Adjustment Method Research Articles

The Side-Cutter Position Adjustment Method for Enhancing Milled Rotor Profile Accuracy using ANN and NSGA-II

The Side-Cutter Position Adjustment Method for Enhancing Milled Rotor Profile Accuracy using ANN and NSGA-II

High-resolution climate projection dataset based on CMIP6 for Peru and Ecuador: BASD-CMIP6-PE

Here, we present BASD-CMIP6-PE, a high-resolution (1d, 10 km) climate dataset for Peru and Ecuador based on the bias-adjusted and statistically downscaled CMIP6 climate projections of 10 GCMs. This dataset includes both historical simulations (1850–2014) and future projections (2015–2100) for precipitation and minimum, mean, and maximum temperature under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). The BASD-CMIP6-PE climate data were generated using the trend-preserving Bias Adjustment and Statistical Downscaling (BASD) method. The BASD performance was evaluated using observational data and through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. Results demonstrated that BASD significantly reduced biases between CMIP6-GCM simulations and observational data, enhancing long-term statistical representations, including mean and extreme values, and seasonal patterns. Furthermore, the hydrological evaluation highlighted the appropriateness of adjusted GCM simulations for simulating streamflow, including mean, low, and high flows. These findings underscore the reliability of BASD-CMIP6-PE in assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.

Fast-facile synthesis of single-phase nanocrystalline β-Ca2P2O7 powder by an aerosol synthesis method

Recently, β-phase of calcium pyrophosphate has gained wide attention for potential in different applications. In this communication, we report for the first time an ultra-fast, cost-effective, and facile one-step synthesis of single-phase β-CPP powder without post-heat treatment by applying liquid flame spray (LFS). LFS is a scalable, continuous, and adjustable one-step flame-based aerosol synthesis method. The effect of Ca/P ratio on structural properties was investigated. β-Ca2P2O7 could be successfully synthesized in spherical nano-and micron-sized particles, as well as plate-like particles. The synthesized single-phase powder exhibited high thermal stability and relatively high specific surface area with different (nano)porous structures.

PBRL-TChain: A performance-enhanced permissioned blockchain for time-critical applications based on reinforcement learning

Permissioned blockchains can provide high security and reliability for various Internet of Things (IoT) systems, such as smart healthcare and vehicular networks. However, the performance issues of permissioned blockchains have been a constraint for fully supporting time-critical tasks with tight requirements of low latency and high throughput. This paper proposes PBRL-TChain, a performance-enhanced permissioned blockchain for time-critical applications based on deep reinforcement learning (DRL). First, we propose a priority ordering mechanism to minimize latency and maximize reliability. Then, we design a fast retransmission mechanism to alleviate the impact of transaction conflicts on the latency performance. Finally, we propose a DRL-based dynamic adjustment method in PBRL-TChain to achieve better performance and reliability. Experiments show that our method outperforms existing methods. Compared with Fabric++ and Athena, it can reduce the latency of time-critical transactions by 10 times, achieving a level of 10 ms, significantly improving the system’s performance and reliability.

Understanding of the Tracking and Adjusting Effect of Deep Profile Control and Flooding Expansion Application Blocks in Pubei Oilfield

In order to explore the development technology of enhancing oil recovery during the ultra-high water cut period in the Pubei Oilfield, deep profile control pilot tests and expanded application tests have been carried out in the Pubei Oilfield. This article mainly starts from the actual production performance of the deep profile control and flooding expansion application test, analyzes the main tracking and adjustment methods and the understanding of the results obtained in the development process of deep profile control and flooding, laying the foundation for the establishment of deep profile control and flooding matching tracking and adjustment technology in Pubei Oilfield.

Decoding time-resolved neural representations of orientation ensemble perception.

The visual system can compute summary statistics of several visual elements at a glance. Numerous studies have shown that an ensemble of different visual features can be perceived over 50-200 ms; however, the time point at which the visual system forms an accurate ensemble representation associated with an individual's perception remains unclear. This is mainly because most previous studies have not fully addressed time-resolved neural representations that occur during ensemble perception, particularly lacking quantification of the representational strength of ensembles and their correlation with behavior. Here, we conducted orientation ensemble discrimination tasks and electroencephalogram (EEG) recordings to decode orientation representations over time while human observers discriminated an average of multiple orientations. We modeled EEG signals as a linear sum of hypothetical orientation channel responses and inverted this model to quantify the representational strength of orientation ensemble. Our analysis using this inverted encoding model revealed stronger representations of the average orientation over 400-700 ms. We also correlated the orientation representation estimated from EEG signals with the perceived average orientation reported in the ensemble discrimination task with adjustment methods. We found that the estimated orientation at approximately 600-700 ms significantly correlated with the individual differences in perceived average orientation. These results suggest that although ensembles can be quickly and roughly computed, the visual system may gradually compute an orientation ensemble over several hundred milliseconds to achieve a more accurate ensemble representation.

