Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation and Optimization for System Availability Under Preventive Maintenance

Engineers design systems to be reliable and work to fulfil their missions without failure for a specific period. However, the system components deteriorate with time and lead to its failures. A frequent system failure increases the management costs, hence posing a challenge to decision-makers. Therefore, for the avoidance of frequent system failures, preventive maintenance is necessary. The objective of any manufacturing firm is to maximize profit and minimize costs. The interval for preventive maintenance can be optimized if the system’s availability is maximized and its cost function minimized. This study evaluates the availability and cost function for a continuous operating series-parallel system under a fixed time environment. A multiobjective model is formulated to maximize the availability and minimize the cost function of the system. The study illustrated a numerical example and solved using goal programming (GP), fuzzy goal programming (FGP), genetic algorithm (GA), and particle swarm optimization (PSO) techniques. The results are compared using a robust statistical test and, the PSO proves to be better. A simulation study was carried out further to evaluate the availability and cost function using R and MATLAB packages.
Unified Position-Attitude Control of A Nonlinear Quadrotor Swarm

In this paper, we propose a novel nonlinear feedback control law to maneuver a swarm of nonlinear quadrotors with interagent collision avoidance. In contrast to the predominant hierarchical control architectures and dynamics linearization in controller synthesis in the literature, we control the position and attitude of each drone simultaneously in one unified step, with no dynamics linearization involved at any stage. Our method is based on generalizations of Gauss’s principle of least constraint that allows higher order constrained dynamics and that identifies, stabilizes, and incorporates the time-varying sets of active constraints. The active constraints are asymptotically stabilized to the controlled space according to a generalized constraint stabilization to provide command control actions. Numerical results are provided for a swarm of up to 80 nonlinear quadrotors executing aggressive flights and for eight nonlinear drones swapping positions on a circle, attesting to the efficacy and efficiency of the proposed scheme.
Effects of strength anisotropy on the stability of slopes

The effects of strength anisotropy of geomaterials in slope stability problems are the focus of this paper. The source of the strength anisotropy could be attributed to many factors and the motivation of this study is to concentrate on the effects of plane of weaknesses, such as joints, bedding planes etc. on the slope stability analysis. Two approaches are used in the finite element simulations to capture the effects of planes of weakness. In the first approach the planes of weakness are explicitly simulated by using joint networks, and in the second one a special constitutive model that considers embedded planes of weakness is utilized. Various slope stability problems have been analyzed using the shear strength reduction method to show the effects of anisotropy on the factor of safety of slopes. RÉSUMÉ Les effets de la résistance anisotropique des géomatériaux dans les problèmes de la stabilité des pentes sont l'objet du présent document. La source de la résistance anisotropique pourrait être attribuée à plusieurs facteurs. La motivation de cette étude est de se concentrer sur les effets du plan de faiblesse, telles que les joints, les plans de stratifications, etc. sur l'analyse de la stabilitée des pentes. Deux approches sont utilisées dans les simulations des éléments finis pour saisir les effets des plans de faiblesses. Dans la première approche, ils sont explicitement simulés à l'aide des réseaux de joints discrets, et dans la deuxième approche un modèle spécial constitutive qui considère les plans de faiblesses intégrés est utilisé. Divers problèmes de stabilités des pentes ont été analysés en utilisant la méthode de résistance au cisaillement de réduction pour démontrer les effets d'anisotropie sur le facteur de sécurité des pentes.
Robust Unit Commitment Optimization under Volatile Wind Power Outputs Assisted by Clustering-based Data-Driven Techniques

This study proposes a novel robust unit commitment (UC) framework with data-driven disjunctive uncertainty sets for volatile wind power generation, which integrates a two-stage adaptive robust UC model with machine learning techniques to flexibly capture the uncertainty space of the wind power forecast errors with disjunctive structures. K-means clustering and density-based spatial clustering of applications with noise (DBSCAN) are applied for clustering, and data-driven disjunctive uncertainty sets are constructed as the union of multiple basic uncertainty sets following the clustering results. The problem is formulated into a two-stage adaptive robust UC model with data-driven disjunctive uncertainty sets and with a multi-level optimization structure. A tailored decomposition-based optimization algorithm is developed to facilitate the solution process. A numerical experiment and a UC case study on the IEEE 39-bus system are presented to demonstrate the effectiveness of the proposed approach. In both applications, using the proposed disjunctive uncertainty sets can effectively reduce the price of robustness compared to the conventional "one-set-fits-all" approach. Furthermore, the disjunctive uncertainty sets using DBSCAN tend to provide less conservative solutions than those using K-means, implying that DBSCAN can handle the outliers and noise of the uncertainty data more efficiently.
Distribution of Real and Imaginary Zeros of Multi-DoF Undamped Flexible Systems

