دانلود و نمایش مقالات مرتبط با مواد::صفحه 1
بلافاصله پس از پرداخت دانلود کنید

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - مواد

تعداد مقالات یافته شده: 645
ردیف عنوان نوع
1 iRestroom : A smart restroom cyberinfrastructure for elderly people
iRestroom: زیرساخت سایبری سرویس بهداشتی هوشمند برای افراد مسن-2022
According to a report by UN and WHO, by 2030 the number of senior people (age over 65) is projected to grow up to 1.4 billion, and which is nearly 16.5% of the global population. Seniors who live alone must have their health state closely monitored to avoid unexpected events (such as a fall). This study explains the underlying principles, methodology, and research that went into developing the concept, as well as the need for and scopes of a restroom cyberinfrastructure system, that we call as iRestroom to assess the frailty of elderly people for them to live a comfortable, independent, and secure life at home. The proposed restroom idea is based on the required situations, which are determined by user study, socio-cultural and technological trends, and user requirements. The iRestroom is designed as a multi-sensory place with interconnected devices where carriers of older persons can access interactive material and services throughout their everyday activities. The prototype is then tested at Texas A&M University-Kingsville. A Nave Bayes classifier is utilized to anticipate the locations of the sensors, which serves to provide a constantly updated reference for the data originating from numerous sensors and devices installed in different locations throughout the restroom. A small sample of pilot data was obtained, as well as pertinent web data. The Institutional Review Board (IRB) has approved all the methods.
keywords: اینترنت اشیا | حسگرها | نگهداری از سالمندان | سیستم های هوشمند | یادگیری ماشین | IoT | Sensors | Elder Care | Smart Systems | Machine Learning
مقاله انگلیسی
2 Predicting social media engagement with computer vision: An examination of food marketing on Instagram
پیش‌بینی تعامل رسانه‌های اجتماعی با بینایی رایانه: بررسی بازاریابی مواد غذایی در اینستاگرام-2022
In a crowded social media marketplace, restaurants often try to stand out by showcasing elaborate “Insta- grammable” foods. Using an image classification machine learning algorithm (Google Vision AI) on restaurants’ Instagram posts, this study analyzes how the visual characteristics of product offerings (i.e., their food) relate to social media engagement. Results demonstrate that food images that are more confidently evaluated by Google Vision AI (a proxy for food typicality) are positively associated with engagement (likes and comments). A follow- up experiment shows that exposure to typical-appearing foods elevates positive affect, suggesting they are easier to mentally process, which drives engagement. Therefore, contrary to conventional social media practices and food industry trends, the more typical a food appears, the more social media engagement it receives. Using Google Vision AI to identify what product offerings receive engagement presents an accessible method for marketers to understand their industry and inform their social media marketing strategies.
keywords: بازاریابی از طریق رسانه های اجتماعی | تعامل با مصرف کننده | یادگیری ماشین | غذا | روان بودن پردازش | هوش مصنوعی گوگل ویژن | Social media marketing | Consumer engagement | Machine learning | Food | Processing fluency | Google Vision AI
مقاله انگلیسی
3 Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks
چشم انداز کامپیوتری برای تجزیه و تحلیل آناتومیکی تجهیزات در پروژه های زیرساختی عمرانی: نظریه پردازی توسعه شبکه های عصبی عمیق مبتنی بر رگرسیون-2022
There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant of the successful delivery of site operations. Although manufacturers provide equipment performance hand- books, additional monitoring mechanisms are required to depart from measuring performance on the sole basis of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated image libraries are used to train and test several backbone architectures. Experimental results reveal the pre- cision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of potentials to influence current practice of articulated machinery monitoring in projects.
keywords: هوش مصنوعی (AI) | سیستم های فیزیکی سایبری | معیارهای ارزیابی خطا | طراحی و آزمایش تجربی | تخمین ژست کامل بدن | صنعت و ساخت 4.0 | الگوریتم های یادگیری ماشین | معماری های ستون فقرات شبکه | Artificial intelligence (AI) | Cyber physical systems | Error evaluation metrics | Experimental design and testing | Full body pose estimation | Industry and construction 4.0 | Machine learning algorithms | Network backbone architectures
مقاله انگلیسی
4 PortiK: A computer vision based solution for real-time automatic solid waste characterization – Application to an aluminium stream
PortiK: یک راه حل مبتنی بر بینایی کامپیوتری برای شناسایی خودکار زباله جامد در زمان واقعی - کاربرد در جریان آلومینیوم-2022
In Material Recovery Facilities (MRFs), recyclable municipal solid waste is turned into a precious commodity. However, effective recycling relies on effective waste sorting, which is still a challenge to sustainable develop- ment of our society. To help the operations improve and optimise their process, this paper describes PortiK, a solution for automatic waste analysis. Based on image analysis and object recognition, it allows for continuous, real-time, non-intrusive measurements of mass composition of waste streams. The end-to-end solution is detailed with all the steps necessary for the system to operate, from hardware specifications and data collection to su- pervisory information obtained by deep learning and statistical analysis. The overall system was tested and validated in an operational environment in a material recovery facility. PortiK monitored an aluminium can stream to estimate its purity. Aluminium cans were detected with 91.2% precision and 90.3% recall, respectively, resulting in an underestimation of the number of cans by less than 1%. Regarding contaminants (i.e. other types of waste), precision and recall were 80.2% and 78.4%, respectively, giving an 2.2% underestimation. Based on five sample analyses where pieces of waste were counted and weighed per batch, the detection results were used to estimate purity and its confidence level. The estimation error was calculated to be within ±7% after 5 minutes of monitoring and ±5% after 8 hours. These results have demon- strated the feasibility and the relevance of the proposed solution for online quality control of aluminium can stream.
