The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology...The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.展开更多
To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and...To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.展开更多
Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).How...Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.展开更多
In this paper,we propose a joint channel estimation and symbol detection(JCESD)algorithm relying on message-passing algorithms(MPA)for orthogonal frequency division multiple access(OFDMA)systems.The channel estimation...In this paper,we propose a joint channel estimation and symbol detection(JCESD)algorithm relying on message-passing algorithms(MPA)for orthogonal frequency division multiple access(OFDMA)systems.The channel estimation and symbol detection leverage the framework of expectation propagation(EP)and belief propagation(BP)with the aid of Gaussian approximation,respectively.Furthermore,to reduce the computation complexity involved in channel estimation,the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula.Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%,compared with the traditional linear minimum mean square error(LMMSE)algorithm,and can approach to the bit error rate(BER)performance bound of perfectly known channel state information within 0.1 dB.展开更多
This paper proposes a digital image processing-based detection algorithm for cross joint traces of coal roadway heading face.Initially,the acquired images were preprocessed,i.e.,adaptive correction was conducted for n...This paper proposes a digital image processing-based detection algorithm for cross joint traces of coal roadway heading face.Initially,the acquired images were preprocessed,i.e.,adaptive correction was conducted for non-uniform illumination images based on the 2D gamma function.The edge detection algorithm was then applied to extract the edges of the structural plane,followed by the filtration of the non-structural plane noises.Moreover,the Hough transform algorithm was applied to extract the linear edges;finally,the edges were locally connected in accordance with the angle and distance criteria.The experimental results show that this algorithm can be used to reduce the noise caused by non-uniform illumination and avoid the mutual interference of multi-scale edges,so as to effectively extract the traces of the cross joint.Furthermore,Q-system and rock mass rating(RMR),were applied to conduct a quantitative evaluation on the stand-up time of unsupported roof in the four test images.The Q-system quality scores are 26.7,43.3,3.1,and 6.7,and the RMR quality scores are 56.84,58.73,48.42,and 51.42,respectively.The stand-up time of unsupported roofs with a span of 4.6 m are 30,36,7.7 and 14 d,respectively.展开更多
In the task of multi-target stance detection,there are problems the mutual influence of content describing different targets,resulting in reduction in accuracy.To solve this problem,a multi-target stance detection alg...In the task of multi-target stance detection,there are problems the mutual influence of content describing different targets,resulting in reduction in accuracy.To solve this problem,a multi-target stance detection algorithm based on a bidirectional long short-term memory(Bi-LSTM)network with position-weight is proposed.First,the corresponding position of the target in the input text is calculated with the ultimate position-weight vector.Next,the position information and output from the Bi-LSTM layer are fused by the position-weight fusion layer.Finally,the stances of different targets are predicted using the LSTM network and softmax classification.The multi-target stance detection corpus of the American election in 2016 is used to validate the proposed method.The results demonstrate that the Bi-LSTM network with position-weight achieves an advantage of 1.4%in macro average F1 value in the comparison of recent algorithms.展开更多
A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE...A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.展开更多
A kind of turbo joint detection scheme based on parallel interference cancellation （PIC） is studied; then, the eigenvalues of iteration matrix is deeply analyzed for studying the ping-pong effects in PIC JD and the ...A kind of turbo joint detection scheme based on parallel interference cancellation （PIC） is studied; then, the eigenvalues of iteration matrix is deeply analyzed for studying the ping-pong effects in PIC JD and the corresponding compensation approach is introduced. Finally, the proposed algorithm is validated through computer simulation in TDD CDMA uplink transmission. The result shows that the ping-pong effects are almost avoided completely in the presence of the compensation scheme, and system performance is greatly improved.展开更多
To achieve much efficient multimedia transmission over an error-prone wireless network, there are still some problem must to be solved, especially in energy limited wireless sensor network. In this paper, we propose a...To achieve much efficient multimedia transmission over an error-prone wireless network, there are still some problem must to be solved, especially in energy limited wireless sensor network. In this paper, we propose a joint detection based on Schur Algorithm for image wireless transmission over wireless sensor network. To eliminate error transmissions and save transmission energy, we combine Schur algorithm with joint dynamic detection for wireless transmission of JPEG 2000 encoded image which we proposed in [1]. Schur algorithm is used to computing the decomposition of system matrix to decrease the computational complexity. We de-scribe our transmission protocol, and report on its performance evaluation using a simulation testbed we have designed for this purpose. Our results clearly indicate that our method could approach efficient images transmission in wireless sensor network and the transmission errors are significantly reduced when compared to regular transmissions.展开更多
