Uncertain Environmental Research Articles

Force Tracking Control Method for Robotic Ultrasound Scanning System under Soft Uncertain Environment

Robotic ultrasound scanning has excellent potential to reduce physician workload, obtain higher-quality imaging, and reduce costs. However, the traditional admittance control strategy for robotics cannot meet the high-precision force control requirements for robots, which are critical for improving image quality and ensuring patient safety. In this study, an integral adaptive admittance control strategy is proposed for contact force control between an ultrasound probe and human skin to enhance the accuracy of force tracking. First, a robotic ultrasound scanning system is proposed, and the system’s overall workflow is introduced. Second, an adaptive admittance control strategy is designed to estimate the uncertain environmental information online, and the estimated parameters are used to modify the reference trajectory. On the basis of ensuring the stability of the system, an integral controller is then introduced to improve the steady-state response. Subsequently, the stability of the proposed strategy is analysed. In addition, a gravity compensation process is proposed to obtain the actual contact force. Finally, through a simulation analysis, the effectiveness of the strategy is discussed. Simultaneously, a series of experiments are carried out on the robotic ultrasound scanning system, and the results show that the strategy can successfully maintain a constant contact force under soft uncertain environments, which effectively improves the efficiency of scanning.

An integrated AI framework for managing organizational risk and climate change concerns in B2B market

The United Nations identified sustainable development goals that focus on the three pillars of sustainability-Economy, Society, and Environment. The capability of Artificial intelligence (AI) for societal good and support to these three pillars is a matter of research importance. Drawing attention to literature on various AI frameworks for climate change and the dynamic capability theory, this study conducts a survey of managers working within their AI ecosystem of B2B firms. Confirmatory factor analysis and structural equation modeling are used to test the reliability, validity, and fitness of the research model. This model illustrates how big data-based AI system can influence business performance due to climate change. Research findings reveals paths involving “big data based artificial intelligence system”, “climate change competency process (prediction competency, mitigation competency, adaptation competency)”, “climate competent decision-making,” and “firm performance”, are statistically significant. As a part of research validation, three business cases are studied that focus on the decision-making capability of business under an uncertain environment. This study justifies that climate competent decision-making is a critical skill for businesses operating under uncertain environmental changes. This research brings out management perceptions generated due to climate change and discusses how they influence managers' behaviors that alter relationship dynamics and structure.

Virtual holonomic constraints based super twisting sliding mode control for motion control of planar snake robot in the uncertain underwater environment

In this article, the snake robot having nine links with eight joints is considered for serpentine motion in the underwater environment. The movement of a snake robot occurs due to nonlinear drag forces and fluidic torque in underwater conditions. The uncertainties in drag forces and fluidic torque are considered to track the head angle and tangential velocity in this work. The virtual holonomic constraints are formulated to track all the joints by designing a controller for single joint. The parameters of gait pattern are obtained using the sliding surface. The sliding surface is formulated for head link angle and velocity tracking. Super twisting sliding mode control approach is proposed to deal with uncertainties, and results are compared with sliding mode control and adaptive sliding mode control. The robustness of controller is verified by inputting measurement noise. The significant improvement has been observed in chattering indicator and control effort by using the proposed super twisting sliding mode control. The investigation of this research article focuses on how to get almost chattering free operation in underwater uncertain environmental conditions with minimum control effort.

When UAVs Meet Cognitive Radio: Offloading Traffic Under Uncertain Spectrum Environment via Deep Reinforcement Learning

