Dynamic Parameter Selection Research Articles

Experimental Evaluation of Under Slab Mats (USMs) Made from End-of-Life Tires for Ballastless Tram Track Applications.

The growing population of urban areas results in the need to deal with the noise pollution from the transportation system. This study presents experimental test results of static and dynamic elastic characteristics of under slab mats (USMs) according to the procedure of DIN 45673-7. Prototype USMs based on recycled elastomeric materials, i.e., SBR granules and fibres produced from waste car tires, are analysed. Vibration isolation mats with different thicknesses (10, 15, 20, 25, 30, and 40 mm), densities (500 and 600 kg/m3), and different degrees of space filling (no holes, medium holes, large holes) are considered. Moreover, a practical application of the laboratory test results of USMs in the design of ballastless track structures of two different types (with a concrete slab and longitudinal beams) is presented. Deflections of the rail and the floating slab system, as well as stresses acting on the mat, are determined according to EN 16432-2. The use of shredded rubber from recycled car tires as a material component of sustainable and environmentally friendly tram track structures may be one of the most effective ways to manage rubber waste within the current trend toward a circular economy, and this study intends to introduce methods for experimental identification and analytical selection of basic static and dynamic parameters of prototype USMs.

Implementation of FOPID controller with modified harmony search optimization for precise modelling and auto-tuning of nonlinear systems

This research aims to optimize the fractional order proportional integral derivative (FOPID) controller for nonlinear process tests. Instead of relying on the traditional trial-and error method for dynamic parameter selection, this proposes a modified harmony search optimization method to compute the optimal dynamic parameters. Based on the physical parameters, using mathematical modelling techniques system transfer function is determined. Additionally, the delay of the control scheme is determined using the Open Loop Transfer Function (OLTF) response. The dynamic parameters of the FOPID controller are evaluated using the Harmonic Search Algorithm – Fractional Order Proportional Integral Derivative (HAS_FOPID) optimization technique, which aims to minimize the system's Integral Square Error (ISE). Time responses, including rise time, peak time, and peak overshoot, are obtained and optimized through the HAS_FOPID algorithm. To verify systems stability, the output response is analysed using various techniques such as bode plots, pole placement, and Nyquist plots. Furthermore, the HAS_FOPID optimization technique is compared with other natural optimization techniques to assess its effectiveness. The research also evaluates the system's robustness by supply/ load disturbances. The objective is to demonstrate that, the optimized HAS_FOPID technique, has significantly enhanced control performance, stability, and robustness of nonlinear systems compared to natural and alternative optimization approaches.

Dynamic Parameters Updating of Earth-rock Dams Based on Modal Parameter Identification

The reasonable selection of dynamic parameters of earth-rock dams is very important for the seismic safety assessment of dams. Because of the size effects, the parameters acquired from the indoor test may additionally no longer exactly reflect the real dynamic characteristics of the dam. Therefore, it is essential to correct and update the dynamic parameters in accordance to the actual seismic dynamic characteristics of the dam. In this paper, by combining the Covariance-driven stochastic subspace identification (SSI-COV) method and the multiple population genetic algorithm (MPGA), an optimization and updating method for the dynamic parameters of earth-rock dams based on modal parameter identification is presented. The collected weak earthquake records of the dam allowed the modal parameters (the natural frequencies and mode shapes) identification based on SSI-COV method. The optimization updated is performed using the MPGA, which can obtain the optimal values of dynamic parameters of the dam and has good robustness within the optimization range of the numerical model. A typical numerical example is given to exhibit the feasibility and effectiveness of the presented method. Then the dynamic parameters of Liyutan Dam are updated by the proposed method according to the weak earthquake records, and the accuracy and applicability of the updated parameters were further verified by the strong earthquake records. The results show that the method proposed in this paper can provide a good idea for the calibration of dynamic parameters of earth-rock dams and has well practical engineering value.

