Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical si...Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%.展开更多
In order to increase the stability of the Mongolia power system, a single-phase automatic reclosing device (SPAR) was introduced on double-circuit power lines built with a size of 330 kV, operating on a voltage of 220...In order to increase the stability of the Mongolia power system, a single-phase automatic reclosing device (SPAR) was introduced on double-circuit power lines built with a size of 330 kV, operating on a voltage of 220 kV and a length of 250 km. These overhead power lines (L-213, L-214) connect the 220/110/35 kV “Songino” substation with the “Mandal” substation and form system networks. This paper presents the challenges encountered when implementing single-phase automatic reclosing (SPAR) devices and compares the changes in power system parameters before and after SPAR deployment for a long 220 kV line. Simulations and analyses were carried out using DIgSILENT PowerFactory software, focusing on rotor angle stability, and the overall impact on the power system during short-circuit faults. The evaluation also utilized measurement data from the Wide Area Monitoring System (WAMS) to compare system behavior pre- and post-implementation of SPAR. The findings reveal that SPAR significantly enhances system reliability and stability, effectively mitigating the risk of oscillations and stability loss triggered by short circuits. This improvement contributes to a more resilient power system, reducing the potential for disturbances caused by faults.展开更多
BACKGROUND A total of 100 patients diagnosed with mixed hemorrhoids from October 2022 to September 2023 in our hospital were randomly divided into groups by dice rolling and compared with the efficacy of different tre...BACKGROUND A total of 100 patients diagnosed with mixed hemorrhoids from October 2022 to September 2023 in our hospital were randomly divided into groups by dice rolling and compared with the efficacy of different treatment options.AIM To analyze the clinical effect and prognosis of mixed hemorrhoids treated with polidocanol injection combined with automatic elastic thread ligation operation(RPH).METHODS A total of 100 patients with mixed hemorrhoids who visited our hospital from October 2022 to September 2023 were selected and randomly divided into the control group(n=50)and the treatment group(n=50)by rolling the dice.The procedure for prolapse and hemorrhoids(PPH)was adopted in the control group,while polidocanol foam injection+RPH was adopted in the treatment group.The therapeutic effects,operation time,wound healing time,hospital stay,pain situation(24 hours post-operative pain score,first defecation pain score),quality of life(QOL),incidence of complications(post-operative hemorrhage,edema,infection),incidence of anal stenosis 3 months post-operatively and recurrence rate 1 year post-operatively of the two groups were compared.RESULTS Compared with the control group,the total effective rate of treatment group was higher,and the difference was significant(P<0.05).The operation time/wound healing time/hospital stay in the treatment group were shorter than those in the control group(P<0.05).The pain scores at 24 hours after operation/first defecation pain score of the treatment group was significantly lower than those in the control group(P<0.05).After surgery,the QOL scores of the two groups decreased,with the treatment group having higher scores than that of the control group(P<0.05).Compared with the control group,the incidence of postoperative complications in the treatment group was lower,and the difference was significant(P<0.05);However,there was no significant difference in the incidence of postoperative bleeding between the two groups(P>0.05);There was no significant difference in the incidence of anal stenosis 3 months after operation and the recurrence rate 1 year after operation between the two groups(P>0.05).CONCLUSION For patients with mixed hemorrhoids,the therapeutic effect achieved by using polidocanol injection combined with RPH was better.The wounds of the patients healed faster,the postoperative pain was milder,QOL improved,and the incidence of complications was lower,and the short-term and long-term prognosis was good.展开更多
Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
BACKGROUND Depression is a leading global health concern with high suicide rates and recurrence.Cognitive models suggest that mental pain and automatic thoughts are central to depression's impact.The hypothesis is...BACKGROUND Depression is a leading global health concern with high suicide rates and recurrence.Cognitive models suggest that mental pain and automatic thoughts are central to depression's impact.The hypothesis is that self-compassion will be negatively associated with mental pain,mediated by automatic thoughts.AIM To determine the mediating role of automatic thoughts in the relationship between self-compassion and mental pain in individuals with depression.METHODS This cross-sectional study included 389 inpatients with depression from Tianjin Anding Hospital.Participants completed the Self-Compassion Scale-Chinese Version(SCS-C),Automatic Thought Questionnaire(ATQ),and Orbach&Mikulincer Mental Pain Scale-Chinese Version(OMMP).Data were analyzed using Pearson correlations,multiple linear regressions,and mediation analysis.RESULTS The SCS-C total score was 68.95±14.89,ATQ was 87.02±28.91,and OMMP was 129.01±36.74.Correlation analysis showed mental pain was positively associated with automatic thoughts(r=0.802,P<0.001)and negatively with selfcompassion(r=-0.636,P<0.001).Regression analysis indicated automatic thoughts(β=0.623,P<0.001)and self-compassion(β=-0.301,P<0.001)significantly predicted mental pain.Mediation analysis confirmed automatic thoughts partially mediated the relationship between self-compassion and mental pain(ab=-0.269,95%CI:-0.363 to-0.212).CONCLUSION Self-compassion is inversely related to mental pain in depression,with automatic thoughts playing a mediating role.These findings suggest potential therapeutic targets for alleviating mental pain in depressed patients.展开更多
