Conventional Performance Index Research Articles

Random Polynomial Neural Networks: Analysis and Design.

In this article, we propose the concept of random polynomial neural networks (RPNNs) realized based on the architecture of polynomial neural networks (PNNs) with random polynomial neurons (RPNs). RPNs exhibit generalized polynomial neurons (PNs) based on random forest (RF) architecture. In the design of RPNs, the target variables are no longer directly used in conventional decision trees, and the polynomial of these target variables is exploited here to determine the average prediction. Unlike the conventional performance index used in the selection of PNs, the correlation coefficient is adopted here to select the RPNs of each layer. When compared with the conventional PNs used in PNNs, the proposed RPNs exhibit the following advantages: first, RPNs are insensitive to outliers; second, RPNs can obtain the importance of each input variable after training; third, RPNs can alleviate the overfitting problem with the use of an RF structure. The overall nonlinearity of a complex system is captured by means of PNNs. Moreover, particle swarm optimization (PSO) is exploited to optimize the parameters when constructing RPNNs. The RPNNs take advantage of both RF and PNNs: it exhibits high accuracy based on ensemble learning used in the RF and is beneficial to describe high-order nonlinear relations between input and output variables stemming from PNNs. Experimental results based on a series of well-known modeling benchmarks illustrate that the proposed RPNNs outperform other state-of-the-art models reported in the literature.

A Novel Parallel Control Method for Optimal Consensus of Nonlinear Multiagent Systems.

This work concentrates on the initial introduction of parallel control to investigate an optimal consensus control strategy for continuous-time nonlinear multiagent systems (MASs) via adaptive dynamic programming (ADP). First, the control input is integrated into the feedback system for parallel control, facilitating an augmented system's optimal consensus control with an appropriate augmented performance index function to be established, which is identical to the original system's suboptimal control with a conventional performance index. Second, the feasibility of the proposed control scheme is evaluated based on the policy iteration algorithm, and the convergence of the algorithm is demonstrated. Then, an online learning algorithm becomes available to implement the ADP-based optimal parallel consensus control protocol without prior knowledge of the system. The Lyapunov approach is employed to indicate that the signals are convergent. Ultimately, the experimental data support the theoretical results.

Wave-Shaped Microstructure Cancer Detection Sensor in Terahertz Band: Design and Analysis

For the quick identification of diverse types of cancer/malignant cells in the human body, a new hollow-core optical waveguide based on Photonic Crystal Fiber (PCF) is proposed and numerically studied. The refractive index (RI) differs between normal and cancerous cells, and it is through this distinction that the other crucial optical parameters are assessed. The proposed cancer cell biosensor’s guiding characteristics are examined in the COMSOL Multiphysics v5.5 environment. The Finite Element Method (FEM) framework is used to quantify the display of the suggested fiber biosensor. Extremely fine mesh elements are additionally added to guarantee the highest simulation accuracy. The simulation results on the suggested sensor model achieve a very high relative sensitivity of 99.9277%, 99.9243%, 99.9302%, 99.9314%, 99.9257% and 99.9169%, a low effective material loss of 8.55×10−5 cm−1, 8.96×10−5 cm−1, 8.24×10−5 cm−1, 8.09×10−5 cm−1, 8.79×10−5 cm−1, and 9.88×10−5 cm−1 for adrenal gland cancer, blood cancer, breast cancer type-1, breast cancer type-2, cervical cancer, and skin cancer, respectively, at a 3.0 THz frequency regime. A very low confinement loss of 6.1×10−10 dB/cm is also indicated by the simulation findings for all of the cancer cases that were mentioned. The straightforward PCF structure of the proposed biosensor offers a high likelihood of implementation when used in conjunction with these conventional performance indexes. So, it appears that this biosensor will create new opportunities for the identification and diagnosis of various cancer cells.

