Статті в журналах з теми "Outdoor vision and weather"
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1
Samo, Madiha, Jimiama Mosima Mafeni Mase, and Grazziela Figueredo. "Deep Learning with Attention Mechanisms for Road Weather Detection." Sensors 23, no. 2 (January 10, 2023): 798. http://dx.doi.org/10.3390/s23020798.
Повний текст джерелаАнотація:
There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous occurrence of multiple weather conditions; (2) imbalanced occurrence of weather conditions throughout the year; and (3) road idiosyncrasies, such as road layouts, illumination, and road objects, etc. In this paper, we explore the use of a focal loss function to force the learning process to focus on weather instances that are hard to learn with the objective of helping address data imbalances. In addition, we explore the attention mechanism for pixel-based dynamic weight adjustment to handle road idiosyncrasies using state-of-the-art vision transformer models. Experiments with a novel multi-label road weather dataset show that focal loss significantly increases the accuracy of computer vision approaches for imbalanced weather conditions. Furthermore, vision transformers outperform current state-of-the-art convolutional neural networks in predicting weather conditions with a validation accuracy of 92% and an F1-score of 81.22%, which is impressive considering the imbalanced nature of the dataset.
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Karoon, Kholud A., and Zainab N. Nemer. "A Review of Methods of Removing Haze from An Image." International Journal of Electrical and Electronics Research 10, no. 3 (September 30, 2022): 742–46. http://dx.doi.org/10.37391/ijeer.100354.
Повний текст джерелаАнотація:
A literature review aids in comprehending and gaining further information about a certain area of a subject. The presence of haze, fog, smoke, rain, and other harsh weather conditions affects outdoor photos. Images taken in unnatural weather have weak contrast and poor colors. This may make detecting objects in the produced hazy pictures difficult. In computer vision, scenes and images taken in a foggy atmosphere suffer from blurring. This work covers a study of many remove haze algorithms for eliminating haze collected in real-world weather scenarios in order to recover haze-free images rapidly and with improved quality. The contrast, viewing range, and color accuracy have been enhanced. All of these techniques it is used in countless fields. Some of the applications that use this technology outdoor surveillance, object recognition, underwater photography, and so on.
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Kim, Bong Keun, and Yasushi Sumi. "Vision-Based Safety-Related Sensors in Low Visibility by Fog." Sensors 20, no. 10 (May 15, 2020): 2812. http://dx.doi.org/10.3390/s20102812.
Повний текст джерелаАнотація:
Mobile service robots are expanding their use to outdoor areas affected by various weather conditions, but the outdoor environment directly affects the functional safety of robots implemented by vision-based safety-related sensors (SRSs). Therefore, this paper aims to set the fog as the environmental condition of the robot and to understand the relationship between the quantified value of the environmental conditions and the functional safety performance of the robot. To this end, the safety functions of the robot built using SRS and the requirements for the outdoor environment affecting them are described first. The method of controlling visibility for evaluating the safety function of SRS is described through the measurement and control of visibility, a quantitative means of expressing the concentration of fog, and wavelength analysis of various SRS light sources. Finally, object recognition experiments using vision-based SRS for robots are conducted at low visibility. Through this, it is verified that the proposed method is a specific and effective method for verifying the functional safety of the robot using the vision-based SRS, for low visibility environmental requirements.
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Liu, Wei, Yue Yang, and Longsheng Wei. "Weather Recognition of Street Scene Based on Sparse Deep Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 3 (May 19, 2017): 403–8. http://dx.doi.org/10.20965/jaciii.2017.p0403.
Повний текст джерелаАнотація:
Recognizing different weather conditions is a core component of many different applications of outdoor video analysis and computer vision. Street analysis performance, including detecting street objects, detecting road lines, recognizing street sign and etc., varies greatly with weather, so modeling based on weather recognition is the key resolution in this field. Features derived from intrinsic properties of different weather conditions contribute to successful classification. We first propose using deep learning features from convolutional neural networks (CNN) for fine recognition. In order to reduce the parameter redundancy in CNN, we used sparse decomposition to dramatically cut down the computation. Recognition results for databases show superior performance and indicate the effectiveness of extracted features.
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Uhm, Taeyoung, Jeongwoo Park, Jungwoo Lee, Gideok Bae, Geonhui Ki, and Youngho Choi. "Design of Multimodal Sensor Module for Outdoor Robot Surveillance System." Electronics 11, no. 14 (July 15, 2022): 2214. http://dx.doi.org/10.3390/electronics11142214.
Повний текст джерелаАнотація:
Recent studies on surveillance systems have employed various sensors to recognize and understand outdoor environments. In a complex outdoor environment, useful sensor data obtained under all weather conditions, during the night and day, can be utilized for application to robots in a real environment. Autonomous surveillance systems require a sensor system that can acquire various types of sensor data and can be easily mounted on fixed and mobile agents. In this study, we propose a method for modularizing multiple vision and sound sensors into one system, extracting data synchronized with 3D LiDAR sensors, and matching them to obtain data from various outdoor environments. The proposed multimodal sensor module can acquire six types of images: RGB, thermal, night vision, depth, fast RGB, and IR. Using the proposed module with a 3D LiDAR sensor, multimodal sensor data were obtained from fixed and mobile agents and tested for more than four years. To further prove its usefulness, this module was used as a monitoring system for six months to monitor anomalies occurring at a given site. In the future, we expect that the data obtained from multimodal sensor systems can be used for various applications in outdoor environments.
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Osorio Quero, C., D. Durini, J. Rangel-Magdaleno, J. Martinez-Carranza, and R. Ramos-Garcia. "Single-Pixel Near-Infrared 3D Image Reconstruction in Outdoor Conditions." Micromachines 13, no. 5 (May 20, 2022): 795. http://dx.doi.org/10.3390/mi13050795.
