Graphic Biostratigraphic Correlation Using Genetic Algorithms

During an earthquake, the better performance of segmental tunnel lining, compared to the continuous in-cast concrete lining, is generally related to the joints between segments. In order to better understand the influence of the segment joints, their effect on the internal forces induced in tunnel lining simultaneously with the effects of the other influential parameters should be considered. In this work, the segmental joints were simulated by the representative stiffnesses and effects of these characteristics in relation to the other parameters such as the soil-liner interface behavior, number of segments in each ring and thickness of segments on the internal forces induced in structure were investigated. For this purpose, 2D numerical analyses were performed and the results obtained were discussed. Results showed that under the seismic condition, the components that had the most significant role on the internal axial forces induced in the segmental lining were rotational stiffness and axial stiffness of joints. Also the bending moments were more affected by the rotational stiffness. Generally, the radial joint stiffness had a less effect on the induced internal forces. With increase in the number of segments and their thickness, the effect of joint stiffness on the internal forces increases and the design of joints should be given more attention; however, the effects of joint stiffness and frictional behavior at the soil-liner interface on the maximum induced forces are almost independent from each other. Also in a specified joint behavior, by variation in each one of the other parameters including the soil-liner interface condition, number of segments and their thickness, the absolute magnitude of the maximum induced internal forces sometimes change significantly.

A riveted graph G(V,E) is a graph whose vertices are fixed on a plane. Its rectilinear drawing D(G) is a configuration of G on the plane in such a way that every edge is composed of horizontal and vertical line segments. The authors consider the problem of reducing the number of line segments and present an algorithm to obtain such a rectilinear drawing that the total number of line segments is no more than 3 mod E mod . It is noted that a drawing so obtained contains no edge composed of five line segments. The algorithm runs in O( mod E mod ) time and space. >

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects of this concept on performance of each AI technique are investigated. Finally, outcomes of the approaches are evaluated and compared by means of the mean absolute percentage error (MAPE). Results show that data preprocessing can significantly improve performance of the AI techniques. Meanwhile, ANFIS outcomes are more approximate to the actual loads than those of ANN and GA, so it can be considered as a suitable tool to deal with STLF problems.

We consider division of an empirical distribution of xi, i being the index of a unit, for which we observe xi (e.g., province i, for which xi is the GDP per capita). Values xi are ordered non-decreasingly. We analyse the cumulative distribution, zi = ∑i'=1,...,ixi. The sequence zi is convex. We want to divide the distribution of zi into subsets of i, with the shape of the distribution {zi} possibly well approximated by the segments of the straight line, determined for the subsets, forming a piecewise linear contour, the number of segments being possibly small. This corresponds to the frequently used categorisations for similar distributions (e.g., “developed”, “developing”,... countries). For such categorisations, usually no formal methods are applied but “substantive” prerequisites, or the methods applied are limited to establishing quantiles of the distribution, without considering its shape and the objective premises for determination of a different number of segments, including optimisation of the criterion mentioned before. A general approach is proposed for optimising division of such distribution conform to the criterion mentioned. A general objective function is proposed and its concrete realisation, as well as algorithms. The methodology proposed allows for obtaining the optimum divisions into categories for arbitrary distributions. Yet, on the basis of concrete empirical distributions, problems are outlined, due to the fact that the distributions obtained often display the features, leading to questioning of the foundations of the methodology proposed, and of the very sense of such categorisations. Examples of distributions of this kind, and consequences for the potential categorisations, are discussed. In summary, the methodology proposed, including the criterion function, constitutes a basis for the categorisation with respect to the cumulative distribution, and a tool for evaluating the rationality of the way, in which the distributions are obtained.

