Accuracy Guarantees Research Articles

Analytical Calculation of Magnetic Field and Analysis of Rotor Permeability Effects on Permanent Magnet Synchronous Motor with Fractional Slot Concentrated Winding

Accurate calculation of the flux and the magnetic field distribution of fractional slot concentrated winding permanent magnet synchronous motor (FSCW PMSM) is the basis for motor performance analysis, and rapid calculation is key. In this paper, to solve the problem of difficult modeling and accuracy guarantee of the flux linkage differential method, a method is proposed to calculate the flux and the no-load back EMF by the slotless subdomain model. By introducing the leakage flux calculation link, the calculation accuracy is improved, the analytical method results are compared with the finite element method results, and the effectiveness of the proposed method is verified. On this basis, the nonlinear variations of the magnetic field and the no-load back EMF with rotor permeability are determined, and the influence mechanism of rotor length and rotor permeability on the main magnetic circuit is revealed. Finally, an experiment of the prototype is carried out, and the correctness and accuracy of the analytical method and the finite element method is verified by comparing with the experimental results.

Digital Assistant for Video KYC Framework in India

The Know Your Customer (KYC) operations in India need to be done securely and efficiently due to the financial services industry's fast digitalization. The creation of a digital assistant that is customized for the Video KYC framework and prioritizes user experience over compliance requirements is suggested in this abstract. The digital assistant uses artificial intelligence and natural language processing to automate the KYC process and guarantee accuracy, dependability, and compliance with rules like those issued by the Reserve Bank of India (RBI). The assistant improves security protocols by utilizing advanced facial recognition, document verification, and biometric authentication, hence reducing the potential for identity fraud and data breaches.

A Counting-based Approach for Efficient k-Clique Densest Subgraph Discovery

Densest subgraph discovery (DSD) is a fundamental topic in graph mining. It has been extensively studied in the literature and has found many real applications in a wide range of fields, such as biology, finance, and social networks. As a typical problem of DSD, the k-clique densest subgraph (CDS) problem aims to detect a subgraph from a graph, such that the ratio of the number of k-cliques over the number of its vertices is maximized. This problem has received plenty of attention in the literature, and is widely used in identifying larger ''near-cliques''. Existing CDS solutions, either k-core or convex programming based solutions, often need to enumerate almost all the k-cliques, which is very inefficient because real-world graphs usually have a vast number of k-cliques. To improve the efficiency, in this paper, we propose a novel framework based on the Frank-Wolfe algorithm, which only needs k-clique counting, rather than k-clique enumeration, where the former one is often much faster than the latter one. Based on the framework, we develop an efficient approximation algorithm, by employing the state-of-the-art k-clique counting algorithm and proposing some optimization techniques. We have performed extensive experimental evaluation on 14 real-world large graphs and the results demonstrate the high efficiency of our algorithms. Particularly, our algorithm is up to seven orders of magnitude faster than the state-of-the-art algorithm with the same accuracy guarantee.

Q-Herilearn: Assessing heritage learning in digital environments. A mixed approach with factor and IRT models.

The assessment of heritage learning in digital environments lacks instruments that measure it with sufficient guarantees of accuracy, validity, and reliability. This study attempts to fill this gap by developing an instrument that has shown solid metric qualities. The process of design and calibration of a scale applied to 1,454 participants between 19 and 63 years of age is presented in this article. Exploratory factor analysis (Exploratory Structural Equation Modeling ESEM) and Item Response Theory models (Graded Response Model GRM) were used. Sufficient evidence of both reliability and validity based on content and internal structure was obtained. Invariance of scores as a function of gender and age of participants has also been demonstrated. The discrimination parameters of the items have been found to be high, and the test information curves have shown that the subscales measure with sufficient precision wide ranges of the respective latent variables. The instrument presents wide possibilities of application to various areas of Heritage Education (e.g., design of programs in HE, definition and planning of teaching objectives, evaluation of programs, etc., in virtual environments).