Повний текст джерелаАнотація:
In the last decade, the vision systems have improved their capabilities to capture 3D images in bad weather scenarios. Currently, there exist several techniques for image acquisition in foggy or rainy scenarios that use infrared (IR) sensors. Due to the reduced light scattering at the IR spectra it is possible to discriminate the objects in a scene compared with the images obtained in the visible spectrum. Therefore, in this work, we proposed 3D image generation in foggy conditions using the single-pixel imaging (SPI) active illumination approach in combination with the Time-of-Flight technique (ToF) at 1550 nm wavelength. For the generation of 3D images, we make use of space-filling projection with compressed sensing (CS-SRCNN) and depth information based on ToF. To evaluate the performance, the vision system included a designed test chamber to simulate different fog and background illumination environments and calculate the parameters related to image quality.
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Su, Cheng, Yuan Biao Zhang, Wei Xia Luan, Zhi Xiong Wei, and Rui Ming Zeng. "Single Image Defogging Algorithm Based on Sparsity." Applied Mechanics and Materials 373-375 (August 2013): 558–63. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.558.
Повний текст джерелаАнотація:
In order to deal with the influence of adverse weather (such as dust fog) over vision system of the outdoor machine, this paper proposes a real-time image defogging method basing on sparsity. This method estimates the radiation intensity of airlight based on the dark channel prior statistical law and adopts sparse decomposition and reconstruction to figure out the atmospheric veil, taking advantage of the sparsity of this issue. By solving the equation for imaging based on the atmospheric scattering model, we can obtain the atmospheric radiation intensity under the ideal condition and succeed in image defogging. Experimental results show that this method can effectively improve the degradation of image in adverse weather (foggy) and raise its sharpness.
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Yang, Hee-Deok. "Restoring Raindrops Using Attentive Generative Adversarial Networks." Applied Sciences 11, no. 15 (July 30, 2021): 7034. http://dx.doi.org/10.3390/app11157034.
Повний текст джерелаАнотація:
Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.
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Kit Ng, Chin, Soon Nyean Cheong, Wen Wen-Jiun Yap, and Yee Loo Foo. "Outdoor Illegal Parking Detection System Using Convolutional Neural Network on Raspberry Pi." International Journal of Engineering & Technology 7, no. 3.7 (July 4, 2018): 17. http://dx.doi.org/10.14419/ijet.v7i3.7.16197.
Повний текст джерелаАнотація:
This paper proposes a cost-effective vision-based outdoor illegal parking detection system, iConvPark, to automatize the detection of illegally parked vehicles by providing real-time notification regarding the occurrences and locations of illegal parking cases, thereby improving effectiveness of parking rules and regulations enforcement. The iConvPark is implemented on a Raspberry Pi with the use of Convolutional Neural Network as the classifier to identify illegally parked vehicles based on live parking lot image retrieved via an IP camera. The system has been implemented at a university parking lot to detect illegal parking events. Evaluation results show that our proposed system is capable of detecting illegally parked vehicles with precision rate of 1.00 and recall rate of 0.94, implying that the detection is robust against changes in light intensity and the presence of shadow effects under different weather conditions, attributed to the superiority offered by CNN.
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Jung-San Lee, Jung-San Lee, Yun-Yi Fan Jung-San Lee, Hsin-Yu Lee Yun-Yi Fan, Gah Wee Yong Hsin-Yu Lee, and Ying-Chin Chen Gah Wee Yong. "Image Dehazing Technique Based on Sky Weight Detection and Fusion Transmission." 網際網路技術學刊 23, no. 5 (September 2022): 967–80. http://dx.doi.org/10.53106/160792642022092305005.
Повний текст джерелаАнотація:
<p>Computer vision techniques are widely applied to the object detection, license plate recognition, remote sensing, and outdoor monitoring system. The performance of these applications mainly relies on the high quality of outdoor image. However, an outdoor image can be led to contrast decrease, color distortion, and unclear structure by poor weather conditions and human factors such as haze, fog, and air pollution. These issues may lower down the sharpness of a photo. Despite of the single-image dehazing is used to solve these issues, it cannot achieve a satisfactory result when the method deals with the bright scene and sky area. In this article, we aim to design an adaptive dehazing technique based on fusion transmission and sky weight detection. The sky weight detection is employed to distinguish the foreground and background, while detected results are applied to the fusion strategy to calculate deep and shallow transmissions. Thus, this can get rids of the subject of over-adjustment. Experimental results have demonstrated that the new method can outperform the latest state-of-the-art methods in terms of subjective and the objective assessments.</p> <p> </p>
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Zhai, Y., and D. Ji. "SINGLE IMAGE DEHAZING FOR VISIBILITY IMPROVEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 27, 2015): 355–60. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-355-2015.
Повний текст джерелаАнотація:
Images captured in foggy weather conditions often suffer from poor visibility, which will create a lot of impacts on the outdoor computer vision systems, such as video surveillance, intelligent transportation assistance system, remote sensing space cameras and so on. In this paper, we propose a new transmission estimated method to improve the visibility of single input image (with fog or haze), as well as the image’s details. Our approach stems from two important statistical observations about haze-free images and the haze itself. First, the famous dark channel prior, a statistics of the haze-free outdoor images, can be used to estimate the thickness of the haze; and second, gradient prior law of transmission maps, which is based on dark channel prior. By integrating these two priors, to estimate the unknown scene transmission map is modeled into a TV-regularization optimization problem. The experimental results show that the proposed approach can effectively improve the visibility and keep the details of fog degraded images in the meanwhile.
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Shi, Zhenghao, Meimei Zhu, Zheng Xia, and Minghua Zhao. "Fast Single-Image Dehazing Method Based on Luminance Dark Prior." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 02 (January 12, 2017): 1754003. http://dx.doi.org/10.1142/s0218001417540039.
Повний текст джерелаАнотація:
Images captured in hazy weather are usually of poor quality, which has a negative effect on the performance of outdoor computer imaging systems. Therefore, haze removal is critical for outdoor imaging applications. In this paper, a quick single-image dehazing method based on a new effective image prior, luminance dark prior, was proposed. This new image prior arose from the observation that most local patches in the luminance image of a haze-free outdoor YUV color space image usually contain pixels of very low intensity, which is similar to the dark channel prior used with HE for RGB images. Using this new prior, a transmission map was used to estimate the thickness of the haze in an image directly from the luminance component of the YUV color image. To obtain a transmission map with a clear edge outline and depth layer of scene objects, a joint filter containing a bilateral filter and Laplacian operator was employed. Experimental results demonstrated that the proposed method unveiled details and recovered vivid colors even in heavily hazy regions, and provided superior visual effects to many other existing methods.
