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Presenting cloud energy storage system (CESS) in the landscape of storage devices exposes microgrids (MGs) to a substantial change. Employing a specific type of inverter namely synchronverter as the connecting interface for CESS gains high inertial response in MGs with high penetration level of inverter‐based distributed generations (IBDGs). However, high fault contribution of synchronverters can lead to challenging issues. Nevertheless, presence of fault resistance which results in malfunctioning of the recloser‐fuse pair intensifies the cruciality of the dilemma. In this paper, at the outset, the effects of contribution of synchronverter‐based CESS and also fault resistance on recloser‐fuse protection are explored and then, a new algorithm is proposed for tackling these bottlenecks. To overcome these hurdles, the conventional recloser‐fuse scheme is applied at first. Then, by using the obtained schemes for fast and slow operations of the recloser, a specific part of them is set in a manner that the fuse‐saving task is performed properly even when the synchronverter‐based CESS is deployed. On the other hand, the other part of the recloser scheme is altered in a way that the maloperation of the recloser‐fuse pair owing to the fault resistance is alleviated. Adequate numerical analyses are conducted to evaluate the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider the estimation algorithm of tensor response on vector covariate regression model. Based on projection theory of tensor and the idea of randomized algorithm for tensor decomposition, three new algorithms named SHOLRR, RHOLRR and RSHOLRR are proposed under the low‐rank Tucker decomposition and some theoretical analyses for two randomized algorithms are also provided. To explore the nonlinear relationship between tensor response and vector covariate, we develop the KRSHOLRR algorithm based on kernel trick and RSHOLRR algorithm. Our proposed algorithms can not only guarantee high estimation accuracy but also have the advantage of fast computing speed, especially for higher‐order tensor response. Through extensive synthesized data analyses and applications to two real datasets, we demonstrate the outperformance of our proposed algorithms over the stat‐of‐art. [ABSTRACT FROM AUTHOR]

Summary: This paper studies distributed resource allocation problem for agents with input dead‐zone, which is not considered in the existing work. At first, the primal problem is transformed to an auxiliary problem by using the exact penalty method to deal with local inequality constraints. It is assumed that the explicit expressions of cost functions and local inequality constraints are unknown to agents but the values of the cost and constraint functions can be obtained. Under such a setup, the extremum seeking control is used to estimate the gradient information. Thus, to obtain the optimal allocation, a novel distributed algorithm is designed by the virtue of the extremum seeking control and a dynamic compensating mechanism which is used to handle the effects of the input dead‐zone. Due to a two time‐scale structure of the designed distributed algorithm, the semi‐globally practically asymptotical convergence of all agents' decisions to the optimal allocation is obtained by the singular perturbation technique. Finally, numerical examples of economic dispatch in smart grids are given to verify the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Summary: Distributed optimization algorithms have various advantages and wide applications, while the distributed nature also makes them flimsy in the face of external attacks. In this paper two Byzantine attack models are designed, in which the edge attack model is seldom considered before. A resilient distributed consensus algorithm based on the trusted nodes and edges is proposed to defend against the two attack models. The agreement and optimality of the proposed consensus algorithm are guaranteed with theoretical analysis. The simulation results and comparison experiments verify the proposed algorithm to be more effective than the compared one. In the perspective of attackers, which attack model is more recommended is also analysed based on the experiments. [ABSTRACT FROM AUTHOR]

A class of graphs ℱ ${\rm{ {\mathcal F} }}$ admits the Erdős–Pósa property if for any graph G $G$, either G $G$ has k $k$ vertex‐disjoint "copies" of the graphs in ℱ ${\rm{ {\mathcal F} }}$, or there is a set S⊆V(G) $S\subseteq V(G)$ of f(k) $f(k)$ vertices that intersects all copies of the graphs in ℱ ${\rm{ {\mathcal F} }}$. For any graph class G ${\mathscr{G}}$, it is natural to ask whether the family of obstructions to G ${\mathscr{G}}$ has the Erdős–Pósa property. In this paper, we prove that the family of obstructions to interval graphs—namely, the family of chordless cycles and asteroidal witnesses (AWs)—admits the Erdős–Pósa property. In turn, this yields an algorithm to decide whether a given graph G $G$ has k $k$ vertex‐disjoint AWs and chordless cycles, or there exists a set of O(k2logk) ${\mathscr{O}}({k}^{2}\mathrm{log}k)$ vertices in G $G$ that hits all AWs and chordless cycles. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of the Internet of Things (IoT) technology has provided a strong technical support for the technological transformation of the logistics industry. The informatization development of logistics industry increasingly relies on the Internet of Things technology represented by Radio Frequency Identification (RFID) technology. These technologies lead the whole business process to optimize the business process in the direction of accurate, efficient and real‐time. In order to solve the problem that the reader cannot identify the label information correctly due to the phenomenon of data collision in the application of RFID technology, this paper proposes an anti‐collision algorithm based on Slotted Random Regressive‐style Binary Search Tree (SR‐RBST). Based on Slotted ALOHA (SA) the method proposed in this paper uses the Regressive‐style Binary Search Tree (RBST) to process the RFID labels in the collision time slot. With the same size of tags, the SR‐RBST algorithm needs less total time slot and has higher efficiency and shorter identification time, while with the increase of the number of tags, the SR‐RBST anti‐collision algorithm has more obvious advantages. The SR‐RBST algorithm effectively improves the time slot utilization efficiency of the system. [ABSTRACT FROM AUTHOR]

Nowadays, missing data in regression model is one of the most well‐known topics. In this paper, we propose a class of efficient importance sampling imputation algorithms (EIS) for quantile and composite quantile regression with missing covariates. They are an EIS in quantile regression (EISQ) and its three extensions in composite quantile regression (EISCQ). Our EISQ uses an interior point (IP) approach, while EISCQ algorithms use IP and other two well‐known approaches: Majorize‐minimization (MM) and coordinate descent (CD). The aims of our proposed EIS algorithms are to decrease estimated variances and relieve computational burden at the same time, which improves the performances of coefficients estimators in both estimated and computational efficiencies. To compare our EIS algorithms with other existing competitors including complete cases analysis and multiple imputation, the paper carries out a series of simulation studies with different sample sizes and different levels of missing rates under different missing mechanism models. Finally, we apply all the algorithms to part of the examination data in National Health and Nutrition Examination Survey. [ABSTRACT FROM AUTHOR]

