Even though image-based prediction of fire events is widely used, the current predictive methods are difficult to implement due to low performance and high specifications. In this work designed to ...overcome such problems, we propose binary semantic segmentation for fire images by employing deep learning that can be applied to embedded devices such as Jetson TX2. To reduce the parameters and consequently the model size while maintaining the performance, we replaced regular convolution with depthwise separable convolution and
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×
1
convolution. Moreover, the addition operation in the long skip connection was replaced with the concatenation operation to properly convey the information in the encoding phase. Besides, we propose the confusion block that can execute the model to proceed training more actively. From these approaches, we achieved a significantly small-sized network for fire segmentation with the highest performance. We compared the performance of the proposed method with various deep learning-based binary segmentation networks and image processing algorithm. Extensive experimental results on the FiSmo Dataset and Corsican Fire Database demonstrated that the proposed network outperforms other models with fewer parameters and is suitable for application in embedded devices.
In this paper, we proposed a semantic fire image segmentation method using a convolutional neural network. The simple but powerful method proposed is middle skip connection achieved through the ...residual network, which is widely used in image-based deep learning. To enhance the middle skip connection, we constructed a pair of convolution layers, hereafter referred to as input convolution and output convolution, to be inserted in front and behind of the entire architecture. Consequently, the middle skip connection yields a stronger feedback effect compared to when only the short skip connection of the residual block and the long skip connection are used. The validity of the proposed method has been confirmed by using the FiSmo dataset and the Corsican Fire Database based on various evaluation metrics. Comparative analysis shows that the proposed model outperforms previous fire segmentation deep learning models and image processing algorithms.
High concentrations of particulate matter (PM) are frequently associated with serious health problems, underlining the importance of accurate PM prediction. This study aimed to predict PM
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...concentrations by analyzing air pollutant data in Korea (Seoul, Incheon, Daejeon, and Busan) using convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods. Real-time data from January 2014 to December 2020 were organized as hourly averages. The SO
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, NO
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, CO, O
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, and PM
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data from 2014 to 2018 were used for training, and data from 2019 to 2020 were used as test data. The highest prediction accuracy was accomplished using all observations. The contribution ratio of each model component to the predictions was verified using SHapley Additive exPlanations (SHAP), and PM
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showed the greatest contribution. The other components, as secondary aerosol precursors, were divided by area. CO and O
3
were found to be high in Seoul (Gwanak), which has been highly urbanized. On the other hand, CO and NO
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were found to be high in Incheon (Namdong), Daejeon (Yuseong), and Busan (Sasang), which are relatively suburban areas. The deep learning results indicated that the predicted PM
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concentration was most affected by past and present concentrations of PM
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. It is considered that the atmospheric PM
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at the study sites mainly originated from direct emissions. We compared the proposed method with recent prediction methods using algorithms, machine learning, and deep learning. The R
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, root mean square error, and mean absolute error evaluation indices supported the suitability of the proposed method for analyses at the study site.
Smart textiles consist of discrete devices fabricated from-or incorporated onto-fibres. Despite the tremendous progress in smart textiles for lighting/display applications, a large scale approach for ...a smart display system with integrated multifunctional devices in traditional textile platforms has yet to be demonstrated. Here we report the realisation of a fully operational 46-inch smart textile lighting/display system consisting of RGB fibrous LEDs coupled with multifunctional fibre devices that are capable of wireless power transmission, touch sensing, photodetection, environmental/biosignal monitoring, and energy storage. The smart textile display system exhibits full freedom of form factors, including flexibility, bendability, and rollability as a vivid RGB lighting/grey-level-controlled full colour display apparatus with embedded fibre devices that are configured to provide external stimuli detection. Our systematic design and integration strategies are transformational and provide the foundation for realising highly functional smart lighting/display textiles over large area for revolutionary applications on smart homes and internet of things (IoT).
Successfully automated sigmoidal curve fitting is highly challenging when applied to large data sets. In this paper, we describe a robust algorithm for fitting sigmoid dose-response curves by ...estimating four parameters (floor, window, shift, and slope), together with the detection of outliers. We propose two improvements over current methods for curve fitting. The first one is the detection of outliers which is performed during the initialization step with correspondent adjustments of the derivative and error estimation functions. The second aspect is the enhancement of the weighting quality of data points using mean calculation in Tukey's biweight function.
