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Bayesian optimization has been demonstrated as an effective methodology for the global optimization. However, it suffers from a computational bottleneck that the inference time grows cubically with the number of observations. In this paper, a Bayesian optimization based on the data-parallel approach is proposed to alleviate this problem. Firstly, an improved geometry motivated clustering algorithm...
In this study, a deep denoising recurrent temporal restricted Boltzmann machine network is proposed for long-term prediction of time series. The network is a deep dynamic network model which is stacked by multiple denoising recurrent temporal restricted Boltzmann machines with strong modeling ability for complex high noise time series data. To better deal with high noise data, a random noise is added...
The blast furnace gas is an important secondary energy for the iron and steel production. Establishing an effective model to describe the state of BFG system is of great significant to maintain the system balance and stability. Considering the strong coupling characteristics of the blast furnace gas system and the high level noises in the industrial data, a simplex unscented Kalman filter-based Wang-Mendel...
In this paper, an extended type-reduction method for spiked concave type-2 fuzzy sets has been studied. First, the concave type-2 fuzzy set is decomposed with α-level, and the α-level sets are obtained. Second, to the certain α-level set, there exists more than one combination (vertical interval sets defined on U) for the concave type-2 fuzzy set that parallels to the x-u plane; then, enhanced Karnik-Mendel...
Traffic classification plays an important and basic role in network management and cyberspace security. With the widespread use of encryption techniques in network applications, encrypted traffic has recently become a great challenge for the traditional traffic classification methods. In this paper we proposed an end-to-end encrypted traffic classification method with one-dimensional convolution neural...
A multiple order model migration (MOMM) algorithm and optimal model selection strategy are proposed here for rapid model development and online glucose prediction. First, the optimal model order is determined for each input and a multiple order prediction model is used. Then a MOMM algorithm is developed based on particle swarm optimization to simultaneously revise multiple parameters. The multiple...
System model is fundamental for accurate motion control of the robot. However, due to the complexity of aquatic environments and the special fin-driven structure, it is difficult to dynamically model the system of robotic fish. This paper proposes an experimental modeling method for yaw motion of a freely swimming robotic fish through system identification. Tail deflection and the corresponding angular...
Characterizing mobile traffic is important to network operators from a network performance and security standpoint. In many cases, such as a performance degradation or security threat, it is useful to know that traffic is being generated by a specific mobile application. Recent methods for identifying mobile traffic have relied on computationally expensive detailed inspection of HTTP flows which may...
Measurement of the lexical properties of domain names enables many types of relatively fast, lightweight web mining analyses. These include unsupervised learning tasks such as automatic categorization and clustering of websites, as well as supervised learning tasks, such as classifying websites as malicious or benign. In this paper we explore whether these tasks can be better accomplished by identifying...
Mining advisor-advisee relationships can benefit many interesting applications such as advisor recommendation and protege performance analysis. Based on the hypothesis that, advisor-advisee relationships among researchers are hidden in scholarly big data, we propose in this work a deep learning based advisor-advisee relationship identification method which considers the personal properties and network...
We introduce a novel high range resolution (HRR) based multi-look automatic target recognition (ATR) method in this paper. First, the scattering center model established offline is used to reduce the data amount that needed in template construction, which makes the method efficient. Then, the correlation among the multiple looks of the same target is considered using the joint sparse representation...
Subspace clustering aims to reveal the latent subspace structure underlying high dimensional data by segmenting the data into corresponding subspaces. It has found wide applications in machine learning and computer vision. Most recent works on subspace segmentation focus on subspace representation based methods, which constructs the affinity matrix from the subspace representation of data points....
Due to the multi-condition characteristic of tobacco ultrahigh-speed cellophane sealing machine, the existing fault monitoring method can't meet the need of efficient cigarette production. Based on the analyzing the running characteristics, a fault monitoring and diagnosis approach method of ultrahigh-speed cellophane sealing machine was proposed to solve several key points, such as condition partition...
The employment, wages and income gap of urban residents in the eastern, central and western of China, were interacted and influenced mutually, how to promote their coordinated development, which has become the top priority of the current government work, but also the focus of scholars research. The number of employees at the end of the year, the total wages and the Gene coefficient of urban residents...
Drop reliability of Ball Grid Array (BGA) packages has been a concern for electronic packages to meet drop performance requirement. Joint Electron Device Engineering Council (JEDEC) has provided a board-level drop standard to investigate the drop performance of electronic packages. Simulation models have been developed to simulate the drop dynamics in drop tests. However, thus far, there is no valid...
In a sponsored search market, the problem of measuring the intensity of competition among advertisers is increasingly gaining prominence today. Usually, search providers want to monitor the advertiser communities that share common bidding keywords, so that they can intervene when competition slackens. However, to the best of our knowledge, not much research has been conducted in identifying advertiser...
Infectious disease epidemics such as influenza and Ebola pose a serious threat to global public health. It is crucial to characterize the disease and the evolution of the ongoing epidemic efficiently and accurately. Computational epidemiology can model the disease progress and underlying contact network, but suffers from the lack of real-time and fine-grained surveillance data. Social media, on the...
The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation-...
This paper presents an evaluation of two different global satellite precipitation products (TRMM 3B42v7 and TRMM 3B42 RT) during 2001–2004 over the Mekong River basin for hydrologic applications with using a distributed hydrological model. The result demonstrates that generally 3B42 V7 is closer to rainfall field interpolated by gauge data and has a better performance in runoff simulation than 3B42...
Blast furnace gas (BFG) is regarded as a very important secondary energy in steel industry, and an effective model to describe the status of BFG system is fairly significant to maintain the system balance and stability. However, the high level noises in industrial data and the disturbances in training samples could lead to the overfitting phenomenon. A fuzzy subset fusion combined with a rule reduction...
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