In this paper,the author follows the trail of C.Malabou,Q.Meillassoux,and G.Deleuze and tries to test three philosophical concepts that seem to be particularly threatened in the era of automatic digital reproduction.T...In this paper,the author follows the trail of C.Malabou,Q.Meillassoux,and G.Deleuze and tries to test three philosophical concepts that seem to be particularly threatened in the era of automatic digital reproduction.These three concepts are plasticity(defended for many years by C.Malabou),contingency(reconstructed by Q.Meillassoux),and virtuality(developed by G.Deleuze).The main task of the text will be to reflect on which of these three concepts better protects our thinking against automation and stays faithful to the ideal of creativity.In what sense are plasticity,contingency,and the possibility of virtualization the a priori condition of any transformation,physical or intellectual,affective or conceptual metamorphosis?In what sense are these three concepts the only conditions for the survival of every living being?Would a being without contingency,plasticity,and disposition to virtualization simply be a dead being?展开更多
Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalizat...Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.展开更多
A rotary sealing device that automatically compensates for wear is designed to address the issues of easy wear and the short service life of the rotary sealing device with automatic wear compensation in mining machine...A rotary sealing device that automatically compensates for wear is designed to address the issues of easy wear and the short service life of the rotary sealing device with automatic wear compensation in mining machinery.After the end face of the guide sleeve wears out,it still tightly adheres to the sealing valve seat under the pressure difference,achieving automatic wear compensation.Based on fluid-solid coupling technology,the structural strength of the rotary sealing device was checked.The influence of factors on the sealing performance of rotary sealing devices was studied using the control variable method.The results show that as the pressure of water increases,the leakage rate of the sealing device decreases,and after 30 MPa,the leakage rate is almost 0 mL/h.The temperature of the rotating sealing device increases with the increase of rotation speed or pressure,and the temperature is more affected by the rotation speed factor.The frictional torque increases with increasing pressure and is independent of rotational speed.Comprehensive analysis shows that the wear resistance and reliability level of the sealing guide sleeve material is PVDF>PEEK>PE>PA.This study designs a high-pressure automatic compensation wear rotary sealing device and selects the optimal sealing material,providing technical support for the application of high-pressure water jet in mining machinery.展开更多
Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empow...Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empowered by the large language model(LLM),we propose a novel multi-agent collaborative framework to streamline the end-to-end OPC UA IM modeling process.Each agent is equipped with meticulously engineered prompt templates,augmenting their capacity to execute specific tasks.We conduct modeling experiments using real textual data to demonstrate the effectiveness of the proposed method,improving modeling efficiency and reducing the labor workload.展开更多
The recognition and positioning of material baskets are key links in the automatic workpiece cleaning device.Aiming at the problems of low recognition accuracy and poor precision of traditional visual methods for mate...The recognition and positioning of material baskets are key links in the automatic workpiece cleaning device.Aiming at the problems of low recognition accuracy and poor precision of traditional visual methods for material basket recognition,a control system of automatic workpiece cleaning device based on YOLOv5 was designed.The YOLOv5 detection algorithm was improved by introducing the attention mechanism and optimizing the loss function,which enhanced the attention to the target area and improved the accuracy of feature extraction,thus realizing the position recognition and coordinate acquisition of workpiece material baskets.In addition,a cleaning system with Siemens S7-1200 PLC as the control core was designed.By controlling servo motors to drive the gantry and adjust the operation of the crane,the automatic grabbing and handling of material baskets were realized,and the automatic control of the cleaning process was achieved.Meanwhile,a human-computer interaction(HMI)and monitoring interface was designed,which could intuitively display the operating status of material baskets and improve the interaction capability of the automatic workpiece cleaning device.展开更多
Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins ar...Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins are limited,especially in automation,heavily dependent on large amounts of data and resources,lacking the flexibility to adapt to different scenarios.To address these challenges,this paper introduces a novel image segmentation model,CableSAM,specifically designed for automated segmentation of cabin cables.CableSAM improves segmentation efficiency and accuracy using knowledge distillation and employs a context ensemble strategy.It accurately segments cables in various scenarios with minimal input prompts.Comparative experiments on three cable datasets demonstrate that CableSAM surpasses other advanced cable segmentation methods in performance.展开更多
Imaging evaluation of lymph node metastasis and infiltration faces problems such as low artificial outline efficiency and insufficient consistency.Deep learning technology based on convolutional neural networks has gr...Imaging evaluation of lymph node metastasis and infiltration faces problems such as low artificial outline efficiency and insufficient consistency.Deep learning technology based on convolutional neural networks has greatly improved the technical effect of radiomics in lymph node pathological characteristics analysis and efficacy monitoring through automatic lymph node detection,precise segmentation and three-dimensional reconstruction algorithms.This review focuses on the automatic lymph node segmentation model,treatment response prediction algorithm and benign and malignant differential diagnosis system for multimodal imaging,in order to provide a basis for further research on artificial intelligence to assist lymph node disease management and clinical decision-making,and provide a reference for promoting the construction of a system for accurate diagnosis,personalized treatment and prognostic evaluation of lymph node-related diseases.展开更多
