کشف دست‌انداز به‌کمک محاسبات نرم

وجود دست انداز در سطح جاده‌ها معضل بزرگی در حمل‌و‌نقل جاده‌ای است و نادیده‌گرفتن آن منجر به افزایش تصادفات، افزایش مصرف سوخت خودرو و هدر‌رفت وقت و انرژی خواهد شد؛ از‌این‌رو حل مساله کشف دست‌انداز، مورد توجه پژوهش‌گران قرار گرفته و الگوریتم‌های مختلفی برای حل آن ارائه شده است. در این مقاله، برای افزایش دقت، یک روش پردازش داده مبتنی بر محاسبات نرم برای کشف دست‌انداز پیشنهاد شده است. در روش پیشنهادی، از ترکیب سیستم فازی و الگوریتم های تکاملی استفاده شده است. این روش، از الگوریتم های ژنتیک و جستجوی هارمونی برای تنظیم توابع عضویت سیستم فازی استفاده می کند. به‌منظور بررسی الگوریتم پیشنهادی، عملکرد این روش برای کشف دست انداز های واقع در خیابان غفاری شهر بیرجند، مورد ارزیابی قرارگرفته است. نتایج حاصل حاکی از عملکرد موفقیت‌آمیز روش پیشنهادی، در مقایسه با سایر روش ها، از نظر دقت است؛ به‌گونه‌ای که دقت الگوریتم فازی ژنتیک 98 درصد و الگوریتم فازی هارمونی 99 درصد به‌دست می‌آید.

pothole detection by soft computing

potholes on roads are regarded as serious problems in the transportation domain, and ignoring them lead to an increase in accidents, traffic, vehicle fuel consumption, and waste of time and energy. as a result, pothole detection has attracted researchers rsquo; attention, and different methods have been presented for it up to now. data analysis methods such as machine learning and soft computing have been widely used for detection purposes. they rely on a dataset and propose a system that can detect a special event in similar datasets. their effectiveness can be measured by evaluating their accuracy in detecting the event.image processing involves a wide range of analytics that are used to extract specific information from images. the majority of image processing programs require massive computational power. the major part of previous research is based on image processing. they utilize dedicated cameras which are embedded in vehicles to take images and analyze them through massive image processing programs. this scheme requires dedicated hardware that is not typically available on vehicles. in this paper, a new scheme is proposed, which uses accelerometer and gps sensors. these types of sensors are available in today rsquo;s smartphones as well as modern vehicles. the data generated by these sensors is processed via soft computing to increase the accuracy of pothole detection. the proposed algorithm uses a combination of a fuzzy system and evolutionary algorithms. fuzzy systems have been widely used to model the realworld problems that are described by uncertainty and ambiguity. evolutionary algorithms (e.g., genetic algorithms) try to imitate evolutionary science in solving hard problems. genetic algorithm and harmony search are used to adjust membership functions of the proposed fuzzy system. for evaluation, a case study has been conducted with regard to detect potholes on ghaffari street in birjand. to this end, a real dataset has been collected and used for implementing the proposed method. experimental results show the high accuracy of the proposed algorithm in comparison to other solutions. they reveal that the accuracy of the proposed genetic fuzzy algorithm is 98 percent and for the proposed harmony fuzzy algorithm is 99 percent.