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The gray Verhulst model has the extremely widespread application in the study of minority, poor information and uncertainty question when the data show saturated state or s-shaped sequences. However the gray Verhulst model built by weakening the randomness of data sequence, lacking of self-organizing and self-learning. Some scholars study on this issue, and put forward a kind of gray Verhulst-BPNN...
We propose LOCO-CV-GP, a method for cross-validating Gaussian process (GP) methods in a leave-one-crown-out (LOCO) manner, when the GP method is applied on hyperspectral data from tree crowns. The fact that spectra within a crown are correlated [1] needs to be taken into consideration when working with airborne HS tree spectra. The experiments are conducted on OSBS2014 dataset to cross-validate OGP,...
We aim to jointly estimate height and semantically label monocular aerial images. These two tasks are traditionally addressed separately in remote sensing, despite their strong correlation. Therefore, a model learning both height and classes jointly seems advantageous and so, we propose a multitask Convolutional Neural Network (CNN) architecture with two losses: one performing semantic labeling, and...
Many new Internet of Things (IoT) applications such a disaster early warning systems, video-streaming, automated driving and similar, are increasingly being built by using advanced component based software engineering approaches. Software components can include various executable images, such as container or Virtual Machine images, scripts and others. Achieving adequate Quality of Service (QoS) for...
How to operate a BFG/coal co-firing boiler in high efficiency is challenging for a gas/solid multi-fuel combustion system. Taking operation data from a real boiler, this study proposes an operation optimization strategy of BFG/coal co-firing boiler based on deep learning. Firstly, the thermal efficiency model is constructed based on deep learning with all the actual sampling data, which outperform...
In this paper, an algorithm that based on pca-bp-bagging model is developed for the prediction of pathological data. This algorithm aims at improving the characteristics of bp neural network that the prediction accuracy of pathological data is low, the generalization ability of single bp neural network model is poor, and the anti-interference ability is weak. To enhance the performance of the whole...
This paper aims at task-oriented action prediction, i.e., predicting a sequence of actions towards accomplishing a specific task under a certain scene, which is a new problem in computer vision research. The main challenges lie in how to model task-specific knowledge and integrate it in the learning procedure. In this work, we propose to train a recurrent longshort term memory (LSTM) network for handling...
Spare parts are indispensable resources to ensure equipment the normal operation and continuous production, especially for urban raü vehicles. When the spare parts storage is insufficient, the equipment can't be replaced or repair ed in time, which can cause serious loss. Therefore, it is important to forecast the demand of the urban rail vehicle spare parts. A combination forecasting method based...
The two-layer restricted Boltzmann machine (RBM) for rating prediction problems in recommendation systems has become one of the significant researches. A dual conditional restricted Boltzmann machine (dCRBM) model is proposed in this paper. In the dCRBM model, the training process uses rated/unrated information. Meanwhile, we also utilize the dual patterns of users and items, and build two CRBMs based...
Energy crisis and environmental pollution stimulate the rapid development of new energy electric vehicles. The state of charge(SOC) is a key parameter of power battery in application, so the accurate estimation is extremely important. Factors affecting the battery SOC are many and complicated, scholars have proposed many methods to estimate SOC, but still does not solve the accuracy and practicability...
In the electricity sector, new sides have emerged with the development of technology and the increasing the electric energy need. Today, electricity has become a product that is bought and sold in the market environment. Forecasting which is the first step of plans and planning have become much more important and have been made mandatory for the market participants by energy market regulators. In...
Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects (referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy...
While there has been recent success with robotic therapy approaches, individual differences in motor impairments motivate the need for customized therapy. Our latest work with healthy participants considered the likelihood of one's error to construct a customized force field training environment, which we termed an error field. We believe error statistics could characterize individual motor impairments...
Identifying bug immediately when it is introduced can help improve the validity and effectiveness of bug fixing. Predicting bugs in software code changes makes such identification possible. Buggy changes, changes that introduce bugs into source code, can be viewed as anomalies relative to clean changes for that they are rare and irregular. Thus, anomaly detection techniques can be applied to buggy...
This paper studies prediction based run-time system reconfiguration strategy to tolerate environment change and hardware malfunction on many-core embedded systems. System reconfiguration will invoke application migration, which may significantly impact system's timing behaviors, therefore, it is vital important to select an appropriate migration strategy after which the system's performance is still...
This Paper reveals the information about Deep Neural Network (DNN) and concept of deep learning in field of natural language processing i.e. machine translation. Now day's DNN is playing major role in machine leaning technics. Recursive recurrent neural network (R2NN) is a best technic for machine learning. It is the combination of recurrent neural network and recursive neural network (such as Recursive...
In the last decade, numerous fake websites have been developed on the World Wide Web to mimic trusted websites, with the aim of stealing financial assets from users and organizations. This form of online attack is called phishing, and it has cost the online community and the various stakeholders hundreds of million Dollars. Therefore, effective counter measures that can accurately detect phishing...
Traffic light detection (TLD) is a vital part of both intelligent vehicles and driving assistance systems (DAS). General for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact performance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light...
Image captioning has recently received much attention. Existing approaches, however, are limited to describing images with simple contextual information, which typically generate one sentence to describe each image with only a single contextual emphasis. In this paper, we address this limitation from a user perspective with a novel approach. Given some keywords as additional inputs, the proposed method...
Visual attention is a dynamic search process of acquiring information. However, most previous studies have focused on the prediction of static attended locations. Without considering the temporal relationship of fixations, these models usually cannot explain the dynamic saccadic behavior well. In this paper, an iterative representation learning framework is proposed to predict the saccadic scanpath...
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