Threshold Judgment Research Articles

A Scene-Adaptive Phase Gradient Autofocus Algorithm

In the process of airborne radar imaging, when encountering certain scenarios (e.g., the Gobi desert), the low number or absence of strong scattering points will lead to azimuth scattering in the imaging results. In this study, we propose an autofocusing method based on the traditional phase gradient autofocus (PGA) algorithm that determines the optimal number of strong scattering points in each azimuth sub-aperture according to the scene (i.e., adaptively) and uses the mean amplitudes of these points in each azimuth sub-aperture of the scene to make a threshold judgment. In this way, the optimal number of strong scattering points is iteratively selected for subsequent PGA processing. The focusing quality of the image is significantly improved through the combination of these algorithms with onboard radar imaging. The simulation results prove the effectiveness of the method proposed in this study, compared with traditional PGA.

Blind Recognition Algorithm of Reed-Solomon Codes Based on Multiple Threshold Judgment and Matrix Recording

Blind Recognition Algorithm of Reed-Solomon Codes Based on Multiple Threshold Judgment and Matrix Recording

A large-scale group decision making model with a clustering algorithm based on a locality sensitive hash function

With the development of science and technology, an expanding array of decision-makers across various fields, including engineering and medicine, have been participating in collaborative decision-making for complex scenarios, such as earthquake relief and disease containment. The rapidly changing dynamics of real-world decision-making and the high complexity of consensus reaching among decision-makers require the development of more sophisticated models to handle these challenges. Considering the diversity and stability of group categories, this study proposes a large-scale group decision-making model based on a locality sensitive hash function. First, the volatility of attributes in real scenarios is considered, and a time-series decision matrix is constructed based on the average growth rates to make the results closer to reality. Then, hash functions are used to map the decision opinions to different dimensions and express the similarity through the Hamming distance, yielding clustering results with high stability and cohesion. To determine whether the decision-making group can reach a consensus, this study conducts hypothesis testing, adopting the idea of small probability counterfactuals to provide objective and fair standards for threshold judgment. Finally, through the case study and comparative analysis, it is proved that the proposed method improved 26.4% and 4.2% under the criteria of integrated cohesion and global consensus degree, respectively, with better clustering effect.

Integrated STL-DBSCAN algorithm for online hydrological and water quality monitoring data cleaning

Online hydrological and water quality monitoring data has become increasingly crucial for water environment management such as assessment and modeling. However, online monitoring data often contains erroneous or incomplete datasets, consequently affecting its operational use. In the study, we developed an automated data cleaning algorithm grounded in Seasonal-Trend decomposition using Loess (STL) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). STL identifies and corrects more obvious anomalies in the time series, followed by DBSCAN for further refinement, in which the reverse nearest neighbor method was employed to enhance the clustering accuracy. To improve anomaly detection, a two-level residual judgment threshold was applied. The algorithm has been successfully applied to three reservoirs in Shanghai, China, achieving the precision rate of 0.91 and recall rate of 0.81 for dissolved oxygen and pH. The proposed algorithm can be potentially applied for cleaning of environment monitoring data with high accuracy and stability.

Ground penetrating radar clutter suppression based on the weighted tensor Schatten p-paradigm minimization of tensor principal components

The issue of clutter suppression in ground-penetrating radar (GPR) detection has consistently been a prominent area of research. The existing tensor robust principal component analysis (TRPCA) approach encounters a challenge when handling different singular values, whereby all singular values are reduced to the same degree, leading to loss of information in the low-rank part. To address the aforementioned issues, this study developed a TRPCA method based on weighted tensor Schatten p-paradigm minimisation (p-TRPCA). This approach assigns distinct weights to each singular value by using a weighted tensor Schatten p-paradigm, facilitating the optimal decomposition of GPR data into low-rank clutter and sparse target components. The processing of target echoes is then completed by applying a threshold judgement to the results. The efficacy of the proposed method was validated through simulations and real-world testing. Its performance was benchmarked against established advanced clutter removal techniques in terms of peak signal-to-noise ratio (PSNR) and signal-to-clutter ratio. The findings demonstrated that the method effectively delineated the low-rank clutter matrix and sparse target matrix within the B-scan image and exhibited superior SNR compared to other methods.

