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Journal articles on the topic 'LIGHT-WEIGHT METHOD'

Author: Grafiati

Published: 11 September 2023

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1

Dohmann, F., and Ch Hartl. "Hydroforming - a method to manufacture light-weight parts." Journal of Materials Processing Technology 60, no. 1-4 (June 1996): 669–76. http://dx.doi.org/10.1016/0924-0136(96)02403-x.

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2

TAKAGI, K., S. SAKAZAWA, and Y. TAKISHIMA. "Light Weight MP3 Watermarking Method for Mobile Terminals." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 9 (September 1, 2008): 2546–54. http://dx.doi.org/10.1093/ietfec/e91-a.9.2546.

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3

Mishra, Sarika, R. Mitra, and M. Vijayakumar. "Light weight silica tiles through foam casting method." Journal of the European Ceramic Society 28, no. 9 (January 2008): 1769–76. http://dx.doi.org/10.1016/j.jeurceramsoc.2007.12.030.

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4

Zilli, Massimiliano, Wolfgang Raschke, Reinhold Weiss, Christian Steger, and Johannes Loinig. "A light-weight compression method for Java card technology." ACM SIGBED Review 11, no. 4 (January 22, 2015): 13–18. http://dx.doi.org/10.1145/2724942.2724944.

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5

Hu, Jian, Huan Xie, Yan Lei, and Ke Yu. "A light-weight data augmentation method for fault localization." Information and Software Technology 157 (May 2023): 107148. http://dx.doi.org/10.1016/j.infsof.2023.107148.

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6

李, 天旭. "Evaluation of Light Pollution Based on Entropy Weight Method." Modeling and Simulation 12, no. 04 (2023): 3903–8. http://dx.doi.org/10.12677/mos.2023.124356.

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7

Zhou, Ying, Jiarong Song, and Haoyu Liu. "Study on Risk Classification of Light Pollution in Different Regions Based on Entropy Weight Method and Variation Coefficient Method." Highlights in Science, Engineering and Technology 50 (May 21, 2023): 151–57. http://dx.doi.org/10.54097/hset.v50i.8502.

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Light pollution is a new source of environmental pollution after exhaust gas, wastewater, waste residue, and noise. Research shows that light pollution is threatening people's health. In order to accurately measure the level of light pollution risk in a region, this paper established a light pollution risk index level model, and used this model to detect the level of light pollution risk in different types of places. Specifically, by determining the main factors that affect the risk of light pollution, and searching for corresponding data indicators from different countries to calculate the weight, this paper uses a combination of entropy weight and coefficient of variation to obtain the index weight, and establishes a light pollution risk index level model based on the weight. Finally, the model is applied to four specific types of locations to determine their risk level of light pollution.

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Zheng, Chuan Xing, and Jian Chen. "Error Compensation Method for Mine's Automatic Light Rail Weight-Bridge." Advanced Materials Research 889-890 (February 2014): 786–89. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.786.

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The method of wheel-weight for mine's automatic light rail weight-bridge is put forward. According to some important factors which cause measure error using wheel-weight method, for example, zero drift, speed and gradient, the error compensation algorithms are studied. The software flow charts of these algorithms are given, which have been put into effect in the ARM microprocessor based embedded system. It has been found that these error compensation methods are efficient to improve the accuracy of the measurement system.

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Wrenn, GeorgeE, LeonardA Abbatiello, and John Lewis. "4818448 Method for fabricating light weight carbonbonded carbon fiber composites." Carbon 27, no. 5 (1989): II—III. http://dx.doi.org/10.1016/0008-6223(89)90220-0.

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10

Hunkeler, D. "A high precision light scattering method for molecular weight determination." Journal of Applied Polymer Science 48 (1991): 335–49. http://dx.doi.org/10.1002/app.1991.070480024.

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11

Somekawa, Hidetoshi. "Nanoindentation method and its application to light-weight metallic materials." Journal of Japan Institute of Light Metals 72, no. 11 (November 15, 2022): 669–79. http://dx.doi.org/10.2464/jilm.72.669.

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12

Wang, Xiao Ping, Tian Han, and Fang Yin. "Light Gauge Steel-Light Weight Aggregate Concrete Structure and Application." Advanced Materials Research 479-481 (February 2012): 23–26. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.23.

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Light gauge steel-light weight aggregate concrete structure is a new type of structure system. This paper introduces the composition, characteristics and production and installation process of this kind of structure. For the Da Yunhe villas, as an example, this paper presents the structure detail and analysis method, and gives the bearing capacity and rigidity calculation result of the typical wall and floor beams, which verify the design code of the steel structure. This paper provides the beneficial reference and basis for future research and promotion.

