Search Step Research Articles

Inverse kinematics solution of demolition manipulator based on global mapping

In solving the inverse kinematics of the redundant hydraulic manipulator of the demolition robot, there are problems of multiple solutions and the contradiction between the real-time and accuracy of the calculation. A series calculation strategy combining global mapping and local search was proposed. Firstly, the inverse kinematics solution was mapped from the global space to the local space of the working point by the global neural network model, and then the inverse solution results were obtained by the local search of the gravitational search algorithm. To avoid the GSA falling into the local minima and improve the search speed and accuracy, an improved gravitational particle adaptive step search strategy was proposed to balance the exploration and development stage of the population. The simulation and experimental results show that the global than local inverse solution algorithm can avoid the multi-solution problems and ensure the stability and accuracy of the solution on the basis of real-time inversion.

Solving the capacitated vehicle routing problem with time windows via graph convolutional network assisted tree search and quantum-inspired computing

Vehicle routing problems are a class of NP-hard combinatorial optimization problems which attract a lot of attention, as they have many practical applications. In recent years there have been new developments solving vehicle routing problems with the help of machine learning, since learning how to automatically solve optimization problems has the potential to provide a big leap in optimization technology. Prior work on solving vehicle routing problems using machine learning has mainly focused on auto-regressive models, which are connected to high computational costs when combined with classical exact search methods as the model has to be evaluated in every search step. This paper proposes a new method for approximately solving the capacitated vehicle routing problem with time windows (CVRPTW) via a supervised deep learning-based approach in a non-autoregressive manner. The model uses a deep neural network to assist finding solutions by providing a probability distribution which is used to guide a tree search, resulting in a machine learning assisted heuristic. The model is built upon a new neural network architecture, called graph convolutional network, which is particularly suited for deep learning tasks. Furthermore, a new formulation for the CVRPTW in form of a quadratic unconstrained binary optimization (QUBO) problem is presented and solved via quantum-inspired computing in cooperation with Fujitsu, where a learned problem reduction based upon the proposed neural network is applied to circumvent limitations concerning the usage of quantum computing for large problem instances. Computational results show that the proposed models perform very well on small and medium sized instances compared to state-of-the-art solution methods in terms of computational costs and solution quality, and outperform commercial solvers for large instances.

A parallel, distributed memory implementation of the adaptive sampling configuration interaction method.

The many-body simulation of quantum systems is an active field of research that involves several different methods targeting various computing platforms. Many methods commonly employed, particularly coupled cluster methods, have been adapted to leverage the latest advances in modern high-performance computing. Selected configuration interaction (sCI) methods have seen extensive usage and development in recent years. However, the development of sCI methods targeting massively parallel resources has been explored only in a few research works. Here, we present a parallel, distributed memory implementation of the adaptive sampling configuration interaction approach (ASCI) for sCI. In particular, we will address the key concerns pertaining to the parallelization of the determinant search and selection, Hamiltonian formation, and the variational eigenvalue calculation for the ASCI method. Load balancing in the search step is achieved through the application of memory-efficient determinant constraints originally developed for the ASCI-PT2 method. The presented benchmarks demonstrate near optimal speedup for ASCI calculations of Cr2 (24e, 30o) with 106, 107, and 3 × 108 variational determinants on up to 16 384 CPUs. To the best of the authors' knowledge, this is the largest variational ASCI calculation to date.

Blasting vibration hazard classification and prediction research

This paper explores the hazard classification method of blasting vibration on nearby buildings. The ratio of the self-vibration frequency f0 of the protected buildings to the main vibration frequency f of blasting vibration and the ratio of the actual measured peak blasting vibration velocity to the permissible vibration velocity of blasting safety regulations are used as two indicators of the blasting vibration hazard classification method, the blasting vibration hazard index calculation formula was proposed, and the blasting vibration hazard was classified into four levels according to the blasting vibration hazard index. An open pit mine blasting project was used for monitoring, the collected data were processed by random forest method. After processing, bursting heart distance, the total explosive quantity required for one blast, the hole distance meter delay, and the row distance meter delay was selected as the input parameters; the optimal kernel function parameter gamma and c were applied to the SVR (Support Vector Regression) model by changing the search step to expand the parameter search, and improved GS-SVR (Grid Search-Support Vector Regression) model was constructed, through which peak blasting vibration velocity and vibration frequency values were predicted. The results show: improved GS-SVR is effective in predicting peak blasting vibration velocity, with the lowest relative error of 0.15% and the average error of 7.96%; the minimum relative error for the prediction of vibration frequency is 0.03% and the average error is 2.54%. The measured data in the literature of related scholars verified that the blasting vibration hazard classification method is scientific and feasible. It can provide reference and reference for blasting vibration hazard classification and prediction of similar blasting projects.

