Exhaustive Search Approach Research Articles

A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy

This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted by the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to the LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels, whereas in both the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance.

Measurements of F1‐ Region Ionosphere State Variables at Arecibo Through Quasi Height‐Independent Exhaustive Fittings of the Incoherent Scatter Ion‐Line Spectra

AbstractWe discuss an exhaustive search approach to fit the incoherent scatter spectrum (ISS) in the F1‐region for molecular ion fraction (fm), ion temperature (Ti), and electron temperature (Te). The commonly used “full profile” approach for F1‐region measurements parameterizes the molecular ion fraction as a function of altitude and fits all the related heights for the state variables. In our approach, we fit the ISS at each height for fm, Ti, Te, and ion velocity (Vi) independently. Our exhaustive search method finds all the major local minima at each altitude. Although a parameterized function is used to guide the algorithm in finding the best solution, the fitting parameters retain their local characteristics. Despite that fitting fm, Ti, and Te without constraints requires Doppler shift to be accurately determined and the ISS signal‐to‐noise ratio higher than the full‐profile method, simulations show that Ti, Te, and fm can be recovered within a few percent accuracy with a moderate signal‐to‐noise ratio. We apply the exhaustive search approach to the Arecibo high‐resolution incoherent scatter radar data taken on 13 September 2014. The derived ion and electron temperatures are sensitive enough to reveal thermosphere gravity waves commonly seen in the electron density previously. Our method is more robust than previous height‐independent fitting methods. Comparison with another Arecibo program indicates our results are likely more accurate. Simultaneous high‐resolution measurements of Ti, Te, fm, Vi, and electron concentration (Ne) in our approach open new opportunities for synergistic studies of the F1‐region dynamics and chemistry.

A New Alternating Suboptimal Dynamic Programming Algorithm with Applications for Feature Selection

Feature selection is predominantly used in machine learning tasks, such as classification, regression, and clustering. It selects a subset of features (relevant attributes of data points) from a larger set that contributes as optimally as possible to the informativeness of the model. There are exponentially many subsets of a given set, and thus, the exhaustive search approach is only practical for problems with at most a few dozen features. In the past, there have been attempts to reduce the search space using dynamic programming. However, models that consider similarity in pairs of features alongside the quality of individual features do not provide the required optimal substructure. As a result, algorithms, which we will call suboptimal dynamic programming algorithms, find a solution that may deviate significantly from the optimal one. In this paper, we propose an iterative dynamic programming algorithm, which invertsthe order of feature processing in each iteration. Such an alternating approach allows for improving the optimization function by using the score from the previous iteration to estimate the contribution of unprocessed features. The iterative process is proven to converge and terminates when the solution does not change in three successive iterations or when the number of iterations reaches the threshold. Results in more than 95% of tests align with those of the exhaustive search approach, being competitive and often superior to the reference greedy approach. Validation was carried out by comparing the scores of output feature subsets and examining the accuracy of different classifiers learned on these features across nine real-world applications, considering different scenarios with various numbers of features and samples. In the context of feature selection, the proposed algorithm can be characterized as a robust filter method that can improve machine learning models regardless of dataset size. However, we expect that the idea of alternating suboptimal optimization will soon be generalized to tasks beyond feature selection.

Alleviating morning commute congestion through school start time optimization: An exhaustive search approach

One of the major causes of road Traffic congestion in urban transportation networks during the morning rush hour can be attributed to the fact that schools start at the same time. In a modern city center, a large portion of commuters need to drop off their children at school before going to work. When the schools start at the same time, commuters pursuing school-related trips enter the network simultaneously, creating a high demand that the network cannot directly accommodate, resulting in performance degradation. To remedy this shortcoming, we propose a novel approach that regulates the start time of schools, anticipating the emergence of congestion during the morning commute. We consider regional Traffic dynamics to capture the movement of vehicles in the urban network through the Macroscopic Fundamental Diagram. The related problem is formulated as a bi-objective mixed integer nonlinear program whose target is to jointly minimize i) the total time spent by all vehicles inside the network and ii) the associated overall delay observed between the initial and the shifted start time of each school located in the urban network. Dealing with such optimization problems is challenging due to their combinatorial and non-convex nature. To this end, we develop an Exhaustive Search Algorithm, which pinpoints the school start time pair that minimizes the total time spent objective metric. We demonstrate that by properly selecting the school start time, we can shift the peak demand to less congested periods and, as a result, alleviate congestion.

