Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalizati...Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.展开更多
Background:Magnetic resonance imaging(MRI)is crucial in modern medical diagnostics,providing detailed insights into soft tissue structures and pathological changes.Traditional grayscale images can sometimes obscure cr...Background:Magnetic resonance imaging(MRI)is crucial in modern medical diagnostics,providing detailed insights into soft tissue structures and pathological changes.Traditional grayscale images can sometimes obscure critical details,complicating accurate interpretations.Automated color coding of the MRI signal intensities may enhance the visualization of various pa-thologies,potentially leading to improved diagnostic accuracy and image quality.This paper aims to explore the effectiveness of color-coded MR image reconstruction in enhancing both diagnostic precision and overall image quality in musculoskeletal MRI.Methods:Two fellowship-trained musculoskeletal radiologists evaluated the images reconstructed with color coding,rating their diagnostic value,image quality,and visual appeal using a five-point Likert scale.To assess interrater reliability,Cohen's Kappa statistical analysis was performed.Additionally,descriptive statistics summarizing the Likert scores for diagnostic value,image quality,and visual appeal of the reconstructed images have been described.Results:Statistical analysis of the data revealed that the diagnostic value,image value,and visual appeal of the color-coded MR images were excellent in almost two-thirds of the data set.The minimum Likert score recorded was 3,signifying a good quality rating.Conclusion:Our study shows positive results,supporting the efficiency of color-coded MR imaging in aiding the conventional gray scale MR imaging to improve its diagnostic efficiency.展开更多
基金Supported by the National Natural Science Foundation of China(61473026,61104131)the Fundamental Research Funds for the Central Universities(JD1413)
文摘Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.
文摘Background:Magnetic resonance imaging(MRI)is crucial in modern medical diagnostics,providing detailed insights into soft tissue structures and pathological changes.Traditional grayscale images can sometimes obscure critical details,complicating accurate interpretations.Automated color coding of the MRI signal intensities may enhance the visualization of various pa-thologies,potentially leading to improved diagnostic accuracy and image quality.This paper aims to explore the effectiveness of color-coded MR image reconstruction in enhancing both diagnostic precision and overall image quality in musculoskeletal MRI.Methods:Two fellowship-trained musculoskeletal radiologists evaluated the images reconstructed with color coding,rating their diagnostic value,image quality,and visual appeal using a five-point Likert scale.To assess interrater reliability,Cohen's Kappa statistical analysis was performed.Additionally,descriptive statistics summarizing the Likert scores for diagnostic value,image quality,and visual appeal of the reconstructed images have been described.Results:Statistical analysis of the data revealed that the diagnostic value,image value,and visual appeal of the color-coded MR images were excellent in almost two-thirds of the data set.The minimum Likert score recorded was 3,signifying a good quality rating.Conclusion:Our study shows positive results,supporting the efficiency of color-coded MR imaging in aiding the conventional gray scale MR imaging to improve its diagnostic efficiency.