A Class of Topological Pseudodistances for Fast Comparison of Persistence Diagrams

Persistence diagrams (PD)s play a central role in topological data analysis, and are used in an ever increasing variety of applications. The comparison of PD data requires computing distances among large sets of PDs, with metrics which are accurate, theoretically sound, and fast to compute. Especially for denser multi-dimensional PDs, such comparison metrics are lacking. While on the one hand, Wasserstein-type distances have high accuracy and theoretical guarantees, they incur high computational cost. On the other hand, distances between vectorizations such as Persistence Statistics (PS)s have lower computational cost, but lack the accuracy guarantees and theoretical properties of a true distance over PD space. In this work we introduce a class of pseudodistances called Extended Topological Pseudodistances (ETD)s, which have tunable complexity, and can approximate Sliced and classical Wasserstein distances at the high-complexity extreme, while being computationally lighter and close to Persistence Statistics at the lower complexity extreme, and thus allow users to interpolate between the two metrics. We build theoretical comparisons to show how to fit our new distances at an intermediate level between persistence vectorizations and Wasserstein distances. We also experimentally verify that ETDs outperform PSs in terms of accuracy and outperform Wasserstein and Sliced Wasserstein distances in terms of computational complexity.

Efficient Greedy Algorithms with Accuracy Guarantees for Combinatorial Restrictions

Efficient Greedy Algorithms with Accuracy Guarantees for Combinatorial Restrictions

Clustering High-Dimensional Noisy Categorical Data

Clustering is a widely used unsupervised learning technique that groups data into homogeneous clusters. However, when dealing with real-world data that contain categorical values, existing algorithms can be computationally costly in high dimensions and can struggle with noisy data that has missing values. Furthermore, except for one algorithm, no others provide theoretical guarantees of clustering accuracy. In this article, we propose a general categorical data encoding method and a computationally efficient spectral-based algorithm to cluster high-dimensional noisy categorical data (nominal or ordinal). Under a statistical model for data on m attributes from n subjects in r clusters with missing probability ϵ, we show that our algorithm exactly recovers the true clusters with high probability when m n ( 1 − ϵ ) ≥ C M r 2 log 3 M , with M = max ( n , m ) and a fixed constant C. In addition, we show that m n ( 1 − ϵ ) 2 ≥ r δ / 2 with 0 < δ < 1 is necessary for any algorithm to succeed with probability at least ( 1 + δ ) / 2 . In cases where m = n and r are fixed, the sufficient condition matches with the necessary condition up to a polylog ( n ) factor. In numerical studies our algorithm outperforms several existing algorithms in both clustering accuracy and computational efficiency. Supplementary materials for this article are available online.

The Study for Standardization of Dose Evaluation Method in Cone-beam CT

This study aimed to compare the dose evaluation methods by constructing simulation models using the Monte Carlo calculation code and propose an evaluation method for cone beam CT (CBCT) that ensures accuracy and practicality. The Particle and Heavy Ion Transport code System (PHITS) ver. 3.26 was used as the Monte Carlo calculation code. CBCT doses were measured by CB dose index (CBDI) and American Association of Physicists in Medicine task group 111 (TG111) methods. The CBDI was compared with the equilibrium doses obtained by the TG111 method. Although CBDI was lower than equilibrium doses obtained by the TG111 method, its practicality was ensured because it can be measured using the dosimeter and phantom that are commonly used. In contrast, the TG111 method guarantees accuracy, but it is difficult to prepare a long phantom to obtain the equilibrium dose. The TG111 method with a phantom length of 15 cm underestimated the equilibrium dose by 20% compared to that with a phantom length of 45 cm that satisfies the dose equilibrium. Therefore, the equilibrium dose obtained by the TG111 method with a phantom length of 15 cm is multiplied by 1.20 to obtain the equilibrium dose equivalent to that with a phantom length of 45 cm. This study has proposed the dose evaluation method that combines guarantees accuracy and practicality in CBCT.

