| All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis.

Driver modeling based on driving behavior and its evaluation in driver identification : Analysis of car-pedal use and car-following habits may allow the help offered by intelligent driver assistance systems to be customized for individual drivers

Exergoeconomic and environmental investigation of an innovative poly‐generation plant driven by a solid oxide fuel cell for production of electricity, cooling, desalinated water, and hydrogen

The rapid growth of transportation sector and related emissions are attracting the attention of policymakers to ensure environmental sustainability. Therefore, the deriving factors of transport emi...

A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability

The framework of SOM-KPCA-SVM patent quality classification system. An automatic patent quality analysis and classification system is developed.The self-organizing map approach is used to cluster patents published before into different quality groups.The kernel principal component analysis is used to transform nonlinear feature space to improve classification performance.The support vector machine is used to build up the patent quality classification model.A series of experiments for patent data of the thin film solar cell are conducted, and the results are very encouraging. A plethora of patents are approved by the patent officers each year and current patent systems face a solemn quandary of evaluating these patents' qualities. Traditional researchers and analyzers have fixated on developing sundry patent quality indicators only, but these indicators do not have further prognosticating power on incipient patent applications or publications. Therefore, the data mining (DM) approaches are employed in this article to identify and to classify the new patent's quality in time. An automatic patent quality analysis and classification system, namely SOM-KPCA-SVM, is developed according to patent quality indicators and characteristics, respectively. First, the self-organizing map (SOM) approach is used to cluster patents published before into different quality groups according to the patent quality indicators and defines group quality type instead of via experts. The kernel principal component analysis (KPCA) approach is used to transform nonlinear feature space in order to improve classification performance. Finally, the support vector machine (SVM) is used to build up the patent quality classification model. The proposed SOM-KPCA-SVM is applied to classify patent quality automatically in patent data of the thin film solar cell. Experimental results show that our proposed system can capture the analysis effectively compared with traditional manpower approach.

A patent quality analysis and classification system using self-organizing maps with support vector machine

Current control techniques for three-phase grid interconnection of renewable power generation systems: A review

Summary

Electricity, heating and cooling usage at the same time constitute an important part of primary energy consumption. Currently, intense efforts are being devoted to the improvement and development of new technologies to promote energy savings and to mitigate the global warming. Among these technologies, tri-generation and poly-generation systems come into prominence due to their high efficiencies, as well as low operating costs and low greenhouse gas emissions. Nevertheless, modelling and sizing these systems is a large-sized problem itself, and the higher the number of inputs, the higher the computational complexity. This paper aims to give a comprehensive review of the optimisation techniques used in tri-generation and poly-generation systems modelling and optimisation. After having reviewed more than a hundred papers, taxonomy is constructed. Self-organising map is used to cluster the reviewed studies based on the optimisation techniques. Variations of the energy resources and the outputs of implemented systems are summarised. The crowd of clusters demonstrated the widely used optimisation techniques and the gaps in research. This study will certainly pioneer the optimisation studies in the field of tri-generation/poly-generation applications. Copyright © 2015 John Wiley & Sons, Ltd.

Optimisation studies on tri‐generation: a review

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.

Patent value analysis using support vector machines

Economic model for an electric vehicle charging station with vehicle-to-grid functionality

C-ITS is one of the most challenging domains in either research or industry. Their social and economic benefits are of importance for fuel consumption, traffic management, and road safety. Consequently, they have been deployed in many countries. Privacy is a critical feature of any shared system. This issue is crucial for deployed C-ITS in order to avoid tracking drivers who send continuous messages containing their locations. Anonymity is ensured by changing the identity of drivers frequently. In the meantime, authentication of messages is highly required. For this reason, adapted public key infrastructure) offers each vehicle a set of pseudonym certificates, which will be used to sign each sent message and will be used as a part of the identity. The identity change is done when the driver changes the pseudonym certificate. This change adds confusion for the tracker and protects the driver's privacy. In this article, we analyze a dataset of messages sent by vehicles, and we show, under some conditions, that it is possible to identify a driver's behavior based only on data correlation between collected data over the air. Then we can track drivers even when they change their pseudonyms frequently. Finally, we give some hints on how to protect drivers' privacy by adding some other confusion mechanisms such as introducing silent periods and exchanging pseudonyms with neighbors.

Pseudonym Limitation for Privacy in Cooperative Transport Systems

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 expertsâ€™ 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.

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

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Secil Ercan

Author Tools

Chat about Author