Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location ...Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.展开更多
This paper examines the impact of power transmission network topology change on locational marginal price(LMP) in real-time power markets. We consider the case where the false status of circuit breakers(CBs) that bypa...This paper examines the impact of power transmission network topology change on locational marginal price(LMP) in real-time power markets. We consider the case where the false status of circuit breakers(CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch.The main goal of this paper is to assess the economic impact of this misconfigured network topology on realtime LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding prices for marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.展开更多
Background:To construct a real-time computerized location system(RCLS)to analyze and display the axis of corneal astigmatism and to compare its accuracy with the Scheimpflug method.Methods:Fifty-seven eyes of 39 volun...Background:To construct a real-time computerized location system(RCLS)to analyze and display the axis of corneal astigmatism and to compare its accuracy with the Scheimpflug method.Methods:Fifty-seven eyes of 39 volunteers with corneal astigmatism more than 1.00 diopter(D)were recruited.The RCLS was composed of a circular light-emitting diode(LED)light source,surgical microscope,surgical video system,computer and self-programming image analysis software.Scheimpflug imaging measurements(Pentacam HR,Oculus,Wetzlar,Germany)were performed on all subjects to determine the axis and power of corneal astigmatism.Thereafter,the axis of corneal astigmatism was analyzed in real-time and displayed by the RCLS on supine position,and videos were recorded.The MB-Ruler 4.0 software was used to measure the astigmatic axis.The accuracy of the RCLS was compared with the Scheimpflug method.Results:The RCLS was able to display the axis of corneal astigmatism in real-time.The axial deviation of corneal astigmatism between the two methods was 0.63±3.78°when astigmatism was 1.00 to 2.00 D and decreased to 0.06±1.38°when astigmatism was greater than 2.00 D.A linear correlation of astigmatic axis was noted between the two methods:Axis_(RCLS)=1.01×Axis_(Scheimpflug)−1.02(R^(2)=0.998,P<0.001).The Bland-Altman analysis revealed that the RCLS agreed sufficiently well with the Scheimpflug method.Conclusions:The RCLS can accurately analyze and display the axis for corneal astigmatism greater than 1.00 D in real-time.The RCLS simplifies marking procedures and may have potential clinical application to improve the postoperative visual outcomes in surgical correction of corneal astigmatism.展开更多
Object recognition and location has always been one of the research hotspots in machine vision.It is of great value and significance to the development and application of current service robots,industrial automation,u...Object recognition and location has always been one of the research hotspots in machine vision.It is of great value and significance to the development and application of current service robots,industrial automation,unmanned driving and other fields.In order to realize the real-time recognition and location of indoor scene objects,this article proposes an improved YOLOv3 neural network model,which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network,which is applied to the detection and recognition of objects in indoor scenes.In this article,RealSense D415 RGB-D camera is used to obtain the RGB map and depth map,the actual distance value is calculated after each pixel in the scene image is mapped to the real scene.Experiment results proved that the detection and recognition accuracy and real-time performance by the new network are obviously improved compared with the previous YOLOV3 neural network model in the same scene.More objects can be detected after the improvement of network which cannot be detected with the YOLOv3 network before the improvement.The running time of objects detection and recognition is reduced to less than half of the original.This improved network has a certain reference value for practical engineering application.展开更多
Actors'relocation is utilized during the network initialization to enhance real-time performance of wireless sensor and actor networks(WSANs)which is an important issue of WSANs.The actor deployment problem in WSA...Actors'relocation is utilized during the network initialization to enhance real-time performance of wireless sensor and actor networks(WSANs)which is an important issue of WSANs.The actor deployment problem in WSANs is proved NP-Hard whether the amount of actors is redundant or not,but to the best of our knowledge,no effective distributed algorithms in previous research can solve the problem.Thus two actor deployment strategies which need not the boundary control compared with present deployment strategies are proposed to solve this problem approximately based on the Voronoi diagram.Through simulation experiment,the results show that our distributed strategies are more effective than the present deployment strategies in terms of real-time performance,convergence time and energy consumption.展开更多
