separating notions when the are not equivalent (Chapter 4). We also apply our knowledge about randomness to solve several questions about provability in axiomatic theories like Peano arithmetic (Chapter 5).
THE NUMBER OF SUBSPACES OF A VECTOR SPACE.

Abstract : The number G sub n of subspaces of an n-dimensional vector space over GF(q) is studied by the symbolic calculus. This calculus provides a general technique for proving theorems in particular identities, involving the G sub n and the Gaussian coefficient (n, k). Examples include a recursion for the G sub n, an infinite product expansion for the Eulerian generating function of the G sub n, and a q-analog of the Pascal triangle for the (n, k). (Author)
On a theory of electricity and gravitation

This is the second paper by Nordstr{\"o}m on his five dimensional theory. It aims at generalizing the relations $\mathbf{D} = \epsilon \mathbf{E}$, $\mathbf{B} = \mu \mathbf{H}$ of electromagnetism to the five dimensional gravito-electric theory and to show that this is consistent with the principle of equivalence. Some steps are taken toward providing a foundation for a theory of matter. Nordstr{\"o}m hopes, as stated in the conclusion, to show ''that the five dimensional perspective, while still having only a formal content, may however provide footholds for further developments of the theory of gravitation, which otherwise would have been hard to find''.