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Li, Chen, Yecai Guo, Qi Liu, and Xiaodong Liu. "DR-Net: A Novel Generative Adversarial Network for Single Image Deraining." Security and Communication Networks 2018 (December 3, 2018): 1–14. http://dx.doi.org/10.1155/2018/7350324.
Повний текст джерелаАнотація:
Blurred vision images caused by rainy weather can negatively influence the performance of outdoor vision systems. Therefore, it is necessary to remove rain streaks from single image. In this work, a multiscale generative adversarial network- (GAN-) based model is presented, called DR-Net, for single image deraining. The proposed architecture includes two subnetworks, i.e., generator subnetwork and discriminator subnetwork. We introduce a multiscale generator subnetwork which contains two convolution branches with different kernel sizes, where the smaller one captures the local rain drops information, and the larger one pays close attention to the spatial information. The discriminator subnetwork acts as a supervision signal to promote the generator subnetwork to generate more quality derained image. It is demonstrated that the proposed method yields in relatively higher performance in comparison to other state-of-the-art deraining models in terms of derained image quality and computing efficiency.
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Gu, Zhenfei, Mingye Ju, and Dengyin Zhang. "A Single Image Dehazing Method Using Average Saturation Prior." Mathematical Problems in Engineering 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/6851301.
Повний текст джерелаАнотація:
Outdoor images captured in bad weather are prone to yield poor visibility, which is a fatal problem for most computer vision applications. The majority of existing dehazing methods rely on an atmospheric scattering model and therefore share a common limitation; that is, the model is only valid when the atmosphere is homogeneous. In this paper, we propose an improved atmospheric scattering model to overcome this inherent limitation. By adopting the proposed model, a corresponding dehazing method is also presented. In this method, we first create a haze density distribution map of a hazy image, which enables us to segment the hazy image into scenes according to the haze density similarity. Then, in order to improve the atmospheric light estimation accuracy, we define an effective weight assignment function to locate a candidate scene based on the scene segmentation results and therefore avoid most potential errors. Next, we propose a simple but powerful prior named the average saturation prior (ASP), which is a statistic of extensive high-definition outdoor images. Using this prior combined with the improved atmospheric scattering model, we can directly estimate the scene atmospheric scattering coefficient and restore the scene albedo. The experimental results verify that our model is physically valid, and the proposed method outperforms several state-of-the-art single image dehazing methods in terms of both robustness and effectiveness.
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Mallesh, S. "Haze Removal Method for Efficient Visualization of Remotely Sensed Satellite Images." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1173–76. http://dx.doi.org/10.22214/ijraset.2022.47556.
Повний текст джерелаАнотація:
Abstract: Images captured in foggy weather conditions often suffer from poor visibility, which will create a lot of impacts on the outdoor computer vision systems, such as video surveillance, intelligent transportation assistance system, remote sensing space cameras and so on. In such situations, traditional visibility restoration approaches usually cannot adequately restore images due to poor estimation of haze thickness and the persistence of color cast problems. In our work, we propose a visibility restoration approach to effectively solve inadequate haze thickness estimation and alleviate color cast problems. By doing so, a high-quality image with clear visibility and vivid color can be generated to improve the visibility of single input image (with fog or haze), as well as the image’s details. Our approach stems from two important statistical observations about haze-free images and the haze itself. First, Wavelet decomposition is applied and LUM Filter is applied on the decomposed image. Finally, we can get the dehazed output.
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Aspragkathos, Sotirios N., George C. Karras, and Kostas J. Kyriakopoulos. "A Hybrid Model and Data-Driven Vision-Based Framework for the Detection, Tracking and Surveillance of Dynamic Coastlines Using a Multirotor UAV." Drones 6, no. 6 (June 15, 2022): 146. http://dx.doi.org/10.3390/drones6060146.
Повний текст джерелаАнотація:
A hybrid model-based and data-driven framework is proposed in this paper for autonomous coastline surveillance using an unmanned aerial vehicle. The proposed approach comprises three individual neural network-assisted modules that work together to estimate the state of the target (i.e., shoreline) to contribute to its identification and tracking. The shoreline is first detected through image segmentation using a Convolutional Neural Network. The part of the segmented image that includes the detected shoreline is then fed into a CNN real-time optical flow estimator. The position of pixels belonging to the detected shoreline, as well as the initial approximation of the shoreline motion, are incorporated into a neural network-aided Extended Kalman Filter that learns from data and can provide on-line motion estimation of the shoreline (i.e., position and velocity in the presence of waves) using the system and measurement models with partial knowledge. Finally, the estimated feedback is provided to a Partitioned Visual Servo tracking controller for autonomous multirotor navigation along the coast, ensuring that the latter will always remain inside the onboard camera field of view. A series of outdoor comparative studies using an octocopter flying along the shoreline in various weather and beach settings demonstrate the effectiveness of the suggested architecture.
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Li, Yongji, Rui Wu, Zhenhong Jia, Jie Yang, and Nikola Kasabov. "Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering." Sensors 21, no. 22 (November 16, 2021): 7610. http://dx.doi.org/10.3390/s21227610.
Повний текст джерелаАнотація:
Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods.
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Kim, Changwon. "Region Adaptive Single Image Dehazing." Entropy 23, no. 11 (October 30, 2021): 1438. http://dx.doi.org/10.3390/e23111438.
Повний текст джерелаАнотація:
Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (DCP). However, dark channel prior tends to underestimate transmissions of bright areas or objects, which may cause color distortions during dehazing. This paper proposes a new single-image dehazing method that combines dark channel prior with bright channel prior in order to overcome the limitations of dark channel prior. A patch-based robust atmospheric light estimation was introduced in order to divide image into regions to which the DCP assumption and the BCP assumption are applied. Moreover, region adaptive haze control parameters are introduced in order to suppress the distortions in a flat and bright region and to increase the visibilities in a texture region. The flat and texture regions are expressed as probabilities by using local image entropy. The performance of the proposed method is evaluated by using synthetic and real data sets. Experimental results show that the proposed method outperforms the state-of-the-art image dehazing method both visually and numerically.
