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A Review of Technology Standards and Patent Portfolios for Enabling Cyber-Physical Systems in Advanced Manufacturing
(2016-01-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Govindarajan, Usharani Hareesh
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Sun, John J.
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Chuang, Allen C.
Cyber-physical systems (CPS) are a collection of transformative technologies for managing interconnected physical and computational capabilities. Recent developments in technology are increasing the availability and affordability of sensors, data acquisition systems, and computer networks. The competitive nature of industry requires manufacturers to implement new methodologies. CPS is a broad area of engineering which supports applications across industries, such as manufacturing, healthcare, electric power grids, agriculture, and transportation. In particular, CPS is the core technology enabling the transition from Industry 3.0 to Industry 4.0 (I 4.0) and is transforming global advanced manufacturing. This paper provides a consolidated review of the latest CPS literature, a complete review of international standards, and a complete analysis of patent portfolios related to the 5C's CPS architecture model by Lee et al. The critical evaluation of international standards and the intellectual property contained in CPS patents is unaddressed by the previous research and will benefit both academic scholars and industry practitioners. The analysis provides a basis for predicting research and development future trends and helps policy makers manage technology changes that will result from CPS in I 4.0. This paper covers the emerging I 4.0 standards from the International Organization for Standardization, the International Electrotechnical Commission, and China's Guobiao standards followed by a patent analysis covering global patents issued in the U.S., Europe, China, and the World Intellectual Property Organization.
An XML based supply chain integration hub for green product lifecycle management
(2010-11-01)
Taghaboni-Dutta, Fataneh
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Trappey, Amy J. C.
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Trappey, Charles V.
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管理科學系
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Department of Management Science
The emergence of new regulations for the disposal of electrical and electronic equipment, directives on the use of certain hazardous substances, and the Kyoto Protocol are changing industries approach to product design and manufacturing. Traditionally, product lifecycle management systems provide a platform for the management of data related to the creation and disposal of products. These systems assist the participants of the products' life cycle processes (manufacturers, suppliers, customers, and regulators) to use data efficiently for planning and control. However, most data collected do not resolve the environmental issues which arise when selling the products or arranging the products to be replaced and disposed. In this paper, we present an Integrated Green Parts Information Platform (IGPIP) framework (The frame work revisited here was published as "A Framework for a Green Product Lifecycle Management System", Issues in Information Systems, Vol. IX, No. (2), 2008, 123-131.) and a working prototype of the system. This system uses XML file transmission to improve the quality, cost and time-to market issues for green designs. (C) 2010 Elsevier Ltd. All rights reserved.
Genetic algorithm dynamic performance evaluation for RFID reverse logistic management
(2010-11-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Wu, Chang-Ru
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管理科學系
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Department of Management Science
Environmental awareness, green directives, liberal return policies, and recycling of materials are globally accepted by industry and the general public as an integral part of the product life cycle. Reverse logistics reflects the acceptance of new policies by analyzing the processes associated with the flow of products, components and materials from end users to re-users consisting of second markets and remanufacturing. The components may be widely dispersed during reverse logistics. Radio frequency identification (RFID) complying with the EPCglobal (2004) Network architecture, i.e., a hardware- and software-integrated cross-platform IT framework, is adopted to better enable data collection and transmission in reverse logistic management. This research develops a hybrid qualitative and quantitative approach, using fuzzy cognitive maps and genetic algorithms, to model and evaluate the performance of RFID-enabled reverse logistic operations (The framework revisited here was published as "Using fuzzy cognitive map for evaluation of RFID-based reverse logistics services", Proceedings of the 2009 international conference on systems, man, and cybernetics (Paper No. 741), October 11-14, 2009, San Antonio, Texas, USA). Fuzzy cognitive maps provide an advantage to linguistically express the causal relationships between reverse logistic parameters. Inference analysis using genetic algorithms contributes to the performance forecasting and decision support for improving reverse logistic efficiency. (C) 2010 Elsevier Ltd. All rights reserved.
Advances in aligning knowledge systems, improving business logistics, driving innovation and adapting customer centric services
(2010-11-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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管理科學系
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Department of Management Science
Using patent data for technology forecasting: China RFID patent analysis
(2011-01-01)
Trappey, Charles V.
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Wu, Hsin-Ying
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Taghaboni-Dutta, Fataneh
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Trappey, Amy J. C.
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管理科學系
China is one of the world's largest manufacturers and consumers of Radio Frequency Identification (RFID) applications. Current estimates show that China will need over 3 billion RFID tags to satisfy demand in the year 2009. The applications for RFID patents have spread across a very diverse range of inventions and in the future it is likely that most products manufactured in China will contain an RFID tag. China's RFID industry has grown along with the demand and researchers are making significant technological advances. In this research, patent data from the State Intellectual Property Office of the People's Republic of China (SIPO) have been used to explore RFID technology development and its trends. Patent abstracts containing the keyword and phrase "RFID" and "Radio Frequency Identification" were collected for analysis, content extraction, and clustering. In total, 1389 patents from the SIPO database covering the years 1995-2008 were retrieved and archived for analysis. Patents provide exclusive rights and legal protection for inventors, play an important role in the development and fair diffusion of technology, and contain detailed specifications necessary to define and protect the boundaries of an invention. Through patent analysis, companies monitor the development of technology and evaluate the position of potential competitors in the market. This research introduce a methodology which combines patent content clustering and technology life cycle forecasting to find a niche space of RFID technology development in China. A patent content clustering method is used to cluster different patent documents into homogenous groups, and then technology forecasting is applied to evaluate possible market opportunities for future inventors and investors. The results suggest that the cluster called RFID wireless communication devices has entered the saturation stage and thus provides limited opportunity for development. Four other clusters; RFID concepts and applications, RFID architecture, RFID tracking implementation, and RFID transmission apparatus, have entered the mature stage. The RFID frequency and waves cluster appears to be in early growth stage with good development potential. Since the technology related to basic RFID concepts and devices has reached a mature stage in China, the research and development seems to be targeting the improvement of the RFID frequencies and waves as a means to develop more reliable RFID systems and applications. (C) 2010 Elsevier Ltd. All rights reserved.