Esophageal cancer(EC),a common malignant tumor of the digestive tract,requires early diagnosis and timely treatment to improve patient prognosis.Automated detection of EC using medical imaging has the potential to inc...Esophageal cancer(EC),a common malignant tumor of the digestive tract,requires early diagnosis and timely treatment to improve patient prognosis.Automated detection of EC using medical imaging has the potential to increase screening efficiency and diagnostic accuracy,thereby significantly improving long-term survival rates and the quality of life of patients.Recent advances in deep learning(DL),particularly convolutional neural networks,have demons-trated remarkable performance in medical imaging analysis.These techniques have shown significant progress in the automated identification of malignant tumors,quantitative analysis of lesions,and improvement in diagnostic accuracy and efficiency.This article comprehensively examines the research progress of DL in medical imaging for EC,covering various imaging modalities such as digital pathology,endoscopy,computed tomography,etc.It explores the clinical value and application prospects of DL in EC screening and diagnosis.Additionally,the article addresses several critical challenges that must be overcome for the clinical translation of DL techniques,including constructing high-quality datasets,promoting multimodal feature fusion,and optimizing artificial intelligence-clinical workflow integration.By providing a detailed overview of the current state of DL in EC imaging and highlighting the key challenges and future directions,this article aims to guide future research and facilitate the clinical implementation of DL technologies in EC management,ultimately contributing to better patient outcomes.展开更多
With the development of machine translation technology,automatic pre-editing has attracted increasing research attention for its important role in improving translation quality and efficiency.This study utilizes UAM C...With the development of machine translation technology,automatic pre-editing has attracted increasing research attention for its important role in improving translation quality and efficiency.This study utilizes UAM Corpus Tool 3.0 to annotate and categorize 99 key publications between 1992 and 2024,tracing the research paths and technological evolution of automatic pre-translation editing.The study finds that current approaches can be classified into four categories:controlled language-based approaches,text simplification approaches,interlingua-based approaches,and large language model-driven approaches.By critically examining their technical features and applicability in various contexts,this review aims to provide valuable insights to guide the future optimization and expansion of pre-translation editing systems.展开更多
The various bioacoustics signals obtained with auscultation contain complex clinical information that has been traditionally used as biomarkers,however,they are not extensively used in clinical studies owing to their ...The various bioacoustics signals obtained with auscultation contain complex clinical information that has been traditionally used as biomarkers,however,they are not extensively used in clinical studies owing to their spatiotemporal limitations.In this study,we developed a wearable stethoscope for wireless,skinattachable,low-power,continuous,real-time auscultation using a lung-sound-monitoring-patch(LSMP).LSMP can monitor respiratory function through a mobile app and classify normal and adventitious breathing by comparing their unique acoustic characteristics.The human heart and breathing sounds from humans can be distinguished from complex sound signals consisting of a mixture of bioacoustic signals and external noise.The performance of the LSMP sensor was further demonstrated in pediatric patients with asthma and elderly chronic obstructive pulmonary disease(COPD)patients where wheezing sounds were classified at specific frequencies.In addition,we developed a novel method for counting wheezing events based on a two-dimensional convolutional neural network deep-learning model constructed de novo and trained with our augmented fundamental lung-sound data set.We implemented a counting algorithm to identify wheezing events in real-time regardless of the respiratory cycle.The artificial intelligence-based adventitious breathing event counter distinguished>80%of the events(especially wheezing)in long-term clinical applications in patients with COPD.展开更多
This article proposes an algebraic model predictive control(MPC)method for automatic landing.While defining the constraint functions in the optimization problem,the tangent hyperbolic function is preferred.Therefore,t...This article proposes an algebraic model predictive control(MPC)method for automatic landing.While defining the constraint functions in the optimization problem,the tangent hyperbolic function is preferred.Therefore,the optimization problem turns into an unconstrained,continuous,and differentiable form.An analytical two-step method is also proposed to solve the rest of the problem.In the first step,it is assumed that only input constraints are active and states are unconstrained.The optimal solution for this case is calculated directly with the optimality condition.The calculated control signal is revised in the second step according to system dynamics and state constraints.Simulation results of the auto-landing system show that the MPC computation speed is significantly increased by the new algebraic MPC(AMPC)without compromising the control performance,which makes the method realistic for using MPC in systems with high-speed changing dynamics.展开更多
