Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

How we Think

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Why are a select few service firms better at what they do - year in and year out - than their competitors? For most senior managers, the profusion of anecdotal "service excellence" books fails to address this key question. In this pathbreaking book, world-renowned Harvard Business School service firm experts James L. Heskett, W. Earl Sasser, Jr. and Leonard A. Schlesinger reveal that leading companies stay on top by managing the service profit chain. Based on five years of painstaking research, the authors show how managers at American Express, Southwest Airlines, Banc One, Waste Management, USAA, MBNA, Intuit, British Airways, Taco Bell, Fairfield Inns, Ritz-Carlton Hotel, and the Merry Maids subsidiary of ServiceMaster employ a quantifiable set of relationships that directly links profit and growth to not only customer loyalty and satisfaction, but to employee loyalty, satisfaction, and productivity. The strongest relationships the authors discovered are those between (1) profit and customer loyalty; (2) employee loyalty and customer loyalty; and (3) employee satisfaction and customer satisfaction. Moreover, these relationships are mutually reinforcing; that is, satisfied customers contribute to employee satisfaction and vice versa. Here, finally, is the foundation for a powerful strategic service vision, a model on which any manager can build more focused operations and marketing capabilities. For example, the authors demonstrate how, in Banc One's operating divisions, a direct relationship between customer loyalty measured by the "depth" of a relationship, the number of banking services a customer utilizes, and profitability led the bank to encourage existing customers to further extend the bank services they use. Taco Bell has found that their stores in the top quadrant of customer satisfaction ratings outperform their other stores on all measures. At American Express Travel Services, offices that ticket quickly and accurately are more profitable than those which don't. With hundreds of examples like these, the authors show how to manage the customer-employee "satisfaction mirror" and the customer value equation to achieve a "customer's eye view" of goods and services. They describe how companies in any service industry can (1) measure service profit chain relationships across operating units; (2) communicate the resulting self-appraisal; (3) develop a "balanced scorecard" of performance; (4) develop a recognitions and rewards system tied to established measures; (5) communicate results company-wide; (6) develop an internal "best practice" information exchange; and (7) improve overall service profit chain performance. What difference can service profit chain management make? A lot. Between 1986 and 1995, the common stock prices of the companies studied by the authors increased 147%, nearly twice as fast as the price of the stocks of their closest competitors. The proven success and high-yielding results from these high-achieving companies will make The Service Profit Chain required reading for senior, division, and business unit managers in all service companies, as well as for students of service management.

The Service Profit Chain: How Leading Companies Link Profit and Growth to Loyalty, Satisfaction, and Value (Цепочка создания прибыли в сфере услуг: как ведущие компании связывают прибыль и рост с лояльностью, удовлетворением и ценностью)

https://ir.nctu.edu.tw:443/bitstream/11536/100881/1/982410H009004.PDF

國際期刊 Transportation Research-Part E---Logistics and Transportation Review 特刊編輯補助

http://sagheb.persiangig.com/Backup/4/scienc.pdf

A critical review of Knowledge-Based Engineering: An identification of research challenges

Existing knowledge-based engineering methodologies offer opportunities for improvement, as the multidisciplinary character of engineering design is not well implemented and as the current methodologies are not optimally substantiated. To better address the integration of multidisciplinary engineering knowledge within a knowledge based engineering (KBE) framework, the KNOMAD methodology has been devised. KNOMAD stands for Knowledge Nurture for Optimal Multidisciplinary Analysis and Design and is a methodology for the analytical utilization, development and evolution of multi-disciplinary engineering knowledge within the design and production realms. The KNOMAD acronym can also be used to highlight KNOMAD's formalized process of: (K)nowledge capture; (N)ormalisation; (O)rganisation; (M)odeling; (A)nalysis; and (D)elivery. These implementation steps are taken and repeated as part of the knowledge life cycle and in this context KNOMAD nurtures the whole Knowledge Management across that life cycle. The main contribution of the paper is to highlight the development of the KNOMAD methodology and to substantiate its individual steps with sufficient detail to support the application of KNOMAD in practice. A discipline-specific case study shows the potential of the KNOMAD methodology.

A multidisciplinary implementation methodology for knowledge based engineering: KNOMAD

Presenting the gradual evolution of the concept of Concurrent Engineering (CE), and the technical, social methods and tools that have been developed, including the many theoretical and practical challenges that still exist, this book serves to summarize the achievements and current challenges of CE and will give readers a comprehensive picture of CE as researched and practiced in different regions of the world. Featuring in-depth analysis of complex real-life applications and experiences, this book demonstrates that Concurrent Engineering is used widely in many industries and that the same basic engineering principles can also be applied to new, emerging fields like sustainable mobility. Designed to serve as a valuable reference to industry experts, managers, students, researchers, and software developers, this book is intended to serve as both an introduction to development and as an analysis of the novel approaches and techniques of CE, as well as being a compact reference for more experienced readers.

Concurrent Engineering in the 21st Century

A systematic methodology for Prognostic and Health Management system architecture definition

Transdisciplinary processes have been the subject of research since several decades already. Transdisciplinary processes are aimed at solving ill-defined and socially relevant problems. Many researchers have studied transdisciplinary processes and have tried to understand the essentials of transdisciplinarity. Many engineering problems can be characterised as ill-defined and socially relevant, too. Although transdisciplinary engineering cannot widely be found in the literature yet, a transdisciplinary approach is deemed relevant for many engineering problems. With this paper we aim to present an overview of the literature on research into transdisciplinary processes and investigate the relevance of a transdisciplinary approach in engineering domains. After a brief description of past research on transdisciplinarity, implications for engineering research, engineering practice, and engineering education are identified. In all three areas, the current situation is described, while challenges are identified that still exist. The paper ends with a research agenda for transdisciplinary engineering.

/pdf/transdisciplinary-systems-engineering-implications-37kr9tgjai.pdf

Wim J.C. Verhagen

Papers

A critical review of Knowledge-Based Engineering: An identification of research challenges

A multidisciplinary implementation methodology for knowledge based engineering: KNOMAD

Concurrent Engineering in the 21st Century

A systematic methodology for Prognostic and Health Management system architecture definition

Transdisciplinary systems engineering: Implications, challenges and research agenda