Foreign object detection in urban rail transit based on deep differentiation segmentation neural network

With the increasing scale of urban rail transit, foreign object intrusion has become a significant operational safety hazard in urban rail transit. Although the laser-based automatic foreign object detection system has advantages such as long-distance detection and insensitivity to light changes, it has drawbacks such as large blind spots and low visualization. In response to the problems existing in laser detection systems, we proposed a novel video-based deep differentiation segmentation neural network for foreign object detection. Firstly, the foreign object detection is transformed into a binary classification problem, and the foreign object is determined as the image's foreground using image segmentation principles. Secondly, build a deep segmentation network based on deep convolution. Finally, perform morphological operations and threshold judgment on the foreground segmentation image to filter out the final detection results. To improve the detection effect, we reduced the impact of airflow disturbance by sampling and calculating the average background image. At the same time, the channel attention model and spatial attention model are added to the deep differentiation neural network. Collecting real data on subway platforms for experiments shows that the proposed method has a detection accuracy of 95.8 %, which is superior to traditional detection methods and recent image segmentation neural networks.

KLSD: a kinase database focused on ligand similarity and diversity.

Due to the similarity and diversity among kinases, small molecule kinase inhibitors (SMKIs) often display multi-target effects or selectivity, which have a strong correlation with the efficacy and safety of these inhibitors. However, due to the limited number of well-known popular databases and their restricted data mining capabilities, along with the significant scarcity of databases focusing on the pharmacological similarity and diversity of SMIKIs, researchers find it challenging to quickly access relevant information. The KLIFS database is representative of specialized application databases in the field, focusing on kinase structure and co-crystallised kinase-ligand interactions, whereas the KLSD database in this paper emphasizes the analysis of SMKIs among all reported kinase targets. To solve the current problem of the lack of professional application databases in kinase research and to provide centralized, standardized, reliable and efficient data resources for kinase researchers, this paper proposes a research program based on the ChEMBL database. It focuses on kinase ligands activities comparisons. This scheme extracts kinase data and standardizes and normalizes them, then performs kinase target difference analysis to achieve kinase activity threshold judgement. It then constructs a specialized and personalized kinase database platform, adopts the front-end and back-end separation technology of SpringBoot architecture, constructs an extensible WEB application, handles the storage, retrieval and analysis of the data, ultimately realizing data visualization and interaction. This study aims to develop a kinase database platform to collect, organize, and provide standardized data related to kinases. By offering essential resources and tools, it supports kinase research and drug development, thereby advancing scientific research and innovation in kinase-related fields. It is freely accessible at: http://ai.njucm.edu.cn:8080.

End-to-end model for automatic seizure detection using supervised contrastive learning

Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures, caused by abnormal electrical activity in cerebral neurons. Given that it is one of the most common neurological disorders globally, the efficient and accurate automatic seizure detection is urgently needed in the diagnosis of epilepsy to reduce the workload of continuous electroencephalogram (EEG) monitoring. Current deep learning based seizure detection approaches usually employ cross-entropy loss as objective function, which generally suffer from inadequate utilization of sample labels and poor classification margins, resulting in decreased performance in seizure detection. In this study, we propose an end-to-end automatic seizure detection framework based on supervised contrastive learning, which effectively utilizes labeled EEG to cluster similar samples while separating dissimilar ones. A supervised contrastive learning loss is employed to optimize classification boundaries by making full use of EEG labels. We employ long-term continuous EEG for evaluation. Given the presence of various noise and interferences, assessment on long-term continuous EEG proves to be more challenging. Post-processing techniques such as smoothing filter, threshold judgment, and collar technique are further adopted to diminish the artifact impacts on seizure detection performance. The proposed method is evaluated on the publicly available Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, achieving an event-based sensitivity of 99.71% and a false detection rate (FDR) of 0.35/h.

FPS-U2Net: Combining U2Net and multi-level aggregation architecture for fire point segmentation in remote sensing images

Traditional methods for fire point segmentation (FPS) in satellite remote sensing images (RSIs) overly rely on threshold judgment, which are greatly affected by factors such as regional time and show poor generalization. Besides, due to the difference between natural scene images (NSIs) and RSIs, directly apply NSIs-based deep learning methods to forest fire RSIs without any modification fails to achieve satisfactory results. To address these issues, first, we construct a Landsat8 RSI-FPS dataset covering different years, seasons and regions. Then, for the first time, we apply salient object detection (SOD) to FPS in forest fire monitoring and propose a novel network FPS-U2Net to improve the performance of FPS. FPS-U2Net is based on U2Netp (a lightweight U2Net), to make better use of the multi-level features from adjacent encoders, we propose multi-level aggregation module (MAM), which is placed between the encoder and decoder at the same stage to aggregate the adjacent multi-scale features and capture richer contextual information. To make up for the weakness of BCE loss, we employ the hybrid loss, BCE + IoU, for the training of the network, which can guide the network learn the salient information from pixel and map levels. Extensive experiments on three datasets demonstrate that our FPS-U2Net significantly outperforms the state-of-the-art semantic segmentation and SOD methods. FPS-U2Net can accurately segment fire regions and predict clear local details.