Considering the challenges posed by external disturbances on carrier-based aircraft land-ing control,higher demands are required for the precision and convergence of the carrier landingcontrol system.First,this paper ...Considering the challenges posed by external disturbances on carrier-based aircraft land-ing control,higher demands are required for the precision and convergence of the carrier landingcontrol system.First,this paper proposes an Adaptive Terminal Sliding Combined Super TwistingControl(ATS-STC)method to address the issues of low precision,slow convergence,and poor dis-turbance rejection capability resulting from external disturbances,such as carrier air-wake and deckmotion.By introducing a nonlinear term into the sliding surface and employing an integralapproach,the proposed ATS-STC method can ensure finite-time convergence and mitigate the chat-tering problem.An adaptive law is also utilized to estimate the external disturbances,therebyenhancing the anti-disturbance performance.Then,the stability and convergence time analysis ofthe designed controller are conducted.Based on the proposed method,an Automatic Carrier Land-ing System(ACLS)is developed to perform the carrier landing control task.Furthermore,a multi-dimensional validation is carried out.For the numerical simulation test,the Terminal Sliding ModeControl(TSMC)method and Proportion Integration Differentiation(PID)method are introducedas comparison,the quantitative assessment results show that the tracking error of TSMC and PIDcan reach 1.5 times and 2 times that of the proposed method.Finally,the Hardware-in-the-Loop(HIL)test and real flight test are conducted.All the experimental results demonstrate that the pro-posed control method is more effective and precise.展开更多
This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 bac...This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 backbone network,followed by adaptive average pooling to scale the features to a fixed length.Subsequently,product quantization with residuals(PQR)is applied to convert continuous feature vectors into discrete features representations,preserving essential information insensitive to image quality variations.The quantized and original features are concatenated and fed into a self-attention mechanism to capture keratitis-related features.Finally,these enhanced features are classified through a fully connected layer.Experiments on clinical low-quality(LQ)images show that ADK_FVQSAM achieves accuracies of 87.7%,81.9%,and 89.3% for keratitis,other corneal abnormalities,and normal corneas,respectively.Compared to DenseNet121,Swin transformer,and InceptionResNet,ADK_FVQSAM improves average accuracy by 3.1%,11.3%,and 15.3%,respectively.These results demonstrate that ADK_FVQSAM significantly enhances the recognition performance of keratitis based on LQ slit-lamp images,offering a practical approach for clinical application.展开更多
Traditional calibration method for the digital inclinometer relies on manual inspection,and results in its disadvantages of complicated process,low-efficiency and human errors easy to be introduced.To improve both the...Traditional calibration method for the digital inclinometer relies on manual inspection,and results in its disadvantages of complicated process,low-efficiency and human errors easy to be introduced.To improve both the calibration accuracy and efficiency of digital inclinometer,an automatic digital inclinometer calibration system was developed in this study,and a new display tube recognition algorithm was proposed.First,a high-precision automatic turntable was taken as the reference to calculate the indication error of the inclinometer.Then,the automatic inclinometer calibration control process and the digital inclinometer zero-setting function were formulated.For display tube recognition,a new display tube recognition algorithm combining threading method and feature extraction method was proposed.Finally,the calibration system was calibrated by photoelectric autocollimator and regular polygon mirror,and the calibration system error and repeatability were calculated via a series of experiments.The experimental results showed that the indication error of the proposed calibration system was less than 4",and the repeatability was 3.9".A digital inclinometer with the resolution of 0.1°was taken as a testing example,within the calibration points'range of[-90°,90°],the repeatability of the testing was 0.085°,and the whole testing process was less than 90 s.The digital inclinometer indication error is mainly introduced by the digital inclinometer resolution according to the uncertainty evaluation.展开更多
Robot-assisted surgery has become an indispensable component in modern neurosurgical procedures.However,existing registration methods for neurosurgical robots often rely on high-end hardware and involve prolonged or u...Robot-assisted surgery has become an indispensable component in modern neurosurgical procedures.However,existing registration methods for neurosurgical robots often rely on high-end hardware and involve prolonged or unstable registration times,limiting their applicability in dynamic and time-sensitive intraoperative settings.This paper proposes a novel fully automatic monocular-based registration and real-time tracking method.First,dedicated fiducials are designed,and an automatic preoperative and intraoperative detection method for these fiducials is introduced.Second,a geometric representation of the fiducials is constructed based on a 2D KD-Tree.Through a two-stage optimization process,the depth of 2D fiducials is estimated,and 2D-3D correspondences are established to achieve monocular registration.This approach enables fully automatic intraoperative registration using only a single optical camera.Finally,a six-degree-of-freedom visual servo control strategy inspired by the mass-spring-damper system is proposed.By integrating artificial potential field and admittance control,the strategy ensures real-time responsiveness and stable tracking.Experimental results demonstrate that the proposed method achieves a registration time of 0.23 s per instance with an average error of 0.58 mm.Additionally,the motion performance of the control strategy has been validated.Preliminary experiments verify the effectiveness of MonoTracker in dynamic tracking scenarios.This method holds promise for enhancing the adaptability of neurosurgical robots and offers significant clinical application potential.展开更多
基金financially supported by the National Key Research and Development Program of China (2022YFB3706802)。
文摘Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%.
