Critical Nodes Identification: A Non-Cooperative Method for Unknown Topology Information in Ad Hoc Networks

The foundation of ad hoc networks lies in the guarantee of continuous connectivity.However,critical nodes,whose failure can easily destroy network connectivity,will influence the ad hoc network connectivity significantly.To protect the network efficiently,critical nodes should be identified accurately and rapidly.Unlike existing critical node identification methods for unknown topology that identify critical nodes according to historical information,this paper develops a critical node identification method to relax the prior topology information condition about critical nodes.Specifically,we first deduce a theorem about the minimum communication range for a node through the number of nodes and deployment ranges,and prove the universality of the theorem in a realistic two-dimensional scenario.After that,we analyze the relationship between communication range and degree value for each node and prove that the greater number of nodes within the communication range of a node,the greater degree value of nodes with high probability.Moreover,we develop a novel strategy to improve the accuracy of critical node identification without topology information.Finally,simulation results indicate the proposed strategy can achieve high accuracy and low redundancy while ensuring low time consumption in the scenarios with unknown topology information in ad hoc networks.

Analysis of Negative Correlation Learning

This paper describes negative correlation learning for designing neural network ensembles. Negative correlation learning has been firstly analysed in terms of minimising mutual information on a regression task. By minimising the mutual information between variables extracted by two neural networks, they are forced to convey different information a-bout some features of their input. Based on the decision boundaries and correct response sets, negative correlation learning has been further studied on two pattern classification problems. The purpose of examining the decision boundaries and the correct response sets is not only to illustrate the learning behavior of negative correlation learning, but also to cast light on how to design more effective neural network ensembles. The experimental results showed the decision boundary of the trained neural network ensemble by negative correlation learning is almost as good as the optimum decision boundary.

An attention graph stacked autoencoder for anomaly detection of electro-mechanical actuator using spatio-temporal multivariate signals

Health monitoring of electro-mechanical actuator(EMA)is critical to ensure the security of airplanes.It is difficult or even impossible to collect enough labeled failure or degradation data from actual EMA.The autoencoder based on reconstruction loss is a popular model that can carry out anomaly detection with only consideration of normal training data,while it fails to capture spatio-temporal information from multivariate time series signals of multiple monitoring sensors.To mine the spatio-temporal information from multivariate time series signals,this paper proposes an attention graph stacked autoencoder for EMA anomaly detection.Firstly,attention graph con-volution is introduced into autoencoder to convolve temporal information from neighbor features to current features based on different weight attentions.Secondly,stacked autoencoder is applied to mine spatial information from those new aggregated temporal features.Finally,based on the bench-mark reconstruction loss of normal training data,different health thresholds calculated by several statistic indicators can carry out anomaly detection for new testing data.In comparison with tra-ditional stacked autoencoder,the proposed model could obtain higher fault detection rate and lower false alarm rate in EMA anomaly detection experiment.

Nearby outdoor environments and seniors physical activities

More than 60% of older Americans have sedentary lifestyles and are recommended more physical activities for health benefit. Nearby outdoor environments on residential sites may impact older inhabitants＇ physical activities there （defined as walking, gardening, yard work, and other outdoor physical activities on residential sites）. This study surveyed 110 assisted-living residents in Houston, Texas, regarding their previous residential sites before moving to a retirement community and physical activities there. Twelve environmental features were studied under four categories （typology, motivators, function, and safety）. Based on data availability, a subset of 57 sample sites was analyzed in Geographic Information Systems. Hierarchical linear modeling was applied to estimate physical activities as a function of the environments. Higher levels of physical activity were found to be positively related with four environmental features （transitional-areas, connecting-paths, walk-ability, and less paving）.

Dynamic statistical information theory

In recent years we extended Shannon static statistical information theory to dynamic processes and established a Shannon dynamic statistical information theory, whose core is the evolution law of dynamic entropy and dynamic information. We also proposed a corresponding Boltzmman dynamic statistical information theory. Based on the fact that the state variable evolution equation of respective dynamic systems, i.e. Fok- ker-Planck equation and Liouville diffusion equation can be regarded as their information symbol evolution equation, we derived the nonlinear evolution equations of Shannon dy- namic entropy density and dynamic information density and the nonlinear evolution equa- tions of Boltzmann dynamic entropy density and dynamic information density, that de- scribe respectively the evolution law of dynamic entropy and dynamic information. The evolution equations of these two kinds of dynamic entropies and dynamic informations show in unison that the time rate of change of dynamic entropy densities is caused by their drift, diffusion and production in state variable space inside the systems and coordinate space in the transmission processes; and that the time rate of change of dynamic infor- mation densities originates from their drift, diffusion and dissipation in state variable space inside the systems and coordinate space in the transmission processes. Entropy and in- formation have been combined with the state and its law of motion of the systems. Fur- thermore we presented the formulas of two kinds of entropy production rates and infor- mation dissipation rates, the expressions of two kinds of drift information flows and diffu- sion information flows. We proved that two kinds of information dissipation rates (or the decrease rates of the total information) were equal to their corresponding entropy produc- tion rates (or the increase rates of the total entropy) in the same dynamic system. We obtained the formulas of two kinds of dynamic mutual informations and dynamic channel capacities reflecting the dynamic dissipation characteristics in the transmission processes, which change into their maximum—the present static mutual information and static channel capacity under the limit case where the proportion of channel length to informa- tion transmission rate approaches to zero. All these unified and rigorous theoretical for- mulas and results are derived from the evolution equations of dynamic information and dynamic entropy without adding any extra assumption. In this review, we give an overview on the above main ideas, methods and results, and discuss the similarity and difference between two kinds of dynamic statistical information theories.

Semantic-Discovery of Construction Project Files

The dynamics of a construction project generates a huge amount of information for processing. Most project information is recorded in digital files and managed in a decentralized way. Current document management tools do not consider the semantic relationships between files and then cannot discover files based on their content or ensure information consistency across various information repositories. This paper presents a semantic-discovery method for accessing project information across repositories. By adopting semantic web technologies, including extensible markup language (XML), ontology, and logic rules, the semantic-discovery tool can access project files by their properties and relations. The method has been applied to searching for files using documents from a real project. The results show that the semantic content of a file can be used to ensure that only related files are retrieved.

Materials analysis of traditional Chinese copper halls using XRF and GIS： Kunming Copper Hall as a case study

This paper presents the framework and results of analysis of the building materials used in traditional Chinese copper halls. The analysis of the Kunming copper hall （KCH） is presented as a typical example. First, the historical building structure of the KCH is investigated. Results of X-ray fluorescence （XRF） spectrometry are presented and analyzed according to the units of each building component. The results indicate that the different components in the same building were cast out of different alloys such as bronze, brass, and red copper. Furthermore, the XRF results are loaded into the geographic information system （GIS） to examine the relations between the building components and their materials. The GIS analysis indicates that the different alloys were deliberately chosen according to the function of each piece in the structure. Finally, the reason and significance of this phenomenon is discussed from the perspective of architectural history and the history of science and technology.

A Syntactic Approach to Knowledge in Finite Games of Perfect Information

In this paper I present a syntactic approach to modeling the interactive knowledge of rationality in finite games of perfect information. This approach allows for a more transparent interpretation. In particular, we have the intuitive picture of viewing knowledge as the input and decisions as the output of a player＇s deliberation. This distinction is blurred in the semantic state-space approach.