Search results

The purpose of this paper is to eliminate instability which may occur when a human stiffens his arms in physical human–robot interaction by estimating the human hand stiffness and presenting a modified vibration index.

Human hand stiffness is first estimated in real time as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A time-domain vibration index based on the interaction force is then modified to reduce the delay in instability detection. The instability is confirmed when the vibration index exceeds a given threshold. The virtual damping coefficient in admittance controller is adjusted accordingly to ensure stability in physical human–robot interaction.

By estimating the human hand stiffness and modifying the vibration index, the instability which may occur in stiff environment in physical human–robot interaction is detected and eliminated, and the time delay is reduced. The experimental results demonstrate significant improvement in stabilizing the system when the human operator stiffens his arms.

The originality is in estimating the human hand stiffness online as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A modification of the vibration index is also an originality to reduce the time delay of instability detection.

The purpose of this paper is to solve the problem of tuning of EKF parameters (process and measurement noise co-variance matrices) designed for attitude estimation using Global Positioning System (GPS) aided inertial sensors by employing a Human Opinion Dynamics (HOD)-based optimization technique and modifying the technique using maximum likelihood estimators and study its performance as compared to Particle Swarm Optimization (PSO) and manual tuning.

A model for the determination of attitude of flight vehicles using inertial sensors and GPS measurement is designed and experiments are carried out to collect raw sensor and reference data. An HOD-based model is utilized to estimate the optimized process and measurement noise co-variance matrix. Added to it, few modifications are proposed in the HOD model by utilizing maximum likelihood estimator and finally the results obtained by the proposed schemes analysed.

Analysis of the results shows that utilization of evolutionary algorithms for tuning is a significant improvement over manual tuning and both HOD and PSO-based methods are able to achieve the same level of accuracy. However, the HOD methods show better convergence and is easier to implement in terms of tuning parameters. Also, utilization of maximum likelihood estimator shows better search during initial iterations which increases the robustness of the algorithm.

The paper is unique in its sense that it utilizes a HOD-based model to solve tuning problem of EKF for attitude estimation.

The purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.

In this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.

The numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.

The paper provides a useful reference for further theoretical research and engineering application.

To develop an overview of generalized scales based on pansystems‐relative quantification.

This is a discussion paper exploring the key issues surrounding generalized measures.

The concrete contents of the study include generalized measure views, dimension theory, concepts, logic, theories, Einstein's relativity, quality‐quantity‐degree, methodology of physics, theorems in pansystems mathematics and physics explained within the framework of pan‐scale transformations.

Provides an overview of generalized scales based on pansystems‐relative quantification.

The purpose of this paper is to state new formulation of the programme‐styled framework of pansystems research and related expansions.

Pansystems‐generalized extremum principle (0**: (dy/dx=0)**) is presented with recognitions to various logoi of philosophy, mathematics, technology, systems, cybernetics, informatics, relativity, biology, society, resource, communications and related topics: logic, history, humanities, aesthetics, journalism, IT, AI, TGBZ* <truth*goodness*beauty*Zen*>, etc. including recent rediscoveries of 50 or so pansystems logoi.

A keynote of the paper is to develop the deep logoi of the analytic mathematics, analytic mechanics, variational principles, Hilbert's sixth/23rd problems, pan‐axiomatization to encyclopedic principles and various applications. The 0**‐universal connections embody the transfield internet‐styled academic tendency of pansystems exploration.

The paper includes topics: history megawave, pansystems sublation‐modes, pan‐metaphysics, pansystems dialogs with logoi of 100 thinkers or so, and pansystems‐sublation for a series of logoi concerning the substructure of encyclopedic dialogs such as systems, derivative, extremum, quantification, variational principle, equation, symmetry, OR, optimization, approximation, yinyang, combination, normality‐abnormality, framework, modeling, simulation, relativity, recognition, practice, methodology, mathematics, operations and transformations, quotientization, product, clustering, Banach completeness theorem, Weierstrass approximation theorem, Jackson approximation theorem, Taylor theorem, approximation transformation theorems due to Walsh‐Sewell mathematical school, Hilbert problems, Cauchy theorem, theorems of equation stability, function theory, logic, paradox, axiomatization, cybernetics, dialectics, multistep decision, computer, synergy, vitality and the basic logoi for history, ethics, economics, society OR, aesthetics, journalism, institution, resource and traffics, AI, IT, etc.

The purpose of this paper is to present the methods of concurrent optimization of the analogue and digital parts (software‐hardware) of estimating, identifying and filtering systems with adaptively adjusted analogue parts – adaptive estimation systems (AES).

Concurrent (complete) optimization of AES permits the determination of the most efficient algorithms for computing the estimates and the controls adjusting analogue units of AES in the way maximally improving the quality of observations delivered by them to the digital part. Performance of AES is assessed by the mean square error (MSE) of estimates which is constructed employing the models of input excitation, analogue and digital parts. Global extremum of MSE is searched by Bayesian methods taking into account the always bounded input range of AES and its possible overloading.

