Standard Test Bed Research Articles

Monte-Carlo Regret Minimization for Adversarial Team Games

We study equilibrium approximation in extensive-form adversarial team games, in which two teams of rational players compete in a zero-sum interaction. The suitable solution concept in these settings is the Team-Maxmin Equilibrium with Correlation (TMECor), which naturally arises when the team players play ex-ante correlated strategies. While computing such an equilibrium is APX-hard, recent techniques show that scalability beyond toy instances is possible. However, even compact representations of the team’s strategy space, such as that exploiting Directed Acyclic Graphs (DAGs), have exponential size prohibiting solving large instances. In the present paper, we show that Monte Carlo sampling for regret minimization in adversarial team games can provide an important advancement. In particular, we design a DAG Monte Carlo Counterfactual Regret Minimization algorithm that performs outcome sampling with O ( d ) time complexity per iteration, where d is the depth of the DAG, and with a convergence rate bound of O ( b kd ) , where b is the branching factor and k is the maximum number of private states in each public state of the team. We empirically evaluate our algorithms with a standard testbed of games, showing their performance when approximating equilibria. We investigate both cases in which only one team is composed of multiple players and cases in which both teams are composed of multiple players. Our empirical results show that our algorithms can provide a rough approximation to equilibrium strategies in all game instances considered, trading off a lower precision in the computed equilibrium for lower resource utilization.

Optimizing the use of a gas diffusion electrode setup for CO2 electrolysis imitating a zero-gap MEA design

The lack of a robust and standardized experimental test bed to investigate the performance of catalyst materials for the electrochemical CO2 reduction reaction (ECO2RR) is one of the major challenges in this field of research. To best reproduce and mimic commercially relevant conditions for catalyst screening and testing, gas diffusion electrode (GDE) setups attract rising attention as an alternative to conventional aqueous-based setups such as the H-cell configuration. Zero-gap electrolyzer designs show promising features for upscaling to the commercial scale. In this study, we scrutinize further our recently introduced “zero-gap GDE” setup or more correct half-cell MEA design for the CO2RR. Using an Au electrocatalyst as a model system we simulate the anode conditions in a zero-gap electrolyzer and identify/report the key experimental parameters to control the catalyst layer preparation to optimize the activity and selectivity of the catalyst. Among others, it is demonstrated that supported Au nanoparticles (NPs) result in significantly higher current densities when compared to unsupported counterparts, however, the supporting also renders the NPs prone to agglomeration during electrolysis.

Targeted multiobjective Dijkstra algorithm

We introduce the Targeted Multiobjective Dijkstra Algorithm (T‐MDA), a label setting algorithm for the One‐to‐One Multiobjective Shortest Path (MOSP) Problem. It is based on the recently published Multiobjective Dijkstra Algorithm (MDA) and equips it with A*‐like techniques. For any explored subpath, a label setting MOSP algorithm decides whether the subpath can be discarded or must be stored as part of the output. A major design choice is how to store subpaths from the moment they are first explored until the mentioned final decision can be made. The T‐MDA combines the polynomially bounded size of the priority queue used in the MDA and a lazy management of paths that are not in the queue. The running time bounds from the MDA remain valid. In practice, the T‐MDA outperforms known algorithms from the literature and the increased memory consumption is negligible. In this paper, we benchmark the T‐MDA against an improved version of the state of the art One‐to‐One MOSP algorithm from the literature on a standard testbed.

Design of Low Power High Speed Hybrid Full Adder: A Review

In this research, I present a new hybrid FA design (a blend of CMOS and transistor logic types) that tries to achieve high speed while consuming minimal power, hence the low PDP designation. Our proposed FA and seven other current FA designs were modelled on the spice with a 45 nm low power model file, a standard test bed, and a standard test pattern (56 input switching), and the impersonation results for eight designs are equivalent. Power outages, distribution delays, and PDP are all issues that need to be addressed. The results of simulations show that our suggested FA design has the shortest broadcast delays and the lowest PDP across the whole power supply and frequency range.

