Miracle-dz/UniFAR-FTUs huggingface dataset:
Observer-based feedback control methods for an underactuated robot system

This thesis is devoted to nonlinear control and state variable estimation of a typical underactuated mechanical system, called "Pendubot". In most cases, the controllers applied to mechanical systems require information of all states for calculating the feedback control signal. For the Pendubot, states are comprised of joint angular positions and velocities. The joint angular positions could be measured accurately. However,the joint angular velocities are obtained either by differentiating the measured joint angular positions or through tachometers in practical situations. In both cases, the velocity signal is contaminated by noise. This degrades the performance of closed-loop Pendubot system. The main focus of this thesis is on how to accurately estimate the joint angular velocities of the Pendubot. To develop advanced observers, model based approaches are considered. High-gain observer (HGO) and sliding mode observer (SMO) are designed and applied respectively. In order to fully investigate the effect of these two observers on the Pendubot, controllers based on each of them are applied to the Pendubot system in both simulation and real-time implementation. The controller employed here includes two parts. One is called swing up controller and swings up the links of the Pendubot from their downward positions to pre-chosen equilibrium positions. The other one is named balancing controller, which is used to balance the Pendubot at the equilibria. As a linear form observer, HGO is simple and easily implemented. The SMO covers all the nonlinearity of the system and therefore is more robust to noise in measurements. Based on both simulation and implementation of the combined system, it is concluded that the HGO and SMO are both more effective than the traditional difference method in supplying accurate estimation of joint angular velocities of the Pendubot and more resistant to noise contamination. In summary, this work aims to study the issues on accurate estimation of the joint angular velocities of the Pendubot using available noisy measurements. The results of this thesis implies a better control of the dynamic states of this underactuated system with substantial modelling uncertainties and disturbances.
Prediction of court decision from Arabic documents using deep learning

The increasing amount of electronic legal documents represents a great opportunity for the development of intelligent computational systems for legal texts processing and classification. Most of these systems use classical machine learning and large datasets in English. This paper proposes an approach to automatically predict legal case outcome from written description of the events in Arabic using deep learning. An in‐house corpus from the decisions of the Moroccan Court of Cassation is built and used to train a deep learning model that predicts judgement. As the created corpus is of limited size, a new data augmentation method is proposed to boost the prediction performance. Two settings for text representation are tested, namely FastText and GloVe embeddings, and multiple deep learning models architectures are tested. The proposed approach succeeds in predicting judicial decisions of the Moroccan Court of Cassation with an accuracy of 80.51% on six classes. Even with a small dataset, the proposed data augmentation method was helpful in improving the overall models' performance. Despite the advancement in the area of legal judgement prediction over the years, this work is the first attempt to predict legal outcome using the documents of the Moroccan Cassation court. The corpus created in the context of this work will be made publicly available to the community.
Optimal design of accelerated life tests with two stresses

Most of the previous works on designing accelerated life test plans were concerned with the case where a single stress is employed for acceleration. In this article we develop optimal accelerated life test plans when two stresses are involved with possible interaction between them. The lifetimes of test items are assumed to follow an exponential distribution, the mean of which depends on the stresses according to the generalized Eyring law. A factorial arrangement of test points is considered for an efficient utilization of equipment, and the low level of each stress and the proportion of test items allocated to each test point are determined such that the asymptotic variance of the maximum-likelihood estimator of the mean lifetime at the use condition or of an acceleration factor is minimized. Patterns of optimal plans are identified and their efficiencies are compared with the corresponding single-stress accelerated life test plans. © 1996 John Wiley & Sons, Inc.
Multiplication operators on Hilbert spaces of Dirichlet series

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy MULTIPLICATION OPERATORS ON HILBERT SPACES OF DIRICHLET SERIES By Eric B. Stetler May 2014 Chair: Scott McCullough Major: Mathematics In this dissertation, certain classes of Hilbert function spaces of one-variable and multivariable Dirichlet series will be examined. Their corresponding multiplier algebras will be explored and, in certain cases, classified up to isometric isomorphism. In the one-variable case, Hilbert spaces of the form Hw = { ∞ ∑
Growing networks with mixed attachment mechanisms

