Deep Learning Based Application for Indoor Scene Recognition

Neural Processing Letters - Recognizing indoor scene and objects and estimating their poses present a wide range of applications in robotic field. This task becomes more challenging especially in...     

碳纤维增强复合材料加固钢板的边界元分析

采用边界元分析对碳纤维增强复合材料加固钢板的疲劳性能进行扩展研究。采用BEASY软件计算应力强度因子、裂缝扩展和疲劳周期。应用面单元模拟组合增强片和带裂缝的钢板,将粘结层作为连接增强片和钢板的界面单元。数值模拟结果与试验结果具有一致性,从而验证了本文方法的有效性。采用边界元方法,对影响应力强度因子和疲劳寿命的因素进行评估,这些因素包括粘结长度、粘结宽度、增强片配置、碳纤维增强复合材料的层数组合增强片模量、粘结组合模量。     

Comparison between three optimization methods for the minimization of maximum bending load and springback in wiping die bending obtained by an experimental approach

The sheet metal bending process is widely used in the automotive industries, and it is actually one of the most important manufacturing processes. The robustness and the reliability of the bending operation, like many other forming operations, depend of several parameters (geometry, material, and process). In this paper, the die radius and the clearance between the punch and the sheet are optimised in order to reduce the maximum bending load and the springback. Two optimization problems are formulated, and three optimization procedures based on the response surface method are proposed and used to find the optimum solutions. Global and local approximations are used to replace the initial optimization problem, which is implicit by an explicit problem, and the optimum is localised using two algorithms: a sequential quadratic programming and an evolution strategies. The objective functions are evaluated experimentally into a limited points number, which are defined using a design of experiments technique. Good results are obtained from the three optimization procedures. The ability of each technique to find the optimal solution is evaluated, and the results show a good agreement between those three methods.     

Experimental investigation of bond characteristics between CFRP fabrics and steel plate joints under impact tensile loads

In recent years, the use of adhesively-bonded fibre-reinforced composite materials has attracted widespread attention as a viable alternative for the retrofitting of civil infrastructure such as buildings and bridges. This has been particularly the case for concrete structures. The retrofitting of metallic bridges and buildings with FRP materials, however, is still in its early stages. In real life, these structures are subjected to dynamic loads. Therefore, it is necessary to understand the bond behaviour between steel and the strengthening materials for both static and dynamic loads. To examine the bond between steel plates and carbon fibre-reinforced polymers (CFRP) fabrics, this paper describes the experimental procedures and results of double strap steel joints loaded at different loading rates (2 mm/min, 3.35, 4.43 and 5 m/s). In this test program, ultimate load-carrying capacity, effective bond length, failure mechanism and strain distribution were examined for all loading rates. Different numbers of CFRP layers with different bond lengths were investigated. Experimental findings reveal that the maximum improvement in joint capacity occurs at a rate of 3.35 m/s. It was observed that the effective bond length is insensitive to loading rate for both joints. The failure modes and strain distributions, however, exhibit little difference between static and dynamic conditions.     

An Evaluation of RetinaNet on Indoor Object Detection for Blind and Visually Impaired Persons Assistance Navigation

Neural Processing Letters - Indoor object detection presents a computer vision task that deals with the detection of specific indoor classes. This task attracts a lot of attention, especially in...     

Covariance-guided landmine detection and discrimination using ground-penetrating radar data

Ground penetrating Radar (GPR) can detect and deliver the response signal from any buried kind of object like plastic or metallic landmines, stones, and wood sticks. It delivers three kinds of data: Ascan, Bscan, and Cscan. However, it cannot discriminate between landmines and inoffensive objects or ‘clutter.’ One-class classification is an alternative to detect landmines, especially, as landmines features data are unbalanced. In this article, we investigate the effectiveness of the Covariance-guided One-Class Support Vector Machine (COSVM) to detect, discriminate, and locate landmines efficiently. In fact, compared to existing one-class classifiers, the COSVM has the advantage of emphasizing low variance directions. Moreover, we will compare the one-class classification to multiclass classification to tease out the advantage of the former over the latter as data are unbalanced. Our method consists of extracting Ascan GPR data. Extracted features are used as an input for COSVM to discriminate between landmines and clutter. We provide an extensive evaluation of our detection method compared to other methods based on relevant state of the art one-class and multiclass classifiers, on the well-known MACADAM database. Our experimental results show clearly the superiority of using COSVM in landmine detection and localization.     