Stochastic Optimization-Based hosting capacity estimation with volatile net load deviation in distribution grids

With the increasing penetration rates of variable renewable energies (VREs), estimating the maximum network capacity without adversely impacting the reliability or voltage quality for power system operation, that is, the hosting capacity (HC), is a significant issue. For system operators, it is challenging to secure flexible resources that can respond to the volatility of the net load resulting from the intermittent generation characteristics of VREs and the appearance of various consumers. Thus, this study proposed a hosting capacity estimation framework that considers the net load deviation. It thereby overcomes the abrupt net load deviation for the economic accommodation of VREs. To evaluate proposed framework, the qualification of the proposed net load deviation limit as a new performance index and investigation for improving HC was verified via performance violation analysis. The proposed net load filter exhibited excellent performance in capturing the net load deviation without distorting the conventional performance index. To consider the net load deviation magnitude and intensity effect on system operation, a multi-time stochastic optimization model was formulated. The proposed framework was tested on an IEEE 33-radial bus system to investigate the effects of the net load deviation limit on the HC, and its potential as a performance index was analyzed. Finally, as an application of the proposed model to help the system operator’s precise decision making, the VRE accommodation costs was quantitatively suggested.

Matrix Manifold-Based Performance Monitoring of Automatic Control Systems

A “smart” automatic feedback control system is supposed to be aware of its operational performance throughout the service time. Driven by this desire, this article addresses the problem of monitoring performance variations caused by multiplicative factors, such as components’ faults, repairing, or replacement. Differing from the conventional performance indices (e.g., the quadratic value function), it detects the performance variation information from symmetric positive-definite (SPD) kernel matrices. As a carrier of performance variation information, the SPD kernel matrix is identified online. Furthermore, the performance variation is given a fresh insight in the sense that it drives the sliding of the SPD kernel matrix on a Riemannian manifold. Thus, performance variation monitoring is achieved by quantizing the geodesic between SPD kernel matrices directly on the Riemannian manifold. At last, the performance variation is visualized on the Riemannian manifold and the proposed schemes are verified via a simulation study.

Thermochromic smart windows with highly regulated radiative cooling and solar transmission

Radiative cooling (RC) is a technique that spontaneously radiates long-wave infrared (LWIR) to the cold outer space, which provides cooling power to buildings, however only preferred in hot seasons. RC has been widely researched on walls and roofs but rarely on windows while windows are the least energy-efficient part of buildings. Moreover, in the current smart window design, consideration of tunable RC is missing. For the first time, we proposed an ideal smart window with a switchable front side LWIR emissivity ( ε Front ) and solar modulation ability (Δ T sol ). Such window needs not only to have high luminous transmission ( T lum ) and Δ T sol , the two major conventional performance indexes but also possess a switchable ε Front to cater for the variable seasonal thermal performance requirements and energy-saving demands. We further fabricated a tunable emissivity thermochromic (TET) smart window with large Δ T sol (51%) and T lum (72%) and switchable ε Front (0.95–0.1) to prove the efficacy of the proposed ideal window. Compared with current smart window technology solely regulating solar transmission and other RC materials with fixed emissivity, the TET smart window panel gives a wide tunability in the selective solar spectrum for dynamic climate conditions in energy-saving buildings, achieving largely enhanced energy saving performance globally • Smart window to regulate both solar transmittance and radiative cooling. • Switchable emissivity to prompt cooling in summer and warm keeping in winter. • High luminous transmittance, good solar regulation. • Better energy efficiency than commercial low-E glass across climate zones.

Effect of Recycled Shell Waste as a Modifier on the High- and Low-Temperature Rheological Properties of Asphalt