Study of electronic structure and optical transition properties of low-lying excited states of AuB molecules based on configuration interaction method

High-level configuration interaction method including the spin-orbit coupling is used to investigate the low-lying excited electronic states of AuB that is not reported experimentally. The electronic structure in our work is preformed through the three steps stated below. First of all, Hartree-Fock method is performed to compute the singlet-configuration wavefunction as the initial guess. Next, we generate a multi-reference wavefunction by using the state-averaged complete active space self-consistent field (SACASSCF). Finally, the wavefunctions from CASSCF are utilized as reference, the exact energy point values are calculated by the explicitly correlated dynamic multi-reference configuration interaction method (MRCI). The Davidson correction (+Q) is put forward to solve the size-consistence problem caused by the MRCI method. To ensure the accuracy, the spin-orbit effect and correlation for inner shell electrons and valence shell electrons are considered in our calculation. The potential energy curves of 12 Λ-S electronic states are obtained. According to the explicit potential energy curves, we calculate the spectroscopic constants through solving radial Schrödinger equation numerically. We analyze the influence of electronic state configuration on the dipole moment by using the variation of dipole moment with nuclear distance. The spin-orbit matrix elements for parts of low-lying exciting states are computed, and the relation between spin-orbit coupling and predissociation is discussed. The predissociation is analyzed by using the obtained spin-orbit matrix elements of the 4 Λ-S states which spilt into 12 Ω states. It indicates that due to the absence of the intersections between the curves of spin-orbit matrix elements related with the 4 low-lying Λ-S states, the predissociation for these low-lying exciting states will not occur. Finally, the properties of optical transition between the ground Ω state &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ {\rm A}^{1}{{{\Pi}}}_{1} $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20231347_M3.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20231347_M3.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; and first excited Ω state &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ {{\mathrm{X}}}^{1}{{{\Sigma }}}_{{0}^{+}} $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20231347_M4.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20231347_M4.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; are discussed in laser-cooling filed by analyzing the Franck-Condon factors and radiative lifetime. And the transition dipole moment is also calculated. But our results reveal that the AuB is not an ideal candidate for laser-cooling. In conclusion, this work is helpful in deepening the understanding of AuB, especially the structures of electronic states, interaction between excited states, and optical transition properties. All the data presented in this paper are openly available at &lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.doi.org/10.57760/sciencedb.j00213.00009"&gt;https://www.doi.org/10.57760/sciencedb.j00213.00009&lt;/ext-link&gt;.

Semi-Supervised Detection Model Based on Adaptive Ensemble Learning for Medical Images.

Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscopes. Moreover, works relying on supervised learning are powerless in the case of inadequate labeled samples. Therefore, for end-to-end medical image detection with overcritical efficiency and accuracy in endoscope detection, an ensemble-learning-based model with a semi-supervised mechanism is developed in this work. To gain a more accurate result through multiple detection models, we propose a new ensemble mechanism, termed alternative adaptive boosting method (Al-Adaboost), combining the decision-making of two hierarchical models. Specifically, the proposal consists of two modules. One is a local region proposal model with attentive temporal-spatial pathways for bounding box regression and classification, and the other one is a recurrent attention model (RAM) to provide more precise inferences for further classification according to the regression result. The proposal Al-Adaboost will adjust the weights of labeled samples and the two classifiers adaptively, and the nonlabel samples are assigned pseudolabels by our model. We investigate the performance of Al-Adaboost on both the colonoscopy and laryngoscopy data coming from CVC-ClinicDB and the affiliated hospital of Kaohsiung Medical University. The experimental results prove the feasibility and superiority of our model.