文摘In order to increase the stability of the Mongolia power system, a single-phase automatic reclosing device (SPAR) was introduced on double-circuit power lines built with a size of 330 kV, operating on a voltage of 220 kV and a length of 250 km. These overhead power lines (L-213, L-214) connect the 220/110/35 kV “Songino” substation with the “Mandal” substation and form system networks. This paper presents the challenges encountered when implementing single-phase automatic reclosing (SPAR) devices and compares the changes in power system parameters before and after SPAR deployment for a long 220 kV line. Simulations and analyses were carried out using DIgSILENT PowerFactory software, focusing on rotor angle stability, and the overall impact on the power system during short-circuit faults. The evaluation also utilized measurement data from the Wide Area Monitoring System (WAMS) to compare system behavior pre- and post-implementation of SPAR. The findings reveal that SPAR significantly enhances system reliability and stability, effectively mitigating the risk of oscillations and stability loss triggered by short circuits. This improvement contributes to a more resilient power system, reducing the potential for disturbances caused by faults.
文摘BACKGROUND A total of 100 patients diagnosed with mixed hemorrhoids from October 2022 to September 2023 in our hospital were randomly divided into groups by dice rolling and compared with the efficacy of different treatment options.AIM To analyze the clinical effect and prognosis of mixed hemorrhoids treated with polidocanol injection combined with automatic elastic thread ligation operation(RPH).METHODS A total of 100 patients with mixed hemorrhoids who visited our hospital from October 2022 to September 2023 were selected and randomly divided into the control group(n=50)and the treatment group(n=50)by rolling the dice.The procedure for prolapse and hemorrhoids(PPH)was adopted in the control group,while polidocanol foam injection+RPH was adopted in the treatment group.The therapeutic effects,operation time,wound healing time,hospital stay,pain situation(24 hours post-operative pain score,first defecation pain score),quality of life(QOL),incidence of complications(post-operative hemorrhage,edema,infection),incidence of anal stenosis 3 months post-operatively and recurrence rate 1 year post-operatively of the two groups were compared.RESULTS Compared with the control group,the total effective rate of treatment group was higher,and the difference was significant(P<0.05).The operation time/wound healing time/hospital stay in the treatment group were shorter than those in the control group(P<0.05).The pain scores at 24 hours after operation/first defecation pain score of the treatment group was significantly lower than those in the control group(P<0.05).After surgery,the QOL scores of the two groups decreased,with the treatment group having higher scores than that of the control group(P<0.05).Compared with the control group,the incidence of postoperative complications in the treatment group was lower,and the difference was significant(P<0.05);However,there was no significant difference in the incidence of postoperative bleeding between the two groups(P>0.05);There was no significant difference in the incidence of anal stenosis 3 months after operation and the recurrence rate 1 year after operation between the two groups(P>0.05).CONCLUSION For patients with mixed hemorrhoids,the therapeutic effect achieved by using polidocanol injection combined with RPH was better.The wounds of the patients healed faster,the postoperative pain was milder,QOL improved,and the incidence of complications was lower,and the short-term and long-term prognosis was good.
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
文摘BACKGROUND Depression is a leading global health concern with high suicide rates and recurrence.Cognitive models suggest that mental pain and automatic thoughts are central to depression's impact.The hypothesis is that self-compassion will be negatively associated with mental pain,mediated by automatic thoughts.AIM To determine the mediating role of automatic thoughts in the relationship between self-compassion and mental pain in individuals with depression.METHODS This cross-sectional study included 389 inpatients with depression from Tianjin Anding Hospital.Participants completed the Self-Compassion Scale-Chinese Version(SCS-C),Automatic Thought Questionnaire(ATQ),and Orbach&Mikulincer Mental Pain Scale-Chinese Version(OMMP).Data were analyzed using Pearson correlations,multiple linear regressions,and mediation analysis.RESULTS The SCS-C total score was 68.95±14.89,ATQ was 87.02±28.91,and OMMP was 129.01±36.74.Correlation analysis showed mental pain was positively associated with automatic thoughts(r=0.802,P<0.001)and negatively with selfcompassion(r=-0.636,P<0.001).Regression analysis indicated automatic thoughts(β=0.623,P<0.001)and self-compassion(β=-0.301,P<0.001)significantly predicted mental pain.Mediation analysis confirmed automatic thoughts partially mediated the relationship between self-compassion and mental pain(ab=-0.269,95%CI:-0.363 to-0.212).CONCLUSION Self-compassion is inversely related to mental pain in depression,with automatic thoughts playing a mediating role.These findings suggest potential therapeutic targets for alleviating mental pain in depressed patients.
文摘In this paper,the author follows the trail of C.Malabou,Q.Meillassoux,and G.Deleuze and tries to test three philosophical concepts that seem to be particularly threatened in the era of automatic digital reproduction.These three concepts are plasticity(defended for many years by C.Malabou),contingency(reconstructed by Q.Meillassoux),and virtuality(developed by G.Deleuze).The main task of the text will be to reflect on which of these three concepts better protects our thinking against automation and stays faithful to the ideal of creativity.In what sense are plasticity,contingency,and the possibility of virtualization the a priori condition of any transformation,physical or intellectual,affective or conceptual metamorphosis?In what sense are these three concepts the only conditions for the survival of every living being?Would a being without contingency,plasticity,and disposition to virtualization simply be a dead being?
