Identification and characterization of information-networks in long-tail data collections

Scientists' ability to synthesize and reuse long-tail scientific data lags far behind their ability to collect and produce these data. Many Earth Science Cyberinfrastructures enable sharing and publishing their data over the web using metadata standards. While profiling data attributes advances the Linked Data approach, it has become clear that building information-networks among distributed data silos is essential to increase their integration and reusability. In this research, we developed a Long-Tail Information-Network (LTIN) model, which uses a metadata-driven approach to build semantic information-networks among datasets published over the web and aggregate them around environmental events. The model identifies and characterizes the spatial and temporal contextual association links and dependencies among datasets. This paper presents the design and application of the LTIN model, and an evaluation of its performance. The model capabilities were demonstrated by inferring the information-network of a stream discharge located at the downstream end of the Illinois River.     

基于云计算的分布式通信电源控制系统研究

通信基站中的电源系统大多以一种分布式的状态存在,其运行数据繁多而复杂,因此应用云计算平台来进行电源系统的数据管理是一种良好的策略。在设计通信电源云计算平台的基础上,采取数据分类匹配的方法对数据进行预处理,以减少云平台的数据冗余,从而达到提高电源系统数据管理效率的目的。     

Probabilistic fault diagnosis method based on the combination of nest-loop fisher discriminant analysis and analysis of relative changes

Bias of data location and increase in data variations are two typical disturbances, which in general, simultaneously exist in the fault process. Targeting their different characteristics, a nested-loop fisher discriminant analysis (NeLFDA) algorithm and relative changes (RC) algorithm are effectively combined for analyzing the fault characteristics. First, a prejudgment strategy is developed to evaluate the fault types and determine what changes are covered in the fault process. Two statistical indexes are defined, which conduct Monte Carlo based center fluctuation analysis and dissimilarity analysis respectively. Second, for the fault data containing those two faults simultaneously, a combined NeLFDA-RC algorithm is proposed for fault deviations modeling, which is termed as CNR-FD. Fault directions concerning bias of data location are extracted by the NeLFDA algorithm and then corresponding fault deviations are removed from the fault data. Then RC algorithm is performed on these fault data to extract directions concerning increase of data variations. These fault directions are used as reconstruction models to characterize each fault class. Particularly, the compromise between these two algorithms is determined by the Monte Carlo based center fluctuation analysis. For online applications, a probabilistic fault diagnosis strategy based on Bayes’ rule is performed to identify fault cause by discovering the right reconstruction models that can make the reconstructed monitoring statistics have the largest probabilities of belonging to normal condition. The motivation of the proposed algorithm is illustrated by a numerical case and the performance of the reconstruction models and the probabilistic fault diagnosis strategy are illustrated using pre-programmed faults from the Tennessee Eastman benchmark process and the real industrial process data from the cut-made process of cigarettes in some cigarette factory.     

Bacterial communication systems: A mathematical formulation of negative chemotaxis

The emergence of molecular communication (MC) has provided a new paradigm for communication at nanoscale, besides the electromagnetic one. Numerous biological systems have been proposed that could be used for communication, using mostly bacteria as information carriers, ensuring biocompatibility and low complexity requirements. In this work, message dissemination dynamics in a bacterial communication system was under investigation by means of a simulation framework that was designed and developed based on a commercial tool. A mathematical formulation is proposed, in an effort to accurately predict the system's behavior under the presence of chemotactic‐like nodes. Simulation results are in strong agreement with the respective mathematical model, for a wide range of biological processes, including phenomena such as chemotaxis, bacterial growth cycle, and biological message transformation dynamics. In the case of a simple simulation scenario exhibiting chemotaxis, the mean accuracy improvement reaches values of up to 19% .     

Supply chain configurations: a model to evaluate performance in customised productions

This paper describes an approach used to evaluate the performance of different supply chain configurations in customised contexts. Based on historical data collected from the supply chain of a shoe producer, different configurations are evaluated based on a discrete-event simulation by highlighting the performance of the supply chain (in terms of supply chain order lead-time and inventory volume) when the production switched from standard production (characterised by batches of large quantities of the same product) to customised production (characterised by a small of series batches with high product variability). The simulation approach relies on experimentation through executable configurations, which enables the creation of different scenarios, and is then applied to the case of an actual firm in the footwear industry. The managerial implications of these findings are discussed.     

Blood supply chain network design considering blood group compatibility under uncertainty

This paper addresses the design of a blood supply chain (SC) network considering blood group compatibility. To this aim, a bi-objective mathematical programming model is developed which minimises the total cost as well as the maximum unsatisfied demand. Due to uncertain nature of some input parameters, two novel robust possibilistic programming models are proposed based on credibility measure. The data of a real case study are then used to illustrate the applicability and performance of the proposed models as well as validating the proposed robust possibilistic programming approach. The obtained results show the superiority of the developed models and significant cost savings compared to current existed blood SC network.     

Separable reversible data hiding in encrypted image based on pixel value ordering and additive homomorphism

This work proposes a separable reversible data hiding scheme in encrypted images based on pixel value ordering (PVO). After the original image is encrypted using homomorphism encryption by the content owner, the data hider embeds the secret data in encrypted domain. The PVO strategy realizes hiding data in each block. Additive homomorphism guarantees the performance of PVO in encrypted domain is close to that in plain domain. Besides, the homomorphism encryption does not cause data expansion, and the payload can be further improved. With the watermarked encrypted image, if the receiver has only the data hiding key, he can extract the additional data. If the receiver has only the encryption key, he can obtain a decrypted image similar to the original one. If the receiver has both the data hiding key and the encryption key, he can extract the additional data without any error and recover the original image losslessly.     

An incremental intrusion detection system using a new semi‐supervised stream classification method

In recent years, the utilization of machine learning and data mining techniques for intrusion detection has received great attention by both security research communities and intrusion detection system (IDS) developers. In intrusion detection, the most important constraints are the imbalanced class distribution, the scarcity of the labeled data, and the massive amounts of network flows. Moreover, because of the dynamic nature of the network flows, applying static learned models degrades the detection performance significantly over time. In this article, we propose a new semi‐supervised stream classification method for intrusion detection, which is capable of incremental updating using limited labeled data. The proposed method, called the incremental semi‐supervised flow network‐based IDS (ISF‐NIDS), relies on an incremental mixed‐data clustering, a new supervised cluster adjustment method, and an instance‐based learning. The ISF‐NIDS operates in real time and learns new intrusions quickly using limited storage and processing power. The experimental results on the KDD99, Moore, and Sperotto benchmark datasets indicate the superiority of the proposed method compared with the existing state‐of‐the‐art incremental IDSs.     

Prediction model of time diversity using Japan rain radar data

The effects of rain attenuation on communication systems will become more pronounced in future satellite communication systems, especially with the planned use of the 21‐GHz band or higher‐frequency bands. Diversity techniques provide a solution to mitigate rain attenuation effects. This study proposes a time diversity technique, one such technique that is likely to demonstrate high effectiveness. To model the system, rainfall rate statistics are necessary, and reliability is improved as the amount of statistical data increases. This paper derives the cumulative distribution of the rainfall rate across Japan over 4 years using rain radar data from the automated meteorological data acquisition system and ground‐based rain radar network and evaluates the rainfall rate at 23 observation points across Japan. We carry out a performance evaluation for all locations within Japan to confirm the efficiency of the time diversity method. Finally, we propose prediction model of the time diversity gain for Japan and other significant parameter which is time correlation of rainfall rate that was found from the time diversity results for further investigation. Copyright © 2016 John Wiley & Sons, Ltd.