Abstract: Abstract The symptoms of plant diseases are evident in different parts of a plant; however leaves are found to be the most commonly observed part for detecting an infection. Researchers have thus attempted to automate the process of plant disease detection and classification using leaf images. Several works utilized computer vision technologies effectively and contributed a lot in this domain. This manuscript summarizes the pros and cons of all such studies to throw light on various important research aspects. A discussion on commonly studied infections and research scenario in different phases of a disease detection system is presented. The performance of state-of-the-art techniques are analyzed to identify those that seem to work well across several crops or crop categories. Discovering a set of acceptable techniques, the manuscript highlights several points of consideration along with the future research directions. The survey would help researchers to gain understanding of computer vision applications in plant disease detection. PubDate: 2019-04-01

Abstract: Abstract Collision between adjoining buildings with aligned slabs is relevant, since the huge impact forces significantly modify the buildings dynamic behavior. The separation required by the regulations avoids pounding; however, even in recent buildings, impact can occur due to not fulfillment of codes and seismicity underestimation. Given the importance of this issue, a significant research effort has been undertaken worldwide, and a considerable number of papers are available. The complexity of this field and this abundance of information might require a review task. This paper presents a summary of the theoretical developments, discusses the most common simulation software, provides an overview of the previous research, offers recommendations to researchers, and identifies research needs. PubDate: 2019-04-01

Abstract: Abstract The Travelling Salesman Problem (TSP) is an NP-hard problem with high number of possible solutions. The complexity increases with the factorial of n nodes in each specific problem. Meta-heuristic algorithms are an optimization algorithm that able to solve TSP problem towards a satisfactory solution. To date, there are many meta-heuristic algorithms introduced in literatures which consist of different philosophies of intensification and diversification. This paper focuses on 6 heuristic algorithms: Nearest Neighbor, Genetic Algorithm, Simulated Annealing, Tabu Search, Ant Colony Optimization and Tree Physiology Optimization. The study in this paper includes comparison of computation, accuracy and convergence. PubDate: 2019-04-01

Abstract: Abstract In this paper, recent pulse coupled neural networks (PCNN) model’s development, especially in the fields related to the image processing, were surveyed. Our review aims to provide a comprehensive and systematic analysis of selected researches from past few decades, having powerful methods to infer the area of study. In this paper, all selected references are categorized in three groups, on the basis of neurons structure, parameters setting, and the inherent characteristics of PCNN. Various applications of these models were mentioned and underlying difficulties, limitations, merits and disadvantages were discussed in applying these models. The researchers will find it helpful to choose and use the appropriate model for a better application. PubDate: 2019-04-01

Abstract: Abstract This paper surveys development using image-based methods for crack analysis in the last two-decade (2002–2016).This study aimed to extract and quantify the individual cracks in concrete surfaces, using a new automated image-based system. In general, an individual crack can appear in concrete structures as one of the three common configurations including longitudinal, transverse, and diagonal cracks. These kinds of cracks propagate inherently as linear, and may be involved in branching and spalling at some point of the original path. The main contribution of this work is twofold. First, the main mother crack is extracted using the graph theory and simulates the crack group proportionally. Second, the exact width of cracks can be measured automatically. The procedure has been automated in this study to calculate the individual crack characteristics including the length, average width, and orientation. Furthermore, the analytical results are presented as the distribution of accurate width variations along the length of the skeleton, maximum crack width and its location on the crack and graph. The results indicated that the proposed image-based crack quantification method can accurately measure changing the crack characteristics like width along it. It is demonstrated that the proposed method is applicable and shows good performance in conventional assessment of distressed concrete surfaces. PubDate: 2019-04-01

Abstract: Abstract Assembly sequence planning (ASP) is an NP-hard problem that involves finding the most optimum sequence to assemble a product. The potential assembly sequences are too large to be handled effectively using traditional approaches for the complex mechanical product. Because of the problem complexity, ASP optimization is required for the efficient computational approach to determine the best assembly sequence. This topic has attracted many researchers from the computer science, engineering, and mathematics background. This paper presents a review of the research that used soft computing approaches to solve and optimize ASP problem. The review on this topic is important for the future researchers to contribute in ASP. The literature review was conducted through finding related published research papers specifically on ASP that used soft computing approaches. This review focused on ASP modeling approach, optimization algorithms and optimization objectives. Based on the conducted review, several future research directions were drawn. In terms of the problem modeling, future research should emphasize to model the flexible part in ASP. Besides, the consideration of sustainable manufacturing and ergonomic factors in ASP will also be the new directions in ASP research. In addition, a further study on new optimization algorithms is also suggested to obtain an optimal solution in reasonable computational time. PubDate: 2019-04-01

