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Existing machine-learning-based vehicle detection algorithms for intelligent vehicles have an obvious disadvantage in that the detection effect decreases dramatically when the distribution of training samples and the scene target samples do not match. To address this issue, a scene-adaptive vehicle detection algorithm based on a composite deep structure is proposed in this paper. Inspired by the Bagging...
We present an advanced dialog state tracking system designed for the 5th Dialog State Tracking Challenge (DSTC5). The main task of DSTC5 is to track the dialog state in a human-human dialog. For each utterance, the tracker emits a frame of slot-value pairs considering the full history of the dialog up to the current turn. Our system includes an encoder-decoder architecture with an attention mechanism...
In modern smart building climate control systems, accurate detection of unusual behavior in temperature sensors (outliers) can help reduce or prevent waste of energy consumption in a Heating, Ventilation and Air Conditioning (HVAC) system. In this work, we propose online learning-distance based outlier detection method. In the new method, we train and tune a multilayer neural network to learn a nonlinear...
In this article, we propose a novel method to detect the occupancy behavior of a building through the temperature and/or possible heat source information, which can be used for energy reduction, security monitoring for emerging smart buildings. Our work is based on a realistic building simulation program, EnergyPlus, from Department of Energy. EnergyPlus can model the various time-series inputs to...
This paper focuses on an important research problem of Big Data classification in intrusion detection system. Deep Belief Networks is introduced to the field of intrusion detection, and an intrusion detection model based on Deep Belief Networks is proposed to apply in intrusion recognition domain. The deep hierarchical model is a deep neural network classifier of a combination of multilayer unsupervised...
To train and evaluate various face recognition algorithms, quite many databases have been created. But most of them have been created under controlled conditions to study the specific variations of the face recognition problem. These variations include position, pose, lighting, background, camera quality and gender. But in real environment, there are also many applications in which there is little...
By the definition of fractal, the BP network is proved to be a fractal whose fractal dimension ranges from 2.5 to 5. The relationship between generalization and learning rate of the BP network and the relationship between generalization and fractal dimension of weights are researched in this paper. The results present that the generalization of BP network becomes better with the increasing of the...
A BP artificial neural network model was established to optimize the final slope angle of open-pit mine, which was based on its powerful self-learning, nonlinear processing capabilities and advantages of simulation for the slope stability with nonlinear relationship among the parameters. The consideration of effect on the stability of the slope includes Protodrakonov scale of hardness, dip angle of...
In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training...
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