Search

The advances in Artificial Intelligence (AI) have led to technological advancements in a plethora of domains. Healthcare, education, and smart city services are now enriched with AI capabilities. These technological advancements would not have been realized without the assistance of fast, secure, and fault-tolerant communication media. Traditional processing, communication and storage technologies cannot maintain high levels of scalability and user experience for immersive services. The metaverse is an immersive three-dimensional (3D) virtual world that integrates fantasy and reality into a virtual environment using advanced virtual reality (VR) and augmented reality (AR) devices. Such an environment is still being developed and requires extensive research in order for it to be realized to its highest attainable levels. In this article, we discuss some of the key issues required in order to attain realization of metaverse services. We propose a framework that integrates digital twin (DT) with other advanced technologies such as the sixth generation (6G) communication network, blockchain, and AI, to maintain continuous end-to-end metaverse services. This article also outlines requirements for an integrated, DT-enabled metaverse framework and provides a look ahead into the evolving topic., Comment: 7 pages, 2 figures, Accepted for publication, IEEE Consumer Electronics Magazine

The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

Driver inattention is among major contributing factors to traffic accidents. There have been and continue to be efforts by governing bodies, car manufacturers, and researchers to prevent driver inattention or, failing that, to mitigate its effects. Many vehicles nowadays come equipped with driver monitoring systems that can alert the driver to, or compensate for, inattention. Moreover, the research community continues to explore and investigate more robust approaches to deal with inattention. Meanwhile, vehicle automation, to various degrees, is becoming more prevalent, with the human’s role in the driving task changing depending on the level of autonomy. This necessitates that inattention detection, moving forward, be studied and designed in view of automation and in the context of a specific level of vehicle autonomy. Driver inattention and vehicle automation interact in a complex way, and that needs to be taken into account in the design of future vehicles. We explore this interaction in this paper in light of research findings, and survey inattention detection systems and attempt to contextualize them within popular frameworks for next-generation autonomous vehicles.

Distracted driving is among the primary causes for serious car accidents. Among the leading cause of death among teenagers today are traffic accidents and major part of them are related to distracted driving. We propose here an end-to-end Convolutional Neural Network-based driver distraction recognition (DDR) system that can generalize to diverse driving conditions. The proposed method consists of two steps: developing generative models to produce images of different driving scenarios and developing a discriminative model for image classification. Unlike traditional methods based on image data-sets collected by simulation experiments, we collect a diverse data-set of drivers in different driving conditions and activity patterns from the Internet and train generative models for multiple driving scenarios. By sampling from these generative models, we augment the collected data-set with new training samples and train a Convolutional Neural Network for distraction recognition. We demonstrate that the generative models are able to generate images of drivers in different driving scenarios. With augmentative images, the DDR system achieves an improvement of 11.45% on image classification performance in a driving simulation environment. Moreover, we demonstrate how the trained DDR systems can be integrated within a driver monitoring system.

Many of today's vehicles come equipped with Advanced Driver Assistance Systems (ADAS). Proactive ADAS have the ability to predict short term driving situations. This provides drivers more time to take adequate actions to avoid or mitigate driving risks. In this work, we address the question of predicting drivers’ imminent maneuvers before they perform an actual steering operation. The proposed system uses deep recurrent neural networks to fuse the information regarding driver observation actions and the driving environment. With new data labeling methods and effective sequential modeling approaches, the system is able to predict with high accuracy driving maneuvers shortly before the actual steering operations. A set of experiments show that the proposed approach anticipates lane change maneuvers 1.50 seconds before cars start to yaw with an accuracy improved to 90.52% and anticipates turn maneuvers at intersections with green lights 2.53 seconds before cars start to yaw with an accuracy improved to 78.59%. We also show in this work how the system can be adapted for driving proficiency assessment.

