The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic techno...The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic technology,plays a pivotal role in ensuring mine safety by enabling real-time identifi cation and accurate classification of vibration signals such as microseismic signals,blasting signals,and noise.These classifications are critical for improving the efficacy of ground pressure monitoring systems,conducting stability analyses of deep rock masses,and implementing timely and precise roadway support measures.Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations.This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals.Based on conventional convolutional neural networks,the proposed model further incorporates long short-term memory(LSTM)networks and attention mechanisms.The LSTM component eff ectively captures temporal correlations in time-series mining vibration data,while the attention mechanism enhances the models’ability to focus on critical features within the data.To validate the eff ectiveness of our proposed model,a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training,validation,and testing purposes.Results demonstrate that the proposed artifi cial intelligence-based classifi cation method achieves a higher recognition accuracy of 92.21%,significantly outperforming traditional manual classification methods.The proposed model represents a signifi cant advancement in ground pressure monitoring and disaster mitigation.展开更多
BACKGROUND:The dynamic monitoring of immune status is crucial to the precise and individualized treatment of sepsis.In this study,we aim to introduce a model to describe and monitor the immune status of sepsis and to ...BACKGROUND:The dynamic monitoring of immune status is crucial to the precise and individualized treatment of sepsis.In this study,we aim to introduce a model to describe and monitor the immune status of sepsis and to explore its prognostic value.METHODS:A prospective observational study was carried out in Zhongshan Hospital,Fudan University,enrolling septic patients admitted between July 2016 and December 2018.Blood samples were collected at days 1 and 3.Serum cytokine levels(e.g.,tumor necrosis factor-α[TNF-α],interleukin-10[IL-10])and CD14+monocyte human leukocyte antigen-D-related(HLA-DR)expression were measured to serve as immune markers.Classifi cation of each immune status,namely systemic inflammatory response syndrome(SIRS),compensatory anti-inflammatory response syndrome(CARS),and mixed antagonistic response syndrome(MARS),was defined based on levels of immune markers.Changes of immune status were classifi ed into four groups which were stabilization(SB),deterioration(DT),remission(RM),and non-remission(NR).RESULTS:A total of 174 septic patients were enrolled including 50 non-survivors.Multivariate analysis discovered that IL-10 and HLA-DR expression levels at day 3 were independent prognostic factors.Patients with MARS had the highest mortality rate.Immune status of 46.1%patients changed from day 1 to day 3.Among four groups of immune status changes,DT had the highest mortality rate,followed by NR,RM,and SB with mortality rates of 64.7%,42.9%,and 11.2%,respectively.CONCLUSIONS:Severe immune disorder defi ned as MARS or deterioration of immune status defi ned as DT lead to the worst outcomes.The preliminary model of the classifi cation and dynamic monitoring of immune status based on immune markers has prognostic values and is worthy of further investigation.展开更多
A novel method is proposed for the supervised classification of multifrequency polarimetric synthetic aperture radar （PolSAR） images. The coherency matrices in P-, L-, and C-bands are mapped onto a 9×9 matrix ...A novel method is proposed for the supervised classification of multifrequency polarimetric synthetic aperture radar （PolSAR） images. The coherency matrices in P-, L-, and C-bands are mapped onto a 9×9 matrix Ω based on the eigenvalue decomposition of the coherency matrix of each band. A boxcar filter is then performed on the matrix Ω. The filtered data are put into a complex Wishart classifier. Finally, the effectiveness of the proposed method is demonstrated with JPL/AIRSAR multifrequency PolSAR data acquired over the Flevoland area.展开更多
Paralytic shellfi sh poisoning(PSP)microalgae,as one of the harmful algal blooms,causes great damage to the of fshore fi shery,marine culture,and marine ecological environment.At present,there is no technique for real...Paralytic shellfi sh poisoning(PSP)microalgae,as one of the harmful algal blooms,causes great damage to the of fshore fi shery,marine culture,and marine ecological environment.At present,there is no technique for real-time accurate identifi cation of toxic microalgae,by combining three-dimensional fluorescence with machine learning(ML)and deep learning(DL),we developed methods to classify the PSP and non-PSP microalgae.The average classifi cation accuracies of these two methods for microalgae are above 90%,and the accuracies for discriminating 12 microalgae species in PSP and non-PSP microalgae are above 94%.When the emission wavelength is 650-690 nm,the fl uorescence characteristics bands(excitation wavelength)occur dif ferently at 410-480 nm and 500-560 nm for PSP and non-PSP microalgae,respectively.The identification accuracies of ML models(support vector machine(SVM),and k-nearest neighbor rule(k-NN)),and DL model(convolutional neural network(CNN))to PSP microalgae are 96.25%,96.36%,and 95.88%respectively,indicating that ML and DL are suitable for the classifi cation of toxic microalgae.展开更多
