Secil Ercan

Patent value analysis using support vector machines

Receiving patents or licenses is an inevitable act of research in order to protect new ideas leading innovation. Request for patents has increased exponentially in order to legalize the intellectual property. Measuring economical value of each patent has been widely studied in the literature. Majority of the research in this field is focused on the patent driver prospect handled for the patent offices. There are a variety of criteria affecting decisions on each patent right; and predicting the possibility of grant may help the researchers to take some precautions. Objective of this study is to propose a robust model to determine if the appeal has a chance of approval. A case study is run on the patents that are accepted and rejected in home appliance industry to construct an intelligent classification model. The support vector machine, Back-Propagation Network and Bayes classification methods are compared on the proposed model. The proposed model in this study will help the decision makers to predict whether the patent appeal will be accepted. The study is unique with the approach that helps the candidate patent owners.

Multi-Criteria Decision Aid for Sustainable Energy Prioritization Using Fuzzy Axiomatic Design

Sustainability has gained tremendous importance and has been an important issue both for policy makers and practitioners. Realizing that the resources on the earth are limited, renewable energy alternatives have flourished and started to replace the conventional energy alternatives. Energy planning using different energy alternatives, for the long term becomes a vital decision. In this study, fuzzy multi criteria decision- making methodologies, axiomatic design (AD) and analytic hierarchy process (AHP) are utilized for ranking both renewable and conventional energy alternatives. In the first phase AHP is utilized to determine the importance of the criteria and in the second phase fuzzy AD is used to evaluate the experts’ assessments. In the case study, both conventional and renewable energy alternatives are evaluated according to technical, economical, air quality and site selection perspectives. The case study results show that the renewable energy alternatives (biomass, geothermal power and wind) perform better than the conventional energy resources.

An Introduction to Self-Organizing Maps

As a special class of artificial neural networks the Self Organizing Map is used extensively as a clustering and visualization technique in exploratory data analysis. This chapter provides a general introduction to the structure, algorithm and quality of Self Organizing Maps and presents industrial engineering related applications reported in the literature.

Review of Collective Intelligence Used in Energy Applications

Low-Carbon Economy policies drive Europe for an integrated approach for utility consumption and management; number of integrated distribution companies are increasing. This new trend will soon cause the need for group decisions, collective intelligence approach to the energy industry. This study aims to review the collective intelligence concepts and methods to give a summary of collective intelligence use in energy applications. It can be considered as a foundation for the future of collective intelligence in the energy industry.

A Multiproduct Multi-vehicle Inventory Routing Problem with Uncertainty

As a result of the increase in environmental problems, green logistics has become an important subject in the supply chain literature. In this study, a multiproduct multi-vehicle inventory routing problem is modeled by considering the cost stemming from fuel consumption as an environmental objective. Demand and inventory holding costs are taken into account as uncertain parameters. A sample average approximation algorithm is used to solve the problem. The performance of the algorithm is evaluated in terms of optimality gap and computational time by using a data set from the literature. The computational experiments give promising results for further research.

Regional Energy Portfolio Construction: Case Studies in Turkey

A geographically large country has different energy resource availabilities and varying environmental, economic and social impacts on the regional energy requests. District energy management is becoming the focus of research, where these differences are to be focused. Hybrid use of the energy resources as a response to the regional requests, forces regional plans. This study has the aim of designing hybrid energy portfolio model with minimum environmental costs and minimum emissions while the rate of energy investment to the regional revenue is increased. The portfolio is constructed based on five resources: solar energy, wind energy, geothermal energy, biomass energy and hydropower besides natural gas. Since in none of the demand is totally responded by renewables only. The lower and upper bounds of European Union allowances are taken as range of targets. Fuzzy Goal Programming method, an intelligent method for handling the uncertainties, is implemented to maximize the membership in the allowed ranges of objectives. Case studies are performed for Yatagan a southwest region of Turkey and Corlu a northwest region, where different renewable sources are available. Remarkable dissimilarities are evaluated that will enlighten the future regional policies.

Socio-Effective Value of Bio-Diesel Production

Increasing air pollution in urban areas has accelerated the interest in biodiesel and vehicles that consume biodiesel. As a caution, majority of the developed countries have started using biodiesel in transportation or determined goals and targets for the near future. Brazil has been a pioneer in the field, whereas the European Union has set the objective of utilizing 10 % of all vehicles using biodiesel by 2020. While the utilization and implementation of biodiesel-based systems severely contribute to economical and environmental savings, the antecedent production process has its own adverse effects such as the demolishment of agricultural sites. This chapter aims to analyze these effects as well as to propose a model for balancing the trade-offs by minimizing the negative consequences and maximizing the positive ones. The related model involves nonlinear constraints and objectives which are dependent of different uncertain scenarios and expectations. A particle swarm optimization (PSO) and self-organizing maps (SOMs) approach are implemented to attain appropriate solutions of the model. This proposition will also provide a new perspective for both academia and investors in the biodiesel field.