Iteration Step Research Articles

Finding the Least Element of a Nonnegative Solution Set of a Class of Polynomial Inequalities

In this paper, we consider the least element problem of a nonnegative solution set of a system of inequalities defined by a homogeneous polynomial mapping and a vector. In the set under consideration, the homogeneous polynomial mapping is defined by a tensor. When the tensor involved is square, the set under consideration is just the feasible region of the tensor complementarity problem (TCP). We first introduce the concept of the generalized -tensor, which is a natural generalization of the -tensor. Then, under the assumption that the considered set is nonempty and the tensor involved is a generalized -tensor, we propose an iterative method for finding the least element of the considered set. Specifically, by recognizing the position indices of positive components in the least element continuously, we solve a series of lower-dimensional system of tensor equations corresponding to these indices and prove that the least element of the set can be obtained within finite step iterations. When the tensor involved is square, the least element obtained is also a solution of the TCP. Compared with the existing methods for finding the least-element solution of the TCP with a -tensor, our method does not require any additional assumptions and has lower computational cost. Preliminary numerical experiments show that the proposed method is effective.

Modeling and Harmonic Analysis of a Fractional-Order Zeta Converter

The Zeta converter is an essential and widely used high-order converter. The current modeling studies on Zeta converters are based on the model that devices, such as capacitors and inductors, are of integer order. For this reason, this paper takes the Zeta converter as the research object and conducts an in-depth study on its fractional-order modeling. However, the existing modeling and analysis methods have high computational complexity, the analytical solutions of system variables are tedious, and it is difficult to describe the ripple changes of state variables. This paper combines the principle of harmonic balance with the equivalent small parameter method (ESPM); the approximate analytic steady-state solution of the state variable can be obtained in only three iterative steps in the whole solving process. The DC components and ripples of the state variables obtained by the proposed method were compared with those obtained by the Oustaloup’s filter-based approximation method; the symbolic period results obtained by ESPM had sufficient precision because they included more combinations of higher harmonics. Finally, the influence of fractional order on harmonics were analyzed. The obtained results show that the proposed method has the advantage of being less computational and easily describing changes in the ripple of the state variables. The simulation results are provided for validity of the theoretical analysis.

Zero‐sum game optimal control for the nonlinear switched systems based on heuristic dynamic programming

AbstractIn this article, the zero‐sum game problem of control and disturbance is studied for discrete affine nonlinear switched systems with unknown dynamical models. A two‐level heuristic dynamic programming iterative algorithm for solving the zero‐sum game problem is proposed, which can be used to solve the Hamilton‐Jacobi‐Isaacs equation associated with the optimal regulation control problem. The convergence analysis based on the value function and control strategy is given. For algorithm implementation, neural networks are used to approximate the control strategy, disturbance strategy, and value function of the game dynamic programming, respectively. A model network is used to approximate the system states with an unknown dynamical model. The iterative algorithm steps are given. Finally, the validity of the method is verified by simulation examples.

The flexible and iterative steps within the NHEJ pathway

Cellular and biochemical studies of nonhomologous DNA end joining (NHEJ) have long established that nuclease and polymerase action are necessary for the repair of a very large fraction of naturally-arising double-strand breaks (DSBs). This conclusion is derived from NHEJ studies ranging from yeast to humans and all genetically-tractable model organisms. Biochemical models derived from recent real-time and structural studies have yet to incorporate physical space or timing for DNA end processing. In real-time single molecule FRET (smFRET) studies, we analyzed NHEJ synapsis of DNA ends in a defined biochemical system. We described a Flexible Synapsis (FS) state in which the DNA ends were in proximity via only Ku and XRCC4:DNA ligase 4 (X4L4), and in an orientation that would not yet permit ligation until base pairing between one or more nucleotides of microhomology (MH) occurred, thereby allowing an in-line Close Synapsis (CS) state. If no MH was achievable, then XLF was critical for ligation. Neither FS or CS required DNA-PKcs, unless Artemis activation was necessary to permit local resection and subsequent base pairing between the two DNA ends being joined. Here we conjecture on possible 3D configurations for this FS state, which would spatially accommodate the nuclease and polymerase processing steps in an iterative manner. The FS model permits repeated attempts at ligation of at least one strand at the DSB after each round of nuclease or polymerase action. In addition to activation of Artemis, other possible roles for DNA-PKcs are discussed.