<div style="text-align:justify;"> STMV beamforming algorithm needs inversion operation of matrix, and its engineering application is limited due to its huge computational cost. This paper proposed bloc...<div style="text-align:justify;"> STMV beamforming algorithm needs inversion operation of matrix, and its engineering application is limited due to its huge computational cost. This paper proposed block iterative STMV algorithm based on one-phase regressive filter, matrix inversion lemma and inversion of block matrix. The computational cost is reduced approximately as 1/4 M times as original algorithm when array number is M. The simulation results show that this algorithm maintains high azimuth resolution and good performance of detecting multi-targets. Within 1 - 2 dB directional index and higher azimuth discrimination of block iterative STMV algorithm are achieved than STMV algorithm for sea trial data processing. And its good robustness lays the foundation of its engineering application. </div>展开更多
Visual localization and object detection both play important roles in various tasks.In many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely together.Howeve...Visual localization and object detection both play important roles in various tasks.In many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely together.However,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such environments.In this paper,we explore multi-task network design and joint refinement of detection and localization.To address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic process.The dataset provides localization and detection information,and is publicly available at https://drive.google.com/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection tasks.Targeting this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding boxes.For dynamic environments,the detection branch also promotes the perception of dynamics.JLDNet includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud regression.Moreover,object-level bundle adjustment is used to further improve localization and detection accuracy.To test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn datasets.Our results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.展开更多
Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body imag...Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body image. Yet, occlusion and robustness are still open challenges. In this paper, we present an automatic, model-free feature point detection and action tracking method using a time-of-flight camera. Our method automatically detects feature points for movement abstraction. To overcome errors caused by miss-detection and occlusion, a refinement method is devised that uses the trajectory of the feature points to correct the erroneous detections. Experiments were conducted using videos acquired with a Microsoft Kinect camera and a publicly available video set and comparisons were conducted with the state-of-the-art methods. The results demonstrated that our proposed method delivered improved and reliable performance with an average accuracy in the range of 90 %.The trajectorybased refinement also demonstrated satisfactory effectiveness that recovers the detection with a success rate of 93.7 %. Our method processed a frame in an average time of 71.1 ms.展开更多
The quality of the exposed avionics solder joints has a significant impact on the stable operation of the inorbit spacecrafts.Nevertheless,the previously reported inspection methods for multi-scale solder joint defect...The quality of the exposed avionics solder joints has a significant impact on the stable operation of the inorbit spacecrafts.Nevertheless,the previously reported inspection methods for multi-scale solder joint defects generally suffer low accuracy and slow detection speed.Herein,a novel real-time detector VMMAO-YOLO is demonstrated based on variable multi-scale concurrency and multi-depth aggregation network(VMMANet)backbone and“one-stop”global information gather-distribute(OS-GD)module.Combined with infrared thermography technology,it can achieve fast and high-precision detection of both internal and external solder joint defects.Specifically,VMMANet is designed for efficient multi-scale feature extraction,which mainly comprises variable multi-scale feature concurrency(VMC)and multi-depth feature aggregation-alignment(MAA)modules.VMC can extract multi-scale features via multiple fix-sized and deformable convolutions,while MAA can aggregate and align multi-depth features on the same order for feature inference.This allows the low-level features with more spatial details to be transmitted in depth-wise,enabling the deeper network to selectively utilize the preceding inference information.The VMMANet replaces inefficient highdensity deep convolution by increasing the width of intermediate feature levels,leading to a salient decline in parameters.The OS-GD is developed for efficacious feature extraction,aggregation and distribution,further enhancing the global information gather and deployment capability of the network.On a self-made solder joint image data set,the VMMAOYOLO achieves a mean average precision mAP@0.5 of 91.6%,surpassing all the mainstream YOLO-series models.Moreover,the VMMAO-YOLO has a body size of merely 19.3 MB and a detection speed up to 119 frame per second,far superior to the prevalent YOLO-series detectors.展开更多