An enhanced N-point interpolation method to eliminate average precision distortion

• Existing N -point interpolation method to calculate average precision raises three anomalies. • The average precision distortion problem is commonly present in the N -point interpolation method. • The proposed enhanced N -point interpolation method reduces more than 90% of the average precision distortion. Existing N -point interpolation methods generate large errors in the average precision calculation for object detection. These errors lead to average precision distortion, which makes it impossible to accurately evaluate the performance of the model. We investigate the reason for the average precision distortion and propose an enhanced N -point interpolation method. These improvements are based on the N -point interpolation method and can be summarized in two parts: (1) The interpolation point position is changed to the middle interpolation. (2) Dynamic selection of parameters for calculating the area of the interpolation interval. Experiments verify the existence of severe average precision distortion in the N -point interpolation method. Furthermore, the proposed enhanced N -point interpolation method reduces the average precision distortion by more than 90% to only 0.04%. In this way, the enhanced N -point interpolation method is able to replace the all-point interpolation method for fast and accurate evaluation of object detection model.

A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market

This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small non-interconnected grids. The core contribution is to propose a probabilistic forecast-driven strategy, modeled as a min-max-min robust optimization problem with recourse. It is solved using a Benders-dual cutting plane algorithm and a column and constraints generation algorithm in a tractable manner. A dynamic risk-averse parameters selection strategy based on the quantile forecasts distribution is proposed to improve the results. A secondary contribution is to use a recently developed deep learning model known as normalizing flows to generate quantile forecasts of renewable generation for the robust optimization problem. This technique provides a general mechanism for defining expressive probability distributions, only requiring the specification of a base distribution and a series of bijective transformations. Overall, the robust approach improves the results over a deterministic approach with nominal point forecasts by finding a trade-off between conservative and risk-seeking policies. The case study uses the photovoltaic generation monitored on-site at the University of Li\`ege (ULi\`ege), Belgium.

Dynamic link adaptation for filterband multicarrier in networks with diverse service quality and throughput requirements

The fast evolution in wireless communication standards and enhancement in cellular applications has created an exponential rise in the data rate requirement over the past few decades. The next generation wireless standards, therefore, need not only to provide ultra high data rates with minimum latency, but also to support diverse quality-of-service (QoS) requirements. Filterbank multicarrier (FBMC) scheme provides quite a few advantages over the conventional orthogonal frequency division multiplexing (OFDM) for future wireless networks. In order to achieve adaptive throughput and diverse service quality requirements under varying channel conditions, a link adaptation algorithm for FBMC scheme is proposed in this paper. The aim is to provide the dynamic selection of optimum parameters resulting in diverse modes of operation to ensure the desired and/or best available communication service quality. The proposed link adaptation algorithm incorporates the parameters of required data rate, available channel condition and QoS demand, and dynamically allocates the appropriate FBMC transmission parameters. The proposed scheme has been evaluated for AWGN as well as multipath fading SUI channel models. Simulation results indicate that the proposed algorithm successfully achieves the desired service quality subject to the availability of suitable channel conditions and is also superior to the existing algorithms in terms of block error rate and effective throughput.

Colour Difference Classification for Dyed Fabrics Based on Differential Evolution with Dynamic Parameter Selection to Optimise the Output Regularisation Extreme Learning Machine

A novel optimisation technique based on the differential evolution (DE) algorithm with dynamic parameter selection (DPS-DE) is proposed to develop a colour difference classification model for dyed fabrics, improve the classification accuracy, and optimise the output regularisation extreme learning machine (RELM). The technique proposed is known as DPS-DE-RELM and has three major differences compared with DE-ELM: (1) Considering that the traditional ELM provides an illness solution based on the output weights, DE is proposed to optimise the output of the RELM. (2) Considering the simple parameter setting of the traditional algorithm, the DE algorithm with DPS is adopted. (3) For DPS, an optimal range of parameters is chosen, and the efficiency of the algorithm is significantly improved. This study analyses the colour difference classification of fabric images captured under standard lighting based on the DPS-DE-RELM algorithm. First, the colour difference of the fabric images is calculated and six color-difference-related features extracted, and second the features are classified into five different levels based on the perception of humans. Finally, a colour difference classification model is built based on the DPS-DE-RELM algorithm, and then the optimal classification model suitable for this study is selected. The experimental results show that the output method with regularisation parameters can achieve a maximum classification accuracy of 98.87%, which is higher compared with the aforementioned optimised original ELM algorithm, which can achieve a maximum accuracy of 84.67%. Therefore, the method proposed has the advantages of greater convergence speed, high classification accuracy, and robustness.

