Alptekin Durmuşoğlu

An analysis of the papers published in Total Quality Management & Business Excellence from 1995 through 2008

In this article, a content analysis is performed for the papers that appeared in Total Quality Management and Business Excellence. A total of 1132 papers which appeared in the volumes from 6 to 19 are analysed into several different dimensions. Country origins and academic unit of authors (engineering/basic sciences/management sciences, etc.), research methods (library search/conceptual study/case study/simulation/field survey, etc.) and unit of analyses (individual/company/firm/regional/national/international level) are included in this analysis. In addition to those attributes, keywords of the papers and the content of case studies (manufacturing/service/information technology) are computed as well. Association mining has also been performed to determine the consequential relationships among the attributes.

Application of possibilistic fuzzy regression for technology watch

Identification and assessment of technological advances have been vital for companies to keep their competitive position or to gain new capabilities for the competition. In this content, Technology Watch Systems (TWS) have been tools of systematic analysis of technology developments that outputs regarding the technological opportunities and threats could be easily interpreted by an analyst. Among several TWS’s, Patent Alert System (PAS) has been a recently developed one which enables users to set or configure alert(s) for the trend changes in a certain technology area of requested sector. Data of associated patent counts is retrieved by extended markup language (XML) mechanism located in PAS, and then an internal alert triggering mechanism is used to search for trend changes on the associated data. This internal alert triggering mechanism is a kind of modified linear regression which sets a newer trend line once a certain amount of deviation (threshold) has risen. Although alerts, indicating the direction of technological changes, provide supportive information to the analysts, extracted trend lines have been narrowed by a strict line where possible deviations have not been reflected. However, deviations in techno-systems are known to occur as the consequence of the vagueness coming from the nature of the system. Therefore, in this work, alert triggering mechanism of PAS is reconsidered using “possibilistic linear fuzzy regression”. Results yielded better and promising outcomes for the reconsidered algorithm.

Enhancing technology clustering through heuristics by using patent counts

International Patent Classification (IPC) system is a hierarchical classification structure used essentially to classify and explore patents along with the technical fields which they are concerned with. Therefore, corresponding number of patents for a certain IPC, can serve as an indicator of technical developments in the relevant area. These numbers can also form a basis for investigating state of the art for a particular field of technology. This paper proposes an approach for clustering of patents for those of the technologies listed by IPC via the number of patent counts. A set of n real numbers indicating the patent counts for different technologies is partitioned into k clusters such that the sum of the squared deviations from the mean-value within each cluster is minimized. With this purpose in mind, two different heuristics have been considered for clustering since complete enumeration would take considerable solution time. The first heuristic is specifically proposed for this study and the second one is Great Deluge Algorithm (GDA) which has been extensively used for solving complicated problems. The proposed heuristics are coded in visual basic (VB) 6.0 and a user interface is developed for the program. The developed program attempts to find the appropriate k value in order to make the best possible clustering. As an application of the proposed clustering approach, patent data that is retrieved from web site of Turkish Patent Institute (TPI) has been used for clustering technologies.

ALERTING COMPANIES THROUGH ON-LINE PATENT TREND ANALYSIS

In a rapidly changing technology environment, companies are seeking reliable and adequate methods or tools to watch and monitor technological changes. The system introduced in this work—Patent Alert System (PAS)—automatically and systematically extracts patent data, which is a well-known and rich source of technological change and extracts the trend changes on the obtained data set. The PAS is initially set or configured to a technology class or subclass on which the users want to be informed of the trend changes. It retrieves the data of associated patent counts by the use of existing XML (Extended Markup Language) schemes of patent offices. Trend analysis is performed through a trend extraction algorithm. Data capture and trend extraction is turned off until alert is made by the user. Trends found in the patent data are expressed as the time evolution of patent and technology with the following symbols: upward (+), downward (−), and steady (stabilized). Consequently, these trends are used to generate alerts, which are forwarded on-line to the people who request/set the alerts. With the proposed software, it is possible evaluate the value of existing technologies and decide on whether the owned technology is trendy or not. Moreover, promising technologies and corresponding investment areas can be determined and irrelevant investments can be avoided. As a consequence, all these findings can be used to establish a long-term strategic technology plan.

Facial expression recognition using geometric features

Recognition of emotions by analyzing the facial expressions has been an important research area in last decade. Existing studies in the literature mostly seek for a classification model that is linking the emotions with the facial expressions that are defined by the basic components of face (such as eyes, mouth, and ear). In this regard, Artificial Neural Networks (ANN) has been a widely employed approach for classification purposes. Being different than the existing approaches, we searched for the points/landmarks that can contribute to accuracy of ANN model. With this purpose we have started our search with the 153 possible distances among 18 critical candidate points/landmarks. The 16 of these distances having significant contribution to the model accuracy was selected by using the correlation-based feature subset selection (CFS) method. The results indicated that our ANN model has 91.2% correct classification rate.

A Framework for Green/Eco-Innovation Through Use of a Novel Measure: E/R

Eco-innovation has been a recent idea used to describe development of products, services and processes that contribute to sustainable development (SD) through commercial application of knowledge considering ecological facts. This chapter introduces a green/eco-innovation framework incorporating the uncertainties and acquisitions together. A measure is used to indicate the ratio between “possible risks” and “values added”. This measure is named as “E/R” ratio, where E represents “eco-innovative acquisitions provided by the proposed novelty” and R denotes the “risks which arise as the consequence of novelty”. A simple evaluation structure has been developed to calculate the value of E/R ratio for a certain novelty. Proposed framework is tested through the case of green buildings providing an insight into the risks associated with them.