Reviewerly/exHarmony huggingface dataset:
A Novel Global Camera Calibration Method Based on Flexible Multidirectional Target

Multi-viewpoint or multi-sensor 3D vision measurement is a widely applied for large-scale measurement. The global coordinates of multiple local measurement coordinates during the measurement process are often unified by using a global camera. However, the parameters of the global camera are easily affected by external factors such as vibrations and lighting, which can reduce the 3D reconstruction accuracy during the unification process. Therefore, it is necessary to use calibration targets to calibrate the parameters of the global camera. However, due to the field of view limitation of the global camera, the size of the calibration target needs to be strictly controlled. Manufacturing large and high-precision targets is difficult. The accuracy of large targets is not easy to guarantee and using large targets can easily exceed the field of view of the global camera. On the other hand, small and high-precision targets only occupy a small part of the image, which leads to a decrease in the accuracy of camera calibration. To solve this problem, a novel camera calibration method based on Flexible Multi-directional Target (FMT) is proposed. The designed FMT consists of Co-planar Small Targets (CST) and a polyhedral skeleton. The FMT is placed in the field of view of the global camera, and target images are collected from multiple angles. An automatic corner extraction algorithm and an optimization algorithm are proposed based on the topological correspondence between the 2D coordinates of the corners in the image and the 3D coordinates of the corners in Euclidean space. These algorithms are used to calculate the parameters and distortion coefficients of the global camera. The algorithm is evaluated through real experiments. The experimental results show that using FMT can improve the measurement accuracy by 83% compared with using chessboard targets, confirming the effectiveness of the algorithm.
Lambertian Reflection Separation Under High Reflectiveness for Worn Surface Reconstruction With Insufficient Samples

Compared with 2D images, 3D image based worn inspection can provide more profound information regarding the in-suit endoscope examination of mechanical parts. With photometric stereo vision, 3D topography can be reconstructed from 2D images with minimal cost. However, this promising technology is limited by the imprecion in calculating normal vector due to the high reflectiveness of worn surfaces. An innovative model is here developed to separate Lambertian reflection from photometric images. The Lambertian reflection variations are estimated by fusing multiple photometric images, while the constants are extracted from the general plane base of worn surfaces. Considering a small sample set of worn surfaces, hybrid samples, including the real and the simulated images, are adopted for parameter optimization with feature-level alignment. Moreover, a label-free prediction loss function is constructed to constrain the optimization direction via real samples. The constructed model is obtained from training 20,000 simulated samples and 1,000 real samples. The results indicate that high reflectiveness can be addressed by separating the Lambertian reflections. Comparatively, the model can improve the normal vector calculation with lower average error than other existing algorithms.
Intelligent Servo Control Strategy for Robot Joints With Incremental Bayesian Fuzzy Broad Learning System

Intelligent servo control significantly reduces the need to adjust control parameters, and is, therefore, widely used in robot joint control. However, existing intelligent servo control strategies for robot joints have problems of computational redundancy, limited prediction accuracy, and insufficient generalization capability. To solve these problems, this article proposes a servo control strategy for robot joints that is based on the incremental Bayesian fuzzy broad learning system (IBFBLS). First, we construct an intelligent servo control strategy with broad learning system on the basis of fuzzy rules to achieve good self-learning and generalization abilities. Second, the learning parameters of the control strategy are optimized by Bayesian inference to achieve precise joint servo control. Finally, the convergence of the control strategy is enhanced by combining it with Lyapunov theory to constrain the learning parameters of the proposed control strategy. The feasibility and superiority of the proposed control strategy are verified by simulation to compare it with existing intelligent servo control methods. In addition, experiments are conducted using robot joint test bed. Both the simulation and experiments verify that the proposed servo control strategy outperforms other servo control methods with respect to tracking accuracy, stability, and convergence. The root-mean-square error in servo control of robot joints was 0.012 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , which has been reduced by 55.56 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> compared with the current state of the art.
Efficient and Fair: Information-Agnostic Online Coflow Scheduling by Combining Limited Multiplexing With DRL

