Using machine learning to predict optimal electromagnetic induction instrument configurations for characterizing the shallow subsurface

Electromagnetic induction (EMI) is used widely for hydrological and other environmental studies. The apparent electrical conductivity (ECa), which can be mapped efficiently with EMI, correlates with a variety of important soil attributes. EMI instruments exist with several configurations of coil spacing, orientation, and height. There are general, rule-of-thumb guides to choose an optimal instrument configuration for a specific survey. The goal of this study was to provide a robust and efficient way to design this optimization task. In this investigation, we used machine learning (ML) as an efficient tool for interpolating among the results of many forward model runs. Specifically, we generated an ensemble of 100 000 EMI forward models representing the responses of many EMI configurations to a range of three-layer subsurface models. We split the results into training and testing subsets and trained a decision tree (DT) with gradient boosting (GB) to predict the subsurface properties (layer thicknesses and EC values). We further examined the value of prior knowledge that could limit the ranges of some of the soil model parameters. We made use of the intrinsic feature importance measures of machine learning algorithms to identify optimal EMI designs for specific subsurface parameters. The optimal designs identified using this approach agreed with those that are generally recognized as optimal by informed experts for standard survey goals, giving confidence in the ML-based approach. The approach also offered insight that would be difficult, if not impossible, to offer based on rule-of-thumb optimization. We contend that such ML-informed design approaches could be applied broadly to other survey design challenges.

Comparision of the walk techniques for fitness state space analysis in vehicle routing problem

The Vehicle Routing Problem (VRP) is a highly researched discrete optimization task. The first article dealing with this problem was published by Dantzig and Ramster in 1959 under the name Truck Dispatching Problem. Since then, several versions of VRP have been developed. The task is NP difficult, it can be solved only in the foreseeable future, relying on different heuristic algorithms. The geometrical property of the state space influences the efficiency of the optimization method. In this paper, we present an analysis of the following state space methods: adaptive, reverse adaptive and uphill-downhill walk. In our paper, the efficiency of four operators are analysed on a complex Vehicle Routing Problem. These operators are the 2-opt, Partially Matched Crossover, Cycle Crossover and Order Crossover. Based on the test results, the 2-opt and Partially Matched Crossover are superior to the other two methods.

A Novel Approach of Synchronization of Microgrid with a Power System of Limited Capacity

Currently, active networks called microgrids are formed on the basis of local power supply systems with a small share of distributed generation. Microgrids operating in an island mode, in some cases, have the ability to transfer electricity excess to an external network leading to a synchronization requirement; thus, the optimization task in terms of the system’s synchronization must be considered. This paper proposes a method for obtaining synchronization between microgrids and power systems of limited capacity based on a passive synchronization algorithm, allowing us to connect a microgrid to an external power system with a minimum impact moment on the shaft of the generating equipment. The algorithm application was demonstrated by considering a real-life object in Tajikistan. The simulation was carried out on RastrWin3. The obtained results show that the microgrid generator is connected to an external power system at an angle of 0.3° and a power surge of 29 kW, unlike the classical synchronization algorithm with an angle of 6.8° and a power surge of 154 kW (a reduction of the shock moment by more than five times). The proposed synchronization method allows us to reduce the resource consumption of the generating equipment and increase the reliability and efficiency of the functioning units of the examined power system.

Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection

An essential criterion for the proper implementation of case-control studies is selecting appropriate case and control groups. In this article, a new simulated annealing-based control group selection method is proposed, which solves the problem of selecting individuals in the control group as a distance optimization task. The proposed algorithm pairs the individuals in the n-dimensional feature space by minimizing the weighted distances between them. The weights of the dimensions are based on the odds ratios calculated from the logistic regression model fitted on the variables describing the probability of membership of the treated group. For finding the optimal pairing of the individuals, simulated annealing is utilized. The effectiveness of the newly proposed Weighted Nearest Neighbours Control Group Selection with Simulated Annealing (WNNSA) algorithm is presented by two Monte Carlo studies. Results show that the WNNSA method can outperform the widely applied greedy propensity score matching method in feature spaces where only a few covariates characterize individuals and the covariates can only take a few values.

Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization

Successful engineering of a microbial host for efficient production of a target product from a given substrate can be viewed as an extensive optimization task. Such a task involves the selection of high activity enzymes as well as their gene expression regulatory control elements (i.e., promoters and ribosome binding sites). Finally, there is also the need to tune expression of multiple genes along a heterologous pathway to relieve constraints from rate-limiting step and help reduce metabolic burden on cells from unnecessary over-expression of high activity enzymes. While the aforementioned tasks could be performed through combinatorial experiments, such an approach incurs significant cost, time and effort, which is a handicap that can be relieved by application of modern machine learning tools. Such tools could attempt to predict high activity enzymes from sequence, but they are currently most usefully applied in classifying strong promoters from weaker ones as well as combinatorial tuning of expression of multiple genes. This perspective reviews the application of machine learning tools to aid metabolic pathway optimization through identifying challenges in metabolic engineering that could be overcome with the help of machine learning tools.

