Monthly Archives: August 2013

Recent Advances in Artificial Immune Systems: Models & Applications

Authors: Dipankar Dasgupta, Senhua Yu, Fernando Nino.

Source: Applied Soft Computing 11 (2011) 1574-1587.

Review: ♦♦♦

This paper revealed that recent research is centered on 4 major AIS algorithms: (1) negative selection algorithms; (2) artificial immune networks; (3) clonal selection algorithms; (4) Danger Theory and dendritic cell algorithms.

1. History of AIS in brief

2. Recent development in AIS

2.1 AIS models: Negative selection algorithms; Artificial immune network; Clonal selection algorithm; Danger Theory inspired algorithms; Dendritic cell algorithms; Other newly developed models.

2.2 Recent AIS applications

3. Remarks

This is a review paper, and the information contained is really big. I’d better read it again to get a better understanding on this topic.

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The Fundamental Axioms of SHM

Authors: Keith Worden, Charles R. Farrar, Graeme Manson and Gyuhae Park.

Source: Proc. R. Soc. A (2007) 463, 1639-1664

Review: ♦♦♦♦

This paper is intended to explicitly state and justify 7 axioms existing in the SHM area. The authors hope that this paper can give new researchers a starting point that alleviates the need to review the vast amounts of literature in this field, and stimulate discussion and thought within the community regarding these axioms.

1. Introduction

The questions need to be answered: (i) the existence of damage, (ii) the damage locations, (iii) the types of damage, and (iv) the damage severity.

Bring up the 7 axioms.

2 – 9 discuss and use examples to support the 7 axioms one by one.

10. Summary

The authors attempted to coalesce information that has been reported in the literature into axioms that form a set of basic principles for SHM.

An Introduction to Structural Health Monitoring

Authors: Charles R. Farrar & Keith Worden

Source: Phil. Trans. R. Soc. A (2007) 365, 303-315

Review: ♦♦♦

This paper is an introduction to some basic concepts, development history and trends, and challenges existing in the Structural Health Monitoring (SHM) area.

The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as SHM. Here, damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system’s performance.

1. Introduction

In the most general terms, damage can be defined as changes introduced into a system that adversely affect its current or future performance.

In terms of length-scales, all damage begins at the material level (defect or flaw), then grow and coalesce to cause component and then system-level damage. The term damage doesn’t necessarily imply a total loss of system functionality, but rather that the system is no longer operating in its optimal manner.

Under an extreme event, SHM is used for rapid condition screening. This screening is intended to provide, in near real-time, reliable information about system performance during such extreme events and the subsequent integrity of the system.

2. Brief historical overview

Challenges include the development of methods to optimally define the # and location of the sensors; identification of the features sensitive to small damage levels; the ability to discriminate changes in these features caused by damage from those caused by changing environmental and/or test conditions; the development of statistical methods to discriminate features from undamaged and damaged structures; and performance of comparative studies of different damage identification methods applied to common datasets.

3. The statistical pattern recognition paradigm

4. Challenges for SHM

5. Theme issue organization

6. Concluding comments

Like so many other technology fields, advancements in SHM will most likely come in small increments requiring diligent, focused and coordinated research efforts over long period of time.

Control Methodologies for A Heterogeneous Group of Untethered Magnetic Micro-Robots

Author: Steven Floyd, Eric Diller, Chytra Pawashe and Metin Sitti

Source: The International Journal of Robotics Research 2011 30: 1553.

Review: ♦♦♦

This paper proposed a methodology to make several micro-robots respond differently to the same driven magnetic field.

To achieve the uniqueness of each robot, the following designs were employed: (1) geometrically similar Mag-uBots with different values of magnetization; (2) geometrically dissimilar Mag-uBots with similar magnetization; and (3) geometrically dissimilar Mag-uBots with dissimilar magnetization. The magnitude and frequency of the imposed driving magnetic fields are used as selection methods among the Mag-uBots.

It is found that while fully decoupled control is not possible with this method, parallel actuation of sub-groups of Mag-uBots is possible and controllable.

1. Introduction

The methods proposed in this paper doesn’t require for a specialized substrate.

2. Tools & System

The robots in this work are actuated by 6 independent electromagnetic coils.

3. Modeling

3.1 Magnetic Torque

The magnetic forces are assumed to be negligible in the analyses.

3.2 Gravitational Rest Torque

To achieve stick-slip motion, the magnetic torque must overcome the gravitational rest torque to lift the Mag-uBots onto an edge.

3.3 Other Forces & Torques

Surface adhesion, electrostatic attraction and viscous damping.

3.4 Natural Frequency

4. Selection Methods

By choosing appropriate values for magnitude, direction, and frequency of the magnetic field, all, none, or some of the Mag-uBots in the workspace can be selected and translated.

4.1 Selection via Internal Magnetization

4.2 Selection via Shape Demagnetization Factor

4.3 Selection via Rotational Inertia

5 Results & Discussion

5.1, 5.2 & 5.3 are same titles with 4.1 – 4.3.

5.4 Demonstration

While independent control of an arbitrary individual was not possible, by establishing the appropriate rules and algorithms, an arbitrary final configuration of Mag-uBots was achieved from an arbitrary initial configuration.

6 Conclusions

3 methods for the control of heterogeneous groups of magnetic micro-robots were demonstrated.

Questions

Q: In section 5.2, the Fig. 12 shows that the R4 has the largest moving area while R6 has the narrowest. But from the analysis and following figures, I think R4 actually has the narrowest moving area and R6 has the largest (R5 has the medium area).

How to create a one-column ABSTRACT in a two-column document

To create a one-column abstract in a two-column document, it is suggested to implement the following codes:

\documentclass[twocolumn…]{…}

\usepackage{abstract}

\twocolumn[

          \maketitle                % need full-width title

          \begin{onecolabstract}

abstract text…

          \end{onecolabstract}

]

\saythanks   % typesets any \thanks commands

The above information comes from the document “The abstract package” by Peter Wilson and Herries Press, 2009/06/08

Stack VS. Heap

Stack

Stack is a special region of your computer’s memory that stores temporary variables created by each function (including the main() function). The stack is a “FILO” (first in, last out) data structure, that is managed and optimized by the CPU quite closely.

When a function exits, all of its variables are popped off of the stack (and hence lost forever). Thus stack variables are local in nature.

Another feature of the stack is that there is a limit (varies with OS) on the size of variables that can be stored on the stack.

Heap

The heap is a region of your computer’s memory that is not managed automatically for you, and is not as tightly managed by the CPU. It is a more free-floating region of memory (and is larger).

Comparison

Stack

  • Very fast access
  • don’t have to explicitly de-allocate variables
  • space is managed efficiently by CPU, memory will not become fragmented
  • local variables only
  • limit on stack size (OS-dependent)
  • variables cannot be resized

Heap

  • Variables can be accessed globally
  • no limit on memory size
  • (relatively) slow access
  • no guaranteed efficient use of space, memory may become fragmented over time
  • variables can be resized

The above information comes from Webpage (http://gribblelab.org/CBootcamp/7_Memory_Stack_vs_Heap.html).