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13

Gao, Tianze, Yunfeng Gao, Yu Li, and Peiyuan Qin. "Revisiting knowledge distillation for light-weight visual object detection." Transactions of the Institute of Measurement and Control 43, no. 13 (August 13, 2021): 2888–98. http://dx.doi.org/10.1177/01423312211022877.

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An essential element for intelligent perception in mechatronic and robotic systems (M&RS) is the visual object detection algorithm. With the ever-increasing advance of artificial neural networks (ANN), researchers have proposed numerous ANN-based visual object detection methods that have proven to be effective. However, networks with cumbersome structures do not befit the real-time scenarios in M&RS, necessitating the techniques of model compression. In the paper, a novel approach to training light-weight visual object detection networks is developed by revisiting knowledge distillation. Traditional knowledge distillation methods are oriented towards image classification is not compatible with object detection. Therefore, a variant of knowledge distillation is developed and adapted to a state-of-the-art keypoint-based visual detection method. Two strategies named as positive sample retaining and early distribution softening are employed to yield a natural adaption. The mutual consistency between teacher model and student model is further promoted through a hint-based distillation. By extensive controlled experiments, the proposed method is testified to be effective in enhancing the light-weight network’s performance by a large margin.

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Ghorai, Shreyasi, Nilanjan Datta, and Mrinal Nandi. "ULBC: An Ultra Light-weight Block Cipher." Journal of Advances in Mathematics and Computer Science 38, no. 8 (June 23, 2023): 86–100. http://dx.doi.org/10.9734/jamcs/2023/v38i81793.

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After explicitly observing the design criteria of two popular block ciphers, namely PRESENT and GIFT, we have proposed a new S-box that would be useful for designing a new light-weight block cipher, we name it as ULBC. The primary goal of the S-Box is to reduce the implementation cost, and make it cheaper than the two block ciphers GIFT and PRESENT. In this design, we have also developed a new property like BOGI which would be extremely helpful in building light-weight block ciphers. Depending on this property we can appropriately design permutation layer, such that no bad output will go bad input. ULBC is composed of a S-box, associated bit wiring and key schedule. Also, we have produced some of cryptanalytic attacks to assure certain security level. We have used two different methods to calculate differential characteristics and linear approximation. By using *-DDT method we have produced tight bounds of them. We find that 24 rounds are sufficient to resist some cryptanalytic attack. It is also found that by using this cipher64-bits plain-text can be encrypted into 64-bits cipher-text.

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15

Tay, Yi Wei Daniel, Ming Jen Tan, and Teck Neng Wong. "Tailoring Light-Weight Aggregates for Concrete 3D Printing Applications." Materials 16, no. 7 (April 1, 2023): 2822. http://dx.doi.org/10.3390/ma16072822.

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Concrete 3D printing is a sustainable solution for manufacturing efficient designs and creating less waste, and selecting the optimal materials to use can amplify the advantages of this technology. In this study, we explore printing lightweight concrete by replacing normal weight aggregate with lightweight aggregates such as cenospheres, perlite, and foam beads. We adopt a systematic approach to investigate mixtures using different formulation methods such as the specific gravity and packing factor methods to improve the printing and mechanical performances of the mixtures. The rheological results showed significant improvement in the flow characteristics of the different mixtures using both the specific gravity method and the packing factor method to formulate the mixtures. Furthermore, a statistical tool was used to achieve optimal performance of the mixtures in terms of high specific compressive strength, high flow characteristics, and good shape retention capability by maximizing the specific compressive strength ratio, slump flow, and the static yield stress, while minimizing the slump, dynamic yield stress, and plastic viscosity. With the above design objectives, the optimal percentages of the aggregate replacements (cenosphere, perlite, and EPS foam beads) were 42%, 68%, and 44%, respectively. Finally, the optimized results also showed that the mixture with cenosphere aggregate replacement had the highest specific strength.

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16

Wei, Bo, Rebeen Ali Hamad, Longzhi Yang, Xuan He, Hao Wang, Bin Gao, and Wai Lok Woo. "A Deep-Learning-Driven Light-Weight Phishing Detection Sensor." Sensors 19, no. 19 (September 30, 2019): 4258. http://dx.doi.org/10.3390/s19194258.

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This paper designs an accurate and low-cost phishing detection sensor by exploring deep learning techniques. Phishing is a very common social engineering technique. The attackers try to deceive online users by mimicking a uniform resource locator (URL) and a webpage. Traditionally, phishing detection is largely based on manual reports from users. Machine learning techniques have recently been introduced for phishing detection. With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. According to the experiments, the true detection rate has been improved. This paper has also verified that the proposed method can run in an energy-saving embedded single board computer in real-time.