Fire rescue challenges and solutions have evolved from straightfor-ward plane rescue to encompass 3D space due to the rise of high-rise city buildings.Hence,this study facilitates a system with quick and simplified on...Fire rescue challenges and solutions have evolved from straightfor-ward plane rescue to encompass 3D space due to the rise of high-rise city buildings.Hence,this study facilitates a system with quick and simplified on-site launching and generates real-time location data,enabling fire rescuers to arrive at the intended spot faster and correctly for effective and precise rescue.Auto-positioning with step-by-step instructions is proposed when launching the locating system,while no extra measuring instrument like Total Station(TS)is needed.Real-time location tracking is provided via a 3D space real-time locating system(RTLS)constructed using Ultra-wide Bandwidth technology(UWB),which requires electromagnetic waves to pass through concrete walls.A hybrid weighted least squares with a time difference of arrival(WLS/TDOA)positioning method is proposed to address real path-tracking issues in 3D space and to meet RTLS requirements for quick computing in real-world applications.The 3D WLS/TDOA algorithm is theoretically constructed with the Cramer-Rao lower bound(CRLB).The computing complexity is reduced to the lower bound for embedded hardware to directly compute the time differential of the arriving signals using the time-to-digital converter(TDC).The results of the experiments show that the errors are controlled when the positioning algorithm is applied in various complicated situations to fulfill the requirements of engineering applications.The statistical analysis of the data reveals that the proposed UWB RTLS auto-positioning system can track target tags with an accuracy of 0.20 m.展开更多
This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 3:Mouse.This standard was released by the China Associ...This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 3:Mouse.This standard was released by the China Association of Acupuncture and Moxibustion on May 15,2020,implemented on October 31,2020,and published by Standards Press of China.The standard was drafted by the Institute of Acupuncture and Moxibustion,China Academy of Chinese Medical Sciences,and the Nanjing University of Chinese Medicine.Principal draftsmen:Xiang-hong JING and Xing-bang HUA.Participating draftsmen:Wan-zhu BAI,Bin XU,Dong-sheng XU,Yi GUO,Tie-ming MA,Xin-jun WANG,and Sheng-feng LU.展开更多
Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in th...Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental condi- tions, without using a set of infrared light emitting diodes （LEDs） on the parachute part of the dro- gue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system, with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effective- ness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.展开更多
Complex event processing (CEP) can extract meaningful events for real-time locating system (RTLS) applications. To identify complex event accurately in RTLS, we propose a new RFID complex event processing method GEEP,...Complex event processing (CEP) can extract meaningful events for real-time locating system (RTLS) applications. To identify complex event accurately in RTLS, we propose a new RFID complex event processing method GEEP, which is based on the timed automata (TA) theory. By devising RFID locating application into complex events, we model the timing diagram of RFID data streams based on the TA. We optimize the constraint of the event streams and propose a novel method to derive the constraint between objects, as well as the constraint between object and location. Experiments prove the proposed method reduces the cost of RFID complex event processing, and improves the efficiency of the RTLS.展开更多
Through a case analysis,this study examines the spatiotemporal evolution of microseismic(MS)events,energy characteristics,volumetric features,and fracture network development in surface well hydraulic fracturing.A tot...Through a case analysis,this study examines the spatiotemporal evolution of microseismic(MS)events,energy characteristics,volumetric features,and fracture network development in surface well hydraulic fracturing.A total of 349 MS events were analyzed across different fracturing sections,revealing significant heterogeneity in fracture propagation.Energy scanning results showed that cumulative energy values ranged from 240 to 1060 J across the sections,indicating notable differences.Stimulated reservoir volume(SRV)analysis demonstrated well-developed fracture networks in certain sections,with a total SRV exceeding 1540000 m^(3).The hydraulic fracture network analysis revealed that during the midfracturing stage,the density and spatial extent of MS events significantly increased,indicating rapid fracture propagation and the formation of complex networks.In the later stage,the number of secondary fractures near fracture edges decreased,and the fracture network stabilized.By comparing the branching index,fracture length,width,height,and SRV values across different fracturing sections,Sections No.1 and No.8 showed the best performance,with high MS event densities,extensive fracture networks,and significant energy release.However,Sections No.4 and No.5 exhibited sparse MS activity and poor fracture connectivity,indicating suboptimal stimulation effectiveness.展开更多