In shared data center networks, communications among users can be modeled as coflows, each comprising of a group of parallel data transmission flows. Efficient and fair scheduling of coflows is critical for improving both system performance and user satisfaction at the application level. Existing coflow scheduling methods maximizing efficiency (coflow completion time, CCT) and fairness (service isolation) simultaneously require prior knowledge of coflow (flow) size that is however not known before completion of coflow execution in reality, which limits their applicability. For information-agnostic scheduling, known results focus either solely on efficiency or fairness, but not both due to the hardness of achieving the desired compromise between them. In this paper, we first present an information-aware non-preemptive coflow scheduling algorithm, and show its provable long-term isolation guarantee under reasonable assumptions. We then adapt this algorithm to information-agnostic online coflow scheduling by combining limited multiplexing with Deep Reinforcement Learning (DRL) framework to achieve long-term isolation guarantee toward fair network sharing and lower average weighted CCT simultaneously. The simulation results show that our algorithm outperforms the state-of-the-art results of both fairness-optimal scheduling (NC-DRF) by 4.92in terms of average weighted CCT and performance-optimal scheduling (Aalo) in the metric of maximum normalized CCT. This fully demonstrates the superiority of our method in simultaneous optimization of efficiency and fairness for information-agnostic coflow scheduling.
Data-Driven Reachability and Support Estimation With Christoffel Functions

In this article, we present algorithms for estimating the forward reachable set of a dynamical system using only a finite collection of independent and identically distributed samples. The produced estimate is the sublevel set of a function called an empirical inverse Christoffel function: empirical inverse Christoffel functions are known to provide good approximations to the support of probability distributions. In addition to reachability analysis, the same approach can be applied to general problems of estimating the support of a random variable, which has applications in data science toward the detection of novelties and outliers in datasets. In applications where safety is a concern, having a guarantee of accuracy that holds on finite datasets is critical. In this article, we prove such bounds for our algorithms under the probably approximately correct (PAC) framework. In addition to applying classical Vapnik–Chervonenkis dimension bound arguments, we apply the PAC-Bayes theorem by leveraging a formal connection between kernelized empirical inverse Christoffel functions and Gaussian process regression models.
Secondary Equipment Evaluation Based on Multi-Parameter Model for Smart Substation

In order to cope with the establishment and practical engineering application of secondary equipment evaluation system for smart substation, the comprehensive evaluation model of secondary equipment which relies on multi-parameter information is introduced. The proposed TOPSIS-based evaluation items, evaluation parameters and evaluation methods are used to evaluate the status of different kinds of secondary equipment. On the basis of various alarm information, telemetry data, remote signal status and other multi-dimensional parameters in process layer, interval layer and station control layer, the status evaluation system of secondary equipment is established to realize monitoring and early warning of normal status and identification and diagnosis of abnormal status.
Research on Intelligent Settlement of Power Marketing Transactions Based on Blockchain Technology

Blockchain technology has the functions of distributed data storage and embedded editable smart contracts, and can provide a flexible, safe and transparent settlement environment and trust foundation for power transaction settlement. Firstly, this paper briefly describes the technical characteristics of blockchain and analyzes the feasibility of transaction settlement application; Then, the application design of private chain, alliance and smart contract is carried out in combination with the power transaction settlement scenario. Finally, the possible problems in the application of power transaction settlement based on blockchain technology are analyzed to provide reference and guidance for practical application.
Person Re-Identification using Background Subtraction and Siamese Network for Pose Varians

Person Re-Identification is a process where the algorithm in charge of matching the similarity of two objects. This method can be used as an alternative solution for the current traditional security surveillance. Many modern technologies that use this model, especially in the use of Video Surveillance. The expected output from the use of this model is the process of monitoring and detecting the similarity of two human objects more efficiently and accurately. However, in its implementation there are still many problems found by previous researchers related to Person Identification. Some of the problems that are often encountered in re-identification are image occlusion, pose variance, illuminati, etc. One of the problems that occur is the difference in poses, the difference in poses causes the re-identification process to often experience errors because the features obtained by the two images may experience differences. In this study, trying to implement the algorithm on a video dataset. There is an additional preprocessing which uses the image segmentation method to extract objects from the video dataset. After pre-processing, the image obtained will be re-identified using the Siamese Network Algorithm. The test results obtained an accuracy of 51% and 54% for each architecture. While the accuracy value of object detection obtained is 0.359 and 0.378, which means that the addition of segmentation using the background subtraction model when compared to previous studies is still not effective in dealing with the problem of different poses.
A Systems Approach to Improving the Spectator Experience at Collegiate Football Games

