A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envel...A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method （E-CICA） is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform（DWT） method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio（ER） of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals.展开更多
The CAAI International Conference on Artificial Intelligence (CICAI 2021) will be held at Hangzhou, China on June 5th-6th. CICAI is organized by Chinese Association for Artificial Intelligence (CAAI). The aim of CICAI...The CAAI International Conference on Artificial Intelligence (CICAI 2021) will be held at Hangzhou, China on June 5th-6th. CICAI is organized by Chinese Association for Artificial Intelligence (CAAI). The aim of CICAI is to promote advanced research in AI, and foster scientific exchange between researchers, practitioners, scientists, students, and engineers in AI and its affiliated disciplines.展开更多
The CAAI International Conference on Artificial Intelligence(CICAI 2021)will be held at Hangzhou,China on May 29th-30th.CICAI is organized by Chinese Association for Artificial Intelligence(CAAI).The aim of CICAI is t...The CAAI International Conference on Artificial Intelligence(CICAI 2021)will be held at Hangzhou,China on May 29th-30th.CICAI is organized by Chinese Association for Artificial Intelligence(CAAI).The aim of CICAI is to promote advanced research in AI,and foster scientific exchange between researchers,practitioners,scientists,students,and engineers in AI and its affiliated disciplines.CICAI 2021 will be a hybrid conference with both online and in-person presentations.展开更多
Background: Detection of extended spectrum beta lactamase producing bacteria is an important issue in the clinical settings. Objective: The purpose of the present study was to validate the Cica Beta Test 1 for detecti...Background: Detection of extended spectrum beta lactamase producing bacteria is an important issue in the clinical settings. Objective: The purpose of the present study was to validate the Cica Beta Test 1 for detection of extended spectrum beta-lactamase (ESBL) producing bacteria. Method: This analytical type of cross-sectional study was carried out in the Department of Microbiology and Immunology at Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka from January 2006 to December 2006 for a period of one (01) year. All the patients presented with the clinical features of urinary tract infection and surgical as well as burn wound infection at any age with both sexes were selected as study population. All bacteria were isolated and identified by their colony morphology, staining characters, pigment production, motility and other relevant biochemical tests. Phenotypic confirmation of ESBLs producing isolates were done by inhibitor potentiated disc diffusion test according to CLSI recommendation. The Cica Beta Test 1 was performed according to the manufacturer’s instructions. Result: A total number of 288 Gram negative bacteria were isolated. Among these isolates Cica Beta test 1 was positive in 97 strains and phenotypic confirmatory test was positive in 89 strains. The test sensitivity of Cica Beta Test 1 was 100% (95% CI 95.9% to 100.0%). Specificity of the test was 96.0% (95% CI 92.2% to 98.2%). The positive predictive value (PPV) and negative predictive value (NPV) were 92.7% (95% CI 84.5% to 95.7%) and 100.0% (95% CI 98.0% to 100.0%) respectively. The accuracy of the test was 97.2% (95% CI 95.1% to 99.1%). Area under ROC curve = 0.980 (95% CI 0.964 to 0.996);p value 0.0001. Conclusion: In conclusion, Cica Beta Test 1 is very high sensitivity and specificity for the detection of ESBL from Gram negative bacteria.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51475034)
文摘A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method （E-CICA） is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform（DWT） method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio（ER） of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals.
文摘The CAAI International Conference on Artificial Intelligence (CICAI 2021) will be held at Hangzhou, China on June 5th-6th. CICAI is organized by Chinese Association for Artificial Intelligence (CAAI). The aim of CICAI is to promote advanced research in AI, and foster scientific exchange between researchers, practitioners, scientists, students, and engineers in AI and its affiliated disciplines.
文摘The CAAI International Conference on Artificial Intelligence(CICAI 2021)will be held at Hangzhou,China on May 29th-30th.CICAI is organized by Chinese Association for Artificial Intelligence(CAAI).The aim of CICAI is to promote advanced research in AI,and foster scientific exchange between researchers,practitioners,scientists,students,and engineers in AI and its affiliated disciplines.CICAI 2021 will be a hybrid conference with both online and in-person presentations.
文摘Background: Detection of extended spectrum beta lactamase producing bacteria is an important issue in the clinical settings. Objective: The purpose of the present study was to validate the Cica Beta Test 1 for detection of extended spectrum beta-lactamase (ESBL) producing bacteria. Method: This analytical type of cross-sectional study was carried out in the Department of Microbiology and Immunology at Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka from January 2006 to December 2006 for a period of one (01) year. All the patients presented with the clinical features of urinary tract infection and surgical as well as burn wound infection at any age with both sexes were selected as study population. All bacteria were isolated and identified by their colony morphology, staining characters, pigment production, motility and other relevant biochemical tests. Phenotypic confirmation of ESBLs producing isolates were done by inhibitor potentiated disc diffusion test according to CLSI recommendation. The Cica Beta Test 1 was performed according to the manufacturer’s instructions. Result: A total number of 288 Gram negative bacteria were isolated. Among these isolates Cica Beta test 1 was positive in 97 strains and phenotypic confirmatory test was positive in 89 strains. The test sensitivity of Cica Beta Test 1 was 100% (95% CI 95.9% to 100.0%). Specificity of the test was 96.0% (95% CI 92.2% to 98.2%). The positive predictive value (PPV) and negative predictive value (NPV) were 92.7% (95% CI 84.5% to 95.7%) and 100.0% (95% CI 98.0% to 100.0%) respectively. The accuracy of the test was 97.2% (95% CI 95.1% to 99.1%). Area under ROC curve = 0.980 (95% CI 0.964 to 0.996);p value 0.0001. Conclusion: In conclusion, Cica Beta Test 1 is very high sensitivity and specificity for the detection of ESBL from Gram negative bacteria.