Intelligent Online Monitoring and Remote Verification of Gateway Meters under the Embedded Sensor and Clustering Algorithm

Since gateway meters cannot simultaneously realize online monitoring and remote verification, the study is aimed at exploring the intelligent online monitoring and remote verification of the gateway meter. First, the similarity and related evaluation indexes of embedded sensors are analyzed based on the relevant theories, like the theories of the embedded sensor and clustering algorithm. Second, the gateway meter is tested on the standard vibration test bed, and the accuracy, timeliness, and environmental adaptability of its intelligent online system are tested. Finally, the remote verification of the gateway meter is carried out from three aspects: the error/load value, secondary voltage drop, and the admittance test. The results show that when the intelligent online monitoring ability of the gateway meter is tested on the standard vibration test bed, the error of the same pilot is controlled between 0.1 mm after multiple peak tests, and the error is within the allowable range. In the intelligent online monitoring system based on the embedded sensor and clustering algorithm, the vibration acceleration is -0.6 cm/s~0.3 cm/s, the speed is -1 cm/s~1 cm/s, and the displacement fluctuates between -0.8 cm/s~0.8 cm/s. This shows that the intelligent online monitoring system can meet the performance requirements of online monitoring. In the process of remote verification of gateway meters, the active error and reactive error are 0.2% mm. The results of the secondary voltage drop and the admittance test show that the relevant technical indexes of the system meet the expected requirements. Therefore, the intelligent online monitoring and remote verification of gateway meters are discussed based on the embedded sensor and clustering algorithm, which provides a reference for the rapid development of gateway meters.

A spectral method algorithm for numerical simulations of gravitational fields

A numerical study of the Einstein field equations, based on the 3 + 1 foliation of the spacetime, is presented. A pseudo-spectral technique has been employed for simulations in vacuum, within two different formalisms, namely the Arnowitt–Deser–Misner (ADM) and the conformal Baumgarte–Shapiro–Shibata–Nakamura (BSSN) approach. The numerical code is based on the Fourier decomposition, accompanied by different filtering techniques. The role of the dealiasing, as well as the influence of the filter type, has been investigated. The algorithms have been stabilized via a novel procedure that controls self-consistently the regularity of the solutions. The accuracy of the model has been validated through standard testbeds, revealing that the filtered pseudo-spectral technique is among the most accurate approaches. Finally, the procedure has been stressed via black hole dynamics and a new strategy, based on hyperviscous dissipation that suppresses spurious boundary problems, has been proposed. The model represents a valid tool of investigation, particularly suitable for the inspection of small scale nonlinear phenomena in gravitational dynamics.

Evaluation of engine performance and emission of African pear seed oil (APO) biodiesel and its prediction via multi‐input‐multi‐output artificial neural network (ANN) and sensitivity analysis

AbstractThe evaluation of engine performance and emission of biodiesel produced from African pear seed oil (APO) and its prediction using multi‐input‐multi‐output (MIMO) artificial neural network (ANN) technique was studied. A standard diesel test bed was used to carry out the evaluation using petrol‐diesel, biodiesel, and its blends, operating at various engine revolutions per minute. Sensitivity analysis used the connection weight method. The performance results showed that petro‐diesel blended with a small amount of APO methyl ester (B20) improved the engine efficiency and emissions better than only petrol‐diesel. The engine performance was predicted well, with the MIMO model having better correlation coefficients of 0.99779, 0.99993, and 0.99774 on training, validation, and testing, respectively, than the Multiple input Single Output (MISO) model. The desired outputs compared between their measured and simulated values exhibited a very low mean square error of 0.0000014488. The blend had the highest relative impact (62.08%) on the output variables. The MIMO model has therefore shown the potential to adopt two input variables equally in predicting the engine performance of biodiesel rather than only complex variables. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd

Optimal Dump Load Allocations in High RBDG Penetrated Microgrid for Voltage and Frequency Regulation