In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owin...In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart performance.Yet,these models fail to consider the classification error while learning the feature representation.Intuitively,the learnt feature representation may degrade the performance of the classification task.For the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion detection.Specially,the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously.This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection.Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model.First,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed model.Next,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed model.Finally,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.展开更多
A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including ...A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including 3 brace damage cases and 2 joint damage cases,were simulated by removing braces and weakening beam鈥揷olumn connections in the structure. The limited acceleration response data generated by hammer impact were used for system identification,and modal parameters were extracted by using the eigensystem realization algorithm. In the first stage,the possible damaged locations are determined by using the damage index and the characteristics of the analytical model itself,and the extent of damage for those substructures identified at stage I is estimated in the second stage by using a second-order eigen-sensitivity approximation method. The main contribution of this paper is to test the two-stage method by using the real dynamic data of a complicated spatial model structure with limited sensors. The analysis results indicate that the two-stage approach is ableto detect the location of both damage cases,only the severity of brace damage cases can be assessed,and the reasonable analytical model is critical for successful damage detection.展开更多
Compared with the traditional phased array radar, the co-located multiple-input multiple-output(MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom deg...Compared with the traditional phased array radar, the co-located multiple-input multiple-output(MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom degree in radar resource management. In order to implement the effective resource management for the co-located MIMO radar in multi-target tracking,this paper proposes a resource management optimization model,where the system resource consumption and the tracking accuracy requirements are considered comprehensively. An adaptive resource management algorithm for the co-located MIMO radar is obtained based on the proposed model, where the sub-array number, sampling period, transmitting energy, beam direction and working mode are adaptively controlled to realize the time-space resource joint allocation. Simulation results demonstrate the superiority of the proposed algorithm. Furthermore, the co-located MIMO radar using the proposed algorithm can satisfy the predetermined tracking accuracy requirements with less comprehensive cost compared with the phased array radar.展开更多
As the combining form of the orthogonal frequency-division multiplexing (OFDM) technique and the vertical Bell Labs layered space-time (V-BLAST) architecture, the V-BLAST OFDM system can better meet the demand of next...As the combining form of the orthogonal frequency-division multiplexing (OFDM) technique and the vertical Bell Labs layered space-time (V-BLAST) architecture, the V-BLAST OFDM system can better meet the demand of next-generation (NextG) broadband mobile wireless multimedia communications. The symbols detection problem of the V-BLAST OFDM system is investigated under the frequency-selective fading environment. The joint space-frequency demultiplexing operation is proposed in the V-BLAST OFDM system. Successively, one novel half-rate rotational invariance joint space-frequency coding scheme for the V-BLAST OFDM system is proposed. By elegantly exploiting the above rotational invariance property, we derive one direct symbols detection scheme without knowing channels state information (CSI) for the frequency-selective V-BLAST OFDM system. Extensive simulation results demonstrate the validity of the novel half-rate rotational invariance joint space-frequency coding scheme and the performance of the direct symbols detection scheme.展开更多