Key time steps selection, i.e., selecting a subset of most representative time steps, is essential for effective and efficient scientific visualization of large time‐varying volume data. In particular, as computer simulations continue to grow in size and complexity, they often generate output that exceeds both the available storage capacity and bandwidth for transferring results to storage, making it indispensable to save only a subset of time steps. At the same time, this subset must be chosen so that it is highly representative, to facilitate post‐processing and reconstruction with high fidelity. The key time steps selection problem is especially challenging in the in situ setting, where we can only process data in one pass in an online streaming fashion, using a small amount of main memory and fast computation. In this paper, we formulate the problem as that of optimal piece‐wise linear interpolation. We first apply a method from numerical linear algebra to compute linear interpolation solutions and their errors in an online streaming fashion. Using that method as a building block, we can obtain a global optimal solution for the piece‐wise linear interpolation problem via a standard dynamic programming (DP) algorithm. However, this approach needs to process the time steps in multiple passes and is too slow for the in situ setting. To address this issue, we introduce a novel approximation algorithm, which processes time steps in one pass in an online streaming fashion, with very efficient computing time and main memory space both in theory and in practice. The algorithm is suitable for the in situ setting. Moreover, we prove that our algorithm, which is based on a greedy update rule, has strong theoretical guarantees on the approximation quality and the number of time steps stored. To the best of our knowledge, this is the first algorithm suitable for in situ key time steps selection with such theoretical guarantees, and is the main contribution of this paper. Experiments demonstrate the efficacy of our new techniques. [ABSTRACT FROM AUTHOR]

The use of selective partial updating method for reducing computational complexity of adaptive algorithms has been proposed recently. However, its performance analysis has not been studied as extensively and exactly as LMS or RLS algorithms for time‐varying channel estimation. This paper provides performance analysis of the low‐complexity family of affine projection algorithms based on selective partial update method on the estimation of multipath Rayleigh fading channels in the presence of carrier frequency offsets (CFO) and random channel variations. The analysis is based on the calculation of the error correlation matrix of the estimation, the mean‐square weight error (MSWE) and the mean‐square estimation error (MSE) parameters. The analysis does not use strong assumptions like small or large step‐size, and explicit closed‐form expressions for the MSE of estimation are obtained only from common hypotheses in wireless communication context. In this paper, the optimum step‐size parameters minimizing the MSE of estimation are analytically derived without any simplifying assumptions. For the sake of comparison with other analytical approaches, the performance of the introduced algorithms is also investigated using the energy conservation relation. Likewise, for exact performance analysis, all the moment terms that appear in closed form expressions for the MSE of estimation are evaluated. Simulations are conduced to corroborate the presented studies and show that the theoretical results agree well with the simulation results over non‐stationary multipath Rayleigh fading channels. [ABSTRACT FROM AUTHOR]

This paper presents a novel computationally efficient Closed Loop Dual‐Mode Nonlinear Model Predictive Control scheme that uses closed loop models for condensing‐based multiple‐shooting frameworks which result in numerically robust optimisations. The proposed approach uses Time‐Varying gains obtained from solving the Time‐Varying Discrete Algebraic Ricatti Equation to embed feedback around the multiple‐shooting trajectory, and proves the equivalence of the solution with the standard approach, thus resulting in the exact same stability, recursive feasibility and convergence properties. Moreover, the paper proposes an efficient algorithm based on an extension of the well‐known O(Np2)$O(N_p^2)$ condensing algorithm, which can be used within the so‐called Real‐Time Iteration Scheme to achieve real‐time performance. Simulations of a nonlinear ball‐plate system, as well as of an inverted pendulum, and its extension ‐ the triple inverted pendulum, are presented along the paper to demonstrate the advantages along with some disadvantages, focusing on the numerical conditioning, the disturbance rejection properties, and the computational performance. [ABSTRACT FROM AUTHOR]

Aims: To develop evidence-based clinical algorithms for assessment and management of abnormal maternal pulse and blood pressure during the intrapartum period.Population: Low risk singleton, term, pregnant women in labour.Setting: Institutional births in low- and middle-income countries.Search Strategy: A review of the literature was performed to retrieve evidence-based guidelines, systematic reviews, and papers on maternal pulse and blood pressure during labour. We searched a number of international clinical guidelines and PubMed using the corresponding key terms in November 2018 and updated the search in May 2020.Case Scenarios: Four common intrapartum case scenarios of abnormal pulse and blood pressure were identified for which algorithms were developed: hypertension, hypotension, tachycardia and bradycardia. Algorithms were constructed after reviewing guidelines and relevant papers, with input from a panel of experts. Thresholds for upper and lower limits of normal maternal pulse and blood pressure measurements are defined, evidence-based interventions for the initial management of abnormal parameters are described (resuscitation and monitoring) and guidance is provided on exploration of the potential causes for each case scenario, with links to pathways for their management.Conclusions: Evidence-based algorithms to support the identification, and management of deviations in pulse and blood pressure during intrapartum care have been developed for hypertension, hypotension, tachycardia and bradycardia. The algorithms focus on initial resuscitation and monitoring, with an exploration of causes and early identification of underlying maternal conditions. These algorithms will help provide a standardised approach to investigation and management of these abnormal parameters to guide clinical practice.Tweetable Abstract: Algorithms for abnormal maternal pulse and blood pressure during labour allow standardised approach to early identification and management of complications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel generic modulo‐2 graph that can be converted to parallel prefix adders (PPA). Also, a conversion algorithm for converting the proposed graph to PPAs is proposed in this paper. The conversation algorithm can be applied on N‐bit Generic modulo‐2 graph, and this graph is converted to N‐bit PPAs. When the proposed conversion algorithm is applied to generic modulo‐2 graph, this graph is converted to Brent‐Kung, Sklansky, Kogge‐Stone, Harris, Ladner‐Fischer, Han‐Carlson, and Knowles adders. Moreover, PPAs are shown in trilinear coordinates to represent the final PPAs, which generic graph converted to them. Furthermore, the add operation is done by generic graph and also the correctness of add function of this graph is investigated by "Boolean decision diagram." [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a common sleep disorder and is diagnosed by polysomnography (PSG) as the gold standard. However, PSG is a time‐consuming and costly test, and patients have to endure long waits before receiving a PSG test in a hospital. In view of this, portable and wearable screening tools for OSA prediction have been developed recently as a low‐cost and easy‐to‐use method. In this paper, an OSA detection model, based on regression approach, using unsegmented electrocardiography (ECG) signals is developed to directly estimate the apnea‐hypopnea index (AHI) value, which is the first report in the literature. In this manner, this work can provide more details of OSA assessment to users and doctors. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Summary: Fire is a serious threat to human life and property, and effective prediction of fire can reduce the harm of fire to human. In the research of fire alarm automation, the fire alarm system based on wireless sensor network is a common method. When using wireless sensor to monitor the fire alarm information, it is necessary to ensure the safety coverage of the network in the monitoring area. Therefore, it is very necessary to study the network coverage in the fire alarm monitoring, which can ensure the effective work of the monitoring network, and good coverage control can reduce the energy consumption of the network and extend the service life of the network. Based on this, this paper designs a network coverage control algorithm based on multiobjective genetic algorithm optimization considering the coverage and energy consumption of wireless network. In this algorithm, the probability perception model is used to design the network coverage control model for the monitoring area with different coverage requirements, taking the maximum area coverage and the minimum network energy consumption rate as the optimization goal. Then the genetic algorithm is used to solve the model. In addition, because the multiobjective genetic algorithm is easy to fall into the local optimal solution when solving the optimization problem, this paper introduces the cross and variation coefficients of self‐adaptive adjustment to improve the genetic algorithm. Through the simulation analysis, it is shown that when solving the multiobjective optimization problem, the introduction of self‐adaptive adjustment of cross and variation coefficients can well overcome the problem that genetic algorithm falls into the local optimal solution, whereas the network coverage control algorithm designed in this paper based on the multiobjective genetic algorithm optimization can achieve greater coverage and lower energy consumption. Compared with the network coverage control algorithm before optimization, the optimized algorithm improves greatly in coverage, network residual energy, and number of nodes, which can well extend the service life of wireless sensor network, and is conducive to the good application of wireless sensor in fire prediction. [ABSTRACT FROM AUTHOR]