Although the future impact of plug-in electric vehicles (PEVs) on distribution grids is disputed, all parties agree that mass operation of PEVs will greatly affect load profiles and grid assets. The large-scale penetration of domestic energy storage, such as with photovoltaics (PVs), into the edges of low-voltage grids is increasing the amount of customer-generated electricity. Distribution grids, which are inherently unbalanced, tend to become even more so with the uneven spread of PVs and PEVs. In combination, PEVs and local generation could provide voltage support for distribution networks, and support increased penetration. This paper develops an interactive energy management system for incorporating PEVs in demand response (DR). Using this system, owners can immediately choose whether they want to discharge their PEV battery back into the grid. The system not only provides owners with a flexible scheme for contributing to DR but also ensures that, through real-time collaboration of PEVs and PVs, the three-phase grid operates within acceptable voltage unbalance. An extensive performance evaluation using MATLAB/GAMS simulation of the 123-bus test system verifies the effectiveness of the proposed approach.

This issue marks the first anniversary issue since I was honored to serve as the Editor-in-Chief (EiC) of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS). I am happy to report that we had a very successful year and here are a few highlights that I would like to share with the community. • The latest impact factor of TNNLS is 4.854 according to the Journal Citation Reports. This marks a record high impact factor for our journal and places TNNLS as the number one scholarly publication in Computer Science (Hardware & Architecture), number three in Computer Science (Theory & Methods), and number ten in Electrical and Electronic Engineering journals.

This paper proposes an online intelligent demand coordination of plug-in electric vehicles (PEVs) in distribution systems. The proposed method is based on the assignment of scores to PEVs through a fuzzy expert system. As well, without violation of grid operational constraints, the PEVs are optimally charged in order to maximize the owners' satisfaction in terms of the energy delivered. The optimization problem of online PEV charging is defined as mixed-integer nonlinear programming. Simulation on a typical distribution network proves the effectiveness of the proposed methodology. Results of the analysis indicate that for more critical PEVs, which have shorter parking duration and higher required charging time, the proposed solution outperforms in more robust energy delivery to the PEV and, accordingly, more satisfaction for the owner.

Driver inattentiveness constitutes the main cause of road accidents, which makes it a major factor in road safety. In this paper, we propose a comprehensive framework to address the road safety problem by tackling it from a high-level information fusion standpoint, considering vehicular ad hoc networks (VANETs) as the deployment platform. The proposed framework relies on the multientity Bayesian networks (MEBNs), which exploit the expressiveness of first-order logic for semantic relations, and the strength of the Bayesian networks in handling uncertainty. First, the entities that influence the inattention phenomenon, as well as both their causal and semantic relationships, are identified. Next, an MEBN-based high-level information fusion framework is proposed through which entities, situations, and their relationships in specific contexts are modeled using MEBN fragments. Furthermore, MEBN inference is used to assess the situations of interest by estimating their states. To demonstrate the capabilities of the proposed framework, a collision warning system simulator has been developed, which evaluates the likelihood of a vehicle being in a near-collision situation using a wide variety of local and global information sources available in various VANET environments. If the threat of being in a near-collision situation is determined to be high, then the driver is warned accordingly. Our experimental results for two distinct single-vehicle and multivehicle categories of driving scenarios, as well as a novel hybrid MEBN inference, demonstrate the capability of the proposed framework to efficiently achieve situation and threat assessment on the road.

