Advanced surface color quality assessment in paper-based full-color 3d printing

Color 3D printing allows for 3D-printed parts to represent 3D objects more realistically, but its surface color quality evaluation lacks comprehensive objective verification considering printing materials. In this study, a unique test model was designed and printed using eco-friendly and vivid paper-based full-color 3D printing as an example. By measuring the chromaticity, roughness, glossiness, and whiteness properties of 3D-printed surfaces and by acquiring images of their main viewing surfaces, this work skillfully explores the correlation between the color representation of a paper-based 3D-printed coloring layer and its attached underneath blank layer. Quantitative analysis was performed using ΔE*ab, feature similarity index measure of color image (FSIMc), and improved color-image-difference (iCID) values. The experimental results show that a color difference on color-printed surfaces exhibits a high linear correlation trend with its FSIMc metric and iCID metric. The qualitative analysis of microscopic imaging and the quantitative analysis of the above three surface properties corroborate the prediction of the linear correlation between color difference and image-based metrics. This study can provide inspiration for the development of computational coloring materials for additive manufacturing

Secure Source-Relay Link Based Threshold DF Relaying Scheme

In this work, a dual-hop cooperative system, in which there are a Source-Destination (S-D) pair, a relay node (R) and an eavesdropper (E), which attempts to eavesdrop the confidential message sent by S and forwarded by R, is considered. In order to enhance the system performance and save the system resource, we propose an S - R link based threshold decode-and-forward (DF) relaying scheme for R to decide whether to aid S-D pair’s information transmission or not, other than the traditional DF relaying scheme. The secrecy outage performance of the considered system is investigated and the closed-form analytical expression for secrecy outage probability is derived and verified via Monte-Carlo simulations

Pigment Penetration Characterization of Colored Boundaries in Powder-Based Color 3D Printing

Color 3D printing has widely affected our daily lives; therefore, its precise control is essential for aesthetics and performance. In this study, four unique test plates were printed using powder-based full-color 3D printing as an example; moreover, the corresponding pigment-penetration depth, chromaticity value and image-based metrics were measured to investigate the lateral pigment penetration characteristics and relative surface-color reproduction of each color patch, and to perform an objective analysis with specific microscopic images. The results show that the lateral pigment-penetration depth correlates with the number of printed layers on the designed 3D test plates, and the qualitative analysis of microscopic images can explain the change in chromaticity well. Meanwhile, there is an obvious linear correlation between the mean structural similarity, color-image difference and color difference for current color samples. Thus, our proposed approach has a good practicality for powder-based color 3D printing, and can provide new insight into predicting the color-presentation efficiency of color 3D-printed substrates by the abovementioned objective metrics

Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures

With the advent of ubiquitous deployment of smart devices and the Internet of Things, data sources for machine learning inference have increasingly moved to the edge of the network. Existing machine learning inference platforms typically assume a homogeneous infrastructure and do not take into account the more complex and tiered computing infrastructure that includes edge devices, local hubs, edge datacenters, and cloud datacenters. On the other hand, recent machine learning efforts have provided viable solutions for model compression, pruning and quantization for heterogeneous environments; for a machine learning model, now we may easily find or even generate a series of models with different tradeoffs between accuracy and efficiency. We design and implement JellyBean, a framework for serving and optimizing machine learning inference workflows on heterogeneous infrastructures. Given service-level objectives (e.g., throughput, accuracy), JellyBean automatically selects the most cost-efficient models that met the accuracy target and decides how to deploy them across different tiers of infrastructures. Evaluations show that JellyBean reduces the total serving cost of visual question answering by up to 58%, and vehicle tracking from the NVIDIA AI City Challenge by up to 36% compared with state-of-the-art model selection and worker assignment solutions. JellyBean also outperforms prior ML serving systems (e.g., Spark on the cloud) up to 5x in serving costs

Advanced Surface Color Quality Assessment in Paper-Based Full-Color 3D Printing

Color 3D printing allows for 3D-printed parts to represent 3D objects more realistically, but its surface color quality evaluation lacks comprehensive objective verification considering printing materials. In this study, a unique test model was designed and printed using eco-friendly and vivid paper-based full-color 3D printing as an example. By measuring the chromaticity, roughness, glossiness, and whiteness properties of 3D-printed surfaces and by acquiring images of their main viewing surfaces, this work skillfully explores the correlation between the color representation of a paper-based 3D-printed coloring layer and its attached underneath blank layer. Quantitative analysis was performed using ΔE*ab, feature similarity index measure of color image (FSIMc), and improved color-image-difference (iCID) values. The experimental results show that a color difference on color-printed surfaces exhibits a high linear correlation trend with its FSIMc metric and iCID metric. The qualitative analysis of microscopic imaging and the quantitative analysis of the above three surface properties corroborate the prediction of the linear correlation between color difference and image-based metrics. This study can provide inspiration for the development of computational coloring materials for additive manufacturing

Pigment Penetration Characterization of Colored Boundaries in Powder-Based Color 3D Printing