This paper investigates the distribution of zeros with respect to the poles on the imaginary and real axes of the s-plane in the transfer function of a multi-DoF undamped flexible LTI system. The transfer function of a multi-DoF undamped flexible LTI system can be modally decomposed i.e. expressed as the sum of second order modes where each mode is characterized by two system parameters – modal residue and modal frequency. It is well known that when all the modal residue signs are the same, all the zeros of the multi-DoF undamped flexible LTI system are minimum phase (MP). However, the same sign for all modal residues is a sufficient condition for the elimination of non-minimum phase (NMP) zeros and not a necessary one. In order to find sufficient conditions for the elimination of NMP zeros when all modal residue signs are not the same, specific results are needed that explain the distribution of zeros with respect to the poles in the s-plane. Therefore, in this paper results are provided that elucidate the distribution of zeros with respect to the poles on the real and imaginary axes of the s-plane for any combination of modal residue signs. The real and imaginary axes are divided into four segments based on the location of the poles and the parity of the number of zeros (i.e. even or odd) in each segment is derived as a function of the system parameters. The results in this paper provide the necessary and sufficient condition for the occurrence of pole-zero flipping on the imaginary axis which is known to be detrimental to the closed-loop dynamic performance of undamped flexible LTI systems. The results from this paper will also enable the derivation of a sufficient condition for the elimination of NMP zeros in multi-DoF undamped flexible LTI systems.
Constructive decomposition of BMO functions and factorization of _{} weights

We give elementary proofs of the decomposition of BMO functions on the line and factorization of AP weights. Our method is an explicit version of a construction due to the third author. We would like to describe an elementary construction leading to an explicit decomposition of BMO functions on R as bounded plus conjugate of bounded functions (i.e. Charles Fefferman's duality theorem) as well as to the factorization theorem proved in [2]. The method described is an explicit version of the construction in [3]. For simplicity we start by-describing the factorization of A2 on R. These are the weights (i.e. nonnegative locally integrable functions) o for which the following inequality holds: f(Mf + I Hf 1)2odx C 2fIf 12C dx, where H is the Hilbert transform and M is the Hardy-Littlewood maximal operator. They have a structural description which can be bound in [1]. In particular, it is known that w-o belongs with o to A2. THEOREM I. For o E A2, there exists weights W. 1, 2, such that w = ol/o2 and I H(J) + M(w ) 3 3Cw. As a corollary one obtains a weak version of the Helson-Szego theorem: for 6 E A2, we can find two bounded functions b,, b, such that lno = b? + H(b2) and hIb211 I 0, we find IW, , cojand IargW. I<arctg3C<7T/2. Thus In -In I W/ W2 bI is bounded, and In W, H( arg WI)e arg WI b2I<2arctg3C. Received by the editors April 29, 1982 and, in revised form, September 10, 1982. 1980 Mathematics Subject Classification. Primary 30D55, 42B30; Secondary 42B20, 42B25.
Demonstration of a 10-kV Class Waffle-Substrate n-Channel IGBT in 4H-SiC

The silicon carbide (SiC) waffle-substrate <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>-channel insulated gate bipolar transistor (IGBT) is a vertical IGBT designed to operate at blocking voltages below about 15 kV. At these voltages, the drift and anode layers are too thin to allow complete removal of the <inline-formula> <tex-math notation="LaTeX">${n}^{+}$ </tex-math></inline-formula> substrate. Instead, a waffle pattern is etched through the substrate to expose the buried anode layer while preserving the structural integrity of the wafer. The feasibility of this approach is demonstrated by fabricating a 10-kV class <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>-channel IGBT with a differential specific ON-resistance of 160 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot \text{cm}^{2}$ </tex-math></inline-formula>.
Automating Operator Oversight in an Autonomous, Regulated, Safety-Critical Research Facility

The deployment at scale of Unmanned Aerial Systems have become increasingly imminent in the last few years, even as concerns regarding the dependability and predictability of their command and control channels remain fully to be addressed. The intersection of ground-to-air wireless communications, aerial networking, and trajectory control has become a research area of sharp interest. The validation of such research, beyond the theoretical/simulation stage, requires a facility that is both realistic, and admits of potentially risky or unsafe operation, while in the end guaranteeing personnel and equipment safety. The AERPAW project is an ambitious project, funded by the PAWR program of the US NSF, to create a remote accessible research platform for a research facility to enable such validation. To enable remote usage of such a testbed, yet provide the researcher with complete experimental freedom, the AERPAW facility includes a combination of architectural mechanisms that balance freedom of experimentation with regulatory compliance and safety. In this paper, we articulate the challenges and considerations of designing such mechanisms, and present the architectural features of AERPAW that attempt to realize these lofty goals.
The Moderating Role of Support Facilities in the Relationships between Entrepreneurial Traits and Identification of Opportunities in Tourism Sector in Khulna City, Bangladesh