Networks grow and evolve when new nodes and links are added in. There are two methods to add the links: uniform attachment and preferential attachment. We take account of the addition of links with mixed attachment between uniform attachment and preferential attachment in proportion. By using numerical simulations and analysis based on a continuum theory, we obtain that the degree distribution P(k) has an extended power-law form P(k) ∼ (k + k0)−γ. When the number of edges k of a node is much larger than a certain value k0, the degree distribution reduces to the power-law form P(k) ∼ k−γ; and when k is much smaller than k0, the degree distribution degenerates into the exponential form . It has been found that degree distribution possesses this extended power-law form for many real networks, such as the movie actor network, the citation network of scientific papers and diverse protein interaction networks.
Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach

Introduction In recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object – in this case a prosthetic device – into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction. Methods Based on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees. Results The classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm).
Lane-change trajectory planning method for driverless vehicles based on trajectory prediction

Lane-change is a common and dangerous condition in the process of vehicle driving. Especially in the multi-vehicle environment, the relative safety between lane-change vehicles and surrounding vehicles is very important. In this paper, a lane-change trajectory planning method based on quintic polynomial is proposed by considering the trajectory prediction results of surrounding vehicles. Firstly, by fusing the driving state of the vehicle, the lane-change trajectory cluster is obtained by using the quintic polynomial programming method, and the stability boundary of the trajectory cluster is obtained according to the three-degree-of-freedom dynamics model of the vehicle. Then, in order to get a relative in trajectory cluster environment vehicle safety of lane-change trajectory, we assume that the information data of surrounding vehicles is available, according to different location of the vehicle, to establish a different objective function, to evaluate different lane-change trajectory of the vehicle relative security, according to the objective function to solve trajectory optimal solution in the cluster. Finally, the co-simulation based on MatLab and CarSim verifies the reliability of the proposed lane-change trajectory planning method.
NeSNet: A Deep Network for Estimating Near-Surface Pollutant Concentrations

With the threat of atmospheric pollution on the rise in recent years, round-the-clock monitoring of the concentration of atmospheric gases has become utterly necessary. As opposed to traditional in situ measurement strategies, satellite monitoring offers a convenient alternative for truly global coverage. However, satellite measurements do not provide information about the vertical profile of concentration, and estimation methods must be used to deduce near-surface concentration. Existing works that address this problem often adopt approaches that use auxiliary variables such as meteorological parameters and population density information along with vertical column density (VCD) measurements. In remote areas where such information is not available, these methods are likely to fail. In our work, we propose a near-surface network, a convolutional neural network that has been designed to perform the estimation of near-surface concentrations of atmospheric trace gases using only VCD values. We demonstrate the working of our method for nitrogen dioxide (NO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>), sulfur dioxide (SO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>), and ozone (O<inline-formula><tex-math notation="LaTeX">$_{3}$</tex-math></inline-formula>). The proposed method shows RMSE scores of 6.272, 7.20, and 16.03 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>g<inline-formula><tex-math notation="LaTeX">$/\text{m}^{3}$</tex-math></inline-formula> for SO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>, NO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>, and O<inline-formula><tex-math notation="LaTeX">$_{3}$</tex-math></inline-formula>, respectively. We also perform a detailed analysis of the impact of various factors on model performance. In the future, this method also use to determine the concentration of additional air pollutants including PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> and PM<inline-formula><tex-math notation="LaTeX">$_{10}$</tex-math></inline-formula>. To possibly improve the effectiveness of the model, other meteorological variables, such as temperature, relative humidity, wind speed, and wind direction can be incorporated.
The polarization characteristics of randomly oriented nonspherical ice particles in mm and sub-mm frequency range: Implications to the remote sensing of cirrus clouds using satellite microwave radiometry

The depolarization effects of randomly oriented nonspherical ice particles are studied for frequencies ranging from 90 to /spl sim/900 GHz. It is found that, given the particle shape and the working frequency, the brightness temperature difference between the vertical and the horizontal polarization measured by a space-borne radiometer is only sensitive to ice particles of sizes within a certain range. The center of this range moves from >1000 /spl mu/m to /spl sim/120 /spl mu/m for nearly spherical particles when the frequency changes from 90 to 874 GHz. The particle size given here is the median mass equivalent sphere diameter. This range also changes with the particle shape. Generally, particles with a greater aspect ratio show a stronger depolarization effect by larger sizes. These features suggest that radiometric measurements from satellite on the polarization difference may be useful in determining ice particle size and shape in the cirrus clouds.
A posteriori error estimates of mixed methods for miscible displacement problems

