Inference-Guiding for Intelligent Agents

In many applications of intelligent agents, initially given facts are not sufficient to reach a decision, and more data are needed. In that case. Inference-guiding is needed to identify the missing information and lead inference to a conclusion. This paper presents a new inferenceguiding strategy that selects the key pieces of missing information in such a way that the total cost of acquiring additional information for reaching a conclusion is the lowest. The computational experiments show that the new strategy is more effective and economical than the inference-guiding strategies currently available for the intelligent systems

Inference-Guiding on Bayesian Knowledge-Based Systems

Bayesian network is a robust structure for representing knowledge containing uncertainties in a knowledge-based system. In applications of expert systems and knowledge-based systems, it often happens that initial data are not sufficient to derive a conclusion of high enough certainty. Inference-guiding is in that case to identify the missing information, pursue its value, and lead inference to a conclusion. This paper presents and characterizes a criterion for effectively selecting key missing information, and thereby develops a “smart” inference approach with the inference-guiding function based on the newly developed criterion for uncertain inference in a Bayesian knowledge-based system

Reliable camera motion estimation from compressed MPEG videos using machine learning approach

As an important feature in characterizing video content, camera motion has been widely applied in various multimedia and computer vision applications. A novel method for fast and reliable estimation of camera motion from MPEG videos is proposed, using support vector machine for estimation in a regression model trained on a synthesized sequence. Experiments conducted on real sequences show that the proposed method yields much improved results in estimating camera motions while the difficulty in selecting valid macroblocks and motion vectors is skipped

A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading.

Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in this paper. In contrast to conventional psychology-based eye movement models, ours is based on a recurrent neural network (RNN) to generate a gaze point prediction sequence, by using the combination of convolutional neural networks (CNN), bidirectional long short-term memory networks (LSTM), and conditional random fields (CRF). The model uses the eye movement data of a reader reading some texts as training data to predict the eye movements of the same reader reading a previously unseen text. A theoretical analysis of the model is presented to show its excellent convergence performance. Experimental results are then presented to demonstrate that the proposed model can achieve similar prediction accuracy while requiring fewer features than current machine learning models

Multiple depth maps integration for 3D reconstruction using geodesic graph cuts

Depth images, in particular depth maps estimated from stereo vision, may have a substantial amount of outliers and result in inaccurate 3D modelling and reconstruction. To address this challenging issue, in this paper, a graph-cut based multiple depth maps integration approach is proposed to obtain smooth and watertight surfaces. First, confidence maps for the depth images are estimated to suppress noise, based on which reliable patches covering the object surface are determined. These patches are then exploited to estimate the path weight for 3D geodesic distance computation, where an adaptive regional term is introduced to deal with the “shorter-cuts” problem caused by the effect of the minimal surface bias. Finally, the adaptive regional term and the boundary term constructed using patches are combined in the graph-cut framework for more accurate and smoother 3D modelling. We demonstrate the superior performance of our algorithm on the well-known Middlebury multi-view database and additionally on real-world multiple depth images captured by Kinect. The experimental results have shown that our method is able to preserve the object protrusions and details while maintaining surface smoothness

DRL-RNP: deep reinforcement learning-based optimized RNP flight procedure execution.

The required navigation performance (RNP) procedure is one of the two basic navigation specifications for the performance-based navigation (PBN) procedure as proposed by the International Civil Aviation Organization (ICAO) through an integration of the global navigation infrastructures to improve the utilization efficiency of airspace and reduce flight delays and the dependence on ground navigation facilities. The approach stage is one of the most important and difficult stages in the whole flying. In this study, we proposed deep reinforcement learning (DRL)-based RNP procedure execution, DRL-RNP. By conducting an RNP approach procedure, the DRL algorithm was implemented, using a fixed-wing aircraft to explore a path of minimum fuel consumption with reward under windy conditions in compliance with the RNP safety specifications. The experimental results have demonstrated that the six degrees of freedom aircraft controlled by the DRL algorithm can successfully complete the RNP procedure whilst meeting the safety specifications for protection areas and obstruction clearance altitude in the whole procedure. In addition, the potential path with minimum fuel consumption can be explored effectively. Hence, the DRL method can be used not only to implement the RNP procedure with a simulated aircraft but also to help the verification and evaluation of the RNP procedure