The emerging Internet of Things (IoT) paradigm makes our telecommunications networks increasingly congested. Unmanned aerial vehicles (UAVs) have been regarded as a promising solution to offload the overwhelming traffic. Considering the limited spectrums, cognitive radio can be embedded into UAVs to build backhaul links through harvesting idle spectrums. For the cognitive UAV (CUAV) assisted network, how much traffic can be actually offloaded depends on not only the traffic demand but also the spectrum environment. It is necessary to jointly consider both issues and co-design the trajectory and communications for the CUAV to make data collection and data transmission balanced to achieve high offloading efficiency, which, however, is non-trivial because of the heterogeneous and uncertain network environment. In this paper, aiming at maximizing the energy efficiency of the CUAV-assisted traffic offloading, we jointly design the Trajectory, Time allocation for data collection and data transmission, Band selection, and Transmission power control ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}^{\mathrm{ 3}}\text{B}$ </tex-math></inline-formula> ) considering the heterogeneous environment on traffic demand, energy replenishment, and spectrum availability. Considering the uncertain environmental information, we develop a model-free deep reinforcement learning (DRL) based solution to make the CUAV achieve the best decision autonomously. Simulation results have shown the effectiveness of the proposed DRL- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}^{\mathrm{ 3}}\text{B}$ </tex-math></inline-formula> strategy.

Compensator-critic structure-based neuro-optimal control of modular robot manipulators with uncertain environmental contacts using non-zero-sum games

In this paper, a novel compensator-critic structure-based neuro-optimal control approach is presented for modular robot manipulators (MRMs) in contact with uncertain environments. Based on joint torque feedback (JTF) technique, the dynamic model of the manipulator systems is described as an integration of joint subsystems associated with the effect of interconnected dynamic coupling (IDC). A local dynamic information-based robust compensator is designed to engage the model uncertainty compensation, and then, the optimal tracking control problem of an MRM system is transformed into an n-player non-zero-sum game issue of multiple joint subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a cost function approximator, which is constructed by using only critic neural networks (NNs), is developed for solving the Hamilton–Jacobi (HJ) equation, thus facilitating the feasible derivation of the neuro-optimal control policy. The tracking error of the closed-loop robotic system is proved to be uniformly ultimately bounded (UUB) on the basis of the Lyapunov theory. Finally, experiment results illustrated the advantage and effectiveness of the developed control method.

Learning-Based Predictive Path Following Control for Nonlinear Systems Under Uncertain Disturbances

Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial for a nonlinear robotic system to achieve high-performance path following control. In this paper, we propose a novel learning-based predictive control scheme that couples a high-level model predictive path following controller (MPFC) with a low-level learning-based feedback linearization controller (LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental disturbances online and tracks the reference state accurately with a probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the linearized system model augmented with a virtual linear path dynamics model to optimize the evolution of path reference targets, and provides the reference states and controls for the low-level LB-FBLC. Simulation results illustrate the effectiveness of the proposed control strategy on a quadrotor path following task under unknown wind disturbances.

Research on the Evaluation Modeling Method of User Experience Quality under Uncertain Noise Environment

Experience economy is a trend of future economic development. Enterprises can only occupy the market more successfully by enhancing the user experience in product design. The user’s product experience is affected by uncertainty noises (such as the user’s environment and different users), rendering the user experience quality evaluation results highly variable. The purpose of this paper is to study the modeling method of user experience quality evaluations under uncertain environmental noises; inspired by normal ordered weighted averaging (OWA) operators, the normal distribution probability density function is implemented to improve the normal ordered weighted averaging (OWA) operators, and a new modeling method designed for the evaluation of user experience quality under uncertainty is proposed, which can overcome the disadvantage of unreasonable weight distribution when the data size and location are different, as well as weigh the importance of both the value and the location of the data. The simulation results show that this method is more effective, accurate, and feasible than the conventional order weighted synthesis operator method. The feasibility and validity of this method are proved by user experience and comparative experiments of multiattribute bread products.

Marketing Environment and governance mechanisms: Focusing on Manufacturer’s Interfirm Benevolence

