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استفاده از مدل رگرسیون گرادیان افزایشی برای مدلسازی حسگرهای گازی در تشخیص کشمش آفتابی، گوگردی و تیزابی | ||
| مهندسی بیوسیستم ایران | ||
| دوره 55، شماره 1، فروردین 1403، صفحه 1-18 اصل مقاله (2.78 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2024.370678.665534 | ||
| نویسندگان | ||
| محمد قوشچیان1؛ سید سعید محتسبی* 2؛ شاهین رفیعی3 | ||
| 1دانشجوی دکتری مهندسی مکانیک بیوسیستم، گروه مهندسی ماشینهای کشاورزی، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی دانشگاه | ||
| 2استاد، گروه مهندسی ماشینهای کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 3استاد، گروه مهندسی ماشینهای کشاورزی، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران | ||
| چکیده | ||
| مدلسازی یادگیری ماشین می‌تواند به غلبه بر برخی از محدودیت‌های حسگرهای گازی، مانند شرایط عملیاتی سخت، خطاهای رانش، انتخاب محدود، نیاز به مقدار زیادی از داده‌های برچسب‌گذاری شده و چالش‌های هزینه و ساخت کمک کند. در این پژوهش یک سامانه بینی الکترونیک جهت تشخیص کشمش آفتابی، گوگردی و تیزابی ساخته شد. تیمارها شامل سه تیمار آفتابی، تیزابی و گوگردی هرکدام در سه تکرار آماده شدند و هرکدام 60 دقیقه در معرض حسگرهای بویایی قرار گرفتند تا پاسخ حسگرها به هر کدام از تیمارها ثبت شود. سپس داده‌های بدست آمده از پاسخ حسگرها توسط مدل‌های یادگیری ماشین مورد بررسی قرار گرفتند تا دقت مدلسازی هر روش مشخص شده و مورد بررسی قرار گیرد. نتایج نشان داد مدل رگرسیون گرادیان افزایشی استفاده شده با ضریب تبیین 9972/0، ریشه میانگین مربعات خطای 0209/0، میانگین مطلق خطای 0026/0 و ریشه میانگین مربعات خطای نسبی 0209/0 برای داده‌های آزمون توانسته است پاسخ حسگرهای گازی را به خوبی نسبت به تیمارهای معرفی شده مدلسازی کند. همچنین با بررسی و تحلیل نتایج بدست آمده، نوع و میزان همبستگی بین پاسخ حسگرها نسبت به هم و نسبت به زمان مشخص شد تا در پیش‌بینی رفتار آنها مورد ارزیابی قرار بگیرد. سپس با مدلسازی انجام شده مشخص شد حسگرهای MQ9، MQ3، MQ5، TGS2620 به ترتیب با ضرایب تبیین 8668/0، 8786/0، 9458/0 و 9074/0 و ریشه میانگین مربعات خطای 0163/0، 0168/0 ، 0083/0 و 0227/0 پاسخ‌های دقیق‌تر و پیش‌بینی پذیرتری نسبت به حسگرهای MQ135، TGS822، TGS810 و MQ4 نشان دادند. | ||
| کلیدواژه‌ها | ||
| حسگرهای گازی؛ رگرسیون گرادیان افزایشی؛ مدلسازی؛ مواد مضر کشمش؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| The Use of Gradient Boost Regression Model to Modeling of Gas Sensors in Diagnosis of Sun-dried, Sulphurous and Acidic solution dried Raisins | ||
| نویسندگان [English] | ||
| mohammad ghoushchian1؛ Seyed Saeid Mohtasebi2؛ shahin rafiee3 | ||
| 1PhD student, Department of Agricultural Machinery Engineering, Faculty of Agriculture, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran | ||
| 2Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
| 3Professor, Department of Agricultural Machinery Engineering, Faculty of Agriculture, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran | ||
| چکیده [English] | ||
| Machine learning modeling can help overcome some of the limitations of gas sensors, such as high operational conditions, drift errors, limited selectivity, the need for a large amount of labeled data, and cost and fabrication challenges. In this research, an electronic nose system was developed for the detection of sulfur dioxide and acetic acid. three treatments, including sunny, acetic, and sulfuric, were prepared in three repetitions, and each was exposed to olfactory sensors for 60 minutes to record the sensor responses to each treatment. Then, the data obtained from the sensor responses were examined by machine learning models to determine the modeling accuracy of each method. The results showed that the utilized Gradient Boost Regression model with a determination coefficient of 0.9972, root mean square error of 0.0209, mean absolute error of 0.0026, and relative root mean square error of 0.0209 was able to model the gas sensor responses well for the introduced treatments. Furthermore, by analyzing the results, the type and degree of correlation between the sensor responses to each other and over time were determined to evaluate their behavior prediction. Then, based on the conducted modeling, it was revealed that MQ9, MQ3, MQ5, and TGS2620 sensors, with determination coefficients of 0.8668, 0.8786, 0.9458, and 0.9074, and root mean square errors of 0.0163, 0.0168, 0.0083, and 0.0227, respectively, provided more accurate and predictable responses compared to MQ135, TGS822, TGS810, and MQ4 sensors. | ||
| کلیدواژه‌ها [English] | ||
| Gradient Boost Regression, Gas sensors. machine learning, Raisin harmful substances, Data modeling | ||
| مراجع | ||
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