基金supported in part by the Jiangsu Province Construction System Science and Technology Project(No.2024ZD056)the Research Development Fund of Xi’an Jiaotong-Liverpool University(No.RDF-24-01-097).
文摘Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.
基金Supported by Jiangsu Provincial Natural Science Foundation(Grant No.BK20231497)Jiangsu Provincial Post graduate Research&Practice Innovation Program(Grant No.KYCX25_2982)+3 种基金China University of Mining and Technology Graduate Innovation Program(Grant No.2025WLKXJ094)National Natural Science Foundation of China(Grant No.51975573)National Key R&D Program of China(Grant No.2022YFC2905600)Priority Academic Program Development of Jiangsu Higher Education Institute of China.
文摘A rotary sealing device that automatically compensates for wear is designed to address the issues of easy wear and the short service life of the rotary sealing device with automatic wear compensation in mining machinery.After the end face of the guide sleeve wears out,it still tightly adheres to the sealing valve seat under the pressure difference,achieving automatic wear compensation.Based on fluid-solid coupling technology,the structural strength of the rotary sealing device was checked.The influence of factors on the sealing performance of rotary sealing devices was studied using the control variable method.The results show that as the pressure of water increases,the leakage rate of the sealing device decreases,and after 30 MPa,the leakage rate is almost 0 mL/h.The temperature of the rotating sealing device increases with the increase of rotation speed or pressure,and the temperature is more affected by the rotation speed factor.The frictional torque increases with increasing pressure and is independent of rotational speed.Comprehensive analysis shows that the wear resistance and reliability level of the sealing guide sleeve material is PVDF>PEEK>PE>PA.This study designs a high-pressure automatic compensation wear rotary sealing device and selects the optimal sealing material,providing technical support for the application of high-pressure water jet in mining machinery.
基金supported supported by the Fundamental Research Funds for the Central Universities(226-2024-00004)the National Natural Science Foundation of China(U23 A20326)Key Research and Development Program of Zhejiang Province(2025C01061).
文摘Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empowered by the large language model(LLM),we propose a novel multi-agent collaborative framework to streamline the end-to-end OPC UA IM modeling process.Each agent is equipped with meticulously engineered prompt templates,augmenting their capacity to execute specific tasks.We conduct modeling experiments using real textual data to demonstrate the effectiveness of the proposed method,improving modeling efficiency and reducing the labor workload.
文摘The recognition and positioning of material baskets are key links in the automatic workpiece cleaning device.Aiming at the problems of low recognition accuracy and poor precision of traditional visual methods for material basket recognition,a control system of automatic workpiece cleaning device based on YOLOv5 was designed.The YOLOv5 detection algorithm was improved by introducing the attention mechanism and optimizing the loss function,which enhanced the attention to the target area and improved the accuracy of feature extraction,thus realizing the position recognition and coordinate acquisition of workpiece material baskets.In addition,a cleaning system with Siemens S7-1200 PLC as the control core was designed.By controlling servo motors to drive the gantry and adjust the operation of the crane,the automatic grabbing and handling of material baskets were realized,and the automatic control of the cleaning process was achieved.Meanwhile,a human-computer interaction(HMI)and monitoring interface was designed,which could intuitively display the operating status of material baskets and improve the interaction capability of the automatic workpiece cleaning device.
基金supported by the Innovation Foundation of National Commercial Aircraft Manufacturing Engineering Technology Research Center(No.COMAC-SFGS-2022-1877)in part by the National Natural Science Foundation of China(No.92048301)。
文摘Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins are limited,especially in automation,heavily dependent on large amounts of data and resources,lacking the flexibility to adapt to different scenarios.To address these challenges,this paper introduces a novel image segmentation model,CableSAM,specifically designed for automated segmentation of cabin cables.CableSAM improves segmentation efficiency and accuracy using knowledge distillation and employs a context ensemble strategy.It accurately segments cables in various scenarios with minimal input prompts.Comparative experiments on three cable datasets demonstrate that CableSAM surpasses other advanced cable segmentation methods in performance.
基金Supported by Clinical Trials from the Nanjing Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University,No.2021-LCYJ-MS-11Nanjing Drum Tower Hospital National Natural Science Foundation Youth Cultivation Project,No.2024-JCYJQP-15.
文摘Imaging evaluation of lymph node metastasis and infiltration faces problems such as low artificial outline efficiency and insufficient consistency.Deep learning technology based on convolutional neural networks has greatly improved the technical effect of radiomics in lymph node pathological characteristics analysis and efficacy monitoring through automatic lymph node detection,precise segmentation and three-dimensional reconstruction algorithms.This review focuses on the automatic lymph node segmentation model,treatment response prediction algorithm and benign and malignant differential diagnosis system for multimodal imaging,in order to provide a basis for further research on artificial intelligence to assist lymph node disease management and clinical decision-making,and provide a reference for promoting the construction of a system for accurate diagnosis,personalized treatment and prognostic evaluation of lymph node-related diseases.