The adaptive charging algorithms of today divide the available charging capacity of a charging site between the electric vehicles without knowing how much current each vehicle draws in reality. Thus, they are not able to detect deviations between the current set point at the charging station and the real charging current. This leads to a situation where the charging capacity of the charging site is not used optimally. This paper presents an algorithm including a novel feature, Expected Characteristic Expectation and tested under realistic circumstances. It is demonstrated that the proposed algorithm enhances the adaptability of the charging site, increasing the efficiency of the used network capacity up to about 2 kWh per charging point per day in comparison with the previous benchmark algorithm. The algorithm is able to increase the average monetary benefits of the charging operators by up to around 5.8%, that is 0.6 € per charging point per day. No input, such as departure time, is required from the user. The proposed algorithm has been tested with real electric vehicles and charging stations and is compatible with the IEC 61851 charging standard. The charging algorithm is applicable in practice as it is described in this paper. [ABSTRACT FROM AUTHOR]

Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases presenting a super‐human performance. DNNs are the state‐of‐the‐art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research, such as spell checking, language detection, entity extraction, author detection, question answering, and other tasks. These supervised algorithms learn patterns from a large number of "right" and "wrong" examples and apply them to new examples. However, using DNNs for analyzing the text resources in DH research presents two main challenges: (un)availability of training data and a need for domain adaptation. This paper explores these challenges by analyzing multiple use‐cases of DH studies in recent literature and their possible solutions and lays out a practical decision model for DH experts for when and how to choose the appropriate deep learning approaches for their research. Moreover, in this paper, we aim to raise awareness of the benefits of utilizing deep learning models in the DH community. [ABSTRACT FROM AUTHOR]

The ability of satellite instruments to accurately observe long‐term changes in atmospheric temperature depends on many factors including the absolute accuracy of the measurement, the stability of the calibration of the instrument, the stability of the satellite orbit, and the stability of the numerical algorithm that produces the temperature data. We present an example of algorithm instability recently discovered in the temperature data set from the SABER instrument on the NASA TIMED satellite. The instability resulted in derived temperatures that were substantially colder than anticipated from mid‐December 2019 to mid‐2022. This algorithm‐induced change in temperature over 1 to 2 years corresponded to the expected temperature change over several decades from increasing anthropogenic CO2. This paper highlights the importance of algorithm stability in developing Geospace Data Records (GDRs) for Earth's mesosphere and lower thermosphere. A corrected version (Version 2.08) of the temperatures from SABER is described. Plain Language Summary: Instruments on Earth orbiting satellites offer the opportunity to detect long‐term changes in atmospheric temperature. Many factors may affect the ability to identify actual long‐term changes in the temperature and to distinguish these from changes in the instrument or from unintended changes in the algorithm that produces the temperature data from the instrument observations. SABER is an instrument on a NASA satellite that has been observing temperatures from 15 to 110 km (10–68 miles) in altitude for over 20 years. An "instability" in the scientific algorithm used to derive temperature from the instrument observations was recently discovered, beginning in late 2019. An unintended change was made in a parameter central to the derivation of temperature from SABER measurements. The consequence was that the atmospheric temperatures between 85 and 110 km (51–68 miles) from 2020 onward were several degrees colder than they would have been without the unintended change. This has been corrected and an updated version of the SABER temperatures and all other SABER data products, called Version 2.08, is now publicly available. Key Points: The concept of Geospace Data Records (GDRs) and their relevance to accurate detection of long‐term change is introducedAlgorithm instability in a GDR of the 20‐year record of SABER temperatures between 85 and 110 km is described and correctedThe field of Geospace Climate is emerging as a frontier with scientific and economic relevance. Accurate GDRs are essential to both [ABSTRACT FROM AUTHOR]

Most machine learning algorithms are configured by a set of hyperparameters whose values must be carefully chosen and which often considerably impact performance. To avoid a time‐consuming and irreproducible manual process of trial‐and‐error to find well‐performing hyperparameter configurations, various automatic hyperparameter optimization (HPO) methods—for example, based on resampling error estimation for supervised machine learning—can be employed. After introducing HPO from a general perspective, this paper reviews important HPO methods, from simple techniques such as grid or random search to more advanced methods like evolution strategies, Bayesian optimization, Hyperband, and racing. This work gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with machine learning pipelines, runtime improvements, and parallelization. This article is categorized under:Algorithmic Development > StatisticsTechnologies > Machine LearningTechnologies > Prediction [ABSTRACT FROM AUTHOR]

This paper presents a new approach to finding optimal patterns for the placement of fields of sonobuoys in a complex undersea environment. We model the problem as a biobjective one, where the aim is to minimise both sensor placement time and uncertainty over target localisation. Both objectives may be important in time‐critical localisation scenarios and our approach allows an operator to choose between different optimal solutions, favouring lower placement time or lower localisation uncertainty as operational circumstances require. We develop a two‐phase algorithm, where an offline multiobjective evolutionary phase finds initial Pareto‐non‐dominated solutions to a static problem and then an online multiobjective reinforcement learning phase finds improved solutions using updated information. We find that the evolutionary algorithm improves significantly on standard grid patterns and that the reinforcement learning algorithm improves further on the evolutionary phase. The number of sonobuoys required may also be reduced. [ABSTRACT FROM AUTHOR]