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Ren, Yu Tao, Yi Chao Zhang, and Ke Zhu. "Background Extraction and Snow Remove Form Video." Advanced Materials Research 756-759 (September 2013): 1382–86. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1382.
Повний текст джерелаАнотація:
Capturing good videos outdoors can be challenging due to harsh lighting, unpredictable scene changes, and most relevant to this work, dynamic weather. Particulate weather, if there were a heavy snow in monitor scene, it would create complex flickering effects that are irritating to people and confusing to computer vision algorithms. The research direction of video image background estimation and background estimation in heavy snow is to improve the visualization of surveillance video, especially to reduce the affection of snowflake in heavy snow. This algorithm can remove snowflakes and retain non-snowflakes moving targets we interested in. We propose a snow removal method of the pixels affected by snowflakes based on probability estimation of snowflakes detected. Firstly, we detect the snowflakes based on the optical of snowflake and time difference method. Then we estimated the coverage rate of snowflakes based on the detecting result. Finally, we build an adaptive estimation method of background pixels value based on the information feedback of snow detection and the model of pixels value. The calculation of this method is simple and the method has good processing results.
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Ngo, Dat, Seungmin Lee, Quoc-Hieu Nguyen, Tri Minh Ngo, Gi-Dong Lee, and Bongsoon Kang. "Single Image Haze Removal from Image Enhancement Perspective for Real-Time Vision-Based Systems." Sensors 20, no. 18 (September 10, 2020): 5170. http://dx.doi.org/10.3390/s20185170.
Повний текст джерелаАнотація:
Vision-based systems operating outdoors are significantly affected by weather conditions, notably those related to atmospheric turbidity. Accordingly, haze removal algorithms, actively being researched over the last decade, have come into use as a pre-processing step. Although numerous approaches have existed previously, an efficient method coupled with fast implementation is still in great demand. This paper proposes a single image haze removal algorithm with a corresponding hardware implementation for facilitating real-time processing. Contrary to methods that invert the physical model describing the formation of hazy images, the proposed approach mainly exploits computationally efficient image processing techniques such as detail enhancement, multiple-exposure image fusion, and adaptive tone remapping. Therefore, it possesses low computational complexity while achieving good performance compared to other state-of-the-art methods. Moreover, the low computational cost also brings about a compact hardware implementation capable of handling high-quality videos at an acceptable rate, that is, greater than 25 frames per second, as verified with a Field Programmable Gate Array chip. The software source code and datasets are available online for public use.
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Vega-Perona, Herminia, María del Mar Bernabé-Villodre, Yolanda Cabrera García-Ochoa, and Vladimir E. Martínez-Bello. "Barriers and Facilitators to Toddlers’ Physical Activity during the COVID-19 Pandemic, as Perceived by Teachers, Principals and Parents: A Challenge for the Early Childhood Educational Environments." Education Sciences 12, no. 5 (May 17, 2022): 349. http://dx.doi.org/10.3390/educsci12050349.
Повний текст джерелаАнотація:
The aim of our study was to explore the barriers and facilitators that teachers, principals, and parents face when adapting to COVID-19 pandemic scenario in terms of promoting toddlers’ physical activity (PA). Thirty-four (20 teachers and principals, and 14 parents) semi-structured qualitative interviews were conducted from October 2020 to March 2021. The socioecological model has enabled the identification of barriers and facilitators, some of which are related to the pandemic and others which are not. The main results suggest that upon reopening the ECEC institutions, regarding environmental barriers, educators mentioned the impact on the use of space, and parents, the modification of daily activities generated by COVID-19. However, educators also considered that the presence of suitable spaces in the school for practicing PA was a facilitator. At the intra- and interpersonal level, facilitators of PA that were unrelated to the pandemic included, for parents, the predisposition of children to be physically active and their own function as role models, and for educators, the curricular practices themselves. At an environmental level, the risk of danger in the traditional classroom plus bad weather were considered barriers by educators, while parents mentioned difficulties accessing outdoor space and the poor suitability of indoor spaces. Our results suggest the simultaneous analysis of the perceptions of different actors in the educational environments offers a broad vision of the ecological alternatives for offering children opportunities for PA in these difficult times.
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Zhang, Zhichao, Hui Chen, Xiaoqing Yin, and Jinsheng Deng. "EAWNet: An Edge Attention-Wise Objector for Real-Time Visual Internet of Things." Wireless Communications and Mobile Computing 2021 (July 10, 2021): 1–15. http://dx.doi.org/10.1155/2021/7258649.
Повний текст джерелаАнотація:
With the upgrading of the high-performance image processing platform and visual internet of things sensors, VIOT is widely used in intelligent transportation, autopilot, military reconnaissance, public safety, and other fields. However, the outdoor visual internet of things system is very sensitive to the weather and unbalanced scale of latent object. The performance of supervised learning is often limited by the disturbance of abnormal data. It is difficult to collect all classes from limited historical instances. Therefore, in terms of the anomaly detection images, fast and accurate artificial intelligence-based object detection technology has become a research hot spot in the field of intelligent vision internet of things. To this end, we propose an efficient and accurate deep learning framework for real-time and dense object detection in VIOT named the Edge Attention-wise Convolutional Neural Network (EAWNet) with three main features. First, it can identify remote aerial and daily scenery objects fast and accurately in terms of an unbalanced category. Second, edge prior and rotated anchor are adopted to enhance the efficiency of detection in edge computing internet. Third, our EAWNet network uses an edge sensing object structure, makes full use of an attention mechanism to dynamically screen different kinds of objects, and performs target recognition on multiple scales. The edge recovery effect and target detection performance for long-distance aerial objects were significantly improved. We explore the efficiency of various architectures and fine tune the training process using various backbone and data enhancement strategies to increase the variety of the training data and overcome the size limitation of input images. Extensive experiments and comprehensive evaluation on COCO and large-scale DOTA datasets proved the effectiveness of this framework that achieved the most advanced performance in real-time VIOT object detection.