基金Supported by Funding for Clinical Trials from the Nanjing Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University,No.2021-LCYJ-MS-11.
文摘Esophageal cancer(EC),a common malignant tumor of the digestive tract,requires early diagnosis and timely treatment to improve patient prognosis.Automated detection of EC using medical imaging has the potential to increase screening efficiency and diagnostic accuracy,thereby significantly improving long-term survival rates and the quality of life of patients.Recent advances in deep learning(DL),particularly convolutional neural networks,have demons-trated remarkable performance in medical imaging analysis.These techniques have shown significant progress in the automated identification of malignant tumors,quantitative analysis of lesions,and improvement in diagnostic accuracy and efficiency.This article comprehensively examines the research progress of DL in medical imaging for EC,covering various imaging modalities such as digital pathology,endoscopy,computed tomography,etc.It explores the clinical value and application prospects of DL in EC screening and diagnosis.Additionally,the article addresses several critical challenges that must be overcome for the clinical translation of DL techniques,including constructing high-quality datasets,promoting multimodal feature fusion,and optimizing artificial intelligence-clinical workflow integration.By providing a detailed overview of the current state of DL in EC imaging and highlighting the key challenges and future directions,this article aims to guide future research and facilitate the clinical implementation of DL technologies in EC management,ultimately contributing to better patient outcomes.
基金supported by Chunhui Collaborative Research Project funded by the Ministry of Education of China[Grant No.202200490]Humanities and Social Sciences Research Project funded by the Ministry of Education of China[Grant No.23YJAZH139].
文摘With the development of machine translation technology,automatic pre-editing has attracted increasing research attention for its important role in improving translation quality and efficiency.This study utilizes UAM Corpus Tool 3.0 to annotate and categorize 99 key publications between 1992 and 2024,tracing the research paths and technological evolution of automatic pre-translation editing.The study finds that current approaches can be classified into four categories:controlled language-based approaches,text simplification approaches,interlingua-based approaches,and large language model-driven approaches.By critically examining their technical features and applicability in various contexts,this review aims to provide valuable insights to guide the future optimization and expansion of pre-translation editing systems.
基金supported by the Korea Environment Industry&Technology Institute(KEITI)through Digital Infrastructure Building Project for Monitoring,Surveying and Evaluating the Environmental Health program,funded by the Korea Ministry of Environment(MOE)(2021003330008)supported by the KIST Internal program(2E32851)+1 种基金supported by the Korea Health Technology Research and Development(R&D)Project through the Korea Health Industry Development Institute(KHIDI)and Korea Dementia Research Center(KDRC),funded by the Ministry of Health&Welfare and Ministry of Science and ICT,Republic of Korea(HU20C0164)the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2022R1A6A3A01087298)。
文摘The various bioacoustics signals obtained with auscultation contain complex clinical information that has been traditionally used as biomarkers,however,they are not extensively used in clinical studies owing to their spatiotemporal limitations.In this study,we developed a wearable stethoscope for wireless,skinattachable,low-power,continuous,real-time auscultation using a lung-sound-monitoring-patch(LSMP).LSMP can monitor respiratory function through a mobile app and classify normal and adventitious breathing by comparing their unique acoustic characteristics.The human heart and breathing sounds from humans can be distinguished from complex sound signals consisting of a mixture of bioacoustic signals and external noise.The performance of the LSMP sensor was further demonstrated in pediatric patients with asthma and elderly chronic obstructive pulmonary disease(COPD)patients where wheezing sounds were classified at specific frequencies.In addition,we developed a novel method for counting wheezing events based on a two-dimensional convolutional neural network deep-learning model constructed de novo and trained with our augmented fundamental lung-sound data set.We implemented a counting algorithm to identify wheezing events in real-time regardless of the respiratory cycle.The artificial intelligence-based adventitious breathing event counter distinguished>80%of the events(especially wheezing)in long-term clinical applications in patients with COPD.
文摘This article proposes an algebraic model predictive control(MPC)method for automatic landing.While defining the constraint functions in the optimization problem,the tangent hyperbolic function is preferred.Therefore,the optimization problem turns into an unconstrained,continuous,and differentiable form.An analytical two-step method is also proposed to solve the rest of the problem.In the first step,it is assumed that only input constraints are active and states are unconstrained.The optimal solution for this case is calculated directly with the optimality condition.The calculated control signal is revised in the second step according to system dynamics and state constraints.Simulation results of the auto-landing system show that the MPC computation speed is significantly increased by the new algebraic MPC(AMPC)without compromising the control performance,which makes the method realistic for using MPC in systems with high-speed changing dynamics.
