Input-output Information Research Articles

Efficiency analysis of viticulture systems in the Portuguese Douro region

PurposeThe purpose of this paper is to estimate and analyse the technical efficiency (TE) component of productivity for a sample of winegrowers from the Douro Demarcated Region in Portugal.Design/methodology/approachThe data were collected through face-to-face surveys and includes a sample of 110 farmers’ vineyards with specific input-output information and other data about production systems during the year of 2017. The authors use a two-stage data envelopment analysis using bootstrap techniques to obtain TE scores in the farmers’ vineyards and to examine the determinants of its efficiency.FindingsThe results show that some farmers’ vineyards have a low efficiency level and that there are essential determinants of the production system, which can influence its efficiency. This suggests considerable opportunities for improvement of wine grape productivity through better use of available resources considering the state of technology.Originality/valueThis work has overcome the lack of data in the farmers’ vineyards, the lack of efficiency studies in the region and also allowed to evaluate the production systems and to assess their impact on efficiency.

In-vitro reconfigurability of native chemical automata, the inclusiveness of their hierarchy and their thermodynamics

Living systems process information using chemistry. Computations can be viewed as language recognition problems where both languages and automata recognizing them form an inclusive hierarchy. Chemical realizations, without using biochemistry, of the main classes of computing automata, Finite Automata (FA), 1-stack Push Down Automata (1-PDA) and Turing Machine (TM) have recently been presented. These use chemistry for the representation of input information, its processing and output information. The Turing machine uses the Belousov-Zhabotinsky (BZ) oscillatory reaction to recognize a representative Context-Sensitive Language (CSL), the 1-PDA uses a pH network to recognize a Context Free Language (CFL) and a FA for a Regular Language (RL) uses a precipitation reaction. By chemically reconfiguring them to recognize representative languages in the lower classes of the Chomsky hierarchy we illustrate the inclusiveness of the hierarchy of native chemical automata. These examples open the door for chemical programming without biochemistry. Furthermore, the thermodynamic metric originally introduced to identify the accept/reject state of the chemical output for the CSL, can equally be used for recognizing CFL and RL by the automata. Finally, we point out how the chemical and thermodynamic duality of accept/reject criteria can be used in the optimization of the energetics and efficiency of computations.

Modified Model-Free Adaptive Control using Compact-Form Dynamic Linearization Technique

Abstract Model-free adaptive control has been used to design feedback in cases when accurate mathematical modeling of the system can not be realized. By using only input-output information, the control approach is attractive with respect to minimal design efforts. In this contribution an improved model-free control program is proposed and applied to flexible systems. The main control idea is based on the compact-form dynamic linearization technique. A linearized controller structure which contains a matrix of unknown time-varying parameters namely pseudo-partial derivative can be designed. First, an equivalent linearized data-based model of the original system is established and corrected locally by using the recursive least-squares estimation algorithm. Then a modified objective function with respect to the controller parameter matrix considering minimization of the current tracking errors and its variations is proposed for control performance improvement. Finally, the required control inputs are calculated to fulfill control requirements. For illustration and as example, the newly introduced method is applied to reduce vibrations of an elastic crane representing a multivariable system. The control effectiveness is verified nummerically and compared with conventional model-free and PI controllers.

A Model for Visual Assessment of Fault Plane Solutions and Active Tectonics Analysis Using the Global Centroid Moment Tensor Catalog

In this study, individual fault plane solutions are developed using various methods to improve the understanding of active tectonics on a regional scale. The comparative analysis of a focal mechanism solution (FMS) has not elicited the attention of researchers. Therefore, this study aims (1) to visually analyze the fault plane solution for 20 local faults that are responsible for all the earthquakes that occurred using visualization techniques such as: fault parameters, the linked Bingham method, the ad hoc pressure (P) axis and tension (T) axis method, and the moment tensor method; (2) to identify the best method for FMS; and (3) to understand the active tectonics of a fault population. A comparative analysis of the models is systematically documented to improve the understanding of the methods. An analysis of the overall fault mechanism is conducted for the analytic determination of fault movement using fault population data from the Global Centroid Moment Tensor catalog. The approach used in this work is a newly designed method for analyzing the reliability of various techniques for fault mechanism and overall fault movement research. Findings show that for the fault mechanism analysis, the P and T axes method and the moment tensor method are better than the fault plane solution from the fault parameters and the linked Bingham method based on the input parameters, output information, model outfit, and accuracy. The moment tensor method is one of the best approaches for analyzing fault mechanism because the errors in the nine components used as input data for the modeling are negligible. Meanwhile, the P and T axes method is one of the best techniques for the overall analysis of fault movement. P and T dihedral analysis using Kamb contouring is modeled. It indicates that the overall mechanisms of compression and dilation are features at the NW–SE and E–W directions, respectively. This comprehensive and consistent analysis of the fault mechanism provides an overview of the seismotectonic settings in Sabah, Malaysia.

Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers

Reconfigurable manufacturing systems (RMSs) are considered the solution of choice when variable production capacity and functionality are required. A combinational approach, which integrates the steady-state probabilities of repairable reconfigurable machine tools (RMTs) and inventory-state probabilities of buffers through an improved universal generating function, is introduced in this study to assess the compound performance indicators (CPIs) of a repairable RMS. This paper contributes to the existing literature by considering the availability of buffers to calculate the CPIs of an RMS. In the proposed approach, the dynamic-state probability for each RMT is determined with a homogeneous continuous-time Markov model, and steady-state probability is obtained as the limit of the dynamic probability as time tends to infinity. In addition, a descriptive input-output information flow, which combines the conveying processes of the machined parts through buffers with the Poisson process, is proposed to determine the inventory-state probabilities of the buffers. Moreover, the explicit expressions of the CPI and expected performance rate (for the RMS and its constituent RMTs) are determined, and the validation procedure and technical details of the performance analysis for the Monte Carlo simulation are presented. Finally, a non-serial, repairable, multi-state RMS with multiple buffers that produces three types of engine cylinder heads is presented to validate the proposed approach. The simulation results verify the accuracy of the performance assessment of the RMS. It is useful for performance improvement in terms of machine reliability, resource utilisation efficiency, and decision-making concerning the configuration of RMS with buffers.

Principles Regarding the Structuring of the Hybrid Models in Engineering

Advancements in computer science offer new concepts and instruments to be used in the modelling of the complex phenomena. Apart from the commercial software applications, researchers may use performant data processing methods based on the latest computing techniques in order to create original software instruments. A hybrid model consists of a series of studies of various types: theoretical (analytical and/or numerical) and experimental. A hybrid model may be represented as a flowchart, each study being an element of the data flow representation. Each element, i.e. distinct study, has input data and output information expressed as data files and/or as visualizations meant to graphically synthesize the results. The output data of an element is the input data of the following study, in this way being developed the flowchart of the hybrid model, similar to the method used in the Unified Modelling Language. The rapid development of the hybrid models depends on the degree of readiness regarding the software libraries to be created. The original software libraries solve two types of problems: solvers of the distinct studies and data integration in the wider context of the hybrid model. The distinct studies which model a particular aspect of the phenomenon are used to automatically and accurately generate the input data of the ‘main’ model which is usually created using a commercial software application. In this way, the distinct studies may be represented as satellites orbiting the ‘main’ model. The paper presents the basic concepts used to conceive hybrid models and it is inspired by the software development activity for research projects in the latest 30 years.

Development of neural­network and fuzzy models of multimass electromechanical systems

The study objective was to construct models of multimass electromechanical systems using neural nets, fuzzy inference systems and hybrid networks by means of MATLAB tools. A model of a system in a form of a neural net or a neuro-fuzzy inference system was constructed on the basis of known input signals and signals measured at the system output. Methods of the theory of artificial neural nets and methods of the fuzzy modeling technology were used in the study.A neural net for solving the problem of identification of the electromechanical systems with complex kinematic connections was synthesized using the Neural Network Toolbox application package of the MATLAB system. A possibility of solving the identification problem using an approximating fuzzy system using the Fuzzy Logic Toolbox package was considered. A hybrid network was synthesized and implemented in a form of an adaptive neuro-fuzzy inference system using the ANFIS editor. Recommendations for choosing parameters that have the most significant effect on identification accuracy when applying the methods under consideration were given. It was shown that the use of neural nets and adaptive neuro-fuzzy inference systems makes it possible to identify systems with accuracy of 2 to 4%.As a result of the conducted studies, efficiency of application of neural nets, fuzzy inference systems and hybrid nets to identification of systems with complex kinematic connections in the presence of input-output information was shown. The neural-network, fuzzy and neuro-fuzzy models of two-mass electromechanical systems were synthesized with the use of modern software tools.The considered approach to using artificial intelligence technologies, that is neural nets and fuzzy logic is a promising line of construction of appropriate neural-network and neuro-fuzzy models of technical objects and systems. The study results can be used in synthesis of regulators for the systems with complex kinematic connections to ensure their high performance.