Quantum‐Based Combinatorial Optimization for Optimal Sensor Placement in Civil Structures

Over the last decade, concepts such as industry 4.0 and the Internet of Things (IoT) have contributed to the increase in the availability and affordability of sensing technology. In this context, structural health monitoring (SHM) arises as an especially interesting field to integrate and develop these new sensing capabilities, given the criticality of structural application for human life and the elevated costs of manual monitoring. Due to the scale of structural systems, one of the main challenges when designing a modern monitoring system is the optimal sensor placement (OSP) problem. The OSP problem is combinatorial in nature, making its exact solution infeasible in most practical cases, usually requiring the use of metaheuristic optimization techniques. While approaches such as genetic algorithms (GAs) have been able to produce significant results in many practical case studies, their ability to scale up to more complex structures is still an area of open research. This study proposes a novel quantum‐based combinatorial optimization approach to solve the OSP problem approximately, within the context of SHM. For this purpose, a quadratic unconstrained binary optimization (QUBO) model formulation is developed, taking as a starting point of the modal strain energy (MSE) of the structure. The framework is tested using numerical simulations of Warren truss bridges of varying scales. The results obtained using the proposed framework are compared against exhaustive search approaches to verify their performance. More importantly, a detailed discussion of the current limitations of the technology and the future paths of research in the area is presented to the reader.

MCClusteringSM: An approach for the multicriteria clustering problem based on a credibility similarity measure

Multicriteria clustering problem has been studied and applied scarcely. When a multicriteria clustering problem is tackled with an outranking approach, it is necessary to include preferences of decision makers on the raw dataset, e.g., weights and thresholds of the evaluation criteria. Then, it is necessary to conduct a process to obtain a comprehensive model of preferences represented in a fuzzy or crisp outranking relation. Subsequently, the model can be exploited to derive a multicriteria clustering. This work presents an exhaustive search approach using a credibility similarity measure to exploit a fuzzy outranking relation to derive a multicriteria clustering. The work includes two experimental designs to evaluate the performance of the algorithm. Results show that the proposed method has good performance exploiting fuzzy outranking relations to create the clusterings.

Hammerstein–Wiener Model Identification for Oil-in-Water Separation Dynamics in a De-Oiling Hydrocyclone System

To reduce the environmental impact of offshore oil and gas, the hydrocarbon discharge regulations tend to become more stringent. One way to reduce the oil discharge is to improve the control systems by introducing new oil-in-water (OiW) sensing technologies and advanced control. De-oiling hydrocyclones are commonly used in offshore facilities for produced water treatment (PWT), but obtaining valid control-oriented models of hydrocyclones has proven challenging. Existing control-oriented models are often based on droplet trajectory analysis. While it has been demonstrated that these models can fit steady-state separation efficiency data, the dynamics of these models have either not been validated experimentally or only describe part of the dynamics. In addition to the inlet OiW concentration, they require the droplet size distribution to be measured, which complicates model validation as well as implementation. This work presents an approach to obtain validated nonlinear models of the discharge concentration, separation efficiency, and discharge rate, which do not require the droplet size distribution to be measured. An exhaustive search approach is used to identify control-oriented polynomial-type Hammerstein–Wiener (HW) models of de-oiling hydrocyclones based on concentration measurements from online OiW monitors. To demonstrate the effectiveness of this modeling approach, a PI controller is designed using the Skogestad internal model control (SIMC) tuning rules to control the discharge OiW concentration directly. The identification experiment emulates an offshore PWT system with installed OiW monitors, which is realistic with the legislative incentive to include online OiW discharge measurements. The proposed approach could enable the application of OiW-based control on existing offshore PWT facilities, resulting in improved de-oiling performance and reduced oil discharge.