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Park, Cheonyu, Baekseok Kim, Yitaek Kim, Younseal Eum, Hyunjong Song, Dongkuk Yoon, Jeongin Moon, and Jeakweon Han. "Carved Turn Control with Gate Vision Recognition of a Humanoid Robot for Giant Slalom Skiing on Ski Slopes." Sensors 22, no. 3 (January 21, 2022): 816. http://dx.doi.org/10.3390/s22030816.
Повний текст джерелаАнотація:
The performance of humanoid robots is improving, owing in part to their participation in robot games such as the DARPA Robotics Challenge. Along with the 2018 Winter Olympics in Pyeongchang, a Skiing Robot Competition was held in which humanoid robots participated autonomously in a giant slalom alpine skiing competition. The robots were required to transit through many red or blue gates on the ski slope to reach the finish line. The course was relatively short at 100 m long and had an intermediate-level rating. A 1.23 m tall humanoid ski robot, ‘DIANA’, was developed for this skiing competition. As a humanoid robot that mimics humans, the goal was to descend the slope as fast as possible, so the robot was developed to perform a carved turn motion. The carved turn was difficult to balance compared to other turn methods. Therefore, ZMP control, which could secure the posture stability of the biped robot, was applied. Since skiing takes place outdoors, it was necessary to ensure recognition of the flags in various weather conditions. This was ensured using deep learning-based vision recognition. Thus, the performance of the humanoid robot DIANA was established using the carved turn in an experiment on an actual ski slope. The ultimate vision for humanoid robots is for them to naturally blend into human society and provide necessary services to people. Previously, there was no way for a full-sized humanoid robot to move on a snowy mountain. In this study, a humanoid robot that transcends this limitation was realized.
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Eslamirad, Nasim, Soheil Malekpour Kolbadinejad, Mohammadjavad Mahdavinejad, and Mohammad Mehranrad. "Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran." Smart and Sustainable Built Environment 9, no. 4 (April 28, 2020): 361–74. http://dx.doi.org/10.1108/sasbe-03-2019-0028.
Повний текст джерелаАнотація:
PurposeThis research aims to introduce a new methodology for integration between urban design strategies and supervised machine learning (SML) method – by applying both energy engineering modeling (evaluating phase) for the existing green sidewalks and statistical energy modeling (predicting phase) for the new ones – to offer algorithms that help to catch the optimum morphology of green sidewalks, in case of high quality of the outdoor thermal comfort and less errors in results.Design/methodology/approachThe tools of the study are the way of processing by SML, predicting the future based on the past. Machine learning is benefited from Python advantages. The structure of the study consisted of two main parts, as the majority of the similar studies follow: engineering energy modeling and statistical energy modeling. According to the concept of the study, at first, from 2268 models, some are randomly selected, simulated and sensitively analyzed by ENVI-met. Furthermore, the Envi-met output as the quantity of thermal comfort – predicted mean vote (PMV) and weather items are inputs of Python. Then, the formed data set is processed by SML, to reach the final reliable predicted output.FindingsThe process of SML leads the study to find thermal comfort of current models and other similar sidewalks. The results are evaluated by both PMV mathematical model and SML error evaluation functions. The results confirm that the average of the occurred error is about 1%. Then the method of study is reliable to apply in the variety of similar fields. Finding of this study can be helpful in perspective of the sustainable architecture strategies in the buildings and urban scales, to determine, monitor and control energy-based behaviors (thermal comfort, heating, cooling, lighting and ventilation) in operational phase of the systems (existed elements in buildings, and constructions) and the planning and designing phase of the future built cases – all over their life spans.Research limitations/implicationsLimitations of the study are related to the study variables and alternatives that are notable impact on the findings. Furthermore, the most trustable input data will result in the more accuracy in output. Then modeling and simulation processes are most significant part of the research to reach the exact results in the final step.Practical implicationsFinding of the study can be helpful in urban design strategies. By finding outdoor thermal comfort that resulted from machine learning method, urban and landscape designers, policymakers and architects are able to estimate the features of their designs in air quality and urban health and can be sure in catching design goals in case of thermal comfort in urban atmosphere.Social implicationsBy 2030, cities are delved as living spaces for about three out of five people. As green infrastructures influence in moderating the cities’ climate, the relationship between green spaces and habitants’ thermal comfort is deduced. Although the strategies to outside thermal comfort improvement, by design methods and applicants, are not new subject to discuss, applying machines that may be common in predicting results can be called as a new insight in applying more effective design strategies and in urban environment’s comfort preparation. Then study’s footprint in social implications stems in learning from the previous projects and developing more efficient strategies to prepare cities as the more comfortable and healthy places to live, with the more efficient models and consuming money and time.Originality/valueThe study achievements are expected to be applied not only in Tehran but also in other climate zones as the pattern in more eco-city design strategies. Although some similar studies are done in different majors, the concept of study is new vision in urban studies.
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Clere, Lynn. "Outdoor play, whatever the weather." Early Years Educator 14, no. 9 (January 2013): 38–44. http://dx.doi.org/10.12968/eyed.2013.14.9.38.
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Próchniak, Piotr, and Agnieszka Próchniak. "Preventive and Proactive Coping with Bad Weather in Outdoor Sports: A Measurement Proposal." Behavioral Sciences 10, no. 4 (April 24, 2020): 80. http://dx.doi.org/10.3390/bs10040080.
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This article presents the Proactive and Preventive Coping with Bad Weather in Outdoor Sports Scale, a tool for diagnosing future oriented coping with bad weather in outdoor sports. A study of the psychometric properties of the Proactive and Preventive Coping with Bad Weather in Outdoor Sports Scale was conducted, with an exploratory and a confirmatory factor analysis being carried out. The first set of data (N = 326) was analysed by exploratory factor analysis, and the second set of data (N = 183) was analysed by confirmatory factor analysis. The results of factor analyses verified the two-factor structure. The Proactive and Preventive Coping with Bad Weather in Outdoor Sports Scale showed satisfactory internal consistency. The coefficient alpha reliabilities were 0.81 for the Preventive scale, and 0.80 for the Proactive scale. The divergent and convergent validity of the Preventive and Proactive Coping in Outdoor Sports Scale was indicated by correlations with scales of coping, general self-efficacy, sensation seeking and the personality NEO-FFI. The results indicate that the Proactive and Preventive Coping with Bad Weather in Outdoor Sports Scale is a valid and reliable instrument.