Purpose - Manufacturers in uncertain environments need to depend on governance mechanisms to reduce the inherent risk in these environments. However, few studies have examined which governance mechanisms a given manufacturers will develop in uncertain environments for managing the relationships with its vertical partner. This study explores how different governance mechanisms function under uncertain environmental circumstances. We also try to investigate the contextual effect of interfirm benevolence as moderator. Research design, data, and methodology - This research provide the conceptual framework of interfirm benevolence on which this research's propositions are predicted. The theoretical background for environmental uncertainty, governance mechanisms and interfirm benevolence will be discussed. Results - The expected results are as follows. Manufacturers in an uncertain environments rely on different governance mechanisms under conditions of high and low interfirm benevolence. In terms of role of interfirm benevolence, interfirm benevolence provides a better understanding of how governance mechanisms can develop in an uncertain supply markets. Conclusions - This research suggests several theoretical and practical implications between channel partners, particularly, this research offers that interfirm benevolence is a crucial competitive factor under environmental uncertainty situation. In future studies, it is necessary to investigate the effect of each governance mechanism structure on performance in an uncertain environment and various level of interfirm benevolence.

Theoretical Guidance on Evacuation Decisions after a Big Nuclear Accident under the Assumption That Evacuation Is Desirable

The development of nuclear power is a major measure for implementing energy-saving and emission reduction strategies all over the world. For a long time, the hazards of nuclear accidents have been obstacles to the development of nuclear power. Temporary evacuation is the fastest and most effective emergency measure to ensure the safety of residents in a short period of time after a nuclear accident. Numerous nuclear accident emergency management personnel make judgments based on personal work experience and subjective awareness when formulating a nuclear accident emergency evacuation plan. How to make a scientific and reasonable decision on the emergency evacuation of nuclear accidents in the shortest time is a common problem faced by many emergency departments when a nuclear accident occurs. In a complex and ever-changing radiation environment, how to maximize the use of limited information and make decisions quickly in an uncertain environment is a core issue that effectively reduces the risk of nuclear accidents. This paper constructs a set of assessment system of nuclear accident emergency evacuation plan selection based on the characteristics of nuclear accident emergencies under uncertain environmental conditions. It uses triangular fuzzy language to describe nuclear accident emergency evacuation decision plans and the weighting of relevant factors. Additionally, the K-means clustering method is used to calculate the weight of experts, which reduces the influence of subjective factors considered by decision makers. Finally, a decision model for emergency evacuation of nuclear accidents is constructed based on the TOPSIS decision model.

Analysis of mode mismatch in uncertain shallow ocean environment

The actual ocean is an uncertain acoustic propagation environment. For the localization algorithms that rely on the precise ocean environmental parameters, the environmental mismatch problems will exist and performance degradation may be very serious. In the uncertain ocean environment, the uncertainties of sound field will have different effects on different normal modes propagating in the sound field, thus analysis of the mismatch characteristics of normal modes affected by uncertain environmental parameters can provide technical guidance for practical engineering applications. Based on the shallow-water acoustic propagation model, this paper simulates and analyzes the mismatch results of the modal depth eigenfunction and the horizontal wave number of each normal mode under conditions of environmental mismatch. Research indicates that the influence of different environmental parameters on normal modes in the sound field is not exactly the same. It was found that the sound-speed profile and seawater depth affect significantly, followed by sediment sound speed, the other parameters appear to be relatively minor importance.

Estimation of the probability density function of transmission loss in the ocean using area statistics

Predictions of acoustic transmission loss (TL) in the ocean and its uncertainty are useful for a variety of ocean and naval engineering applications. Previously, an ad-hoc technique, area statistics (AS), was developed as a computationally efficient method to estimate the probability density function (PDF) of TL in any uncertain ocean environment from a single range-depth TL-field calculation in that environment. Here, the performance of AS is reported for ten ocean environments having uncertain sound speed profile, bathymetry, and seabed properties. In each environment, a parabolic-equation-based prediction of TL from a point source was generated as a function of range and depth at frequencies of 100, 200, and 300 Hz using the most probable values of the uncertain environmental parameters. From these calculations, AS was used to estimate the PDF of TL at more than 3000 locations within the ten environments. These estimated PDFs are then quantitatively compared, via an L1-error norm, to PDFs generated from traditional Monte Carlo techniques for the same locations and frequencies. Current results show that the L1-error of the AS PDF is less than 0.5, indicating 75% or greater overlap between the AS and Monte Carlo PDFs, at 90% of the test locations. [Sponsored by ONR.]