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17

Gao, Jianbo, Sihui Dai, Jingjing Huang, Xu Xiao, Li Liu, Longhan Wang, Xia Sun, Yemin Guo, and Ming Li. "Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4." Agronomy 12, no. 9 (August 31, 2022): 2081. http://dx.doi.org/10.3390/agronomy12092081.

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In order to detect kiwifruit quickly and accurately in orchard environments for the picking robot, this paper proposed a detection method based on a lightweight YOLOv4-GhostNet network. The implementations of the method are as follows: The original CSP-Darknet53 backbone network model was replaced by GhostNet, a feature layer facilitating small object detection was introduced in the feature fusion layer, and part of the ordinary convolution was replaced by a combination of 1 × 1 convolution and depth-separable convolution to reduce the computational pressure caused by the fused feature layer. The parameters of the new network are reduced, and the generalization ability of the model is improved by loading pre-training weights and freezing some layers. The trained model was tested, and the results showed that the detection performances were better than that of the original YOLOv4 network. The F1 value, map, and precision were improved on the test set, which were 92%, 93.07%, and 90.62%, respectively. The size of weight parameters was reduced to 1/6 of the original YOLOv4 network, and the detection speed reached 53 FPS. Therefore, the method proposed in this study shows the features of fast recognition, lightweight parameters, and high recognition accuracy, which can provide technical support for vision systems of kiwifruit picking robots.

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18

Rao, Vidya, and Prema K.V. "Light-weight hashing method for user authentication in Internet-of-Things." Ad Hoc Networks 89 (June 2019): 97–106. http://dx.doi.org/10.1016/j.adhoc.2019.03.003.

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19

Nguyen, Jason, Sang-in Park, and David Rosen. "Heuristic optimization method for cellular structure design of light weight components." International Journal of Precision Engineering and Manufacturing 14, no. 6 (June 2013): 1071–78. http://dx.doi.org/10.1007/s12541-013-0144-5.

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20

Lee, Tae-Woo, Ji-Yong Cho, and Yong-Hwan Cho. "A Method of Embedded Linux Light-Weight for Efficient Application Execution." Journal of the Korea Society of Computer and Information 18, no. 3 (March 30, 2013): 1–10. http://dx.doi.org/10.9708/jksci.2013.18.3.001.

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21

Xu, Mei, Dong-Sheng Li, Bin Li, Chao Wang, Yu-Peng Zhu, Wen-Ping Lv, and Bi-Jun Xie. "Comparative Study on Molecular Weight of Konjac Glucomannan by Gel Permeation Chromatography-Laser Light Scattering-Refractive Index and Laser Light-Scattering Methods." Journal of Spectroscopy 2013 (2013): 1–4. http://dx.doi.org/10.1155/2013/685698.

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The gel permeation chromatography, combined with laser light scattering and refractive index (GPC-LLS-RI) online method and laser light scattering (LLS) single method, researched the weight-average molecular weight (Mw) of konjac glucomannan (KGM) in this paper. The results show that the determination results of molecular weight of KGM are similar by two method, the molecular weight of KGM is2.476×105 g/mol by online method measured, and it is2.508×105 g/mol by single method measured, compared with only 1.3% difference between the two methods. At the same time, the molecular weight distributions of KGM and conformation index were analyzed by online method, while the second virial coefficient (A2) that characterizes the interaction between KGM molecules and solvent molecules was acquired by single method, which can provide reliable foundation for the molecular chain morphology research of KGM.

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22

Li, Bei, Ziyang Wang, Hui Liu, Quan Du, Tong Xiao, Chunliang Zhang, and Jingbo Zhu. "Learning Light-Weight Translation Models from Deep Transformer." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 15 (May 18, 2021): 13217–25. http://dx.doi.org/10.1609/aaai.v35i15.17561.

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Recently, deep models have shown tremendous improvements in neural machine translation (NMT). However, systems of this kind are computationally expensive and memory intensive. In this paper, we take a natural step towards learning strong but light-weight NMT systems. We proposed a novel group-permutation based knowledge distillation approach to compressing the deep Transformer model into a shallow model. The experimental results on several benchmarks validate the effectiveness of our method. Our compressed model is 8 times shallower than the deep model, with almost no loss in BLEU. To further enhance the teacher model, we present a Skipping Sub-Layer method to randomly omit sub-layers to introduce perturbation into training, which achieves a BLEU score of 30.63 on English-German newstest2014. The code is publicly available at https://github.com/libeineu/GPKD.