Background: Auditory–perceptual assessment of voice is a subjective procedure. Artificial intelligence with deep learning (DL) may improve the consistency and accessibility of this task. It is unclear how a DL model performs on different acoustic features. Aims: To develop a generalizable DL framework for identifying dysphonia using a multidimensional acoustic feature. Methods & Procedures: Recordings of sustained phonations of /a/ and /i/ were retrospectively collected from a clinical database. Subjects contained 238 dysphonic and 223 vocally healthy speakers of Chinese Mandarin. All audio clips were split into multiple 1.5‐s segments and normalized to the same loudness level. Mel frequency cepstral coefficients and mel‐spectrogram were extracted from these standardized segments. Each set of features was used in a convolutional neural network (CNN) to perform a binary classification task. The best feature was obtained through a five‐fold cross‐validation on a random selection of 80% data. The resultant DL framework was tested on the remaining 20% data and a public German voice database. The performance of the DL framework was compared with those of two baseline machine‐learning models. Outcomes & Results: The mel‐spectrogram yielded the best model performance, with a mean area under the receiver operating characteristic curve of 0.972 and an accuracy of 92% in classifying audio segments. The resultant DL framework significantly outperformed both baseline models in detecting dysphonic subjects on both test sets. The best outcomes were achieved when classifications were made based on all segments of both vowels, with 95% accuracy, 92% recall, 98% precision and 98% specificity on the Chinese test set, and 92%, 95%, 90% and 89%, respectively, on the German set. Conclusions & Implications: This study demonstrates the feasibility of DL for automatic detection of dysphonia. The mel‐spectrogram is a preferred acoustic feature for the task. This framework may be used for vocal health screening and facilitate automatic perceptual evaluation of voice in the era of big data. WHAT THIS PAPER ADDS: What is already known on this subject: Auditory–perceptual assessment is the current gold standard in clinical evaluation of voice quality, but its value may be limited by the rater's reliability and accessibility. DL is a new method of artificial intelligence that can overcome these disadvantages and promote automatic voice assessment. This study explored the feasibility of a DL approach for automatic detection of dysphonia, along with a quantitative comparison of two common sets of acoustic features. What this study adds to existing knowledge: A CNN model is excellent at decoding multidimensional acoustic features, outperforming the baseline parameter‐based models in identifying dysphonic voices. The first 13 mel‐frequency cepstral coefficients (MFCCs) are sufficient for this task. The mel‐spectrogram results in greater performance, indicating the acoustic features are presented in a more favourable way than the MFCCs to the CNN model. What are the potential or actual clinical implications of this work?: DL is a feasible method for the detection of dysphonia. The current DL framework may be used for remote vocal health screening or documenting voice recovery after treatment. In future, DL models may potentially be used to perform auditory–perceptual tasks in an automatic, efficient, reliable and low‐cost manner. [ABSTRACT FROM AUTHOR]

Turbines are generally oversimplified in conventional algorithms for steady‐state analysis of hydropower plant (HPP) networks. Moreover, conventional algorithms are hardly suitable for serial HPPs. In this paper, a modified global gradient algorithm (MGGA) is developed by extending turbine models and optimizing turbine interpolation. First, the nonlinear model of turbines is established with the proposed variable resistance factor and added to the global matrix. Then turbine unit quantity interpolation is proposed to compute the turbine operating parameters. Finally, turbine links and pipes can be iterated synchronously, and the global matrix is solved using the Newton–Raphson method. Characteristics of MGGA are evaluated by employing two test HPPs, including rapidity of convergence, computational stability of turbine, and applicability for serial HPPs. The results show that: (a) The iterative numbers of the MGGA are 0.72 and 4.98 times on average less than that of CA1 and CA2 for the accuracy of 10−6 in 400 operating conditions, even though MGGA has an obvious hysteretic nature; (b) Only MGGA can successfully calculate the extreme cases that large head losses in HPP networks; (c) Although fixed head nodes are not present between turbine links, such as serial HPPs, MGGA still works. [ABSTRACT FROM AUTHOR]

In this paper, the Barton–Bandis model for rock joints is extended to cyclic loading conditions, without any new material parameter. Also developed herein is an implicit solution algorithm for the extended Barton–Bandis model, which can also be used for the original Barton–Bandis model for which an implicit algorithm has been unavailable. To this end, we first cast the Barton–Bandis model into an incremental elasto‐plastic framework, deriving an expression for the elastic shear stiffness being consistent with the original model formulation. We then extend the model formulation to cyclic loading conditions, incorporating the dependence of shear stress and dilation on the joint position and the shearing direction. The extension is achieved by introducing a few state‐dependent variables which can be calculated with the existing material parameters. For robust and accurate utilization of the model, we also develop an implicit algorithm based on return mapping, which is unconditionally stable and guarantees the satisfaction of the strength criterion. We verify that the proposed model formulation and algorithm produce virtually the same results as the original Barton–Bandis model under monotonic shearing conditions. We then validate the extended Barton–Bandis model against experimental data on natural rock joints under cycling loading conditions. The present work thus enables the Barton–Bandis model, which has been exceptionally popular in research and practice, to be applicable to a wider range of problems in rock mechanics and rock engineering. [ABSTRACT FROM AUTHOR]

In this paper, an approximate Gaussian state estimator is developed based on generalised polynomial chaos expansion for target tracking applications. Motivated by the fact that calculating conditional moments in an approximate Gaussian filter involves computing integrals with respect to Gaussian density, the authors approximate the non‐linear dynamics using polynomial chaos expansion. Second‐order as well as third‐order polynomial chaos expansions were used for approximate filtering, to derive the necessary recursive algorithm and also provide certain algebraic simplifications which reduce the computational burden without significantly affecting the filtering performance. Two comprehensive numerical experiments for multivariate systems, including one for a multi‐model system, demonstrate the potential of the new algorithms. [ABSTRACT FROM AUTHOR]