Specification Mining over Temporal Data

Current specification mining algorithms for temporal data rely on exhaustive search approaches, which become detrimental in real data settings where a plethora of distinct temporal behaviours are recorded over prolonged observations. This paper proposes a novel algorithm, Bolt2, based on a refined heuristic search of our previous algorithm, Bolt. Our experiments show that the proposed approach not only surpasses exhaustive search methods in terms of running time but also guarantees a minimal description that captures the overall temporal behaviour. This is achieved through a hypothesis lattice search that exploits support metrics. Our novel specification mining algorithm also outperforms the results achieved in our previous contribution.

Systematic hyperparameter selection in Machine Learning-based engine control to minimize calibration effort

For automotive powertrain control systems, the calibration effort is exploding due to growing system complexity and increasingly strict legal requirements for greenhouse gas and real-world pollutant emissions. These powertrain systems are characterized by their highly dynamic operation, so transient performance is key. Currently applied control methods require tuning of an increasing number of look-up tables and of parameters in the applied models. Especially for transient control this state-of-the-art calibration process is unsystematic and requires a large development effort. Also, embedding models in a controller can set challenging requirements to production control hardware. In this work, we assess the potential of Machine Learning to dramatically reduce the calibration effort in transient air path control development. This is not only done for the existing benchmark controller, but also for a new preview controller. In order to efficiently realize preview, a strategy is proposed where the existing reference signal is shifted in time. These reference signals are then modeled as a function of engine torque demand using a Long Short-Term Memory (LSTM) neural network, which can capture the dynamic input–output relationship. A multi-objective optimization problem is defined to systematically select hyperparameters that optimize the trade-off between model accuracy, system performance, calibration effort and computational requirements. This problem is solved using an exhaustive search approach. The control system performance is validated over a transient driving cycle. For the LSTM-based controllers, the proposed calibration approach achieves a significant reduction of 71% in the control calibration effort compared to the benchmark process. The expert effort and turbocharger experiments used in calibrating transient compensation maps in physics-based feedforward controller are replaced by little simulation time and parametrization effort in ML-based controller, which requires significantly less expert effort and system knowledge compared to benchmark process. The best trade-off between multi-objective cost terms is achieved with one layer and 32 cells LSTM neural network for both non-preview and preview control. For non-preview control, a comparable control system performance is achieved with the LSTM-based controller, while 5% reduction in cumulative NOx emissions and similar fuel consumption is achieved with preview controller.

Energy-efficient UAV-wireless networks for data collection

In this study, a singular unmanned aerial vehicle (UAV) is utilized to gather data from a set of ground sensors in wireless networks. The main objective is to minimize the total energy consumption of the ground sensors by finding the UAV’s optimal trajectory through an optimization problem. Two distinct channel models are implemented to represent the connection between the UAV and the sensors, which are compatible with fourth-generation (4G), fifth-generation (5G), and beyond 5G (B5G) systems. The optimization problem’s constraints involve ensuring the quality of service (QoS) and limiting the transmitted power of each ground sensor. To solve the intractable optimization problem, three different approaches are employed to obtain the UAV’s trajectory: (1) exhaustive search (ES) approach, (2) particle swarm optimization (PSO) approach, and (3) fixed placement approach. Finally, the proposed approaches’ effectiveness is validated using simulation results.

