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نتیجه جستجو - Assessment

تعداد مقالات یافته شده: 995
ردیف عنوان نوع
1 ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision
ChickenNet - یک رویکرد انتها به انتها برای ارزیابی وضعیت پرهای مرغ های تخمگذار در مزارع تجاری با استفاده از بینایی کامپیوتر-2022
Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive, manual task. This study proposes a novel approach for automated plumage condition assessment using com- puter vision and deep learning. It presents ChickenNet, an end-to-end convolutional neural network that detects hens and simultaneously predicts a plumage condition score for each detected hen. To investigate the effect of input image characteristics, the method was evaluated using images with and without depth information in resolutions of 384 × 384, 512 × 512, 896 × 896 and 1216 × 1216 pixels. Further, to determine the impact of subjective human annotations, plumage condition predictions were compared to manual assessments of one observer and to matching annotations of two observers. Among all tested settings, performance metrics based on matching manual annotations of two observers were equal or better than the ones based on annotations of a single observer. The best result obtained among all tested configurations was a mean average precision (mAP) of 98.02% for hen detection while 91.83% of the plumage condition scores were predicted correctly. Moreover, it was revealed that performance of hen detection and plumage condition assessment of ChickenNet was not generally enhanced by depth information. Increasing image resolutions improved plumage assessment up to a resolution of 896 × 896 pixels, while high detection accuracies (mAP > 0.96) could already be achieved using lower resolutions. The results indicate that ChickenNet provides a sufficient basis for automated monitoring of plumage conditions in commercial laying hen farms.
keywords: طیور | ارزیابی پر و بال | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی نمونه | Poultry | Plumage assessment | Computer vision | Deep learning | Instance segmentation
مقاله انگلیسی
2 Non-destructive and contactless estimation of chlorophyll and ammonia contents in packaged fresh-cut rocket leaves by a Computer Vision System
تخمین غیر مخرب و بدون تماس محتویات کلروفیل و آمونیاک در برگ های موشک تازه برش خورده بسته بندی شده توسط یک سیستم کامپیوتر ویژن-2022
Computer Vision Systems (CVS) offer a non-destructive and contactless tool to assign visual quality level to fruit and vegetables and to estimate some of their internal characteristics. The innovative CVS described in this paper exploits the combination of image processing techniques and machine learning models (Random Forests) to assess the visual quality and predict the internal traits on unpackaged and packaged rocket leaves. Its perfor- mance did not depend on the cultivation system (traditional soil or soilless). The same CVS, exploiting its ma- chine learning components, was able to build effective models for either the classification problem (visual quality level assignment) and the regression problems (estimation of senescence indicators such as chlorophyll and ammonia contents) just by changing the training data. The experiments showed a negligible performance loss on packaged products (Pearson’s linear correlation coefficient of 0.84 for chlorophyll and 0.91 for ammonia) with respect to unpackaged ones (0.86 for chlorophyll and 0.92 for ammonia). Thus, the non-destructive and con- tactless CVS represents a valid alternative to destructive, expensive and time-consuming analyses in the lab and can be effectively and extensively used along the whole supply chain, even on packaged products that cannot be analyzed using traditional tools.
keywords: Contactless quality level assessment | Diplotaxis tenuifolia L | Image analysis | Packaged vegetables | Senescence indicators prediction
مقاله انگلیسی
3 A systematic review on computer vision-based parking lot management applied on public datasets
مرور سیستماتیک مدیریت پارکینگ مبتنی بر بینایی ماشین اعمال شده بر روی مجموعه داده های عمومی-2022
Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness. To evaluate such methods authors often employ publicly available parking lot image datasets. In this study, we surveyed and compared robust publicly available image datasets specifically crafted to test computer vision-based methods for parking lot management approaches and consequently present a systematic and comprehensive review of existing works that employ such datasets. The literature review identified relevant gaps that require further research, such as the requirement of dataset-independent approaches and methods suitable for autonomous detection of position of parking spaces. In addition, we have noticed that several important factors such as the presence of the same cars across consecutive images, have been neglected in most studies, thereby rendering unrealistic assessment protocols. Furthermore, the analysis of the datasets also revealed that certain features that should be present when developing new benchmarks, such as the availability of video sequences and images taken in more diverse conditions, including nighttime and snow, have not been incorporated.
