SAFDetection:Sensor Analysis based Fault Detection in Tightly-CoupledMulti-Robot Team Tasks

This dissertation addresses the problem of detecting faults based on sensor analysis for tightly-coupled multi-robot team tasks. The approach I developed is called SAFDetection, which stands for Sensor Analysis based Fault Detection, pronounced “Safe Detection”. When dealing with robot teams, it is challenging to detect all types of faults because of the complicated environment they operate in and the large spectrum of components used in the robot system. The SAFDetection approach provides a novel methodology for detecting robot faults in situations when motion models and models of multi-robot dynamic interactions are unavailable. The fundamental idea of SAFDetection is to build the robots’ normal behavior model based on the robots’ sensor data. This normal behavior model not only describes the motion pattern for the single robot, but also indicates the interaction among the robots in the same team. Inspired by data mining theory, it combines data clustering techniques with the generation of a probabilistic state transition diagram to model the normal operation of the multi-robot system. The contributions of the SAFDetection approach include: (1) providing a way for a robot system to automatically generate a normal behavior model with little prior knowledge; (2) enabling a robot system to detect physical, logic and interactive faults online; (3) providing a way to build a fault detection capability that is independent of the particular type of fault that occurs; and (4) providing a way for a robot team to generate a normal behavior model for the team based the individual robot’s normal behavior models. SAFDetection has two different versions of implementation on multi-robot teams: the centralized approach and the distributed approach; the preferred approach depends on the size of the robot team, the robot computational capability and the network environment. The SAFDetection approach has been successfully implemented and tested in three robot task scenarios: box pushing (with two robots) and follow-the-leader (implemented with two- and five-robot teams). These experiments have validated the SAFDetection approach and demonstrated its robustness, scalability, and applicability to a wide range of tightly-coupled multi-robot applications

Graph Learning and Its Applications: A Holistic Survey

Graph learning is a prevalent domain that endeavors to learn the intricate relationships among nodes and the topological structure of graphs. These relationships endow graphs with uniqueness compared to conventional tabular data, as nodes rely on non-Euclidean space and encompass rich information to exploit. Over the years, graph learning has transcended from graph theory to graph data mining. With the advent of representation learning, it has attained remarkable performance in diverse scenarios, including text, image, chemistry, and biology. Owing to its extensive application prospects, graph learning attracts copious attention from the academic community. Despite numerous works proposed to tackle different problems in graph learning, there is a demand to survey previous valuable works. While some researchers have perceived this phenomenon and accomplished impressive surveys on graph learning, they failed to connect related objectives, methods, and applications in a more coherent way. As a result, they did not encompass current ample scenarios and challenging problems due to the rapid expansion of graph learning. Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning. Specifically, we commence by proposing a taxonomy from the perspective of the composition of graph data and then summarize the methods employed in graph learning. We then provide a detailed elucidation of mainstream applications. Finally, based on the current trend of techniques, we propose future directions.Comment: 20 pages, 7 figures, 3 table

Reproducible and Portable Big Data Analytics in the Cloud

Cloud computing has become a major approach to help reproduce computational experiments because it supports on-demand hardware and software resource provisioning. Yet there are still two main difficulties in reproducing big data applications in the cloud. The first is how to automate end-to-end execution of analytics including environment provisioning, analytics pipeline description, pipeline execution, and resource termination. The second is that an application developed for one cloud is difficult to be reproduced in another cloud, a.k.a. vendor lock-in problem. To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds. We propose and develop an open-source toolkit that supports 1) fully automated end-to-end execution and reproduction via a single command, 2) automated data and configuration storage for each execution, 3) flexible client modes based on user preferences, 4) execution history query, and 5) simple reproduction of existing executions in the same environment or a different environment. We did extensive experiments on both AWS and Azure using four big data analytics applications that run on virtual CPU/GPU clusters. The experiments show our toolkit can achieve good execution performance, scalability, and efficient reproducibility for cloud-based big data analytics

Robots with Display Screens: A Robot with a More Humanlike Face Display Is Perceived To Have More Mind and a Better Personality