The rising renewable penetrations has paved ways for the microgrids to operate independent of the conventional centrally located power plants. At low load hours, the microgrid operation is often disrupted due the excess power generations creating voltage (V) and frequency (f) regulation issues. The use of energy storage devices to absorb such excess generations would be infeasible due to their less usability, high cost and uneconomical trading. The offline planning of distributing dump loads (DLs) in the network can coordinate with the online control in maintaining V and f of the system. Thus, the present work provides an offline analysis to optimally allocate DLs in the power electronic (PE) interfaced DGs powered autonomous microgrid to minimize the V and f deviations at off-peak hours. The V and f deviations as per the droop characteristics of PE interfaced DGs have been taken into consideration in the load flow. The optimization problem is formulated and solved using heuristic techniques viz MWOA and NSGA-II. The weakly meshed standard test beds of IEEE 33 and 69 bus distribution systems have been used in the present work as autonomous microgrids.

Benders decomposition for multiple-allocation hub-and-spoke network design with economies of scale and node congestion

This paper is the first to consider a novel and more realistic variant of the uncapacitated hub location problem where both flow-dependent economies of scale and congestion considerations are incorporated into the multiple-allocation version of the problem. Given an undirected graph with edge per-unit costs and flow requirements for a given set of source-destination pairs of nodes, the goal is to decide where to locate hub nodes and then to route flow through these hubs in a way that minimizes total cost. Transportation costs are influenced by two conflicting effects: consolidation discounts on hub-to-hub flow due to economies of scale, and congestion penalties due to excessive demand through hubs. The problem is formulated as a mixed-integer linear program by piecewise-linearizing non-linear cost elements. Since the resulting model is found to be difficult to solve directly using commercial solvers, a specialized Benders decomposition approach is devised to solve the problem. The proposed method is tested on two standard testbeds (the Civil Aeronautics Board and Australian Post datasets). While the commercial solver is unable to obtain any feasible solution for networks larger than 25 nodes, the proposed algorithm is shown to be able to obtain optimal solutions for networks as large as 70 nodes, and is able to obtain small-gap feasible solutions for networks with up to 100 nodes. Whenever both algorithms are able to solve instances to optimality, the Benders algorithm is able to do so in a fraction of the time needed by the commercial solver. The incorporation of congestion costs into the multiple-allocation topology as opposed to the single-allocation one is also found to lead to significant cost savings.

Generation Rejection Scheme Based-on a Combinational Rotor Angle Trajectory Prediction

This paper presents a response-based generating rejection scheme (GRS) based on an angular stability prediction logic to initiate the outage of accelerated generating units while saving the rest of generating units from the loss of synchronism. First trigonometric, polynomial, and hybrid models of rotor angle trajectory based on the reasonable assumptions are proofed. Then, by taking these models in the prediction step, through the maximum use of measured data based on defining the forecast horizon (FH) and data window with incremental length, the stability/instability of generating units is separately predicted. Next, the status of tripping signal based on a combinational logic of the output results of the angular stability prediction method is specified. In the developed logic, if at least two models of the three designated models yield the same response about the unit stability status, the trip signal is accordingly fired or blocked. The proposed method is examined on the one machine infinite bus and the WSCC standard test bed under different operation and fault scenarios. The obtained results demonstrate that beside simplicity, low computational burden, and very short processing time, the proposed combinatorial method outperforms the existing ones working with individual prediction models.

A Response-Based Approach for Online Prediction of Generating Unit Angular Stability

In this paper, first, a rotor angle trajectory model based on polynomial functions is proposed. Afterwards, a response-based approach for online prediction of power system angular instability is presented. The proposed method utilizes bus phase angle data measured by phasor measurement unit at the point of common coupling of power plant transformer to the bulk power grid. In the prediction process, by computing the second order derivative of post-fault data, the starting point of the calculation data window is determined. Next, a fifth-degree polynomial curve is fitted on the designated data window to predict the angular curve of generating unit. Based on the sign of the first order derivative of predicted curve, the angular stability of generating unit is judged. This approach is testified on the western system coordinating council standard test bed under different operation and fault type scenarios. Taking into account various fault conditions and their associated occurrence probability, a probabilistic index is also defined to sum up the overall performance of the new method. Simulation results confirm that the proposed method outperforms the existing ones in terms of both accuracy and speed. Prediction results could be used in generator rejection schemes to prevent severe power plant outages.