keywords: امکانات بازیابی مواد | شناسایی مواد زائد جامد | یادگیری عمیق | شبکه عصبی عمیق | بینایی کامپیوتر | Material recovery facilities | MRF | Solid waste characterization | Deep-learning | Deep neural network | Computer vision
مقاله انگلیسی
5 Computer vision technique for freshness estimation from segmented eye of fish image
تکنیک بینایی کامپیوتری برای تخمین تازگی از چشم تقسیم شده تصویر ماهی-2022
Preserving the quality of fish is a challenging task. Several different cooling methods and materials are used during their storage, transportation purpose. These are responsible factors that decide the freshness of a post harvested fish. In this proposed algorithm, a computer vision-based technique is developed to predict the freshness level of fish from its image. Eyes of the fish are considered as the region of interest, as a good corre- lation has been observed between the colour of the eye and different duration of storage day. It is segmented from the image of a fish sample and then a strategic framework is used for extraction of the discriminatory features. These extracted features show a degradation pattern which acts as an indicative parameter to determine the level of freshness of sample of fish. The proposed method provides a recognition accuracy of 96.67%. The experimental results indicate that this is an efficient and non-destructive methodology for detecting the fish freshness. The high accuracy of freshness detection and low computation time makes this non-destructive methodology efficient for real-world usage in the fish industry and market.
keywords: استخراج ویژگی | چشم ماهی | تکنیک های پردازش تصویر | سطح تازگی | تقسیم بندی | Feature extraction | Fish eye | Image processing techniques | Level of freshness | Segmentation
مقاله انگلیسی
6 In-situ optimization of thermoset composite additive manufacturing via deep learning and computer vision
بهینه سازی درجای تولید افزودنی کامپوزیت ترموست از طریق یادگیری عمیق و بینایی کامپیوتری-2022
With the advent of extrusion additive manufacturing (AM), fabrication of high-performance thermoset com- posites without the need of tooling has become a reality. However, finding an optimal set of printing parameters for these thermoset composites during extrusion requires tedious experimentation as composite ink properties can vary significantly with respect to environmental parameters such as temperature and relative humidity. Addressing this challenge, this study presents a novel optimization framework that utilizes computer vision and deep learning (DL) to optimize the calibration and printing processes of thermoset composite AM. Unlike traditional DL models where printing parameters are determined prior to printing, our proposed framework dynamically and autonomously adjusts the printing parameters during extrusion. A novel DL integrated extrusion AM system is developed to determine the optimal printing parameters including print speed, road width, and layer height for a given composite ink. This closed loop system is consisted of a computer communicating with an extrusion AM system, a camera to perform in-situ imaging and several high accuracy convolution neural net- works (CNNs) selecting the ideal process parameters for composite AM. The results show that our proposed process optimization framework was able to autonomously determine these parameters for a carbon fiber- composite ink. Consequently, specimens with complex geometries could be fabricated without visible defects and with maximum fiber alignment and thus enhancing the mechanical performance of the specimen’s com- posite material. Moreover, our proposed framework minimizes a labor-intensive procedure required to additively manufacture thermoset composites by optimizing the extrusion process without any user intervention.
keywords: یادگیری عمیق | بینایی کامپیوتر | اکستروژن | پرینت سه بعدی کامپوزیت | Deep learning | Computer vision | Extrusion | Composite 3D printing
مقاله انگلیسی
7 بیوپلیمر: ماده ای پایدار برای کاربردهای غذایی و پزشکی
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 22 - تعداد صفحات فایل doc فارسی: 48
پلیمرهای زیستی یک گروه پیشرو از مواد کاربردی مناسب برای کاربردهای با ارزش بالا هستند که مورد توجه محققان و متخصصان در رشته‌های مختلف قرار گرفته اند. برای درک جنبه های اساسی و کاربردی بیوپلیمرها برای رسیدگی به چندین مشکل پیچیده مرتبط با سلامت و رفاه مهم به تحقیقات بین رشته ای نیاز است. برای کاهش اثرات زیست محیطی و وابستگی به سوخت های فسیلی، تلاش زیادی برای جایگزینی پلیمرهای مصنوعی با مواد زیست تخریب پذیر، به ویژه آنهایی که از منابع طبیعی به دست می آیند، انجام شده است. در این راستا، بسیاری از انواع پلیمرهای طبیعی یا زیستی برای رفع نیازهای کاربردهای روزافزون توسعه یافته اند. این بیوپلیمرها در حال حاضر در مصارف غذایی مورد استفاده قرار می گیرند و به دلیل خواص منحصر به فردشان در حال گسترش در صنایع دارویی و پزشکی هستند. این بررسی بر روی کاربردهای مختلف پلیمرهای زیستی در صنایع غذایی و پزشکی تمرکز دارد و چشم انداز آینده را برای صنعت بیوپلیمر ارائه می دهد.
