Wireless Robotics Networks for Search and Rescue in Underground Mines

To ensure the safety of miners, reliable and continuous monitoring of underground mine environment plays a significant role. Moreover, such a reliable communication network is essential to provide speedy rescue and recovery operations in case of an emergency situation in a mine. However, due to the hostile nature and unique characteristics of underground mine workings, emergency response communication and disaster management are very challenging tasks. This chapter presents an overview of evolving technology wireless robotics networks (WRN) which may be a promising alternative to support search and rescue (SAR) operation in underground mine emergencies. The chapter first outlines the introduction followed by a detailed discussion on the current state of the art on WRNs and their development in the context of underground mines. Finally, this chapter provides some insights on open research areas targeting the current wireless research design community and those interested in pursuing such challenging problems in this field.

Overview and Optimized Design for Energy Recovery Patents Applied to Hydraulic Systems

Due to their large application quantities with extremely low efficiency, pollutant emissions, high fuel consumption, and oil price, researches on the environment protection and the energy saving of construction machinery, especially hydraulic excavators, become very necessary and urgent. In this chapter, the authors proposed a complete study for the excavators' hydraulic energy recovery systems. This study is divided into two parts. In the first one, an overview for the energy saving principles is discussed and classed based on the type of the energy recovered. In the second part and once the energy recovery system is selected, the authors proposed a new approach to design the energy recovery system under a typical working cycle. This approach, the global optimization method for parameter identification (GOMPI), uses an optimization technique coupled with the simulated model on simulation software. Finally, results concluded that applying GOMPI model was an efficient solution as it proves its accuracy and efficiency to design any energy recovery patent applied to hydraulic systems.

An Improved Hybridized Evolutionary Algorithm Based on Rules for Local Sequence Alignment

In bioinformatics, sequence alignment is the heart of the sequence analysis. Sequence can be aligned locally or globally depending upon the biologist's need for the analysis. As local sequence alignment is considered important, there is demand for an efficient algorithm. Due to the enormous sequences in the biological database, there is a trade-off between computational time and accuracy. In general, all biological problems are considered as computational intensive problems. To solve these kinds of problems, evolutionary-based algorithms are proficiently used. This chapter focuses local alignment in molecular sequences and proposes an improvised hybrid evolutionary algorithm using particle swarm optimization and cellular automata (IPSOCA). The efficiency of the proposed algorithm is proved using the experimental analysis for benchmark dataset BaliBase and compared with other state-of-the-art techniques. Using the Wilcoxon matched pair signed rank test, the significance of the proposed algorithm is explicated.

Recent Neuro-Fuzzy Approaches for Feature Selection and Classification

Classification is the task of assigning objects to one of several predefined categories. However, developing a classification system is mostly hampered by the size of data. With the increase in the dimension of data, the chance of irrelevant, redundant, and noisy features or attributes also increases. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy, and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and classification with better insight by representing knowledge in symbolic forms. The neuro-fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied to standard datasets to demonstrate their applicability and performance.

Guided Search-Based Multi-Objective Evolutionary Algorithm for Grid Workflow Scheduling

The computational grid provides the global computing infrastructure for users to access the services over a network. However, grid service providers charge users for the services based on their usage and QoS level specified. Therefore, in order to optimize the grid workflow execution, a robust multi-objective scheduling algorithm is needed considering economic cost along with execution performance. Generally, in multi-objective problems, simulations rely on running large number of evaluations to obtain the accurate results. However, algorithms that consider the preferences of decision maker, convergence to optimal tradeoff solutions is faster. Thus, in this chapter, the author proposed the preference-based guided search mechanism into MOEAs. To obtain solutions near the pre-specified regions of interest, the author has considered two MOEAs, namely R-NSGA-II and R-ε-MOEA. Further, to improve the diversity of solutions, a modified form called M-R-NSGA-II is used. Finally, the experimental settings and performance metrics are presented for the evaluation of the algorithms.

Nature-Inspired Algorithms for Bi-Criteria Parallel Machine Scheduling

Nature has always been a source of inspiration for human beings. Nature-inspired search-based algorithms have an enormous computational intelligence and capabilities and are observing diverse applications in engineering and manufacturing problems. In this chapter, six nature-inspired algorithms, namely artificial bee colony, bat, black hole, cuckoo search, flower pollination, and grey wolf optimizer algorithms, have been investigated for scheduling of multiple jobs on multiple potential parallel machines. Weighted flow time and tardiness have been used as optimization criteria. These algorithms are very efficient in identifying optimal solutions, but as the size of the problem increases, these algorithms tend to get stuck at local optima. In order to extract these algorithms from local optima, genetic algorithm has been used. Flower pollination algorithm, when appended with GA, is observed to perform better than other counterpart nature-inspired algorithms as well as existing heuristics and meta-heuristics based on MOGA and NSGA-II algorithms.

DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm

DNA fragment assembly (DFA) is one of the most important and challenging problems in computational biology. DFA problem involves reconstruction of target DNA from several hundred (or thousands) of sequenced fragments by identifying the proper orientation and order of fragments. DFA problem is proved to be a NP-Hard combinatorial optimization problem. Metaheuristic techniques have the capability to handle large search spaces and therefore are well suited to deal with such problems. In this chapter, quantum-inspired genetic algorithm-based DNA fragment assembly (QGFA) approach has been proposed to perform the de novo assembly of DNA fragments using overlap-layout-consensus approach. To assess the efficacy of QGFA, it has been compared genetic algorithm, particle swarm optimization, and ant colony optimization-based metaheuristic approaches for solving DFA problem. Experimental results show that QGFA performs comparatively better (in terms of overlap score obtained and number of contigs produced) than other approaches considered herein.

An Approach to License Plate Recognition System Using Neural Network

This chapter presents an approach for automatic recognition of license plates. The system basically consists of four modules: image acquisition, license plate extraction, segmentation, and recognition. It starts by capturing images of the vehicle using a digital camera. An algorithm for the extraction of license plate has been designed and an algorithm for segmentation of characters is proposed. Recognition is done using neural approach. The performance of the system has been investigated on real images of about 610 Saudi Arabian vehicles captured under various conditions. Recognition of about 90% shows that the system is efficient.

An Enhanced Clustering Method for Image Segmentation

The findings of image segmentation reflect its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this chapter, the authors have evaluated the performances of three different clustering algorithms used in image segmentation: the classical k-means, its modified k-means++, and proposed enhanced clustering method. Brief explanations of the fundamental working principles implicated in these methods are presented. Thereafter, the performance which affects the outcome of segmentation are evaluated considering two vital quality measures, namely structural content (SC) and root mean square error (RMSE). Experimental result shows that the proposed method gives impressive result for the computed values of SC and RMSE as compared to k-means and k-means++. In addition to this, the output of segmentation using the enhanced technique reduces the overall execution time as compared to the other two approaches irrespective of any image size.

Hybrid Honey Bees Meta-Heuristic for Benchmark Data Classification

Some bio-inspired methods are cuckoo search, fish schooling, artificial bee colony (ABC) algorithms. Sometimes, these algorithms cannot reach to global optima due to randomization and poor exploration and exploitation process. Here, the global artificial bee colony and Levenberq-Marquardt hybrid called GABC-LM algorithm is proposed. The proposed GABC-LM will use neural network for obtaining the accurate parameters, weights, and bias values for benchmark dataset classification. The performance of GABC-LM is benchmarked against NNs training with the typical LM, PSO, ABC, and GABC methods. The experimental result shows that the proposed GABC-LM performs better than that standard BP, ABC, PSO, and GABC for the classification task.