The deteriorating ecological environment and the concept of sustainable development have highlighted the importance of waste reuse. This article investigates the performance changes resulting from the incorporation of shellac into asphalt binders. Seashell powder-modified asphalt was prepared with 5%, 10%, and 15% admixture using the high-speed shear method. The microstructure of the seashell powder was observed by scanning electron microscope test (SEM); the physical-phase analysis of the seashell powder was carried out using an X-ray diffraction (XRD) test; the surface characteristics and pore structure of shellac were analyzed by the specific surface area Brunauer-Emmett-Teller (BET) test; and Fourier infrared spectroscopy (FTIR) qualitatively analyzed the composition and changes of functional groups of seashell powder-modified asphalt. The conventional performance index of seashell powder asphalt was analyzed by penetration, softening point, and ductility (5 °C) tests; the effect of seashell powder on asphalt binder was studied using a dynamic shear rheometer (DSR) and bending beam rheometer (BBR) at high and low temperatures, respectively. The results indicate the following: seashell powder is a coarse, porous, and angular CaCO3 bio-material; seashell powder and the asphalt binder represent a stable physical mixture of modified properties; seashell powder improves the consistency, hardness, and high-temperature performance of the asphalt binder but weakens the low-temperature performance of it; seashell powder enhances the elasticity, recovery performance, and permanent deformation resistance of asphalt binders and improves high-temperature rheological properties; finally, seashell powder has a minimal effect on the crack resistance of asphalt binders at very low temperatures. In summary, the use of waste seashells for recycling as bio-modifiers for asphalt binders is a practical approach.

A Study on New Performance Index of Granular Models

In this paper, we propose the performance evaluation of granular model instead of the conventional performance index, which is a root mean square error. In contrast to most fuzzy models developed in the previous researches, the outputs obtained by granular models have the form of information granules rather than plain numeric entities. Thus, we use a concept of information granularity as a network of information granules with contexts produced in the output space and a set of information granules formed in the input space. Granular model is constructed by information granules that are extracted by specialized context-based fuzzy clustering. Furthermore, this model is designed by the rules connecting the association of information granules obtained in input-output space. The performance evaluation method of granular model needs new criterion, because the output of granular model has triangular fuzzy number. The performance index is expressed by the product between fuzzy coverage of predicted output and specificity of information granules. Thus, we can quantify the model output representing information granules. Granular models offer a new performance evaluation method of system modeling by designing models at the level of information granules. For this, we deal with a synthesis function approximation and nonlinear regression and demonstrate the effectiveness of the proposed performance evaluation in the design of granular model.

클론선택기반 유전자 알고리즘을 이용한 자기부상 RGV의 PID 제어기 설계

This paper proposes a novel optimum design method for the PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV) by a genetic algorithm using clone selection method and a new performance index function with performances of both time and frequency domain. Generally, since an attraction type levitation system is intrinsically unstable and requires a delicate controller that is designed considering overshoot and settling time, it is difficult to completely satisfy the desired performance through the methods designed by conventional performance indexes. In the paper, the conventional performance indexes are analyzed and then a new performance index for Maglev-based RGV is proposed. Also, an advanced genetic algorithm which is designed using clonal selection algorithm for performance improvement is proposed. To verify the proposed algorithm and the performance index, we compare the proposed method with a simple genetic algorithm and particle swarm optimization. The simulation results show that the proposed method is more effective than conventional optimization methods.

Optimal control of LQG problem with an explicit trade-off between mean and variance

For discrete-time linear-quadratic Gaussian (LQG) control problems, a utility function on the expectation and the variance of the conventional performance index is considered. The utility function is viewed as an overall objective of the system and can perform the optimal trade-off between the mean and the variance of performance index. The nonlinear utility function is first converted into an auxiliary parameters optimisation problem about the expectation and the variance. Then an optimal closed-loop feedback controller for the nonseparable mean–variance minimisation problem is designed by nonlinear mathematical programming. Finally, simulation results are given to verify the algorithm's effectiveness obtained in this article.

Efficient Micromixing Using Induced-Charge Electroosmosis

Lab-on-a-chip (LOC) devices which utilize electrokinetics for fluid transport are invariably poor mixers due to the nature of low-Reynolds-number flows. For many such devices, efficient mixing is needed for fast analysis, but the predominant mechanism of equalizing concentration differences is often diffusion-a relatively slow form of mass transfer. In this numerical study, we propose a novel micromixer design which utilizes the recent concept of induced-charge electroosmosis for enhancing fluid mixing. As validation, it is shown that numerical simulations of fluid flow in the proposed system are in good agreement with analytical solutions available for electrokinetic flow and electrokinetic mixing in traditional microchannels. The conventional mixing performance index is modified so that it accounts for the length required for desired mixing as well as the concentration gradients across the channel width. With the help of the modified mixing index, the proposed mixer is compared with the traditional mixer design and found to be superior in performance. Furthermore, the effects of design parameters on mixing performance are analyzed for possible device implementation.