Recently, a supervised dictionary learning (SDL) approach based on the Hilbert-Schmidt independence criterion (HSIC) has been proposed that learns the dictionary and the corresponding sparse coefficients in a space where the dependency between the data and the corresponding labels is maximized. In this paper, two multiview dictionary learning techniques are proposed based on this HSIC-based SDL. While one of these two techniques learns one dictionary and the corresponding coefficients in the space of fused features in all views, the other learns one dictionary in each view and subsequently fuses the sparse coefficients in the spaces of learned dictionaries. The effectiveness of the proposed multiview learning techniques in using the complementary information of single views is demonstrated in the application of speech emotion recognition (SER). The fully-continuous sub-challenge (FCSC) of the AVEC 2012 dataset is used in two different views: baseline and spectral energy distribution (SED) feature sets. Four dimensional affects, i.e., arousal, expectation, power, and valence are predicted using the proposed multiview methods as the continuous response variables. The results are compared with the single views, AVEC 2012 baseline system, and also other supervised and unsupervised multiview learning approaches in the literature. Using correlation coefficient as the performance measure in predicting the continuous dimensional affects, it is shown that the proposed approach achieves the highest performance among the rivals. The relative performance of the two proposed multiview techniques and their relationship are also discussed. Particularly, it is shown that by providing an additional constraint on the dictionary of one of these approaches, it becomes the same as the other.

Input-output monotonicity is an important constraint found in many application domains. A monotonic fuzzy system (MFS) is defined as a Takagi-Sugeno-Kang (TSK) system whose output is monotonically increasing or decreasing with respect to one or more inputs. This paper reviews the authors' previous work, which derived the parametric conditions for the MFS, and discusses the rationale lying behind the conditions. An MFS is developed by creating a monotonic rule base while preserving the relative monotonicity among the membership functions corresponding to the fuzzy rules. This paper also proves that the parametric conditions are necessary and sufficient to build a single-input zeroth-order MFS with two rules. Only the sufficiency of the conditions holds for a multi-input first-order or higher order TSK fuzzy system with three or more rules.

The goal of the cognitive robotics research is to design robots with human-like cognition (albeit reduced complexity) in perception, reasoning, action planning, and decision making. Such a venture of cognitive robotics has developed robots with redundant number of sensors and actuators in order to perceive the world and act up on it in a human-like fashion. A major challenge to deal with these robots is managing the enormous amount of information continuously arriving through multiple sensors. The primates master this information management skill through their custom-built attention mechanism. Mimicking the attention behavior of the primates, therefore, has gained tremendous popularity in robotic research in the recent years ( Bar-Cohen , Biologically Inspired Intelligent Robots, 2003, and B. Webb , Biorobotics, 2003). The difficulties of redundant information management, however, is the most severe in case of visual perception of the robots. Even a moderate size image of the natural scene generally contains enough visual information to easily overload the on-line decision making process of an autonomous robot. Modeling primates-like visual attention mechanism for the robot, therefore, is becoming more popular among the robotic researchers. A visual attention model enables the robot to selectively (and autonomously) choose a “behaviorally relevant” segment of visual information for further processing while relative exclusion of the others. This paper sheds light on the ongoing journey of robotics research to achieve a visual attention model which will serve as a component of cognition of the modern-day robots.

IAM ABOUT to start my second year of service as the Editor-in-Chief (EiC) of the IEEE TRANSACTIONS ON NEURAL NETWORKS (TNN). Needless to say, my first year as the EiC has been full of excitement and challenges. Transitioning this position from my predecessor to me went very smoothly during the months of September 2009 to January 2010. During the past year, we have accumulated 50+ Associate Editors (AEs) handling roughly 600 new submissions (not counting resubmissions and revised submissions). With the help of these AEs and my predecessor, I was quickly able to learn to do my job, and as such, the transition had very few glitches. The easy part of my job is checking whether a submission is in compliance with our guidelines and where it is within the scope of the TRANSACTIONS, before it is assigned to an AE for handling. The difficult part of my job has been dealing with some papers with three or more reviewers, all of whom agreed to review them but for some reason failed to respond to repeated automatic-review reminders. AEs handling these papers have to take several extra steps to remind reviewers through phone calls or e-mails, look for replacement reviewers, or review the papers themselves. Most authors have been appreciative of the work of the AEs and reviewers, and they accept our decisions without a problem. The backlog of papers has been kept short over the last year. We have maintained an organized printing and paperacceptance schedule, with papers typically printed in the journal within 2‐3 months of acceptance. Our page budget has been kept constant in the past few years (roughly 2060 pages per year), and we expect to hold the same page count for next year.

