Underwater dual manipulators-Part II: Kinematics analysis and numerical simulation

1104-1112This paper introduces dual-arm underwater manipulators mounted on an autonomous underwater vehicle (AUV), which can accomplish the underwater handling task. Firstly, the mechanical structure of the dual-arm system is briefly introduced, wherein each 4-DOF manipulator has an additional grasping function. In addition, the kinematics model of the manipulator is derived by using the improved D-H method. Secondly, the working space of the underwater dual-arm system is analyzed, which is obtained by using Monte Carlo method. The cubic polynomial interpolation and the five polynomial interpolation trajectory planning methods are compared in the joint space. Finally, with the help of the Robotics Toolbox software, the numerical test is conducted to verify the functions of the underwater dual-arm manipulator system

Underwater dual manipulators-Part I: Hydrodynamics analysis and computation

1098-1103This paper introduces two 4-DOF underwater manipulators mounted on autonomous underwater vehicle (AUV) with grasping claws, such that the AUV can accomplish the underwater task by using dual manipulators. Mechanical design of the manipulator is briefly presented and the feature of the simple structure of dual manipulators is simulated by using Solid Works. In addition, the hydrodynamics of the manipulator is analyzed, considering drag force, added mass and buoyancy. Then, hydrodynamic simulations of the manipulator are conducted by using 3-D model with Adams software, from which the torque of each joint is calculated. This paper presents an integrated result of computed torques by combining the theoretical calculation and simulation results, which is instrumental in determining the driving torque of the manipulators

Soft Scattering Evaporation of Dark Matter Subhalos by Inner Galactic Gases

The large gap between a galactic dark matter subhalo's velocity and its own gravitational binding velocity creates the situation that dark matter soft-scattering on baryons to evaporate the subhalo, if kinetic energy transfer is efficient by low momentum exchange. Small subhalos can evaporate before dark matter thermalize with baryons due to the low binding velocity. In case dark matter acquires an electromagnetic dipole moment, the survival of low-mass subhalos requires stringent limits on the photon-mediated soft scattering. We calculate the subhalo evaporation rate via soft collision by ionization gas and accelerated cosmic rays, and show the stability of subhalos lighter than $10^{-5}M_{\odot}$ in the gaseous inner galactic region is sensitive to dark matter's effective electric and magnetic dipole moments below current direct detection limits.Comment: 8 pages, 4 figure

AMD, an Automated Motif Discovery Tool Using Stepwise Refinement of Gapped Consensuses

Motif discovery is essential for deciphering regulatory codes from high throughput genomic data, such as those from ChIP-chip/seq experiments. However, there remains a lack of effective and efficient methods for the identification of long and gapped motifs in many relevant tools reported to date. We describe here an automated tool that allows for de novo discovery of transcription factor binding sites, regardless of whether the motifs are long or short, gapped or contiguous

Distributed Decision-making in Disrupted Industrial Environments Using a Multi-agent Framework

The modern industrial environment is becoming more complex and dynamic due to customized and shifting market demands, highly connected businesses, and frequently upgraded technologies. In such environments, varying uncertainties and disruptions could occur and highly impact the performance of the manufacturing factories and supply chain networks. Conventional centralized decision-making approaches handle disruptions by re-optimizing across the entire system regardless of disruption type and scale, which require significant computational efforts, especially for complex and large-scale systems. Therefore, to stay competitive, industrial enterprises need a dynamic and flexible decision-making method that enables an agile and resilient response to unexpected disruptions. Enabled by current Artificial Intelligence (AI) techniques, multi-agent control has been proposed to conduct distributed decision-making to provide an agile response to disruptions. A multi-agent system consists of various autonomous agents, which are cyber representations of their associated physical objects and have their own knowledge and goals. Agents communicate and interact with each other to make high-level decisions for their associated physical objects. In different industrial environments, agents could represent different system entities, such as the products and machines in manufacturing systems, or suppliers and customers in supply chains. However, most existing industrial multi-agent systems require prior knowledge of disruptions and predetermined rules and strategies to generate responses, which makes it difficult to handle unexpected disruptions. Aiming to improve the agility and resiliency of industrial systems, this dissertation develops a model-based multi-agent framework to address risk management within an agile and resilient response to various unexpected industrial disruptions. Specifically, this dissertation focuses on the disruptions that affect the occurrence of pre-scheduled events, such as machine breakdown and loss of suppliers. The proposed multi-agent framework comprises model-based agents, heuristic-based communication, and optimization-based decision-making. The model-based agent architecture enables agents to update their knowledge and local environments dynamically. When agents need to make decisions, they utilize their knowledge as heuristics to guide their communication strategies. Then based on their knowledge and communication information, agents can identify new actions by solving risk-aware optimizations to respond to unexpected disruptions dynamically. The proposed framework is tested in a simulated manufacturing environment and a supply chain instance, showcasing the improved flexibility, agility, and resiliency of the multi-agent systems. To conclude, this dissertation pushes the fields of distributed decision-making for industrial systems closer to satisfying the requirements of modern industry: flexibility, agility, and resiliency, especially for manufacturing systems and supply chain networks. Enterprises could apply this framework to address a disruption quickly by computing a resilient recovery plan to minimize the negative effects. In addition, this dissertation contributes to standardizing the design of system-level decision-making using a multi-agent framework. The proposed methodology to design a multi-agent framework is transferable to other complex systems, such as multi-robot systems and autonomous vehicle teams, that consist of multiple intelligent entities.PhDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/177884/1/mingjieb_1.pd