The miscible displacement of one incompressible fluid by another in a porous medium is governed by a system of two equations. One is an elliptic equation of the pressure and the other is a parabolic equation of the concentration of one of the fluids. Since the pressure appears in the concentration only through its velocity field, we choose a mixed finite element method to approximate the pressure equation and for the concentration we use the standard Galerkin method. We shall obtain an explicit a posteriori error estimator in L2(L2) for the semi‐discrete scheme applied to the non‐linear coupled system. Copyright © 2007 John Wiley & Sons, Ltd.
SE-CNN: Convolution Neural Network Acceleration via Symbolic Value Prediction

CNNs are difficult to achieve inter-layer parallelism because of the data dependence between layers. In the paper, we propose Symbolic-Execution CNN (SE-CNN), which breaks data dependence between CNN layers via value prediction. Our insight is that in post-trained CNNs, only a subset (less than 10%) of neurons are activated (producing non-zero values) to identify patterns in inputs while most of the other neurons remain silent (producing zeros). This is because given an input image there are only a limited number of features presented. Thus, within the CNN, the neurons that are sensitive to the given features are more exercised than the others. Based on this insight, SE-CNN works in two successive phases: A parallel computation phase and a serial correction phase. In the parallel computation phase, each CNN layer starts computation simultaneously based on predicted inputs: we predict most of the neurons having zeros as inputs. For non-zero input neurons we predict their inputs for the next input image the same as the previous ones. In the serial correction phase, each layer compares the predicted inputs with the real ones to correct its computation results if necessary. If a neuron has predicted correctly its input during the parallel phase, thus the corresponding neuron passes its serial phase. Otherwise the neuron will amend its prediction with a light-weight result amendment mechanism based on the real inputs. We implement SE-CNN on top of the streaming processor of a state-of-the-art general purpose GPU (GPGPU) architecture, adding marginal hardware overheads in area and power consumption. We also provide application programming interfaces (APIs) so that CNNs that have already been implemented can directly enjoy the benefits of our technique. We utilize GPGPU-sim as our experimental platform, benchmarked with 9 well-accepted CNNs from recent years’ ILSVRC contests. Experimental results show that compared to other three state-of-the-art GPU based CNN acceleration mechanisms, SE-CNN can averagely achieve <inline-formula> <tex-math notation="LaTeX">$13.4\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$10.4\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$7.9\times $ </tex-math></inline-formula> (maximally <inline-formula> <tex-math notation="LaTeX">$22.0\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$18.7\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$16.4\times $ </tex-math></inline-formula>) CNN execution speedup while maintaining over 95% of classification accuracy.
THE PREPARATION AND PROPERTIES OF VAPOR‐DEPOSITED SINGLE‐CRYSTAL‐LINE GaN

Single‐crystalline, colorless, GaN has been prepared by a vapor‐phase growth technique previously used to prepare GaAs, GaP, and GaSb. These crystals are the first reported speciments of GaN suitable for good electrical and optical evaluation of this compound. It has been determined that GaN has a direct energy bandgap of 3.39 eV, and that undoped crystals prepared by this method have a very high inherent electron concentration, typically above 1019/cm3, which is probably related to a high density of nitrogen vacancies. Conducting p‐type specimens have been prepared using Ge as the dopant; but this result has been difficult to reproduce, and the samples have been electrically inhomogeneous.
Galangin-Loaded Gold Nanoparticles: Molecular Mechanisms of Antiangiogenesis Properties in Breast Cancer