Transfer-learning based gas path analysis method for gas turbines

Data-driven gas path analysis is a state-of-health diagnostic method. The method utilizes input-output information to solve the health assessment problem of gas turbine engines. In recent years, the rapid development and intense competition of the gas turbine industry bring new challenges to the method. A novel method is required to achieve life-cycle monitoring and further increase diagnostic accuracy. This study proposes a transfer-learning based gas path analysis method. The method combines transfer learning with data-driven gas path analysis for the first time. The proposed method can not only learn fault patterns from engine operating data but also recognize the different diagnostic value of data. This ability enables the developed GPA model to automatically adjust its training dataset to maintain a steady diagnostic accuracy in life-cycle monitoring. Experiments were presented and discussed on a two-spool split flow turbofan engine platform. The transfer-learning based gas path analysis method achieves averaged 94% diagnostic accuracy in a life-cycle simulation. The method improves diagnostic accuracy by 11–20% as compared with traditional data-driven methods.

Tracking Control of Robot Manipulators with Unknown Models: A Jacobian-Matrix-Adaption Method

Tracking control of robot manipulators is a fundamental and significant problem in robotic industry. As a conventional solution, the Jacobian-matrix-pseudo-inverse (JMPI) method suffers from two major limitations: one is the requirement on known information of the robot model such as parameter and structure; the other is the position error accumulation phenomenon caused by the open-loop nature. To overcome such two limitations, this paper proposes a novel Jacobian-matrix-adaption (JMA) method for the tracking control of robot manipulators via the zeroing dynamics. Unlike existing works requiring the information of the known robot model, the proposed JMA method uses only the input-output information to control the robot with unknown model. The solution based on the JMA method transforms the internal, implicit, and unmeasurable model information to the external, explicit, and measurable input-output information. Moreover, simulation studies including comparisons and tests substantiate the efficacy and superiority of the proposed JMA method for the tracking control of robot manipulators subject to unknown models.

Two-party secure connection in Bluetooth-enabled devices

ABSTRACTSecure Simple Pairing, a Bluetooth-pairing protocol, suffers from passive off-line and active online-guessing attack. These assaults are a direct result of the shortcomings in Bluetooth specification. Bluetooth technology uses the principles of device inquiry and inquiry scan. Scanning devices listen in on known frequencies for devices that are actively inquiring. If two Bluetooth devices know absolutely nothing about each other, one must run an inquiry to try to discover the other. One device sends out the inquiry request, and any device listening for such a request will respond with its address, and possibly its name, Input/Output capability and other information. Before connection, each device knows the address, their name, their capability, Quality-of-Service, etc. During pairing, Man-In-The-Middle attacker may capture all the information of connecting devices and impersonate them. This paper introduces the security augmentation in Bluetooth pairing by postponing exchange of Input-Output capability and other information like Quality-of-Service until it is essentially required and by casing the link key with a pair of Elliptic Curve Diffie-Hellman keys. Consequently, this leads to increased pairing time. Yet, we overlooked the increased pairing time, as the proposed Bluetooth-pairing protocol improves security by strengthening the link key.

Subspace Identification of Local Systems in One-Dimensional Homogeneous Networks

This note considers the identification of large-scale one-dimensional networks consisting of identical LTI dynamical systems. A subspace identification method is developed that only uses local input-output information and does not rely on knowledge about the local state interaction. The proposed identification method estimates the Markov parameters of a locally lifted system, following the state-space realization of a single subsystem. The Markov-parameter estimation is formulated as a rank minimization problem by exploiting the low-rank property and the two-layer Toeplitz structural property in the data equation, whereas the state-space realization of a single subsystem is formulated as a structured low-rank matrix-factorization problem. The effectiveness of the proposed identification method is demonstrated by simulation examples.