This study advances the understanding of entrepreneurship in tourism sector of Bangladesh investigating the relationship between entrepreneurial traits and opportunity identification. Drawing on the personality trait theory, we hypothesize the relationships between identification of opportunity and personality traits, like need for achievement, risk-taking propensity, locus of control, self-confidence, tolerance, innovativeness, decision making capability, ability to learn from failure, capacity for hard work, and independence. Personality trait theory has been used to identify the individual determinants of opportunity identification. We also investigate whether the presence of tourism facilities strengthens the hypothesized relationships. Applying a hierarchical regression model with original data collected from a survey of 150 respondents applying convenience sampling strategy, our results reveal that the need for achievement, tolerance, innovativeness, hardworking and independence have a statistically significant relationship with the identification of tourism opportunities. Our results also support that relationship of personality traits (need for achievement, learn from the failure and independence) with opportunity identification is strengthened when tourism facilities are available. The findings enhance our understanding of opportunity recognition in tourism and hospitality industry. Tourism researchers and prospective tourism entrepreneurs will be benefitted from the research findings.
Learning-based Wildfire Tracking with Unmanned Aerial Vehicles

This paper designs a path planning algorithm for a group of unmanned aerial vehicles (UAVs) to track multiple spreading wildfire zones. Due to limited observable information, the fire evolution is hard to model. A regression neural network is online trained with real-time UAV observation data and applied for fire front prediction. To track fire fronts effectively, a UAV path planning algorithm is proposed by Q-learning. Various practical factors are taken into account by cost function designs such as moving target tracking, field of view of UAVs, spreading speed of fire zones, collision/obstacle avoidance, and maximum information collection. Simulation results validate the fire prediction accuracy and UAV tracking performance.
Upper bound estimations of misclassification rate in the heteroscedastic clustering model with sub‐Gaussian noises

Clustering is an important tool in statistics, machine learning and applied mathematics. This paper considers the clustering model Y=μlT+Z∈ℝp×n , where the noise matrix Z consists of independent sub‐Gaussian entries Zij and the variance Var(Zij)=σij2 may vary across different coordinates. Our aim is to estimate the error between the label vector l and its defined estimator l^ . We provide upper bound estimations for the misclassification rate in the sense of expectation and probability, respectively. Finally, some simulations have been carried out to support our theoretical results and illustrate the advantage of our proposed estimator.
Acne Classification with Gaussian Mixture Model based on Texture Features

—This paper presents an acne detection method on face images using a Gaussian Mixture Model (GMM). First, the skin area in the face image is segmented based on color information using the GMM. Second, the candidates of the acne region are then extracted using a Laplacian of Gaussian-based blob detection strategy. Then, texture features are extracted from acne candidates using either a Gabor Filter or Gray Level Co-occurrence Matrix (GLCM). Lastly, these features are then utilized as input in the GMM for verifying whether these regions are acne or not. In our experiment, the proposed method was evaluated using face images from ACNE04 dataset. Based on the experiment, it is found that the best classification results were obtained when GLCM features in the Cr-YCbCr channel are applied. In addition, the proposed method has competitive performance compared to K-Nearest Neighbor (KNN).
A Node-Merging based Approach for Generating iStar Models from User Stories (ADPBD)

—User story is a widely adopted requirement notation in agile development. Generally, user stories are written by customers or users in natural language with limited form to describe user’s needs for the software system from their perspectives. However, since user stories are generally presented in a flat list, the relations derived from the user stories are difficult to capture. It reduces the understanding of the system as a whole. One solution to this problem is to build goal-oriented models that provide explicit relations among user stories. But extracting concepts and relationships from a large number of discrete user stories often take a lot of time for the agile development team. This paper proposes an iStar model generating approach based on node-merging from user stories. The method first extracts the iStar nodes from the semi-structured user stories, then uses a BERT (Bidirectional Encoder Representations from Transformers) model to measure the similarity between the nodes, and then nodes to be merged are identified and the edges between the iStar nodes are connected. Experiments are designed to illustrate the effectiveness of the proposed approach.
TEMT: A Transient Electronic–Magnetic–Thermal-Coupled Simulation Framework for STT-MTJs