Automatic curve fitting of 19,236 dose-response experiments shows that our proposed method outperforms the current fitting methods provided by MATLAB®;'s nlinfit function and GraphPad's Prism software.
•Organic-inorganic hybrid films are effective for managing surface cracks.•A thick inorganic zinc oxide layer resulted in improved mechanical properties.•A thin inorganic zinc oxide layer resulted in ...improved crack resistance.
The integration of molecular/atomic layer deposition (MLD/ALD) techniques offers a versatile platform for the fabrication of functional organic-inorganic hybrid thin-film structures. In this study, we utilized a hybrid thin-film approach comprising of a zinc oxide (ZnO) ALD layer and a diethylzinc (DEZ) + hydroquinone (HQ) MLD layer to enhance the surface quality and mechanical properties. The objective of this research was to investigate the changes in surface crack pattern and mechanical properties of the hybrid thin films by varying the composition and number of MLD/ALD layers while maintaining a fixed MLD/ALD ratio of 1:5. The hybrid films were synthesized using the (DEZ + HQ) m + (DEZ + H2O) 5 mn structure, where the value of mxn was fixed at 200, and the value of m was varied between 5, 10, 20, 100, and 200. The hybrid films were designated as Hm, and their surface morphology was observed by scanning electron microscopy to identify the presence of cracks. The mechanical properties of the films were evaluated by measuring the hardness and elastic modulus of an ALD-coated ultra-high molecular weight polyethylene using nano-indentation examinations, and the films' behavior in the wear environment was studied through micro-scratch testing. The results indicate that H5 and H10 hybrid films exhibited no surface cracks, while serious surface cracks were observed in H20 and H100 films. X-ray diffraction analysis showed that structural changes occurred in the hybrid films, and it was speculated that this was related to surface failure. The hardness and elastic modulus values increased with an increase in the m value and showed significant differences between hybrid films. This study demonstrated that the MLD/ALD-based hybrid thin-film approach is an effective means of enhancing surface quality and mechanical properties. The findings of this study have significant implications for the development of advanced materials and coatings for various industrial applications.
Dynamic adaptive streaming over HTTP (DASH) technique, the most popular streaming method, requires a large number of hard disk drives (HDDs) to store multiple bitrate versions of many videos, ...consuming significant energy. A solid-state drive (SSD) can be used to cache popular videos, thus reducing HDD energy consumption by allowing I/O requests to be handled by an SSD, but this requires effective HDD power management due to limited SSD bandwidth. We propose a new SSD cache management scheme to minimize the energy consumption of a video storage system with heterogeneous HDDs. We first present a technique that caches files with the aim of saving more HDD energy as a result of I/O processing on an SSD. Based on this, we propose a new HDD power management algorithm with the goal of increasing the number of HDDs operated in low-power mode while reflecting the heterogeneous HDD power characteristics. For this purpose, it assigns a separate parameter value to each I/O task based on the ratio of HDD energy to bandwidth and greedily selects the I/O tasks handled by the SSD within limits on its bandwidth. Simulation results show that our scheme consumes between 12% and 25% less power than alternative schemes under the same HDD configuration.
In multiaccess edge computing (MEC), tasks are offloaded from mobile devices to servers at the edge of the network. This speeds up task processing without incurring the latency required to reach ...central servers. However, the power used by edge servers is significant and needs to be cost-effective. We propose a scheme in which tasks are offloaded to servers with the aim of maximizing a reward within a limited power budget, server processing capacities, and wireless network coverage. Our algorithm determines the maximum utilization of each server while favoring the offloading of tasks with high ratios of reward to power requirement. We model the task allocation problem using a minimum-cost-maximum-flow graph, and propose two edge allocation algorithms, one of which is extended to allow task splitting, which offload tasks subject to server capacity by searching for the highest reward. In simulations, our scheme achieved between 8% and 80% higher rewards than alternative schemes, under the same power constraints.
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