سامانه مدیریت نشریات علمی دانشگاه قم

Ebrahimi, Aboulfazl, Shamsi, Mahboubeh, Mohajjel, Morteza. (1402). Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks. پژوهش‌های فلسفی -کلامی, 9(1), 162-188. doi: 10.22091/JEMSC.2022.7422.1162
Aboulfazl Ebrahimi; Mahboubeh Shamsi; Morteza Mohajjel. "Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks". پژوهش‌های فلسفی -کلامی, 9, 1, 1402, 162-188. doi: 10.22091/JEMSC.2022.7422.1162
Ebrahimi, Aboulfazl, Shamsi, Mahboubeh, Mohajjel, Morteza. (1402). 'Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks', پژوهش‌های فلسفی -کلامی, 9(1), pp. 162-188. doi: 10.22091/JEMSC.2022.7422.1162
Ebrahimi, Aboulfazl, Shamsi, Mahboubeh, Mohajjel, Morteza. Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks. پژوهش‌های فلسفی -کلامی, 1402; 9(1): 162-188. doi: 10.22091/JEMSC.2022.7422.1162

Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks

Journal of Engineering Management and Soft Computing
مقاله 11، دوره 9، شماره 1 - شماره پیاپی 16، بهمن 2023، صفحه 162-188    اصل مقاله (2.04 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22091/JEMSC.2022.7422.1162
نویسندگان
Aboulfazl Ebrahimi* 1؛ Mahboubeh Shamsi2؛ Morteza Mohajjel3
1MSC. Department of computer engineering, Faculty of Electrical and Computer Engineering, Qom University of technology. Email: ebrahimi.a@qut.ac.ir
2Assistance professor, Department of computer engineering, Faculty of Electrical and Computer Engineering, Qom University of technology. Email: shamsi@qut.ac.ir
3Assistance professor, Department of computer engineering, Faculty of Electrical and Computer Engineering, Qom University of technology. Email: mohajjel@qut.ac.ir
چکیده
In a wireless sensor network (WSN), due to various factors such as limited power, sensor transferability, hardware failure and network problems such as packet collisions, unreliable connection and unexpected damage, the amount sensed to the header or base station is not Arrives. Therefore, data loss is very common in wireless sensor networks. Loss of measured data greatly reduces WBAN accuracy. Because WBAN deals with the vital signs of the human body, network reliability is very important. To solve this problem, missing data must be estimated. In order to predict the missing values, a model for estimating lost data based on LSTM (short-term memory) neural network is presented in this paper. This model combines five vital signs as input to predict the amount lost. The results show that sgdm-LSTM is a good way to estimate the amount lost. In addition, experimental results show that the mean square root error of the estimated value is lower than other methods. This value is 4.1495 with the best network parameters.
کلیدواژه‌ها
WBAN؛ Deep Learning؛ Artificial Neural Network؛ Missing Data؛ Estimation
عنوان مقاله [English]
-تنظیم بهینه پارامترهای شبکه عصبی عمیق در برآورد داده های از دست رفته ی علائم حیاتی در شبکه های حسگر بی سیم بدن
نویسندگان [English]
ابوالفضل ابراهیمی1؛ محبوبه شمسی2؛ مرتضی محجل3
1کارشناس ارشد، گروه مهندسی کامپیوتر، دانشکده برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران. رایانامه: ebrahimi.a@qut.ac.ir
2استادیار، گروه مهندسی کامپیوتر، دانشکده برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران. رایانامه: shamsi@qut.ac.ir
3استادیار، گروه مهندسی کامپیوتر، دانشکده برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران. رایانامه: mohajjel@qut.ac.ir
چکیده [English]
در شبکه های حسگر بی سیم به دلیل عوامل مختلفی از قبیل انرژی محدود، قابلیت انتقال سنسورها، خرابی سخت افزار و مشکلات شبکه مانند برخورد بسته ها، پیوند غیرقابل اطمینان و آسیب های غیر منتظره، مقدار حس شده به سرخوشه یا ایستگاه پایه نمی رسد. لذا از بین رفتن داده ها در شبکه های حسگر بی سیم بسیار متداول است. از دست دادن داده های سنجیده شده، دقت WBAN را کاهش می‌دهد. برای حل این مشکل، داده های گم شده باید برآورد شوند. به منظور پیش بینی مقادیر گم شده، یک مدل برآورد داده از دست رفته بر اساس شبکه عصبی LSTM (حافظه کوتاه مدت) در این مقاله ارائه شده است. این مدل پنج علامت حیاتی را به عنوان ورودی برای پیش بینی مقدار از دست رفته ترکیب می کند. نتایج نشان می دهد که sgdm-LSTM روش خوبی برای برآورد مقدار از دست رفته است. ضمنا، نتایج تجربی نشان می دهد که میانگین خطای مربع ریشه مقدار برآورد شده کمتر از سایر روش ها است. این مقدار، با بهترین ابر پارامترهای شبکه 4.1495 است.
کلیدواژه‌ها [English]
برآورد, داده ی از دست رفته, شبکه حسگر بی سیم بدن, شبکه ی عصبی مصنوعی, یادگیری عمیق
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