[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Internet of Things in agriculture, recent advances and future challenges

Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling

Six years and more than seventy publications later this paper looks back and analyzes the development of prognostic algorithms using C-MAPSS datasets generated and disseminated by the prognostic center of excellence at NASA Ames Research Center. Among those datasets are five run-to-failure CMAPSS datasets that have been popular due to various characteristics applicable to prognostics. The C-MAPSS datasets pose several challenges that are inherent to general prognostics applications. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than seventy publications have used the C-MAPSS datasets for developing data driven prognostic algorithms. However, in the absence of performance benchmarking results and due to common misunderstandings in interpreting the relationships between these datasets, it has been difficult for the users to suitably compare their results. In addition to identifying differentiating characteristics in these datasets, this paper also provides performance results for the PHM’08 data challenge wining entries to serve as performance baseline. This paper summarizes various prognostic modeling efforts that used C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.

/pdf/performance-benchmarking-and-analysis-of-prognostic-methods-5gte6d426q.pdf

Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets.

Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAVs networks.

/pdf/artificial-intelligence-for-uav-enabled-wireless-networks-a-4tag21fj4n.pdf

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Accurate fault prediction or remaining useful life (RUL) estimation can obviously reduce cost of maintenance and decrease the probability of accidents so as to improve the performance of the system test and maintenance. At the same time, it is difficult to apply the model-based methods for fault prediction in most applications for complex systems. Due to continuously improving of automation, increasing of sampling frequency and development of computing technology and memory capacity, it gradually promotes data-driven technology into practical methods. Therefore, data-driven fault prognostics based on the sensor or historical test data has become the primary prediction means of complex systems, such as Artificial Neural Networks (ANN), Support Vector Regression (SVR) and other computational intelligence methods. In the other hands, most of traditional forecasting methods are always off-line that are not suitable for on-line prediction and real-time processing. Furthermore, for some on-line prediction methods such as Online Support Vector Regression (Online SVR), there is conflicts and trade-offs between prediction efficiency and accuracy. In real prognostics and health management (PHM) systems, such as the operating status monitoring and forecasting of complex systems like airplane and aircraft, it requires that the algorithms are flexible and adaptive in realization of balance between prediction efficiency and accuracy to meet different complicated requirements. To solve the problem above, an on-line adaptive data-driven fault prognosis and prediction strategy is presented in this paper. Considering the complex characteristics of trends and neighborhood of time series data, the multi-scale reconstruction strategy is applied to effectively reduce the size of on-line data and preserve rich history knowledge of samples. Therefore, the prediction efficiency could be improved and faster forecasting can be achieved with adaptive multi-scale sub-models. To evaluate the proposed prediction strategy, we have executed experiments with Tennessee Eastman (TE) process data. Experimental results with TE process fault data prove its effectiveness. The experiments and tests confirm the algorithms can be effectively applied to the on-line status monitoring and prediction with excellent performance in both efficiency and precision. New on-line fault status prediction strategy shows better prospect in real-time and on-line application for complex system. It can be applied in industrial fields for system maintenance and prognostics and health management.

Online adaptive status prediction strategy for data-driven fault prognostics of complex systems

For mobile communication traffic, an accurate prediction result plays an important role in network management, capacity planning, traffic congestion control, channel equalization, etc. This paper proposes a clustered complex echo state network for mobile communication traffic forecasting with prior knowledge. In order to reflect some learning mechanisms of real world organization, various complexities such as small-world features and scale-free node degree distribution were introduced to the dynamic reservoir of original Echo State Network (ESN) and viewed as promising tools for function approximation, modeling of nonlinear dynamic systems, and chaotic time series prediction. Our further observation on the real traffic collected by China Mobile Communications Corporation (CMCC) Heilongjiang Co. Ltd., showed the property of multi-periodicities. Based on this characteristic, Fourier spectrum was chosen as the prior knowledge in this paper for generating multiple functional clusters, corresponding to the frequency components revealed in traffic series of each cell, in the dynamic reservoir of complex echo state network. Experiment results on real traffic showed that: the forecasting accuracy of proposed prior knowledge based clustered complex echo state network (PCCESN) model is superior to that of original ESN models and provides decision-making support for network planning and optimization of mobile network.

Clustered complex echo state networks for traffic forecasting with prior knowledge

Unmanned aerial vehicle (UAV) has attracted more and more attention for its unique advantages and has been widely used in the military and civilian areas. With expansion of functions of UAV and increased improvement of technical complexity, UAV reliability and security are particularly essential, especially large amount of fleets and insufficient redundancy cause high operating risk and more failures. Sensors are important components in UAVs. Detecting the sensing data can monitor UAV flight condition effectively and analyze the operating status, thus, it is necessary and feasible to detect anomaly with sensing data. Due to the large number of UAV sensors and the high dimensionality of sensing data, it poses a great challenge to the sensor anomaly detection of UAVs. While kernel principal component analysis (KPCA) can effectively deal with large numbers of samples and high dimensional data, and has advantages of simple model and high efficiency of dimensionality reduction. Thus, this work proposes a KPCA based sensor data anomaly detection method. Experimental results with UAV simulated data indicates that using the proposed method to detect the UAV sensing data can obtain satisfied performance.

Unmanned aerial vehicle sensor data anomaly detection using kernel principle component analysis

To meet the special requirements on micro-climate raised by the greenhouse in Northeast China, we develop a WSN-based greenhouse environment monitoring system. We decrease the power consumption of nodes, reduce the complexity of system development and simplify the deployment of nodes by dormancy mechanism, single-hop transmission, and on-demand deployment. Gateway node transmits the sensor data to remote database by TD-SCDMA technology. Then, through Internet, people can get access to the environmental information of crops by network terminal. The advantages of this system include low power, low cost, simple structure and flexible extension. The experiments further proved the practicality and reliability of the system.

GEMS: A WSN-based greenhouse environment monitoring system

This paper targets the identification of faulty sensor nodes, which is one of the most important sources of faults in wireless sensor networks. To explore an effectual technique for node self detection, a new algorithm, Node Self Detection by History data and Neighbors (NDHN) is proposed in this paper. This algorithm collects the characteristics of the nodes to compute the biases through historical measurements and neighbors' data to make a judgment. The algorithm is hierarchical and thus can be extended for large networks. Simulation results indicate that the identify accuracy is high.

A self detection technique in fault management in WSN

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