A multi-layer trust-based middleware framework for handling interoperability issues in heterogeneous IOTs

Traditional wireless technologies have evolutionary converged to Internet of Thing (IoT) for devices and service interactions. In the past decade, the academia, industry 4.0 and end-user interest has also grown drastically in IoT applications and their services. However, this increase in IoT services demand has witnessed a new challenge of seamless interaction among heterogeneous devices that are varied, diverse and dynamic in nature. In this connection, another challenge is tracing the footprint of these IoT interactions for trust-based interactions, that further becomes complex with the introduction of new applications. For instance, in many situations, IoT services are generally not self-contained and sufficient. They need to coordinate and interact with other IoT services held in the surroundings. The large-scale deployment of IoT based services is not conceivable without addressing interoperability and services coordination related challenges. In this regard, a comprehensive set of tools and techniques associated with IoT heterogeneity and interoperability have been explored. This article proposes a middleware framework to consider IoT heterogeneity and interoperability issues in different service interactions. Later, the article investigates experimentally, the trust measurements among IOTs, their decay, and the effectiveness of dynamic selection of trust parameters along with their thresholds. Appropriateness of time intervals have also been tested in various types of service interactions. To demonstrate middleware applicability, the trust-based algorithm has been applied in a service-oriented environment along with different types of services.

Optimization of Damage Equivalent Accelerated Test Spectrum Derivation Using Multiple Non‐Gaussian Vibration Data

The response spectra are widely used in the damage assessment of non‐Gaussian random vibration environments and the derivation of damage equivalent accelerated test spectrum. The effectiveness of the latter is strongly affected by modal parameter uncertainties, multiple field data processing, and the nonsmooth shape of the derived power spectral density (PSD). Optimization of accelerated test spectrum derivation based on dynamic parameter selection and iterative update of spectrum envelope is presented in this paper. The extreme response spectrum (ERS) envelope of the field data is firstly taken as the limiting spectrum, and the corresponding relationship between damping coefficient, fatigue exponent, and damage equivalent PSD under different test times is constructed to achieve the dynamic selection of uncertain parameters in the response spectrum model. Then, an iterative update model based on the weighted sum of fatigue damage spectrum (FDS) error is presented to reduce the error introduced by the nonsmooth shape of the derived PSD. The case study shows that undertest can be effectively avoided by the dynamic selection of model parameters. The weighted error is reduced from 80.1% to 7.5% after 7 iterations. Particularly, the error is close to 0 within the peak and valley frequency band.

Single-Phase Fault Line Selection in Distribution Network Based on Signal Injection Method

The distribution network with small current grounding mode can ensure high power supply reliability, but it is difficult to locate single-phase grounding fault line. Most of the faults in distribution network are single-phase to ground fault. It is important to locate the fault line timely and accurately for the normal operation of the power grid. In this article, a fault line selection method based on signal injection is proposed. The dynamic selection of parameters and injection frequency in real-time monitoring distribution network by injection method is analyzed. The accuracy of signal frequency domain conversion is improved by using all phase Fourier transform. Taking the active power consumption of injected signal after fault as the criterion, the accuracy of line selection is improved. Through theoretical analysis and simulation, as well as experiments in a 600V distribution system, the fault line can be accurately selected.

