Traffic Behavior Analysis During the Transition from One Level of the Network Hierarchy to Another

The hierarchical network model is the most preferable in the design of computer networks, as it allows you to create a more stable structure of network, rationally allocate available resources, and also provide a higher degree of data protection. In this work, the study of the behavior of the traffic during the transition from one level of the network hierarchy to another, based on the study of the values of the traffic self-similarity degree during this transition. For the study, a simulation model of a computer network with a hierarchical topology was developed using the NS-3 simulator. Also, a window application was developed in the Visual C# programming language. With the help of this application the degree of self-similarity of the traffic was investigated using the files obtained as a result of processing the trace file. Thus, as a result of the study, it can be stated that any changes in the degree of self-similarity of the network traffic when this traffic moves from one level of the hierarchy to another level depends on such a condition as the direction of traffic movement. The initial degree of selfsimilarity of network traffic also effects on the network traffic self-similarity degree.

Parametric simulator of cyclic and non-cyclic impulsive vibration signals for diagnostic research applications

In recent years cyclostationary analysis of vibration signals is considered to be one of the most potent approaches for diagnostics of machines with rotating components. However, it is a subject of an extensive research towards extending its robustness due to its significant inefficiency in the presence of non-cyclic impulsive components in measured data. This problem is especially visible in datasets measured on machines such as ore crushers, where the high-energy impacts are a natural phenomenon. Unfortunately, due to practical inaccessibility, real-life datasets necessary to properly study this problem are extremely difficult to obtain. To address this issue, the authors propose an easy to use simulator of impulsive components. It covers both cyclic components that can describe various types of fault signatures, and non-cyclic ones that can represent impacts occurring naturally due to the nature of machine operation. Simulated signals have been compared with real ones to ensure a high similarity degree, which in turn guarantees a relatively high level of realism.

Probabilistic Linguistic-Based Group DEMATEL Method with Both Positive and Negative Influences

Decision-making trial and evaluation laboratory (DEMATEL) is a widely accepted factor analysis algorithm for complex systems. The rationality of the evaluation scale is the basis of sound DEMATEL decision-making. Unfortunately, the existing evaluation scales of DEMATEL failed to reasonably distinguish and describe the positive and negative influences between factors. Generally, the positive and negative influences between factors should be considered at the same time. In other words, negative influence between factors should not be directly ignored, which is improper and unrealistic. To better address this issue, we extend the evaluation scale of DEMATEL. We also integrate the scale-based group DEMATEL method with probabilistic linguistic term sets (PLTSs) to increase its effectiveness, which allows experts to express incomplete and uncertain linguistic preferences in DEMATEL decision-making. An experts’ subjective weight adjustment method based on the similarity degree between PLTSs is introduced to determine experts’ weights. Finally, an algorithm of probabilistic linguistic-based group DEMATEL method with both positive and negative influences is summarized, and an example is used to illustrate the proposed method and demonstrate its superiority. Our results demonstrate that the method proposed in this paper deals reasonably with realistic problems.

The Comprehensive Identification of Roof Risk in a Fully Mechanized Working Face Using the Cloud Model