Intelligent Transfer Optimization for Ironmaking Process With Nonanalytic Constraints

In order to guarantee the smooth operation of the blast furnace ironmaking process, it is essential to consider the constraints for the optimization of this process. Due to the complexity of the process, it is challenging to obtain the description of the constraint functions and quantify the feasibility of solutions, making traditional optimization methods helpless. To address this problem, a transfer optimization framework is proposed that consists of a two-stage generation mapping algorithm, and an improved grey wolf optimizer (GWO) algorithm. The method achieves the transformation from constrained optimization to unconstrained optimization by establishing a proper mapping with distribution- and boundary-sensitive two-stage generation algorithm. Meanwhile, the density-amended GWO algorithm (DAGWO) with adaptive search steps depended on the solution density distribution is applied to locate the optimal solutions. The intelligent transfer optimization method is validated by both numerical tests and practical data, and the results demonstrate the effectiveness of the proposed algorithm.

The p-hub centre routing problem with emissions budget: Formulation and solution procedure

In recent years, the use of fossil fuels has led to a drastic increase in the emissions of CO2 and other greenhouse polluting gases. The transportation sector stands out as one of the main contributors to this pollution. Thus, several network design problems are being revisited to uncover cost and energy-efficient solutions. In this paper, we formulate and solve a p-hub centre routing problem under a CO2 emissions budget. The aim is to locate hub nodes, allocate client nodes in local tours of capacitated vehicles, and decide on vehicle speeds during transportation. The objective is to minimise the maximum time of service subject to a budget constraint on the total CO2 emissions cost of the hub network. To solve the introduced problem, we implement an efficient simulated annealing algorithm with a temperature-dependent penalty cost function, a fixed size prohibited solutions list, and a speed-based reparation heuristic. Additionally, we present a novel clustering-based construction heuristic to generate initial starting solutions for our algorithm, while hub location–allocation, vehicle routing, and speed optimisation operators are considered during the local search step. Extensive computational experiments on adapted AP data set instances show that the proposed solution approach outperforms a state-of-art solver in terms of CPU time and solution quality. Finally, we study the trade-off between the quality of service and the CO2 emission costs and discuss the effect of the CO2 budget on the design of hub networks.

Self-correction of automatic speech recognition

The rapid development of modern technology and society has raised the need for a large number of real-time translations, such as Automatic Speech Recognition (ASR) technology. One of the important factors determining the fluency of cooperation is that ASR translates the input speech into readable text so that both parties can understand each other's meaning. There is also a huge demand for and reliance on automatic speech recognition technology for modern applications. This article focuses on the word matching algorithm for backward search (WMA-BS), which requires two separate steps, one for the word matching algorithm and the other for the backward search step, for the first step, which can also be done by applying the word-level N-Gram language model. n-Gram model is a language model in which when n words appear in a text, we can use these n words to predict the structure of the text. we can use these n words to predict the structure of the text. In order to make the word-level N-gram language model suitable for the method, it is more useful for us to integrate it into shallow fusion. One of the most important parts to be addressed in the word matching algorithm is the boundary, or distance, between two words in English.

An end-to-end deep learning method for protein side-chain packing and inverse folding

Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to tackle this problem, but their speed or accuracy is still unsatisfactory. To address this, we present AttnPacker, a deep learning (DL) method for directly predicting protein side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone 3D geometry to simultaneously compute all side-chain coordinates without delegating to a discrete rotamer library or performing expensive conformational search and sampling steps. This enables a significant increase in computational efficiency, decreasing inference time by over 100× compared to the DL-based method DLPacker and physics-based RosettaPacker. Tested on the CASP13 and CASP14 native and nonnative protein backbones, AttnPacker computes physically realistic side-chain conformations, reducing steric clashes and improving both rmsd and dihedral accuracy compared to state-of-the-art methods SCWRL4, FASPR, RosettaPacker, and DLPacker. Different from traditional PSCP approaches, AttnPacker can also codesign sequences and side chains, producing designs with subnative Rosetta energy and high in silico consistency.

