Design of Air Cushion Vehicles Using Artificial Intelligence: Expert System and Genetic Algorithm

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

The United States Navy Landing Craft Air Cushion (LCAC) vehicles, under the Service Life Extension Program (SLEP), use the Enhanced TF40B (ETF40B) gas turbine engines for main propulsion and lift. These engines provide the additional engine horsepower needed under high ambient temperature conditions, have an increase in operation reliability, and use modern digital engine control equipment. During craft operations, ETF40B engines are exposed to harsh saltwater and sand environments that will cause severe engine corrosion problems if corrosion resistant design features are not utilized. Proper material selection and utilization of anti-corrosion coatings are extremely important to ensure reliability of ETF40B engines on LCAC. This paper will document and outline the Navy’s efforts to improve the ETF40B engine’s resistance to the LCAC operating environment with a focus on the following components/features: • Carbon and Graphite Free Bushing and Washer Materials for Compressor Guide Vanes; • Coatings between the Compressor Casing and Compressor Guide Vane Bushings; • Use of a Silicone Rubber Abradable Material in the First 4 Stages of the Compressor; • Use of Anti-Corrosion Coatings on all Compressor Guide Vanes and Stems.

Four Textron Lycoming TF40B marine gas turbine engines are used to power the U.S. Navy’s Landing Craft Air Cushion (LCAC) vehicle. This is the first hovercraft of this configuration to be put in service for the Navy. Operation and test of the first production craft revealed deficiencies and less than desirable reliability, but confirmed the validity of its design and ability to perform the mission. After intensive efforts to resolve these problems, reliability trends began to improve as a result of corrective actions incorporated. Today, the LCAC fleet has accrued over 50,000 engine operating hours. Presented here are the changes which have been incorporated into the configuration of the TF40B engine to eliminate both engine unique and vehicle related discrepancies revealed through fleet experience. These changes have contributed significantly toward the improvement of the engine’s mean time between removal (MTBR) and mean time between failure (MTBF) rates.

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.

A major environmental concern of today’s scientists is the inefficient exploitation of natural resources. The land is the ultimate source of wealth and the foundation on which civilization is constructed. Inappropriate land use, leads to destruction of the land resource, poverty and other social problems, and even to the destruction of civilization. To avoid such phenomena, land evaluation is employed, for rational land use planning and appropriate and sustainable use of natural and human resources (Rossiter, 1994). The management of land use is an interdisciplinary activity that relies on large amounts of information from different sources. Land evaluators need to collect information from soil surveyors, climatologists and census takers on land resource. They also need the expert knowledge of soil scientists, agronomists and economists on land use. In addition, land evaluators must select and apply the most appropriate analytical methods to evaluate land qualities and to combine these into overall physical and/or economic suitability. This evaluation is then calibrated against expert judgement and related experience. Finally they must present the results of the evaluation with reports and maps. This output has to be dynamic, considering the continuous refinement of the whole land evaluation process. The above characteristics of land evaluation denote that the management of such a process definitely requires the support of computer systems, especially expert systems, remote sensing and image processing systems, and geographical information systems (GIS). Such systems exist, but they are usually stand-alone units, hence human intervention (land evaluators) for the flow of information from one system towards the other is indispensable. Therefore, integrated systems are highly desirable. The latest research and development trends in this area progressively encompass Artificial Intelligence (AI) techniques to a greater extent, in order to achieve an optimal performance in the analysis of the vast geographical data. Expert systems were included early on, in an effort to model the domain knowledge of land evaluation from experts. Now, such systems introduce fuzzy logic to cope with uncertainty within the data sources and the inference procedure. Machine learning techniques are also included to model the land evaluation procedures when expert knowledge is insufficient or even absent. In general, there exists an amount of both symbolic and non-symbolic AI techniques, which scientists are keen on combining and integrating with traditional land information systems. This chapter is structured as follows. An overview of three of the most used AI techniques in land evaluation problems is given. Following that, the next section introduces ISLE (Tsoumakas and Vlahavas, 1999), an Intelligent System for Land Evaluation that is designed as a framework for the integration of AI techniques with a geographical information system. The final section discusses conclusions and future trends in this field.

In this paper, a new approach to production planning and control for job shop is proposed. A general approach is: using in the first phase expert systems to create production plan and using in the second phase genetic algorithms to construct a schedule. The author discusses an expert system designed to help companies in the short-term production planning and control in supply net. The proposed expert system considers alternative process plans for a workpiece and outsourcing. In this paper, a new approach with the genetic algorithm to the Job shop Scheduling Problem (JSP) with makespan as the criterion has been developed. The proposed combination of expert system and genetic algorithm has been tested using data from real factories. The research indicates that for a scheduling problem, the concept using genetic algorithm yields better results when using the methods based on dispatching rule.

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.