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23

Tang, Jinghao, Jiebin Huang, and Qiufen Ni. "Evaluation System of Light Pollution Risk Index Based on Entropy Weight Method." Highlights in Science, Engineering and Technology 48 (May 16, 2023): 155–62. http://dx.doi.org/10.54097/hset.v48i.8300.

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In recent years, the issue of light pollution has garnered widespread attention. To address this concern, the state of California in the United States has developed a grading format to regulate lighting areas for light pollution protection. In order to further study this problem, we have selected six factors related to light pollution, namely population density, per capita gross regional product, total passenger transport, industrial electricity consumption, real estate development investment, and green area of built-up areas in different years in each prefecture-level city in China. Based on the VIKOR model, we have established a light pollution risk assessment system using the obtained compromise value as an assessment index for light pollution levels in different regions. The results have been analyzed using VIIRS satellite remote sensing images, and the average error is 5.24%, indicating good sensitivity to the system. Finally, we have analyzed the assessment results to show that over-development of areas can seriously exacerbate the risk level of light pollution. Therefore, development should be planned rationally to reduce the impact of light pollution.

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24

Jeong, Mira, MinJi Park, Jaeyeal Nam, and Byoung Chul Ko. "Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection." Sensors 20, no. 19 (September 25, 2020): 5508. http://dx.doi.org/10.3390/s20195508.

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As the need for wildfire detection increases, research on wildfire smoke detection combining low-cost cameras and deep learning technology is increasing. Camera-based wildfire smoke detection is inexpensive, allowing for a quick detection, and allows a smoke to be checked by the naked eye. However, because a surveillance system must rely only on visual characteristics, it often erroneously detects fog and clouds as smoke. In this study, a combination of a You-Only-Look-Once detector and a long short-term memory (LSTM) classifier is applied to improve the performance of wildfire smoke detection by reflecting on the spatial and temporal characteristics of wildfire smoke. However, because it is necessary to lighten the heavy LSTM model for real-time smoke detection, in this paper, we propose a new method for applying the teacher–student framework to deep LSTM. Through this method, a shallow student LSTM is designed to reduce the number of layers and cells constituting the LSTM model while maintaining the original deep LSTM performance. As the experimental results indicate, our proposed method achieves up to an 8.4-fold decrease in the number of parameters and a faster processing time than the teacher LSTM while maintaining a similar detection performance as deep LSTM using several state-of-the-art methods on a wildfire benchmark dataset.

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Li, Shunming, Kun Xu, and Jinrui Wang. "The Light Weight Design of Mowing Vehicle Frame with a Combined Method." Modern Mechanical Engineering 08, no. 03 (2018): 192–203. http://dx.doi.org/10.4236/mme.2018.83013.

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Singh, Rimjhim Padam, and Poonam Sharma. "A Light-Weight Change Detection Method Using YCbCr-Based Texture Consensus Model." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 09 (December 6, 2019): 2050023. http://dx.doi.org/10.1142/s0218001420500238.

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Background subtraction is a prerequisite and often the very first step employed in several high-level and real-time computer vision applications. Several parametric and non-parametric change detection algorithms employing multiple feature spaces have been proposed to date but none has proven to be robust against all challenges that can possibly be posed in a complex real-time environment. Amongst the varied challenges posed, illumination variations, shadows, dynamic backgrounds, camouflaged and bootstrapping artifacts are some of the well-known problems. This paper presents a light-weight hybrid change detection algorithm that integrates a novel combination of RGB color space and conditional YCbCr-based XCS-LBP texture descriptors (YXCS-LBP) into a modified pixel-based background model. The conditional employment of light-weight YXCS-LBP texture features with the modified Visual background extractor (ViBe) aiming at reduction in false positives, produces outperforming results without incurring much memory and computational cost. The random and time-subsampled update strategy employed with the proposed classification procedure ensures the efficient suppression of shadows and bootstrapping artifacts along with the complete retention of long-term static objects in the foreground masks. Comprehensive performance analysis of the proposed technique on publicly available Change Detection dataset (2014 CDnet dataset) demonstrates the superiority of the proposed technique over different state-of-the-art-methods against varied challenges.

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Lee, Hyun-Tack, Soo-Heon Park, Bong-Ho Choi, Bong-Gu Kim, Han-Sang Kim, and Seok-Moo Hong. "Light Weight of the Leg rest Module using the Response Surface Method." Journal of the Korea Academia-Industrial cooperation Society 24, no. 2 (February 28, 2023): 568–75. http://dx.doi.org/10.5762/kais.2023.24.2.568.