We propose a joint exponential function and Woods–Saxon stochastic resonance(EWSSR)model.Because change of a single parameter in the classical stochastic resonance model may cause a great change in the shape of the p...We propose a joint exponential function and Woods–Saxon stochastic resonance(EWSSR)model.Because change of a single parameter in the classical stochastic resonance model may cause a great change in the shape of the potential function,it is difficult to obtain the optimal output signal-to-noise ratio by adjusting one parameter.In the novel system,the influence of different parameters on the shape of the potential function has its own emphasis,making it easier for us to adjust the shape of the potential function.The system can obtain different widths of the potential well or barrier height by adjusting one of these parameters,so that the system can match different types of input signals adaptively.By adjusting the system parameters,the potential function model can be transformed between the bistable model and the monostable model.The potential function of EWSSR has richer shapes and geometric characteristics.The effects of parameters,such as the height of the barrier and the width of the potential well,on SNR are studied,and a set of relatively optimal parameters are determined.Moreover,the EWSSR model is compared with other classical stochastic resonance models.Numerical experiments show that the proposed EWSSR model has higher SNR and better noise immunity than other classical stochastic resonance models.Simultaneously,the EWSSR model is applied to the detection of actual bearing fault signals,and the detection effect is also superior to other models.展开更多
Objective:To explore the clinical diagnostic value of combined detection of different tumor markers for primary hepatic carcinoma, and to provide the reference for the clinical diagnosis. Methods: 72 patients who were...Objective:To explore the clinical diagnostic value of combined detection of different tumor markers for primary hepatic carcinoma, and to provide the reference for the clinical diagnosis. Methods: 72 patients who were diagnosed with primary hepatic carcinoma were collected as observation group, 65 patients with benign liver disease as benign liver disease group and 80 cases of health examination as healthy control group, the contents of tumor markers alpha fetoprotein(AFP), carcinoembryonic antigen(CEA), carbohydrate antigen-199(CA199), carbohydrate antigen-125(CA125) and carbohydrate antigen-153(CA153) were determined by electrochemiluminescence in all subjects, then the results of five kinds of tumor markers and the positive rates of each index between the two groups were compared, the diagnostic value of separate and combined detection of different tumor markers in primary hepatic carcinoma were analyzed.Results: The values of AFP, CA199 and CA153 in the observation group were higher than the benign liver disease group, the values of AFP, CEA, CA199, CA125 and CA153 in the observation group were higher than the control group, the values of CA199 and CA125 in the benign liver disease group were higher than the control group, the differences were statistically significant (P<0.05). The positive rates of AFP, CEA, CA199 and CA153 in the observation group were higher than the benign liver disease group, the positive rates of AFP, CEA, CA199 and CA125 in the observation group were higher than the control group, the positive rates of AFP, CEA, CA199 and CA125 in the benign liver disease group were higher than the control group, the differences were statistically significant (P<0.05). The sensitivity of combined detection of all indicators for primary hepatic carcinoma was 86.4%, specificity, correct index, misdiagnosis rate and missed diagnosis rate were 86.4%, 89.2%, 75.6%, 13.6% and 10.8% respectively, and the combined detection was higher than the correct index of each index.Conclusion: Combined detection of serum tumor markers AFP, CEA, CA199, CA125 and CA153 can improve the sensitivity and specificity of diagnosis of primary hepatic carcinoma, it has better diagnostic value for primary hepatic carcinoma.展开更多
Objective: Significance of combined detection of serum 25(OH)D3, hsa-miR-17-5p and HCV-RNA in diagnosis of hepatitis C disease. Methods: A total of 52 patients with benign ovarian tumor resection were selected from Ap...Objective: Significance of combined detection of serum 25(OH)D3, hsa-miR-17-5p and HCV-RNA in diagnosis of hepatitis C disease. Methods: A total of 52 patients with benign ovarian tumor resection were selected from April 2016 to April 2018 in our hospital. We paid attention to the implementation of perioperative nursing intervention, and observed the prognosis and the occurrence of adverse reactions. Results: The expression of hsa-mir-17-5p in the observation group was significantly higher than that in the control group, the expression of 25(OH)D3 was lower than that in the control group, the expression of hsa-mir-17-5p in the HCV-RNA positive group was higher than that in the HCV-RNA negative group, and the expression of 25(OH) D3 was lower than that in the HCV-RNA negative group. The expression of HCV-RNA in the observation group was positively and negatively correlated with the expression of 25(OH) D3 and hsa-mir-17-5p in the 25(OH)D3 and the critical values of serum 25 (OH)D3, hsa-mir-17-5p and HCV-RNA were 24.23 ug/L, 1.89 relative expression and 1.22 × 103 IU/mL, respectively. The area of AUC detected by hsa-miR-17-5p+ 25 (OH) D3+25(OH)D3 is larger than that detected separately. Conclusion: The combined detection of serum 25(OH)D3, hsa-mir-17-5p and HCV-RNA has high diagnostic efficiency and sensitivity for hepatitis C. It is suggested that they be used in clinical practice.展开更多
基金supported by the Stable-Support Scientific Project of the China Research Institute of Radio-wave Propagation(Grant No.A13XXXXWXX)the National Natural Science Foundation of China(Grant Nos.42174210,4207202,and 42188101)the Strategic Pioneer Program on Space Science,Chinese Academy of Sciences(Grant No.XDA15014800)。
文摘The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.