Improving the Computational Efficiency of ROVIO

ROVIO is one of the state-of-the-art monocular visual inertial odometry algorithms. It uses an Iterative Extended Kalman Filter (IEKF) to align visual features and update the vehicle state simultaneously by including the feature locations in the state vector of the IEKF. This algorithm is single-core intensive, which allows the other cores to be used for other algorithms, such as object detection and path optimization. However, the computational cost of the algorithm grows rapidly with the maximum number of features to track. Each feature adds three new states (a 2D bearing vector and inverse depth), leading to bigger matrix multiplications that are computationally expensive. The main computational load of ROVIO is the iterative update step of the IEKF. In this work, we reduce the average computational cost of ROVIO by [Formula: see text] on an NVIDIA Jetson TX2, without affecting the accuracy of the algorithm. This computational gain is mainly achieved by exploiting the sparse matrices in ROVIO. Furthermore, we reduce the computational peaks by pre-selecting new features based on their already calculated FAST score. The combination of both modifications allows us to run ROVIO on the computationally restricted Raspberry Pi Zero 2[Formula: see text]W.

Stochastic mirror descent method for linear ill-posed problems in Banach spaces

Consider linear ill-posed problems governed by the system for , where each A i is a bounded linear operator from a Banach space X to a Hilbert space Y i . In case p is huge, solving the problem by an iterative regularization method using the whole information at each iteration step can be very expensive, due to the huge amount of memory and excessive computational load per iteration. To solve such large-scale ill-posed systems efficiently, we develop in this paper a stochastic mirror descent method which uses only a small portion of randomly selected equations at each iteration step and incorporates convex regularization terms into the algorithm design. Therefore, our method scales very well with the problem size and has the capability of capturing features of sought solutions. The convergence property of the method depends crucially on the choice of step-sizes. We consider various rules for choosing step-sizes and obtain convergence results under a priori early stopping rules. In particular, by incorporating the spirit of the discrepancy principle we propose a choice rule of step-sizes which can efficiently suppress the oscillations in iterates and reduce the effect of semi-convergence. Furthermore, we establish an order optimal convergence rate result when the sought solution satisfies a benchmark source condition. Various numerical simulations are reported to test the performance of the method.

GridNetOpt: Fast Full-Chip EM-Aware Power Grid Optimization Accelerated by Deep Neural Networks

This article presents a fast full-chip electromigration (EM) aware IR drop constrained optimization framework, named GridNetOpt, for on-chip power grid networks accelerated by deep neural networks (DNN). Compared to the existing linear programming-based methods, the new method employs more flexible conjugate gradient-based optimization to size the wire width of the power grids. To mitigate the high cost of sensitivity calculation of the adjoint network using full-chip IR drop analysis at every iteration step, the sensitivity is computed via a trained conditional generative adversarial network (CGAN). The new method exploits the differentiable characteristics of DNNs for fast sensitivity computation. The sensitivity, which is the node voltage with respect to wire resistance, will guide the search direction during the optimization process. In order to consider more accurate EM failure effects, the training data is obtained from the power grids under different wire widths and current loads analyzed by a state-of-the-art full-chip multi-physics-based coupled EM-IR drop analysis tool. This is in contrast with the existing linear programming-based methods, in which only immortal wires or wires with non-zero resistance can be dealt with. Numerical results on a number of synthesized power grid benchmarks from ARM Cortex-M0 processor designs show that the proposed GridNetOpt can lead to at least an order of magnitude speedup over the conjugate gradient-based method using the traditional adjoint network method. Compared to the previous localized power grid fixing work with GridNet, GridNetOpt leads to smaller area overhead for all the benchmarks we tested. It can also reduce IR drops for power grid circuits with immortal wires, which is not possible with the localized GridNet method.

Asynchronous and Adaptive State Estimation of Integrated Electricity–Gas Energy Systems

The combination of Internet of Things technology and power grid technology greatly strengthens the situational awareness in integrated energy systems. However, present development fails to consider the internal dynamic characteristics of the natural gas system. This is addressed in the present study by proposing an asynchronous distributed dynamic state estimation method for integrated electricity-gas energy systems. First, static state estimation based on a weighted least square method is applied to the electric power network, and an improved extended Kalman filter state estimation method based on a transient model is applied to the natural gas network. The asynchronous exchange of information between the two subsystems is addressed by applying an iterative update step correction to the dynamic state estimation of the natural gas network. Second, we propose an adaptive state estimation execution cycle adjustment method to track the state of the natural gas subsystem in real time under conditions of large disturbances in that subsystem. Application of the proposed algorithm to simulation case studies demonstrates that it can greatly improve the estimation accuracy and trajectory tracking performance of the state estimation process relative to conventional methods.