واژگان کلیدی: پلیمرهای زیستی | کاربردهای پزشکی و غذایی | مواد زیست تخریب پذیر | پلی ساکاریدهای میکروبی | کیتوزان
مقاله ترجمه شده
8 MagLoc : A magnetic induction based localization scheme for fresh food logistics
MagLoc: یک طرح محلی سازی مبتنی بر القای مغناطیسی برای تدارکات مواد غذایی تازه-2022
An IoT infrastructure to continuously monitor the fresh food supply chain can quickly detect food quality and contamination issues and thereby reduce costs and food wastage. This, in turn, involves several challenges including the development of inexpensive quality/contamination sensors to be deployed in a fine grain manner in the food boxes, technologies for sensor level communications, online data management and analytics, and logistics driven by such analytics. In this paper, we study the issues related to the communication among sensing modules deployed in the fresh food boxes and thereby an automated localization of the boxes that may have quality/contamination issues. In this context we study the near-field magnetic induction (NFMI) based communication and localization, as the ubiquitous RF communications suffer high attenuation through the water/mineral rich tissue media. An accurate localization of the sensors inside boxes within the food pallets is very challenging in this environment. In this paper we propose a novel magnetic induction based localization scheme, and show that with a small number of anchor nodes, the localization can be done without any errors for boxes as small as 0.5 meter on the side, and with small errors even for boxes half as big.
Keywords: Smart sensing | Industrial sensors | Food supply chain | Physical Internet | Magnetic communication | Localization
مقاله انگلیسی
9 Smart mask – Wearable IoT solution for improved protection and personal health
ماسک هوشمند – راه حل پوشیدنی اینترنت اشیا برای بهبود حفاظت و سلامت شخصی-2022
The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials, AI, wireless, IoT, and software will help the gathering of health data and health-related event detection in real time from the user as well as from their environment. In the larger scale, with the help of AI-based analysis for health data it is possible to predict and decrease medical costs with accurate diagnoses and treatment plans, where the comparison of personal data to large-scale public data enables drawing up a personal health trajectory, for example. Key research prob- lems for smart respiratory protective equipment are identified in addition to future research di- rections. A Smart Mask prototype was developed with accompanying user application, backend and heath AI to study the concept.
keywords: کووید-۱۹ | محاسبات لبه | اینترنت اشیا | سلامت شخصی | پوشیدنی | COVID-19 | Edge computing | IoT | Personal health | Wearable
مقاله انگلیسی
10 FANETs in Agriculture - A routing protocol survey
FANETs در کشاورزی - مرور پروتکل مسیریابی-2022
Breakthrough advances on communication technology, electronics and sensors have led to integrated commercialized products ready to be deployed in several domains. Agriculture is and has always been a domain that adopts state of the art technologies in time, in order to optimize productivity, cost, convenience, and environmental protection. The deployment of Unmanned Aerial Vehicles (UAVs) in agriculture constitutes a recent example. A timely topic in UAV deployment is the transition from a single UAV system to a multi-UAV system. Collaboration and coordination of multiple UAVs can build a system that far exceeds the capabilities of a single UAV. However, one of the most important design problems multi- UAV systems face is choosing the right routing protocol which is prerequisite for the co- operation and collaboration among UAVs. In this study, an extensive review of Flying Ad- hoc network (FANET) routing protocols is performed, where their different strategies and routing techniques are thoroughly described. A classification of UAV deployment in agri- culture is conducted resulting in six (6) different applications: Crop Scouting, Crop Survey- ing and Mapping, Crop Insurance, Cultivation Planning and Management, Application of Chemicals,and Geofencing. Finally, a theoretical analysis is performed that suggests which routing protocol can serve better each agriculture application, depending on the mobility models and the agricultural-specific application requirements.
keywords: کشاورزی هوشمند | کشاورزی دقیق | وسایل نقلیه هوایی بدون سرنشین (UAV) | شبکه های ادوک پرنده (FANET) | پروتکل های مسیریابی | مدل های تحرک | smart farming | precision agriculture | unmanned aerial vehicles (UAVs) | flying adhoc networks (FANETs) | routing protocols | mobility models
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 890 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 890 :::::::: افراد آنلاین: 47