Miracle-dz/UniFAR-FTUs huggingface dataset:
A Component Construction Progress Surveillance Method Using LiDAR and BIM

Laser imaging detection and ranging (LiDAR) and Building Information Model (BIM) are widely used in building construction. But there are problems such as inconvenient interaction and inaccurate data registration due to different data coordinate systems. This paper proposes a method for component construction progress surveillance using LiDAR and BIM. Component-level interactions can be performed by splitting the model into components and converting them into point clouds. Then, the problem of inaccurate registration between BIM and LiDAR can be solved by filtering and feature point calibration. Finally, the construction progress can be obtained by performing the k-neighborhood algorithm. To verify the validity and reliability of the proposed method, this paper uses the actual data of the factory under renovation to conduct experiments. The results show that the proposed method achieves a registration tolerance of less than 0.05m. The proposed method has good robustness on outlier points for evaluating the construction progress of components.
A Review Study on Feedback Models for Information Retrieval

The benefits of feedback for researchers as achievement and development are undisputed. For this reason, researchers have sought to develop ideas and frameworks that describe how feedback works and what factors may influence feedback engagement in it. This review paper aimed to summarize the best-known models and ideas we discovered through a methodical, three-step process. We discussed the definitions, models, historical context, and certain basic mechanisms of feedback systems. At the end of our examination of the models, we reached eight major conclusions. This study aims to advance knowledge in this area and help researchers and educators select the best models for their studies and policy development in feedback model. We further evaluated and analyzed the fourteen models in our supplemental study to classify common features and integrate them into a new comprehensive model.
A collaborative communication-less frequency support control for the DRU-HVDC integrated offshore wind farm

The utilization of diode rectifier units (DRU) in high voltage direct current (HVDC) transmission systems holds significant promise as a cost-effective solution for the integration of large-scale offshore wind farms (OWFs). A collaborative communication-less frequency support (FS) control strategy has been proposed in this paper for OWFs using DRU-HVDC transmission. The onshore-grid frequency change is introduced to the HVDC in the form of a small sinusoidal disturbance and is accurately reflected in offshore grid by the frequency coupling between the AC and the DC side of the DRU. On this basis, the wind turbines (WTs) could realize the FS by measuring the frequency variation of the small disturbance current in the offshore grid. Finally, the results of PSCAD/EMTDC simulation are presented to validate the efficacy of the proposed FS strategy.
Deep reinforcement learning based hierarchical eco-driving strategy for a series-parallel hybrid electric truck

It has been well recognized that the development of information technology and the construction of intelligence infrastructure are becoming better solution to energy consumption and pollutant emissions. In this paper, deep deterministic policy gradient (DDPG) algorithm is embedded into a hierarchical eco-driving framework which is designed to improve the driving performance. The structure is divided into two layers, namely, the upper adaptive cruise control (ACC) and the lower energy management strategy (EMS). Because of the complex power model, pre-optimized progress is embedded into the DDPG loop. The experimental results show that compared with the Green Light Optimal Speed Advice (GLOSA) strategy with similar travel time, the proposed EMS can be reduced by about 8% and the fuel economy can reach 87.2% to 90.7% of dynamic programming (DP) based EMS. Overall, the proposed framework shows better energy saving advantages.
A Motion Planning Framework with Integrating Transformer-based Trajectory Prediction on Highway

In the realm of autonomous vehicles (AVs), ensuring safe and effective motion planning is a paramount and challenging task, particularly in complex and dynamic scenarios. To enhance driving safety, a low-risk, high-efficiency motion planning framework is established for AVs based on surrounding vehicles (SVs) trajectory prediction in highway scenarios. Firstly, a Transformer-based predictive model is constructed for trajectory prediction of SVs. Subsequently, a driving risk assessment method is designed based on the trajectory prediction results. Furthermore, a motion planning framework utilizing polynomial curve technique is proposed while considering driving risk, efficiency and comfort. Finally, the predictive model is trained and tested by collecting the highway dataset on the Crala simulation experiment platform, and the effectiveness of our constructed motion planning framework is verified through two lane-changing events. The simulation results affirm that our proposed method achieves both safe and efficient motion planning for AVs.
Lane change trajectory planning method for intelligent vehicles considering surrounding risk