Roof accidents seriously affect the safe and efficient mining of the working faces. Therefore, it is necessary to assess and identify the possible and influencing factors on the occurrence of roof risk in a fully mechanized mining workface. In this study, based on the analytic hierarchy process and fuzzy comprehensive evaluation, a comprehensive standard cloud model was established through constructing a quantitative grade interval and calculating the weight of each index to achieve the aim of a roof risk assessment and identification. The accuracy of risk assessment was ensured by using the comprehensive analyses of various aspects, such as cloud digital features, risk assessment cloud image and standard cloud image. This showed that the main influencing factors on the occurrence of roof accidents were roof separation distance, weighting intensity and rib spalling followed by the coal body stress concentration, initial support force and geological conditions. Taking 42,115 fully mechanized working faces in the Yushen coal mining area as an engineering background, this model was adopted to assess and identify the risk of roof accidents through generating comprehensive assessment cloud images and introducing the Dice coefficient to calculate the similarity degree. The results showed that the overall risk of roof accidents in 42,115 working faces was regarded as grade II (general risk) through the overall index of comprehensive risk evaluation and a similarity degree of 0.8606. The impact of roof condition was mainly influenced by the risk of roof accidents, while the support status, personal working status and coal body condition had a limited effect on the risk of roof accidents. The comprehensive standard cloud model proposed in this study had strong visibility and discovered the key parts of risk indexes easily to solve the problems of ambiguity and quantitative identification in traditional roof risk evaluation methods. Therefore, this model was worth promoting, because it laid the foundation for the intelligent identification and early warning system of roof accident risk in a fully mechanized mining workface.

Attribute selection approaches for incomplete interval-value data

Attribute selection in an information system (IS) is an important issue when dealing with a large amount of data. An IS with incomplete interval-value data is called an incomplete interval-valued information system (IIVIS). This paper proposes attribute selection approaches for an IIVIS. Firstly, the similarity degree between two information values of a given attribute in an IIVIS is proposed. Then, the tolerance relation on the object set with respect to a given attribute subset is obtained. Next, θ-reduction in an IIVIS is studied. What is more, connections between the proposed reduction and information entropy are revealed. Lastly, three reduction algorithms base on θ-discernibility matrix, θ-information entropy and θ-significance in an IIVIS are given.

Relative Similarity Programming Model for Uncertain Multiple Attribute Decision-Making Objects and Its Application

This paper is concerned with the uncertain multiattribute decision-making (UMADM) of which the attribute value is triangular fuzzy number. Firstly, the max-relative similarity degree and min-relative similarity degree of alternative decision-making objects are given based on the relative similarity degree of triangular fuzzy number, the advantage relation theories to comparative relative similarity degree of triangular fuzzy number are proposed, and some good properties, relations, and conclusions are derived. Secondly, in order to determine the attribute weight vector, a triangular fuzzy number-based decision-making object relative similarity programming model is established with the help of maximizing possibility degree algorithm rules in the cooperative game theory. Subsequently, by aggregating the comparison overall relative similarity degree values of all decision-making objects, we could pick over and sort the set of alternative objects and gather a new model algorithm for the relative similarity programming of triangular fuzzy number-based multiple attribute decision-making alternatives. Finally, an example is given to illustrate the feasibility and practicability of the model algorithm presented in this paper.

Rough set models of interval rough number information system

Classical rough set theory (RST) is based on equivalence relations, and does not have an effective mechanism when the attribute value of the objects is uncertain information. However, the information in actual problems is often uncertain, and an accurate or too vague description of the information can no longer fully meet the actual needs. Interval rough number (IRN) can reflect a certain degree of certainty in the uncertainty of the data when describing the uncertainty of the data, and can enable decision makers to make decisions more in line with actual needs according to their risk preferences. However, the current research on rough set models (RSMs) whose attribute values are interval rough numbers is still very scarce, and they cannot analyze the interval rough number information system (IRNIS) from the perspective of similar relation. therefore, three new interval rough number rough set models (IRNRSMs) based on similar relation are proposed in this paper. Firstly, aiming at the limitations of the existing interval similarity degree (ISD), new interval similarity degree and interval rough number similarity degree (IRNSD) are proposed, and their properties are discussed. Secondly, in the IRNIS, based on the newly proposed IRNSD, three IRNRSMs based on similar class, β-maximal consistent class and β-equivalent class are proposed, and their properties are discussed. And then, the relationships between these three IRNRSMs and those between their corresponding approximation accuracies are researched. Finally, it can be found that the IRNRSM based on the β-equivalent classes has the highest approximation accuracy. Proposing new IRNRSMs based on similar relation is a meaningful contribution to extending the application range of RST.