Effect of positioning errors of frames on fuselage panel assembly deformation

In aircraft assembly, frames are usually used as the skeleton shape benchmarks of a fuselage panel to locate other parts accurately, thereby coordinating the relative position relationships between each part. In order to guarantee the assembly quality, a methodology for analyzing the effect of positioning errors of frames on fuselage panel assembly deformation is proposed. A finite element model of the fuselage panel is established first and then a mathematical model for representing the assembly deformation is derived. To contain more assembly deformation information, the optimal measurement point placement method is adopted to extract a set of finite element nodes as the measurement point locations from a large candidate set. Meanwhile, the mathematical relationship between the position errors of measurement points and the positioning errors of frames is also determined, which is then combined with the Monte Carlo simulation and the grey relational analysis to quantitatively analyze the impacts of the positioning error(s) generated by single or multiple frames on fuselage panel assembly deformation

Cryptanalysis of a homomorphic encryption scheme from ISIT 2008

International audienceAt ISIT 2008, Aguilar Melchor, Castagnos and Gaborit presented a lattice-based homomorphic encryption scheme (abbreviated as MCG). Its security is based on the Computational Knapsack Vector Problem. In this paper, we explore a secret linear relationship between the public keys and the secret keys, which can be used to construct a reduced-dimension lattice, and then we obtain a group of equivalent private keys by solving the Closest Vector Problem of the lattice. Moreover, our attack is practical on all the three settings of recommended parameters, and the running time to recover the equivalent private keys is only several hours on a single PC

Improved Nguyen-Vidick heuristic sieve algorithm for shortest vector problem

Abstract. In this paper, we present an improvement of the Nguyen-Vidick heuristic sieve algorithm for shortest vector problem in general lattices, which time complexity is 2 0.3836n polynomial computations, and space complexity is 2 0.2557n. In the new algorithm, we introduce a new sieve technique with two-level instead of the previous one-level sieve, and complete the complexity estimation by calculating the irregular spherical cap covering

Spatial-temporal dynamics of NDVI and Chl-a concentration from 1998 to 2009 in the East coastal zone of China: integrating terrestrial and oceanic components

Annual normalized difference vegetation index (NDVI) and chlorophyll-a (Chl-a) concentration are the most important large-scale indicators of terrestrial and oceanic ecosystem net primary productivity. In this paper, the Sea-viewing Wide Field-of-view Sensor level 3 standard mapped image annual products from 1998 to 2009 are used to study the spatial-temporal characters of terrestrial NDVI and oceanic Chl-a concentration on two sides of the coastline of China by using the methods of mean value (M), coefficient of variation (CV), the slope of unary linear regression model (Slope), and the Hurst index (H). In detail, we researched and analyzed the spatial-temporal dynamics, the longitudinal zonality and latitudinal zonality, the direction, intensity, and persistency of historical changes. The results showed that: (1) spatial patterns of M and CV between NDVI and Chl-a concentration from 1998 to 2009 were very different. The dynamic variation of terrestrial NDVI was much mild, while the variation of oceanic Chl-a concentration was relatively much larger; (2) distinct longitudinal zonality was found for Chl-a concentration and NDVI due to their hypersensitivity to the distance to shoreline, and strong latitudinal zonality existed for Chl-a concentration while terrestrial NDVI had a very weak latitudinal zonality; (3) overall, the NDVI showed a slight decreasing trend while the Chl-a concentration showed a significant increasing trend in the past 12 years, and both of them exhibit strong self-similarity and long-range dependence which indicates opposite future trends between land and ocean.Annual normalized difference vegetation index (NDVI) and chlorophyll-a (Chl-a) concentration are the most important large-scale indicators of terrestrial and oceanic ecosystem net primary productivity. In this paper, the Sea-viewing Wide Field-of-view Sensor level 3 standard mapped image annual products from 1998 to 2009 are used to study the spatial-temporal characters of terrestrial NDVI and oceanic Chl-a concentration on two sides of the coastline of China by using the methods of mean value (M), coefficient of variation (CV), the slope of unary linear regression model (Slope), and the Hurst index (H). In detail, we researched and analyzed the spatial-temporal dynamics, the longitudinal zonality and latitudinal zonality, the direction, intensity, and persistency of historical changes. The results showed that: (1) spatial patterns of M and CV between NDVI and Chl-a concentration from 1998 to 2009 were very different. The dynamic variation of terrestrial NDVI was much mild, while the variation of oceanic Chl-a concentration was relatively much larger; (2) distinct longitudinal zonality was found for Chl-a concentration and NDVI due to their hypersensitivity to the distance to shoreline, and strong latitudinal zonality existed for Chl-a concentration while terrestrial NDVI had a very weak latitudinal zonality; (3) overall, the NDVI showed a slight decreasing trend while the Chl-a concentration showed a significant increasing trend in the past 12 years, and both of them exhibit strong self-similarity and long-range dependence which indicates opposite future trends between land and ocean

A Model-based Multi-agent Framework to Enable an Agile Response to Supply Chain Disruptions

Due to the COVID-19 pandemic, the global supply chain is disrupted at an unprecedented scale under uncertain and unknown trends of labor shortage, high material prices, and changing travel or trade regulations. To stay competitive, enterprises desire agile and dynamic response strategies to quickly react to disruptions and recover supply-chain functions. Although both centralized and multi-agent approaches have been studied, their implementation requires prior knowledge of disruptions and agent-rule-based reasoning. In this paper, we introduce a model-based multi-agent framework that enables agent coordination and dynamic agent decision-making to respond to supply chain disruptions in an agile and effective manner. Through a small-scale simulated case study, we showcase the feasibility of the proposed approach under several disruption scenarios that affect a supply chain network differently, and analyze performance trade-offs between the proposed distributed and centralized methods.Comment: 7 pages, 4 figures, accepted by IEEE International Conference on Automation Science and Engineering (CASE) 202