Intelligent vehicle trajectory planning is a multi-objective coupling optimization problem with multiple constraints. Rapid generation of collision-free smooth trajectories that meet kinematic and dynamic constraints can improve the safety and comfort of intelligent vehicle lane-changing process. In order to improve the efficiency of trajectory planning, this paper uses the path and speed decoupling method to generate a feasible lane-changing trajectory set. First, the B-spline curve is used to generate a feasible lane-changing path, and then the speed planning is completed based on Dynamic programming (DP) and Quadratic programming (QP). Considering safety, comfort and efficiency, a comprehensive cost function is established, and the trajectory with the smallest cost function is selected as the output target. The safety cost function is based on the theory of driving risk field, and the safety potential energy index is used to evaluate the safety of the trajectory, which can quantify the risk level of the surrounding traffic factors on the whole process of lane-changing. The simulation results show that the proposed method can quickly plan a reasonable trajectory in the lane-changing scene, and can effectively avoid collision with dynamic obstacles while maintaining safety and efficiency.
Numerical Derivation of High-Resolution Detector Response Matrices for Airborne Gamma-Ray Spectrometry Systems

The objective of this work is to derive high-resolution spectral and angular detector response matrices of NaI(Tl) scintillation detectors for airborne gamma-ray spectrometry (AGRS) applications using a simulation-based numerical approach.The coupled gamma-ray and electron detector response was simulated using the high-fidelity multi-purpose Monte Carlo code FLUKA in an event-by-event mode and adopting high-performance computing. The simulation model incorporates a state-of-the-art non-proportional scintillation model. To validate the developed model, radiation measurements were conducted using calibration point sources with known activities and source-detector geometries under laboratory conditions.The detector response shows strong energy dispersion by various spectral features such as backscatter or annihilation peaks and also a pronounced sensitivity on scintillation non-proportionality effects. Furthermore, the simulations revealed a significant directional dependence of the detector response caused by the various hardware detector components and scintillation crystal geometry.These results show the importance for a detailed modelling of AGRS systems in a numerical calibration approach to prevent systematic errors in the simulated spectral and angular detector response. The general methodology presented herein can be adopted for numerical calibration as well as for spectral deconvolution of any inorganic-scintillator-based AGRS system.
A Reconfiguration Method for Distribution Network with Countywide PV Generation Using Information Gap Decision Theory

Reconfiguration is an important method to optimize the operation of distribution networks in the context of photovoltaic integration throughout the county. Due to the uncertainty of PV power and the limited accuracy of prediction techniques, the existing reconfiguration methods do not adequately consider the error between the actual power and the predicted power. This paper proposes a distribution network reconfiguration method using information gap decision theory to improve the active adaptability to photovoltaic fluctuations from the decision point of view, while limiting the active power loss and ensuring the safety of operation. The method is tested on IEEE 33-bus system with PV integrated in each bus to verify its effectiveness. The economic benefits, tolerable PV fluctuation ranges and bus voltages are also discussed.
Multi-Sensor Weighted Fusion Algorithm for High Voltage Transformer Based on Neural Network

T High-voltage, flexible DC transformers are crucial components of electric power systems, and their online monitoring is essential for enhancing reliability. Data fusion, a key technology in wireless sensor networks, reduces sensor transmissions and significantly improves network performance, lifespan, and response time. Multi-sensor data fusion provides more precise information than single-sensor systems. To obtain accurate estimations of monitoring variables from noisy data, we have improved the adaptive weighted fusion algorithm. First, we preprocess the data using the trust method. Next, we employ a neural network-based error correction technique to adaptively match each sensor's weights. This process generates more accurate estimation values. Comparative simulation studies reveal that our proposed algorithm outperforms both the mean estimation algorithm and the adaptive weighted fusion algorithm regarding accuracy and fault tolerance. Consequently, it is better suited for the big data environment's demands on data accuracy.
The Expression of Alamandine Receptor MrgD in Clear Cell Renal Cell Carcinoma Is Associated with a Worse Prognosis and Unfavorable Response to Antiangiogenic Therapy

Renal cell carcinoma (RCC) ranks among the most prevalent malignancies in Western countries, marked by its notable heterogeneity, which contributes to an unpredictable clinical trajectory. The insufficiency of dependable biomarkers adds complexity to assessing this tumor progression. Imbalances of several components of the intrarenal renin–angiotensin system (iRAS) significantly impact patient prognoses and responses to first-line immunotherapies. In this study, we analyzed the immunohistochemical expression of the Mas-related G-protein-coupled receptor D (MrgD), which recognizes the novel RAS peptide alamandine (ALA), in a series of 87 clear cell renal cell (CCRCCs), 19 papillary (PRCC), 7 chromophobe (ChRCC) renal cell carcinomas, and 11 renal oncocytomas (RO). MrgD was expressed in all the renal tumor subtypes, with a higher mean staining intensity in the PRCCs, ChRCCs, and ROs. A high expression of MrgD at the tumor center and at the infiltrative front of CCRCC tissues was significantly associated with a high histological grade, large tumor diameter, local invasion, and locoregional node and distant metastasis. Patients with worse 5-year cancer-specific survival and a poorer response to antiangiogenic tyrosine-kinase inhibitors (TKIs) showed higher MrgD expression at the center of their primary tumors. These findings suggest a possible role of MrgD in renal carcinogenetic processes. Further studies are necessary to unveil its potential as a novel biomarker for CCRCC prognosis and response to frontline therapies.
The Performance of a High-Resolution Algorithm for Estimating the Complex Relative Permittivity of Materials at X-band Using the Transmission/Reflection Method in Free-Space