Robust State Estimation via Maximum Correntropy EKF on Matrix Lie Groups With Application to Low-Cost INS/GPS-Integrated Navigation System

This paper focuses on solving state estimation problems of nonlinear systems whose states evolve on matrix Lie groups and measurements live in the Euclidean space under non-Gaussian measurement noise. Building upon the maximum correntropy criterion and the invariant Kalman filtering theory, a generalized robust extended Kalman filtering algorithm on matrix Lie groups is derived from maximizing a novel optimization cost function. This cost function is introduced by considering the correntropy and the system geometry structure. Then, two alternative versions of the proposed filtering algorithm are provided according to the distinct processing approaches of the nonlinear measurement function in the cost function. A Gaussian-Newton method on Lie groups and a fixed-point iterative method are applied to update the posterior estimation. In addition, sufficient conditions are given to ensure the convergence of fixed-point iterative steps. Extensive Monte-Carlo simulations on an INS/GPS integrated navigation system and experiments on open-source datasets validate the theoretical analysis and illustrate the effectiveness and robustness of the proposed filter.

FIXED POINT APPROXIMATION USING UNIFIED ITERATION SCHEME FOR ASYMPTOTICALLY NONEXPANSIVE MAPPINGS IN CAT(0) SPACES

In the present paper, we focus on reexamination of unified three step iteration scheme in more general infinite-dimensional manifolds i.e. in geodesic CAT(0) spaces for asymptotically non-expansive mappings. The findings hold true for both asymptotically non-expansive type mappings and asymptotically quasi nonexpansive mappings. Since, numerous iteration schemes have been introducing for so long and also claimed new and different from other which shows huge lacking of existing iteration based literature. It is to be noted that there are several iteration schemes which are claimed to be different and unique but is special case of some existing scheme. Our results improve the existing iteration scheme based literature.

Computation of the Stabilizing Solution of Nash-Riccati Equations

Consider an iterative modification of the linearized Newton method for computing the minimal nonnegative solution of a a nonsymmetric Nash-Riccati equation. The equation has arisen in linear quadratic games for positive systems. The Newton procedure for computing the minimal nonnegative solution is well known in the the literature. Our proposal is effective one because it employs small number of matrix multiplication at each iteration step and there is a variant to exploit the block structure of matrix coefficients of the Nash-Riccati equation. Moreover, in this reason, it is easy to extend the proposed iterative modification depending on the number of players of a given game model. We provide a numerical example where compare the results from experiment with the proposed iteration an the linearized Newton method.

The impact thinking framework: a process for advancing research-to-practice initiatives in neuroaesthetics.

Neuroaesthetics research explores brain, body and behavioral responses to engagement with the arts and other aesthetic sensory experiences. Evidence indicates that such experiences can help address various psychological, neurological and physiological disorders, and that they can support mental and physical well-being and learning in the general population. The interdisciplinary nature of this work contributes to its impact and promise; however, it also creates challenges as various disciplines approach and define research and practice in varied ways. Recent field-wide reports have noted that a consensus translational framework is needed to support further neuroaesthetics research that can deliver meaningful knowledge and interventions. The Impact Thinking Framework (ITF) was designed to meet this need. Through a description of the framework's nine iterative steps and a presentation of three case studies, this paper posits that the ITF can support researchers and practitioners in understanding and applying aesthetic experiences and the arts to advance health, well-being, and learning.

Optimal allocation method of multi-energy system based on hybrid optimization algorithm

With the rapid development of industry, the research of energy storage technology and renewable energy continues to be hot, and the energy industry opens the era of diversification. Multi-energy complementary has become a new trend in sustainable energy development, leading the energy industry to a new energy system of deep integration and integration of multiple energy sources. This paper proposes a hybrid optimization algorithm that combines particle swarm algorithms and Hooke–Jeeves​ (HJ) with a comprehensive evaluation index as the optimization objective, aiming to improve the speed of solving the capacity optimization of integrated energy systems. The multi-energy system configuration optimization platform that covers the index system, optimization model, and system analysis module was established to systematically solve the integrated energy system optimization configuration problem, moreover provide an important reference for integrated energy system design and implementation. Besides, the influence of optimization algorithms on the configuration results was analyzed. Taking the combination of soil source heat pump system and combined cooling, heating and power system as an example, this study quantifies and compares the optimization results and solution speeds of the hybrid algorithm and the traditional single optimization calculation. It is shown that the hybrid optimization algorithm reduces the amount of iteration steps by approximately 31% compared with the particle swarm algorithm and by approximately 48% compared with the HJ algorithm. This significantly improves the speed of the optimization computation while ensuring the accuracy of the computation results.