Compressive System Identification for Multiple Line Outage Detection in Smart Grids

Real-time power line outage detection (POD) and localization is an important monitoring task for the modern smart grid. Reliable monitoring of power lines status plays a critical role in the system-wide blackout prevention. In this paper, the main aim is to address the multiple POD problems by exploiting the compressive system identification—a time-efficient approach in a complex network analysis. A typical power network is considered as a single graph, and the mathematical formulation of the POD problem is initialized using the dc power-flow model and graph theory concepts. Next, a sparse representation-based formulation for this problem (POD-SRP) is reported and further improved and generalized in case of multiple large-scale outages. Practical and technical challenges associated with this sparse recovery problem are partially addressed by developing new SRP solvers. Furthermore, a new sparse-based mathematical formulation for POD is introduced and termed as “Binary-POD-SRP,” which specifically deals with two particular issues, namely, the high coherence and the signal dynamic outrange. Finally, the identification performance of the proposed framework is evaluated by a variety of case studies, which are modeled using IEEE standard test-beds. We specifically discuss how the inherent challenges within large-scale multiple-outages can be solved by applying these new techniques and formulations.

Constraint guided accelerated search for mixed blocking permutation flowshop scheduling

Mixed Blocking Permutation Flowshop Scheduling Problem (MBPFSP) with the objective of makespan minimisation is NP-Hard. It has important industrial applications that include the cider production industry. MBPFSP has made some progress in recent years. However, within practical time limits, existing incomplete algorithms still either find low quality solutions or struggle with large problems. One key reason behind this is the typical way of using generic heuristics or metaheuristics that usually lack problem specific structural knowledge. In MBPFSP, a machine could be blocked with the currently finished job until the subsequent machine is available to process the same job. These blocking constraints affect the makespan. So MBPFSP search should naturally take explicit steps to take the blocking constraints into account. Unfortunately, existing research on MBPFSP just uses only the makespan to compare generated solutions, but the search otherwise is not aware of the blocking constraints. Moreover, existing such methods use either an exhaustive or a random neighbourhood generation strategy. In this work, we aim to advance MBPFSP search by better exploiting the problem specific structural knowledge. We use the constraint and the objective functions to obtain such problem specific knowledge and we exploit such knowledge both in a constructive search method and in a local search method. In this paper, we also present an acceleration method to efficiently evaluate insertion-based neighbourhoods of MBPFSP. Our experimental results on three standard testbeds demonstrate that our proposed algorithms significantly improve over existing best-performing algorithms.

Spectral Eigen Index: Military vehicle fingerprinting using Eigen analysis in spectral domain

It is of great interest for military to identify specific vehicles using vibrating data collected by sensors. In view of the special spectral properties of military vehicles, for a vibration measurement, we first formulate a matrix by stacking the overlapped short-duration measures, the eigenvector of the largest eigenvalue is taken to represent the original measurement after a sequence of Gaussian smoothing, high frequency suppression, sub-sampling and an additional Fourier transform. A new vibration index, denoted by spectral eigenvector index, is formed as the fingerprint of the vehicle in consequence. A feed-forward Neural Network N with 70 hidden nodes is trained as the vehicle type classifier. Vehicle classification using N trained based on this new index can achieve valuable accuracies for a standard testbed of military vehicles.