Evaluation Criteria Based on Mutual Information for Classifications Including Rejected Class

Different from the conventional evaluation criteria using performance measures, information theory based criteria present a unique beneficial feature in applications of machine learning. However, we are still far from possessing an in-depth understanding of the “entropy” type criteria, say, in relation to the conventional performance-based criteria. This paper studies generic classification problems, which include a rejected, or unknown, class. We present the basic formulas and schematic diagram of classification learning based on information theory. A closed-form equation is derived between the normalized mutual information and the augmented confusion matrix for the generic classification problems. Three theorems and one set of sensitivity equations are given for studying the relations between mutual information and conventional performance indices. We also present numerical examples and several discussions related to advantages and limitations of mutual information criteria in comparison with the conventional criteria.

Performance evaluation for the logistics system in case that capacity weight varies from arcs and types of commodity

Most of Taiwan high-technology companies establish their factories in China to enhance the logistics systems. In order to show and improve the company's competitive advantage, it is an important issue to measure the performance of the logistics system. Hence, we try to propose an index to evaluate the performance of the logistics system between China and Taiwan by constructing a two-commodity capacitated-flow network. In the proposed model, each arc has both the capacity and cost attributes. Each type of commodity consumes the capacity of each arc differently. That is, the capacity weight varies from the arcs and types of commodity. It is known that the system capacity is the maximum flow from the source to the sink for a single-commodity capacitated-flow network. However, the conventional performance indexes proposed for a single-commodity case are not suitable for a two-commodity case. Hence, we define the system capacity as a vector for a two-commodity capacitated-flow network. Then a performance index, the probability that the upper bound of the system capacity equals a given pattern under the budget constraint, is proposed to measure the performance of logistics systems. An efficient algorithm based on minimal cuts is presented to evaluate such a performance index.

Efficiency Measurement of Factories Via Data Envelopment Analysis

The aim of this paper is to illustrate how a mathematical programming technique called Data Envelopment Analysis (DEA) can be used to measure the efficiency of factories of a multinational company, given the existence of multiple inputs and outputs in the transformation process with no a priori weightings. Productivity measures encompass measures of technical change, technical efficiency, scale efficiency and allocation efficiency. In a competitive environment, industrial enterprises must continuously improve their productivity to sustain long-term growth and profitability. Productivity measurement and analysis can therefore play an important role in strategic planning. In contrast to conventional performance index measures, DEA assessment incorporates all the relevant factors (classified as either inputs or outputs) into a single model. In this sense, it is non-biased, as no single factor is given preference over another. Moreover, DEA measures the relative efficiency with respect to the best observed performance, as opposed to other techniques based on the observed average values or some predetermined performance. To show the DEA potentiality, an analysis on 12 factories in the paper industry is presented. Besides identifying the inefficient units, DEA yields valuable assessments by factories that can be used to determine a suitable course of action for improving the performance of the inefficient units.

SEISMIC BEHAVIOUR OF BUTTRESS DAMS: NON-LINEAR MODELLING OF A DAMAGED BUTTRESS BASED ON ARX/NARX MODELS

The paper reports a full model identification study carried out on a scale model of a dam buttress subjected to seismic-like excitations generated by means of a shake table. The use of linear and non-linear models is discussed, since the buttress has an artificial crack and is subjected to high-intensity inputs. In particular, a suitable class of polynomial NARX models is considered, which captures most of the system dynamics.Several questions related to the NARX identification methodology are addressed in the paper; use of non-linear models greatly increases model accuracy and reliability, but many specific operational problems arise in practice. In particular, the validity of classical model selection approaches is questionable; satisfactory non-linear models are obtained in this case with many fewer parameters than suggested by conventional performance indices. Also, it is difficult to guarantee a satisfactory model performance in simulation and, sometimes, even stability is hard to obtain.With reference to specific identified models a further analysis step is carried out, which shows the evolution of the dynamic characteristics of the model in the various phases of the earthquake-like excitation. Also, the role of the particular non-linearities included in the model is discussed.