Prior-information-guided corresponding point regression network for 6D pose estimation

A key step in bin-picking is to obtain the 6DoF pose of the object. However, One of the reasons why many pose estimation methods fail to deal with occlusions and noise is that they lack reliable correspondence and effective utilization of CAD model information. In order to solve such issue, we propose a new prior-information-guided 6D pose estimation network based on 3D-3D correspondence prediction. Specifically, the network establishes dense correspondences between the object and the CAD model by predicting the object’s corresponding points on the CAD point cloud. This correspondence prediction method allows the network to learn more geometric details and perform better than direct regression. Furthermore, we implement the embedding of CAD model features in the network, which utilizes the prior information of the CAD model to compensate for the missing object information due to occlusion and viewpoint and provide more precise point regression. In addition, thanks to this points regression method, we can intuitively eliminate erroneous points via distance threshold judgment, thus achieving robustness to noise and outliers. Finally, the pose are then estimated through a SVD solver. Experiments show that our method outperforms most methods on LM, LM-O and YCB-V datasets, and its performance on LM-O datasets is 9% higher than the most advanced method. This proves that our method has excellent robustness and achieves the state-of-the-art performance.

Transformer Monitoring with Electromagnetic Energy Transmission and Wireless Sensing.

To ensure stable and normal transformer operation, light gas protection of the transformer Buchholz relay is essential. However, false alarms related to light gas protection are common, and troubleshooting them often requires on-site gas sampling by personnel. During this time, the transformer's operating state may rapidly deteriorate, posing a safety threat to field staff. To tackle these challenges, this work presents the near-field, thin-sliced transformer monitoring system that uses Electromagnetic Energy Transmission and Wireless Sensing Device (ETWSD). The system leverages external wireless energy input to power gas monitoring sensors. Simultaneously, it employs Near-Field Communication to obtain real-time concentrations of light gases, along with the electrified state and temperature. In field testing conducted on transformer relays' gas collection chambers, the ETWSD effortlessly monitors parameters within warning ranges, encompassing methane gas concentrations around 1000 ppm, leakage voltage ranging from 0-100 V, and relay working temperatures up to 90 °C. Additionally, to facilitate real-time diagnosis for electrical workers, we have developed an Android-based APP software that displays current light gas concentrations, leakage voltage collection values, and temperature, while also enabling threshold judgment, alarms, and data storage. The developed ETWSD is expected to aid on-site personnel in promptly and accurately evaluating transformer light gas protection error alarm faults. It provides a method for simultaneous, contactless, and rapid monitoring of multiple indicators.

Gas pipeline leakage detection and location by using w-FBG array based micro-strain sensing technology

In addressing the significant security concerns associated with urban natural gas transportation, particularly the potential catastrophic consequences of leaks, this paper introduces a novel approach for detecting and locating leaks in natural gas pipelines using weak fiber Bragg grating (w-FBG) array micro-strain sensing technology. The field pipeline model is established, w-FBG array is applied to pipeline leakage experiment, and the pipeline leakage is detected by analyzing the change trend of micro-strain. The pipeline leakage experiments under different pressures were carried out. The experimental results show that there is a good linear relationship between the pipeline leakage pressure and the wavelength change of w-FBG, and the linearity is greater than 0.99, which is consistent with the theoretical analysis and verifies the applicability of w-FBG. According to the characteristics of micro-strain transfer in pipeline leakage process, a ‘abrupt change point’ capturing method based on peak to peak slope and threshold judgment is proposed. Results indicate that the proposed algorithm effectively detects pipeline leaks and accurately locates the point of leakage, with a positioning error of approximately 0.5 m. Compared with the traditional method, the proposed method has higher precision.

Wavelet-based saturated absorption line detection for laser frequency locking

Owing to the presence of noise and the Doppler background, accurate saturated absorption (SA) peak automatic identification technology poses a significant challenge for laser frequency tuning and locking. To address this issue, a novel peak identification algorithm for the SA spectrum is proposed. First, a Gaussian filter based on a Gaussian continuous wavelet transform is proposed to mitigate the spectral high-frequency noise. Subsequently, a hybrid method combining a first-order Gaussian continuous wavelet transformation and adaptive threshold judgment was designed for multi-peak boundary segmentation. Finally, we obtained the target peak and its sweeping voltage based on an adaptive nonlinear fitting algorithm, which was almost unaffected by the peak asymmetry caused by the Doppler background.

A parallel Canny edge detection algorithm based on OpenCL acceleration.