As ticket sales and student attendance for University of Virginia (UVA) home football games decline, the university must find ways to engage fans with the football program. The following technical evaluation used a systems methodology to improve the customer experience for Scott Stadium spectators, with the additional hope of paralleling an improvement in the school’s football community. Taking a three-pronged approach, the analysis focused on traffic, in-game experience, and website design. A ride-along and interviews with the University Police Department (UPD) yielded observational data regarding game day pedestrian and vehicular traffic. The UVA Athletics Department provided ticketing data. Concessions numbers supplied by Aramark, a student survey, and the team’s observations from game days offered information regarding in-game experience. The research team’s examination of the department’s digital presence gave an analysis of the website design. The interview data and analysis of patron and vehicular traffic patterns indicated that a paucity of signage, GPS directions that only route drivers to prepaid parking, and a dated traffic plan contribute to pregame traffic backups. Investigating ticketing statistics showed that tardy students and inefficient distribution of stadium staff create sparsely attended kickoffs and entrance bottlenecks. An assessment of the game day website revealed a User Experience (UX) design that hinders fans from finding parking, concessions, and general information efficiently. Analysis of concessions data revealed that stadium staff fail to make student-preferred food items available in multiple convenient locations. Finally, the survey data revealed that many students leave before halftime, find the in-game entertainment in need of improvement, and attend games to fraternize with friends rather than watch football. Due to these results, the primary traffic recommendations involve increasing parking signage during game days and an updated traffic plan. To improve the in-game experience, suggestions include prioritizing student-preferred food items, rearranging event staff at entrance gates, incorporating incentives that encourage students and fans to arrive early and stay late at games, and updating in-game entertainment to shift student focus to on-field activities. Finally, recommendations to restructure the game day website include reducing text by utilizing images and bullet points, highlighting critical content through bolding and underlining, and grouping similar information with panels and icons.
Development and Application of Big Data Cloud Platform for Tower Grounding Parameters

In this paper, through the comparative analysis of various commonly used measurement methods in tower grounding resistance measurement, a series of shortcomings in grounding operation and maintenance are put forward, and a set of big data cloud platform system for tower grounding parameters that users can query is developed. It is realized that the measured information can be directly uploaded to the cloud server at the measurement site, and the measured "offline results" can be converted into "online data". On the one hand, it effectively improves the working efficiency and accuracy of grounding operation and maintenance, and at the same time, it also records the whole business process of operation and maintenance, which is conducive to the supervision and management of operation and maintenance, and provides data support for the operation state evaluation and fault risk analysis of grounding devices. Finally, this information equipment is applied to the field measurement test, and the advantages of this equipment are pointed out by comparing and analyzing the measurement results with the shaking table method and different wiring methods.
Operation Level Evaluation and Development Path Planning of Intelligent Distribution Network in Multiple Dimensions

With the adjustment of energy structure and the increase of uncertainty source load, the physical structure of the distribution network becomes more complex, and its intelligent and low-carbon operation face greater challenges. In addition, business and information flows of the distribution network have diversified and grown more complicated, placing more demands on the dispatching department's capacity for management and data collecting. It is challenging to evaluate the distribution network's overall operational status since its physical infrastructure and information system are continually being updated and changed. In order to meet the actual engineering needs and consider the intelligent development of the distribution network, it is important to build an evaluation system of the smart distribution network operation level from different dimensions. Firstly, a systematic analysis of the smart distribution network operation is carried out from three dimensions: physical, information and social, and an evaluation index system of the smart distribution network operation level is constructed. On this premise, a comprehensive evaluation method based on normal gray cloud clustering is proposed, and the distribution network development form is divided. Finally, the actual dispatching of the distribution network is used as an example to evaluate its current operation level, determine the division of the distribution network development form under each dimension, and provide path guidance for the balanced development of the smart distribution network.
Application of Artificial Intelligence and Its Interpretability Analysis in Power Grid Dispatch and Control

Deep learning, an advanced kind of artificial intelligence, has recently aided in the intelligent growth of many different sectors. Power grid regulation, one of the key areas for using artificial intelligence technology, has to take inspiration from Internet culture and fully utilize the capabilities of AI to raise the bar for business intelligence. This paper first compares the intelligent enhanced scheduling computing architecture with the traditional scheduling computing architecture, and discusses the current situation and limitations of traditional methods in the field of power system regulation. On this basis, the usual artificial intelligence application scenarios in power flow calculation adjustment, operation optimization scheduling, and power grid static security analysis are examined. Finally, the credibility of artificial intelligence is summarized, and the development of artificial intelligence in the field of power system regulation is prospected.
Nonlinear Elements in Traveling-Wave Parametric Amplifiers for Dispersive Qubit Readout