Damage identification of large-scale space truss structures based on stiffness separation method

Optimization-based damage identification of large-scale space truss structures is challenging because with increasing degrees of freedom for the structures, the objective function becomes complex that is difficult to converge. To solve this issue, this study introduces a stiffness separation method for damage identification of large-scale space truss structures based on static response. The separate method splits the global stiffness matrix into the required sub-stiffness matrices, thereby significantly simplifying the objective function for damage identification. Two types of space truss structures are used to verify the accuracy and effectiveness of the proposed method. The results show that the proposed method reduces the number of iteration steps required to search for optimal values.

Adaptive Fourier decomposition‐type sparse representations versus the Karhunen–Loève expansion for decomposing stochastic processes

The present study devotes to introducing and analyzing the use of the adaptive Fourier decomposition (AFD)‐type methods in the area of stochastic processes and random fields. We involve two types of algorithms, namely, stochastic AFD (SAFD) and stochastic pre‐orthogonal AFD (SPOAFD), for, respectively, the Hardy space format and non‐Hardy space ones, as may be regarded. We provide both their theoretical results and practical algorithms and compare them with the well adopted and, in fact, dominating Karhunen–Loève (KL)‐type expansions. The AFD methods involve a finite or infinite sequence of optimally chosen parameters; they, in contrast, do not rely on and hence not have to compute the eigenpairs of the second type Fredholm integral equation with the covariance function as the kernel. Apart from such computational conveniences, they with the same convergence rate have flexibility of choosing best suitable dictionaries for doing the specific task in the practice. We include a number of experiments showing that in terms of effectiveness, the AFD methods give better approximations before all the positive eigenvalues running out in the case the integral operator being of a finite rank or before the KL iteration step becomes excessively large: The AFD methods normally give better approximations from the very beginning of the iterations.

Strength Back - A qualitative study on the co-creation of a positive psychology digital health intervention for spinal surgery patients.

Spinal surgery patients often experience pain as well as stress, anxiety or even depression before surgery, highlighting the need for better mental preparation before undergoing surgery. Acceptance and Commitment Therapy and positive psychology have proven effective in coping with chronic pain and providing long-term skills that enhance psychological flexibility and mental well-being.The aim of this study is to develop a digital intervention (app) based on Acceptance and Commitment Therapy and positive psychology in co-creation with all stakeholders, including patients and professionals. The aim of the intervention is to increase psychological flexibility and positive skills of spinal surgery patients to promote long-term resilience. In this qualitative study, individual, semi-structured interviews were held with healthcare professionals (N = 9) and spinal surgery patients (N = 12) to identify contextual factors and needs for the app. Subsequently, three focus-group sessions were held with healthcare professionals and newly recruited patients to specify relevant values. Also, a first version of the app, named Strength Back, was developed using a participatory design. The interviews confirmed the need for information and digital support to cope with insecurity, anxiety and pain, both before and after surgery. Based on iterative steps in the focus-group sessions, thirteen modules were developed focusing on procedural information, pain education, psychological flexibility and mental well-being. The intervention Strength Back, containing information as well as Acceptance and Commitment Therapy and positive psychology exercises, has the potential to increase psychological flexibility, enhance well-being and improve postoperative recovery after spinal surgery.

Greedy double subspaces coordinate descent method for solving linear least-squares problems

In this paper, we propose a version of the coordinate descent method for solving linear least-squares problems. At each iteration step, the new estimate is obtained by successive projection and orthogonalization onto a solution space given by two greedily selected columns. It is theoretically proved that this new method converges linearly to the unique solution of the least-squares problem when its coefficient matrix is of full column rank and overdetermined. Additionally, experimental results further confirm that our method has a faster convergence than the well-known greedy coordinate descent (GCD) and the two-step Gauss–Seidel (2SGS) methods, in particular when the coefficient matrix has highly coherent columns.

A Few-Shot Malicious Encrypted Traffic Detection Approach Based on Model-Agnostic Meta-Learning

Existing malicious encrypted traffic detection approaches need to be trained with many samples to achieve effective detection of a specified class of encrypted traffic data. With the rapid development of encryption technology, various new types of encrypted traffic are emerging and difficult to label. Therefore, it is an urgent problem to train a deep learning model using only a small number of samples to detect new classes of malicious encrypted traffic. This paper proposes a few-shot malicious encrypted traffic detection (FMETD) approach based on model-agnostic meta-learning (MAML), integrating feature selection and classification into an end-to-end framework. The FMETD approach first converts the raw traffic data into two-dimensional grayscale images. Then, FMETD trains a deep learning model (2D-CNN) using the MAML, which is to learn an optimal set of model initialization parameters for the model from a set of classification tasks consisting of grayscale images. The model with this set of parameters can detect new classes of maliciously encrypted traffic data efficiently with a few samples by a few iterations steps. The experimental results show that the FMETD approach has 99.8% accuracy for two-class classification encrypted traffic and 98.5% average accuracy for multi-classification. When the number of grayscale images of each class in the support set and validation set is reduced to 20, the accuracy of our approach to multi-class classification is 97.9% for new classes of traffic.

