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

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Segmentation is a critical task in image processing. Bi-level segmentation involves dividing the whole image into partitions based on a threshold value, whereas multilevel segmentation involves multiple threshold values. A successful segmentation assigns proper threshold values to optimise a criterion such as entropy or between-class variance. High computational cost and inefficiency of an exhaustive search for the optimal thresholds leads to the use of global search heuristics to set the optimal thresholds. An emerging area in global heuristics is swarm-intelligence, which models the collective behaviour of the organisms. In this paper, two successful swarm-intelligence-based global optimisation algorithms, particle swarm optimisation (PSO) and artificial bee colony (ABC), have been employed to find the optimal multilevel thresholds. Kapur's entropy, one of the maximum entropy techniques, and between-class variance have been investigated as fitness functions. Experiments have been performed on test images using various numbers of thresholds. The results were assessed using statistical tools and suggest that Otsu's technique, PSO and ABC show equal performance when the number of thresholds is two, while the ABC algorithm performs better than PSO and Otsu's technique when the number of thresholds is greater than two. Experiments based on Kapur's entropy indicate that the ABC algorithm can be efficiently used in multilevel thresholding. Moreover, segmentation methods are required to have a minimum running time in addition to high performance. Therefore, the CPU times of ABC and PSO have been investigated to check their validity in real-time. The CPU time results show that the algorithms are scalable and that the running times of the algorithms seem to grow at a linear rate as the problem size increases.

A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding

Purpose – Lean Six Sigma (LSS) is a continuous improvement methodology that aims to reduce the costs of poor quality, improve the bottom-line results and create value for both customers and shareholders. The purpose of this paper is to explore the critical failure factors for LSS in different sectors, such as manufacturing, services, higher education, etc. Design/methodology/approach – The following research is based on a systematic literature review of 56 papers that were published on Lean, Six Sigma and LSS in well-known academic databases from 1995 to 2013. Findings – There are 34 common failure factors of LSS cited in this paper. There are some common factors for failure, such as a lack of top management commitment and involvement, lack of communication, lack of training and education, limited resources and others. Many gaps and limitations are discussed in this paper and need to be explored in future research. Originality/value – The paper is one of the first systematic literature reviews to explore ...

Critical failure factors of Lean Six Sigma: a systematic literature review

The integration of lean manufacturing, Six Sigma and sustainability: A literature review and future research directions for developing a specific model

A study of the kinematic characteristics of a three degree-of-freedom (dof) parallel mechanism is presented. The architecture of the mechanism is comprised of a mobile platform attached to a base through three identical prismatic-revolute-spherical jointed serial linkages. The prismatic joints are considered to be actuated. These prismatic actuators lie on a common plane and have radial directions of action. The mechanism's inverse displacement solution is obtained. Since the mechanism has only 3 dof, constraint equations describing the inter-relationship between the six motion coordinates are derived. These constraints allow the definition of parasitic motions, i.e., motions in the three unspecified motion coordinates. Architecture optimization of the device is undertaken demonstrating that specific values of design variables allow minimization of parasitic motion.

Kinematic Analysis and Optimization of a New Three Degree-of-Freedom Spatial Parallel Manipulator

Purpose – Lean and Six Sigma are the two most important continuous improvement (CI) methodologies for achieving operational and service excellence in any organization. The purpose of this paper is to explain how lean compares to the Six Sigma and outline the benefits for integrating them. Also, this paper discusses the existing models that describe how Six Sigma and lean fit together. A new detailed description for integrating Six Sigma and lean is developed to provide an improved approach for CI.Design/methodology/approach – The following research included proposals and discussion, which were mainly based on the authors' own findings and experience, in addition to a literature‐based review of some of the most common and traditional lean and Six Sigma models.Findings – The paper proposes a new lean Six Sigma (LSS) approach and provides a detailed description of its phases. The paper also presents the views on the integration benefits as well as on how Six Sigma compares to lean. Six Sigma and lean are rel...

Juan A. Carretero

Papers

Kinematic Analysis and Optimization of a New Three Degree-of-Freedom Spatial Parallel Manipulator

The integration of Six Sigma and lean management

Formulating Jacobian matrices for the dexterity analysis of parallel manipulators

3-PRRR redundant planar parallel manipulator: Inverse displacement, workspace and singularity analyses

Quantitative dexterous workspace comparison of parallel manipulators