Purpose This study aims to identify the impact of adopting different techniques of artificial intelligence including (expert systems, machine learning, neural networks and algorithms) in improving the quality of accounting information characteristics such as; appropriateness, faithful representation and verifiability in Palestinian industrial enterprises. Design/methodology/approach Employees from 13 companies registered on the Palestine Stock Exchange for 2023 were selected. Moreover, the sample included 326 randomly chosen participants. A questionnaire was distributed to participants to collect data, and a descriptive-analytical approach was followed to achieve the study’s aim and examine its hypotheses. Findings The results showed that the use of artificial intelligence techniques (expert systems, machine learning, neural networks and algorithms) has a positive effect on improving the quality of accounting information characteristics (relevance, faithful representation and verifiability). Expert systems, neural network applications and algorithms contribute to developing solutions to various problems in industrial companies; discovering fraudulent practices in financial statements; and obtaining more accurate, faster and more reliable results. Machine learning also links the company’s systems together simultaneously and in an integrated and effective manner. Research limitations/implications The research relied on the industrial sector only because expanding society is difficult due to the general conditions in Palestine, and the results may vary between different sectors due to the nature of their work and activity. Practical implications Industrial companies’ efforts to benefit from artificial intelligence applications in their work increase the quality of accounting information, which in turn reflects the company’s real situation and helps in making the necessary decisions efficiently and effectively. Originality/value This study contributes to directing the attention of financiers and accountants working in Palestinian industrial companies to the importance of applying artificial intelligence techniques to ensure the highest quality characteristics of accounting information through the preparation of accounting programmes that rely on artificial intelligence to operate, thus achieving the maximum degree of advantage in compiling financial data from its sources, operation and conversion into useful financial information for its users.

Increased automation of manufacturing is necessary to compete in today's worldwide markets. The role of artificial intelligence (AI) techniques for incorporating the automation with changing manufacturing environments needs to be investigated. AI techniques can assist in meeting the challenge of transforming shop floor production engineering data into appropriate production engineering labor standards in a timely, consistent, and cost effective manner. Production heuristics can be incorporated into an expert system that can learn from the changing manufacturing environment. This paper presents a prototype expert system which transfers the knowledge of experienced methods engineers into a rule-based system to develop the appropriate job elements and standard times for each engineering task. Manufacturing data taken from a leading US company are used for the testing and validation of the prototype system. The prototype system demonstrated the applicability of automated generation of knowledge transfer to the decomposition of the job into tasks. Further implications of the automated systems are discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

The development of computer-aided manufacturing systems is evolving towards that of intelligent manufacturing systems (IMS). In an intelligent manufacturing system, unprecedented and unforeseen situations are expected to be solved within certain limits, even on the basis of incomplete and imprecise information. Therefore, a tremendous amount of manufacturing knowledge is needed. Artificial intelligence (AI) based techniques are designed for capturing, representing, organizing, and utilizing knowledge by computers, and hence will play an important role in intelligent manufacturing. As an AI technique, knowledge-based expert systems have been used in manufacturing for nearly two decades. Recently, another AI technique, namely, neural networks, is gaining more and more visibility and has been successfully applied in manufacturing practice. Expert systems and neural networks are individually useful but not sufficient on their own in dealing with manufacturing problems. In fact, expert systems and neural networks are complementary. The weaknesses of expert systems are offset by the strengths of neural networks, and vice versa. Quite a few researchers are interested in merging the structures and functions of expert systems with those of neural networks. Some evidence has shown that the neural-expert hybrid technique is a promising tool for intelligent manufacturing. This paper reviews the current research on the neural-expert hybrid approach and its applications in manufacturing, which will provide some guidelines and references for the research and implementation.

Results from an engineering investigation, including findings, root cause and corrective action of an ETF40B compressor incident, are presented in this paper. Vericor’s ETF40B is used to power US NAVY Landing Craft Air Cushion (LCAC) vehicles that have undergone the Service Life Extension Program (SLEP). This paper discusses the initial review of an engine incident, the results of the engineering investigation that led to identifying root cause and the successful validation testing providing fleet corrective action. This paper will present the history of the investigation, the metallurgical analysis findings, the results of finite element stress analysis and investigative testing performed on the vane. The paper will conclude with the testing conducted to verify the design solution and how the final field and production compressor changes were incorporated.

Simulations of nonlinear waves generated by an air-cushion vehicle

Smart grids have emerged as a transformative technology in the power sector, enabling efficient energy management. However, the increased reliance on digital technologies also exposes smart grids to various cybersecurity threats and attacks. This article provides a comprehensive exploration of cyberattacks and cybersecurity in smart grids, focusing on critical components and applications. It examines various cyberattack types and their implications on smart grids, backed by real-world case studies and quantitative models. To select optimal cybersecurity options, the study proposes a multi-criteria decision-making (MCDM) approach using the analytical hierarchy process (AHP). Additionally, the integration of artificial intelligence (AI) techniques in smart-grid security is examined, highlighting the potential benefits and challenges. Overall, the findings suggest that “security effectiveness” holds the highest importance, followed by “cost-effectiveness”, “scalability”, and “Integration and compatibility”, while other criteria (i.e., “performance impact”, “manageability and usability”, “compliance and regulatory requirements”, “resilience and redundancy”, “vendor support and collaboration”, and “future readiness”) contribute to the evaluation but have relatively lower weights. Alternatives such as “access control and authentication” and “security information and event management” with high weighted sums are crucial for enhancing cybersecurity in smart grids, while alternatives such as “compliance and regulatory requirements” and “encryption” have lower weighted sums but still provide value in their respective criteria. We also find that “deep learning” emerges as the most effective AI technique for enhancing cybersecurity in smart grids, followed by “hybrid approaches”, “Bayesian networks”, “swarm intelligence”, and “machine learning”, while “fuzzy logic”, “natural language processing”, “expert systems”, and “genetic algorithms” exhibit lower effectiveness in addressing smart-grid cybersecurity. The article discusses the benefits and drawbacks of MCDM-AHP, proposes enhancements for its use in smart-grid cybersecurity, and suggests exploring alternative MCDM techniques for evaluating security options in smart grids. The approach aids decision-makers in the smart-grid field to make informed cybersecurity choices and optimize resource allocation.