Graphene Oxide on the Microstructure and Mechanical Properties of Cement Based Composite Material

To investigate the mixing amount of graphene oxide and water cement ratio on the microstructure and mechanical properties of graphene oxide reinforced cement based composite material, graphene oxide suspension was developed using improved Hummers method, and the structure, size and morphology of graphene oxide were represented using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and AFM. The results demonstrated that the bending and compressive strength of graphene oxide reinforced cement based composite material improved firstly and then declined with the increase of the mixing amount of graphene oxide, and moreover the improvement of the bending strength was obvious than that of the compressive strength. When the content of graphene oxide was 0.03%, the bending strength reached the maximum, 13.73 MPa. Under a high water cement ratio, the addition of graphene oxide was more effective in enhancing the strength of cement mortar. The representation of the microstructure of cement based composite material with scanning electron microscope (SEM) suggested that graphene oxide could optimize the microstructure of cement hydration products, improve the pore structure of set cement, reduce the volume of micropore in set cement, and increase the compactness of set cement, i.e. apparently strengthen the toughening effect of set cement. The research achievements are useful to improve the mechanical properties of cement based composite materials

Fast implementation of singular spectrum analysis for effective feature extraction in hyperspectral imaging

As a recent approach for time series analysis, singular spectrum analysis (SSA) has been successfully applied for feature extraction in hyperspectral imaging (HSI), leading to increased accuracy in pixel-based classification tasks. However, one of the main drawbacks of conventional SSA in HSI is the extremely high computational complexity, where each pixel requires individual and complete singular value decomposition (SVD) analyses. To address this issue, a fast implementation of SSA (F-SSA) is proposed for efficient feature extraction in HSI. Rather than applying pixel-based SVD as conventional SSA does, the fast implementation only needs one SVD applied to a representative pixel, i.e., either the median or the mean spectral vector of the HSI hypercube. The result of SVD is employed as a unique transform matrix for all the pixels within the hypercube. As demonstrated in experiments using two well-known publicly available data sets, almost identical results are produced by the fast implementation in terms of accuracy of data classification, using the support vector machine (SVM) classifier. However, the overall computational complexity has been significantly reduced

Graphene Oxide on the Microstructure and Mechanical Properties of Cement Based Composite Material

To investigate the mixing amount of graphene oxide and water cement ratio on the microstructure and mechanical properties of graphene oxide reinforced cement based composite material, graphene oxide suspension was developed using improved Hummers method, and the structure, size and morphology of graphene oxide were represented using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and AFM. The results demonstrated that the bending and compressive strength of graphene oxide reinforced cement based composite material improved firstly and then declined with the increase of the mixing amount of graphene oxide, and moreover the improvement of the bending strength was obvious than that of the compressive strength. When the content of graphene oxide was 0.03%, the bending strength reached the maximum, 13.73 MPa. Under a high water cement ratio, the addition of graphene oxide was more effective in enhancing the strength of cement mortar. The representation of the microstructure of cement based composite material with scanning electron microscope (SEM) suggested that graphene oxide could optimize the microstructure of cement hydration products, improve the pore structure of set cement, reduce the volume of micropore in set cement, and increase the compactness of set cement, i.e. apparently strengthen the toughening effect of set cement. The research achievements are useful to improve the mechanical properties of cement based composite materials

Ranking highlight level of movie clips : a template based adaptive kernel SVM method

This paper looks into a new direction in movie clips analysis – model based ranking of highlight level. A movie clip, containing a short story, is composed of several continuous shots, which is much simpler than the whole movie. As a result, clip based analysis provides a feasible way for movie analysis and interpretation. In this paper, clip-based ranking of highlight level is proposed, where the challenging problem in detecting and recognizing events within clips is not required. Due to the lack of publicly available datasets, we firstly construct a database of movie clips, where each clip is associated with manually derived highlight level as ground truth. From each clip a number of effective visual cues are then extracted. To bridge the gap between low-level features and highlight level semantics, a holistic method of highlight ranking model is introduced. According to the distance between testing clips and selected templates, appropriate kernel function of support vector machine (SVM) is adaptively selected. Promising results are reported in automatic ranking of movie highlight levels