Most of the common techniques in text mining are based on the statistical analysis of a term, either word or phrase. Statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. Thus, the underlying text mining model should indicate terms that capture the semantics of text. In this case, the mining model can capture terms that present the concepts of the sentence, which leads to discovery of the topic of the document. A new concept-based mining model that analyzes terms on the sentence, document, and corpus levels is introduced. The concept-based mining model can effectively discriminate between nonimportant terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. The proposed mining model consists of sentence-based concept analysis, document-based concept analysis, corpus-based concept-analysis, and concept-based similarity measure. The term which contributes to the sentence semantics is analyzed on the sentence, document, and corpus levels rather than the traditional analysis of the document only. The proposed model can efficiently find significant matching concepts between documents, according to the semantics of their sentences. The similarity between documents is calculated based on a new concept-based similarity measure. The proposed similarity measure takes full advantage of using the concept analysis measures on the sentence, document, and corpus levels in calculating the similarity between documents. Large sets of experiments using the proposed concept-based mining model on different data sets in text clustering are conducted. The experiments demonstrate extensive comparison between the concept-based analysis and the traditional analysis. Experimental results demonstrate the substantial enhancement of the clustering quality using the sentence-based, document-based, corpus-based, and combined approach concept analysis.

Visual attention is one of the major requirements for a robot to serve as a cognitive companion for human. The robotic visual attention is mostly concerned with overt attention which accompanies head and eye movements of a robot. In this case, each movement of the camera head triggers a number of events, namely transformation of the camera and the image coordinate systems, change of content of the visual field, and partial appearance of the stimuli. All of these events contribute to the reduction in probability of meaningful identification of the next focus of attention. These events are specific to overt attention with head movement and, therefore, their effects are not addressed in the classical models of covert visual attention. This paper proposes a Bayesian model as a robot-centric solution for the overt visual attention problem. The proposed model, while taking inspiration from the primates visual attention mechanism, guides a robot to direct its camera toward behaviorally relevant and/or visually demanding stimuli. A particle filter implementation of this model addresses the challenges involved in overt attention with head movement. Experimental results demonstrate the performance of the proposed model.

Designing a reliable network becomes a time-consuming task if it involves All-Terminal Reliability (ATR) calculation, which belongs to the class of NP-hard problems. To make this task easier to address, we propose a new algorithm to decide the ATR feasibility of a given network G without performing exhaustive calculation. The proposed algorithm cumulatively updates the lower and upper bounds of the ATR using the set of subnetworks decomposed or branched from G. Once the lower or upper bound reaches the predetermined ATR requirement, the feasibility of G is determined. The proposed algorithm is characterized by four existing ATR calculation methods, which decompose or branch G into multiple subnetworks. The four implementations of the proposed algorithm will be tested via computer experiments. The results show that the proposed algorithm can make feasibility decision dramatically faster. The arrangement of subnetworks that can improve the performance of the proposed algorithm is also discussed.

We consider here the design aspect of a real-time scheduler for a class of embedded systems. For this purpose, we design a feedback controller for a reservation-based CPU scheduler for soft real-time systems. The execution time of soft real-time systems, such as multimedia systems, portable MP3 players, personal digital assistants, cellular phones, and embedded Web servers is highly variable. Hence, it is crucial to assign an adequate amount of CPU resources for the running tasks to guarantee the quality of service. On the other hand, it is also important not to allocate the large amount of resources to avoid waste. The purpose of this paper is to attain the aforementioned crucial objectives for a class of embedded systems under real-time computing constraints. Specifically, we provide an analytical model for a real-time scheduler in terms of a switched system with time-varying uncertainty. Moreover, by using Lyapunov stability in a linear matrix inequalities (LMIs) framework, we design a state feedback controller that stabilizes the switched system. This, in fact, achieves the regulation of scheduling errors caused by time-varying uncertainty to a desired level. We extend an LMI-framework-based control scheme to a relatively new control application domain, i.e., a soft realtime scheduling domain. We provide performance analysis under scheduler simulation environments and implement a feedback bandwidth server scheduler under a real-time kernel simulator. In the simulation studies, the advantages of the controller design scheme are clearly highlighted in comparison with some conventional existing open-loop systems.