Color 3D printing has widely affected our daily lives; therefore, its precise control is essential for aesthetics and performance. In this study, four unique test plates were printed using powder-based full-color 3D printing as an example; moreover, the corresponding pigment-penetration depth, chromaticity value and image-based metrics were measured to investigate the lateral pigment penetration characteristics and relative surface-color reproduction of each color patch, and to perform an objective analysis with specific microscopic images. The results show that the lateral pigment-penetration depth correlates with the number of printed layers on the designed 3D test plates, and the qualitative analysis of microscopic images can explain the change in chromaticity well. Meanwhile, there is an obvious linear correlation between the mean structural similarity, color-image difference and color difference for current color samples. Thus, our proposed approach has a good practicality for powder-based color 3D printing, and can provide new insight into predicting the color-presentation efficiency of color 3D-printed substrates by the abovementioned objective metrics

Relay protection mirror operation technology based on digital twin

Abstract When conducting relay protection research, research costs can be significantly reduced if protection principle development, protection parameter verification and debugging can be carried out without relying on actual protection devices. The concept of ‘digital twin’ has made this possible, but the existing research has shortcomings in real-time data interaction ability, protection logic transparency, interface standardization, human–computer interaction etc., and consequently, mirror operation of relay protection in digital space has not been fully realized. Therefore, referring to the characteristics of digital twin, and combining with the practical application requirements in relay protection, this paper proposes the concept and characteristics of relay protection mirror operation based on digital twin. Key solutions are proposed to address the difficulties that may be encountered in the implementation of relay protection mirror operation in terms of protection principles, interfaces, real-time operation of the system, and human–computer interaction function simulation. Finally, an example of 110 kV double-bus and double-branch bus protection is used to verify the feasibility and progressiveness of the scheme proposed in this paper by comparing the action behavior and external characteristics of the twin protection and the actual protection device. The presented research can provide a reference for further in-depth research and application of relay protection using digital means

Path Planning of Unmanned Aerial Vehicle in Complex Environments Based on State-Detection Twin Delayed Deep Deterministic Policy Gradient

This paper investigates the path planning problem of an unmanned aerial vehicle (UAV) for completing a raid mission through ultra-low altitude flight in complex environments. The UAV needs to avoid radar detection areas, low-altitude static obstacles, and low-altitude dynamic obstacles during the flight process. Due to the uncertainty of low-altitude dynamic obstacle movement, this can slow down the convergence of existing algorithm models and also reduce the mission success rate of UAVs. In order to solve this problem, this paper designs a state detection method to encode the environmental state of the UAV’s direction of travel and compress the environmental state space. In considering the continuity of the state space and action space, the SD-TD3 algorithm is proposed in combination with the double-delayed deep deterministic policy gradient algorithm (TD3), which can accelerate the training convergence speed and improve the obstacle avoidance capability of the algorithm model. Further, to address the sparse reward problem of traditional reinforcement learning, a heuristic dynamic reward function is designed to give real-time rewards and guide the UAV to complete the task. The simulation results show that the training results of the SD-TD3 algorithm converge faster than the TD3 algorithm, and the actual results of the converged model are better

Astaxanthin Alleviates Ochratoxin A-Induced Cecum Injury and Inflammation in Mice by Regulating the Diversity of Cecal Microbiota and TLR4/MyD88/NF-κB Signaling Pathway

Ochratoxin A (OTA) is a common environmental pollutant found in a variety of foods and grains, and excessive OTA consumption causes serious global health effects on animals and humans. Astaxanthin (AST) is a natural carotenoid that has anti-inflammatory, antiapoptotic, immunomodulatory, antitumor, antidiabetes, and other biological activities. The present study is aimed at investigating the effects of AST on OTA-induced cecum injury and its mechanism of action. Eighty C57 mice were randomly divided into four groups, including the control group, OTA group (5 mg/kg body weight), AST group (100 mg/kg body weight), and AST intervention group (100 mg/kg body weight AST+5 mg/kg body weight OTA). It was found that AST decreased the endotoxin content, effectively prevented the shortening of mouse cecum villi, and increased the expression levels of tight junction (TJ) proteins, consisting of occludin, claudin-1, and zonula occludens-1 (ZO-1). AST increased the number of goblet cells, the contents of mucin-2 (MUC2), and defensins (Defa5 and β-pD2) significantly, while the expression of mucin-1 (MUC1) decreased significantly. The 16S rRNA sequencing showed that AST affected the richness and diversity of cecum flora, decreased the proportion of lactobacillus, and also decreased the contents of short-chain fatty acids (SCFAs) (acetate and butyrate). In addition, AST significantly decreased the expression of TLR4, MyD88, and p-p65, while increasing the expression of p65. Meanwhile, the expression of inflammatory factors including TNF-α and INF-γ decreased, while the expression of IL-10 increased. In conclusion, AST reduced OTA-induced cecum injury by regulating the cecum barrier function and TLR4/MyD88/NF-κB signaling pathway