keywords: Parking lot | Dataset | Benchmark | Machine learning | Image processing
مقاله انگلیسی
4 Using social media photos and computer vision to assess cultural ecosystem services and landscape features in urban parks
استفاده از عکس های رسانه های اجتماعی و بینایی کامپیوتری برای ارزیابی خدمات اکوسیستم فرهنگی و ویژگی های چشم انداز در پارک های شهری-2022
Urban parks are important public places that provide an opportunity for city dwellers to interact with nature. In recent years, social media data have become a promising data source for the assessment of cultural ecosystem services (CES) and landscape features in urban parks. However, it is a challenging task to identify and classify the CES and landscape features from social media photos by manual content analysis. In addition, relatively few studies focused on the differences in landscape preferences between tourists and locals in urban parks. In this study, we used geotagged social media photos from Flickr and computer vision methods (scene recognition, image clustering and image labeling) based on the convolutional neural networks (CNN) and the Google Cloud Vision platform to assess the spatial preferences and landscape preferences (cultural ecosystem services and landscape features) of tourists and locals in the urban parks of Brussels. The spatial analysis results showed that the tourists’ photos were spatially concentrated on well-known parks located in the city center while the locals’ photos were rather spatially dispersed across all parks of the city. We identified 10 main landscape themes (corresponding to 4 CES categories and 10 landscape feature categories) from 20 image clusters by automated image analysis on social media photos. We also noticed that tourists paid more attention to the place identity featured by symbolic sculptures and buildings, while locals showed more interest in local species of plants, flowers, insects, birds, and animals. This research contributes to social media-based user preferences analysis and CES assessment, which could provide insights for urban park planning and tourism management.
keywords: داده های رسانه های اجتماعی | خدمات اکوسیستم فرهنگی | ویژگی های چشم انداز | پارک های شهری | بینایی کامپیوتر | Social media data | Cultural ecosystem services | Landscape features | Urban parks | Computer vision
مقاله انگلیسی
5 Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems
ارزیابی بیومتریک حیوانات با استفاده از بینایی کامپیوتری غیرتهاجمی و یادگیری ماشینی پیش‌بینی‌کننده خوبی برای سن و رفاه گاوهای شیری هستند: آینده سیستم‌های پشتیبانی خودکار دامپزشکی-2022
Digitally extracted biometrics from visible videos of farm animals could be used to automatically assess animal welfare, contributing to the future of automated veterinary support systems. This study proposed using non- invasive video acquisition and biometric analysis of dairy cows in a robotic dairy farm (RDF) located at the Dookie campus, The University of Melbourne, Australia. Data extracted from dairy cows were used to develop two machine learning models: a biometrics regression model (Model 1) targeting (i) somatic cell count, (ii) weight, (iii) rumination, and (iv) feed intake and a classification model (Model 2) mapping features from dairy cow’s face to predict animal age. Results showed that Model 1 achieved a high correlation coefficient (R = 0.96), slope (b = 0.96), and performance, and Model 2 had high accuracy (98%), low error (2%), and high performance without signs of under or overfitting. Models developed in this study can be used in parallel with other models to assess milk productivity, quality traits, and welfare for RDF and conventional dairy farms.
keywords: هوش مصنوعی | فیزیولوژی گاو | ماستیت | بیومتریک حیوانات | سنجش از راه دور برد کوتاه | Artificial intelligence | Cows physiology | Mastitis | Animal biometrics | Short range remote sensing
مقاله انگلیسی
6 A graphics-based digital twin framework for computer vision-based post-earthquake structural inspection and evaluation using unmanned aerial vehicles
یک چارچوب دیجیتال دوقلوی مبتنی بر گرافیک برای بازرسی و ارزیابی ساختاری پس از زلزله مبتنی بر بینایی کامپیوتری با استفاده از وسایل نقلیه هوایی بدون سرنشین-2022
Rapid structural inspections and evaluations are critical after earthquakes. Computer vision-based methods have attracted the interest of researchers for their potential to be rapid, safe, and objective. To provide an end-to-end solution for computer vision-based post-earthquake inspection and evaluation of a specific as-built structure, the concepts of physics-based graphics model (PBGM) and digital twin (DT) are combined to develop a graphics-based digital twin (GBDT) framework. The GBDT framework comprises a finite element (FE) model and a computer graphics (CG) model whose state is informed by the FE analysis, representing the state of the structure before and after an earthquake. The CG model is first created making use of the FE model and the photographic survey of the structure, yielding the virtual counterpart of the as-built structure quickly and accurately. Then damage modelling approaches are proposed to predict the location and extent of structural and nonstructural damage under seismic loading, from which photographic representation of the predicted damage is realized in the CG model. The effectiveness of the GBDT framework is demonstrated using a five-story reinforced concrete benchmark building through the design and assessment of various UAV (Unmanned Aerial Vehicle) inspection trajectories for post-earthquake scenarios. The results demonstrate that the proposed GBDT framework has significant potential to enable rapid structural inspection and evaluation, ultimately leading to more efficient allocation of scarce resources in a post-earthquake setting.