In 5G new radio(NR), polar codes are adopted for e MBB downlink control channels where the blind detection is employed in user equipment(UE) to identify the correct downlink control information(DCI). However, differen...In 5G new radio(NR), polar codes are adopted for e MBB downlink control channels where the blind detection is employed in user equipment(UE) to identify the correct downlink control information(DCI). However, different from that in the 4G LTE system, the cyclic redundancy check(CRC) in polar decoding plays both error correction and error detection roles. Consequently, the false alarm rates(FAR) may not meet the system requirements(FAR<1.52 × 10^(−5)). In this paper, to mitigate the FAR in polar code blind detection, we attach a binary classifier after the polar decoder to further remove the false alarm results and meanwhile retain the correct DCI. This classifier works by tracking the squared Euclidean distance ratio(SEDR) between the received signal and hypothesis. We derive an analytical method to fast compute proper classification threshold that is implementation-friendly in practical use. Combining the well-designed classifier, we show that some very short CRC sequences can even be used to meet the FAR requirements. This consequently reduces the CRC overhead and contributes to the system error performance improvements.展开更多
为进一步提高矿山高边坡稳定性,文章以实际工程为例,深入分析基于岩土工程勘察成果的矿山高边坡稳定性分析关键技术,通过工程地质勘查,采用质量指标(basic quality,BQ)、边坡质量分级(slope mass rating,SMR)和简化毕肖普法,对研究区内...为进一步提高矿山高边坡稳定性,文章以实际工程为例,深入分析基于岩土工程勘察成果的矿山高边坡稳定性分析关键技术,通过工程地质勘查,采用质量指标(basic quality,BQ)、边坡质量分级(slope mass rating,SMR)和简化毕肖普法,对研究区内重点边坡的稳定性进行分析。结果显示,研究区内重点边坡安全储备不足,不利于后续矿产资源开发工作的开展。基于此,文章提出采用主动柔性防护系统与预应力锚杆相结合的综合性边坡防护手段,对高边坡稳定性分析和加固工作的开展具有重要的现实意义。展开更多
基金supported in part by the National Science Fund for Distinguished Young Scholars under Grant (42025403)the National Key Research and Development Plan of China (2021YFA0716800)the National Key Research and Development Plan of China (2022YFC2903804)。
文摘The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic technology,plays a pivotal role in ensuring mine safety by enabling real-time identifi cation and accurate classification of vibration signals such as microseismic signals,blasting signals,and noise.These classifications are critical for improving the efficacy of ground pressure monitoring systems,conducting stability analyses of deep rock masses,and implementing timely and precise roadway support measures.Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations.This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals.Based on conventional convolutional neural networks,the proposed model further incorporates long short-term memory(LSTM)networks and attention mechanisms.The LSTM component eff ectively captures temporal correlations in time-series mining vibration data,while the attention mechanism enhances the models’ability to focus on critical features within the data.To validate the eff ectiveness of our proposed model,a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training,validation,and testing purposes.Results demonstrate that the proposed artifi cial intelligence-based classifi cation method achieves a higher recognition accuracy of 92.21%,significantly outperforming traditional manual classification methods.The proposed model represents a signifi cant advancement in ground pressure monitoring and disaster mitigation.
基金the National Natural Science Foundation of China(81471840,81171837)the Shanghai Traditional Medicine Development Project(ZY3-CCCX3-3018,ZHYY-ZXYJH-201615)the Key Project of Shanghai Municipal Health Bureau(2016ZB0202).
文摘BACKGROUND:The dynamic monitoring of immune status is crucial to the precise and individualized treatment of sepsis.In this study,we aim to introduce a model to describe and monitor the immune status of sepsis and to explore its prognostic value.METHODS:A prospective observational study was carried out in Zhongshan Hospital,Fudan University,enrolling septic patients admitted between July 2016 and December 2018.Blood samples were collected at days 1 and 3.Serum cytokine levels(e.g.,tumor necrosis factor-α[TNF-α],interleukin-10[IL-10])and CD14+monocyte human leukocyte antigen-D-related(HLA-DR)expression were measured to serve as immune markers.Classifi cation of each immune status,namely systemic inflammatory response syndrome(SIRS),compensatory anti-inflammatory response syndrome(CARS),and mixed antagonistic response syndrome(MARS),was defined based on levels of immune markers.Changes of immune status were classifi ed into four groups which were stabilization(SB),deterioration(DT),remission(RM),and non-remission(NR).RESULTS:A total of 174 septic patients were enrolled including 50 non-survivors.Multivariate analysis discovered that IL-10 and HLA-DR expression levels at day 3 were independent prognostic factors.Patients with MARS had the highest mortality rate.Immune status of 46.1%patients changed from day 1 to day 3.Among four groups of immune status changes,DT had the highest mortality rate,followed by NR,RM,and SB with mortality rates of 64.7%,42.9%,and 11.2%,respectively.CONCLUSIONS:Severe immune disorder defi ned as MARS or deterioration of immune status defi ned as DT lead to the worst outcomes.The preliminary model of the classifi cation and dynamic monitoring of immune status based on immune markers has prognostic values and is worthy of further investigation.