Background: Select and enact appropriate learning tactics that advance learning has been considered a critical set of skills to successfully complete highly flexible online courses, such as Massive open online courses (MOOCs). However, limited by analytic methods that have been used in the past, such as frequency distribution, sequence mining and process mining, we lack a deep, complete and detailed understanding of the learning tactics used by MOOC learners. Objectives: In the present study, we proposed four major dimensions to better interpret and understand learning tactics, which are frequency, continuity, sequentiality and role of learning actions within tactics. The aim of this study was to examine to what extent can a new analytic technique, the ordered network analysis (ONA), deepen the understanding of MOOC learning tactics compared to using other methods. Methods: In particular, we performed a fine‐grained analysis of learning tactics detected from more than 4 million learning events in the behavioural trace data of 8788 learners who participated in a large‐scale MOOC 'Flipped Classroom'. Results and Conclusions: We detected eight learning tactics, and then chose one typical tactic as an example to demonstrate how the ONA technique revealed all four dimensions and provided deeper insights into this MOOC learning tactic. Most importantly, based on the comparison with different methods such as process mining, we found that the ONA method provided a unique opportunity and novel insight into the roles of different learning actions in tactics which was neglected in the past. Takeaway: In summary, we conclude that ONA is a promising technique that can benefit the research on learning tactics, and ultimately benefit MOOC learners by strengthening the strategic support. Lay Description: What is already known about this topic?: Since external support from instructors or peers are usually limited in MOOCs, the ability to self‐regulate learning (SRL), that is, select and enact goal‐oriented learning tactics that advance learning comes to be a critical set of skills to successfully complete coursework.Researchers have utilised analytic techniques that go beyond frequency‐based approaches, for example, process mining and sequence mining, to detect and understand learning tactics.However, limited by previous analytic methods that have been used in the past, we still lack a deep, complete and detailed understanding of the learning tactics used by MOOC learners. What this paper adds?: We proposed four major dimensions to better interpret and understand learning tactics, which are frequency, continuity, sequentiality and role of learning actions within tactics.We examined to what extent can a new analytic technique, the ordered network analysis (ONA), deepen the understanding of learning tactics compared to other methods.We found that the ONA method provided a unique opportunity and novel insight into the roles of different learning actions in tactics which was neglected in the past. Implications for practice and/or policy: Researchers who study learning tactics unpacked using the ONA technique can gain a deeper insight into a theorised learning processes that interplay within and across learning tactics and, in that way, improve their understanding of how self‐regulated learners enact and monitor learning tactics.The analytics on learning tactics use in a form of ONA graphs has the potential to provide learners with a detailed overview of their learning engagement over the selected learning period in MOOCs.The ONA graphs of learning tactics, combined with the information about learning performance and course requirements, instructors or learners (prompted by instructors) can evaluate whether the way learners have studied in a MOOC was beneficial to learners' learning success. [ABSTRACT FROM AUTHOR]

In the newest generation of video coding standard, Versatile Video Coding (VVC), a new technique called Quad Tree with nested Multi‐type Tree (QTMT) structure is introduced. QTMT significantly improves the coding efficiency, but the improvement in compression performance comes at the cost of drastically increased complexity. This paper proposes a fast intra partition algorithm using Lightweight Neural Network (LNN) to skip QTMT partition steps which are unlikely to be chosen as the best split modes. Specifically, five LNNs (for five QTMT split modes) are trained, using features that consider the characteristic of 360‐degree videos. The experimental results demonstrate that the proposed QTMT‐LNN can reduce the encoding time from 52.28% to 72.17% on average, with coding efficiency losses ranging from 0.71% to 1.78%. Compared to other fast algorithms for intra coding unit (CU) partition, the method outperforms the related works in terms of Bjontegaard delta bit‐rate (BDBR) and encoding time reduction. [ABSTRACT FROM AUTHOR]

At present, the image encryption algorithm based on the bit‐plane has the problem that the encryption speed and the encryption effect cannot be well balanced. To address the problem, an image layered scrambling encryption algorithm is proposed in this paper. First, a new one‐dimensional Cosine Logistic compound chaotic map (1DCLC) is proposed. Several characteristics of the new chaotic map are analyzed by using various measures, such as bifurcation diagram, Lyapunov exponent, sample entropy etc. And the results show that the novel chaotic map has a better chaotic performance. Second, based on the chaotic map, an image encryption algorithm is proposed. The algorithm first performs an XOR encryption operation on the image. Second, the layered scrambling is applied to the XOR encrypted image. Then the layered and scrambled image is recombined for the second XOR encryption operation. In particular, in the layered scrambling, not only can the position of the pixel be changed but also the value of the pixel can be modified, which improves the security of the algorithm. In addition, in order to defend against various plaintext attacks and increase the security of the overall algorithm, the Hash function is introduced to update the key of the proposed algorithm. Finally, the original algorithm is improved and the improved algorithm's ability to resist differential attacks is analyzed. The experimental simulation and security test results show that the proposed encryption algorithm has a comparable encryption effect and a faster encryption speed. [ABSTRACT FROM AUTHOR]

Trimap is required for most image matting algorithms, as it provides partial regions with known opacity. As capturing elaborate trimaps is a time‐consuming process, in practice, users prefer to provide coarse trimaps with large unknown regions. However, extant image matting algorithms cannot provide high‐quality alpha mattes based on coarse trimaps. Although some matting algorithms include trimap expansion in the pre‐processing stage, if this is done by directly comparing the similarity of image features between pixels, errors and omissions can easily occur. To overcome this issue, in this paper, a coarse trimap expansion model based on one‐class classification is presented, in which the problem is treated as a process of reclassifying pixels in unknown regions. For this purpose, a coarse trimap expansion method denoted as CTE‐OC is proposed, in which the similarity between pixels is reliably determined by measuring semantic features, allowing newly developed one‐class classifiers to adequately classify pixels in entire unknown regions. The validity of these strategies is tested experimentally, and the results show that CTE‐OC can significantly improve the quality of alpha mattes obtained by extant image matting methods when provided with coarse trimaps. [ABSTRACT FROM AUTHOR]

Particle swarm optimization is widely applied in self‐potential inversion, but most of these applications are limited to simple polarized bodies like inclined sheets and spheres. In this paper, two variants of particle swarm optimization are formed by introducing the resistivity constraint matrix and the damping factor and are then applied to two‐dimensional self‐potential source inversion. The two variants are combined to balance the exploration and exploitation capabilities in different situations, and the rationality of the parameter selection is validated through the stability analysis. To ensure both compactness and smoothness of the inversion results, we adopt an objective function based on L1–L2 norm regularization. Finally, we verify the effectiveness of the proposed algorithm through synthetic examples, the Cu–Fe model sandbox experiment and a field example. By imposing certain constraints, particle swarm optimization also shows great potential in high‐dimensional self‐potential source inversion, instead of being limited to the inversion of a few parameters. [ABSTRACT FROM AUTHOR]

In this paper, by applying the theory of fractional calculus to quantum back propagation (BP) neural network, a quantum BP algorithm based on the definition of fractional Grünwald–Letnikoff (G‐L) is proposed. We choose the Sigmoid linear superposition function to replace the activation function of the traditional neural network to construct a fractional quantum BP neural network structure. Experimental results prove that this algorithm improves the convergence speed of the network and reduces the convergence error. [ABSTRACT FROM AUTHOR]