Soft computing techniques are generally well suited for vehicular control systems that are usually modeled by highly nonlinear differential equations and working in unstructured environments. To demonstrate their applicability in real-world applications, two intelligent controllers based on fuzzy logic and artificial neural network are designed for performing a wall-following task. Based on performance and flexibility considerations, the two controllers are implemented onto a reconfigurable hardware platform, namely a field-programmable gate array. As comparative studies of these two embedded hardware controllers designed for the same vehicular application are limited in literature, this research also presents an evaluation of the two controllers, comparing them in terms of hardware resource requirements, operational speeds, and trajectory tracking errors in following different predefined trajectories.

Novel network technology combined with advances in hardware development have permitted the enabling of distributed real-time systems and have shortened the time-to-market period. Distributed frameworks, also known as middleware, are often used to integrate enterprise systems, shorten the development time, and reduce complexity. However, to deploy standard middleware in robotics and control applications, we have to deal with the challenge of producing predictable outputs. Most real-time applications in these areas are developed in ad hoc manner, and as such, it is hard to migrate them to new platforms. To overcome this issue while minimizing development effort and increasing reusability for distributed real-time systems, we propose a control framework for distributed real-time systems based on standard middleware specifications. The control framework is composed of asynchronously running task modules, which can be located on either the local or the remote machines. The task modules are connected by an event channel, which uses the publish/subscribe communication method. We also have developed an adaptive event channel in order to meet real-time system requirements and to produce predictable outputs. Detailed development of the control framework along with the adaptive event channel are assessed through a set of experimental results.

We examine in this paper the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive hybrid intelligent control scheme is proposed here. The controller makes use of a multi-input multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed controller does not require a precise dynamical model of the system's dynamics. As a matter of fact, the controller can achieve its control objectives starting from partial or no a priori knowledge of the system's dynamics. The ability to incorporate the already acquired knowledge about the system's dynamics is among what distinguishes the proposed controller from its predecessor adaptive fuzzy controllers. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying intensity levels of the aforementioned uncertainties. The position and the internal force errors are also shown to asymptotically converge to zero under such conditions

Dempster-Shafer evidence theory (DSET) is a popular paradigm for dealing with uncertainty and imprecision. Its corresponding evidential reasoning framework is theoretically attractive. However, there are outstanding issues that hinder its use in real-life applications. Two prominent issues in this regard are 1) the issue of basic probability assignments (masses) and 2) the issue of dependence among information sources. This paper attempts to deal with these issues by utilizing neural networks in the context of pattern classification application. First, a multilayer perceptron neural network with the mean squared error as a cost function is implemented to calculate, for each information source, posteriori probabilities for all classes. Second, an evidence structure construction scheme is developed for transferring the estimated posteriori probabilities to a set of masses along with the corresponding focal elements, from a Bayesian decision point of view. Third, a network realization of the Dempster-Shafer evidential reasoning is designed and analyzed, and it is further extended to a DSET-based neural network, referred to as DSETNN, to manipulate the evidence structures. In order to tackle the issue of dependence between sources, DSETNN is tuned for optimal performance through a supervised learning process. To demonstrate the effectiveness of the proposed approach, we apply it to three benchmark pattern classification problems. Experiments reveal that the DSETNN out-performs DSET and provide encouraging results in terms of classification accuracy and the speed of learning convergence.