This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 2:Rat.This standard was released by the China Associat...This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 2:Rat.This standard was released by the China Association of Acupuncture and Moxibustion on May 15,2020,implemented on October 31,2020,and published by Standards Press of China.The standard was drafted by the Institute of Acupuncture and Moxibustion,China Academy of Chinese Medical Sciences,and the Nanjing University of Chinese Medicine.Principal draftsmen:Xiang-hong JING and Xing-bang HUA.Participating draftsmen:Wan-Zhu BAI,Bin XU,Dong-sheng XU,Yi GUO,Tie-ming MA,Xin-jun WANG,and Sheng-feng LU.展开更多
The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for he...The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.展开更多
Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does no...Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.展开更多
Unmanned aerial vehicle(UAV)swarm network consisting of a collection of micro UAVs can be used for many applications.It is well established that packet routing is a fundamental problem to achieve UAV collaboration.How...Unmanned aerial vehicle(UAV)swarm network consisting of a collection of micro UAVs can be used for many applications.It is well established that packet routing is a fundamental problem to achieve UAV collaboration.However,the highly dynamic nature of UAVs,frequently changing network topologies and security issues,poses significant challenges to packet forwarding in UAV networks.The existing topology-based routing protocols are not well suited in UAV network due to their high controlling overhead or excessive end-to-end delay.Geographic routing is regarded as a promising solution,as it only requires local information.In order to enhance the accuracy and security of geographic routing in highly dynamic UAV network,in this paper,we propose a new predictive geographic(PGeo)routing strategy with location verification.First,a detection mechanism is adopted to recognize malicious UAVs falsifying their location.Then,an accurate average service time of a packet in the medium access control(MAC)layer is derived to assist location prediction.The proposed delay model can provide a theoretical basis for future work,and our simulation results reveal that PGeo outstrips the existing geographic routing protocols in terms of packet delivery ratio in the presence of location spoofing behavior.展开更多
A novel method is developed by utilizing the fractional frequency based multirange rulers to precisely position the passive inter-modulation(PIM)sources within radio frequency(RF)cables.The proposed method employs a s...A novel method is developed by utilizing the fractional frequency based multirange rulers to precisely position the passive inter-modulation(PIM)sources within radio frequency(RF)cables.The proposed method employs a set of fractional frequencies to create multiple measuring rulers with different metric ranges to determine the values of the tens,ones,tenths,and hundredths digits of the distance.Among these rulers,the one with the lowest frequency determines the maximum metric range,while the one with the highest frequency decides the highest achievable accuracy of the position system.For all rulers,the metric accuracy is uniquely determined by the phase accuracy of the detected PIM signals.With the all-phase Fourier transform method,the phases of the PIM signals at all fractional frequencies maintain almost the same accuracy,approximately 1°(about 1/360 wavelength in the positioning accuracy)at the signal-to-noise ratio(SNR)of 10 d B.Numerical simulations verify the effectiveness of the proposed method,improving the positioning accuracy of the cable PIM up to a millimeter level with the highest fractional frequency operating at 200 MHz.展开更多
Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring.Traditional methods,such as diffraction stacking,are time-consuming and challenging for real-time monit...Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring.Traditional methods,such as diffraction stacking,are time-consuming and challenging for real-time monitoring.In this study,we propose an approach to locate microseismic events using a deep learning algorithm with surface data.A fully convolutional network is designed to predict source locations.The input data is the waveform of a microseismic event,and the output consists of three 1D Gaussian distributions representing the probability distribution of the source location in the x,y,and z dimensions.The theoretical dataset is generated to train the model,and several data augmentation methods are applied to reduce discrepancies between the theoretical and field data.After applying the trained model to field data,the results demonstrate that our method is fast and achieves comparable location accuracy to the traditional diffraction stacking location method,making it promising for real-time microseismic monitoring.展开更多
Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation...Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.展开更多
Despite advances in surgery,chemotherapy,and radiotherapy,the treatment of colorectal cancer(CRC)requires more personalized approaches based on tumor biology and molecular profiling.While some relevant mutations have ...Despite advances in surgery,chemotherapy,and radiotherapy,the treatment of colorectal cancer(CRC)requires more personalized approaches based on tumor biology and molecular profiling.While some relevant mutations have been associated with differential response to immunotherapy,such as RAS and BRAF mutations limiting response to anti-epithelial growth factor receptor drugs or microsatellite instability predisposing susceptibility to immune checkpoint inhibitors,the role of inflammation in dictating tumor progression and treatment response is still under investigation.Several inflammatory biomarkers have been identified to guide patient prognosis.These include the neutrophil-lymphocyte ratio,Glasgow prognostic score(GPS)and its modified version,lymphocyte-Creactive protein ratio,and platelet-lymphocyte ratio.However,these markers are not yet included in the standard clinical management of patients with CRC,and further research is needed to evaluate their efficacy in different patient populations.A recent study by Wang et al,published in the World Journal of Gastroenterology,sheds light on the prognostic significance of pan-immune-inflammation value(PIV)in CRC,particularly concerning primary tumor location.Specifically,the authors found that a high PIV was strongly correlated with worse disease-free survival in patients with left-sided colon cancer,whereas no such association was observed in patients with right-sided colon cancer.Integrating tumor location into the prognostic assessment of CRC may improve our ability to more accurately identify high-risk patients and develop personalized treatment plans that are more likely to improve patient outcomes.展开更多
Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilize...Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.展开更多
文摘Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.
基金supported in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIP)(No.2015R1C1A1A01051890)part by the National Science Foundation DGE-1303378
文摘This paper examines the impact of power transmission network topology change on locational marginal price(LMP) in real-time power markets. We consider the case where the false status of circuit breakers(CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch.The main goal of this paper is to assess the economic impact of this misconfigured network topology on realtime LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding prices for marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.
基金funded by the Nature and Science Foundation of China(Grant No.81570869)Nature and Science Foundation of Zhejiang Province,China(Grant No.Y2110784)+2 种基金Zhejiang Provincial Foundation of China for Distinguished Young Talents in Medicine and Health(Grant No.2010QNA018)Foundation of Wenzhou City Science&Technology Bureau(Grant No.Y20140705)Engineering Development Project of Ophthalmology and Optometry(Grant No.GCKF201601).
文摘Background:To construct a real-time computerized location system(RCLS)to analyze and display the axis of corneal astigmatism and to compare its accuracy with the Scheimpflug method.Methods:Fifty-seven eyes of 39 volunteers with corneal astigmatism more than 1.00 diopter(D)were recruited.The RCLS was composed of a circular light-emitting diode(LED)light source,surgical microscope,surgical video system,computer and self-programming image analysis software.Scheimpflug imaging measurements(Pentacam HR,Oculus,Wetzlar,Germany)were performed on all subjects to determine the axis and power of corneal astigmatism.Thereafter,the axis of corneal astigmatism was analyzed in real-time and displayed by the RCLS on supine position,and videos were recorded.The MB-Ruler 4.0 software was used to measure the astigmatic axis.The accuracy of the RCLS was compared with the Scheimpflug method.Results:The RCLS was able to display the axis of corneal astigmatism in real-time.The axial deviation of corneal astigmatism between the two methods was 0.63±3.78°when astigmatism was 1.00 to 2.00 D and decreased to 0.06±1.38°when astigmatism was greater than 2.00 D.A linear correlation of astigmatic axis was noted between the two methods:Axis_(RCLS)=1.01×Axis_(Scheimpflug)−1.02(R^(2)=0.998,P<0.001).The Bland-Altman analysis revealed that the RCLS agreed sufficiently well with the Scheimpflug method.Conclusions:The RCLS can accurately analyze and display the axis for corneal astigmatism greater than 1.00 D in real-time.The RCLS simplifies marking procedures and may have potential clinical application to improve the postoperative visual outcomes in surgical correction of corneal astigmatism.