The parameter identification problem of linear discrete period time‐varying (LDPV) systems is studied in this paper and a two‐step identification algorithm based on the recursive least squares principle is proposed. The recursive principle‐based identification algorithm is mostly used to deal with the parameter identification of time‐invariant systems. Combine with the cyclic reconstruction method, the LDPV system is converted to a linear time‐invariant system. Then, based on this and combined with recursive least squares identification, a discrimination algorithm is proposed for the parameter estimation of the LDPV system. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents and studies three two‐grid stabilized quadratic equal‐order finite element algorithms based on two local Gauss integrations for the steady Navier–Stokes equations with damping. In these algorithms, we first solve a stabilized nonlinear problem on a coarse grid, and then pass the coarse grid solution to a fine grid and solve a stabilized linear problem. Using some nonlinear analysis techniques, we analyze stability of the algorithms and derive optimal order error estimates of the approximate solutions. Theoretical and numerical results show that, when the algorithmic parameters are chosen appropriately, the accuracy of the approximate solutions computed by our two‐grid stabilized algorithms is comparable to that of solving a fully stabilized nonlinear problem on the same fine grid; however, our two‐grid algorithms save a large amount of CPU time than the one‐grid stabilized algorithm. [ABSTRACT FROM AUTHOR]

Summary: Terrorist attacks have contributed significantly to using wireless technologies to identify concrete destruction survivors. A dynamic ad hoc mobile network (MANET) consists of wireless linked nodes, which route hop‐by‐hop without the support of a fixed infrastructure acquires information from trapped survivors. The energy efficiency that extends the lifespan of the network is an essential prerequisite of MANET. Researchers have suggested many strategies to accomplish this purpose and a cluster of these techniques in MANETs are used to provide an energy‐efficient approach. In this paper we are proposing a red deer multi‐objective constraint applied for an energy efficient QoS routing (RD‐MOCER) algorithm to the number of clusters in an ad hoc network and the energy dispensing in nodes to provide an energy effective solution and to minimise network traffic. Intracluster and intercluster traffic is handled by the cluster heads in the proposed approach. The algorithm suggested takes account of mobile nodes' node degrees, transmitting capacity and battery power usage. This approach gives a variety of options at a time, the key benefit of which is that the ideal Pareto front results in these solutions. We correlate the findings with two other well‐known methods of clustering; MOPSO and MOEAQ‐based clustering with different results. We conduct detailed simulations to demonstrate that the solution proposed is an effective and stronger solution to clustering in ad hoc cell networks than the other two techniques. [ABSTRACT FROM AUTHOR]

As the current multi‐segment signal fusion algorithms cannot meet the requirement of high‐precision frequency estimation at different signal‐to‐noise ratios (SNRs), an adaptive fusion algorithm is proposed in this paper. At high SNR, an autocorrelation weighted fusion algorithm is utilised, which adopts the real‐time weighted fusion of multi‐segment autocorrelation signals, resulting in denoised sinusoidal signals. Then the frequency is obtained by minimising the error function based on coarse estimation. At low SNR, an improved spectrum fusion algorithm is adopted, which uses the three‐parameter sine‐fitting method to determine the phase difference compensation factor, and the multi‐segment signal spectrum is weighted to obtain a long signal spectrum with continuous phase. As a result, the frequency can be estimated by spectral peak search. Simulation results show that the proposed algorithm, compared with existing algorithms, not only improves the estimation accuracy by about 20%–50% but also can perform high‐precision frequency estimation for multi‐segment signals of different lengths, frequencies, and abnormal situations. [ABSTRACT FROM AUTHOR]

Aiming at the problem of motion blur in the inspection image of wind power generation equipment, an improved fast motion blur removal method based on deblurganv2 is proposed in this paper. Firstly, according to the characteristics of disparate local motion blur degrees and different global motion blur directions of the wind power inspection image, the real wind power fuzzy image data set is collected and produced. Secondly, according to the characteristics of the single background of the wind power inspection image and the requirements of fast and effective processing, the lightweight network Ghostnet is redesigned as the backbone network of the Deblurganv2 generator to reduce the number of network parameters and calculations. Finally, five SE channel attention mechanism layers are added to GhostNet to strengthen feature extraction, and the upsampling process is optimized through bilinear interpolation, to improve the deblurring performance of the wind power inspection image. The experimental results show that compared with other algorithms, the proposed algorithm has a higher peak signal‐to‐noise ratio and structural similarity, the network model parameters are compressed to 6.1 MB, and the reasoning speed is improved to 0.42 s. [ABSTRACT FROM AUTHOR]

A novel hue‐preserving and color enrichment color image enhancement algorithm without gamut problem is proposed in this paper. First, the pixel values of the input image are stretched to the edge of RGB color space to improve the saturation. Then, the pixel values are converted to the complementary color space for saturation enhancement, so that the whole method has no gamut problem and keeps the contrast intensity of the output picture unchanged. Experimental results demonstrate that the proposed algorithm can overcome the poor color effect of traditional methods in the condition of low exposure and can restore the picture color better. Besides, higher saturation can be achieved through the proposed algorithm than up‐to‐date color image enhancement methods. [ABSTRACT FROM AUTHOR]

This study investigates scholars' citation behaviors from a fine‐grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision‐making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest‐based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision‐making, followed by citation authority and citation inertia. However, big‐name scholars whose h‐indexes rank among the top 1% exhibit a unique pattern of citation behaviors—their citation decision‐making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia. [ABSTRACT FROM AUTHOR]