Team-Maxmin Equilibrium: Efficiency Bounds and Algorithms

The Team-maxmin equilibrium prescribes the optimal strategies for a team of rational players sharing the same goal and without the capability of correlating their strategies in strategic games against an adversary. This solution concept can capture situations in which an agent controls multiple resources - corresponding to the team members - that cannot communicate. It is known that such equilibrium always exists and it is unique (except degenerate cases) and these properties make it a credible solution concept to be used in real-world applications, especially in security scenarios. Nevertheless, to the best of our knowledge, the Team-maxmin equilibrium is almost completely unexplored in the literature. In this paper, we investigate bounds of (in)efficiency of the Team-maxmin equilibrium w.r.t. the Nash equilibria and w.r.t. the Maxmin equilibrium when the team members can play correlated strategies. Furthermore, we study a number of algorithms to find and/or approximate an equilibrium, discussing their theoretical guarantees and evaluating their performance by using a standard testbed of game instances.

Ellipsoid Segmentation Model for Analyzing Light-Attenuated 3D Confocal Image Stacks of Fluorescent Multi-Cellular Spheroids.

In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP) and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation.

Towards a Standard Testbed for Optical Music Recognition: Definitions, Metrics, and Page Images

We posit that progress in Optical Music Recognition (OMR) has been held up for years by the absence of anything resembling the standard testbeds in use in other fields that face difficult evaluation problems. One example of such a field is text information retrieval (IR), where the Text Retrieval Conference (TREC) has annually-renewed IR tasks with accompanying data sets. In music informatics, the Music Information Retrieval Exchange (MIREX), with its annual tests and meetings held during the ISMIR conference, is a close analog to TREC; but MIREX has never had an OMR track or a collection of music such a track could employ. We describe why the absence of an OMR testbed is a problem and how this problem may be mitigated. To aid in the establishment of a standard testbed, we provide (1) a set of definitions for the complexity of music notation; (2) a set of performance metrics for OMR tools that gauge score complexity and graphical quality; and (3) a small corpus of music for use as a baseline for a proper OMR testbed.

Evaluating performance of biomedical image retrieval systems—An overview of the medical image retrieval task at ImageCLEF 2004–2013

Medical image retrieval and classification have been extremely active research topics over the past 15 years. Within the ImageCLEF benchmark in medical image retrieval and classification, a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluation campaigns. A detailed analysis of the data also highlights the value of the resources created.

Memetic Algorithm with Local Search as Modified Swine Influenza Model-Based Optimization and Its Use in ECG Filtering

The Swine Influenza Model Based Optimization (SIMBO) family is a newly introduced speedy optimization technique having the adaptive features in its mechanism. In this paper, the authors modified the SIMBO to make the algorithm further quicker. As the SIMBO family is faster, it is a better option for searching the basin. Thus, it is utilized in local searches in developing the proposed memetic algorithms (MAs). The MA has a faster speed compared to SIMBO with the balance in exploration and exploitation. So, MAs have small tradeoffs in convergence velocity for comprehensively optimizing the numerical standard benchmark test bed having functions with different properties. The utilization of SIMBO in the local searching is inherently the exploitation of better characteristics of the algorithms employed for the hybridization. The developed MA is applied to eliminate the power line interference (PLI) from the biomedical signal ECG with the use of adaptive filter whose weights are optimized by the MA. The inference signal required for adaptive filter is obtained using the selective reconstruction of ECG from the intrinsic mode functions (IMFs) of empirical mode decomposition (EMD).

Solution of travelling salesman problem using bacterial foraging optimisation algorithm

Travelling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. It is a standard test bed for new algorithmic ideas. A good performance of an algorithm on TSP indicates its usefulness in the field of optimisation. This paper presents a new method to solve the TSP using bacterial foraging optimisation algorithm (BFOA). The key step of BFOA used in the proposed work is computation of chemotaxis, where a bacterium takes steps over the foraging landscape in order to reach the regions with high-nutrient content. In the proposed algorithm, the movement of bacterium is decided by computing direction and distance matrix. If a bacterium does not encounter a city within the minimum distance constraint in its first step, then it searches for another city by taking another step and so on. The new approach is also analysed quantitatively with genetic algorithm based on running time of the algorithm.