Abstract: Abstract The current paper establishes different axisymmetric and two-dimensional models for an electrostatic, magnetostatic and electromagnetic induction process. Therein, the Maxwell equations are combined in a monolithic solution strategy. A higher order finite element discretization using Galerkin’s method in space as well as in time is developed for the electromagnetic approach. In addition, time integration procedures of the Runge–Kutta family are evolved. Furthermore, the residual error is introduced to open an alternative way for a numerically efficient estimation of the time integration accuracy of the Galerkin time integration method. Runge–Kutta methods are enriched by the embedded error estimate. A family of electrostatic, magnetostatic and electromagneto dynamic boundary and initial boundary value problems with existing analytical solutions are introduced, which will serve as benchmark examples for numerical solution procedures. PubDate: 2019-04-01

Abstract: Abstract Multispectral remote sensing images are the primary source in the land use and land cover (LULC) monitoring. This is achieved by LULC classification and LULC change detection. The change detection in LULC includes the detection of water bodies, forest fire, forest degradation, agriculture areas monitoring, etc. Various change detection and LULC classification methods have their own advantages and disadvantages, and no single method is optimal and finds applicability for all cases. This paper summarizes and analyses the various soft computing and feature extraction techniques used for LULC classification and change detection. Based on the average error rate, performances of the different soft computing techniques are evaluated. The broad usage of multispectral remote sensing images, object-based change detection, neural networks and various levels of image fusion methods offer more potential in LULC monitoring. PubDate: 2019-04-01

Abstract: Abstract We discuss the use of hierarchical collocation to approximate the numerical solution of parametric models. With respect to traditional projection-based reduced order modeling, the use of a collocation enables non-intrusive approach based on sparse adaptive sampling of the parametric space. This allows to recover the low-dimensional structure of the parametric solution subspace while also learning the functional dependency from the parameters in explicit form. A sparse low-rank approximate tensor representation of the parametric solution can be built through an incremental strategy that only needs to have access to the output of a deterministic solver. Non-intrusiveness makes this approach straightforwardly applicable to challenging problems characterized by nonlinearity or non affine weak forms. As we show in the various examples presented in the paper, the method can be interfaced with no particular effort to existing third party simulation software making the proposed approach particularly appealing and adapted to practical engineering problems of industrial interest. PubDate: 2019-04-01

Abstract: Abstract We review the literature on patient-specific vascular modeling, with particular attention paid to three-dimensional arterial networks. Patient-specific vascular modeling typically involves three main steps: image processing, analysis suitable model generation, and computational analysis. Analysis suitable model generation techniques that are currently utilized suffer from several difficulties and complications, which often necessitate manual intervention and crude approximations. Because the modeling pipeline spans multiple disciplines, the benefits of integrating a computer-aided design (CAD) component for the geometric modeling tasks has been largely overlooked. Upon completion of our review, we adopt this philosophy and present a CAD-integrated template-based modeling framework that streamlines the construction of solid non-uniform rational B-spline vascular models for performing isogeometric finite element analysis. Examples of arterial models for mouse and human circles of Willis and a porcine coronary tree are presented. PubDate: 2019-04-01

Abstract: Abstract This paper documents all the important works in the field of conjugate heat transfer study. Theoretical and applied aspects of conjugate heat transfer analysis are reviewed and summarized to a great extent on the light of available literature in this field. Over the years, conjugate heat transfer analysis has been evolved as the most effective method of heat transfer study. In this approach the mutual effects of thermal conduction in the solid and convection in the fluid are considered in the analysis. Various analytical and computational studies are reported in this field. Comprehension of analytical as well as computational studies of this field will help the researchers and scientists who work in this area to progress in their research. That is the focus of this review. Early analytical studies related to conjugate heat transfer are reviewed and summarised in the first part of this paper. Background of theoretical studies is discussed briefly. More importance is given in summarising the computational studies in this field. Different coupling techniques proposed to date are presented in great detail. Important studies narrating the application of conjugate heat transfer analysis are also discussed under separate headings. Hence the present paper gives complete theoretical background of Conjugate heat transfer along with direction to its application envelope. PubDate: 2019-04-01