In order to accurately determine the dielectric characteristics of a planar material slab, this paper presents a sophisticated algorithm with high precision, drawing inspiration from the advancements made in the Multiple Signal Classification (MUSIC) algorithm. The transmission coefficients are acquired with a planar material sample placed in the middle of the two antennas. The reflection coefficients are determined with a metal-backed planar material sample using only one antenna. Through the application of measured transmission or reflection coefficients across known sample thicknesses via free-space transmission or reflection measurements, the effectiveness of the proposed algorithm is validated. The algorithm's validity is established through experiments involving varied thicknesses of Teflon-PTFE materials at the X-band. The outcomes of estimating the complex relative permittivity for the planar Teflon-PTFE slab using the presented algorithm showcase precision for both methods, particularly when the sample's minimum thickness equates to half of a wavelength.
Development of an Inductor Design Tool Based on Multi-Objective Optimization and Finite Element Simulation

This paper proposes an inductor design tool based on multi-objective optimization and finite element simulation. It provides a clear graphical user interface. Designers only need to provide simple circuit parameters and the tool can complete the complex design process automatically. It uses Non-Dominated Sorting Genetic Algorithm II to obtain Pareto front of inductors. It enables automated modeling and finite element simulation to correct electrical parameters. In addition, an improved method for calculating AC winding loss based on finite element simulation is used. The feasibility of the tool has been verified through an optimization process of an inductor in an interleaved boost converter.
A Survivability Assessment Model for the Weak-Link Power Grid under Extreme Events

This paper proposed a survivability assessment model for the waek-link power grid (WLPG) under extreme events. Firstly, the survivability index system is constructed to reflect the impact of extreme events. It mainly includes secondary index from multiple dimensions and the corresponding tertiary index. Secondly, the objective secondary index information entropy model and primary index principal component analysis model are proposed. Finally, IEEE RTS79 was tested as case study to validate the proposed index assessment model. The proposed model can effectively characterize the survivability of WLPG under extreme events of different severity with a simple calculation process and not require human weighting, making it relatively objective.
Construction Project Management Using Natural Language Processing Technology

The effectiveness of the structure business is generally because of innovative headways. Utilizing an intelligent thought of natural language processing innovation, this study expects to determine correspondence cooperation issues to foster a stage for project management. This paper looks at the review and execution of Natural Language Processing (NLP) innovation in the management of construction projects, determined to resolve issues like the developing size and volume of construction projects and records, the intrinsic trouble and intricacy of their management, the tedious and blunder inclined nature of manual resemblance and acceptance, and the absence of mastery among experts to give complete, opportune, and precise conferences. The most predominant issues in design can be tended to by changing over unstructured reports into organized data.
Validation of IAEA Electron Beam Data for Radiotherapy and Their Use to Model Total Skin Irradiation

Monte Carlo simulation for external beam radiotherapy requires precise modeling of the treatment linear accelerator head and of the primary electron beam. Detailed modeling of medical accelerators is complex and could require proprietary data, and the production of an adequate sample of primary particles to simulate statistically significant dosimetric observables requires intensive computational resources. To facilitate these tasks, IAEA (International Atomic Energy Agency) released a phase-space database for external beam radiotherapy, consisting of primary particle distributions deriving from Monte Carlo simulations of widely utilized linear accelerators, which can be used as input to dosimetric simulations. The IAEA data have been released more that 10 years ago along with a Geant4 interface. This presentation addresses the ability to produce realistic dosimetric distributions in Geant4-based simulations using IAEA electron beam data and to endeavour the simulation of beams for non-conventional radiotherapy settings that are not covered by the IAEA database, such as those needed for Total Skin Irradiation (TSI, as performed in Total Skin Electron Therapy procedures). We report the results of validation tests, involving the comparison of dosimetric quantities measured in quality control standard procedures and simulated with Geant4 using IAEA phase-space data.
An Online Bridge-Level IGBT Temperature Estimation Approach Via Emitter Parasitic Inductance Voltage Undershoot

