مقاله انگلیسی آموزش شبکه های عصبی عمیق برای شبکه های حسگر بی سیم با استفاده از تصاویر
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مقاله انگلیسی آموزش شبکه های عصبی عمیق برای شبکه های حسگر بی سیم با استفاده از تصاویر

عنوان فارسی مقاله: آموزش شبکه های عصبی عمیق برای شبکه های حسگر بی سیم با استفاده از تصاویر برچسب گذاری شده به طور آزاد و ضعیف
عنوان انگلیسی مقاله: Training deep neural networks for wireless sensor networks using loosely and weakly labeled images
مجله/کنفرانس: Neurocomputing - محاسبات نورونی
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: هوش مصنوعی، شبکه های کامپیوتری
کلمات کلیدی فارسی: شبکه های عصبی عمیق، شبکه های حسگر بی سیم، برچسب گذاری داده های خودکار، تشخیص تصویر، یادگیری انتقالی، فشرده سازی مدل
کلمات کلیدی انگلیسی: Deep Neural Networks, Wireless Sensor Networks, Automated Data Labeling, Image Recognition, Transfer Learning, Model Compression
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.neucom.2020.09.040
دانشگاه: Zhejiang University of Technology, China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2021
ایمپکت فاکتور: 7.083 در سال 2020
شاخص H_index: 143 در سال 2021
شاخص SJR: 1.085 در سال 2020
شناسه ISSN: 0925-2312
شاخص Quartile (چارک): Q1 در سال 2020
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 12
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E15445
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
نوع رفرنس دهی: vancouver
فهرست انگلیسی مطالب

Abstract


Keywords


1. Introduction


2. Cost-effective domain generalization


3. An implementation of cost-effective domain generalization


4. Experiments and results


5. Conclusion


CRediT authorship contribution statement


Declaration of Competing Interest


Acknowledgment


Research Data


References


Vitae

نمونه متن انگلیسی مقاله

Abstract


Although deep learning has achieved remarkable successes over the past years, few reports have been published about applying deep neural networks to Wireless Sensor Networks (WSNs) for image targets recognition where data, energy, computation resources are limited. In this work, a Cost-Effective Domain Generalization (CEDG) algorithm has been proposed to train an efficient network with minimum labor requirements. CEDG transfers networks from a publicly available source domain to an application-specific target domain through an automatically allocated synthetic domain. The target domain is isolated from parameters tuning and used for model selection and testing only. The target domain is significantly different from the source domain because it has new target categories and is consisted of low-quality images that are out of focus, low in resolution, low in illumination, low in photographing angle. The trained network has about 7 M (ResNet-20 is about 41 M) multiplications per prediction that is small enough to allow a digital signal processor chip to do real-time recognitions in our WSN. The category-level averaged error on the unseen and unbalanced target domain has been decreased by 41.12%.


 


1. Introduction

Wireless sensor networks (WSNs) typically are designed to detect and identify neighboring objects in wild [1, 2, 3, 4] with sound or vibration sensors in the form of single [5] or microarrays [6]. The sound or vibration sensor has many advantages [7, 8, 5], such as low cost, low energy consumption, and relatively low in algorithm complexity. However, they are unsuitable for mixed objects detection because their spatial resolutions are usually too low to distinguish each person in a group of pedestrians. To overcome this shortage, we have employed cameras in our WSNs which has been proved to be effective for dense targets identification [9]. Unfortunately, images captured by WSNs are noisy, such as low in illumination, resolution and photographing angle, which are different from most publicly available datasets. Because the severe limitation in data and resources, despite the rapid development in deep learning [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], WSN-applicable deep-learning-based image classification algorithms evolve slowly. So, cost-effective dataset construction methods are needed urgently to build datasets that corresponding to specific WSN applications. Several random images of the target application (target domain) that was captured during our field experiments have been shown in Fig. 1, where targets like persons and cars are hard to identify.

Because of limited communication bandwidth, WSNs cannot run deep neural networks (DNNs) in a remote cloud (or fog) which is a common strategy for embedded devices [21, 22, 23, 24, 25]. To run DNNs in such devices locally [26, 27], a training strategy is wanted to cut computation costs without decreasing identification accuracy significantly. Fortunately, Han et al. [28, 29] have pointed out that only parts of weight parameters in neural networks are playing essential roles during predictions. Therefore, it is possible to train an efficient DNN for WSNs with fewer parameters if we can fully utilize key weight parameters.

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