Dynamic rate and channel use allocation for cooperative wireless networks

In this paper, we investigate the performance of link adaptation (LA) algorithms for cooperative multiple access multiple relay networks. In the first transmission phase, the sources transmit in turn in consecutive time slots. In the second cooperative retransmission phase, the destination schedules one relaying node to send redundancies. LA is a fundamental mechanism allowing the source nodes to adapt to the radio channel conditions. In this paper, we investigate the problem of rate allocation using a centralized method, where the destination is the central node that determines the source rates. Furthermore, and in sharp contrast with existing cooperative transmission schemes, we consider the packet size to be time-varying. In other words, we propose adapting the slot duration of each of the sources during the transmission phase as a new degree of freedom. Accordingly, the LA process determines the rate and the time slot duration of each source. We consider a practical performance metric namely, the network spectral efficiency. This metric being difficult to be optimized (given the complex multi-variable optimization problem), we propose best-response dynamic (BRD) algorithms to solve the rate and time duration allocation. Then, we conduct a thorough performance analysis using Monte-Carlo simulations. Our numerical results validate the effectiveness of the proposed BRD algorithms as they yield performance close to the corresponding exhaustive search approach. Furthermore, the results ensure the gain of exploiting the new degree of freedom of the variable slot duration.

Surrogate-assisted uncertainty modeling of embankment settlement

The structural optimization of basal reinforced piled embankments is usually conducted by examining design alternatives while ignoring the inherent variability of soil properties and studying only a limited number of structural variables. As an alternative, this paper proposes a hybrid modeling framework to introduce soil property uncertainty into embankment settlement calculations. This is important because settlement is critical in the serviceability assessments considered during structural optimization. The proposed framework consists of uncertainty modeling, finite element method, surrogate modeling, and probabilistic analysis. More specifically, a neural network with Monte Carlo dropout that accounts for uncertainty is employed to correlate the soil properties which affect the long-term performance of embankments over soft clay. Next, a coupled finite element analysis is performed using two constitutive soil parameters generated by the neural network to predict post-construction settlements. Combining the finite element (input source) with a surrogate model (data-driven approximation) yields substantial settlement outcomes for structure evaluations. A case study is then used to validate the effectiveness and applicability of this framework. Finally, an exhaustive search approach is used to design a cost-effective improved ground within ultimate and serviceability limit state constraints. Pareto front is computed using a logistic function at different settlement reliability levels.

Optimal User Pairing Approach for NOMA-Based Cell-Free Massive MIMO Systems

This study investigates a non-orthogonal multiple access (NOMA)-assisted cell-free massive multiple-input multiple-output (MIMO) system, considering the impact of both individual and linear-combination channel estimations. To make the best use of NOMA as an enabler for cell-free massive MIMO systems, user pairing should be employed effectively. Random user pairing naturally leads to a non-optimal solution, whereas an exhaustive search approach is unfavorable for practical systems owing to the high complexity. In this study, we propose an optimal user pairing strategy to group users that jointly optimize the minimum downlink rate per user and power allocation at an acceptable cost of complexity. To address this problem, we first relax the binary variables to continuous variables and then develop an iterative algorithm based on the inner approximation method, yielding at least one locally optimal solution. Numerical results show that the proposed user pairing algorithm outperforms existing counterparts, such as conventional beamforming, random pairing, far pairing, and close-pairing strategies, while it can be performed dynamically, that is, two arbitrary users satisfying the formulated problem can be paired regardless of geographical distance. Finally, our approach demonstrates that the combination channel estimation-based NOMA-assisted cell-free massive MIMO achieves the best result in terms of the downlink rate per user when associated with the proposed algorithm.

Solving Yin-Yang Puzzles Using Exhaustive Search and Prune-and-Search Algorithms

We investigate some algorithmic and mathematical aspects of Yin-Yang/Shiromaru-Kuromaru puzzles. Specifically, we discuss two algorithms for solving arbitrary Yin-Yang puzzles, namely the exhaustive search approach and the prune-and-search technique. We show that both algorithms have an identical asymptotic running time of O(max{mn, 2^(mn−h)}) for finding all solutions of a Yin-Yang instance with h hints of size m x n. Nevertheless, our experiments show that the practical running time of the prune-and-search technique outperforms the conventional exhaustive search approach.