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Timmermans, Erik J., Suzan van der Pas, Elaine M. Dennison, Stefania Maggi, Richard Peter, Maria Victoria Castell, Nancy L. Pedersen, et al. "The Influence of Weather Conditions on Outdoor Physical Activity Among Older People With and Without Osteoarthritis in 6 European Countries." Journal of Physical Activity and Health 13, no. 12 (December 2016): 1385–95. http://dx.doi.org/10.1123/jpah.2016-0040.
Повний текст джерелаАнотація:
Background:Older adults with osteoarthritis (OA) often report that their disease symptoms are exacerbated by weather conditions. This study examines the association between outdoor physical activity (PA) and weather conditions in older adults from 6 European countries and assesses whether outdoor PA and weather conditions are more strongly associated in older persons with OA than in those without the condition.Methods:The American College of Rheumatology classification criteria were used to diagnose OA. Outdoor PA was assessed using the LASA Physical Activity Questionnaire. Data on weather parameters were obtained from weather stations.Results:Of the 2439 participants (65–85 years), 29.6% had OA in knee, hand and/or hip. Participants with OA spent fewer minutes in PA than participants without OA (Median = 42.9, IQR = 20.0 to 83.1 versus Median = 51.4, IQR = 23.6 to 98.6; P < .01). In the full sample, temperature (B = 1.52; P < .001) and relative humidity (B = –0.77; P < .001) were associated with PA. Temperature was more strongly associated with PA in participants without OA (B = 1.98; P < .001) than in those with the condition (B = 0.48; P = .47).Conclusions:Weather conditions are associated with outdoor PA in older adults in the general population. Outdoor PA and weather conditions were more strongly associated in older adults without OA than in their counterparts with OA.
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Mountain, Julie. "Your outdoor calendar: November." Nursery World 2021, no. 11 (November 2, 2021): 26–43. http://dx.doi.org/10.12968/nuwa.2021.11.26.
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Abdul-Niby, M., M. Farhat, M. Abdullah, and A. Nazzal. "A Low Cost Automated Weather Station for Real Time Local Measurements." Engineering, Technology & Applied Science Research 7, no. 3 (June 12, 2017): 1615–18. http://dx.doi.org/10.48084/etasr.1187.
Повний текст джерелаАнотація:
In this paper, we present an automated weather station for real time and local measurements, based on an embedded system that continuously measures several weather factors such as temperature, humidity, barometric pressure, wind speed, wind direction, and rainfall. This weather station consists of two parts which are located indoor and outdoor and connected together wirelessly. The outdoor weather station measures the current temperature, humidity, barometric pressure, wind speed, wind direction and recent rain amount. The indoor station displays the outdoor reading as well as the temperature and humidity for the room it is located in, on a graphic liquid crystal display. In addition, this weather information can be accessed from any place through an iOS and Android application called Blynk.
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Mountain, Julie. "A shared vision." Nursery World 2020, no. 8 (May 2, 2020): 24–26. http://dx.doi.org/10.12968/nuwa.2020.8.24.
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Buluswar, Shashi D., and Bruce A. Draper. "Color Models for Outdoor Machine Vision." Computer Vision and Image Understanding 85, no. 2 (February 2002): 71–99. http://dx.doi.org/10.1006/cviu.2001.0950.
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Tang, Ji-Yu, and Zi-Qin Sang. "Image synthesizing for all-weather outdoor scenes." Applied Optics 40, no. 29 (October 10, 2001): 5183. http://dx.doi.org/10.1364/ao.40.005183.
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Mountain, Julie. "Your Outdoor Calendar: April." Nursery World 2022, no. 4 (April 2, 2022): 26–27. http://dx.doi.org/10.12968/nuwa.2022.4.26.
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Wet, wild and windy April is a brilliant month to spend quality time outdoors, experiencing everything the weather can throw at us and using nature's springtime bounties for inspirational natural play, says Julie Mountain
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Araźny, Andrzej. "Weather Types in the Norwegian Arctic." Bulletin of Geography. Physical Geography Series 2, no. 1 (December 1, 2009): 107–20. http://dx.doi.org/10.2478/bgeo-2009-0015.
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Abstract In order to assess the usefulness of Norwegian Arctic bioclimatic conditions for outdoor activities in the years 1971-2000, the author applied his own weather typology developed on the basis of the classification proposed by Błażejczyk (1979). The typology classified four weather groups for the study area. Atmospheric circulation types proposed by Niedźwiedź (2002) were used to determine the synoptic situations and types of favourable weather conditions for outdoor recreation, tourism and work, as well as those that may pose a threat to human life.
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Allen-Collinson, Jacquelyn. "‘Weather work’: embodiment and weather learning in a national outdoor exercise programme." Qualitative Research in Sport, Exercise and Health 10, no. 1 (July 31, 2017): 63–74. http://dx.doi.org/10.1080/2159676x.2017.1360382.
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Kunimitsu, Satoshi, Hajime Asama, Kuniaki Kawabata, and Taketoshi Mishima. "Development of Crane Vision for Positioning Container." Journal of Robotics and Mechatronics 16, no. 2 (April 20, 2004): 186–93. http://dx.doi.org/10.20965/jrm.2004.p0186.
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Pattern recognition under the outdoor environment is very difficult in general because of the change in the brightness distribution of the object based on the change in the lighting condition. Combining distributed sensing with expanded template matching, the technique for achieving effective pattern recognition under the outdoor environment was developed. We applied it to the detection of a relative position of a spreader of a quayside gantry crane and the target container, made a prototype machine, and named Crane Vision. This paper details Crane Vision’s measurement principle, system configuration, experimental results, and demonstrates the validity of pattern recognition under the outdoor environment.
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Ekta, Ekta, and Saharsh Gera. "Deep WeatherNet: A Scalable Deep Learning Approach for Weather Image Classification on Large Datasets." International Journal of Innovative Research in Science,Engineering and Technology 12, no. 02 (November 7, 2022): 1191–201. http://dx.doi.org/10.15680/ijirset.2023.1202066.
