Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.

The knowledge-creating company

Knowledge Management

Industry 4.0: A Survey on Technologies, Applications and Open Research Issues

Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0

In this article we propose the concept, its framework, and implementation methodology for Virtual Engineering Objects (VEO). A VEO is the knowledge representation of an engineering object that embodies its associated knowledge and experience. A VEO is capable of adding, storing, improving, and sharing knowledge through experience. Moreover, it is demonstrated that VEO is a specialization of a Cyber-Physical System (CPS). In this article, it is shown through test models how the concept of VEO can be implemented with the Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). The test model confirmed that the concept of VEO is able to capture and reuse the experience of engineering artifacts, which can be beneficial for efficient decision-making in industrial design and manufacturing.

Virtual Engineering Object VEO: Toward Experience-Based Design and Manufacturing for Industry 4.0

Together with the fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing HAR methods depend on exploratory case-based shallow feature learning architectures, which struggle with correct activity recognition when put into real-life practice. To tackle this problem, we propose a novel approach that utilizes the convolutional neural networks (CNNs) and the attention mechanism for HAR. In the presented method, the activity recognition accuracy is improved by incorporating attention into multihead CNNs for better feature extraction and selection. Proof of concept experiments are conducted on a publicly available data set from wireless sensor data mining (WISDM) lab. The results demonstrate a higher accuracy of our proposed approach in comparison with the current methods.

A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multihead Convolutional Attention

Collecting, distributing and sharing knowledge in a knowledge-explicit way is a significant task for any company. However, collecting decisional knowledge in the form of formal decision events as the fingerprints of a company is an utmost advance. Such decisional fingerprint is called decisional DNA. Set of experience knowledge structure can assist on accomplishing this purpose. In addition, Ontology-based technology applied to set of experience knowledge structure would facilitate distributing and sharing companies' decisional DNA. Such possibility would assist in the development of an e-decisional community, which will support decision-makers on their overwhelming job. The purpose of this paper is to explain the development of .an OWL decisional Ontology built upon set of experience, which would make decisional DNA, that is, explicit knowledge of formal decision events, a useful element in multiple systems and technologies, as well as in the construction of the e-decisional community.

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An OWL Ontology of Set of Experience Knowledge Structure

When managers make decisions, they use previous, similar, or equal experiences to help themselves in a new decision-making situation. Thus, keeping record of previous decision events appears to be of the utmost importance as part of the decision making process. For us, every formal decision event has to be collected and stored as experienced knowledge, and any technology able to do this will allow us to improve the decision-making process by reducing decision time, as well as by avoiding duplication in the process. However, one of the most complicated issues about knowledge is its representation. Developing a knowledge structure that stores and administers experience from the day-to-day decision processes would improve decision-making quality and efficiency. We are proposing such a knowledge structure and have named it set of experience knowledge structure. A set of experience knowledge structure (SOEKS) is a combination of organized information obtained from a formal decision event. Fully applied, the set of experience knowledge structure would advance the notion of administering knowledge in the current decision-making environment.

Edward Szczerbicki

Papers

Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0

Virtual Engineering Object VEO: Toward Experience-Based Design and Manufacturing for Industry 4.0

A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multihead Convolutional Attention

An OWL Ontology of Set of Experience Knowledge Structure

Experience-Based Knowledge Representation: SOEKS