Navigation of Multiple Robots in Formative Manner in an Unknown Environment using Artificial Potential Field Based Path Planning Algorithm

This paper proposes a new algorithm for multiple robots that can navigate from source to goal position in a coordinated and formative manner in an optimum path and time, efficiently avoiding static and dynamic obstacles. The proposed algorithm is applied here to track surveillance for two and four robots from starting point to the goal point in an obstacle environment without collision. This algorithm is inspired by the concept of the Artificial Potential Field (APF) method and behavior-based approximate reasoning. An exhaustive search approach is used to avoid the randomly appeared obstacles, approximately estimate positions of obstacles, thus reaching the surveillance point and then tracking the allotted path for the robots. The robots are governed to move in real-time, maintaining a formation between them to track the assigned path cooperatively. The performance of this algorithm is validated in a real-time and simulation environment using MATLAB/Simulink. It is observed from the results of performance measuring parameters path length and travel time that the proposed algorithm outperforms than existing A*, RRT, and GA algorithm.

Leveraging genetic algorithm to address multi-failure localization in optical networks

Fault management has long been an indispensable component for controlling and managing telecommunication networks. To prevent huge data losses, it is necessary to develop a fast and efficient fault localization mechanism. In this work, we study the problem of multi-failure localization in transparent optical networks. In this context, a correlation-based approach is introduced to exploit the quality of transmission of acquired lightpaths to localize the faulty links. The proposed search-based framework can be implemented by leveraging any search algorithm. One may utilize the exhaustive search method to localize faulty links more accurately, but at the cost of taking more time. On the other hand, one may utilize intelligent search methods with the aim of reducing the required time for localization at the expense of accuracy. However, we propose to use both of the search approaches together. In this way, faulty links are first localized by the intelligent search methods to reroute and restore the failed traffic as fast as possible to prevent further loss of data. To this aim, a genetic algorithm (GA) is proposed to search among the suspected links. Subsequently, exhaustive search method can be utilized to localize failures more accurately without time constraint and then send technicians to the right site to recover the faulty links. The obtained results reveal that the proposed GA approach achieves overall high localization accuracy (98.6%–100%) that is insignificantly affected as the traffic load decreases. Dual and triple-failure incidents are localized within 42–80 ms and 596–2180 ms, respectively. It is shown that the mean time required for localizing failures using the GA search algorithm is significantly lower than exhaustive search approach by several orders of magnitude. Hence, the proposed GA-based fault localization algorithm can reduce the average time required to restore the traffic in case of failures, applicable for the restoration applications.

A method for mixed additive and multiplicative random error models with inequality constraints in geodesy

In the geodetic data processing field, most methods for dealing with inequality constraints model are based on additive random error (ARE) models, and there have been few studies on mixed additive and multiplicative random error (MAAMRE) models with inequality constraints. To address this problem, a MAAMRE model with inequality constraints is first established based on the definition of inequality constraint equations, and then, a corresponding parameter estimation algorithm is proposed based on the idea of an exhaustive search method. In addition, considering a MAAMRE model for an ill-posed problem, an iterative regularization solution for an ill-posed MAAMRE model is first derived, and then, a specific parameter estimation algorithm for an ill-posed MAAMRE model with inequality constraints is further proposed by applying the exhaustive search approach. Finally, the feasibility and advantages of the proposed algorithms are verified by global positioning system (GPS) elevation fitting model and digital terrain model (DTM) examples.Graphical