Composite materials offer a wide range of advantages in terms of strength, weight, and versatility, making them a popular choice for structural applications. The performance of composite structures depends not only on the material constituents but also on the topology and arrangement of these components. However, designing an optimal configuration that maximizes the desired performance metrics can be a challenging task due to the large design space and complex interactions between the components. It presents a novel approach for optimizing the multi-component topology and material orientation design of composite structures using artificial intelligence (AI) techniques. The objective is to develop an automated and efficient methodology that can identify the most optimal configuration for a given set of performance requirements. The proposed methodology combines AI algorithms, such as genetic algorithms and machine learning, with advanced computational modeling techniques. The genetic algorithms are employed to explore the design space and search for the best combination of topology and material orientations. Concurrently, machine learning techniques are utilized to model the complex relationships between the design variables and performance metrics, enabling the identification of design patterns and accelerating the optimization process.
 To achieve this, a comprehensive framework is established, encompassing the generation of an initial population of candidate designs, evaluation of their performance using numerical simulations or experimental testing, application of genetic algorithms to iteratively evolve the population by selecting the fittest designs and introducing variations, and utilization of machine learning models to predict the performance of new designs and guide the optimization process. The effectiveness of the proposed methodology is demonstrated through a case study involving the design of a composite aerospace structure. The results reveal that the AI-based optimization approach significantly outperforms traditional trial-and-error methods, leading to improved performance metrics such as strength, stiffness, and weight.
 The findings have significant implications for the design and manufacturing of composite structures in various industries, including aerospace, automotive, and civil engineering. The automated optimization process enabled by AI techniques can efficiently explore the design space, identify innovative solutions, and ultimately enhance the overall performance of composite structures.

Path planning is one of the most important tasks for autonomous mobile robots. The purpose of path planning is to find a collision-free path from a starting position to a target position due to specific criteria such as distance. This paper proposes a modified path improvement algorithm based on the former and later points. In the initial path, some points are randomly placed on the line segments that have the necessary condition. New positions of points in the path are calculated by their adjacent points. This approach explores the most optimal path between different values of angle for the different number of segments. A method is proposed in which the initial path in the line segments that contain the turns adheres to the obstacles in the way. This operation makes a shorter path. Experiments are conducted on six maps and the results indicate that the proposed method achieves a considerable optimization in the final path.

In this study, an artificial intelligence technique is proposed to be implemented in the parameter tuning of a PVD process. Due to its previous adaptation in similar optimization problems, genetic algorithm (GA) is selected to optimize the parameter tuning of the RF magnetron sputtering process. The most optimized parameter combination obtained from GA’s optimization result is expected to produce the desirable zinc oxide (ZnO) thin film from the sputtering process. The parameters involved in this study were RF power, deposition time and substrate temperature. The algorithm was tested to optimize the 25 datasets of parameter combinations. The results from the computational experiment were then compared with the actual result from the laboratory experiment. Based on the comparison, GA had shown that the algorithm was reliable to optimize the parameter combination before the parameter tuning could be done to the RF magnetron sputtering machine. In order to verify the result of GA, the algorithm was also been compared to other well known optimization algorithms, which were, particle swarm optimization (PSO) and gravitational search algorithm (GSA). The results had shown that GA was reliable in solving this RF magnetron sputtering process parameter tuning problem. GA had shown better accuracy in the optimization based on the fitness evaluation.

In the thesis, initial design of an Air Cushion Vehicle (ACV)is performed with the expert system and its skirt system is further optimized with the genetic algorithm. Both the expert system and genetic algorithm are advanced computerized design techniques of artifical intelligence. Those techniques are specifically developed for the ACVs with programming codes in this thesis. Then the main objective is to show the successful implementation of those techniques in the design of ACVs. The thesis work is divided into two parts. In the first part, the general configuration of ACVs, including the overall dimensions, weight distribution, parametric properties, and several subsystems, is studied and designed by the expert system as an initial design phase. In the second part of the thesis, the skirt system of ACVs is further optimized. In particular, the properties of the bag and finger skirt are optimized for improved ride quality and stability by the genetic algorithm. For the validation of these two artificial intelligence techniques, the CCG (Canadian Coast Guard) 37 ton Waban-Aki and U.S. Navy's 150 ton LCAC (Landing Craft Air Cushion) are selected for the tests. The results of the tests proved that the expert system was successfully implemented and was a powerful tool for the initial design of ACVs. Furthermore, the genetic algorithm optimized the skirt system with significantly improved ride quality and stability. It was also shown that the skirt mass was an important design factor in the heave response of the bag and finger skirt. Hence, this thesis work opened the new possibility of designing ACVs with artificial intelligence techniques.