Direct Solution of Fuzzy Project Crashing Problem with Fuzzy Decision Variables using Tabu Search and Simulated Annealing Algorithms

Project management is a very important field employed for scheduling activities and monitoring the progress, in competitive and fluctuating environments. Project crashing analysis is concerned with shortening the project duration time by accelerating some of its activities at an additional cost. In reality, because of uncertain environment conditions there can be ambiguity in the parameters of the problem. The uncertainty in the parameters can be modeled via fuzzy set theory. Using fuzzy models give the chance of better project scheduling with more stability under uncertain environmental factors. In this study, a fuzzy project crashing problem with fuzzy decision variable - occurrence time of events - and fuzzy normal activity duration times is handled. The fuzzy project crashing problem is solved without any transformation process by employing a fuzzy ranking method and the tabu search and simulated annealing algorithms.

THE EFFECT OF POTENTIAL ABSORPTIVE CAPACITY AND INNOVATION ON COMPETITIVE ADVANTAGE: A MODEL FROM PARTICIPATIVE LEADERSHIP PERSPECTIVE

Increasing uncertain environmental conditions and changing business environment have inevitably pushed organizations to quest for knowledge. The new knowledge obtained by the end of this quest moves an organization to further position from the current one. Similarly, organizations should adapt themselves based on knowledge in order to survive within the context of changing customer demands and developing technologies. This adaptation, herewith, often requires product/service, process and method innovation that enable the organizations to have a competitive advantage over the others. The aim of this study is to exert the effect of participative leadership style on potential absorptive capacity, which means knowledge acquisition and assimilation, innovation and competitive advantage by addressing participative leadership style on organizational level rather than individual and team level, and also to discuss the mediating roles of potential absorptive capacity and innovation between participative leadership styles and competitive advantage

Efficient estimation of the probability density function of transmission loss in uncertain ocean environments

Predictions of acoustic transmission loss (TL) in the ocean are useful in a variety of naval and ocean engineering applications. However, real ocean environments are imperfectly known and have many uncertainties. Consequently, TL calculations performed for these environments are also uncertain. Traditional Monte-Carlo methods used to obtain probability density functions (PDF) of TL in such circumstances require many field calculations, and are impractical for real-time applications. This presentation describes how the PDF of TL at a point of interest may be estimated using a single TL-field calculation and the statistics of TL within a range-depth area containing that point. To document the performance of this technique, RAM was used to perform TL field calculations for 100 Hz and 300 Hz sound sources in realistic ocean sound channels with eight uncertain environmental parameters describing the bathymetry, sound speed profile, and bottom properties. Preliminary results indicate that TL PDFs generated with area statistics have an L1-error ≤ 0.5 when compared with 1000-calculation Monte-Carlo PDF results throughout most of the sound channel. However, greater errors were observed in the near field as well as in areas where the sound channel depth was less than 100 m, for 100 Hz sound. [Sponsored by ONR.]

The Determinants of Green Product Innovation Performance

AbstractThis study utilizes structural equation modeling (SEM) to explore the positive effects of corporate environmental commitment and green human capital on green product innovation performance via the mediator: green adaptive ability. Green adaptive ability is the capability to comply with uncertain environmental regulations and environmentalism. This study tests the hypotheses in a sample of 136 of Taiwan's manufacturing companies. The findings in this study highlight the importance of corporate environmental commitment and green human capital that are determinants of green adaptive ability to develop green product innovation. The empirical results show corporate environmental commitment positively affects green product innovation performance directly and positively influences it indirectly via green adaptive ability. Green human capital, however, does not affect green product innovation performance directly. Green adaptive ability is a full mediator between green human capital and green product innovation performance but a partial mediator between corporate environmental commitment and green product innovation performance. Copyright © 2014 John Wiley &amp; Sons, Ltd and ERP Environment

Three-dimensional multiple-source focalization in an uncertain ocean environment

This letter develops a Bayesian focalization approach for three-dimensional localization of an unknown number of sources in shallow water with uncertain environmental properties. The algorithm minimizes the Bayesian information criterion using adaptive hybrid optimization for environmental parameters, Metropolis sampling for source bearing, and Gibbs sampling for source ranges and depths. Maximum-likelihood expressions are used for unknown complex source strengths and noise variance, which allows these parameters to be sampled implicitly. An efficient scheme for adding/deleting sources is used during the optimization. A synthetic example considers localizing a quiet source in the presence of multiple interferers using a horizontal line array.