基金supported by the National Natural Science Foundation of China(No.51876114)the Shanghai Engineering Research Center of Marine Renewable Energy(Grant No.19DZ2254800).
文摘To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.
基金Supported by National Key Research and Development Program of China(Grant No.2021YFB1600402)National Natural Science Foundation of China(Grant No.52072212)+1 种基金Dongfeng USharing Technology Co.,Ltd.,China Intelli‑gent and Connected Vehicles(Beijing)Research Institute Co.,Ltd.“Shuimu Tsinghua Scholarship”of Tsinghua University of China.
文摘Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.
文摘In this paper,we propose a joint channel estimation and symbol detection(JCESD)algorithm relying on message-passing algorithms(MPA)for orthogonal frequency division multiple access(OFDMA)systems.The channel estimation and symbol detection leverage the framework of expectation propagation(EP)and belief propagation(BP)with the aid of Gaussian approximation,respectively.Furthermore,to reduce the computation complexity involved in channel estimation,the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula.Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%,compared with the traditional linear minimum mean square error(LMMSE)algorithm,and can approach to the bit error rate(BER)performance bound of perfectly known channel state information within 0.1 dB.
基金supported by the National Natural Scieince Foundation of China(Nos.52004204 and 52034007).
文摘This paper proposes a digital image processing-based detection algorithm for cross joint traces of coal roadway heading face.Initially,the acquired images were preprocessed,i.e.,adaptive correction was conducted for non-uniform illumination images based on the 2D gamma function.The edge detection algorithm was then applied to extract the edges of the structural plane,followed by the filtration of the non-structural plane noises.Moreover,the Hough transform algorithm was applied to extract the linear edges;finally,the edges were locally connected in accordance with the angle and distance criteria.The experimental results show that this algorithm can be used to reduce the noise caused by non-uniform illumination and avoid the mutual interference of multi-scale edges,so as to effectively extract the traces of the cross joint.Furthermore,Q-system and rock mass rating(RMR),were applied to conduct a quantitative evaluation on the stand-up time of unsupported roof in the four test images.The Q-system quality scores are 26.7,43.3,3.1,and 6.7,and the RMR quality scores are 56.84,58.73,48.42,and 51.42,respectively.The stand-up time of unsupported roofs with a span of 4.6 m are 30,36,7.7 and 14 d,respectively.
基金Supported by the National Natural Science Foundation of China(No.61972040)the Science and Technology Projects of Beijing Municipal Education Commission(No.KM201711417011)the Premium Funding Project for Academic Human Resources Development in Beijing Union University(No.BPHR2020AZ03)。
文摘In the task of multi-target stance detection,there are problems the mutual influence of content describing different targets,resulting in reduction in accuracy.To solve this problem,a multi-target stance detection algorithm based on a bidirectional long short-term memory(Bi-LSTM)network with position-weight is proposed.First,the corresponding position of the target in the input text is calculated with the ultimate position-weight vector.Next,the position information and output from the Bi-LSTM layer are fused by the position-weight fusion layer.Finally,the stances of different targets are predicted using the LSTM network and softmax classification.The multi-target stance detection corpus of the American election in 2016 is used to validate the proposed method.The results demonstrate that the Bi-LSTM network with position-weight achieves an advantage of 1.4%in macro average F1 value in the comparison of recent algorithms.
基金Supported by the National Natural Science Foundation of China （No. 61001105）, the National Science and Technology Major Projects （No. 2011ZX03001- 007- 03） and Beijing Natural Science Foundation （No. 4102043）.
文摘A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.
文摘A kind of turbo joint detection scheme based on parallel interference cancellation （PIC） is studied; then, the eigenvalues of iteration matrix is deeply analyzed for studying the ping-pong effects in PIC JD and the corresponding compensation approach is introduced. Finally, the proposed algorithm is validated through computer simulation in TDD CDMA uplink transmission. The result shows that the ping-pong effects are almost avoided completely in the presence of the compensation scheme, and system performance is greatly improved.