Abstract: Abstract Hole drilling is one of the major basic operations in part manufacturing. It follows without surprise then that the optimization of this process is of great importance when trying to minimize the total financial and environmental cost of part manufacturing. In multi-hole drilling, 70% of the total process time is spent in tool movement and tool switching. Therefore, toolpath optimization in particular has attracted significant attention in cost minimization. This paper critically reviews research publications on drilling path optimization. In particular, this review focuses on three aspects; problem modeling, objective functions, and optimization algorithms. We conclude that most papers being published on hole drilling are simply basic Traveling Salesman Problems (TSP) for which extremely powerful heuristics exist and for which source code is readily available. Therefore, it is remarkable that many researchers continue developing “novel” metaheuristics for hole drilling without properly situating those approaches in the larger TSP literature. Consequently, more challenging hole drilling applications that are modeled by the Precedence Constrained TSP or hole drilling with sequence dependent drilling times do not receive much research focus. Sadly, these many low quality hole drilling research publications drown out the occasional high quality papers that describe specific problematic problem constraints or objective functions. It is our hope through this review paper that researchers’ efforts can be refocused on these problem aspects in order to minimize production costs in the general sense. PubDate: 2019-04-01

Abstract: Abstract In the field of image processing, there are several problems where an efficient search of the solutions has to be performed within a complex search domain to find an optimal solution. Multi-thresholding which is a very important image segmentation technique is one of them. The multi-thresholding problem is simply an exponential combinatorial optimization process which traditionally is formulated based on complex objective function criterion which can be solved using only nondeterministic methods. Under such circumstances, there is also no unique measurement which quantitatively judges the quality of a given segmented image. Therefore, researchers are solving those issues by using Nature-Inspired Optimization Algorithms (NIOAs) as alternative methodologies for the multi-thresholding problem. This study presents an up-to-date review on all most important NIOAs employed in multi-thresholding based image segmentation domain. The key issues which are involved during the formulation of NIOAs based image multi-thresholding models are also discussed here. PubDate: 2019-03-20

Abstract: Abstract Modeling of manufacturing processes and especially machining has proven to be particularly demanding, due to the complex phenomena occurring, leading to the necessity of employing special material and contact models, developing the appropriate thermo-mechanical coupling, and determining the chip forming mechanism and final morphology. Finite element method (FEM) models are proven to be adequate for metal cutting simulations up to some extent but still exhibit several shortcomings. During the past few years, a shift towards meshless methods was noticed, in order to avoid the deficiencies of FEM models. In the present work, a thorough review of the most important meshless methods employed for the modeling of machining processes is presented. After a concise description of each method, further discussions are conducted with a view to illustrate the strengths and weaknesses of each method, highlight its capabilities towards more reliable simulations, as well as propose potential future applications. PubDate: 2019-03-16

Abstract: Abstract Driverless cars and autonomous vehicles have significantly changed the face of transportation those days. Efficient use of vision system in the recent development of advanced driver assistance systems since last two decades have equipped cars and light vehicles to reduce accidents, congestion, crashes and pollution. The robust performance of the driver assistance systems absolutely depend on the flawless detection of the vehicles from the images. Developments of vigorous computer vision techniques based on various Image level features have enabled intelligent Transportation systems to solve some of the core challenges in vehicle detection. A detailed study of the vehicle detection in dynamic conditions is presented in this paper. The complexity of the vehicle detection in variable on-road driving conditions is evident from the diverse challenges illustrated in this paper. Dynamic vehicle detection mechanism has obviously attracted numerous approaches like feature based techniques and model based techniques. Different set of visual information representation as edge, shadow, light are used to detect the vehicles. Out of all low level features shape representation for vehicle detection is observed more efficient. The need of handling massive visual data for processing is addressed using novel feature representation like object proposal methods is discussed in more detail. The efficacy of ongoing research in Autonomous vehicles is validated using deep learning techniques on aerial image analysis. PubDate: 2019-03-12