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

In this study, we examined the ability of Artificial Intelligence (AI) techniques in deriving parameters of NMR log starting from conventional logs. To perform this, it was applied Fuzzy Logic (FL) and Artificial Neural Networks (ANN) techniques separately, forming independent schemes. On the other hand, Genetic Algorithms (GA) and Simple Mean (SM) approaches were used to assign weighting factors to FL and ANN estimates, with the objective to optimize the individual contributions of each one. This methodology made use of, as input data, gamma rays (GR), resistivity laterolog (RLA1), density (RHOB), neutron (NPOR) and sonic (DTCO) logs from two wells drilled through a carbonate reservoirs in Campos Basin - Brazil. The output responses were compared with free fluid porosity (CMFF) and SDR lateral permeability (KSDR - Schlumberger Doll Research), both derived from NMR log of the same wells. The results indicate that ANN performed better when compared with FL, but this last was essential in the success of SM and GA estimates. However, each approach showed a good fit with the parameter curves of NMR log, confirming the utility of the present methodology in the case when there are only conventional logs in the studied well.

4 - Soft computing techniques

Mark Watson Morgan Kaufmann Publishers, San Francisco, 1997, 377 pp. ISBN 1-55860-420-0, $44.95 This book is well written and organized to cover how to apply AI (Artificial Intelligence) techniques for practical Internet and Intranet tools by using the Java programming language. In the book, the author uses neural network and genetic algorithms to develop distributed systems that are applied for the Internet and Intranets, such as client/server model and information retrieval tool and so on. Part 1 begins with a good general introduction to AI. The history and survey of Artificial Intelligence research are given briefly. In addition, symbolic processing versus neural networks approaches are discussed. This author opts to use neural network to develop example Internet/Intranet systems in the book. Part 2 consists of Java programs developed for AI, neural networks, genetic algorithms, natural language processing, and expert systems. Chapter 2 illustrates Graphical User Interface (GUI) class that is derived from Applet and Canvas classes in Java, which is used in most example programs in the book. Chapter 3 introduces a frame data structure that is used as a message in distributed systems in the later chapters. Chapter 4 shows neural network classes developed in Java and its theory and implementation. Chapter 5 explains the genetic algorithms and the Java genetic algorithm classes. Chapter 6 describes how to develop and implement natural language processing classes using Java. Chapter 7 shows how to share data objects in distributed systems. For example, distributed AI agent classes are developed to share data objects, WWW data collection agent classes are shown to retrieve documents in the Internet, and small POP (Post Office Protocol) mail classes are developed to send and receive mails. Chapter 8 illustrates how to develop expert systems by using Java Jess, which is based on OPS5/CLIPS expert systems. Part 3 explains how to generate and implement the projects using the Java classes that are developed in Part 2. Chapter 9 shows the project that demonstrates the use of genetic algorithms in chapter 5 to control enemy ships in a scrolling arcade game. Chapter 10 describes the real-time handwriting recognition system developed by using the neural network classes in chapter 4. Chapter 11 illustrates a greedy algorithm to optimize neural networks that is based on the author’s hand-tuning neural training data. Chapter 12 improves neural network training data by applying genetic algorithms, to discard bad training data and to avoid discarding good training data. Chapter 13 describes the distributed natural language processing system that uses natural language processing classes in chapter 6. The data structures in chapter 2 are used to store historical data in a separate server application on any other computer in the Internet or Intranets. The final chapter provides the design and implementation of a complete agent-based information retrieval tool for the Internet. The retrieved documents are stored in ASCII or HTML format. This book is a very good reference book for anyone interested in developing systems using Java in distributed environments, especially applying AI techniques. Also, the author provides the CD-ROM that includes the source code for Java classes developed in the book. Haklin Kimm, University of Tennessee