基金supported in part by the National Natural Science Fundation of China(4117131761132008+1 种基金61490693)Aeronautical Science Foundation of China(20132058003)
文摘A novel method is proposed for the supervised classification of multifrequency polarimetric synthetic aperture radar （PolSAR） images. The coherency matrices in P-, L-, and C-bands are mapped onto a 9×9 matrix Ω based on the eigenvalue decomposition of the coherency matrix of each band. A boxcar filter is then performed on the matrix Ω. The filtered data are put into a complex Wishart classifier. Finally, the effectiveness of the proposed method is demonstrated with JPL/AIRSAR multifrequency PolSAR data acquired over the Flevoland area.
基金Supported by the National Natural Science Foundation of China(No.41972244)partially supported by the Science and Technology Basic Resources Survey of the Ministry of Science and Technology(No.2018FY100201)+3 种基金the National Key Research and Development Program(No.2019YFC1407900)to Siyu GOUShuai ZHANGWenyu GANand Tianjiu JIANG。
文摘Paralytic shellfi sh poisoning(PSP)microalgae,as one of the harmful algal blooms,causes great damage to the of fshore fi shery,marine culture,and marine ecological environment.At present,there is no technique for real-time accurate identifi cation of toxic microalgae,by combining three-dimensional fluorescence with machine learning(ML)and deep learning(DL),we developed methods to classify the PSP and non-PSP microalgae.The average classifi cation accuracies of these two methods for microalgae are above 90%,and the accuracies for discriminating 12 microalgae species in PSP and non-PSP microalgae are above 94%.When the emission wavelength is 650-690 nm,the fl uorescence characteristics bands(excitation wavelength)occur dif ferently at 410-480 nm and 500-560 nm for PSP and non-PSP microalgae,respectively.The identification accuracies of ML models(support vector machine(SVM),and k-nearest neighbor rule(k-NN)),and DL model(convolutional neural network(CNN))to PSP microalgae are 96.25%,96.36%,and 95.88%respectively,indicating that ML and DL are suitable for the classifi cation of toxic microalgae.
基金supported in part by National Natural Science Foundation of China(No.62471054)in part by National Natural Science Foundation of China(No.92467301)+3 种基金in part by the National Natural Science Foundation of China(No.62201562)in part by the National Natural Science Foundation of China(No.62371063)in part by the National Natural Science Foundation of China(No.62321001)in part by Liaoning Provincial Natural Science Foundation of China(No.2024–BSBA–51).
文摘In 5G new radio(NR), polar codes are adopted for e MBB downlink control channels where the blind detection is employed in user equipment(UE) to identify the correct downlink control information(DCI). However, different from that in the 4G LTE system, the cyclic redundancy check(CRC) in polar decoding plays both error correction and error detection roles. Consequently, the false alarm rates(FAR) may not meet the system requirements(FAR<1.52 × 10^(−5)). In this paper, to mitigate the FAR in polar code blind detection, we attach a binary classifier after the polar decoder to further remove the false alarm results and meanwhile retain the correct DCI. This classifier works by tracking the squared Euclidean distance ratio(SEDR) between the received signal and hypothesis. We derive an analytical method to fast compute proper classification threshold that is implementation-friendly in practical use. Combining the well-designed classifier, we show that some very short CRC sequences can even be used to meet the FAR requirements. This consequently reduces the CRC overhead and contributes to the system error performance improvements.
文摘为进一步提高矿山高边坡稳定性,文章以实际工程为例,深入分析基于岩土工程勘察成果的矿山高边坡稳定性分析关键技术,通过工程地质勘查,采用质量指标(basic quality,BQ)、边坡质量分级(slope mass rating,SMR)和简化毕肖普法,对研究区内重点边坡的稳定性进行分析。结果显示,研究区内重点边坡安全储备不足,不利于后续矿产资源开发工作的开展。基于此,文章提出采用主动柔性防护系统与预应力锚杆相结合的综合性边坡防护手段,对高边坡稳定性分析和加固工作的开展具有重要的现实意义。