Abstract: Abstract Wide research has been carried out for recognition of handwritten text on various languages that include Assamese, Bangla, English, Gujarati, Hindi, Marathi, Punjabi, Tamil etc. Recognition of multi-lingual text documents is still a challenge in the pattern recognition field. In this paper, a study of various features and classifiers for recognition of pre-segmented multi-lingual characters consisting of English, Hindi and Punjabi has been presented. In feature extraction phase, various techniques, namely, zoning features, diagonal features, horizontal peak extent based features and intersection and open end point based features are considered. In classification phase, three different classifiers, namely, k-NN, Linear-SVM, and MLP are attempted. Different combinations of various features and classifiers have been also performed. For script identification, we have achieved maximum accuracy of 92.89% using a combination of Linear-SVM, k-NN, and MLP classifiers, and for character recognition of English, Hindi and Punjabi, we have achieved a recognition accuracy of 92.18%, 84.67% and 86.79%, respectively. PubDate: 2019-03-08

Abstract: Abstract This work grew out of rapid developments of topology optimization approaches and emerging industry trends of “3D printing” techniques, the latter bridging to a large extent the gap between innovative design and advanced manufacturing. In the present work, we first make an application-oriented review of topology optimization approaches in an attempt to illustrate their efficacy in the design of high-performance structures. Subsequently, a broad panorama of additive manufacturing is provided with a particular interest in its application in the automotive and the aerospace sectors. Taking an aerospace bracket as an example, we further go through an entire procedure from topology optimization design to additive manufacturing, then to performance verification. In the interest of cultivating a long-term partnership upon this combination, we finally examine, in face of present and near future, limitations of additive manufacturing in the loss of geometric accuracy and performance deterioration, and provide a roadmap for future work. PubDate: 2019-03-07

Abstract: Abstract The paper discusses Trefftz discretization techniques with a focus on their coupling with shape functions computed by the method of Taylor series. The paper highlights are, on one hand the control of ill-conditioning and the solving of large scale problems, on the other hand the applications to non-linear Partial Differential Equations. Indeed, despite excellent convergence properties, the practical use of Trefftz methods remains very limited because of their difficulty in treating nonlinearities and large systems. PubDate: 2019-03-06

Abstract: Abstract Dynamic simulation of revolved solids plays an important role in many fields. Aiming at the lacks of solutions in some key aspects, this study establishes governing equation of motion based on theory of variational inequality; designs a compatibility iteration algorithm for solving contact forces; deduces parametric equations of arbitrary cylinder and cone in three-dimensional space; provides corresponding analytical methods to identify contact points between bodies and to calculate volume integral over bodies; proposes a rotation matrix modification approach to conserve volume and shape of rigid body in the case of large rotation. The accuracy, availability, competence, robustness, and application prospects of the presented methodology are demonstrated by several interesting and challenging problems. PubDate: 2019-03-02

Abstract: Abstract Reliable prediction of damage and failure in structural parts is a major challenge posed in engineering mechanics. Damage criterion is one of the most useful pieces of information about the RC structures when subjected to extreme dynamic loads. The damage index is the numerical approach to quantify the damage following a blast or an earthquake. The damage index was used to identify the capacity degradation of the structural elements under high strain rate loads. The few investigations conducted on evaluation of damage criterion and mode of failure for RC structures when subjected to blast loads. Therefore, the overall aim of this research is to overview on the damage criterion and failure modes of RC structures when subjected to extreme dynamic loads. In this study different damage criterion are demonstrated for structural elements. The results indicate the damage criterion using residual axial load carrying capacity is the appropriate method to evaluate the damage degree in columns as well as three main damage modes are observed for RC columns when subjected to blast loads. Also the maximum support rotation is a suitable approach to determine the level of damage in RC panel and slabs. The data collected from this research are being used to improve the knowledge of how structures will respond to a blast event, and improve analytical models for predicting the level of damage in concrete structures. PubDate: 2019-03-02