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28

Wu, Peng, Xueqing Kong, and Jiayin Chen. "Research on Comprehensive Evaluation Model of Light Pollution Based on Entropy Weight Method." Highlights in Science, Engineering and Technology 59 (July 15, 2023): 315–20. http://dx.doi.org/10.54097/hset.v59i.10143.

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With the development of urbanization, light pollution has become a serious problem that needs the public to pay attention. In this paper, the factors that affect the degree of light pollution are summarized. As a result, 9 evaluation indicators are selected, and the entropy weight method is used to objectively weigh the selected indicators. Among them, the impact range of curtain walls and the lightness of LED screens occupy the largest weight. Then, by introducing the degree of coordinated development and considering the interaction among various indexes, a widely applicable evaluation model of light pollution degree was established. The model is used to evaluate the degree of light pollution in different regions while Beijing was used as an instance in this paper. It is found that the economic development level of the region is positively correlated with the degree of light pollution. Aiming at the increasingly serious problem of light pollution in the world, the model established in this paper not only provides a new idea for evaluating the degree of light pollution in each region but also defines the level of light pollution, which is helpful to solve the problem of light pollution on a global scale.

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Prima, Giulia Di, Fabio Librizzi, and Rita Carrotta. "Light Scattering as an Easy Tool to Measure Vesicles Weight Concentration." Membranes 10, no. 9 (September 3, 2020): 222. http://dx.doi.org/10.3390/membranes10090222.

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Over the last few decades, liposomes have emerged as promising drug delivery systems and effective membrane models for studying biophysical and biological processes. For all applications, knowing their concentration after preparation is crucial. Thus, the development of methods for easily controlling vesicles concentration would be of great utility. A new assay is presented here, based on a suitable analysis of light scattering intensity from liposome dispersions. The method, tested for extrusion preparations, is precise, easy, fast, non-destructive and uses a tiny amount of sample. Furthermore, the scattering intensity can be measured indifferently at different angles, or even by using the elastic band obtained from a standard spectrofluorimeter. To validate the method, the measured concentrations of vesicles of different matrix compositions and sizes, measured by light scattering with different angles and instruments, were compared to the data obtained by the standard Stewart assay. Consistent results were obtained. The light scattering assay is based on the assessment of the mass fraction lost in the preparation, and can be applied for methods such as extrusion, homogenization, French press and other microfluidic procedures.

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30

Angadi, S. A., and M. M. Kodabagi. "A Light Weight Text Extraction Technique for Hand-Held Device." International Journal of Image and Graphics 15, no. 04 (August 12, 2015): 1550017. http://dx.doi.org/10.1142/s0219467815500175.

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Automated systems for understanding display boards are finding many applications useful in guiding tourists, assisting visually challenged and also in providing location aware information. Such systems require an automated method to detect and extract text prior to further image analysis. In this paper, a new approach that uses zonewise profile features to identify and segment text regions from low resolution images of display boards captured from mobile phone cameras is presented. The method computes zonewise profile features on every 40 × 40 pixel image block and identifies potential text blocks using newly defined discriminant functions. Further, a merging algorithm is used to merge text blocks to obtain text regions. The method is implemented using the android software development kit and experimented on Sony X-PeriaTM Z C6603/C6602 mobile. The proposed methodology is evaluated on 3240 low resolution images of display boards captured from 2 and/or 5 mega pixel cameras on mobile phones at various pixel sizes 240 × 320, 480 × 640 and 960 × 1280 and reports an average processing time of 13 s and detection rate of 95.5%. The proposed method is found to be robust and insensitive to the variations in size and style of font, thickness and spacing between characters.

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31

Gu, Geonmo, Byungsoo Ko, SeoungHyun Go, Sung-Hyun Lee, Jingeun Lee, and Minchul Shin. "Towards Light-Weight and Real-Time Line Segment Detection." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 726–34. http://dx.doi.org/10.1609/aaai.v36i1.19953.

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Previous deep learning-based line segment detection (LSD) suffers from the immense model size and high computational cost for line prediction. This constrains them from real-time inference on computationally restricted environments. In this paper, we propose a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD). We design an extremely efficient LSD architecture by minimizing the backbone network and removing the typical multi-module process for line prediction found in previous methods. To maintain competitive performance with a light-weight network, we present novel training schemes: Segments of Line segment (SoL) augmentation, matching and geometric loss. SoL augmentation splits a line segment into multiple subparts, which are used to provide auxiliary line data during the training process. Moreover, the matching and geometric loss allow a model to capture additional geometric cues. Compared with TP-LSD-Lite, previously the best real-time LSD method, our model (M-LSD-tiny) achieves competitive performance with 2.5% of model size and an increase of 130.5% in inference speed on GPU. Furthermore, our model runs at 56.8 FPS and 48.6 FPS on the latest Android and iPhone mobile devices, respectively. To the best of our knowledge, this is the first real-time deep LSD available on mobile devices.