In the process of Canny edge detection, a large number of high complexity calculations such as Gaussian filtering, gradient calculation, non-maximum suppression, and double threshold judgment need to be performed on the image, which takes up a lot of operation time, which is a great challenge to the real-time requirements of the algorithm. The traditional Canny edge detection technology mainly uses customized equipment such as DSP and FPGA, but it has some problems, such as long development cycle, difficult debugging, resource consumption, and so on. At the same time, the adopted CUDA platform has the problem of poor cross-platform. In order to solve this problem, a fine-grained parallel Canny edge detection method is proposed, which is optimized from three aspects: task partition, vector memory access, and NDRange optimization, and CPU-GPU collaborative parallelism is realized. At the same time, the parallel Canny edge detection methods based on multi-core CPU and CUDA architecture are designed. The experimental results show that OpenCL accelerated Canny edge detection algorithm (OCL_Canny) achieves 20.68 times acceleration ratio compared with CPU serial algorithm at 7452 × 8024 image resolution. At the image resolution of 3500 × 3500, the OCL_Canny algorithm achieves 3.96 times the acceleration ratio compared with the CPU multi-threaded Canny parallel algorithm. At 1024 × 1024 image resolution, the OCL_Canny algorithm achieves 1.21 times the acceleration ratio compared with the CUDA-based Canny parallel algorithm. The effectiveness and performance portability of the proposed Canny edge detection parallel algorithm are verified, and it provides a reference for the research of fast calculation of image big data.

Control strategy and capacity optimization of energy-storage-based railway power conditioner

Electrified railroad has the characteristics of high safety factors, high comfort, large transportation capacity, and less time-consuming, which is an effective means to solve traffic congestion. However, with the rapid development of China’s electrified railroad, harmonic, negative sequence and other power quality problems have also been widely concerned, while more and more regenerative braking energy in the traction power supply system can not be effectively utilized. The input of railroad power regulators makes the power quality problem be solved effectively, this paper studies the control strategy of traditional railroad power regulators. Combined with the actual working conditions of railroad vehicles, the simulation model of traction network, vehicles, and railroad power conditioner is established, and the load characteristics of traction substation are simulated by this model. Secondly, for a domestic substation field collection data processing to obtain the actual train operation load changes, to the battery energy storage device life within the maximum economic returns as the optimization goal, for the peak load reduction judgment threshold and the capacity of the battery energy storage device through the genetic algorithm to process the data of previous years to optimize the design.

A Proposal for Lodging Judgment of Rice Based on Binocular Camera

Rice lodging is a crucial problem in rice production. Lodging during growing and harvesting periods can decrease rice yields. Practical lodging judgment for rice can provide effective reference information for yield prediction and harvesting. This article proposes a binocular camera-based lodging judgment method for rice in real-time. As a first step, the binocular camera and Inertial Measurement Unit (IMU) were calibrated. Secondly, Census and Grayscale Level cost features are constructed for stereo matching of left and right images. The Cross-Matching Cost Aggregation method is improved to compute the aggregation space in the LAB color space. Then, the Winner-Takes-All algorithm is applied to determine the optimal disparity for each pixel. A disparity map is constructed, and Multi-Step Disparity Refinement is applied to the disparity map to generate the final one. Finally, coordinate transformation obtains 3D world coordinates corresponding to pixels. IMU calculates the real-time pose of the binocular camera. A pose transformation is applied to the 3D world coordinates of the rice to obtain its 3D world coordinates in the horizontal state of the camera (pitch and roll angles are equal to 0). Based on the distance between the rice and the camera level, thresholding was used to determine whether the region to be detected belonged to lodging rice. The disparity map effect of the proposed matching algorithm was tested on the Middlebury Benchmark v3 dataset. The results show that the proposed algorithm is superior to the widely used Semi-Global Block Matching (SGBM) stereo-matching algorithm. Field images of rice were analyzed for lodging judgments. After the threshold judgment, the lodging region results were accurate and could be used to judge rice lodging. By combining the algorithms with binocular cameras, the research results can provide practical technical support for yield estimation and intelligent control of rice harvesters.

Research on the construction of intelligent dance teaching ecosystem in universities based on multi-data chain network

Abstract In this paper, the Link16 data chain is used to cycle dance movement teaching resources by time and build the function of different nodes of dance teaching to form a distributed network topology without a central node. The network synchronization method integrates and updates the latest dance teaching resources. The MSK demodulation method is used to regulate the multi-data network information, and the network synchronization sequence is constructed by setting the relevant performance of the synchronization code and the judgment threshold. The motion error optimization algorithm reduces motion errors during high-speed dance. The results show that the variance of each classroom in the dance ecological teaching system is within 0.40, with minor fluctuations. In the evaluation results of the three evaluators, the scores of scientific knowledge were all 5, and the scores of ability, attitude and value were slightly different, but the variance of the total score and each index was under 0.10.