Numerical verification of variable friction cladding connection for multihazard mitigation

The motion of cladding systems can be leveraged to mitigate natural and man-made hazards. The literature counts various examples of connections enhanced with passive energy dissipation capabilities at connections. However, because such devices are passive, their mitigation performance is typically limited to specific excitations. The authors have recently proposed a novel variable friction cladding connection capable of mitigating hazards semi-actively. The variable friction cladding connection is engineered to transfer lateral forces from the cladding element to the structural system. Its variation in friction force is generated by a toggle-actuated variable normal force applied onto sliding friction plates. In this study, a multiobjective motion-based design methodology integrating results from the previous work is proposed to leverage the variable friction cladding connection for nonsimultaneous wind, seismic, and blast hazard mitigation. The procedure starts with the quantification of each hazard and performance objectives. It is followed by the selection of dynamic parameters enabling prescribed performance under wind and seismic loads, after which an impact rubber bumper is designed to satisfy motion requirements under blast. Last, the peak building responses are computed and iterations conducted on the design parameters on the satisfaction of the motion objectives. The motion-based design procedure is verified through numerical simulations on two example buildings subjected to the three nonsimultaneous hazards. The performance of the variable friction cladding connection is also assessed and compared against different control cases. Results show that the motion-based design procedure yields a conservative design approach in meeting all of the motion requirements and that the variable friction cladding connection performs significantly well at mitigating vibrations.

A Dynamic Firefly Algorithm-Based Fractional Order Fuzzy-PID Approach for the Control of a Heavy-Duty Gas Turbine

Heavy duty gas turbines have long played an important role in energy production. Advanced turbine designs are expected to be highly efficient, able to quickly ramp the output power up and down and promptly tolerate loading variations without breaching emissions regulations. Control design plays an important role in ensuring high efficiency and performance of gas turbines. This paper proposes a fractional order fuzzy-PID approach for a heavy-duty gas turbine. The controller gains are optimized using a Firefly algorithm enhanced with a dynamic parameter selection. This latter is used to speed up the convergence rate of the Firefly algorithm, optimize the gains of the fractional order fuzzy-PID and enhance the performance and efficiency of the gas turbine. The proposed approach is implemented to the speed loop of a gas turbine in order to maintain the output temperature and the turbine’s speed within their desired values during either a sudden change in loading or a drop in frequency. A comparison analysis with a standard Firefly algorithm-based approach was carried out to further assess the performance of the proposed evolved firefly algorithm-based approach.

P2726Extremely boosted prediction of cardiac amyloidosis by routine laboratory paramaters

Abstract Background/Introduction Cardiac amyloidosis (CA) is a rare and complex condition with poor prognosis. Novel therapies have been shown to improve outcome, however, most of the affected individuals remain undiagnosed, mainly due to a lack in awareness among clinicians. One approach to overcome this issue is to use automated diagnostic algorithms that act based on routinely available laboratory results. Purpose We tested the performance of flexible machine learning and traditional statistical prediction models for non-invasive CA diagnosis based on routinely collected laboratory parameters. Since laboratory routines vary between hospitals or other health care providers, special attention has been taken to adaptive and dynamic parameter selection, and to dealing with the frequent occurrence of missing values. Methods Our cohort consisted of 376 clinically accepted patients with various types of heart failure. Of these, 69 were diagnosed with CA via endomyocardial biopsy (positives), and 307 had unrelated cardiac disorders (negatives). A total of 63 routine laboratory parameters were collected from these patients, with a high incidence of missing values (on average 60% of patients for each parameter). We tested the performance of two prediction models: logistic regression, and extreme gradient boosting with regression trees. To deal with missing values we adopted two strategies: a) finding an optimal overlap of parameters and deleting all patients with missing values (reduction of parameters and samples), and b) retaining all features and imputing missing values with parameter-wise means. To fairly assess the performance of prediction models we employed a 10-fold cross validation (stratified to preserve sample class ratio). Finally, area under curve for receiver-operator characteristic (ROC AUC) was used as our final performance measure. Results A complex machine learning model based on forests of regression trees proved to be the most performant (ROC AUC 0.94±4%) and robust to missing values. The best regression model was obtained with the 25 most frequent variables and patient deletion in case of missing values (ROC AUC 0.82±0.8%). While progressive inclusion of predictor variables worsened the performance of the logistic regression, it increased that of the machine learning approach. Conclusions Extreme gradient boosting of regression trees by routine laboratory parameters achieved staggering accuracy results for the automated diagnosis of CA. Our data suggest that implementations of such algorithms as independent interpreters of routine laboratory results may help to establish or suggest the diagnosis of CA in patients with heart failure symptoms, even in the absence of specialized experts.