The Problem of the Reliability of Bending Models for Composite Plates of Medium Thickness

Most refined bending models of medium-thick plates, which consider transverse shear and partial compression deformations, differ little. However, despite a significant increase in the order of the governing differential equations, the results obtained from their equations give mainly a small increase in accuracy compared to the existing theories. On the other hand, such an increase in the order of the constructed systems of differential equations requires a significant increase in the effort required to solve them, complicates their physical interpretation, and narrows the range of people who can use them, primarily engineers and designers. Therefore, developing a plate-bending model that incorporates all the above factors and is on par with previously applied theories regarding the complexity of the calculation equations remains relevant. For example, most of the applied theories that do not consider transverse compression cannot be used to solve problems of contact interaction with rigid and elastic dies and bases because it is impossible to satisfy the conditions at the contact boundary of the outer surface of the plate, as well as the boundary conditions at the edges of the plate. Therefore, to provide guaranteed accuracy of the results, some researchers of these problems have introduced such a concept as “energy consistency” between the functions of representation of the displacement vector components, their number, the order of equations, and the number of boundary conditions. The authors, based on the developed version of the model of orthotropic plates of medium thickness, investigate the problem of taking into account the so-called “energy consistency” effect of the bending model, depending on the order of the design equations and the number of boundary conditions, as well as its usefulness and disadvantages in practical calculations. The equations of equilibrium in displacements and expressions for stresses in terms of force and moment forces are recorded. For rectangular and circular plates of medium thickness, test problems are solved, and the numerical data are compared with those obtained using spatial problems of elasticity theory, as well as the refined Timoshenko and Reissner theories. An analysis of the obtained results is provided.

Spectral pursuit for simultaneous sparse representation with accuracy guarantees

Spectral pursuit for simultaneous sparse representation with accuracy guarantees

Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)

In this paper, we are interested in the acceleration of numerical simulations. We focus on a hypersonic planetary reentry problem whose simulation involves coupling fluid dynamics and chemical reactions. Simulating chemical reactions takes most of the computational time but, on the other hand, cannot be avoided to obtain accurate predictions. We face a trade-off between cost-efficiency and accuracy: the numerical scheme has to be sufficiently efficient to be used in an operational context but accurate enough to predict the phenomenon faithfully. To tackle this trade-off, we design a hybrid numerical scheme coupling a traditional fluid dynamic solver with a neural network approximating the chemical reactions. We rely on their power in terms of accuracy and dimension reduction when applied in a big data context and on their efficiency stemming from their matrix-vector structure to achieve important acceleration factors (×10 to ×18.6). This paper aims to explain how we design such cost-effective hybrid numerical schemes in practice. Above all, we describe methodologies to ensure accuracy guarantees, allowing us to go beyond traditional surrogate modeling and to use these schemes as references.

Using artificial neural networks to model single bead geometries processed by laser-wire direct energy deposition

Wire-feed laser additive manufacturing processes have gained researchers’ attention because of their potential to reduce material waste, guarantee accuracy, increase material quality and density, and produce a wide dimensional range of final products. Nevertheless, printing materials with desired geometrical properties of the beads is still challenging in such processes. This might be attributed to the need for more sufficient experimental data and precise modeling approaches. In this study, an architecture based on Artificial Neural Networks (ANNs) is developed to model the bead geometries (width, height, and area), considering the wire feed rate, laser power, and travel speed as process parameters. A design-of-experiment based on full factorial design is considered for processing single beads with a Fraunhofer coaxial wire-feed laser system. Inconel 625 wire with a diameter of 1.14 mm and stainless steel substrate are utilized as the experimental materials. Geometrical data is obtained using a laser scanner model RA-7525 SE with 0.026mm volumetric accuracy. The beads’ geometrical details are provided as the feeding data for the proposed ANN. For each bead, a length of 10 mm is considered to calculate the average geometrical parameters, which increases the accuracy of the dataset in comparison to the values acquired via a macroscopic picture of the cross-section of each weld bead. A variety of hyperparameters are chosen and compared regarding precision criteria, including Mean Square Error (MSE), to increase the model‘s accuracy. A train-test separation strategy is considered to evaluate the model‘s accuracy on independent data points. The outcome of this research is an ANN-based geometry prediction model that can be utilized to enhance the development of offline path planners and optimize process parameter selection for a precise geometry toward process control.