The notion of digital health often remains an empty signifier, employed strategically for a vast array of demands to attract investments and legitimise reforms. Rather scarce are attempts to develop digital health towards an analytic notion that provides avenues for understanding the ongoing transformations in health care. This article develops a sociomaterial approach to understanding digital health, showing how digitalisation affords practices of health and medicine to cope with and utilise the combined and interrelated challenges of increases in quantification (data‐intensive medicine), varieties of connectivity (telemedicine), and unprecedented modes of instantaneous calculation (algorithmic medicine). This enables an engagement with questions about what forms of knowledge, relationships and control are produced through different manifestations of digital health. The paper then sets out, in detail, three innovative strategies that can guide explorations and negotiations into the type of care we want to achieve through digital transformation. These strategies embed Karen Barad's concept of agential cuts suggesting that responsible cuts towards the materialisation of digital health require participatory efforts that recognise the affordances and the generativity of technology developments. Through the sociomaterial approach presented in this article, we aim to lay the foundations to reorient and sensitise innovation and care processes in order to create new possibilities and value‐centric approaches for promoting health in digital societies as opposed to promoting digital health per se. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents a sufficient condition for observability of continuous-time switched nonlinear systems that also involve state jumps. Without the assumption of observability of individual modes, the sufficient condition is based on gathering partial information from each mode so that the state is completely recovered after several switchings. Based on the sufficient condition, a hybrid-type observer is designed, which comprises a copy of the actual plant and an error correction scheme at discrete time instants. In order to execute the proposed design, the observable component of the state at each mode needs to be estimated without transients or 'peaking' (caused by high-gain observers), and this motivates us to introduce a back-and-forth estimation technique. Under the assumption of persistent switching, analysis shows that the estimate thus generated converges asymptotically to the actual state of the system. Simulation results validate the utility of the proposed algorithm. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Design of event‐triggered functional observers for linear systems subject to input and output disturbances is presented in this paper. A new dynamic event‐triggered mechanism is first proposed and then a novel event‐triggered interval functional observer is designed. The designed observer provides upper and lower bounds of the unknown linear function of the state vectors. By constructing a Lyapunov function, conditions ensuring the existence of the event‐triggered functional observer are established. The smallest bound of the estimated error and an effective algorithm for determining observer matrices are also presented. Finally, the obtained results in this paper are used to estimate linear functions of the state vector of a battery and motor circuit model of electric vehicles. [ABSTRACT FROM AUTHOR]

One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem. [ABSTRACT FROM AUTHOR]

Images in video may include both Gaussian noise and geometric rotation. Thus, it is challenging to represent an image sequence in its intrinsically low‐dimensional space in a noise‐robust and rotation‐robust manner. In this paper, we propose a novel‐ordered sparse subspace clustering algorithm based on a p‐norm to achieve an effective clustering of sequential data under heavy noise conditions. We also use the wavelet‐histogram of oriented gradient (HOG) transform in the kernel view to extract both the global features (with the wavelet process) and the local features (with the HOG process) from the image. In addition, we assign different weights to different features to obtain a sparse coefficient matrix that helps to emphasize the global and local correlations in each sample. Similarly, the clustering algorithm based on the p‐norm for sequential images emphasizes the within‐class correlations amongst samples. Therefore, in this paper, we select additional denoising main components under a Laplacian constraint to achieve a better block‐diagonal structure and highlight the independence of different clusters. Extensive experiments performed on various public datasets (including the ordered face dataset, handwritten recognition dataset, video scene segmentation dataset, and object recognition dataset) demonstrate that the proposed method is more resilient to noise and rotation than other representative sparse subspace clustering methods. [ABSTRACT FROM AUTHOR]

In the process of detecting materials inside a tray in an automated production line, it is necessary to detect not only the known materials and blank space in the designated area, but also the unknown materials misplaced inside the tray. However, the supervised detection algorithm based on deep learning can only detect the known and blank material areas. Therefore, this paper proposed a phased material detection. The first stage is to detect the tray and then identify the material area in the second stage. In order to improve the tray detection accuracy during the first stage under the condition of a high intersection ratio, an improved YOLOv5s tray detection method is proposed. The structure of YOLOv5s is improved using the SENet. This paper proposes to use the rich geometric information of the shallow network and the high‐level semantic information to integrate the bypass features. MAP@0.5:0.95 of the improved model increased from 95.7% to 96.6% and MAP@0.95 from 78.5% to 90.8%. The challenge of detecting unknown wrong materials on a tray can be resolved through the recognition of material area segmentation images processed by using the improved pre‐detection algorithm, together with the relative position reference between the material area and the tray. The experimental results showed that the improved method proposed meets the industrial detection requirements with an overall recognition accuracy of 91% within a 250 ms detection interval. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of intermittently connected sensor networks where sensors are sleep most of time and become active randomly and rarely. In the networks, packet collision probability and power consumption can be reduced. Meanwhile, the delivery delay increases with the use of the low activation values. In this paper, we design the energy‐efficient intermittently connected sensor networks, considering this trade‐off of activation control. We first provide the algorithm that computes the optimal packet forwarding rule achieving the minimum delivery delay, which can be calculated using the activation rates. We then propose the activation rate control methods, which control the activation rates of the sensors based on the density of the sensors and/or the number of wake‐ups of sensors having relay packets. By doing this, we can improve the delay performance, while suppressing the increase in the packet collision probability and the power consumption of sensors. Through simulation experiments, we demonstrate the effectiveness of our activation control methods in the intermittently connected sensor networks. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has become a rising sophisticated technological application trend in the electrical industry in recent years. Such innovation provides optional methodologies for many existing applications, such as power and load profile forecasting, reliability evaluation, substation behavior detection and state observation of electrical equipment, and so on. This paper presents a review of various supervised and unsupervised ML techniques and applications for electrical power systems, including generation, transmission, distribution and micro‐grid. The algorithms and applications are mainly summarized from IEEE journals and the interest of this paper shows the roles and developments of most used algorithms and its corresponding extensions and performance in different applications. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Purpose: Limited‐angle computed tomography is a challenging but important task in certain medical and industrial applications for nondestructive testing. The limited‐angle reconstruction problem is highly ill‐posed and conventional reconstruction algorithms would introduce heavy artifacts. Various models and methods have been proposed to improve the quality of reconstructions by introducing different priors regarding to the projection data or ideal images. However, the assumed priors might not be practically applicable to all limited‐angle reconstruction problems. Convolutional neural network (CNN) exhibits great promise in the modeling of data coupling and has recently become an important technique in medical imaging applications. Although existing CNN methods have demonstrated promising results, their robustness is still a concern. In this paper, in light of the theory of visible and invisible boundaries, we propose an alternating edge‐preserving diffusion and smoothing neural network (AEDSNN) for limited‐angle reconstruction that builds the visible boundaries as priors into its structure. The proposed method generalizes the alternating edge‐preserving diffusion and smoothing (AEDS) method for limited‐angle reconstruction developed in the literature by replacing its regularization terms by CNNs, by which the piecewise constant assumption assumed by AEDS is effectively relaxed. Methods: The AEDSNN is derived by unrolling the AEDS algorithm. AEDSNN consists of several blocks, and each block corresponds to one iteration of the AEDS algorithm. In each iteration of the AEDS algorithm, three subproblems are sequentially solved. So, each block of AEDSNN possesses three main layers: data matching layer, x‐direction regularization layer for visible edges diffusion, and y‐direction regularization layer for artifacts suppressing. The data matching layer is implemented by conventional ordered‐subset simultaneous algebraic reconstruction technique (OS‐SART) reconstruction algorithm, while the two regularization layers are modeled by CNNs for more intelligent and better encoding of priors regarding to the reconstructed images. To further strength the visible edge prior, the attention mechanism and the pooling layers are incorporated into AEDSNN to facilitate the procedure of edge‐preserving diffusion from visible edges. Results: We have evaluated the performance of AEDSNN by comparing it with popular algorithms for limited‐angle reconstruction. Experiments on the medical dataset show that the proposed AEDSNN effectively breaks through the piecewise constant assumption usually assumed by conventional reconstruction algorithms, and works much better for piecewise smooth images with nonsharp edges. Experiments on the printed circuit board (PCB) dataset show that AEDSNN can better encode and utilize the visible edge prior, and its reconstructions are consistently better compared to the competing algorithms. Conclusions: A deep‐learning approach for limited‐angle reconstruction is proposed in this paper, which significantly outperforms existing methods. The superiority of AEDSNN consists of three aspects. First, by the virtue of CNN, AEDSNN is free of parameter‐tuning. This is a great facility compared to conventional reconstruction methods; Second, AEDSNN is quite fast. Conventional reconstruction methods usually need hundreds even thousands of iterations, while AEDSNN just needs three to five iterations (i.e., blocks); Third, the learned regularizer by AEDSNN enjoys a broader application capacity, which could work well with piecewise smooth images and surpass the piecewise constant assumption frequently assumed for computed tomography images. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is at the heart of modern society with computers now capable of making process decisions in many spheres of human activity. In education, there has been intensive growth in systems that make formal and informal learning an anytime, anywhere activity for billions of people through online open educational resources and massive online open courses. Moreover, new developments in Artificial Intelligence‐related educational assessment are attracting increasing interest as means of improving assessment efficacy and validity, with much attention focusing on the analysis of the large volumes of process data being captured from digital assessment contexts. In evaluating the state of play of Artificial Intelligence in formative and summative educational assessment, this paper offers a critical perspective on the two core applications: automated essay scoring systems and computerized adaptive tests, along with the Big Data analysis approaches to machine learning that underpin them. Lay Description: What is already known about this topic?: AI and machine learning are established in summative assessments (SA) of learningThe main types are Automated Essay Scoring (AES) and Computerised Adaptive Testing (CAT)Machine learning, based on Big Data and Learning Analytics, offers exciting possibilities in formative assessment (FA) What this paper adds?: A review of current AI and machine learning platforms in FA and SAInsights into the structure and design of CATs and AESsA review of current research on usage of Big Data/Learning Analytics for FA and SAA critique of the future for AI and machine learning in assessing higher order learning Implications for practice and/or policy: The use of AI for the assessment of higher order learning is not yet a feasible realitySophisticated learning analytics can now offer closer alignment of AI to human judgment'Trained' machine feedback can support self‐regulated learning in online environmentsIndicators are emerging from process data analysis on a learner's behaviour and affective state [ABSTRACT FROM AUTHOR]

