Wind turbine tower detection using feature descriptors and deep learning

Abedini, Fereshteh; Bahaghighat, Mahdi; Shoyan, Misak T.

doi:10.2298/fuee2001133a

Cited by 19 publications

(12 citation statements)

References 42 publications

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“…In the current study, the performance of our proposed algorithm was evaluated on static data as well. In future works, we study the performance and effectiveness of the proposed method on dynamic data found on more complex outlier removal algorithm found on the machine learning and deep learning algorithms [36][37][38][39][40][41][42][43].…”

Section: Discussionmentioning

confidence: 99%

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

Khorasani¹,

Zanjireh²,

Bahaghighat³

et al. 2022

Computer Systems Science and Engineering

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With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster's inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.

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Section: Discussionmentioning

confidence: 99%

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

Khorasani¹,

Zanjireh²,

Bahaghighat³

et al. 2022

Computer Systems Science and Engineering

Self Cite

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“…Nowadays, data scientists are more interested in deploying emerging and strong deep learning (DL) algorithms [26][27][28][29][30]. DL can perform big data analytics efficiently.…”

Section: Discussionmentioning

confidence: 99%

Developing a Parallel Classifier for Mining in Big Data Sets

Shamseen¹,

Zanjireh

Bahaghighat

et al. 2021

IIUMEJ

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Data mining is the extraction of information and its roles from a vast amount of data. This topic is one of the most important topics these days. Nowadays, massive amounts of data are generated and stored each day. This data has useful information in different fields that attract programmers’ and engineers’ attention. One of the primary data mining classifying algorithms is the decision tree. Decision tree techniques have several advantages but also present drawbacks. One of its main drawbacks is its need to reside its data in the main memory. SPRINT is one of the decision tree builder classifiers that has proposed a fix for this problem. In this paper, our research developed a new parallel decision tree classifier by working on SPRINT results. Our experimental results show considerable improvements in terms of the runtime and memory requirements compared to the SPRINT classifier. Our proposed classifier algorithm could be implemented in serial and parallel environments and can deal with big data. ABSTRAK: Perlombongan data adalah pengekstrakan maklumat dan peranannya dari sejumlah besar data. Topik ini adalah salah satu topik yang paling penting pada masa ini. Pada masa ini, data yang banyak dihasilkan dan disimpan setiap hari. Data ini mempunyai maklumat berguna dalam pelbagai bidang yang menarik perhatian pengaturcara dan jurutera. Salah satu algoritma pengkelasan perlombongan data utama adalah pokok keputusan. Teknik pokok keputusan mempunyai beberapa kelebihan tetapi kekurangan. Salah satu kelemahan utamanya adalah keperluan menyimpan datanya dalam memori utama. SPRINT adalah salah satu pengelasan pembangun pokok keputusan yang telah mengemukakan untuk masalah ini. Dalam makalah ini, penyelidikan kami sedang mengembangkan pengkelasan pokok keputusan selari baru dengan mengusahakan hasil SPRINT. Hasil percubaan kami menunjukkan peningkatan yang besar dari segi jangka masa dan keperluan memori berbanding dengan pengelasan SPRINT. Algoritma pengklasifikasi yang dicadangkan kami dapat dilaksanakan dalam persekitaran bersiri dan selari dan dapat menangani data besar.

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“…Decreasing wasting of resources affecting industries and businesses can be very advantageous [1][2][3][4][5][6][7]. The rectangle packing problem (RPP) is one of the most practical issues in the industrial sector.…”

Section: Introductionmentioning

confidence: 99%

A Time-Efficient and Exploratory Algorithm for the Rectangle Packing Problem

Bozorgi¹,

Zanjireh²,

Bahaghighat³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Today, resource waste is considered as one of the most important challenges in different industries. In this regard, the Rectangle Packing Problem (RPP) can affect noticeably both time and design issues in businesses. In this study, the main objective is to create a set of non-overlapping rectangles so that they have specific dimensions within a rectangular plate with a specified width and an unlimited height. The ensued challenge is an NP-complete problem. NP-complete problem, any of a class of computational problems that still there are no efficient solution for them. Most substantial computer-science problems such as the traveling salesman problem, satisfiability problems (sometimes called propositional satisfiability problem and abbreviated SAT or B-SAT), and graph-covering problems are belong to this class. Essentially, it is complicated to spot the best arrangement with the highest rate of resource utilization by emphasizing the linear computation time. This study introduces a time-efficient and exploratory algorithm for the RPP, including the lowest front-line strategy and a Best-Fit algorithm. The obtained results confirmed that the proposed algorithm can lead to a good performance with simplicity and time efficiency. Our evaluation shows that the proposed model with utilization rate about 94.37% outperforms others with 87.75%, 50.54%, and 87.17% utilization rate, respectively. Consequently, the proposed method is capable to of achieving much better utilization rate in comparison with other mentioned algorithms in just 0.023 s running-time, which is much faster than others.

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Wind turbine tower detection using feature descriptors and deep learning

Cited by 19 publications

References 42 publications

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

Developing a Parallel Classifier for Mining in Big Data Sets

A Time-Efficient and Exploratory Algorithm for the Rectangle Packing Problem

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