Three-dimensional localization of multiple sources in an uncertain ocean environment

This paper develops a Bayesian focalization approach to simultaneous three-dimensional localization of multiple sources in shallow water with uncertain environmental properties, for application to horizontal line array (HLA) data. The algorithm maximizes the posterior probability density over unknown environmental (seabed and water column) and source parameters (includes source-bearing) using an adaptive hybrid optimization algorithm. Maximum-likelihood expressions for source strengths and noise variance are used that allows these parameters to be sampled implicitly rather than explicitly. An extension to the algorithm that optimizes for a priori unknown number of sources, based on minimizing the Bayes information criterion, is developed and presented. The algorithm is applied to simulated multi-frequency data in a continental shelf environment.

Solution of fuzzy multi-objective project crashing problem

Project management is a very important field employed for scheduling activities and monitoring the progress, in competitive and fluctuating environments. The feasible duration time required to perform a specific project is determined using critical path method. However, because of competitive priorities, time is important and the completion time of a project determined using critical path method should be reduced to meet a deadline requested. In this situation, project crashing problem arises. Project crashing analysis is concerned with shortening the project duration time by accelerating some of its activities at an additional cost. In general, the parameters of the problem are accepted as certain and the project crashing problems are solved using deterministic solution techniques. In reality, because of uncertain environment conditions, incomplete or unobtainable information, there can be ambiguity in the parameters of the problem. The uncertainty in the parameters can be modeled via fuzzy set theory. Using fuzzy models gives the chance of better project management decisions with more stability under uncertain environmental factors. In the literature, various authors solved different fuzzy versions of project management problems via transforming them into their crisp equivalents. In this study, a fuzzy multi-objective project crashing problem with fuzzy parameters is handled. The fuzzy project crashing problem is solved with a direct solution approach based on fuzzy ranking methods and the tabu search algorithm.

The effects of uncertain environment parameters on sonar operation range in shallow water

Traditional sonar detection performance prediction is based deterministic prediction method, which does not take the impact of uncertain environmental parameters on transmission loss into consideration. Taking passive sonar detection as example, the farthest operation range is certainly unique for fixed frequency, of course, SL, NL, DI and DT are all well known. In this paper, a probabilistic prediction method is proposed in which transmission loss is subject to a probability distribution even for fixed frequency and range. An acoustic field modeling and numeric calculation framework is raised to fill the gap between underwater physics and signal analysis. For simple physical model depicting an infinite half-space consisting of two fluid layers (water and sediment), calculation results indicate that transmission loss is subject to gamma distribution due to roughness at the boundaries and sound speed fluctuation. In the end, some potential applicable prospects of this new acoustic field prediction method are discussed.

Methodology for Determining the Acceptability of Given Designs in Uncertain Environments

Managers wish to verify that a particular engineering design meets their requirements. This design's future environment will differ from the environment assumed during the design. Therefore it is crucial to determine which variations in the environment may make this design unacceptable. The proposed methodology estimates which uncertain environmental parameters are important (so managers can become pro-active) and which parameter combinations (scenarios) make the design unacceptable. The methodology combines simulation, bootstrapping, and metamodeling. The methodology is illustrated through a simulated manufacturing system, including fourteen uncertain parameters of the input distributions for the various arrival and service times. These parameters are investigated through sixteen scenarios, selected through a two-level fractional-factorial design. The resulting simulation Input/Output (I/O) data are analyzed through a first-order polynomial metamodel and bootstrapping. A second experiment gives some outputs that are indeed unacceptable. Polynomials fitted to the I/O data estimate the border line (frontier) between acceptable and unacceptable environments.