Dynamic optimization of a cryogenic air separation unit using a derivative-free optimization approach

Optimal dynamic product transition is a challenging and important issue in manufacturing plants. When a reliable dynamic model is available, gradient-based optimization algorithms can be used to achieve this aim. However, in some cases a first principles dynamic model may not be available. In this work, we will assume that only input-output information from a dynamic model embedded in a dynamic process simulator is available for optimal product transitions between products. We present a derivative-free optimization trust region approach to deal with the product dynamic optimization problem of an air separation unit used in several processing plants to obtain pure oxygen. High-purity oxygen is required in intensive energy applications such as steel plants and in combustion processes. A closed-loop model predictive control strategy is used where the system to be optimized is embedded in the ASPEN Dynamics simulation environment. The results demonstrate that black-box dynamic models can be dynamically optimized when model of the dynamic model and/or its gradient information are not available. We have successfully applied the non-linear model predictive control and derivate-free approach to several oxygen composition and productivity transition issues.

Noise-modulated neural networks as an application of stochastic resonance

Stochastic resonance (SR) is a phenomenon by which the input signal of a nonlinear system, with magnitude too small to affect the output, becomes observable by adding a non-zero level of noise to the system. SR is known to assist biological beings in coping with noisy environments, providing sophisticated information processing and adaptive behaviors. The SR effect can be interpreted as a decrease in the input-output information loss of a nonlinear system by making it stochastically closer to a linear system. This work shows how SR can improve the performance of a system even when the desired input-output relationship is nonlinear, specifically for the case of a neural networks whose hidden layers consist of threshold functions. Universal approximation capability of neural networks exploiting SR is then discussed: although a network consisting of threshold activation functions has been proven to be an universal approximator in the context of the extreme learning machine (ELM), once SR is taken into account, the system can be deemed as a classic three-layer neural network whose universality has been previously proven by simpler proofs. After proving the universal approximation capability for an infinite number of hidden units, the performance achieved with a finite number of hidden units is evaluated using two training algorithms, namely backpropagation and ELM. Results highlight the SR effect occurring in the proposed system, and the relationship among the number of hidden units, noise intensity, and approximation performance.

Multilayer Perceptron for Classification of Structural delamination and Transducers Debonding in Smart Composite Laminates

This paper investigates the feasibility of multilayer perceptron (MLP) for the classification of structural delamination and transducers debonding in smart composite laminates. Structural vibration response is employed to extract the discriminative features for multiple damages. The dynamic model of the smart structure with inter-ply delaminations and partially debonded piezoelectric sensor and actuator is developed by incorporating improved layerwise theory, higher order electric potential field and finite element method. The developed model is solved in the time domain to obtain the transient response of the healthy and damaged structures through a surface bondedpiezoelectric sensor for random input excitations applied through a piezoelectric actuator. The input-output information is fed into a system identification algorithm to identify damage sensitive features for the healthy anddamaged state of the smart composite laminate. The discriminative features are classified through MLP in a supervised manner and its classification accuracy is evaluated in terms of true positive (TP) rate, false positive (FP) rate, precision and area under the receiver operating characteristic curve (ROC area).

HOSM Differentiator with Varying Gains for Global/Semi-Global Output Feedback

This paper develops a higher-order sliding mode (HOSM) differentiator with adaptive gains to address the exact tracking control problem using only input-output information of a wider class of nonlinear systems with disturbances and parametric uncertainties. The complete state of the plant is assumed unmeasured so that a norm state estimator is constructed to norm bound the state-dependent disturbances and dynamically update the gains of the proposed differentiator. Global/semi-global stability properties and robust exact tracking can be achieved when the proposed adaptive HOSM based differentiator is applied to output-feedback purposes. Numerical simulations are presented for different sliding mode control designs, such as: (a) first-order sliding mode control, (b) non-singular terminal sliding modes, (c) second order sliding mode (SOSM) algorithms (twisting and super-twisting) as well as (d) quasi-continuous HOSM finite-time controllers.