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Classifying weather conditions from outdoor images can have various applications such as improving road safety, scheduling outdoor activities, and enhancing the reliability of vehicle assistant driving and outdoor video surveillance systems. However, traditional methods of weather classification involved the use of expensive sensors and extensive manpower, which were both time-consuming and tedious. Automating the task of weather classification from images can save time and resources. In this paper, we propose a framework for classifying weather images using transfer learning techniques. Our approach involves using pre-trained deep CNN models to learn features, thereby reducing the time required for classification. We also recognize that the size and quality of the training dataset are critical factors in the efficiency of the model. Therefore, we implemented our framework using the Spark platform, making it scalable for big datasets. We conducted extensive experiments on a weather image dataset, and our results demonstrate the reliability of our proposed framework. We concluded that weather classification using the InceptionV3 model and Logistic Regression classifier yields the best results, with a maximum accuracy of 97.77%. Our framework has potential applications in various fields such as agriculture, aquaculture, transportation, tourism, etc.
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Giddy, Julia K., Jennifer M. Fitchett, and Gijsbert Hoogendoorn. "Insight into American tourists’ experiences with weather in South Africa." Bulletin of Geography. Socio-economic Series 38, no. 38 (December 20, 2017): 57–72. http://dx.doi.org/10.1515/bog-2017-0034.
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Abstract Weather and climate are often important factors determining the success of a tourism destination and resultant satisfaction among tourists. This is particularly true for South Africa due the predominance of outdoor tourist attractions. Increasing numbers of international tourists have visited South Africa since the fall of apartheid, particularly those from the United States (U.S.), which is an important market for South African tourism. Therefore, this paper seeks to examine a sample of American tourists’ experience with day-to-day weather and climatic conditions in South Africa. The results show that although respondents did not feel that climatic conditions were an important factor in motivations to visit the country, the day-to-day weather did often impact the enjoyment of their visit. Most notably, weather controlled their ability to participate in outdoor activities. In correlating accounts of unpleasant weather conditions with the meteorological records, a close association emerged, particularly for excessively high temperatures. This indicates that the experiences of American tourists are an accurate indication of climatic unsuitability for tourism, which poses threats to the South African outdoor tourism sector.
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Helen Meisfjord Jørgensen, Grete, and Knut Egil Bøe. "Outdoor yards for sheep during winter – Effects of feed location, roof and weather factors on resting and activity." Canadian Journal of Animal Science 91, no. 2 (June 2011): 213–20. http://dx.doi.org/10.4141/cjas10062.
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Jørgensen, G. H. M. and Bøe, K. E. 2011. Outdoor yards for sheep during winter – Effects of feed location, roof and weather factors on resting and activity. Can. J. Anim. Sci. 91: 213–220. The aim of this experiment was to investigate the effect of roof cover and location of feed on sheep's use of an outdoor yard under different weather conditions. A 2×2 factorial experiment was conducted with roof covering of outdoor yard (yes or no) and location of feed (indoors or outdoors) in four different pens, each with one of four possible combinations of these factors. Twenty adult ewes of the Norwegian White breed were randomly allotted to four groups with five animals. Weather parameters were automatically recorded. The following behavioural parameters were scored using instantaneous sampling every 15 min throughout 24-h video recordings: location (indoors or outdoors), general behaviour (stand/walk, resting, feeding). Weather factors did not seem to have any large influence on sheep behaviour. A roof covering the outdoor yard increased time spent in the yard, had no effect on feeding time, a limited effect on resting time, but increased the time spent resting outdoors. Locating the feed outdoors increased time spent in the yard, but also increased the time spent resting indoors, indicating that if a dry and comfortable resting area is offered indoors, the feed should be located in the outdoor yard.
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Brocherie, Franck, Olivier Girard, and Grégoire Millet. "Emerging Environmental and Weather Challenges in Outdoor Sports." Climate 3, no. 3 (July 14, 2015): 492–521. http://dx.doi.org/10.3390/cli3030492.
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Pyataeva, Anna. "Dynamic texture recognition under adverse lighting and weather conditions for outdoor environments." E3S Web of Conferences 75 (2019): 01008. http://dx.doi.org/10.1051/e3sconf/20197501008.
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Recognizing dynamic patterns based on visual processing is significant for many applications. In this paper dynamic texture recognition focuses on outdoor scenarios where a crisis event might occur (i.e. fire in a forest, floods/flooding etc.) Real outdoor scenes may include the objects with dynamic behaviour due to illumination, blurring, or weather conditions effects. Under bad weather conditions the imaging systems is degraded and produce low visibility images. In this work precipitation artefacts and lightning effects for dynamic texture analysis were studied. Experimental results show that the proposed method of weather and adverse lighting effects compensation is feasible and effective for videobased dynamic texture analysis under bad weather conditions.
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Schilling, Fabian, Fabrizio Schiano, and Dario Floreano. "Vision-Based Drone Flocking in Outdoor Environments." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 2954–61. http://dx.doi.org/10.1109/lra.2021.3062298.
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Jordan, Deb. "A new vision for outdoor leadership theory." Leisure Studies 8, no. 1 (January 1989): 35–47. http://dx.doi.org/10.1080/02614368900390041.
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TSUDA, T. "Visualization Methods for Outdoor See-Through Vision." IEICE Transactions on Information and Systems E89-D, no. 6 (June 1, 2006): 1781–89. http://dx.doi.org/10.1093/ietisy/e89-d.6.1781.
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Abdallah, Samer M., Daniel C. Asmar, and John S. Zelek. "A Benchmark for Outdoor Vision SLAM Systems." Journal of Field Robotics 24, no. 1-2 (January 2007): 145–65. http://dx.doi.org/10.1002/rob.20180.
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Talebi, Mehdi, Abbas Vafaei, and Amirhassan Monadjemi. "Vision-based entrance detection in outdoor scenes." Multimedia Tools and Applications 77, no. 20 (March 5, 2018): 26219–38. http://dx.doi.org/10.1007/s11042-018-5846-3.