Smoldering charcoal detection in forest soil by multiple CO sensors

Cleaning up residual fires is an important part of forest fire management to avoid the loss of forest resources caused by the recurrence of a residual fire. Existing residual fire detection equipment is mainly infrared temperature detection and smoke identification. Due to the isolation of ground, temperature and smoke characteristics of medium and large smoldering charcoal in some forest soils are not obvious, making it difficult to identify by detection equipment. CO gas is an important detection index for indoor smoldering fire detection, and an important identification feature of hidden smoldering ground fires. However, there is no research on locating smoldering fires through CO detection. We studied the diffusion law of CO gas directly above covered smoldering charcoal as a criterion to design a detection device equipped with multiple CO sensors. According to the motion decomposition search algorithm, the detection device realizes the function of automatically searching for smoldering charcoal. Experimental data shows that the average CO concentration over the covered smoldering charcoal decreases exponentially with increasing height. The size of the search step is related to the reliability of the search algorithm. The detection success corresponding to the small step length is high but the search time is lengthy which can lead to search failure. The introduction of step and rotation factors in search algorithm improves the search efficiency. This study reveals that the average ground CO concentration directly above smoldering charcoal in forests changes with height. Based on this law, a CO gas sensor detection device for hidden smoldering fires has been designed, which enriches the technique of residual fire detection.

Reinforcement Learning for Guiding the E Theorem Prover

Automated Theorem Proving (ATP) systems search for aproof in a rapidly growing space of possibilities. Heuristicshave a profound impact on search, and ATP systems makeheavy use of heuristics. This work uses reinforcement learn-ing to learn a metaheuristic that decides which heuristic to useat each step of a proof search in the E ATP system. Proximalpolicy optimization is used to dynamically select a heuristicfrom a fixed set, based on the current state of E. The approachis evaluated on its ability to reduce the number of inferencesteps used in successful proof searches, as an indicator of in-telligent search.

Damping technique empowered robust energy flow calculation for integrated energy systems

Steady-state energy flow calculation (EFC) is the cornerstone of the long-term operation assessment of integrated energy systems (IES). The conventional EFC methods mainly focus on calculation speed and efficiency. However, the robustness is often ignored, which may result in a failure in finding the solutions under ill-conditioned and unsolvable situations. To address this issue, this paper proposes a robust EFC method for integrated electricity-gas-heat system. First, a novel robust EFC method based on Levenberg–Marquardt (L–M) method is proposed for a comprehensive formulation of the energy flow model combining the electricity, natural gas, district heating system and detailed interdependence. Further, a consecutive adaptive damping factor updating approach is proposed. It enables a secure search step to avoid the flutter in iteration process, thus enhancing computational efficiency and convergence for EFC. In addition, the influence of the direction and the size of search step are jointly considered. It is helpful to guarantee solutions for both ill-conditioned and unsolvable situations. Numerical simulation results demonstrate the strong robustness, convergence and universality of the proposed EFC method that works under diversified operation modes, e.g., well-conditioned mode, inappropriate initial values, heavy load mode, and ill-conditioned operation mode.

An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy.

Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic algorithm derived from the distant sense of hearing of sand cats, which shows excellent performance in some large-scale optimization problems. However, the SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, and the tendency to be trapped in the topical optimum. To escape these demerits, an adaptive sand cat swarm optimization algorithm based on Cauchy mutation and optimal neighborhood disturbance strategy (COSCSO) are provided in this study. First and foremost, the introduction of a nonlinear adaptive parameter in favor of scaling up the global search helps to retrieve the global optimum from a colossal search space, preventing it from being caught in a topical optimum. Secondly, the Cauchy mutation operator perturbs the search step, accelerating the convergence speed and improving the search efficiency. Finally, the optimal neighborhood disturbance strategy diversifies the population, broadens the search space, and enhances exploitation. To reveal the performance of COSCSO, it was compared with alternative algorithms in the CEC2017 and CEC2020 competition suites. Furthermore, COSCSO is further deployed to solve six engineering optimization problems. The experimental results reveal that the COSCSO is strongly competitive and capable of being deployed to solve some practical problems.