On the Dynamic Capacity of Concrete Dams

The purpose of this joint contribution is to study the maximum dynamic load concrete dams can withstand. The so-called “dynamic capacity functions” for these infrastructures seems now technically and commercially feasible thanks to the modern finite element techniques, hardware capabilities, and positive experiences collected so far. The key topics faced during the dynamic assessment of dams are also discussed using different point of view and examples, which include: the selection of dynamic parameters, the progressive level of detail for the numerical simulations, the implementation of nonlinear behaviors, and the concept of the service and collapse limit states. The approaches adopted by local institutions and engineers on the subject of dam capacity functions are discussed using the authors’ experiences, and an overview of time and resources is outlined to help decision makers. Three different concrete dam types (i.e., gravity, buttress, and arch) are used as case studies with different complexities. Finally, the paper is wrapped up with a list of suggestions for analysts, the procedure limitations, and future research needs.

An evolutionary algorithm based hyper-heuristic framework for the set packing problem

In recent years, hyper-heuristics have received massive attention from the research community as an alternative of meta-heuristics. In a hyper-heuristic, generation or selection of an effective heuristic among a pool of heuristics is an important and challenging task in the search process. At each iteration, a suitable heuristic can take the search process toward the global optimal solution. Moreover, some additional factors such as quality and the number of heuristics also affect the performance. In this paper, we propose an evolutionary algorithm based hyper-heuristic framework that incorporates dynamic selection of parameters. To test its generality, effectiveness and robustness, we apply this approach on two different NP-hard problems - set packing problem (SPP) and minimum weight dominating set (MWDS) problem. The proposed approach for the SPP and the MWDS problem has been evaluated respectively on their respective set of benchmark instances. Computational results show that the proposed approach for the SPP and MWDS problem perform much better than their respective state-of-the-art approaches in terms of the solution quality and computational time.

Approximate residual-minimizing shift parameters for the low-rank ADI iteration

The low-rank alternating directions implicit (LR-ADI) iteration is a frequently employed method for efficiently computing low-rank approximate solutions of large-scale Lyapunov equations. In order to achieve a rapid error reduction, the iteration requires shift parameters whose selection and generation is often a difficult task, especially for nonsymmetric matrices in the Lyapunov equation. This article represents a follow up of Benner et al. [Electron. Trans. Numer. Anal., 43 (2014–2015), pp. 142–162] and investigates self-generating shift parameters based on a minimization principle for the Lyapunov residual norm. Since the involved objective functions are too expensive to evaluate and hence intractable, objective functions are introduced which are efficiently constructed from the available data generated by the LR-ADI iteration. Several numerical experiments indicate that these residual-minimizing shifts using approximated objective functions outperform existing precomputed and dynamic shift parameter selection techniques, although their generation is more involved.

Fractional-order PID control of a MIMO distillation column process using improved bat algorithm

In this paper, a new bat algorithm (BA) based on dynamic control parameters selection is presented. The dynamic BA (DBA) uses a new mechanism to dynamically select the best performing combination of the pulse rate coefficient, the pulse frequency coefficient, and the population size. A fractional-order PID (FOPID) controller based on the DBA is implemented to improve the performance of a distillation column process. The proposed FOPID controller is used to control the distillate and bottom mole fractions. The influence of the feed rate disturbance is considered for this model. The efficacy of the DBA-based FOPID is compared with the performance of the controllers based on the conventional BA, directional BA, enhanced BA, genetic algorithm, and particle swarm optimization algorithm. The analyses and simulation results show the superiority of the proposed method.