In this paper, we present models for the nonlinearity of different nonlinear elements for superconducting Josephson traveling-wave parametric amplifiers. Such devices are typically used as a first-stage readout amplifier for superconducting qubits in a cryogenic quantum computing environment. The nonlinear coefficients of different designs are evaluated as functions of an externally applied bias field, taking into account direct and external pumping schemes. Finally, we present results of the total gain for two different nonlinear transmission lines, one that is embedded with single Josephson junctions operating in four-wave mixing mode with direct pumping, and an externally pumped SNAIL-based amplifier operating in the three-wave mixing regime with a corresponding constant external flux bias. The gain is evaluated using a quantum circuit model including substrate losses and chromatic dispersion.
Anemia Detection Through Conjunctiva on Eyes Using Histogram of Oriented Gradients and Decision Tree Classification

Generally, an invasive examination is carried out by taking a sample of a person's blood to detect hemoglobin levels. However, as science develops, hemoglobin levels can be measured through alternative non-invasive examinations by looking at the paleness of the eye's conjunctiva. This research aims to detect anemia through images of the eye's conjunctiva with the histogram of oriented gradient (HOG) characteristics extraction method and decision tree also logistic regression comparison classification methods. Based on the test results using HOG and decision trees on 40 testing images, with an image size of 256x128, the best parameters were obtained, namely cell size of 8x8, block size of 8x8, bin numbers of 15, normalization of L2-Hys blocks, entropy criteria, best splitter, random state 10, and minimum impurity decrease of 0.15, the system was able to produce the largest performance of 82.5% with a computing time of 25.63 seconds. Meanwhile, for logistic regression method with a stochastic gradient descent (SGD) optimizer methods, the best parameters are cell size of 16x16, block size of 8x8, bin numbers of 11, block normalization of L2-Hys, and random state 30, the system is able to produce the largest performance of 92.5% with a computational time of 24.20 seconds.
Anemia Detection Through Conjunctiva on Eyes Using Principal Component Analysis Method and K-Nearest Neighbor

Anemia is a condition where red blood cells and hemoglobin in a person's body are below normal values. Most anemia are characterized by some symptoms such as the conjunctiva of the eyes becoming pale, weakened endurance, and decreased physical labor. To carry out the detection of anemia is usually done by invasive means or taking a sample of a person's blood using a syringe on the fingertips of the hand. In addition to the invasive way, the detection of anemia can also be done in a non-invasive way or without taking a pair of blood using a syringe on the fingertips of the hand. In this paper, the detection of anemia is carried out non-invasively through the conjunctiva of the eye using the Principal Component Analysis (PCA) method and the K-Nearest Neighbor (K-NN) method. The results obtained based on the best parameters with an image size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$256\times 128$</tex> pixels, PCA percentage parameters of 40%, cityblock distance, with a value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{K}=9$</tex> systems resulted in an accuracy of 87.5% with a computing time of 1,317 seconds using 60 training data and 40 test data.
Enhancing 3D Pose Estimation Accuracy from Multiple Camera Perspectives through Machine Learning Model Integration

In this investigation, we propose a machine learning approach to integrate estimations from two orthogonal camera views, separated by approximately 90 degrees, using a three-layer feed-forward neural network to refine and unify 3D pose estimations. The primary objective is to minimize the discrepancies between the estimated joint coordinates and the ground truth, consequently improving the overall accuracy of the 3D pose estimation process. Our neural network architecture comprises two hidden layers with the ReLU activation function and an output layer with the linear activation function to generate the final 3D coordinates of human skeleton joints. Integration of estimations from two orthogonal camera perspectives allows the model to account for occlusions, varying lighting conditions, and pose diversity, providing a more comprehensive representation of the 3D pose. The network is trained and evaluated on a public CMU Panoptic dataset that contains videos with a wide range of poses.
Design of Body Function Index Mobile Query System for College Students Based on Decision Tree Algorithm

Decision tree is the core technology of data mining. According to different characteristics, classification or decision sets are represented by tree structure, and rules and development rules are generated. The decision tree algorithm takes the information gain as the basis of attribute division and selects the attribute with the maximum information gain as the split node to generate the decision tree. Based on comparison of typical decision tree algorithms, CART algorithm is used in this paper. This paper studies the pruning of decision tree based on the principle of CART algorithm, designs the logical structure of data storage based on SQL Server database management system, and constructs the moving query process of college students’ physical function index. The research results solve the key technical problems of the system development and provide a complete solution for the construction of the mobile inquiry system of the physical function index of college students.