It is important for robot designers to know how to make robots that interact effectively with humans. One key dimension is robot appearance and in particular how humanlike the robot should be. Uncanny Valley theory suggests that robots look uncanny when their appearance approaches, but is not absolutely, human. An underlying mechanism may be that appearance affects users’ perceptions of the robot’s personality and mind. This study aimed to investigate how robot facial appearance affected perceptions of the robot’s mind, personality and eeriness. A repeated measures experiment was conducted. 30 participants (14 females and 16 males, mean age 22.5 years) interacted with a Peoplebot healthcare robot under three conditions in a randomized order: the robot had either a humanlike face, silver face, or no-face on its display screen. Each time, the robot assisted the participant to take his/her blood pressure. Participants rated the robot’s mind, personality, and eeriness in each condition. The robot with the humanlike face display was most preferred, rated as having most mind, being most humanlike, alive, sociable and amiable. The robot with the silver face display was least preferred, rated most eerie, moderate in mind, humanlikeness and amiability. The robot with the no-face display was rated least sociable and amiable. There was no difference in blood pressure readings between the robots with different face displays. Higher ratings of eeriness were related to impressions of the robot with the humanlike face display being less amiable, less sociable and less trustworthy. These results suggest that the more humanlike a healthcare robot’s face display is, the more people attribute mind and positive personality characteristics to it. Eeriness was related to negative impressions of the robot’s personality. Designers should be aware that the face on a robot’s display screen can affect both the perceived mind and personality of the robot

The Aroma Composition of Baby Ginger Paocai

The purpose of this study was to analyze the volatile compounds in baby ginger paocai and the fresh baby ginger and identify the key aroma components that contribute to the flavor of baby ginger paocai. A total of 86 volatile compounds from the two baby ginger samples were quantified; these compounds were extracted by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography–mass spectrometry (GC-MS). The aroma composition of baby ginger paocai was different from that of fresh baby ginger. Baby ginger paocai was characterized by the presence of aroma-active compounds which varied in concentration from 0.03 to 28.14%. Geranyl acetate was the aroma component with the highest relative content in baby ginger paocai. β-myrcene, eucalyptol, trans-β-ocimene, Z-ocimene, linalool, decanal, cis-citral, geraniol, geranyl acetate, curcumene, and β-bisabolene contributed to the overall aroma of the product of baby ginger paocai which had gone through a moderate fermentation process

A comparative metabolomics analysis of domestic yak (Bos grunniens) milk with human breast milk

Yaks are tough animals living in Tibet’s hypoxic stress environment. However, the metabolite composition of yak milk and its role in hypoxic stress tolerance remains largely unexplored. The similarities and differences between yak and human milk in hypoxic stress tolerance are also unclear. This study explored yak colostrum (YC) and yak mature milk (YMM) using GC–MS, and 354 metabolites were identified in yak milk. A comparative metabolomic analysis of yak and human milk metabolites showed that over 70% of metabolites were species-specific. Yak milk relies mainly on essential amino acids- arginine and essential branched-chain amino acids (BCAAs): L-isoleucine, L-leucine, and L-valine tolerate hypoxic stress. To slow hypoxic stress, human breast milk relies primarily on the neuroprotective effects of non-essential amino acids or derivates, such as citrulline, sarcosine, and creatine. In addition, metabolites related to hypoxic stress were significantly enriched in YC than in YMM. These results reveal the unique metabolite composition of yak and human milk and provide practical information for applying yak and human milk to hypoxic stress tolerance

Effect of Chitosan Coating with Different Molecular Weights on the Storage Quality of Postharvest Passion Fruit (Passiflora edulis Sims)

To study the preservation effect of chitosan coating with different molecular weights on postharvest passion fruit, the "Qinmi No.9" was coated with chitosan of molecular weights of 30, 50, 100, 150 and 200 kDa (1.5%, w/v) to determine the quality of passion fruit during storage. The results showed that chitosan coating with different molecular weights was able to delay the shrinkage and yellowing, reduce the weight loss rate and inhibit the decay of passion fruit. Moreover, chitosan with a larger molecular weight was more conducive to delaying the ripening and senescence of passion fruit, as well as reducing shrinkage, and decay. At the end of storage, the weight loss of fruits coated with 200 kDa chitosan was nearly 10% less than that coated with 30 kDa chitosan, and the fruits coated with 150 and 200 kDa chitosan did not decay. The lower molecular weight (30 and 50 kDa) and higher molecular weight (150 kDa) chitosan were more effective in inhibiting weight loss, total soluble solids and soluble sugar metabolism, and maintaining titratable acid, flavonoid and total phenol contents of fruit during storage. The chitosan with 150 kDa had the best effect in maintaining the vitamin C content, which was 1.12 times higher than the control group at the end of storage. In conclusion, chitosan with different molecular weights was effective to delay senescence, slow down water loss and shrink of passion fruit and maintain the quality, chitosan with 150 kDa was more suitable to maintain the quality of postharvest passion fruit