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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
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A Case Study on Attribute Recognition of Heated Metal Mark Image Using Deep Convolutional Neural Networks.

Keming Mao1, Duo Lu2, Dazhi E3

  • 1College of Software, Northeastern University, Shenyang 110004, China. maokm@mail.neu.edu.cn.

Sensors (Basel, Switzerland)
|June 9, 2018
PubMed
Summary

Computer vision and machine learning accurately recognize heated metal mark attributes from fire investigation images. This approach enhances fire cause analysis by identifying material type, heating, and cooling conditions with high precision.

Keywords:
attribute recognitionconvolutional neural networksheated metal mark

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Area of Science:

  • Forensic Science
  • Computer Vision
  • Machine Learning

Background:

  • Heated metal marks are crucial fire investigation evidence.
  • Traditional analysis relies on physics and chemistry, posing challenges for qualitative assessment.
  • Automated attribute recognition is needed to improve fire cause determination.

Purpose of the Study:

  • To develop and evaluate a computer vision and machine learning model for recognizing attributes of heated metal marks.
  • To establish a benchmark dataset for heated metal mark attribute recognition.
  • To assess the model's performance across various parameters and conditions.

Main Methods:

  • A benchmark dataset of heated metal mark images was created, selecting seven key attributes.
  • A deep convolutional neural network (CNN) model was implemented for feature representation and classification.
  • Extensive experiments were conducted, varying model structures, data augmentation, training modes, optimization, and batch sizes.

Main Results:

  • The fine-tuned CNN model achieved high recognition rates for multiple attributes.
  • Specific recognition rates included: metal type (0.925), heating mode (0.908), heating temperature (0.835), heating duration (0.917), cooling mode (0.928), placing duration (0.805), and relative humidity (0.92).
  • Analysis included parameter influence, recognition efficiency, and execution time.

Conclusions:

  • The proposed computer vision and machine learning approach effectively recognizes heated metal mark attributes.
  • This method offers a preferable and potentially practical solution for fire investigation.
  • The study demonstrates the utility of AI in enhancing forensic analysis of fire evidence.