Insulated gate bipolar transistor (IGBT) junction temperature estimation is critical for lifetime prediction. However, most traditional temperature evaluation methods need a set of testing equipment corresponding to an IGBT chip. In this article, an online bridge-level junction temperature evaluation method via the induced voltage undershoot between Kelvin and power emitters of lower IGBT for junction temperatures extraction of two IGBTs in half-bridge is presented. The VeE undershoot of lower IGBT during turn-off transient has a good linear relationship and high relative sensitivity for estimating temperatures of upper and lower IGBTs. So that, the bridge-level junction temperature monitoring method can be implemented online in converters with no disruption to operation. Double-pulse and three-phase inverter are established and the experimental results reveal the relation between Tj of IGBT and VeE undershoot under different working conditions. It can be concluded that the VeE undershoot is a potentially practical TSEP for online bridge-level IGBT temperature evaluation.
A Reactive Power Substitution Control Strategy for Wind Farm Considering the Reactive Capacity of Wind Turbines

Under steady-state, through the reactive power substitution between reactive power compensation equipment and wind turbines, the output power of centralized reactive power compensation devices can be reduced to satisfy the dynamic reactive power demands such as fault ride-through. In this paper, a reactive power substitution control strategy for wind farm is proposed. This control strategy prioritizes the centralized reactive power compensation devices to respond to the requirements of grid dispatching and voltage regulation. Then, to address different types of grid voltage faults, the control objective is to minimize the output of centralized reactive power compensation equipment and the terminal voltage deviation of the wind turbines. An optimization model is established to obtain the reactive power command of wind turbines online and realize real-time control for wind farms. Finally, an actual wind farm is taken to verify the feasibility of the proposed reactive power replacement control strategy under steady-state and grid voltage faults.
Integral Backstepping Control of Sinusoidal Ripple Current Charge for Battery Chargers

The sinusoidal ripple current charge algorithm enables the battery charger to achieve high power density and high charging efficiency. However, the conventional PI control method is limited by high-frequency variable charging current. In this paper, an integral backstepping control method is proposed to regulate the sinusoidal ripple current, which is modeled and analyzed on a full-bridge DC/DC converter. The backstepping controller is a nonlinear control method that can provide fast output current tracking and good steady-state performance. Moreover, the introduction of the integral term can adaptively compensate the parameter uncertainties and external disturbances, which enhances the robustness and anti-interference ability of the system. The proposed controller achieves fast dynamic response and accurate tracking performance with variable frequency. The simulation verification is carried out, and the comparison results can provide reference for the optimization of the current control strategy of the battery chargers.
Improved NSGA-III algorithm for solving multi-objective flexible job shop scheduling problem

A workshop scheduling model considering machine resources, spatial resources, and human resources is established for the flexible job shop scheduling problem (FJSP). An improved NSGA-III algorithm is proposed to solve the multi-objective optimization problem that incorporates the makespan, total jobs movement distance, and total workshop payroll. The algorithm enhances the diversity of the population by introducing the Logistic chaotic mapping initialization to the NSGA-III algorithm. Furthermore, a adaptive local search strategy based on the upper confidence bound algorithm (UCB) is employed to enhance the algorithm’s search capability. Finally, the effectiveness and practicality of the algorithm are validated through instance verification.
Generating Visually Evoked Potentials Using a Diffusion Probabilistic Model

We present a diffusion probabilistic model (DPM) capable of generating high-quality electroencephalography (EEG) signals. In particular, we investigate the fidelity of synthesized visually evoked potentials and the performance of the novel class-conditioned continuous-time DPM that generates the signals. We carefully select our neural network architecture to prevent generated frequency artifacts in the multi-channel synthesized EEG signals. With progressive distillation, we are able to achieve a single-step diffusion model that still has a competitive performance. With generative metrics and statistical analysis, we show that using DPMs for brain signal generation is feasible.
Study on Core Loss Model of Super High Frequency PWM Wave Excitation

Magnetic core loss is one of the important parameters of power magnetic components. In view of the problem that AC power method has a large error in measuring the core loss at high frequency and low loss angle, as well as the disadvantages of calorimetric method, such as high time cost and tedious measurement process, DC power method is proposed. The DC method obtains magnetic core loss by measuring DC input power of inverter circuit, greatly weakening the impact of impedance angle of the measured magnetic component on measurement accuracy, and can accurately measure magnetic core loss under PWM wave excitation. This article conducts research on the following aspects: Firstly, based on the DC power method, this article measured the losses of magnetic cores under asymmetric PWM wave and symmetric PWM wave excitation, established a prediction model for magnetic core losses, and verified the accuracy of the model. Verify the existence of relaxation loss when measuring the magnetic core loss under symmetric PWM wave excitation, and establish a relaxation loss prediction model.