Sunflower optimization algorithm for multi-strategy-assist parameter identification of solar cell models

A novel optimization method, namely, the elite opposition learning and polynomial steps-based sunflower optimization (EOPSFO) algorithm, has been proposed to solve engineering problems. To speed up the convergence, the elite opposition-based learning and polynomial steps strategy is applied to automatically determine the search step adapted in each iteration. To verify the performance of EOPSFO, the feasibility of EOPSFO is first verified using various benchmarking and some standard optimization problems. In addition, EOPSFO is used to determine the parameters of the single diode model and double diode model. Results show that EOPSFO can be regarded as a competitive algorithm in optimization problems.

Single-objective and multi-objective optimization for variance counterbalancing in stochastic learning

Artificial neural networks have proved to be useful in a host of demanding applications, therefore becoming increasingly important in science and engineering. Large-scale problems constitute a challenging task for training neural networks using the stochastic gradient descent method and variations, which are based on the random selection of mini-batches of training points at every iteration. The challenge lies on the mandatory use of diminishing search step sizes in order to retain mild error fluctuations throughout the training set preserving so the quality of the network’s generalization capability. Variance counterbalancing was recently proposed as a remedy for addressing the diminishing step sizes in neural network training using stochastic gradient methods. It is based on the concurrent minimization of the average mean squared error of the network along with the error variance over random sets of mini-batches. Also, it promotes the use of advanced optimization algorithms instead of the slowly convergent gradient descent. The present work aims at enriching our understanding of the original variance counterbalancing approach, as well as reformulating it as a multi-objective problem by taking advantage of its bi-objective nature. Experimental analysis reveals the performances of the studied approaches and their competitive edge over the established Adam method.

Speed Adaptive Robot Trajectory Generation Based on Derivative Property of B-Spline Curve

In order to improve the safety of autonomous flight of quadrotor in three-dimensional complex environments, this paper proposes a speed adaptive motion planning method by limiting the distance between two adjacent control points based on the derivative property of B-Spline curve. First the initial B-Spline curve control points and the speed constraints based on derivative property are efficiently obtained by the front-end search that is divided into the shortest safe path search and reasonable step targeted expansion satisfying dynamic constraints. Then the back-end optimization assigns the corresponding safe flight corridor to the local trajectories according to the stage of the initial control points and uses MINVO basis ([1]) to limit the simplexes with minimum volume enclosing local trajectories. The trajectory generation is finally formulated as a Quadratically Constrained Quadratic Program (QCQP) which can be solved efficiently. Both simulations and real-world experiments validate the efficiency and performance of this method in generating safe, smooth, speed adaptive trajectories.

An INS/Geomagnetic Integrated Navigation Method for Coarse Estimation of Positioning Error and Search Area Adaption Applied to High-Speed Aircraft

The geomagnet aided inertial navigation algorithm has become one of the current research hotspots in the field of aircraft navigation. It can overcome the shortcomings of pure inertial navigation error accumulation over time and achieve long-term, autonomous, and all-weather navigation. The traditional iterative closest contour point (ICCP) algorithm, however, requires high sensor performance, has poor tolerance to noise, and has a complex algorithm with poor real-time performance. The timeliness of the traditional magnetic contour matching (MAGCOM) algorithm is related to the search area, the matching search area is fixed, and the algorithm is inflexible. To address the above problems, this article proposes a geomagnetic navigation method based on coarse estimation of navigation error and search area adaption. When the aircraft flies into the geomagnetic database region, the navigation error is roughly estimated based on the error characteristics of the inertial navigation system. Considering the influence of noise and uncertainty, three times the value of the estimated error is used as the edge length of the geomagnetic matching search area, and geomagnetic matching is performed for positioning. The product of velocity and time provided by the inertial navigation system is used as a constraint to determine whether the geomagnetic matching results are abnormal. If multiple consecutive geomagnetic matching results are normal, the navigation system automatically reduces the matching search area and search step to improve the matching accuracy and timeliness. Simulation experiments show that the accuracy of the adaptive search area geomagnetic matching method proposed in this article can reach within 200 m.