Group-based optimisation of signal timings using the TRANSYT traffic model

This paper presents the group-based optimisation of signal timings for area traffic control. Using group-based control variables, the optimisation of signal timings is formulated as a set of non-linear mathematical programs. A conventional performance index, which is a weighted combination of estimated delays and stops, is evaluated by the traffic model from the well-known stage-based procedure TRANSYT and is used as the optimand, but it is considered here as a function of group-based control variables, the common cycle time, the start time and duration of the period of right of way for each signal group. These programs are then solved by an integer programming method. The cases of specified and unspecified cycle time are both discussed. Together with a previous hill-climbing technique some optimisation heuristics for the optimisation of signal timings using group-based notation are developed. These heuristics consist of a mixture of network-wide steps, in which control variables for signal settings at all junctions are changed simultaneously, and junction-based steps, in which the variables at each junction are changed in turn as happens in TRANSYT. A trial network from Leicestershire, England is used in examples to demonstrate the application and effectiveness of the optimisation heuristics.

Derivatives of the performance index for the traffic model from TRANSYT

This paper forms a starting point for the investigation of phase-based optimisation of signal timings for area traffic control. A conventional performance index, which is a weighted combination of delays of stops, is evaluated by the traffic model from the well-known stage-based procedure TRANSYT, but is considered here as a function of phase-based control variables, start and duration of green phases. In the paper, approximate expressions for the derivatives of performance index with respect to phase-based control variables are derived. The contribution from upstream, downstream and further downstream links and platoon dispersion are taken into account. The derivatives calculated from the expressions are compared with those from numerical differentiation using a trial network from Leicestershire as an example to illustrate the effectiveness of the formulae.

Highly stable power supply using digitally controlled phase-controlled rectifier and active filter

A design of a highly stable current-controlled power supply combining a phase controlled rectifier (PCR), passive filter and active filter is investigated. A digital phase-locked voltage control (PLVC) with a capability of compensating the thyristor firing angles under an unbalanced power source is proposed; otherwise the PCR output voltage has low-order subharmonics whose suppression requires a bulky passive filter. The digital PLVC has a fast dynamic characteristic as an inner control loop of the PCR. To suppress further the output ripple, an active filter using a transformer is introduced and its design is described through a frequency-domain analysis. An optimal, proportional and measurable-variable feedback (IPM) controller is designed using a time-weighted performance index based on a time-domain analysis. The design method based on the time-weighted performance index gives a better response characteristic than that based on the conventional performance index. It is shown via experimental results that the proposed scheme gives good dynamic and static performance.

Assessment of Effectiveness of Renewable Energy Systems in Mitigating Global Warming Based on Net Energy Analysis.

Renewable energy technologies are expected to be capable of contributing significantly to solution of the global warming problem because of their inherent advantages : resources can be considered as virtually inexhaustible and no carbon dioxide release will occur during the energy conversion process. On the other hand, their generally high energy cost due to low availability inevitably makes it difficult, under the prevailing decision-making process based on conventional performance indices, to exploit their advantages on the large scale of substitution for conventional energy technologies such as fossil fuel or nuclear power. In the present paper, we introduce a methodology based on indices derived from net energy analysis to assess the significance of renewable energy technologies as an option to mitigate global warming as well as to satisfy future energy demand, and discuss the effectiveness of some new carbon-free energy technologies, including the proposed Hydrogen-Based Global Renewable Energy Network System.

Digital stabilizer design for optimal performance of power systems

Abstract A new method of designing a digital power system stabilizer is presented. In this method, linear discrete-time models of assumed order are used to describe the power system dynamics and the recursive least-squares algorithm is employed to identify the model parameters. Based on the identified parameters, the digital stabilizer is designed such that a modified version of a conventional quadratic performance index is minimized. As a result, the proposed digital power system stabilizer possesses a derivative term not found in the conventional stabilizer, and therefore is suitable for further enhancement of a power system performance. This feature of the proposed stabilization scheme is illustrated using two numerical examples.