Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning

Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.     

Prediction of Alzheimer’s Using Random Forest with Radiomic Features

Alzheimer’s disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%–80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Alzheimer’s disease is the last phase of the disease where the brain is severely damaged, and the patients are not able to live on their own. Radiomics is an approach to extracting a huge number of features from medical images with the help of data characterization algorithms. Here, 105 number of radiomic features are extracted and used to predict the alzhimer’s. This paper uses Support Vector Machine, K-Nearest Neighbour, Gaussian Naïve Bayes, eXtreme Gradient Boosting (XGBoost) and Random Forest to predict Alzheimer’s disease. The proposed random forest-based approach with the Radiomic features achieved an accuracy of 85%. This proposed approach also achieved 88% accuracy, 88% recall, 88% precision and 87% F1-score for AD vs. CN, it achieved 72% accuracy, 73% recall, 72% precisionand 71% F1-score for AD vs. MCI and it achieved 69% accuracy, 69% recall, 68% precision and 69% F1-score for MCI vs. CN. The comparative analysis shows that the proposed approach performs better than others approaches.     

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives, and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multiobjective optimization, genetic algorithms, and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multiobjective approaches show promising results in fitness convergence, design diversity, and user satisfaction metrics.     

A study on measuring surface deformation of the L’Aquila region using the StaMPS technique

This research compares two time-series interferometric synthetic aperture radar (InSAR) methods, namely persistent scatterer SAR interferometry (PS-InSAR) and small baseline subset (SBAS) to retrieve the deformation signal from pixels with different scattering characteristics. These approaches are used to estimate the surface deformation in the L’Aquila region in Central Italy where an earthquake of magnitude Mw 6.3 occurred on 6 April 2009. Fourteen Environmental Satellite (ENVISAT) C-band Advanced Synthetic Aperture Radar (ASAR) images, covering the pre-seismic, co-seismic, and post-seismic period, are used for the study. Both the approaches effectively extract measurement pixels and show a similar deformation pattern in which the north-west and south-east regions with respect to the earthquake epicentre show movement in opposite directions. The analysis has revealed that the PS-InSAR method extracted more number of measurement points (21,103 pixels) as compared to the SBAS method (4886 pixels). A comparison of velocity estimates shows that out of 833 common pixels in both the methods, about 62% (517 pixels) have the mean velocity difference below 3 mm year^?1 and nearly 66% pixels have difference below 5 mm year^?1. It is concluded that StaMPS-based PS-InSAR method performs better in terms of extracting more number of measurement pixels and in the estimation of mean line of sight (LOS) velocity as compared to SBAS method.     

Two-echelon fuzzy stochastic supply chain for the manufacturer–buyer integrated production–inventory system

This paper deals with two-echelon integrated procurement production model for the manufacturer and the buyer integrated inventory system. The manufacturer procures raw material from outside suppliers (not a part of supply chain) then proceed to convert it as finished product, and finally delivers to the buyer, who faces imprecise and uncertain, called fuzzy random demand of customers. The manufacturer and the buyer work under joint channel, in which a centralized decision maker makes all decisions to optimize the joint total relevant cost (JTRC) of entire supply chain. In this account, in one production cycle of the manufacturer we determine an optimal multi-ordering policy for the buyer. To be part of this, we first derive the JTRC in stochastic framework, and then extend it in fuzzy stochastic environment. In order to scalarize the fuzzy stochastic JTRC, we use an evaluation method wherein randomness is estimated by probabilistic expectation and fuzziness is estimated by possibilistic mean based on possibility evaluation measure. To derive the optimal policies for both parties, an algorithm is proposed. A numerical illustration addresses the situations of paddy procurement, conversion to rice and fulfillment of uncertain demand of rice. Furthermore, sensitivity of parameters is examined to illustrate the model and algorithm.     

Identifying crop water stress using deep learning models

Neural Computing and Applications - The identification of water stress is a major challenge for timely and effective irrigation to ensure global food security and sustainable agriculture. Several...     

Information channel diagrams: an approach for modelling information flows

In this article, the ‘information channel diagram’ (ICD) approach is introduced as a diagrammatical tool for modelling information flow during the delivery phase of organisations in which goods are deployed or delivered to customers. An initial review and evaluation of current tools for modelling information flow will be conducted based on the characteristics of information flow during the delivery phases in organisations. Diagrammatic primitives and a prescribed modelling methodology for developing an ICD will be presented, and a case scenario of the delivery phase of an organisation within the health care sector will be applied to demonstrate the use of the ICD. The article concludes by discussing some applications, generalisation potential and limitations of the ICD approach.     

Novel analysis of the size and orientation dependent resonance phenomenon of the microstrip line coupled complementary split‐ring resonator

The purpose of this article is to provide a comprehensive investigation on the resonance phenomenon of microstrip line coupled complementary split‐ring resonator (CSRR) with different orientation and relative size. It is shown that when the relative size of the CSRR is smaller than the host line, the CSRR with its slit oriented orthogonal to the line axis will not excite effectively and show weak resonance behavior. However, when the slit is positioned along the line axis, the cross‐polarization effect comes into play, which excites the CSRR through the mixed coupling. To ensure the correctness, several numerical simulations are carried out for different substrate height and relative permittivity. Finally, a prototype is fabricated and measured for the experimental validation.     

Memory leak detection in Plumbr

Platforms with automatic memory management, such as the JVM, are usually considered free of memory leaks. However, memory leaks can happen in such environments, as the garbage collector cannot free objects, which are not used by the application anymore, but are still referenced. Such unused objects can eventually fill up the heap and crash the application. Although this problem has been studied extensively, nevertheless, there are still many rooms for improvement in this area. This paper describes the statistical approach for memory leak detection, as an alternative, along with a commercial tool, Plumbr, which is based on the method. The tool is later analyzed with three case studies of real applications and in the process also analyzes strengths and weaknesses of the statistical approach for memory leak detection. Copyright © 2014 John Wiley & Sons, Ltd.     

Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach

In general, distributed scheduling problem focuses on simultaneously solving two issues: (i) allocation of jobs to suitable factories and (ii) determination of the corresponding production scheduling in each factory. The objective of this approach is to maximize the system efficiency by finding an optimal planning for a better collaboration among various processes. This makes distributed scheduling problems more complicated than classical production scheduling ones. With the addition of alternative production routing, the problems are even more complicated. Conventionally, machines are usually assumed to be available without interruption during the production scheduling. Maintenance is not considered. However, every machine requires maintenance, and the maintenance policy directly affects the machine's availability. Consequently, it influences the production scheduling. In this connection, maintenance should be considered in distributed scheduling. The objective of this paper is to propose a genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint. The optimization performance of the proposed GADG will be compared with other existing approaches, such as simple genetic algorithms to demonstrate its reliability. The significance and benefits of considering maintenance in distributed scheduling will also be demonstrated by simulation runs on a sample problem.