文摘To achieve much efficient multimedia transmission over an error-prone wireless network, there are still some problem must to be solved, especially in energy limited wireless sensor network. In this paper, we propose a joint detection based on Schur Algorithm for image wireless transmission over wireless sensor network. To eliminate error transmissions and save transmission energy, we combine Schur algorithm with joint dynamic detection for wireless transmission of JPEG 2000 encoded image which we proposed in [1]. Schur algorithm is used to computing the decomposition of system matrix to decrease the computational complexity. We de-scribe our transmission protocol, and report on its performance evaluation using a simulation testbed we have designed for this purpose. Our results clearly indicate that our method could approach efficient images transmission in wireless sensor network and the transmission errors are significantly reduced when compared to regular transmissions.
文摘<div style="text-align:justify;"> STMV beamforming algorithm needs inversion operation of matrix, and its engineering application is limited due to its huge computational cost. This paper proposed block iterative STMV algorithm based on one-phase regressive filter, matrix inversion lemma and inversion of block matrix. The computational cost is reduced approximately as 1/4 M times as original algorithm when array number is M. The simulation results show that this algorithm maintains high azimuth resolution and good performance of detecting multi-targets. Within 1 - 2 dB directional index and higher azimuth discrimination of block iterative STMV algorithm are achieved than STMV algorithm for sea trial data processing. And its good robustness lays the foundation of its engineering application. </div>
基金supported by the National Natural Science Foundation of China(No.62072020)Key-Area Research and the Leading Talents in Innovation and Entrepreneurship of Qingdao(No.19-3-2-21-zhc).
文摘Visual localization and object detection both play important roles in various tasks.In many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely together.However,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such environments.In this paper,we explore multi-task network design and joint refinement of detection and localization.To address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic process.The dataset provides localization and detection information,and is publicly available at https://drive.google.com/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection tasks.Targeting this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding boxes.For dynamic environments,the detection branch also promotes the perception of dynamics.JLDNet includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud regression.Moreover,object-level bundle adjustment is used to further improve localization and detection accuracy.To test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn datasets.Our results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.
文摘Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body image. Yet, occlusion and robustness are still open challenges. In this paper, we present an automatic, model-free feature point detection and action tracking method using a time-of-flight camera. Our method automatically detects feature points for movement abstraction. To overcome errors caused by miss-detection and occlusion, a refinement method is devised that uses the trajectory of the feature points to correct the erroneous detections. Experiments were conducted using videos acquired with a Microsoft Kinect camera and a publicly available video set and comparisons were conducted with the state-of-the-art methods. The results demonstrated that our proposed method delivered improved and reliable performance with an average accuracy in the range of 90 %.The trajectorybased refinement also demonstrated satisfactory effectiveness that recovers the detection with a success rate of 93.7 %. Our method processed a frame in an average time of 71.1 ms.
基金supported by the National Natural Science Foundation of China(Grant No.52305623)the Natural Science Foundation of Hubei Province,China(Grant No.2022CFB589)the Natural Science Foundation of Chongqing,China(Grant No.CSTB2023NSCQ-MSX0636).
文摘The quality of the exposed avionics solder joints has a significant impact on the stable operation of the inorbit spacecrafts.Nevertheless,the previously reported inspection methods for multi-scale solder joint defects generally suffer low accuracy and slow detection speed.Herein,a novel real-time detector VMMAO-YOLO is demonstrated based on variable multi-scale concurrency and multi-depth aggregation network(VMMANet)backbone and“one-stop”global information gather-distribute(OS-GD)module.Combined with infrared thermography technology,it can achieve fast and high-precision detection of both internal and external solder joint defects.Specifically,VMMANet is designed for efficient multi-scale feature extraction,which mainly comprises variable multi-scale feature concurrency(VMC)and multi-depth feature aggregation-alignment(MAA)modules.VMC can extract multi-scale features via multiple fix-sized and deformable convolutions,while MAA can aggregate and align multi-depth features on the same order for feature inference.This allows the low-level features with more spatial details to be transmitted in depth-wise,enabling the deeper network to selectively utilize the preceding inference information.The VMMANet replaces inefficient highdensity deep convolution by increasing the width of intermediate feature levels,leading to a salient decline in parameters.The OS-GD is developed for efficacious feature extraction,aggregation and distribution,further enhancing the global information gather and deployment capability of the network.On a self-made solder joint image data set,the VMMAOYOLO achieves a mean average precision mAP@0.5 of 91.6%,surpassing all the mainstream YOLO-series models.Moreover,the VMMAO-YOLO has a body size of merely 19.3 MB and a detection speed up to 119 frame per second,far superior to the prevalent YOLO-series detectors.
