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تعداد مقالات یافته شده: 868
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 A computer vision-based method for bridge model updating using displacement influence lines
یک روش مبتنی بر بینایی کامپیوتری برای به‌روزرسانی مدل پل با استفاده از خطوط موثر جابجایی-2022
This paper presents a new computer vision-based method that simultaneously provides the moving vehicle’s tire loads, the location of the loads on a bridge, and the bridge’s response displacements, based on which the bridge’s influence lines can be constructed. The method employs computer vision techniques to measure the displacement influence lines of the bridge at different target positions, which is then later used to perform model updating of the finite element models of the monitored structural system.
The method is enabled by a novel computer vision-based vehicle weigh-in-motion method which the coauthors recently introduced. A correlation discriminating filter tracker is used to estimate the displacements at target points and the location of single or multiple moving loads, while a low-cost, non-contact weigh-in-motion technique evaluates the magnitude of the moving vehicle loads.
The method described in this paper is tested and validated using a laboratory bridge model. The system was loaded with a vehicle with pressurized tires and equipped with a monitoring system consisting of laser displacement sensors, accelerometers, and cameras. Both artificial and natural targets were considered in the experimental tests to track the displacements with the cameras and yielded robust results consistent with the laser displacement measurements.
The extracted normalized displacement influence lines were then successfully used to perform model updating of the structure. The laser displacement sensors were used to validate the accuracy of the proposed computer vision-based approach in deriving the displacement measurements, while the accelerometers were used to derive the system’s modal properties employed to validate the updated finite element model. As a result, the updated finite element model correctly predicted the bridge’s displacements measured during the tests. Furthermore, the modal parameters estimated by the updated finite element model agreed well with those extracted from the experimental modal analysis carried out on the bridge model. The method described in this paper offers a low-cost non-contact monitoring tool that can be efficiently used without disrupting traffic for bridges in model updating analysis or long-term structural health monitoring.
keywords: Computer vision | Displacement influence line | Vehicle weigh-in-motion | Structural identification | Finite element method model | Model updating | Modal analysis | Bridge systems
مقاله انگلیسی
3 Monitoring crop phenology with street-level imagery using computer vision
پایش فنولوژی محصول با تصاویر سطح خیابان با استفاده از بینایی ماشین-2022
Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant thematic information. We present a framework to collect and extract crop type and phenological information from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side- looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed 200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures. At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds, maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley, winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g. green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology was developed to obtain the best performing model among 160 models. This best model was applied on an independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data collection and suggests avenues for massive data collection via automated classification using computer vision.
keywords: Phenology | Plant recognition | Agriculture | Computer vision | Deep learning | Remote sensing | CNN | BBCH | Crop type | Street view imagery | Survey | In-situ | Earth observation | Parcel | In situ
مقاله انگلیسی
4 Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method
روش اندازه گیری جابجایی ساختاری همزمان با روشنایی مبتنی بر بینایی کامپیوتری-2022
Computer vision-based techniques for structural displacement measurement are rapidly becoming popular in civil structural engineering. However, most existing computer vision-based displace- ment measurement methods require man-made targets for object matching or tracking, besides usually the measurement accuracies are seriously sensitive to the ambient illumination variations. A computer vision-based illumination robust and multi-point simultaneous measuring method is proposed for structural displacement measurements. The method consists of two part, one is for segmenting the beam body from its background, the segmentation is perfectly carried out by fully convolutional network (FCN) and conditional random field (CRF); another is digital image cor- relation (DIC)-based displacement measurement. A simply supported beam is built in laboratory. The accuracy and illumination robustness are verified through three groups of elaborately designed experiments. Due to the exploitation of FCN and CRF for pixel-wise segmentation, numbers of locations along with the segmented beam body can be chosen and measured simul- taneously. It is verified that the method is illumination robust since the displacement measure- ments are with the smallest fluctuations to the illumination variations. The proposed method does not require any man-made targets attached on the structure, but because of the exploitation of DIC in displacement measurement, the regions centered on the measuring points need to have texture feature.
keywords: پایش سلامت سازه | اندازه گیری جابجایی | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی شی | همبستگی تصویر دیجیتال | Structural health monitoring | Displacement measurement | Computer vision | Deep learning | Object segmentation | Digital image correlation
مقاله انگلیسی
5 High-Stability Cryogenic System for Quantum Computing With Compact Packaged Ion Traps
سیستم برودتی با پایداری بالا برای محاسبات کوانتومی با تله های یونی بسته بندی شده فشرده-2022
Cryogenic environments benefit ion trapping experiments by offering lower motional heating rates, collision energies, and an ultrahigh vacuum (UHV) environment for maintaining long ion chains for extended periods of time. Mechanical vibrations caused by compressors in closed-cycle cryostats can introduce relative motion between the ion and the wavefronts of lasers used to manipulate the ions. Here, we present a novel ion trapping system where a commercial low-vibration closed-cycle cryostat is used in a custom monolithic enclosure. We measure mechanical vibrations of the sample stage using an optical interferometer, and observe a root-mean-square relative displacement of 2.4 nm and a peak-to-peak displacement of 17 nm between free-space beams and the trapping location. We packaged a surface ion trap in a cryopackage assembly that enables easy handling while creating a UHV environment for the ions. The trap cryopackage contains activated carbon getter material for enhanced sorption pumping near the trapping location, and source material for ablation loading. Using 171Yb+ as our ion, we estimate the operating pressure of the trap as a function of package temperature using phase transitions of zig-zag ion chains as a probe. We measured the radial mode heating rate of a single ion to be 13 quanta/s on average. The Ramsey coherence measurements yield 330-ms coherence time for counter-propagating Raman carrier transitions using a 355-nm mode-locked pulse laser, demonstrating the high optical stability.