Setting the Initial Value for Single Exponential Smoothing and the Value of the Smoothing Constant for Forecasting Using Solver in Microsoft Excel

Although single exponential smoothing is a popular forecasting method for a wide range of applications involving stationary time series data, consistent rules about choosing the initial value and determining the value for the smoothing constant (α) are still required, because they directly impact the forecast accuracy. The purpose of this study is to mitigate these shortcomings. First, a new method for setting the initial value by weighting is derived, and its performance is compared with two other traditional methods. Second, the optimal (α) was automatically solved using Solver in Microsoft Excel, after which 𝛼 was determined by minimizing the mean squared error (MSE). This was accomplished by comparing the 𝛼 from Solver with step search by setting the smoothing constant by varying its value from 0.001 to 1 in increments of 0.001 and then choosing the optimal 𝛼 value from this range that has the lowest MSE. The experimental results show that 𝛼 from Solver and the optimal 𝛼 with step search are not different, and the initial value set by the proposed method outperformed the existing ones regarding the MSE.

Machine-learning accelerated annealing with fitting-search style for multicomponent alloy structure predictions

Structural prediction for the discovery of novel materials is a long sought-after goal of computational physics and materials sciences. The success is rather limited for methods such as the simulated annealing method (SA) that require expensive density functional theory (DFT) calculations and follow unintelligent search paths. Here a machine-learning based crystal combinatorial optimization program (CCOP) with a fitting-search style is proposed to drastically improve the efficiency of structural search in SA. CCOP uses a graph neural network energy prediction model to reduce the DFT cost and a deep reinforcement learning algorithm to direct the search path. Tests on six multicomponent alloys show the energy prediction model is capable of extracting the bonding characteristics of the complex alloys to achieve interpretability. It also achieves high accuracy with a tiny training set (an increment of 30 samples per iteration) by active learning in less than five iterations. Comparison with a few conventional methods shows that CCOP finds the lowest-energy structures with the smallest number of search steps. CCOP cuts the computing cost of SA by two orders of magnitude, while providing better search results than SA. CCOP is promising for serving as a broadly applicable tool for the efficient crystal structure predictions.

Fast Autofocusing Algorithm Based on Variable Step Search and Curve Fitting Applied in the Near- and Far-Field Detection System With a Common Optical Path

We design a set of near- and far-field detection system with a common optical path. And we propose a fast autofocusing algorithm based on variable step search and curve fitting for defocus aberration correction of the system. By denoising the image with the image processing method, improving the traditional hill-climbing search method with variable step search and curve fitting, and taking the sum of the squares of grayscale differences and the image similarity function as criteria, we effectively improve the speed and accuracy of autofocus. The theoretical positioning accuracy reaches 0.076 μm, the code statement frequency count of the algorithm is reduced by 2MN times, and the resolution enhances from 28.51 lp mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> to 71.8 lp mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> . Numerical simulations and experimental results have validated the effectiveness of the proposed method.

Impact of educational and psychological interventions compared with standard care in college/university students with autism spectrum disorder: a systematic review protocol.

This review will explore the impact of educational and psychological interventions on educational, social, behavioral, and mental health outcomes in students with autism spectrum disorder in tertiary education. This systematic review will inform a new guideline on support for students with autism spectrum disorder in the tertiary education environment. These students face multiple educational, behavioral, social, and health-related problems that require effective interventions. Participants are students with autism spectrum disorder in a tertiary education study program. Educational and psychological interventions will be included, such as accommodations, meta-cognitive and self-regulation training, psychological counseling, social skills training, and peer-mentoring/academic coaching. The comparator will be standard care. The study outcomes will include academic drop-out rates and evaluations, learning skills, social skills and social engagement, behavior, mental health (including anxiety, stress, and depression), and employment after graduation. This review will consider quantitative studies only. A 3-step search strategy will be used to find both published and unpublished studies in MEDLINE, CINAHL, APA PsycINFO, SocINDEX, Web of Science, Clinical Trials, ProQuest Dissertations and Theses, Open Dissertations, ERIC, WHO ICRTP, and Google Scholar. There will be no date or language limitations. All stages of article screening, critical appraisal, and data extraction will be conducted by 2 independent reviewers with the resolution of any disagreements done via consensus or through a senior reviewer. If possible, the included studies will be pooled using meta-analysis. The degree of certainty of the evidence will be assessed according to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. PROSPERO CRD42022323554.