For decades, the slide drilling system (SDS) has played a critical role in the area of directional drilling. Drilling in the sliding mode is much more time-consuming than drilling in the rotating mode for a SDS. Therefore, it generally takes more time to drill a curved wellbore segment than a straight segment of the same length. Based on this fact, a novel wellbore trajectory design for SDSs is proposed in this paper to reduce the total drilling time and improve economy. The basic idea of our strategy is to replace a long curved wellbore section, e.g., the build-up and drop-off sections, with a series of short circular-arc and straight-line segments in the design stage. Assuming that rotating and sliding modes are only used for straight-line and circular-arc segments, respectively, the total drilling time is reduced by increasing the average rate of penetration. Therefore, the problem lies in how to replace a long circular arc with multiple short circular arcs and straight lines to minimize the total drilling time, which is the aim of this research. In this paper, a piecewise trial strategy is proposed, where the optimized wellbore trajectory is generated by adding circular-arc and straight-line segments with equal length one by one according to the “line first” principle. This strategy converts the original optimization problem, which involves both combinatorial and parameter optimization of unknown dimension, to a much easier one, where there are only three design variables and one set of constraint equations. Due to the multimodal characteristics of the objective function, genetic algorithm (GA) is adopted to obtain the optimal solution. A great reduction of the drilling time is obtained by applying our method to two typical cases, which shows a clear potential for future field applications.

Choroidal neovascularization (CNV) in neovascular age-related macular degeneration (nAMD) undergoing anti-VEGF therapy transforms into a fibrotic lesion. This fibrovascular transformation is associated with a great variety of functional and morphological effects. The aim of this study was to investigate the vascular morphology of fibrotic CNV, to compare it with its surrounding tissue and to identify phenotypes using optical coherence tomography angiography (OCTA). In 18 eyes with fibrotic CNV in nAMD spectral domain OCT (SD-OCT) and OCTA were performed. The automated segmentation lines were manually adjusted. A slab from 60 µm beneath Bruch's membrane to the inner edge of the subretinal hyperreflective material was applied. Quantitative analysis of the vascular morphology was performed using skeletonized OCTA images. Compared to the perilesional rim, the number of segments per area was significantly lower (234.75 ± 25.68 vs. 255.30 ± 20.34 1/mm2, p = 0.0003) within the fibrovascular lesion. Two phenotypes could be identified within the lesion. The phenotypic traits of cluster 1 were few, long and thick vascular segments; Cluster 2 was characterized by many, short and thin vascular segments (number of segments per area: 219.4 ± 18.8 vs. 258.8 ± 13.2 1/mm2, p = 0.00009, segment length: 49.6 ± 2.7 vs. 45.0 ± 1.3 µm, p = 0.0002, vascular caliber: 26.6 ± 1.2 vs. 23.5 ± 1.8 µm, p = 0.003). The clusters did not differ significantly regarding visual acuity (0.52 ± 0.44 vs. 0.54 ± 0.18 logMAR, p = 0.25), differentiability of subretinal (OR = 3.43, CI = [0.30, 39.64], p = 0.6) and intraretinal fluid (OR = 5.34, CI = [0.48, 89.85], p = 0.14). Less normalized ellipsoid zone (EZ) loss could be observed in cluster 1 (131.0 ± 161.3 vs. 892.4 ± 955.6 1/m, p = 0.006). In this study the vascular morphology of fibrotic CNV was analyzed using OCTA. Differences between the lesion and a perilesional rim could be detected. Two phenotypes within the fibrovascular lesion were identified. These morphological clusters could indicate different patterns of fibrovascular transformation of the CNV under long-term anti-VEGF therapy and be useful identifying possible predictive biomarkers in future studies.

A method for modeling photovoltaic (PV) arrays - based on artificial intelligence techniques, namely genetic algorithm (GA) and cuckoo optimization algorithm (COA) - is presented. COA and GA are used to obtain the parameters of the PV array model using the PV cell’s datasheet information. The adopted models – using GA and COA - are implemented on a simulation platforms using MATLAB 2020a environment for two-diode and single-diode models. The proposed optimization method fits the mathematical current-voltage (I-V) characteristic to the three (V, I) remarkable points without the need to guess or to estimate any other parameter. The obtained models are tested and validated with experimental data taken from the Mutah university’s PV power plant. The results show that for both of the employed optimization algorithms, the two-diode model is more accurate than the single-diode model. The results also disclose that, at different values of temperature and solar irradiance, the COA – compared to the GA – better handles the optimization problem with low iterations and better fitness value.