基金supported by Henan Province Science and Technology Project under Grant No.182102210065.
文摘Object recognition and location has always been one of the research hotspots in machine vision.It is of great value and significance to the development and application of current service robots,industrial automation,unmanned driving and other fields.In order to realize the real-time recognition and location of indoor scene objects,this article proposes an improved YOLOv3 neural network model,which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network,which is applied to the detection and recognition of objects in indoor scenes.In this article,RealSense D415 RGB-D camera is used to obtain the RGB map and depth map,the actual distance value is calculated after each pixel in the scene image is mapped to the real scene.Experiment results proved that the detection and recognition accuracy and real-time performance by the new network are obviously improved compared with the previous YOLOV3 neural network model in the same scene.More objects can be detected after the improvement of network which cannot be detected with the YOLOv3 network before the improvement.The running time of objects detection and recognition is reduced to less than half of the original.This improved network has a certain reference value for practical engineering application.
基金Supported by the National Natural Science Foundation of China(No.60803148,60973124)
文摘Actors'relocation is utilized during the network initialization to enhance real-time performance of wireless sensor and actor networks(WSANs)which is an important issue of WSANs.The actor deployment problem in WSANs is proved NP-Hard whether the amount of actors is redundant or not,but to the best of our knowledge,no effective distributed algorithms in previous research can solve the problem.Thus two actor deployment strategies which need not the boundary control compared with present deployment strategies are proposed to solve this problem approximately based on the Voronoi diagram.Through simulation experiment,the results show that our distributed strategies are more effective than the present deployment strategies in terms of real-time performance,convergence time and energy consumption.
文摘Fire rescue challenges and solutions have evolved from straightfor-ward plane rescue to encompass 3D space due to the rise of high-rise city buildings.Hence,this study facilitates a system with quick and simplified on-site launching and generates real-time location data,enabling fire rescuers to arrive at the intended spot faster and correctly for effective and precise rescue.Auto-positioning with step-by-step instructions is proposed when launching the locating system,while no extra measuring instrument like Total Station(TS)is needed.Real-time location tracking is provided via a 3D space real-time locating system(RTLS)constructed using Ultra-wide Bandwidth technology(UWB),which requires electromagnetic waves to pass through concrete walls.A hybrid weighted least squares with a time difference of arrival(WLS/TDOA)positioning method is proposed to address real path-tracking issues in 3D space and to meet RTLS requirements for quick computing in real-world applications.The 3D WLS/TDOA algorithm is theoretically constructed with the Cramer-Rao lower bound(CRLB).The computing complexity is reduced to the lower bound for embedded hardware to directly compute the time differential of the arriving signals using the time-to-digital converter(TDC).The results of the experiments show that the errors are controlled when the positioning algorithm is applied in various complicated situations to fulfill the requirements of engineering applications.The statistical analysis of the data reveals that the proposed UWB RTLS auto-positioning system can track target tags with an accuracy of 0.20 m.
文摘This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 3:Mouse.This standard was released by the China Association of Acupuncture and Moxibustion on May 15,2020,implemented on October 31,2020,and published by Standards Press of China.The standard was drafted by the Institute of Acupuncture and Moxibustion,China Academy of Chinese Medical Sciences,and the Nanjing University of Chinese Medicine.Principal draftsmen:Xiang-hong JING and Xing-bang HUA.Participating draftsmen:Wan-zhu BAI,Bin XU,Dong-sheng XU,Yi GUO,Tie-ming MA,Xin-jun WANG,and Sheng-feng LU.
