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To ensure that the mixed vehicle group can reach minimum total travel time and consistency travel within the traffic light proximity under the constraint of the signal light, this paper presents a new distributed cooperative control strategy incorporating heterogeneous time delays, packet losses and information uncertainty from the perspective of the cyber-physical system. First, to guarantee the current vehicle can maintain the desired spacing from the preceding vehicle and the rearing vehicle, a new car following model is proposed. Second, a unified mixed vehicle group model is developed by incorporating human-driven vehicles (HVs) car-following model and connected and automated vehicles (CAVs) control model, and the heterogeneous time delays, packet losses and information uncertainty are embedded into unified mixed vehicle group model. Third, from the perspective of the cyber-physical system, a distributed cooperative control strategy is proposed by considering constraints on the consensus and the traffic light sequence to ensure the mixed vehicles group consensus pass the in the vicinity of traffic signal light (VTSL) on the green light phase. Finally, the stability condition of the mixed vehicle group system is derived, and the effectiveness of the developed control strategy is demonstrated by simulation experiments.

This article discusses the management of autonomous energy complex with renewable energy within the smart grid solutions in terms of different types of input data uncertainty. On the basis of the theory of fuzzy logic and a fuzzy situational network, an approach has been developed that allows one to calculate the type of uncertainty of the input information, and then select the most appropriate mathematical model and control strategy for AEC with RES.

Distributed generation (DG) plays an important role in the development of modern systems of electric energy distribution and the formation of Smart Grid. The appearance of generating sources in the structure of electric networks can have a significant effect on the whole spectrum of technical and economic indicators of distribution systems. At the same time, in many cases, renewable energy sources are used as DG. Their peculiarity is the inability to accurately predict the output power. Thus, a such energy sources introduce some peculiar properties into the process of modeling of distribution network modes of operation, which is related to the need to take into account the uncertainty factor. In this regard, the paper deals with the justification of the power and operating modes of DG sources when they are integrated into certain nodes of distribution networks in order to minimize operational costs, considering information uncertainty.

The paper presents an approach regarding the reliability estimation of complex repairable equipment of electrical utilities. Large quantities of maintenance data have been acquired over the past decades through maintenance data collecting systems. Before using the data, attention must be paid to the uncertainties in data and biases regarding the reliability estimates they generate. The aim of this paper is to propose a novel methodology regarding reliability assessment and evaluation of critical repairable equipment to support maintenance strategy considering uncertain maintenance data. The method integrates two different statistical models for analyzing historical failure data considering their uncertainties. A new validation framework is proposed to evaluate model performance considering those uncertainties. A case study is presented for Hydro-Quebec’s major transmission equipment. The limits of the models and their dependability on information uncertainty are discussed.

The article deals with the synthesis the automated control system (ACS) multidimensional object with incomplete information about the object. To select the structure and parameters of regulators provided the use of the criterion of acceptable quality and principle of guaranteed dynamics. In this initial data and engineering requirements for the quality of regulation and the dynamic properties of an object. Sought for control law includes both the desired state of the object, the speed of change of the vector components of the control errors and system requirements. The proposed approach is simple realization on the real object, because expression for the control is obtained in an analytical form.

Vehicular fog computing (VFC) has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize stable and reliable task offloading under information uncertainty remains a critical challenge. In this paper, we propose a matching-learning-based task offloading algorithm to address this challenge. First, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Second, we extend the work to the scenario of information uncertainty, and develop a matching-learning-based task offloading algorithm by combining matching theory and upper confidence bound (UCB) algorithm. Simulation results demonstrate that the proposed algorithm can achieve bounded deviation from the optimal performance without the global information.

With the wide development of smart devices, fog computing has emerged as a promising solution to accommodate the ever-increasing computational demands in Internet of things (IoT). However, there are two major obstacles hindering the wide deployment of IoT fog computing, i.e., how to realize server recruitment under information asymmetry and reliable task assignment under information uncertainty. In this article, we develop a robust two-stage task offloading algorithm by integrating contract theory with computational intelligence. In the first stage, we propose a contract based server recruitment scheme to motivate servers to share residual computational resources. In the second stage, by leveraging multi-armed bandit (MAB), we develop a reliable volatile upper confidence bound (RV-UCB) algorithm to minimize the long-term delay of task assignment, which takes into account task awareness, occurrence awareness and location awareness. Finally, a series of stimulation results are carried out to validate the performance of the proposed algorithm.

Determining and distinguishing the capability of a node is an extremely important and appealing issue in complex networks, especially in many text processing tasks. There are many factors to inuence the evaluation, such as the topology structure character, the related statistical information and the transmission mechanism of the network. However, few previous studies are considered this issue from the information uncertainty or information missing of the network system in the transmission mechanism therein. In this paper, we focus on discussing the evaluation method of the importance of the node in the language system, in order to nd a more suitable evaluation and quantized metric to accomplish a well understanding of the text from the uncertainty of the whole system point of view. For doing this, a novel metric is proposed based on entropy to evaluate the importance of a node. The experimental results on actual information retrieval demonstrate that our approach can obtain well understanding of a text.