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32

Zhilina, Tatyana. "Decreasing of Thermal Losses of the Light-Weight Building Envelope." Applied Mechanics and Materials 729 (January 2015): 224–27. http://dx.doi.org/10.4028/www.scientific.net/amm.729.224.

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33

Zhang, Yuyang, and Long Cheng. "Evaluation Model of Location Light Pollution Level based on Analytic Hierarchy Process and Entropy Weight Method." E3S Web of Conferences 393 (2023): 03035. http://dx.doi.org/10.1051/e3sconf/202339303035.

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With the development of society, the impact of light pollution has gradually intensified. It not only endangers human health and animal and plant activities but also indirectly damages the climate and environment. In the past, the research on the evaluation of urban light pollution often focused on the comparison between the light pollution level measured by the relevant photometry equipment and the natural light level. This means that many sites need to be selected for measurement in the evaluation city. To save resources and ensure the evaluation results have a certain validity, we built a Location Light Pollution Risk Index (LLPRI) model. First, we selected 16 indicators from 6 aspects and built a three-level indicator evaluation system. After that, based on combining the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to calculate the index weight, we also use the Weight Combination Method to obtain the final weight of the index. Finally, we will bring the collected site sample data into the model and then use K-means Algorithm to cluster the model results and obtain the model metric We compare the results of the assessment of location light pollution risk level by the model with the data from the Military Meteorological Satellite Program (DMSP) and Visible Infrared Imaging Radiometer (VIIRS). The experimental results show that the LLRPI model has good effectiveness.

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Tian, Run Zhu, Zheng Wu Jiang, Qing Yun Huang, and Zheng Hong Yang. "Study on Utilization of Biomass in Light-Weight Aggregate Production." Materials Science Forum 743-744 (January 2013): 245–51. http://dx.doi.org/10.4028/www.scientific.net/msf.743-744.245.

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In this paper, the property of common biomass-rice husk was compared with that of coal. The combustion characteristics of rice husk were investigated by Thermogravimetry (TG) method, and the barriers for biomass fuels utilization as an alternative fuel to manufacture artificial aggregates were also analyzed. The key techniques of using biomass as an alternative fuel in light-weight aggregate production, such as preprocessing of the biomass feedstock, proper design of combustion system for biomass fuel and control of sintering process were proposed. Properties of artificial aggregates sintered by biomass fuel were also studied according to GB/T 17421.2-2010 test method, and the test results showed that all the properties meet the requirement of national standard.

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Gao, Wei, Linjie Zhou, and Lvfang Tao. "A Fast View Synthesis Implementation Method for Light Field Applications." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (November 30, 2021): 1–20. http://dx.doi.org/10.1145/3459098.

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View synthesis (VS) for light field images is a very time-consuming task due to the great quantity of involved pixels and intensive computations, which may prevent it from the practical three-dimensional real-time systems. In this article, we propose an acceleration approach for deep learning-based light field view synthesis, which can significantly reduce calculations by using compact-resolution (CR) representation and super-resolution (SR) techniques, as well as light-weight neural networks. The proposed architecture has three cascaded neural networks, including a CR network to generate the compact representation for original input views, a VS network to synthesize new views from down-scaled compact views, and a SR network to reconstruct high-quality views with full resolution. All these networks are jointly trained with the integrated losses of CR, VS, and SR networks. Moreover, due to the redundancy of deep neural networks, we use the efficient light-weight strategy to prune filters for simplification and inference acceleration. Experimental results demonstrate that the proposed method can greatly reduce the processing time and become much more computationally efficient with competitive image quality.

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NAKASHIMA, Tatsushi, Yoshihiko MATSUI, Syuuichi MATSUMOTO, Yoshiaki NISHIJIMA, and Hiroshi ENOMOTO. "A study on Measurement of Particulate Matter Weight by Transmitted Light Attenuation Method." Journal of the Japan Institute of Energy 100, no. 1 (January 20, 2021): 1–4. http://dx.doi.org/10.3775/jie.100.1.

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37

Bekhet, Saddam, Monagi H. Alkinani, Reinel Tabares-Soto, and M. Hassaballah. "An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network." Computers, Materials & Continua 69, no. 2 (2021): 2475–91. http://dx.doi.org/10.32604/cmc.2021.018514.

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38

Lee, Sung‐Soo, Tae‐joon Song, Myung‐Soo Ko, and Sung Min Cho. "Low‐molecular‐weight white organic‐light‐emitting‐devices using direct color mixing method." Journal of Information Display 3, no. 2 (January 2002): 6–12. http://dx.doi.org/10.1080/15980316.2002.9651890.