Summary: Vertical handoff is a major concern in the operation of mobile connections. Multiple wireless networks collate to provide smooth and quality service to the users over mobile connections. This paper formulates a Markov decision process for handoff decisions with a sample space that includes a union of parameters that are important for making a handoff decision. The major contribution of this paper is to propose three different yet closely related algorithms for reducing the computational complexity of the original problem. In particular, we propose a feature‐wise assessment algorithm, a network‐wise assessment algorithm, and a hybrid approach for computational complexity reduction. Discussed algorithms give pseudo‐optimal solutions with a significant reduction in computational complexity. Results indicate that different complexity reduction algorithms perform best under different circumstances. This provides a guideline for the selection of complexity reduction algorithms based on real scenarios. [ABSTRACT FROM AUTHOR]

Summary: Data center networks (DCNs) are facing challenging control problems such as flow scheduling, congestion control, load balancing, and bandwidth allocation when dynamically handling heterogeneous mice and elephant flows. This paper focuses on the load balancing in DCNs as an online decision‐making problem with high complexity (NP‐hard problem). Traditional load balancing approaches are usually the heuristic algorithms relying on the operator's viewpoint and a short‐term knowledge of the traffic conditions and network environment. This paper proposes DeepRLB, a deep reinforcement learning (DRL)‐based load balancing approach for software‐defined networking‐based DCNs which exploits the deep deterministic policy gradient (DDPG) algorithm to adaptively learn the link‐weight values by observing the traffic flow characteristics. The actor and critic neural networks in the DDPG model have been designed based on two learning models: fully connected (DeepRLB‐Full) and convolutional (DeepRLB‐Conv). The output of the model is further used by the controller to determine the forwarding paths for traffic flows accordingly. We evaluated the performance of DeepRLB by comparing it with ECMP as well as a per‐packet load balancing algorithm in three different data center topologies under 30 heterogeneous traffic demand matrices. The obtained results showed that the DeepRLB algorithm outperforms the ECMP with respect to studied load balancing metrics considerably (DeepRLB‐Conv achieved the best performance). As a sample, DeepRLB‐Full and DeepRLB‐Conv were able to reach 19.14% and 24.46% reduction in the total number of over‐utilized and under‐utilized network links compared to ECMP, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a variable projection approach based on the BFGS (Broyden–Fletcher–Goldfarb–Shanno) method for image reconstruction problems is proposed, which is an alternative to the common alternating minimisation scheme. The image restoration is expressed as a nonlinear least‐squares problem with reduced parameter space. To improve the efficiency of the algorithm, the BFGS method is proposed to be used to optimise the reduced objective function. The large‐scale problem considered in this paper is projected on to a small Krylov subspace using Lanczos bidiagonalisation. The regularisation parameter is selected by a weighted generalised cross validation criterion. Numerical examples demonstrate the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces a key encapsulation mechanism based on polar codes, called as KEM‐PC, with secure and efficient key generation, encapsulation and decapsulation algorithms. In the proposed KEM‐PC, the ephemeral public encapsulation and secret decapsulation keys, which are generated at each key exchange session, are employed. By introducing secure polar coding and exploiting the characteristics of polar codes, the ephemeral secret decapsulation key size is reduced. Also, by exploiting the secure polar encoding, KEM‐PC has a proper security level against information set decoding and indistinguishable chosen plaintext attack. In fact, decreasing the ephemeral secret decapsulation key size of KEM‐PC has no impact on its security level. Moreover, the proposed KEM‐PC has a potential to be exploited in an authenticated key exchange protocol. The aforementioned properties make the proposed KEM‐PC usable in secure communication devices. [ABSTRACT FROM AUTHOR]