基金This work was supported by the Research Deanship of Prince Sattam Bin Abdulaziz University,Al-Kharj,Saudi Arabia(Grant No.2020/01/17215).Also,the author thanks Deanship of college of computer engineering and sciences for technical support provided to complete the project successfully。
文摘In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart performance.Yet,these models fail to consider the classification error while learning the feature representation.Intuitively,the learnt feature representation may degrade the performance of the classification task.For the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion detection.Specially,the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously.This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection.Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model.First,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed model.Next,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed model.Finally,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.
基金supported by the National Natural Science Foundation of China (90815025, 90715032 and 50808013)
文摘A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including 3 brace damage cases and 2 joint damage cases,were simulated by removing braces and weakening beam鈥揷olumn connections in the structure. The limited acceleration response data generated by hammer impact were used for system identification,and modal parameters were extracted by using the eigensystem realization algorithm. In the first stage,the possible damaged locations are determined by using the damage index and the characteristics of the analytical model itself,and the extent of damage for those substructures identified at stage I is estimated in the second stage by using a second-order eigen-sensitivity approximation method. The main contribution of this paper is to test the two-stage method by using the real dynamic data of a complicated spatial model structure with limited sensors. The analysis results indicate that the two-stage approach is ableto detect the location of both damage cases,only the severity of brace damage cases can be assessed,and the reasonable analytical model is critical for successful damage detection.
基金supported by the National Natural Science Fundation of China (61671137)。
文摘Compared with the traditional phased array radar, the co-located multiple-input multiple-output(MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom degree in radar resource management. In order to implement the effective resource management for the co-located MIMO radar in multi-target tracking,this paper proposes a resource management optimization model,where the system resource consumption and the tracking accuracy requirements are considered comprehensively. An adaptive resource management algorithm for the co-located MIMO radar is obtained based on the proposed model, where the sub-array number, sampling period, transmitting energy, beam direction and working mode are adaptively controlled to realize the time-space resource joint allocation. Simulation results demonstrate the superiority of the proposed algorithm. Furthermore, the co-located MIMO radar using the proposed algorithm can satisfy the predetermined tracking accuracy requirements with less comprehensive cost compared with the phased array radar.
文摘As the combining form of the orthogonal frequency-division multiplexing (OFDM) technique and the vertical Bell Labs layered space-time (V-BLAST) architecture, the V-BLAST OFDM system can better meet the demand of next-generation (NextG) broadband mobile wireless multimedia communications. The symbols detection problem of the V-BLAST OFDM system is investigated under the frequency-selective fading environment. The joint space-frequency demultiplexing operation is proposed in the V-BLAST OFDM system. Successively, one novel half-rate rotational invariance joint space-frequency coding scheme for the V-BLAST OFDM system is proposed. By elegantly exploiting the above rotational invariance property, we derive one direct symbols detection scheme without knowing channels state information (CSI) for the frequency-selective V-BLAST OFDM system. Extensive simulation results demonstrate the validity of the novel half-rate rotational invariance joint space-frequency coding scheme and the performance of the direct symbols detection scheme.