基金supported by the National Natural Science Foundation of China(Nos.61473307,61304120)
文摘Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental condi- tions, without using a set of infrared light emitting diodes （LEDs） on the parachute part of the dro- gue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system, with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effective- ness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.
文摘Complex event processing (CEP) can extract meaningful events for real-time locating system (RTLS) applications. To identify complex event accurately in RTLS, we propose a new RFID complex event processing method GEEP, which is based on the timed automata (TA) theory. By devising RFID locating application into complex events, we model the timing diagram of RFID data streams based on the TA. We optimize the constraint of the event streams and propose a novel method to derive the constraint between objects, as well as the constraint between object and location. Experiments prove the proposed method reduces the cost of RFID complex event processing, and improves the efficiency of the RTLS.
基金supported by Yunlong Lake Laboratory of Deep Underground Science and Engineering Project(No.104024008)the National Natural Science Foundation of China(Nos.52274241 and 52474261)the Natural Science Foundation of Jiangsu Province(No.BK20240207).
文摘Through a case analysis,this study examines the spatiotemporal evolution of microseismic(MS)events,energy characteristics,volumetric features,and fracture network development in surface well hydraulic fracturing.A total of 349 MS events were analyzed across different fracturing sections,revealing significant heterogeneity in fracture propagation.Energy scanning results showed that cumulative energy values ranged from 240 to 1060 J across the sections,indicating notable differences.Stimulated reservoir volume(SRV)analysis demonstrated well-developed fracture networks in certain sections,with a total SRV exceeding 1540000 m^(3).The hydraulic fracture network analysis revealed that during the midfracturing stage,the density and spatial extent of MS events significantly increased,indicating rapid fracture propagation and the formation of complex networks.In the later stage,the number of secondary fractures near fracture edges decreased,and the fracture network stabilized.By comparing the branching index,fracture length,width,height,and SRV values across different fracturing sections,Sections No.1 and No.8 showed the best performance,with high MS event densities,extensive fracture networks,and significant energy release.However,Sections No.4 and No.5 exhibited sparse MS activity and poor fracture connectivity,indicating suboptimal stimulation effectiveness.
文摘This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 2:Rat.This standard was released by the China Association of Acupuncture and Moxibustion on May 15,2020,implemented on October 31,2020,and published by Standards Press of China.The standard was drafted by the Institute of Acupuncture and Moxibustion,China Academy of Chinese Medical Sciences,and the Nanjing University of Chinese Medicine.Principal draftsmen:Xiang-hong JING and Xing-bang HUA.Participating draftsmen:Wan-Zhu BAI,Bin XU,Dong-sheng XU,Yi GUO,Tie-ming MA,Xin-jun WANG,and Sheng-feng LU.
基金funded by the ICT Division of theMinistry of Posts,Telecommunications,and Information Technology of Bangladesh under Grant Number 56.00.0000.052.33.005.21-7(Tracking No.22FS15306)support from the University of Rajshahi.
文摘The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.
文摘Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.
基金co-supported by the National Key Research and Development Program of China(No.2024YFE0107900)the National Natural Science Foundation of China(No.62222105)+1 种基金the Natural Science Foundation of Guangdong Province,China(No.2024A1515010235)the 2024 China Unicom Guangdong low-altitude communication and sensing key technology research and digital twin platform research and development project(No.20241890).
文摘Unmanned aerial vehicle(UAV)swarm network consisting of a collection of micro UAVs can be used for many applications.It is well established that packet routing is a fundamental problem to achieve UAV collaboration.However,the highly dynamic nature of UAVs,frequently changing network topologies and security issues,poses significant challenges to packet forwarding in UAV networks.The existing topology-based routing protocols are not well suited in UAV network due to their high controlling overhead or excessive end-to-end delay.Geographic routing is regarded as a promising solution,as it only requires local information.In order to enhance the accuracy and security of geographic routing in highly dynamic UAV network,in this paper,we propose a new predictive geographic(PGeo)routing strategy with location verification.First,a detection mechanism is adopted to recognize malicious UAVs falsifying their location.Then,an accurate average service time of a packet in the medium access control(MAC)layer is derived to assist location prediction.The proposed delay model can provide a theoretical basis for future work,and our simulation results reveal that PGeo outstrips the existing geographic routing protocols in terms of packet delivery ratio in the presence of location spoofing behavior.