INDEX TERMS: Optomechanical design | quantum computing | trapped ions.
مقاله انگلیسی
6 Automated bridge surface crack detection and segmentation using computer vision-based deep learning model
تشخیص و تقسیم خودکار ترک سطح پل با استفاده از مدل یادگیری عمیق مبتنی بر بینایی کامپیوتری-2022
Bridge maintenance will become a widespread trend in the engineering industry as the number of bridges grows and time passes. Cracking is a common problem in bridges with concrete structures. Allowing it to expand will result in significant economic losses and accident risks This paper proposed an automatic detection and segmentation method of bridge surface cracks based on computer vision deep learning models. First, a bridge surface crack detection and segmentation dataset was established. Then, according to the characteristics of the bridge, we improved the You Only Look Once (YOLO) algorithm for bridge surface crack detection. The improved algorithm was defined as CR-YOLO, which can identify cracks and their approximate locations from multi-object images. Subsequently, the PSPNet algorithm was improved to segment the bridge cracks from the non-crack regions to avoid the visual interference of the detection algorithm. Finally, we deployed the proposed bridge crack detection and segmentation algorithm in an edge device. The experimental results show that our method outperforms other baseline methods in generic evaluation metrics and has advantages in Model Size(MS) and Frame Per Second (FPS).
keywords: Bridge crack Crack detection | Crack segmentation | Deep learning | Computer vision
مقاله انگلیسی
7 Intelligent Reflecting Surface (IRS) Allocation Scheduling Method Using Combinatorial Optimization by Quantum Computing
روش زمان‌بندی تخصیص سطح بازتابنده هوشمند (IRS) با استفاده از بهینه‌سازی ترکیبی توسط محاسبات کوانتومی-2022
Intelligent Reflecting Surface (IRS) significantly improves the energy utilization efficiency in 6th generation cellular communication systems. Here, we consider a system with multiple IRS and users, with one user communicating via several IRSs. In such a system, the user to which an IRS is assigned for each unit time must be determined to realize efficient communication. The previous studies on the optimization of various parameters for IRS based wireless systems did not consider the optimization of such IRS allocation scheduling. Therefore, we propose an IRS allocation scheduling method that limits the number of users who allocate each IRS to one unit time and sets the reflection coefficients of the IRS specifically to the assigned user resulting in the maximum IRS array gain. Additionally, as the proposed method is a combinatorial optimization problem, we develop a quadratic unconstrained binary optimization formulation to solve this using quantum computing. This will lead to the optimization of the entire system at a high speed and low power consumption in the future. Using computer simulation, we clarified that the proposed method realizes a more efficient communication compared to the method where one IRS is simultaneously used by multiple users.
INDEX TERMS: Intelligent reflecting surface | IRS allocation scheduling | quantum computing | quantum annealing | combinatorial optimization
مقاله انگلیسی
8 Towards automatic waste containers management in cities via computer vision: containers localization and geo-positioning in city maps
به سمت مدیریت خودکار ظروف زباله در شهرها از طریق بینایی کامپیوتری: محلی سازی ظروف و موقعیت جغرافیایی در نقشه های شهر-2022
This paper describes the scientific achievements of a collaboration between a research group and the waste management division of a company. While these results might be the basis for several practical or commercial developments, we here focus on a novel scientific contribution: a methodology to automatically generate geo- located waste container maps. It is based on the use of Computer Vision algorithms to detect waste containers and identify their geographic location and dimensions. Algorithms analyze a video sequence and provide an automatic discrimination between images with and without containers. More precisely, two state-of-the-art object detectors based on deep learning techniques have been selected for testing, according to their perfor- mance and to their adaptability to an on-board real-time environment: EfficientDet and YOLOv5. Experimental results indicate that the proposed visual model for waste container detection is able to effectively operate with consistent performance disregarding the container type (organic waste, plastic, glass and paper recycling,…) and the city layout, which has been assessed by evaluating it on eleven different Spanish cities that vary in terms of size, climate, urban layout and containers’ appearance.
keywords: Waste container localization | Deep Learning | Computer Vision | Object detection
مقاله انگلیسی
9 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
10 Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions
یادگیری ماشینی الهام گرفته از کوانتومی برای 6G: مبانی، امنیت، تخصیص منابع، چالش‌ها و دستورالعمل‌های تحقیقاتی آینده-2022
Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality, and data structures. Hence, the conventional machine learning approaches in data training and processing have exhibited their limited computing capabilities to support the sixth-generation (6G) networks with highly dynamic applications and services. In this regard, the fast developing quantum computing with machine learning for 6G networks is investigated. Quantum machine learning algorithm can significantly enhance the processing efficiency and exponentially computational speed-up for effective quantum data representation and superposition framework, highly capable of guaranteeing high data storage and secured communications. We present the state-of-the-art in quantum computing and provide a comprehensive overview of its potential, via machine learning approaches. Furthermore, we introduce quantum-inspired machine learning applications for 6G networks in terms of resource allocation and network security, considering their enabling technologies and potential challenges. Finally, some dominating research issues and future research directions for the quantum-inspired machine learning in 6G networks are elaborated.
INDEX TERMS: 6G networks | machine learning | quantum machine learning | quantum security.
مقاله انگلیسی
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