Angiogenesis is important for tissue during normal physiological processes as well as in a number of diseases, including cancer. Drug resistance is one of the largest difficulties to antiangiogenesis therapy. Due to their lower cytotoxicity and stronger pharmacological advantage, phytochemical anticancer medications have a number of advantages over chemical chemotherapeutic drugs. In the current study, the effectiveness of AuNPs, AuNPs-GAL, and free galangin as an antiangiogenesis agent was evaluated. Different physicochemical and molecular approaches have been used including the characterization, cytotoxicity, scratch wound healing assay, and gene expression of VEGF and ERKI in MCF-7 and MDA-MB-231 human breast cancer cell line. Results obtained from MTT assay show cell growth reduction in a time- and dose-dependent aspect; also, in comparison to individual treatment, a synergistic impact was indicated. CAM assay results demonstrated galangin-gold nanoparticle capacity to suppress angiogenesis in chick embryo. Additionally, altering VEGF and ERKI gene expression was recorded. Taken together, all the results can conclude that galangin-conjugated gold nanoparticles can be a promising antiangiogenesis supplemental drug in breast cancer treatment.
Frontiers and hotspots evolution in anti-inflammatory studies for coronary heart disease: A bibliometric analysis of 1990–2022

Background Coronary heart disease (CHD) is characterized by forming of arterial plaques composed mainly of lipids, calcium, and inflammatory cells. These plaques narrow the lumen of the coronary artery, leading to episodic or persistent angina. Atherosclerosis is not just a lipid deposition disease but an inflammatory process with a high-specificity cellular and molecular response. Anti-inflammatory treatment for CHD is a promising therapy; several recent clinical studies (CANTOS, COCOLT, and LoDoCo2) provide therapeutic directions. However, bibliometric analysis data on anti-inflammatory conditions in CHD are lacking. This study aims to provide a comprehensive visual perspective on the anti-inflammatory research in CHD and will contribute to further research. Materials and methods All the data were collected from the Web of Science Core Collection (WoSCC) database. We used the Web of Science’s systematic tool to analyze the year of countries/regions, organizations, publications, authors, and citations. CiteSpace and VOSviewer were used to construct visual bibliometric networks to reveal the current status and emerging hotspot trends for anti-inflammatory intervention in CHD. Results 5,818 papers published from 1990 to 2022 were included. The number of publications has been on the rise since 2003. Libby Peter is the most prolific author in the field. “Circulation” was ranked first in the number of journals. The United States has contributed the most to the number of publications. The Harvard University System is the most published organization. The top 5 clusters of keywords co-occurrence are inflammation, C-reactive protein, coronary heart disease, nonsteroidal anti-inflammatory, and myocardial infarction. The top 5 literature citation topics are chronic inflammatory diseases, cardiovascular risk; systematic review, statin therapy; high-density lipoprotein. In the past 2 years, the strongest keyword reference burst is “Nlrp3 inflammasome,” and the strongest citation burst is “Ridker PM, 2017 (95.12). ” Conclusion This study analyzes the research hotspots, frontiers, and development trends of anti-inflammatory applications in CHD, which is of great significance for future studies.
A Comprehensively Improved Interval Type-2 Fuzzy Neural Network for NOx Emissions Prediction in MSWI Process

The accurate and timely prediction of nitrogen oxides (NOx) emissions ensures eco-friendly and efficient operations for municipal solid waste incineration (MSWI) plants. Due to the high nonlinearity and uncertainty in MSWI processes, constructing an efficient prediction model remains challenging. This article proposes a comprehensively improved interval type-2 fuzzy neural network (CI-IT2FNN) for NOx emissions prediction. First, the neighborhood rough set is introduced to determine the structure of this fuzzy neural network automatically, including the number of fuzzy rules and their corresponding consequent parameters. Second, an adaptive shape factor is added to the fuzzy membership function to better cope with the uncertainty, which can help to improve the generalization ability of network. Furthermore, to reduce the computational complexity, the Begian–Melek–Mendel method is utilized as the defuzzification method in this article. Then, by integrating the linear least square estimation and gradient decent, a hierarchical learning algorithm is applied to adjust the network parameters to further enhance the learning efficiency and accuracy. Finally, after being evaluated by a benchmark simulation, the proposed CI-IT2FNN demonstrates its effectiveness and superiority on NOx emissions prediction.
A Variational Approach to Loaded Ply Structures

We show how an energy analysis can be used to derive the equilibrium equations and boundary conditions for an end-loaded variable ply much more efficiently than in previous works. Numerical results are then presented for a clamped balanced ply approaching lock-up. We also use the energy method to derive the equations for a more general ply made of imperfect anisotropic rods and we briefly consider their helical solutions.