Being a promising candidate for future nonvolatile memory and neuromorphic computing, spin-transfer-torque magnetic tunnel junction (STT-MTJ) devices are gaining substantial momentum in industrial adoption in recent years, calling for EDA support for higher modeling capabilities. However, the complex interplay of multiple physical mechanisms featured in MTJ devices imposes substantial challenges to their numerical modeling. In this work, we propose a fully coupled, transient electronic–magnetic–thermal (TEMT) modeling approach for STT-MTJ devices. Aiming to be the first principle and physically holistic, TEMT combines the atomistic nonequilibrium Green’s function (NEGF) model for tunneling currents, the Landau–Lifshitz–Gilbert–Slonczewski (LLGS) equation for magnetic dynamics and the heat conduction equation (HCE) for thermal dynamics in a self-consistent manner. To alleviate computational burden, we also devise an analytical approximation approach to reduce the number of NEGF solutions in the TEMT framework, which leads to over $15\times $ speedup with a very mild accuracy loss. An in-depth investigation of STT-MTJ devices using the TEMT framework is conducted to demonstrate its benefits and performance.
Extension of Traveling Salesperson Problem with Drone for Manufacturing Digital Twin Data Collection

This work investigates dual-agent path planning for marsupial robots with different energy time scales. We extend upon the studied Traveling Salesman Problem with Drone (TSP-D) originally formulated for last-mile package delivery. We contribute a drone energy model and constraints to allow for drone dock charging in a variation we refer to as the TSP-D with Energy Management. A moving-horizon heuristic method is also proposed to reduce the computational load and allow for on-line system feedback. We demonstrate that this heuristic method is computationally more efficient and provides significant path time savings as compared to a single agent solution. Furthermore, it enables the desired system feedback.
A PDE Based Approach to Path Finding in Three Dimensions: Solving a Path Finding Problem for an Unmanned Aerial Vehicle

This thesis presents a general three-dimensional method for pathfinding, based on a partial differential equation. The method relies on a grid with hazard-values, describing the risk associated with every point in the domain. Analogous to a fluid flow problem, we construct an artificial permeability based on the hazard- values, and we use this to calculate streamlines that constitute the potential paths from a starting point to the target. We investigate the different parameters and ways to manipulate the problem to yield sufficiently flyable streamlines. The method is geared towards finding a terrain-following, flyable path for an unmanned aerial vehicle(UAV) through a hostile terrain. In special, we consider the potential for a program implementation to run on-board the UAV during mission flight. For this application, the available memory and processor resources can be restricted. This sets strict requirements on the pathfinding algorithm. Particularly fast solvers exist for solving PDEs discretized using finite differences on regular grids. We implement a multigrid method for the resulting linear set of equations, with optimal memory usage, linear complexity and a potential for parallelization.
Bias Correction in Wind Direction Forecasting Using the Circular-Circular Regression Method

Abstract Wind direction forecasting plays an important role in wind power prediction and air pollution management. Weather quantities such as temperature, precipitation, and wind speed are linear variables in which traditional model output statistics and bias correction methods are applied. However, wind direction is an angular variable; therefore, such traditional methods are ineffective for its evaluation. This paper proposes an effective bias correction technique for wind direction forecasting of turbine height from numerical weather prediction models, which is based on a circular-circular regression approach. The technique is applied to a 24-h forecast of 65-m wind directions observed at Yangmeishan wind farm, Yunnan Province, China, which consistently yields improvements in forecast performance parameters such as smaller abso-lute mean error and stronger similarity in wind rose diagram pattern.
Applying Ferromagnetic Iron Spin Dynamics Methods to Uranium Dioxide

Experiments conducted on Uranium Dioxide ( UO 2 ) under the Manhattan Project led to the creation of the first self-sustaining nuclear reaction at Chicago-Pile 1 in 1942. Eighty years later, UO 2 functions as the primary fuel for nuclear fission reactors, providing around 10% of global electric output [1] . The importance of understanding UO 2 ‘s thermal and magnetic properties is instrumental in ensuring safe operation and handling, which can be done computationally using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). Simulating spin-lattice coupling with LAMMPS requires the usage of magneto-mechanical potentials instead of classical potentials, which captures more of the relevant quantum-mechanical physics that is dominant in low-temperature magnetic subsystems. UO 2 at low temperatures contains a non-trivial magnetic subsystem which is believed to be a result of quadrupole-quadrupole, magnetic exchange, and phonon-magnon interactions. We discuss progress made with simulating the magnetic subsystem of elemental Iron and possible applications to the technologically important material, UO 2 . approximately for the and smaller for the