OmniSketch: Efficient Multi-Dimensional High-Velocity Stream Analytics with Arbitrary Predicates

A key need in different disciplines is to perform analytics over fast-paced data streams, similar in nature to the traditional OLAP analytics in relational databases - i.e., with filters and aggregates. Storing unbounded streams, however, is not a realistic, or desired approach due to the high storage requirements, and the delays introduced when storing massive data. Accordingly, many synopses/sketches have been proposed that can summarize the stream in small memory (usually sufficiently small to be stored in RAM), such that aggregate queries can be efficiently approximated, without storing the full stream. However, past synopses predominantly focus on summarizing single-attribute streams, and cannot handle filters and constraints on arbitrary subsets of multiple attributes efficiently. In this work, we propose OmniSketch, the first sketch that scales to fast-paced and complex data streams (with many attributes), and supports count aggregates with filters on multiple attributes, dynamically chosen at query time. The sketch offers probabilistic guarantees, a favorable space-accuracy tradeoff, and a worst-case logarithmic complexity for updating and for query execution. We demonstrate experimentally with both real and synthetic data that the sketch outperforms the state-of-the-art, and that it can approximate complex ad-hoc queries within the configured accuracy guarantees, with small memory requirements.

Attack-detection and multi-clock source cooperation-based accurate time synchronization for PLC-AIoT in smart parks

Power Line Communications-Artificial Intelligence of Things (PLC-AIoT) combines the low cost and high coverage of PLC with the learning ability of AI to provide data collection and transmission capabilities for PLC-AIoT devices in smart parks. With the development of smart parks, their emerging services require secure and accurate time synchronization of PLC-AIoT devices. However, the impact of attackers on the accuracy of time synchronization cannot be ignored. To solve the aforementioned problems, we propose a tampering attack-aware Deep Q-Network (DQN)-based time synchronization algorithm. First, we construct an abnormal clock source detection model. Then, the abnormal clock source is detected and excluded by comparing the time synchronization information between the device and the gateway. Finally, the proposed algorithm realizes the joint guarantee of high accuracy and low delay for PLC-AIoT in smart parks by intelligently selecting the multi-clock source cooperation strategy and timing weights. Simulation results show that the proposed algorithm has better time synchronization delay and accuracy performance.

The Synergy between a Humanoid Robot and Whisper: Bridging a Gap in Education

Students may encounter problems concentrating during a lecture due to various reasons, which can be related to the educator’s accent or the student’s auditory difficulties. This may lead to reduced participation and poor performance in the class. In this paper, we explored whether the incorporation of the humanoid robot Pepper can help in improving the learning experience. Pepper can capture the audio of a person; however, there is no guarantee of accuracy of the recorded audio due to various factors. Therefore, we investigated the limitations of Pepper’s speech recognition system with the aim of observing the effect of distance, age, gender, and the complexity of statements. We conducted an experiment with eight persons including five females and three males who spoke provided statements at different distances. These statements were classified using different statistical scores. Pepper does not have the functionality to transcribe speeches into text. To overcome this problem, we integrated Pepper with a speech-to-text recognition tool, Whisper, which transcribes speech into text that can be displayed on Pepper’s screen using its service. The purpose of the study is to develop a system where the humanoid robot Pepper and the speech-to-text recognition tool Whisper act in synergy to bridge the gap between verbal and visual communication in education. This system could be beneficial for students as they will better understand the content through the visual representation of the teacher’s spoken words regardless of any hearing impairments and accent problems. The methodology involves recording the participant’s speech, followed by its transcription to text by Whisper, and then evaluation of the generated text using various statistical scores. We anticipate that the proposed system will be able to increase the student’s learning experience, engagement, and immersion in a classroom environment.