Macroscopic Real-Time Timetable Rescheduling Approach for High-Speed Railway under Complete Blockages Using a Three-Stage Algorithm

The rescheduling of train timetables under a complete blockage is a challenging process, which is more difficult when timetables contain lots of trains. In this paper, a mixed integer linear programming (MILP) model is formulated to solve the problem, following the rescheduling strategy that blocked trains wait inside the stations during the disruption. When the exact end time of the disruption is known, trains at stations downstream of the blocked station can depart early. The model aims at minimizing the total delay time and the total number of delayed trains under the constraints of station capacities, activity time, overtaking rules, and rescheduling strategies. Because there are too many variables and constraints of the MILP model to be solved, a three-stage algorithm is designed to speed up the solution. Experiments are carried out on the Beijing–Guangzhou high-speed railway line from Chibibei to Guangzhounan. The original timetable contains 162 trains, including 29 cross-line trains and 133 local trains. The simulation results show that our model can handle the optimization task of the timetable rescheduling problem very well. Compared with the one-stage algorithm, the three-stage algorithm is proved to greatly improve the solving speed of the model. All instances can get a better optimized disposition timetable within 450 to 600 s, which is acceptable for practical use.

Analysis of optimization methods in marine objects control synthesis

The article presents four main chapters that allow you to formulate an optimization task and choose a method for solving it from static and dynamic optimization methods to single-criterion and multi-criteria optimization. In the group of static optimization methods, the methods are without constraints and with constraints, gradient and non-gradient and heuristic. Dynamic optimization methods are divided into basic - direct and indirect and special. Particular attention has been paid to multi-criteria optimization in single-object approach as static and dynamic optimization, and multi-object optimization in game control scenarios. The article shows not only the classic optimization methods that were developed many years ago, but also the latest in the field, including, but not limited to, particle swarms.

Model Updating of a Hydraulic Chain Oscillator

A hydraulic system in form of a chain oscillator has been set up, measured and described by a mathematical model in the frequency domain. By analysing the hydraulic circuit, it is possible to derive a suitable mathematical model, with the focus on keeping it as simple as possible, but still representing all the important properties. Unknown system parameters are identified with the help of a two-step procedure using a non-linear optimization task. Excited hydraulically, by adjusting the flow rate, the pressures in this setup are measured. From this data, frequency responses between flow rate and the pressures can be calculated, which are used to validate the mathematical model and the identification strategy. Different system configurations were investigated to further confirm the validity of the model and the identification methodology.

Development of a Power and Communication Bus Using HIL and Computational Intelligence

This paper deals with the development of a power and communication bus named DLN (Device Lightweight Network) that can be seen as a new interface with auto-addressing functionality to transfer power and data by means of two wires in modern cars. The main research goal of this paper is to elaborate a new method based on a hardware in the loop technique aided by computational intelligence algorithms in order to search for the optimal structure of the communication modules, as well as optimal features of hardware parts and the values of software parameters. The desired properties of communication modules, which have a strong influence on the performance of the bus, cannot be found using a classical engineering approach due to the large number of possible combinations of configuration of the hardware and software parts of the whole system. Therefore, an HIL-based optimization method for bus prototyping is proposed, in which the optimization task is formulated as a multi-criteria optimization problem. Several criterion functions are proposed, corresponding to the automotive objectives and requirements. Different soft computing optimization algorithms, such as a single-objective/multi-objectives evolutionary algorithm and a particle swarm optimization algorithm, are applied to searching for the optimal solution. The verification study was carried out in order to show the merits and limitations of the proposed approach. Attention was also paid to the problem of the selection of the behavioural parameters of the heuristic algorithms. The overall results proved the high practical potential of the DLN, which was developed using the proposed optimization method.

Energy optimization during walking involves implicit processing

ABSTRACT Gait adaptations, in response to novel environments, devices or changes to the body, can be driven by the continuous optimization of energy expenditure. However, whether energy optimization involves implicit processing (occurring automatically and with minimal cognitive attention), explicit processing (occurring consciously with an attention-demanding strategy) or both in combination remains unclear. Here, we used a dual-task paradigm to probe the contributions of implicit and explicit processes in energy optimization during walking. To create our primary energy optimization task, we used lower-limb exoskeletons to shift people's energetically optimal step frequency to frequencies lower than normally preferred. Our secondary task, designed to draw explicit attention from the optimization task, was an auditory tone discrimination task. We found that adding this secondary task did not prevent energy optimization during walking; participants in our dual-task experiment adapted their step frequency toward the optima by an amount and at a rate similar to participants in our previous single-task experiment. We also found that performance on the tone discrimination task did not worsen when participants were adapting toward energy optima; accuracy scores and reaction times remained unchanged when the exoskeleton altered the energy optimal gaits. Survey responses suggest that dual-task participants were largely unaware of the changes they made to their gait during adaptation, whereas single-task participants were more aware of their gait changes yet did not leverage this explicit awareness to improve gait adaptation. Collectively, our results suggest that energy optimization involves implicit processing, allowing attentional resources to be directed toward other cognitive and motor objectives during walking.