There are determined upper boundaries of potentially achievable accuracy of estimates, as well as optimal estimating and controlling observation units' algorithms, ensuring their achievement. New effects appearing in completely optimal AES are analysed.

The paper presents the backgrounds of new and analytically complex approach. To clarify basic ideas and methods, the simplest but useful for applications single input‐single output and single input‐multiple output models of ASE were considered. The obtained results create wide field for further investigations.

The results of the paper can be applied in the development of new classes of high‐efficient adaptive data acquisition, measurement, controlling, communication and other systems.

Concurrent optimisation of AES is important task having no general solution until now. Known approaches allow only the separate optimisation of the analogue and digital parts. Presented original approach enables the correct formalisation and solution of this task that permits the design and realization of systems with characteristics close to theoretically achievable ones and exceeding the characteristics of the known systems of similar predestination.

Episodes of compulsive eating may lead to addiction. Changing relative prices does not always work for many food addicts turned overweight or obese individuals. This paper points to when such situations may arise and how they can be remedied.

We modify the standard neoclassical economics model assumption of indifference curves being convex to the origin. It becomes violated in situations when compulsive eaters become food addicts. As a result of that, the assumption of the concave (quasi-concave) utility function is violated too. We also introduce the possibility that compulsive eaters may have stable but nonconstant preferences.

Most important finding of our model is that a smooth dynamic path to addiction, caused by habit, disappears. Hence, the ability for smooth adjustment to relative price changes due to policies targeting obesity may not be applicable for a compulsive addict. We postulate the existence of thresholds past in which irreversible harm to addicted overeaters may occur. Reaching such states implies that no economic tools at our disposal could reverse the harm, which, in turn, deem that many policies directed at altering relative prices are ineffective in correcting overeating addiction and its consequences.

Even if we believe in consumer sovereignty, it is possible to shape consumer behavior via policy actions, including the behavior of extremum seekers turned addicts. The public policy of obesity should consider, in this case, its social cost.

No prior research has considered food addiction in light of compulsive eating caused by extremum-seeking behavior. Addiction correcting food policies always relied on either rational or myopic addiction models.

This paper aims to present a new method for identification of some flight modes, including natural and non-standard modes, and extraction of their characteristics, directly from measurements of flight parameters in the time domain.

The Hilbert-Huang transform (HHT), as a novel prevailing tool in the signal analysis field, is used to attain the purpose. The study shows that the HHT has superior potential capabilities to improve the airplane flying quality analysis and to conquer some drawbacks of the classical method in flight dynamics.

The proposed method reveals the existence of some non-standard modes with small damping ratios at non-linear flight regions and obtains their characteristics.

The paper examines only airplane longitudinal dynamics. Further research is needed regarding lateral-directional dynamic modes and coupling effects of the longitudinal and lateral modes.

Application of the proposed method to the flight test data may result in real-time flying quality analysis, especially at the non-linear flight regions.

First, to utilize the empirical mode decomposition (EMD) capabilities in real time, a local-online algorithm is introduced which estimates the signal trend by the Savitzky-Golay sifting process and eliminates it from the signal in the EMD algorithm. Second, based on the local-online EMD algorithm, a systematic method is proposed to identify flight modes from flight parameters in the time domain.

Highlights two works being carried out by the French Laboratoire Central des Ponts et Chaussées in the field of smart sensors. The first concerns the knowledge of loads applied to bridges in order to evaluate extreme load effects and fatigue load effects over their lifetime. To achieve these goals, a data acquisition system based on smart sensors extracting and classifying extrema in the traffic loads signal has been developed. The second concerns distributed systems software cost reduction by means of a generic model. The aim of the model is the design of a software generator for smart sensor‐based systems. The key of the system is in the description of an instrumentation plan under the form of a data dependence graph (DDG). The goal of the generator is to map and “execute” that DDG on the physical architecture according to the number of transducers, their affectation to the smart sensors and a PC‐based system controller.

This paper aims to describe two Web-based technologies of geographic information systems (GIS) to be used in monitoring and analysis of environmental processes, proposed by the authors. The technologies analyze the temporal aspect of the process together with the spatial aspect, which defers them from most other works on environmental processes, as these are usually limited either to spatial statistics or to temporal statistics. The approach is instrumental in dynamically finding the relationships between the processes and predicting critical incidents.

Often, the study of natural processes is limited to the analysis of their spatial properties presented by individual time series. The principal idea of this approach consists in supplementing this traditional analysis with the analysis of time fields. In this way, the authors are able to analyze temporal and spatial properties of environmental processes together.

The paper presents two technologies which provide the analysis of spatial and temporal data obtained in natural environment monitoring. The discussed spatio-temporal data mining methods are shown to enable the research into environmental processes, and the solution of practical issues of critical situation forecasts.

The paper discussed Web-based GIS technologies for the analysis of the temporal aspect of the environmental process together with the spatial aspect. Application examples demonstrate the ability of this approach to find the relationships in dynamics of the processes and to predict critical incidents.