Due to the rapid growth in scale and complexity of information networks, self-organizing systems have been focused on for realizing new network control architectures that have high scalability, adaptability, and robustness. However, in self-organizing systems, the uncertainty (incompleteness, ambiguity, and dynamicity) of information observable for components in the system can lead to the slow adaptation to environmental changes and the lack of a global optimality, which complicates a practical use of self-organizing systems in industrial and business fields. In this study, we adopt the principle of collective decision making, in which a coordinated decision in a group is achieved through local interactions of components, in order to realize a network control mechanism adaptable to such information uncertainty. Specifically, we apply Effective Leadership model, which is a mathematical model of collective decision making, to a self-organizing control mechanism. In Effective Leadership model, there are two types of individuals, informed and non-informed ones, and collective decision is achieved through local interaction of them. Through simulation experiments, we reveal the advantages and characteristics of the network control mechanism based on Effective Leadership model.

The model is based on the criteria of cost-effectiveness and information uncertainty. The created model has a fundamental difference from the current classical economic-mathematical models. It is proposed to use the mathematical expressions of the information entropy determination of two states of the system: operable and non-operable as the constraints. These expressions are the constraints imposed in the task that creates an optimal radial network with redundancy. Expressions are constructed as all the elements reserving each other are similar, i.e. they have the same performance. The given example shows how to calculate the information entropy where a constraint is a problem solution, as well as the possibility of both applications of probabilities and numerical values for the information entropy determination is proved.

Qualitative reasoning makes use of qualitative assessments provided by subject matter experts to model factors such as security risk. Confidence in a result is important and useful when comparing competing results. Quantifying the confidence in an evidential reasoning result must be consistent and based on the available information. A novel method is proposed to relate confidence to the available information uncertainty in the result using fuzzy sets. Information uncertainty can be quantified through measures of non-specificity and conflict. Fuzzy values for confidence are established from information uncertainty values that lie between the measured minimum and maximum information uncertainty values.

This paper presents a model for determining the behavior of pedestrians attempting to avoid collision when approaching an indoor blind corner from both sides. We constructed a decision model that takes tactics into account by using a mixed strategy based on game theory. In addition, we considered changes in the behavior of pedestrians caused by information uncertainty proportional to the distance from the other person, and derived a formula for such changes by using the Dempster-Shafer theory of evidence. The effectiveness of the developed decision model was verified through simulation. Based on this model, control and information provision considering human behavior can be used to ensure safe and efficient operation of mobile robots even at blind corners of indoor aisle intersections, where the risk of collisions is particularly high.

In this paper, we studied the conditions require to ensure the safety and efficient traffic of pedestrians walking in an indoor blind corner-based decision model by determining their behavior. This model was constructed with consideration for tactics that affect when person encounter each other using a mixed strategy from game theory, which we expanded using the Dempster-Shafer theory of evidence for the adaptation of behavioral changes caused by information uncertainty. We determined the conditions required for safe and efficient traffic, and we verified their effectiveness in a simulation using a decision model. Based on the knowledge obtained from this verification, we tested an implementation of a system that supported safety and efficient traffic in an indoor blind corner-based model.

This study investigates how consumers evaluate a product when they read conflicting online consumer reviews of evaluations from previous consumers. If consumers are rational, as is assumed in economics, they prefer the product with the low-variance of review-rating scores rather than the product with the high- variance of review-rating scores because the variance of review-rating scores is positively related to the perceived uncertainty of product performance. Our study finds the condition in which consumers are likely to act irrationally, and explain why consumers have irrational behavior with attribution theory. When attributional bias occurs, consumers with a favorable prior brand attitude prefer the product with high-variance reviews over the product with low-variance reviews. Whereas, if attributional bias does not exist, rational behavior about the information uncertainty is found. Our findings show the underlying mechanism for the consumers' responses to the disagreements of others' opinions and give practical implications for online sellers.

Date of Conference: 5-9 Dec. 2011 In this paper, stochastic signaling is studied for power-constrained scalar valued binary communications systems in the presence of uncertainties in channel state information (CSI). First, it is shown that, for a given decision rule at the receiver, stochastic signaling based on the available CSI at the transmitter results in a randomization between at most two different signal levels for each symbol. Then, the performance of stochastic signaling and conventional deterministic signaling is compared, and sufficient conditions are derived for improvability and nonimprovability of deterministic signaling via stochastic signaling in the presence of CSI uncertainty. Finally a numerical example is presented to explore the theoretical results. © 2011 IEEE.

The maintenance of good situational awareness by government agencies for the maritime surface picture is of critical importance for national security, regional safety, pollution mitigation, immigration enforcement and protection from smuggling etc. Unfortunately, this picture, as currently implemented, provides the user with no immediate awareness of the quality or timeliness of the information for any specific vessel track that is plotted. The present research describes the development of visualization concepts in the form of icons that can be integrated into the existing symbology used to plot the picture. Several design concepts were initially developed and evaluated heuristically against Human Factors principles. Subsequently, the best candidates were evaluated in the laboratory for their capability for improving situational awareness in a search task. The results provided evidence of which designs were more effective, and how they could potentially make operational decisions more effective by increasing awareness of the underlying data uncertainty. Each of the designs was then evaluated by Navy operators who performed simulated operational tasks and assessed the utility and usability of the new functionality. The results of the experiment and the outcome of this validation process were then used to support a recommendation as to which design would be proposed for future implementation.

The paper studies the problem of information uncertainty evaluation in modern engineering and technology applications, and especially the system design. It analyses the virtual environment design and engineering measurement. Information typical for those applications is classified according to its uncertainty types. Uncertainty sources are identified. Fuzzy theory models are proposed. Examples of their applications in characteristic problems are given.