Efficient Complex Aggregate Queries with Accuracy Guarantee Based on Execution Cost Model over Knowledge Graphs

Knowledge graphs (KGs) have gained prominence for representing real-world facts, with queries of KGs being crucial for their application. Aggregate queries, as one of the most important parts of KG queries (e.g., “ What is the average price of cars produced in Germany?”), can provide users with valuable statistical insights. An efficient solution for KG aggregate queries is approximate aggregate queries with semantic-aware sampling (AQS). This balances the query time and result accuracy by estimating an approximate aggregate result based on random samples collected from a KG, ensuring that the relative error of the approximate aggregate result is bounded by a predefined error. However, AQS is tailored for simple aggregate queries and exhibits varying performance for complex aggregate queries. This is because a complex aggregate query usually consists of multiple simple aggregate queries, and each sub-query influences the overall processing time and result quality. Setting a large error bound for each sub-query yields quick results but with a lower quality, while aiming for high-quality results demands a smaller predefined error bound for each sub-query, leading to a longer processing time. Hence, devising efficient and effective methods for executing complex aggregate queries has emerged as a significant research challenge within contemporary KG querying. To tackle this challenge, we first introduced an execution cost model tailored for original AQS (i.e., supporting simple queries) and founded on Taylor’s theorem. This model aids in identifying the initial parameters that play a pivotal role in the efficiency and efficacy of AQS. Subsequently, we conducted an in-depth exploration of the intrinsic relationship of the error bounds between a complex aggregate query and its constituent simple queries (i.e., sub-queries), and then we formalized an execution cost model for complex aggregate queries, given the accuracy constraints on the error bounds of all sub-queries. Harnessing the multi-objective optimization genetic algorithm, we refined the error bounds of all sub-queries with moderate values, to achieve a balance of query time and result accuracy for the complex aggregate query. An extensive experimental study on real-world datasets demonstrated our solution’s superiority in effectiveness and efficiency.

Data-Driven Reachability and Support Estimation With Christoffel Functions

We present algorithms for estimating the forward reachable set of a dynamical system using only a finite collection of independent and identically distributed samples. The produced estimate is the sublevel set of a function called an empirical inverse Christoffel function: empirical inverse Christoffel functions are known to provide good approximations to the support of probability distributions. In addition to reachability analysis, the same approach can be applied to general problems of estimating the support of a random variable, which has applications in data science towards detection of novelties and outliers in data sets. In applications where safety is a concern, having a guarantee of accuracy that holds on finite data sets is critical. In this paper, we prove such bounds for our algorithms under the Probably Approximately Correct (PAC) framework. In addition to applying classical Vapnik- Chervonenkis (VC) dimension bound arguments, we apply the PAC-Bayes theorem by leveraging a formal connection between kernelized empirical inverse Christoffel functions and Gaussian process regression models.

ASTREAM: Data-Stream-Driven Scalable Anomaly Detection With Accuracy Guarantee in IIoT Environment

Intrusion detection exerts a crucial influence on securing the IIoT driven by anomaly detection approaches. Dissimilar with the static data, the intrusion detection data is in the form of a dynamic data stream possessing the properties of infiniteness, correlations, and data distribution change.However, these properties cause some issues for current anomaly detection approaches.Firstly, it is impractical to save the whole dataset due to the infiniteness.Secondly, the correlations are hardly considered.Thirdly, the data distribution change cant be appropriately handled due to a lack of model update and change detection strategy. Thus, we propose ASTREAM(anomaly detection in data streams), a novel anomaly detection approach that merges sliding window, model update, and change detection strategies into LSHiForest to achieve accurate and efficient anomaly detection with better scalability. ASTREAM has the following characteristics:(a)the sliding window can be utilized to handle the infiniteness of data streams;(b)the introduced PCA can consider the correlations between different attributes;(c)the change detection and model update can detect data distribution change in time and train the new model. Comprehensive experiments are implemented on the KDDCUP99 dataset to validate ASTREAM performance.Experiment results reveal that ASTREAM outperforms baselines in aspects of accuracy and efficiency and has better scalability.