文摘A novel method is developed by utilizing the fractional frequency based multirange rulers to precisely position the passive inter-modulation(PIM)sources within radio frequency(RF)cables.The proposed method employs a set of fractional frequencies to create multiple measuring rulers with different metric ranges to determine the values of the tens,ones,tenths,and hundredths digits of the distance.Among these rulers,the one with the lowest frequency determines the maximum metric range,while the one with the highest frequency decides the highest achievable accuracy of the position system.For all rulers,the metric accuracy is uniquely determined by the phase accuracy of the detected PIM signals.With the all-phase Fourier transform method,the phases of the PIM signals at all fractional frequencies maintain almost the same accuracy,approximately 1°(about 1/360 wavelength in the positioning accuracy)at the signal-to-noise ratio(SNR)of 10 d B.Numerical simulations verify the effectiveness of the proposed method,improving the positioning accuracy of the cable PIM up to a millimeter level with the highest fractional frequency operating at 200 MHz.
基金supported by National Natural Science Foundation of China Grant(No.42004040,42474092,U2239204,and 42304145)Natural Science Foundation of Jiangxi Province Grant(20242BAB25190 and 20232BAB213077).
文摘Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring.Traditional methods,such as diffraction stacking,are time-consuming and challenging for real-time monitoring.In this study,we propose an approach to locate microseismic events using a deep learning algorithm with surface data.A fully convolutional network is designed to predict source locations.The input data is the waveform of a microseismic event,and the output consists of three 1D Gaussian distributions representing the probability distribution of the source location in the x,y,and z dimensions.The theoretical dataset is generated to train the model,and several data augmentation methods are applied to reduce discrepancies between the theoretical and field data.After applying the trained model to field data,the results demonstrate that our method is fast and achieves comparable location accuracy to the traditional diffraction stacking location method,making it promising for real-time microseismic monitoring.
文摘Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.
文摘Despite advances in surgery,chemotherapy,and radiotherapy,the treatment of colorectal cancer(CRC)requires more personalized approaches based on tumor biology and molecular profiling.While some relevant mutations have been associated with differential response to immunotherapy,such as RAS and BRAF mutations limiting response to anti-epithelial growth factor receptor drugs or microsatellite instability predisposing susceptibility to immune checkpoint inhibitors,the role of inflammation in dictating tumor progression and treatment response is still under investigation.Several inflammatory biomarkers have been identified to guide patient prognosis.These include the neutrophil-lymphocyte ratio,Glasgow prognostic score(GPS)and its modified version,lymphocyte-Creactive protein ratio,and platelet-lymphocyte ratio.However,these markers are not yet included in the standard clinical management of patients with CRC,and further research is needed to evaluate their efficacy in different patient populations.A recent study by Wang et al,published in the World Journal of Gastroenterology,sheds light on the prognostic significance of pan-immune-inflammation value(PIV)in CRC,particularly concerning primary tumor location.Specifically,the authors found that a high PIV was strongly correlated with worse disease-free survival in patients with left-sided colon cancer,whereas no such association was observed in patients with right-sided colon cancer.Integrating tumor location into the prognostic assessment of CRC may improve our ability to more accurately identify high-risk patients and develop personalized treatment plans that are more likely to improve patient outcomes.
基金supported by the National Natural Science Foundation of China(No.22306076)the Natural Science Foundation of Jiangsu Province(No.BK20230676)the Natural Science Foundation of Jiangsu Higher Education Institutions of China(No.22KJB610011).
文摘Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.