SmartLA: Reinforcement learning-based link adaptation for high throughput wireless access networks

High throughput wireless standards based on IEEE 802.11n and IEEE 802.11ac have been developed and released within the last few years as new amendments over the commercially popular IEEE 802.11. IEEE 802.11n and IEEE 802.11ac support a large pool of parameter set such as increased number of spatial streams via multiple input multiple output (MIMO) communications, channel bonding, guard intervals, different modulation and coding schemes, several levels of frame aggregation, block acknowledgement etc. As a consequence, they boost up physical data rate in the order of Gigabits per second. However, all these enhancements have their internal trade-offs with the channel quality, as explored in the existing literature. For example, higher channel bonding levels result in poor performance under high bit error rate. In a free wireless environment, multiple heterogeneous stations share the wireless channel which is again a time-varying system. Consequently, none of these link level parameters provide an optimal performance for all channel quality instances. Therefore, to practically meet the theoretical high throughput, each wireless device should adapt its physical data transmission rate dynamically by an appropriate tuning of different link parameters. Otherwise, high transmission failure may arise. In this paper, we design an adaptive automated on-line learning mechanism, called “Smart Link Adaptation” (SmartLA), for dynamic selection of link parameters, motivated by “State-Action-Reward-State-Action” (SARSA) model, a variant of reinforcement learning. SmartLA can make a wireless station quite intelligent to cope up with various network conditions by exploiting the best suited data rate observed so far for various channel conditions from the past experience as well as by exploring different possible set of parameters. We analyze the performance of SmartLA in both from simulation analysis and over a 26 nodes IEEE 802.11ac testbed (6 access points and 20 client devices). We observe that the proposed link adaptation mechanism performs significantly better compared to other competing mechanisms mentioned in the literature.

Fractional order fuzzy-PID control of a combined cycle power plant using Particle Swarm Optimization algorithm with an improved dynamic parameters selection

The effectiveness of the Particle Swarm Optimization (PSO) algorithm in solving any optimization problem is highly dependent on the right selection of tuning parameters. A better control parameter improves the flexibility and robustness of the algorithm. In this paper, a new PSO algorithm based on dynamic control parameters selection is presented in order to further enhance the algorithm's rate of convergence and the minimization of the fitness function. The powerful Dynamic PSO (DPSO) uses a new mechanism to dynamically select the best performing combinations of acceleration coefficients, inertia weight, and population size. A fractional order fuzzy-PID (fuzzy-FOPID) controller based on the DPSO algorithm is proposed to perform the optimization task of the controller gains and improve the performance of a single-shaft Combined Cycle Power Plant (CCPP). The proposed controller is used in speed control loop to improve the response during frequency drop or change in loading. The performance of the fuzzy-FOPID based DPSO is compared with those of the conventional PSO, Comprehensive Learning PSO (CLPSO), Heterogeneous CLPSO (HCLPSO), Genetic Algorithm (GA), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithm. The simulation results show the effectiveness and performance of the proposed method for frequency drop or change in loading.

Mathematical model and software for control of commissioning blast furnace

Blowing-in is a starting period of blast furnace operation after construction or major repair. The current approximation methods of blowing-in burden analysis are based on blowing-in practice of previously commissioned blast furnaces. This area is theoretically underexplored; there are no common scientifically based methods for selection of the burden composition and blast parameters. The purpose of this paper is development and scientific substantiation of the methods for selection of the burden composition and blast parameters in the blast furnace during the blowing-in period. Research methods are based on physical regularities of main processes running in the blast furnace, system analysis, and application of modern principles for development and construction of mathematical models, algorithms and software designed for automated control of complex production processes in metallurgy. As consequence of the research made by the authors the following results have been achieved: 1. A set of mathematical models for analysis of burden arrangement throughout the height of the blast furnace and for selection of optimal blast and gas dynamic parameters has been developed. 2. General principles for selection of the blowing-in burden composition and blast and gas dynamic parameters have been set up. 3. The software for the engineering and process staff of the blast furnace has been developed and introduced in the industry.