Active regulation of battery charge-sustaining in ECMS: Application in energy management for engine waste heat recovery system

Equivalent consumption minimization strategy (ECMS) is a widely used energy management method in the battery equipped bio-sources energy system, in which an equivalence factor is adopted to weigh the two kinds of energy, usually fuel and electricity. The regulation of the equivalence factor will affect not only the fuel consumption but also the battery's state of charge (SOC) sustainability, which results great influence upon the energy management of the system. In this paper, a new perspective of treating the relationship between the equivalence factor and instantaneous fuel consumption as well as instantaneous electricity consumption as a first-order dynamic system is proposed, thus the relationship between the equivalence factor and SOC is deduced as a second-order dynamic system. Based on the perspective, a linear second-order Active Disturbance Rejection Control (ADRC) is employed to regulate the equivalence factor, which can estimate and compensate, in real-time, disturbances in the system from input-output information only. An engine waste heat recovery energy network constructed by - - - - - is selected as the research object, simulation verifications upon 11 different driving cycles based on the simulation platform established by GT-SUITE software are conducted to test the fuel-saving capability and SOC self-sustainability by the proposed approach with a same set of tuning parameters. Simulation results indicate that both the fuel-saving capability and the SOC self-sustainability can be ensured by the active control of equivalent factor with a same set of tuning parameters upon all the 11 driving cycles, demonstrating a great adaptability of the control parameters.

Sub-kBT micro-electromechanical irreversible logic gate.

In modern computers, computation is performed by assembling together sets of logic gates. Popular gates like AND, OR and XOR, processing two logic inputs and yielding one logic output, are often addressed as irreversible logic gates, where the sole knowledge of the output logic value is not sufficient to infer the logic value of the two inputs. Such gates are usually believed to be bounded to dissipate a finite minimum amount of energy determined by the input–output information difference. Here we show that this is not necessarily the case, by presenting an experiment where a OR logic gate, realized with a micro-electromechanical cantilever, is operated with energy well below the expected limit, provided the operation is slow enough and frictional phenomena are properly addressed.

Data-driven output-feedback fault-tolerant control for unknown dynamic systems with faults changing system dynamics

This paper studies the data-driven output-feedback fault-tolerant control (FTC) problem for unknown dynamic systems with faults changing system dynamics. In a framework of active FTC, two basic issues are addressed: the fault detection employing only the measured input–output information; the controller reconfiguration to achieve optimal output-feedback control in the presence of multiple faults. To detect faults and write the system state via the input–output data, an approach to data-driven design of a residual generator with a full-rank transformation matrix is presented. An output-feedback approximate dynamic programming method is developed to solve the optimal control problem under the condition that the unknown linear time-invariant discrete-time plant has multiple outputs. According to the above results and the proposed input–output data-based value function approximation structure of time-varying plants, a model-free output-feedback FTC scheme considering optimal performance is given. Finally, two numerical examples and a practical example of a DC motor control system are used to demonstrate the effectiveness of the proposed methods.

Robust and simple intelligent observer-based fault estimation and reconstruction for a class of non-linear systems: HIRM aircraft

ABSTRACTThis paper introduces an observer strategy, namely a Sliding Mode Observer (SMO), to realise the fault detection and estimation of general uncertain non-linear systems. The use of a non-linear observer is considered for monitoring the states of a high incidence research model (HIRM) aircraft system. For a special class of Lipschitz non-linear system, a fault reconstruction scheme is presented where the reconstructed signal can approximate the fault signal to any accuracy. The proposed method is based only on the available plant input-output information and can be calculated online. Moreover, the globally asymptotic stability of the closed-loop system is mathematically proved. Finally, an HIRM aircraft system example is given to illustrate the efficiency of the proposed approach.

Stochastic iterative learning control for discrete linear time-invariant system with batch-varying reference trajectories

In this paper, we present adaptive iterative learning control (ILC) schemes for discrete linear time-invariant (LTI) stochastic system with batch-varying reference trajectories (BVRT). If reference trajectories change every batch, ILC shows a different convergence property from that of the identical reference trajectory. First, we derive the convergence property and propose deterministic adaptive ILC combined with iterative learning identification for LTI system with BVRT. If the state noise and measurement noise exist, convergence rate and tracking performance are degraded because the controller considers the difference arising from the noise as tracking error. To deal with such a problem, we propose two approaches. The first is based on a batch-domain Kalman filter, which uses the difference between the current output trajectory and the next reference trajectory as a state vector, while the second is based on a time-domain Kalman filter. In the second approach, the system is identified at the end of each batch in an iterative fashion using the observer/Kalman filter identification (OKID). Then, the stochastic problem is handled using Kalman filter with a steady-state Kalman gain obtained from the identification. Therefore, the second approach can track the reference trajectories of discrete LTI stochastic system using only the input–output information. Simulation examples are provided to show the effectiveness of the proposed schemes.