A novel approach for identification of a class of nonlinear systems is introduced. It implements a variable-structure based fuzzy-logic identifier (VSFI) model. The proposed approach adopts a serial-parallel structure and, unlike most fuzzy identifiers, does not require measurements of all the system's states. Based on output measurements, the system states are estimated using a recently developed scheme of a high-gain observer. It is shown that the proposed VSFI is stable provided that the system identified is stable. Furthermore, it can be shown that the estimator state-errors tend exponentially to an arbitrarily small ball of convergence. Simulation results illustrate that the identification scheme proposed might serve as a potential candidate for nonlinear system identification.

We examine the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive fuzzy controller scheme is proposed here. The controller makes use of a multi-input-multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed algorithm requires neither a precise mathematical model of the system's dynamics nor a linear parameterization of the system's uncertain physical parameters. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying intensity levels of the aforementioned uncertainties. The payload position/orientation error and that of the internal forces are also shown to asymptotically converge to zero under such conditions. The performance of the controller proposed is then compared with that of a well-known conventional adaptive controller.

The problem of simultaneous approximations of a given function and its derivatives, has been addressed frequently in pure and applied mathematics. In pure mathematics, Bernstein polynomials get their importance from the fact that they provide simultaneous approximation of a function and its derivatives. In neural network theory, feedforward networks were shown to be universal approximators of an unknown function and its derivatives. In this paper, we consider fuzzy logic systems with the membership functions of each input variables are chosen as the translations and dilations of one appropriately fixed function. We prove, by a constructive proof based on discretization of the convolution operator, that under certain conditions made on the input variables membership functions, fuzzy logic systems of Sugeno type are universal approximators of a given function and its derivatives.

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

Game theory deals with decision-making processes involving two or more parties, also known as players, with partly or completely conflicting interests. Decision-makers in a conflict must often make their decisions under risk and under unclear or fuzzy information. In this paper, two distinct fuzzy approaches are employed to investigate an extensively studied 2/spl times/2 game model-the game of Chicken. The first approach uses a fuzzy multicriteria decision analysis method to obtain optimal strategies for the players. It incorporates subjective factors into the decision-makers' objectives and aggregates objectives using a weight vector. The second approach applies the theory of fuzzy moves (TFM) to the game of Chicken. The theory of moves (TOM) is designed to bring a dynamic dimension to the classical theory of games by allowing decision-makers to look ahead for one or several steps so that they can make a better decision. TOM is the crisp counterpart of TFM, the approach we implement here to deal with games that include fuzzy and uncertain information. The application of fuzzy approaches to the game of Chicken demonstrates their effectiveness in manipulating subjective, uncertain, and fuzzy information and provides valuable insights into the strategic aspects of Chicken.

The layout of temporary facilities in a construction site deals with the selection of their most efficient layout in order to operate efficiently and cost effectively. The layout design seeks the best arrangement of facilities within the available area. In the design process of the layout, many objectives must be considered to effectively utilize people resources, equipment, space, and energy. This study proposes a soft-computing-based approach to improve the layout process of facilities. The main objective is on obtaining the closeness relationship values between each pair of facilities in a construction site. To achieve this, an integrated approach, using fuzzy set theory and genetic algorithms, is used to investigate the layout of temporary facilities in relation with the planned building(s) in a construction site. An example application is presented to illustrate the proposed approach and the results are then discussed along with recommendations for further work. Depending on the importance of relationships among the various facilities in the construction site, this study is expected to provide engineers with an appropriate tool to compare and evaluate different layouts and select the most appropriate and efficient one.

A control procedure is proposed for dealing with the active stiffening motion of a class of flexible structures characterized by nonlinear affine dynamics. Based on recent developments in the area of differential geometry, the procedure allows for determining the critical area for placing a sensor on a given flexible structure beyond which a centralized controller located on the rigid part becomes ineffective. This is used subsequently for locating instrumentation devices and hardware components on the elastic parts of the system. Numerical simulations are carried out to assess the validity of the theoretical framework. Several meaningful results are obtained, and propositions for further findings are outlined.