keywords: بینایی کامپیوتر | مهندسی زلزله | دوقلو دیجیتال | ارزیابی پس از زلزله | دوقلو دیجیتال مبتنی بر گرافیک | مدل گرافیکی مبتنی بر فیزیک | Computer vision | Earthquake engineering | Digital twin | Post-earthquake assessment | Graphics-based digital twin | Physics-based graphics model
مقاله انگلیسی
7 A computer vision-based method to identify the international roughness index of highway pavements
یک روش مبتنی بر بینایی کامپیوتری برای شناسایی شاخص ناهمواری بین‌المللی روسازی بزرگراه-2022
The International Roughness Index (IRI) is one of the most critical parameters in the field of pavement performance management. Traditional methods for the measurement of IRI rely on expensive instrumented vehicles and well-trained professionals. The equipment and labor costs of traditional measurement methods limit the timely updates of IRI on the pavements. In this article, a novel imaging-based Deep Neural Network (DNN) model, which can use pavement photos to directly identify the IRI values, is proposed. This model proved that it is possible to use 2-dimensional (2D) images to identify the IRI other than the typically used vertical accelerations or 3-dimensional (3D) images. Due to the fast growth in photography equipment, small and convenient sports action cameras such as the GoPro Hero series are able to capture smooth videos at a high framerate with built-in electronic image stabilization systems. These significant improvements make it not only more convenient to collect high-quality 2D images, but also easier to process them than vibrations or accelerations. In the proposed method, 15% of the imaging data were randomly selected for testing and had never been touched during the training steps. The testing results showed an averaged coefficient of determination (R square) of 0.6728 and an averaged root mean square error (RMSE) of 0.50.
keywords: شاخص بین المللی زبری | شبکه عصبی عمیق | بینایی کامپیوتر | ارزیابی وضعیت روسازی | International roughness index | Deep neural network | Computer vision | Pavement condition assessment
مقاله انگلیسی
8 Quantum-Inspired Power System Reliability Assessment
ارزیابی قابلیت اطمینان سیستم قدرت با الهام از کوانتومی-2022
To enable an in-depth study of power system operation and planning, the assessment of standard reliability indices is inevitable. The Monte Carlo Simulation (MCS) approach is a broadly used method in replacing the analytical methods in reliability indices assessment. The accuracy of MCS, however, highly depends on the sampling size, and hence, a complicated system with large number of components requires a large sampling size and daunting computational effort. To address this shortcoming, this paper attempts to take advantage of potentials of the quantum computing (QC) for power system reliability assessment by realizing the following contributions: 1) an innovative quantum model designed for reliability assessment; 2) a quantum circuit that achieves the quadratic speed up compared to the classical MCS method; 3) an efficient quantum amplitude estimation (QAE) algorithm to accurately evaluate the reliability indices. The accuracy and efficacy of the quantum reliability method are extensively verified and demonstrated on both radial and mesh distribution systems.
Index Terms—Quantum computing | Quantum amplitude estimation | Reliability assessment | Distribution systems
مقاله انگلیسی
9 TUI Model for data privacy assessment in IoT networks
مدل TUI برای ارزیابی حریم خصوصی داده ها در شبکه های اینترنت اشیا-2022
The development of the Internet of Things (IoT) has been at the forefront of progressing societal functionality. However, the addition of IoT devices in conventional information technology (IT) infrastructure has raised and prioritized the concern of information security and data privacy. The Common Vulnerability Scoring System (CVSS) is a framework for providing information to the public about the impact of vulnerabilities and exploits executed on a multitude of devices. While the CVSS addresses a plethora of conditions for vulnerabilities, it does not adequately make end- users aware of the impact data privacy can have on their devices. The primary objective of this research work is to extend the existing CVSS and propose a new model that acknowledges Transparency, Unlinkability, and Intervenability (TUI) to address the data privacy issues of IoT devices when scoring impacts of vulnerabilities. Our research has developed this model to provide a new sufficient score for analyzing the true impact of compromised data privacy. After the development of the new scoring for TUI, our research highlights case studies to emphasize the impact our TUI model will have on the CVSS. We strongly believe that our proposed model benefit both the individual users (consumers of IoT devices) and the industry to portray the possible vulnerabilities from a user standpoint as well as a manufacturer standpoint.
keywords: حریم خصوصی داده ها | امنیت اینترنت اشیا | مدل سیا | امتیازدهی آسیب پذیری | امنیت دستگاه | ارزیابی امنیتی | Data privacy | IoT security | CIA model | Vulnerability scoring | Device security | Security assessment
مقاله انگلیسی
10 A Multiscale Simulation Approach for Germanium-Hole-Based Quantum Processor
یک رویکرد شبیه سازی چند مقیاسی برای پردازنده کوانتومی مبتنی بر حفره ژرمانیوم-2022
A multiscale simulation method is developed to model a quantum dot (QD) array of germanium (Ge) holes for quantum computing. Guided by three-dimensional numerical quantum device simulations of QD structures, an analytical model of the tunnel coupling between the neighboring hole QDs is obtained. Two-qubit entangling quantum gate operations and quantum circuit characteristics of the QD array processor are then modeled. Device analysis of two-qubit Ge hole quantum gates demonstrates faster gate speed, smaller process variability, and less stringent requirement of feature size, compared to its silicon counterpart. The multiscale simulation method allows assessment of the quantum processor circuit performance from a bottom-up, physics-informed perspective. Application of the simulation method to the Ge QD array processor indicates its promising potential for preparing high-fidelity ansatz states in quantum chemistry simulations.
keywords: Quantum computing | Germanium | Hole | Quantum dot | Quantum gate | Multiscale simulation
مقاله انگلیسی
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