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39

Nasir Hussain, N., Srinivasa Prakash Regalla, and Yendluri V. Daseswara Rao. "Economical method for manufacturing of advanced light weight material crash box for automobiles." Materials Today: Proceedings 27 (2020): 1186–91. http://dx.doi.org/10.1016/j.matpr.2020.02.081.

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HAGA, Seiji, Takashi KOBAYASHI, and Tetsuro OGUSHI. "Development of Optimization Method for a Light Weight Heatsink (Irregular Pitch Fin) Design." Proceedings of Design & Systems Conference 2003.13 (2003): 158–61. http://dx.doi.org/10.1299/jsmedsd.2003.13.158.

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41

Gremaud, R., C. P. Broedersz, D. M. Borsa, A. Borgschulte, P. Mauron, H. Schreuders, J. H. Rector, B. Dam, and R. Griessen. "Hydrogenography: An Optical Combinatorial Method To Find New Light-Weight Hydrogen-Storage Materials." Advanced Materials 19, no. 19 (October 5, 2007): 2813–17. http://dx.doi.org/10.1002/adma.200602560.

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42

He, Qingguo, Hongmin Huang, Fenglian Bai, and Yong Cao. "A Facile Method for Controlling the Molecular Weight of Hyperbranched Light-Emitting Polymers." Macromolecular Rapid Communications 27, no. 4 (February 24, 2006): 302–5. http://dx.doi.org/10.1002/marc.200500671.

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43

Gao, Yue Hua, Qi Peng Liu, and Wen Zhong Zhao. "A Light-Weight Design Method for Vehicle with Welded Structure Based on Surrogate Model." Advanced Materials Research 756-759 (September 2013): 3172–76. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3172.

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In this paper, a multi-disciplinary optimization frame considering weld fatigue constraints is constructed based on kriging surrogate model in order to improve the fatigue reliability of vehicle structures. Under this frame, kriging surrogate model is used to construct weight, strength, stiffness, frequency, fatigue life responses for optimization, which improves effectively the computing efficiency. For weld fatigue analysis, master S-N curve method in AMSE criterion is used for reliable fatigue life. The curb girders of a high-speed train are taken as an example, a light-weight optimization model considering static strengthen and weld fatigue constrains is constructed and solved by use of submodelling technology under the proposed optimization frame. After optimization, the weight of the curb girders is reduced about 33% from 1.73 ton to 1.16 ton. These results show the proposed optimization method is effective, and provide the preference for the light-weight design of high-speed trains.

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Li, Fang Hui, and Fan Meng. "Effects of Snow Load on Light-Weight Steel Structures." Applied Mechanics and Materials 501-504 (January 2014): 777–81. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.777.

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With the rapid development of economic and construction industry, light-weight steel structural system is widely used in the modern building construction. While, the light-weight steel structures are more sensitive under the effect of snow load than the other structure systems according to the data from the large amount of accidents of steel structure failures, which indicated the necessary and urged us to focus on the impact of snow on the light-weight steel structures. This paper introduces the current research situation and analyzes the method of snow load determination on gable frames in the load code of different countries, and provides some suggestions on the snow load design of light-weight steel structures finally.

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Gu, Li Yang, Peng Han, and Duo Nian Yu. "Study on Optimal Design of Light-Weight Composite Hood." Advanced Materials Research 415-417 (December 2011): 214–20. http://dx.doi.org/10.4028/www.scientific.net/amr.415-417.214.

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In this paper, the traditional steel engine hood is analyzed using finite element method firstly, and then the basic performance requirements of the hood is obtained. Based on the principle of equal stiffness, the design process is executed on carbon fiber composite hood. Afterwards according to the property of this composite, the whole optimization process of composite hood from conceptual design to detailed design is accomplished by ways of free size optimization, parameters optimization and stacking sequence optimization. The optimal structure of carbon fiber composite engine hood can be obtained by comparing the results of optimization design. Carbon fiber can put lightweight into practice , and the developing time of composite hood can be sharply reduced .

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Wang, Ziyi, and Tengda Xie. "Study on the risk assessment of light pollution based on entropy weight method-TOPSIS model." Advances in Engineering Technology Research 5, no. 1 (May 4, 2023): 233. http://dx.doi.org/10.56028/aetr.5.1.233.2023.