Blind CFO Estimation in a UFMC System Aided With Virtual Carriers

This letter studies the design of blind estimation technique for carrier frequency offset (CFO) in a universal filtered multicarrier (UFMC) system. We first propose a preliminary scheme, and then further devise a series of improved approaches used in the novel two-stage solution, including stages of initial and residual CFO estimation. For Stage 1, we devise two useful estimation algorithms, respectively. Especially, the latter approach using the frequency-domain received signal avoids intensive computation of the cost function. For Stage 2, we derive a closed-form formula used in the iterations to estimate the residual CFO. It can be shown that the proposed schemes not only provide a performance comparable to that of the non-blind least square-based scheme, but also enjoy much lower computational complexity compared with the conventional exhaustive search approach. The conducted computer simulation results verify the superior performance of the blind CFO estimation for UFMC.

Outage Energy Efficiency Maximization for UAV-Assisted Energy Harvesting Cognitive Radio Networks

The UAV-assisted energy harvesting cognitive wireless network has the feature of fast layout, remarkable spectrum efficiency (SE) and energy efficiency (EE), thereby being envisioned as one significant cutting-edge communication technology in the 5G/6G era. In this study, we consider an UAV-assisted energy harvesting cognitive radio network (UAV-EH-CRN), where an UAV is employed as one cognitive user (CU), hovering in the air to perform spectrum sensing and communicating with the dedicated receiver (DR) on the ground. Based on the sensing results of the primary user (PU), the UAV can adaptively adjust power to transmit with the DR. Moreover, by harvesting renewable energy, the UAV can replenish energy actively for transmission. By introducing outage rate (OR) for guaranteeing the UAVs SE requirements, our goal is to maximize the outage energy efficiency (OEE) for the UAV-EH-CRN, subject to constraints of energy, transmission power, and interference power. To make the non-convex OEE maximization problem solvable, one resource allocation policy portrayed as outage related convex approximation strategy (OR-CAS) is presented. Specifically, the OEE problem is firstly converted into a tractable counterpart by the proposed OR-CAS. Then, making use of the Lagrange duality method and one-dimensional linear programming, the optimal resource allocation solution for the OEE maximization is obtained. Simulation results show that our scheme enable the UAV-EH-CRN to adapt to various on-demand outage SE scenarios by setting different OR thresholds. Moreover, our algorithm can obtain significant EE gains and less time expenditure in comparison with the traditional exhaustive search approach.

ExhauFS: exhaustive search-based feature selection for classification and survival regression.

Feature selection is one of the main techniques used to prevent overfitting in machine learning applications. The most straightforward approach for feature selection is an exhaustive search: one can go over all possible feature combinations and pick up the model with the highest accuracy. This method together with its optimizations were actively used in biomedical research, however, publicly available implementation is missing. We present ExhauFS—the user-friendly command-line implementation of the exhaustive search approach for classification and survival regression. Aside from tool description, we included three application examples in the manuscript to comprehensively review the implemented functionality. First, we executed ExhauFS on a toy cervical cancer dataset to illustrate basic concepts. Then, multi-cohort microarray breast cancer datasets were used to construct gene signatures for 5-year recurrence classification. The vast majority of signatures constructed by ExhauFS passed 0.65 threshold of sensitivity and specificity on all datasets, including the validation one. Moreover, a number of gene signatures demonstrated reliable performance on independent RNA-seq dataset without any coefficient re-tuning, i.e., turned out to be cross-platform. Finally, Cox survival regression models were used to fit isomiR signatures for overall survival prediction for patients with colorectal cancer. Similarly to the previous example, the major part of models passed the pre-defined concordance index threshold 0.65 on all datasets. In both real-world scenarios (breast and colorectal cancer datasets), ExhauFS was benchmarked against state-of-the-art feature selection models, including L1-regularized sparse models. In case of breast cancer, we were unable to construct reliable cross-platform classifiers using alternative feature selection approaches. In case of colorectal cancer not a single model passed the same 0.65 threshold. Source codes and documentation of ExhauFS are available on GitHub: https://github.com/s-a-nersisyan/ExhauFS.