基金Project supported by the National Natural Science Foundation of China(Grant No.61501525)the National Natural Science Foundation of Hunan Province of China(Grant No.2018JJ3680)。
文摘We propose a joint exponential function and Woods–Saxon stochastic resonance(EWSSR)model.Because change of a single parameter in the classical stochastic resonance model may cause a great change in the shape of the potential function,it is difficult to obtain the optimal output signal-to-noise ratio by adjusting one parameter.In the novel system,the influence of different parameters on the shape of the potential function has its own emphasis,making it easier for us to adjust the shape of the potential function.The system can obtain different widths of the potential well or barrier height by adjusting one of these parameters,so that the system can match different types of input signals adaptively.By adjusting the system parameters,the potential function model can be transformed between the bistable model and the monostable model.The potential function of EWSSR has richer shapes and geometric characteristics.The effects of parameters,such as the height of the barrier and the width of the potential well,on SNR are studied,and a set of relatively optimal parameters are determined.Moreover,the EWSSR model is compared with other classical stochastic resonance models.Numerical experiments show that the proposed EWSSR model has higher SNR and better noise immunity than other classical stochastic resonance models.Simultaneously,the EWSSR model is applied to the detection of actual bearing fault signals,and the detection effect is also superior to other models.
基金Projects Funded by the National Natural Science Foundation of China.Project No:81700537.
文摘Objective:To explore the clinical diagnostic value of combined detection of different tumor markers for primary hepatic carcinoma, and to provide the reference for the clinical diagnosis. Methods: 72 patients who were diagnosed with primary hepatic carcinoma were collected as observation group, 65 patients with benign liver disease as benign liver disease group and 80 cases of health examination as healthy control group, the contents of tumor markers alpha fetoprotein(AFP), carcinoembryonic antigen(CEA), carbohydrate antigen-199(CA199), carbohydrate antigen-125(CA125) and carbohydrate antigen-153(CA153) were determined by electrochemiluminescence in all subjects, then the results of five kinds of tumor markers and the positive rates of each index between the two groups were compared, the diagnostic value of separate and combined detection of different tumor markers in primary hepatic carcinoma were analyzed.Results: The values of AFP, CA199 and CA153 in the observation group were higher than the benign liver disease group, the values of AFP, CEA, CA199, CA125 and CA153 in the observation group were higher than the control group, the values of CA199 and CA125 in the benign liver disease group were higher than the control group, the differences were statistically significant (P<0.05). The positive rates of AFP, CEA, CA199 and CA153 in the observation group were higher than the benign liver disease group, the positive rates of AFP, CEA, CA199 and CA125 in the observation group were higher than the control group, the positive rates of AFP, CEA, CA199 and CA125 in the benign liver disease group were higher than the control group, the differences were statistically significant (P<0.05). The sensitivity of combined detection of all indicators for primary hepatic carcinoma was 86.4%, specificity, correct index, misdiagnosis rate and missed diagnosis rate were 86.4%, 89.2%, 75.6%, 13.6% and 10.8% respectively, and the combined detection was higher than the correct index of each index.Conclusion: Combined detection of serum tumor markers AFP, CEA, CA199, CA125 and CA153 can improve the sensitivity and specificity of diagnosis of primary hepatic carcinoma, it has better diagnostic value for primary hepatic carcinoma.
文摘Objective: Significance of combined detection of serum 25(OH)D3, hsa-miR-17-5p and HCV-RNA in diagnosis of hepatitis C disease. Methods: A total of 52 patients with benign ovarian tumor resection were selected from April 2016 to April 2018 in our hospital. We paid attention to the implementation of perioperative nursing intervention, and observed the prognosis and the occurrence of adverse reactions. Results: The expression of hsa-mir-17-5p in the observation group was significantly higher than that in the control group, the expression of 25(OH)D3 was lower than that in the control group, the expression of hsa-mir-17-5p in the HCV-RNA positive group was higher than that in the HCV-RNA negative group, and the expression of 25(OH) D3 was lower than that in the HCV-RNA negative group. The expression of HCV-RNA in the observation group was positively and negatively correlated with the expression of 25(OH) D3 and hsa-mir-17-5p in the 25(OH)D3 and the critical values of serum 25 (OH)D3, hsa-mir-17-5p and HCV-RNA were 24.23 ug/L, 1.89 relative expression and 1.22 × 103 IU/mL, respectively. The area of AUC detected by hsa-miR-17-5p+ 25 (OH) D3+25(OH)D3 is larger than that detected separately. Conclusion: The combined detection of serum 25(OH)D3, hsa-mir-17-5p and HCV-RNA has high diagnostic efficiency and sensitivity for hepatitis C. It is suggested that they be used in clinical practice.