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In the process of rapid human development, artificial light has gradually increased. People use artificial light more consider light to bring us benefits, but light pollution and the use of light at the same time harm human beings, even to the point of endangering human health and affecting the ecological environment. The problems caused by light pollution are waiting to be solved by us and all human beings together. In this paper, regional vulnerability (e.g., regional development level, population, biodiversity, etc.) is considered. Together with the intensity of pollution sources (e.g. road glare level, light hours, light violation level, etc.), the risk level of light pollution is determined. The risk level model of light pollution was established by calculating the weights of each index using TOPSIS entropy weighting method and hierarchical analysis method. The natural interruption point method was used to classify the risk level of light pollution into level I, level II, level III, level IV, and level v. The risk level model of light pollution was applied to four regions. We derived the risk level as level IV for urban communities, level II for protected land and suburban communities, and level II for rural communities.

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Lyu, Aihong. "Vehicle Detection Using the Light Weight Model Based on YOLOv5." Journal of Physics: Conference Series 2560, no. 1 (August 1, 2023): 012043. http://dx.doi.org/10.1088/1742-6596/2560/1/012043.

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Abstract Vehicle detection is important for the administration because it’s an important part of the intelligent traffic management system. It can help to make the best decision for the government and provide the data for intelligent roadside equipment. And can reduce road congestion and emissions, reduce the incidence of traffic accidents, improve road safety and reduce damage caused by accidents. The current method of object detection needs large computing resources and makes the roadside equipment costly or unable to meet the computing resources. So, this paper proposed a light weight detection model based on YOLOv5. This model reduces the parameter and FLOPs by using the deepthwise separable convolution CBAM to improve the performance of vehicle detection. And this paper reduces the input image size and halves the feature channel to reduce the amount of computation. The experiment result shows that the proposed model is 8 times smaller than YOLOv5-s while the mAP only decreases 6% on the UA-DETRAC dataset, significantly outperforming the other methods. This paper provided an idea for implementing a light weight model and a way to reduce the application cost of roadside smart devices.

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Liu, Botao, Huiru Yang, and Jia Li. "Assessment and mitigation of light pollution." Highlights in Science, Engineering and Technology 64 (August 21, 2023): 132–40. http://dx.doi.org/10.54097/hset.v64i.11269.

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With the improvement of human development level, excessive optical radiation has caused adverse effects on human living and production environment, namely, light pollution. In this paper, a system for assessing the degree of light pollution is studied and methods for more effective intervention of light pollution are proposed. On the basis of collecting data related to light pollution, we first preprocessed and extracted information related to 14 indicators, including brightness, light range, population and distance from the sea, from five dimensions of light intensity, social development, human activities, biodiversity and geographical climate, so as to quantitatively analyze the degree of light pollution. Then combined with the analytic hierarchy process (AHP) and entropy weight method (EWM) to calculate the weight of each index, and at last, the sensitivity analysis was used to verify its robustness. Among them, the building distribution density, brightness and distance from the sea have the greatest weight. At the same time, K-means clustering algorithm was used to calculate the standard, and finally an evaluation system was calculated to measure the severity of light pollution.

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Liu, Sibei, Xinkai Liang, Yumeng Yang, and Changsheng Zhang. "The evaluation model of light pollution degree based on Topsis method." Highlights in Science, Engineering and Technology 48 (May 16, 2023): 143–54. http://dx.doi.org/10.54097/hset.v48i.8299.

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Light pollution is an environmental problem that arises along with the economic development of modern society. To explore the influencing factors of light pollution degree, firstly, the evaluation indicator system was constructed. The system modified by factor analysis, then entropy weight method and multiple linear regression are used to calculate the weight values of factors respectively, and the comprehensive score index of 271 prefecture-level cities in China is obtained by substituting into TOPSIS. And K-Means++ clustering is performed on the scores to derive the grade classification boundary values, thus dividing the degree of light pollution into five criteria. Then the 271 prefecture-level cities in China are divided and visualized by the two rating systems, and the advantages and disadvantages are analyzed by comparing with satellite images. And the DN values were calculated with the composite score series derived from the two models separately to calculate the spearman coefficients, and the results showed a better fit of the multivariate linear TOPSIS. This article applies the superiority rating system of the previous work, selects four representative areas from each of the four scenarios with different levels of light pollution, calculates their comprehensive score index, and adopts the light pollution level classification and cause analysis. The results show that light pollution is very low in conservation land locations, low in rural communities, medium in suburban communities, and high in urban communities.

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Zhu, Xiaoyu, Md Moinul Hossain, Jian Li, Biao Zhang, and Chuanlong Xu. "Weight coefficient calculation through equivalent ray tracing method for light field particle image velocimetry." Measurement 193 (April 2022): 110982. http://dx.doi.org/10.1016/j.measurement.2022.110982.

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