GRADE guidance 37: rating imprecision in a body of evidence on test accuracy

ObjectivesTo provide guidance on rating imprecision in a body of evidence assessing the accuracy of a single test. This guide will clarify when Grading of Recommendations Assessment, Development and Evaluation (GRADE) users should consider rating down the certainty of evidence by one or more levels for imprecision in test accuracy. Study Design and SettingA project group within the GRADE working group conducted iterative discussions and presentations at GRADE working group meetings to produce this guidance. ResultsBefore rating the certainty of evidence, GRADE users should define the target of their certainty rating. GRADE recommends setting judgment thresholds defining what they consider a very accurate, accurate, inaccurate, and very inaccurate test. These thresholds should be set after considering consequences of testing and effects on people-important outcomes. GRADE's primary criterion for judging imprecision in test accuracy evidence is considering confidence intervals (i.e., CI approach) of absolute test accuracy results (true and false, positive, and negative results in a cohort of people). Based on the CI approach, when a CI appreciably crosses the predefined judgment threshold(s), one should consider rating down certainty of evidence by one or more levels, depending on the number of thresholds crossed. When the CI does not cross judgment threshold(s), GRADE suggests considering the sample size for an adequately powered test accuracy review (optimal or review information size [optimal information size (OIS)/review information size (RIS)]) in rating imprecision. If the combined sample size of the included studies in the review is smaller than the required OIS/RIS, one should consider rating down by one or more levels for imprecision. ConclusionThis paper extends previous GRADE guidance for rating imprecision in single test accuracy systematic reviews and guidelines, with a focus on the circumstances in which one should consider rating down one or more levels for imprecision.

Judgment method of the dynamic threshold value of motor protection at a dynamic load rate

To address the shortcomings of traditional motor thermal protection, which uses the motor’s temperature or temperature rise threshold operating at full load as the basis for the judgment method, a dynamic threshold judgment method based on a study of the motor’s temperature rise characteristics under a dynamic load rate is proposed. Taking a forced air-cooled, three-phase asynchronous motor with 11 kW power as the research object, a 2D/3D motor model under steady-state conditions is established. The loss distribution of asynchronous motors at different load rates is obtained through electromagnetic field simulation calculations. The finite element method is used to simulate and calculate the motor’s temperature field. Taking ambient temperature and load factors into account, the study takes place from the perspective of motor temperature rise in order to acquire the law governing the change in motor temperature increase under various working circumstances. A method of converting the temperature and warming between the motor’s stator, winding, and rotor is proposed. A dynamic threshold judgment system for accurate warning of overheating problems in motors under any load rate is set up so that the thermal protection of motors can accurately be judged under variable load conditions, which offers a crucial theoretical foundation for the design of intelligent motor temperature rise prediction systems. It contributes to a methodological underpinning for the diagnosis and early warning of thermal health in smart motor systems.

Relationships between the magnitude of representational momentum and the spatial and temporal anticipatory judgments of opponent's kicks in taekwondo.

For successful actions in a fast, dynamic environment such as sports, a quick successful anticipation of a forthcoming environmental state is essential. However, the perceptual mechanisms involved in successful anticipation are not fully understood. This study examined the relationships between the magnitude of representational momentum (RM) as a forward displacement of the memory representation of the final position of a moving object (which implies that observers perceptually "see" a near future forthcoming dynamic environmental state) and the temporal and spatial anticipatory judgments of the opponent's high or middle kicks in taekwondo. Twenty-seven participants (university taekwondo club members and non-members) observed video clips of taekwondo kicks that vanished at one of 10 frame positions prior to the kick impact and performed three tasks consecutively: anticipatory coincidence timing (CT) with the arrival of kick impact, judgment of the kick type (high and middle kicks) by forced choice, and judgment of the vanishing frame position (measuring RM). Our results showed significant group effects for the number of correct kick-type judgments and the judgment threshold for kick-type choice (kick-typeJT), which was estimated in terms of individual psychometric function curves. A significant correlation was found between the magnitude of RM (estimated at kick-typeJT) and kick-typeJT, but not between the CT errors (estimated at kick-typeJT) and kick-typeJT. This indicates that the magnitude of RM may play an influential role in quick kick-type judgments, but not in coincidence timing while observing an opponent's kick motion. These findings suggest that subjective anticipatory perception or judgment of the future spatial state is vital to anticipatory actions under severe time constraints.