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Ryti, Niilo R. I., Anton Korpelainen, Olli Seppänen, and Jouni J. K. Jaakkola. "Paradoxical home temperatures during cold weather: a proof-of-concept study." International Journal of Biometeorology 64, no. 12 (August 27, 2020): 2065–76. http://dx.doi.org/10.1007/s00484-020-01998-7.
Повний текст джерелаАнотація:
Abstract There is substantial epidemiological evidence on the associations between cold weather and adverse health effects. Meteorological alarm systems are being developed globally, and generalized protective advice is given to the public based on outdoor exposure parameters. It is not clear how these shared outdoor exposure parameters relate to the individual-level thermal exposure indoors, where the majority of time is spent. We hypothesized a priori that there are opposite correlations between indoor and outdoor temperatures in residential apartments. Apartments were classified into 3 categories according to their response to declining outdoor temperature: under-controlled apartments cool down, controlled apartments maintain constant indoor temperature level, and over-controlled apartments warm up. Outdoor and indoor temperatures were measured in 30-min intervals in 417 residential apartments in 14 buildings in Kotka, Finland, between February and April 2018 with outdoor temperatures ranging from − 20.4 °C to + 14.0 °C. Different apartment types were present in all buildings. Floor and orientation did not explain the divergence. Indoor temperatures below the limit value + 20 °C by building code occurred in 26.2%, 7.9%, and 23.6% of the under-controlled, controlled, and over-controlled apartments, some in conjunction with increasing outdoor temperatures. Indoor temperatures above the limit + 25 °C occurred but were more rare. This proof-of-concept study demonstrates that while the home environment may be a source of thermal stress during cold weather, generalized advice for adjusting the heating may lead to paradoxical exposures in some cases. More elaborate conceptualizations of everyday thermal exposures are needed to safely reduce weather-related health risks using shared meteorological alarm systems.
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Bøe, Knut Egil, and Rebecca Ehrlenbruch. "Thermoregulatory behavior of dairy goats at low temperatures and the use of outdoor yards." Canadian Journal of Animal Science 93, no. 1 (March 2013): 35–41. http://dx.doi.org/10.4141/cjas2012-028.
Повний текст джерелаАнотація:
Bøe, K. E. and Ehrlenbruch, R. 2013. Thermoregulatory behavior of dairy goats at low temperatures and the use of outdoor yards. Can. J. Anim. Sci. 93: 35–41. The aim of this experiment was to investigate the effect of roof cover and location of feed on thermoregulatory behavior and the goat's use of an outdoor yard under different weather conditions. A total of four groups of five pregnant dairy goats (20 goats in total) were allocated to a 4×4 Latin square experiment with four groups, four treatments and four experimental periods. Each treatment period lasted for 7 d. The goats were exposed to the following treatments: roof covering of outdoor yard (yes or no) and location of feed (indoors or in yard). The goats were video recorded for two 24-h periods at the end of each experimental period (totally 32, 24-h periods). When the air temperature dropped and when there was rain or snow, the goats spent less time in the outdoor yard (P<0.0001), but total lying time (P=0.47) and time spent feeding (P=0.77) were not affected by weather conditions. Lying in the outdoor yard was reduced as the air temperature decreased (P<0.001) and time spent standing/walking inside increased (P<0.001). Irrespective of weather conditions, the goats spent significantly more time in the outdoor yard in pens when the outdoor yards were covered with a roof (P <0.01), but time spent lying was not affected by roof cover (P=0.12) or feed location (P=0.40). We conclude that even if the outdoor yard was less used at decreasing temperatures, the time spent lying and feeding was not affected by inclement weather. Hence, a housing system with an inside resting area and an outside activity area provides adequate environmental protection for the goats even at low temperatures. A roof covering the outdoor yard had only a limited positive effect.
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Bahler, Lonneke, Jan W. Deelen, Joost B. Hoekstra, Frits Holleman, and Hein J. Verberne. "Seasonal influence on stimulated BAT activity in prospective trials: a retrospective analysis of BAT visualized on 18F-FDG PET-CTs and 123I-mIBG SPECT-CTs." Journal of Applied Physiology 120, no. 12 (June 15, 2016): 1418–23. http://dx.doi.org/10.1152/japplphysiol.00008.2016.
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Retrospective studies have shown that outdoor temperature influences the prevalence of detectable brown adipose tissue (BAT). Prospective studies use acute cold exposure to activate BAT. In prospective studies, BAT might be preconditioned in winter months leading to an increased BAT response to various stimuli. Therefore the aim of this study was to assess whether outdoor temperatures and other weather characteristics modulate the response of BAT to acute cold. To assess metabolic BAT activity and sympathetic outflow to BAT, 64 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography-computed tomography (PET-CT) and 56 additional 123I- meta-iodobenzylguanidine (123I- mIBG) single-photon emission computed tomography-CT (SPECT-CT) scans, respectively, of subjects participating in previously executed trials were retrospectively included. BAT activity was measured in subjects after an overnight fast, following 2 h of cold exposure (∼17°C). The average daytime outdoor temperatures and other weather characteristics were obtained from the Dutch Royal Weather Institute. Forty-nine subjects were BAT positive. One week prior to the scan, outdoor temperature was significantly lower in the BAT-positive group compared with the BAT-negative group. Higher outdoor temperatures on preceding days resulted in lower stimulated metabolic BAT activity and volume (all P < 0.01). Outdoor temperatures did not correlate with sympathetic outflow to BAT. In conclusion, outdoor temperatures influence metabolic BAT activity and volume, but not sympathetic outflow to BAT, in subjects exposed to acute cold. To improve the consistency of the findings of future BAT studies in humans and to exclude bias introduced by outdoor temperatures, these studies should be planned in periods of similar outdoor temperatures.
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Zhang, Jifeng, Wenjun Jiang, Jinrui Zhang, Jie Wu, and Guojun Wang. "Exploring Weather Data to Predict Activity Attendance in Event-based Social Network." ACM Transactions on the Web 15, no. 2 (May 2021): 1–25. http://dx.doi.org/10.1145/3440134.
Повний текст джерелаАнотація:
Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.