Deriving industrial logistics hub reference models for manufacturing based economies
(2011-02-01)
Trappey, Charles V.
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Lin, Gilbert Y. P.
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Trappey, Amy J. C.
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Liu, C. S.
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Lee, W. T.
Global enterprises require extended logistics operations that integrate channel intermediaries into the network The method for deriving Integrated models for logistics hubs that improve the efficiency of manufacturers global operations is the focus of this paper Under government sponsorship six industrial logistics hubs were implemented by leading manufacturing sectors across Taiwan over a five year period from 2004 until 2008 Each industry sector had unique industry characteristics business strategies and logistics models Therefore this paper describes how generalized and quick to implement integrated logistics hubs are developed by studying the successful reference models and systems used by six independent industrial sectors The research results provide a field tested method for deriving integrated logistics hub models in different manufactunng economies with notes that provide sufficient methodological detail for repeating the construction of logistics hubs in other manufacturing economies (C) 2010 Elsevier Ltd All rights reserved
A knowledge centric methodology for dental implant technology assessment using ontology based patent analysis and clinical meta-analysis
(2014-04-01)
Trappey, Charles V.
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Trappey, Amy J. C.
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Peng, Hsin-Yi
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Lin, Li-Deh
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Wang, Tong-Mei
The medical equipment industry has been one of the fastest growing sectors of the decade with predicted global sales reaching US$ 430 billion in 2017 [22]. During the period from 1995 to 2008, the patent applications in medical technology increased rapidly worldwide (World Intellectual Property Organization, 2012). Patent analysis, although useful in forecasting technology development trends, has posed a challenging analysis task since the volume and diversity of new patent applications has surpassed the ability of regular firms and research teams to process and identify relevant information. Further, medical related technologies rely on clinical trials to validate and gain regulatory approval for patient treatment even though patents, protecting the intellectual property rights of inventors, have been granted. This research focuses on developing a knowledge centric methodology and system to analyze and assess viable medical technology innovations and trends considering both patents and clinical reports. Specifically, the design innovations of dental implant connections are used as a case study. A novel and generic methodology combining ontology based patent analysis and clinical meta-analysis is developed to analyze and identify the most effective patented techniques in the dental implant field. The research establishes and verifies a computer supported analytical approach and system for the strategic prediction of medical technology development trends. (C) 2014 Elsevier Ltd. All rights reserved.
Data driven modeling for power transformer lifespan evaluation
(2014-03-01)
Trappey, Charles V.
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Trappey, Amy J. C.
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Ma, Lin
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Tsao, Wan-Ting
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管理科學系
Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation's energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit (RTU) system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.
Intelligent patent recommendation system for innovative design collaboration
(2013-11-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Wu, Chun-Yi
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Fan, Chin Yuan
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Lin, Yi-Liang
Patents' search is increasingly critical for a company's technological advancement and sustainable marketing strategy. When most innovative designs are created collaboratively by a diverse team of researchers and technologists, patent knowledge management becomes time consuming with repeated efforts creating additional task conflicts. This research develops an intelligent recommendation methodology and system to enable timely and effective patent search prior, during, and after design collaboration to prevent potential infringement of existing intellectual property rights (IPR) and to secure new IPR for market advantage. The research develops an algorithm to dynamically search related patents in global patent databases. The system clusters users with similar patent search behaviors and, subsequently, infers new patent recommendations based on inter-cluster group member behaviors and characteristics. First, the methodology evaluates the filtered information obtained from collaborative patent searches. Second, the system clusters existing users and identifies users' neighbors based on the collaborative filtering algorithm. Using the clusters of users and their behaviors, the system recommends related patents. When collaborative design teams are planning R&D policies or searching patents and prior art claims to create new IP and prevent or settles IP legal disputes, the intelligent recommendation system identifies and recommends patents with greater efficiency and accuracy than previous systems and methods described in the literature. (C) 2013 Elsevier Ltd. All rights reserved.
A hierarchical cost learning model for developing wind energy infrastructures
(2013-12-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Liu, Penny H. Y.
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Lin, Lee-Cheng
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Ou, Jerry J. R.
Renewable energy has been increasingly promoted and used to substitute non-renewable fossil-fuels, which cause negative effects on the environment. The Taiwan Statute for Renewable Energy Development has regulated and promoted renewable energy since 2009. A feed-in tariff (FIT) for renewable energy is one of the incentives that the government uses to promote the installation of green power generation facilities. The price of the electricity feed-in tariff is based on the current and future costs of renewable energy generation. When analyzing cost trends for renewable energy installation, many researchers use a single factor cost learning curve model. However, past studies indicate that there are multiple factors affecting the overall cost of installing renewable energy. Hence, this research develops a hierarchical installation cost learning model which considers multiple factors to accurately model and forecast wind energy development. This research uses wind power development data from Taiwan as a case study. We identify the cost factors, evaluate the learning effects, and compare the hierarchical learning curve model to the basic (non-hierarchical) learning curve model. The research results show an improved fit between the hierarchical model and the actual data when compared to the basic learning model. The study also provides new insights between the wind power learning progression of Taiwan and three countries in Europe. (C) 2013 Elsevier B.V. All rights reserved.

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