基金supported by the National Natural Science Foundation of China(No.T2288101)the National Key Research and Development Project,China(No.2020YFC1512500)the Academic Excellence Foundation of Beijing University of Aeronautics and Astronautics(BUAA)。
文摘Considering the challenges posed by external disturbances on carrier-based aircraft land-ing control,higher demands are required for the precision and convergence of the carrier landingcontrol system.First,this paper proposes an Adaptive Terminal Sliding Combined Super TwistingControl(ATS-STC)method to address the issues of low precision,slow convergence,and poor dis-turbance rejection capability resulting from external disturbances,such as carrier air-wake and deckmotion.By introducing a nonlinear term into the sliding surface and employing an integralapproach,the proposed ATS-STC method can ensure finite-time convergence and mitigate the chat-tering problem.An adaptive law is also utilized to estimate the external disturbances,therebyenhancing the anti-disturbance performance.Then,the stability and convergence time analysis ofthe designed controller are conducted.Based on the proposed method,an Automatic Carrier Land-ing System(ACLS)is developed to perform the carrier landing control task.Furthermore,a multi-dimensional validation is carried out.For the numerical simulation test,the Terminal Sliding ModeControl(TSMC)method and Proportion Integration Differentiation(PID)method are introducedas comparison,the quantitative assessment results show that the tracking error of TSMC and PIDcan reach 1.5 times and 2 times that of the proposed method.Finally,the Hardware-in-the-Loop(HIL)test and real flight test are conducted.All the experimental results demonstrate that the pro-posed control method is more effective and precise.
基金supported by the National Natural Science Foundation of China(Nos.62276210,82201148 and 62376215)the Key Research and Development Project of Shaanxi Province(No.2025CY-YBXM-044)+3 种基金the Natural Science Foundation of Zhejiang Province(No.LQ22H120002)the Medical Health Science and Technology Project of Zhejiang Province(Nos.2022RC069 and 2023KY1140)the Natural Science Foundation of Ningbo(No.2023J390)the Ningbo Top Medical and Health Research Program(No.2023030716).
文摘This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 backbone network,followed by adaptive average pooling to scale the features to a fixed length.Subsequently,product quantization with residuals(PQR)is applied to convert continuous feature vectors into discrete features representations,preserving essential information insensitive to image quality variations.The quantized and original features are concatenated and fed into a self-attention mechanism to capture keratitis-related features.Finally,these enhanced features are classified through a fully connected layer.Experiments on clinical low-quality(LQ)images show that ADK_FVQSAM achieves accuracies of 87.7%,81.9%,and 89.3% for keratitis,other corneal abnormalities,and normal corneas,respectively.Compared to DenseNet121,Swin transformer,and InceptionResNet,ADK_FVQSAM improves average accuracy by 3.1%,11.3%,and 15.3%,respectively.These results demonstrate that ADK_FVQSAM significantly enhances the recognition performance of keratitis based on LQ slit-lamp images,offering a practical approach for clinical application.
基金the National Natural Science Foundation of China(No.61927822)。
文摘Traditional calibration method for the digital inclinometer relies on manual inspection,and results in its disadvantages of complicated process,low-efficiency and human errors easy to be introduced.To improve both the calibration accuracy and efficiency of digital inclinometer,an automatic digital inclinometer calibration system was developed in this study,and a new display tube recognition algorithm was proposed.First,a high-precision automatic turntable was taken as the reference to calculate the indication error of the inclinometer.Then,the automatic inclinometer calibration control process and the digital inclinometer zero-setting function were formulated.For display tube recognition,a new display tube recognition algorithm combining threading method and feature extraction method was proposed.Finally,the calibration system was calibrated by photoelectric autocollimator and regular polygon mirror,and the calibration system error and repeatability were calculated via a series of experiments.The experimental results showed that the indication error of the proposed calibration system was less than 4",and the repeatability was 3.9".A digital inclinometer with the resolution of 0.1°was taken as a testing example,within the calibration points'range of[-90°,90°],the repeatability of the testing was 0.085°,and the whole testing process was less than 90 s.The digital inclinometer indication error is mainly introduced by the digital inclinometer resolution according to the uncertainty evaluation.
基金Supported by National Natural Science Foundation of China(Grant No.92148206).
文摘Robot-assisted surgery has become an indispensable component in modern neurosurgical procedures.However,existing registration methods for neurosurgical robots often rely on high-end hardware and involve prolonged or unstable registration times,limiting their applicability in dynamic and time-sensitive intraoperative settings.This paper proposes a novel fully automatic monocular-based registration and real-time tracking method.First,dedicated fiducials are designed,and an automatic preoperative and intraoperative detection method for these fiducials is introduced.Second,a geometric representation of the fiducials is constructed based on a 2D KD-Tree.Through a two-stage optimization process,the depth of 2D fiducials is estimated,and 2D-3D correspondences are established to achieve monocular registration.This approach enables fully automatic intraoperative registration using only a single optical camera.Finally,a six-degree-of-freedom visual servo control strategy inspired by the mass-spring-damper system is proposed.By integrating artificial potential field and admittance control,the strategy ensures real-time responsiveness and stable tracking.Experimental results demonstrate that the proposed method achieves a registration time of 0.23 s per instance with an average error of 0.58 mm.Additionally,the motion performance of the control strategy has been validated.Preliminary experiments verify the effectiveness of MonoTracker in dynamic tracking scenarios.This method holds promise for enhancing the adaptability of neurosurgical robots and offers significant clinical application potential.
