IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

A Continuous Mathematical Model of the One-Dimensional Sedimentation Process of Flocculated Sediment Particles

A new continuous one-dimensional sedimentation model incorporating a new continuous flocculation model that considers aggregation and fragmentation processes was derived and tested. Additionally, a new procedure to model sediment particle size distribution (PSD) was derived. Basic to this development were three different parametric models: Jaky, Fredlund and the Gamma probability distribution (GPD) were chosen to fit three different glass micro-spheres PSDs having average particle sizes of 7, 25 and 35 microns. The GPD provided the best fit with the least parameters. The bimodal GPD was used to fit ten sediment samples with excellent results (\u3c 5% average error). A continuous flocculation model was derived using the method of moments for solving the continuous Smoluchowski coagulation equation with fragmentation. The initial sediment PSD was modeled using a bimodal GPD. This new flocculation model resulted in a new general moments’ equation that considers aggregation and fragmentation processes, which is represented by a system of ordinary differential equations. The model was calibrated using a genetic algorithm with initial and flocculated PSDs of four sediment samples and four anionic polyacrylamides flocculants. The results show excellent correlation between predicted and observed values (R2 \u3e 0.9878). A new continuous one-dimensional sedimentation model that resulted in a scalar hyperbolic conservation law was derived from the well-known Kynch kinematic sedimentation model. The model was calibrated using column tests results with glass micro-spheres particles. Two different glass microspheres particle size distributions (PSDs) were used with average diameters of 7 and 37 microns. Excellent values of coefficient of determination (R2 \u3e 0.89, except for one test replicate) were obtained for both the small and large glass micro-spheres PSDs. These results suggest that the proposed sedimentation model can be expanded to model the sedimentation process inside a sediment pond

Optimisation algorithms inspired from modelling of bacterial foraging patterns and their applications

Research in biologically-inspired optimisation has been fl<;lurishing over the past decades. This approach adopts a bott0!ll-up viewpoint to understand and mimic certain features of a biological system. It has been proved useful in developing nondeterministic algorithms, such as Evolutionary Algorithms (EAs) and Swarm Intelligence (SI). Bacteria, as the simplest creature in nature, are of particular interest in recent studies. In the past thousands of millions of years, bacteria have exhibited a self-organising behaviour to cope with the natural selection. For example, bacteria have developed a number of strategies to search for food sources with a very efficient manner. This thesis explores the potential of understanding of a biological system by modelling the' underlying mechanisms of bacterial foraging patterns and investigates their applicability to engineering optimisation problems. :rvlodelling plays a significant role in understanding bacterial foraging behaviour. Mathematical expressions and experimental observations have been utilised to represent biological systems. However, difficulties arise from the lack of systematic analysis of the developed models and experimental data. Recently, Systems Biology has be,en proposed to overcome this barrier, with the effort from a number of research fields, including Computer Science and Systems Engineering. At the same time, Individual-based Modelling (IbM) has emerged to assist the modelling of a biological system. Starting from a basic model of foraging and proliferation of bacteria, the development of an IbM of bacterial systems of this thesis focuses on a Varying Environment BActerial Model (VEBAM). Simulation results demonstrate that VEBAM is able to provide a new perspective to describe interactions between the bacteria and their food environment. Knowledge transfer from modelling of bacterial systems to solving optimisation problems also composes an important part of this study. Three Bacteriainspired Algorithms (BalAs) have been developed to bridge the gap between modelling and optimisation. These algorithms make use of the. self-adaptability of individual bacteria in the group searching activities described in VEBAM, while incorporating a variety of additional features. In particular, the new bacterial foraging algorithm with varying population (BFAVP) takes bacterial metabolism into consideration. The group behaviour in Particle Swarm Optimiser (PSO) is adopted in Bacterial Swarming Algorithm (BSA) to enhance searching ability. To reduce computational time, another algorithm, a Paired-bacteria Optimiser (PBO) is designed specifically to further explore the capability of BalAs. Simulation studies undertaken against a wide range of benchmark functions demonstrate a satisfying performance with a reasonable convergence speed. To explore the potential of bacterial searching ability in optimisation undertaken in a varying environment, a dynamic bacterial foraging algorithm (DBFA) is developed with the aim of solving optimisation in a time-varying environment. In this case, the balance between its convergence and exploration abilities is investigated, and a new scheme of reproduction is developed which is different froin that used for static optimisation problems. The simulation studies have been undertaken and the results show that the DBFA can adapt to various environmental changes rapidly. One of the challenging large-scale complex optimisation problems is optimal power flow (OPF) computation. BFAVP shows its advantage in solving this problem. A simulation study has been performed on an IEEE 30-bus system, and the results are compared with PSO algorithm and Fast Evolutionary Programming (FEP) algorithm, respectively. Furthermore, the OPF problem is extended for consideration in varying environments, on which DBFA has been evaluated. A simulation study has been undertaken on both the IEEE 30-bus system and the IEEE l1S-bus system, in compariso~ with a number of existing algorithms. The dynamic OPF problem has been tackled for the first time in the area of power systems, and the results obtained are encouraging, with a significant amount of energy could possibly being saved. Another application of BaIA in this thesis is concerned with estimating optimal parameters of a power transformer winding model using BSA. Compared with Genetic Algorithm (GA), BSA is able to obtain a more satisfying result in modelling the transformer winding, which could not be achieved using a theoretical transfer function model

Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Development of an Integrated Intelligent Multi -Objective Framework for UAV Trajectory Generation

This thesis explores a variety of path planning and trajectory generation schemes intended for small, fixed-wing Unmanned Aerial Vehicles. Throughout this analysis, discrete and pose-based methods are investigated. Pose-based methods are the focus of this research due to their increased flexibility and typically lower computational overhead.;Path planning in 3 dimensions is also performed. The 3D Dubins methodology presented is an extension of a previously suggested approach and addresses both the mathematical formulation of the methodology, as well as an assessment of numerical issues encountered and the solutions implemented for these.;The main contribution of this thesis is a 3-dimensional clothoid trajectory generation algorithm, which produces flyable paths of continuous curvature to ensure a more followable commanded path. This methodology is an extension of the 3D Dubins method and the 2D clothoid method, which have been implemented herein. To ensure flyability of trajectories produced by 3D pose-based trajectory generation methodologies, a set of criteria are specified to limit the possible solutions to only those flyable by the aircraft. Additionally, several assumptions are made concerning the motion of the aircraft in order to simplify the path generation problem.;The 2D and 3D clothoid and Dubins trajectory planners are demonstrated through a trajectory tracking performance comparison between first the 2D Dubins and 2D clothoid methods using a position proportional-integral-derivative controller, then the 3D Dubins and 3D clothoid methods using both a position proportional-integral-derivative controller and an outer-loop non-linear dynamic inversion controller, within the WVU UAV Simulation Environment. These comparisons are demonstrated for both nominal and off-nominal conditions, and show that for both 2D and 3D implementations, the clothoid path planners yields paths with better trajectory tracking performance as compared to the Dubins path planners.;Finally, to increase the effectiveness and autonomy of these pose-based trajectory generation methodologies, an immunity-based evolutionary optimization algorithm is developed to select a viable and locally-optimal trajectory through an environment while observing desired points of interest and minimizing threat exposure, path length, and estimated fuel consumption. The algorithm is effective for both 2D and 3D routes, as well as combinations thereof. A brief demonstration is provided for this algorithm. Due to the calculation time requirements, this algorithm is recommended for offline use

Open-ended Search through Minimal Criterion Coevolution

Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field\u27s biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, the methods by which search is driven remain unnatural. In particular, nature has no need to characterize and enforce novelty; rather, it is guided by a single, simple constraint: survive long enough to reproduce. The key insight is that such a constraint, called the minimal criterion, can be harnessed in a coevolutionary context where two populations interact, finding novel ways to satisfy their reproductive constraint with respect to each other. Among the contributions of this dissertation, this approach, called minimal criterion coevolution (MCC), is the primary (1). MCC is initially demonstrated in a maze domain (2) where it evolves increasingly complex mazes and solutions. An enhancement to the initial domain (3) is then introduced, allowing mazes to expand unboundedly and validating MCC\u27s propensity for open-ended discovery. A more natural method of diversity preservation through resource limitation (4) is introduced and shown to maintain population diversity without comparing genetic distance. Finally, MCC is demonstrated in an evolutionary robotics domain (5) where it coevolves increasingly complex bodies with brain controllers to achieve principled locomotion. The overall benefit of these contributions is a novel, general, algorithmic framework for the continual production of open-ended dynamics without the need for a characterization of behavioral novelty

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

A Multi-objective Genetic Algorithm for Peptide Optimization

The peptide-based drug design process requires the identification of a wide range of candidate molecules with specific biological, chemical and physical properties. The laboratory analysis in terms of in vitro methods for the discovery of several physiochemical properties of theoretical candidate molecules is time- and cost-intensive. Hence, in silico methods are required for this purpose. Metaheuristics like evolutionary algorithms are considered to be adequate in silico methods providing good approximate solutions to the underlying multiobjective optimization problems. The general issue in this area is the design of a multi-objective evolutionary algorithm to achieve a maximum number of high-quality candidate peptides that differ in their genetic material, in a minimum number of generations. A multi-objective evolutionary algorithm as an in silico method of discovering a large number of high-quality peptides within a low number of generations for a broad class of molecular optimization problems of different dimensions is challenging, and the development of such a promising multi-objective evolutionary algorithm based on theoretical considerations is the major contribution of this thesis. The design of this algorithm is based on a qualitative landscape analysis applied on a three- and four-dimensional biochemical optimization problem. The conclusions drawn from the empirical landscape analysis of the three- and four-dimensional optimization problem result in the formulation of hypotheses regarding the types of evolutionary algorithm components which lead to an optimized search performance for the purpose of peptide optimization. Starting from the established types of variation operators and selection strategies, different variation operators and selection strategies are proposed and empirically verified on the three- and four-dimensional molecular optimization problem with regard to an optimized interaction and the identification of potential interdependences as well as a fine-tuning of the parameters. Moreover, traditional issues in the field of evolutionary algorithms such as selection pressure and the influence of multi-parent recombination are investigated