510 Co-design of an evidence and theory-based hospital deprescribing behaviour change intervention

Abstract Introduction 50% of older adults are prescribed a medicine that is unnecessary or harmful that should be deprescribed (1). CompreHensive geriAtRician-led MEdication Review (CHARMER) is a behaviour change intervention to equip geriatricians and pharmacists to proactively deprescribe. Five determinants of deprescribing have been prioritised and the following six Behaviour Change Techniques (BCTs) selected that now require designing (2): 1&2. Pros and cons and salience of consequences to address misconceptions that deprescribing is risky, 3. Restructuring pharmacists working to facilitate contribution to deprescribing 4. Social comparison to address misconceptions that patients/carers are resistant to deprescribing 5. Action planning to prioritise deprescribing and 6. Incentivising deprescribing. Aim We aimed to co-design the content, mode of delivery and intensity of BCTs to develop a hospital deprescribing behaviour change intervention. Methods We maximum variation sampled three hospitals to represent contextual factors likely to influence CHARMER implementation: diversity of patient population, geography, IT infrastructure maturity and include a combination of larger teaching and smaller district general hospitals. We recruited geriatricians, pharmacists and staff likely to be involved in implementation to join one co-design panel per hospital. We convened two rounds of workshops with each hospital to design BCT prototypes following the five iterative steps of design thinking: empathise, define, ideate, prototype and test. We introduced geriatrician and pharmacist personas in round 1 and panels discussed how barriers and enabler influence practice through a journey mapping exercise, followed by brainstorming ideas for how BCT(s) to address them could be operationalised. Round 2 aimed to reach consensus about which of the Round 1 BCT operationalisation ideas were most promising, and then refine their content, mode of delivery and duration/intensity. Behavioural scientist and practitioner members of the research team prepared prototypes for feedback at a final workshop attended by the three hospital panels. Results The six BCTs were operationalised into an intervention comprising: (1 & 2) 2-hour workshop with pros and cons activities and videos of salient patient cases (3) Weekly short face-to-face pharmacist:geriatrician deprescribing discussions (4) 5-minute videos of geriatricians navigating challenging deprescribing consultations (5) Hospital deprescribing action plan template (6)Dashboard updated weekly to benchmark deprescribing activity with other hospitals Automated prompts to flag high-risk patients for deprescribing and a primary and secondary care deprescribing forum were proposed as additional BCTs. These were later excluded as they address ‘memory, attention and decision processes’ and a ‘primary care social influence’ respectively which does not align with the determinants of deprescribing (1). Conclusion This study illustrates the integration of theory and co-design methodology for a hospital deprescribing behaviour change intervention. The development of an intervention that remains faithful to the underpinning mechanisms of action of behaviour change is a strength of this approach. Maximum variation sampling for hospital permitted representation of the ‘extremes’ of contextual factors but may result in an intervention that is not optimised for the majority. The intervention is undergoing feasibility testing across four English hospitals and will proceed to a definitive trial across 42 hospitals in Spring 2023 to determine effectiveness and cost effectiveness.

A foreground digital calibration algorithm for time-interleaved ADCs with low computational complexity

In front of the offset, gain, timing, and bandwidth mismatch errors, time-interleaved analog-to-digital converters (TIADCs) are usually calibrated to achieve satisfying performance. In this paper, we propose a new digital calibration approach for TIADCs, including the direction-distance search algorithm and multiplier-free gradient descent method. Compared to state-of-the-art multiplication-less techniques, this approach significantly reduces time complexity by minimizing search space dimension, subtracting the number of searches, and varying iteration step size. Notably, no multiplier is needed in the mismatch estimation, resulting in a much lower computational complexity than in previous work. For a 12-bit 4-channel TIADC, simulation results show that the signal-to-noise-and-distortion ratio is improved from 25 dB to 65 dB within the duration of 1600 samples. The proposed calibration circuit is synthesized targeting standard 28-nm technology, occupying the area of 0.014 mm2 and dissipating the power of 19 mW at 2.8 GHz clock frequency.