Tomographic Reconstruction of 3D Objects Using Marked Point Process Framework

For reconstructing sparse volumes of 3D objects from projection images taken from different viewing directions, several volumetric reconstruction techniques are available. Most popular volume reconstruction methods are algebraic algorithms (e.g. the multiplicative algebraic reconstruction technique, MART). These methods which belong to voxel-oriented class allow volume to be reconstructed by computing each voxel intensity. A new class of tomographic reconstruction methods, called “object-oriented” approach, has recently emerged and was used in the Tomographic Particle Image Velocimetry technique (Tomo-PIV). In this paper, we propose an object-oriented approach, called Iterative Object Detection—Object Volume Reconstruction based on Marked Point Process (IOD-OVRMPP), to reconstruct the volume of 3D objects from projection images of 2D objects. Our approach allows the problem to be solved in a parsimonious way by minimizing an energy function based on a least squares criterion. Each object belonging to 2D or 3D space is identified by its continuous position and a set of features (marks). In order to optimize the population of objects, we use a simulated annealing algorithm which provides a “Maximum A Posteriori” estimation. To test our approach, we apply it to the field of Tomo-PIV where the volume reconstruction process is one of the most important steps in the analysis of volumetric flow. Finally, using synthetic data, we show that the proposed approach is able to reconstruct densely seeded flows.     

Constructability Concepts in West Port Highway in Malaysia

This paper presents a case study of the applications and nonapplications of constructability concepts to illustrate, in a practical way, the impact that these concepts can have on a project’s success. This case study, which conveys an important message with regard to the application of constructability concepts, was purposely chosen from among prestigious projects in peninsular Malaysia. The basic message, as viewed by the interviewees, is that applying these constructability concepts will enhance a project’s constructability, consequently optimizing the schedule, cost, and quality of the project for the benefit of all the parties involved. The interviewees for the case study agreed that the applied constructability concepts were derived from their own experience and not based on any existing formal program. The absence of a systematic technique for transferring construction experience and knowledge to all the participants in all phases of a construction project is the reason behind the lack of constructability in our construction industry today.     

SN-SVM: a sparse nonparametric support vector machine classifier

This paper introduces a novel sparse nonparametric support vector machine classifier (SN-SVM) which combines data distribution information from two state-of-the-art kernel-based classifiers, namely, the kernel support vector machine (KSVM) and the kernel nonparametric discriminant (KND). The proposed model incorporates some near-global variations of the data provided by the KND and, hence, may be viewed as an extension to the KSVM. Similarly, since the support vectors improve the choice of \(\kappa \) -nearest neighbors ( \(\kappa -NN\) ’s), it can also serve as an extension to the KND. The proposed model is capable of dealing with both heteroscedastic and non-normal data while avoiding the small sample size problem. The model is a convex quadratic optimization problem with one global optimal solution, so it can be estimated easily and efficiently using numerical methods. It can also be reduced to the classical KSVM model and as such existing SVM programs can be used for easy implementation. Through the Bayesian interpretation with the help of a Gaussian prior, we show that our method provides a sparse solution by assigning non-zero weights to only a fraction of the total number of training samples. This sparsity can be used by existing sparse classification algorithms to obtain better computational efficiency. The experimental results on real-world datasets and face recognition applications show that the proposed SN-SVM model improves the classification accuracy over contemporary classifiers and also provides sparser solution than the KSVM.