A Multilayer Nested Space Ray Telescope Assembly and Adjustment System Based on Online Monitoring Feedback

In order to adapt to the rapid development of deep space exploration, pulsar navigation and space astronomical observation technology, the development of high-efficiency and high-performance X-ray space telescopes has become one of the hotspots for research in the fields of autonomous spacecraft navigation and astronomical observation. Since the X-ray multilayer nested mirror shells are thin-walled and deformable cylindrical structures with high requirements for surface shape accuracy, a slight change in the position or surface shape will lead to a reduction in the focusing imaging quality, so it is necessary to develop a high-precision assembly and adjustment system based on closed-loop real-time monitoring. In this paper, a multi-layer nested space ray telescope assembly and adjustment system with online monitoring feedback is developed, and a method for precision assembly and adjustment of multi-layer nested mirror shells based on online real-time detection and feedback is proposed, which specifically includes key innovative assembly technologies such as three-dimensional face shape measurement, flexible vacuum adsorption, precision adjustment that have been broken through in the process, and the imaging quality of the focused spot of the mirror shells is evaluated and analyzed in the end. The results of the experiment indicate that the assembly and adjustment of the multilayer nested mirror shells can effectively complete the high-precision assembly, and the relevant results can provide a certain reference significance for the further enhancement of the assembly and adjustment performance of the multilayer nested mirror shells.

ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging.

Numerical simulation serves as a cornerstone in scientific modeling, yet the process of fine-tuning simulation parameters poses significant challenges. Conventionally, parameter adjustment relies on extensive numerical simulations, data analysis, and expert insights, resulting in substantial computational costs and low efficiency. The emergence of deep learning in recent years has provided promising avenues for more efficient exploration of parameter spaces. However, existing approaches often lack intuitive methods for precise parameter adjustment and optimization. To tackle these challenges, we introduce ParamsDrag, a model that facilitates parameter space exploration through direct interaction with visualizations. Inspired by DragGAN, our ParamsDrag model operates in three steps. First, the generative component of ParamsDrag generates visualizations based on the input simulation parameters. Second, by directly dragging structure-related features in the visualizations, users can intuitively understand the controlling effect of different parameters. Third, with the understanding from the earlier step, users can steer ParamsDrag to produce dynamic visual outcomes. Through experiments conducted on real-world simulations and comparisons with state-of-the-art deep learning-based approaches, we demonstrate the efficacy of our solution.

INFLUÊNCIA DO PROCEDIMENTO DE AJUSTE GRANULOMÉTRICO DE AMOSTRAS DE CERÂMICA VERMELHA AGREGADA COM RESÍDUOS DE ROCHAS ORNAMENTAIS PARA CLASSIFICAÇÃO CONFORME CRITÉRIOS DA ABNT

INFLUENCE OF THE PROCEDURE FOR GRANULOMETRIC SAMPLE ADJUSTMENT OF RED CERAMIC AGGREGATED WITH DIMENSION STONE WASTES FOR CLASSIFICATION ACCORDING TO THE ABNT CRITERIA’S. In order to clarify questions about the granulometric preparation of samples for the classification of solid wastes according to the Brazilian technical standard (NBR 10004/04), solubilization tests were carried out on red ceramic specimens made with conventional clay aggregated with ornamental rock residues. Samples were prepared by fragmentation and milling to verify the effects on metals releasing to the extractor medium. The specimens were made with 80% clay, 20% residue and 10% (m/m) moisture and sintered at 1000 °C. The data show that ceramics made with conventional clay, when discarded, will produce non inert wastes, as well as the ceramics made with dimension stone wastes added to the conventional raw material, according to the granulometry of its particles. After all, the metals concentration in the solubilization test are influenced by the sample granulometry. This paper shows that the method of granulometric adjustment should be detailed in the technical standard for solubilization tests (NBR 10006/04), in order to ensure the reliability of the method for classification of solid wastes.

Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases - Weifang City, Shandong Province, China, 2020-2023.

Respiratory infectious diseases, such as influenza and coronavirus disease 2019 (COVID-19), present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems. The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three methodologies were evaluated: the adaptive dynamic threshold method (ADTM) for dynamic threshold adjustments, the machine learning supervised method (MLSM), and the machine learning unsupervised method (MLUM) utilizing anomaly detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency. ADTM issued 37 warnings with a sensitivity of 71% and a specificity of 85%. MLSM generated 35 warnings, with a sensitivity of 82% and a specificity of 87%. MLUM produced 63 warnings with a sensitivity of 100% and specificity of 80%. The initial warnings from ADTM and MLUM preceded those from MLSM by five days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (P<0.05). The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning's reliability and underscores the unique advantages of each method. Furthermore, it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response, alongside acknowledging limitations and the need for broader validation.

Internal Short-Circuit Fault Location of Rail Transit Auxiliary Inverter Based on Voltage Deviation

In order to improve the ability of locating and detecting the internal short-circuit fault of rail transit auxiliary inverter, a method of locating the internal short-circuit fault of rail transit auxiliary inverter based on voltage deviation is proposed. The fault sensing information acquisition module of rail transit auxiliary inverter is constructed by using the detection method of fundamental frequency characteristics of differential mode current of excitation winding, and the voltage deviation analysis of rail transit auxiliary inverter under internal short-circuit fault state is realized by using the methods of MVCR excitation winding control and output reactive power adjustment. Combined with the dynamic distribution characteristics of output harmonics and initial phase of rail transit auxiliary inverter, the equivalent compensation and capacity expansion methods of excitation winding circuit are adopted. The signal test model of fault location is established, and the spectral characteristics of the internal short-circuit fault signal of rail transit auxiliary inverter are extracted. A fault joint feature location and monitoring model is established by using the data clustering analysis method of fusion difference, and the internal short-circuit fault location detection of inverter is realized according to the voltage deviation distribution characteristics of inverter in short-circuit state. The test results show that the accuracy of short-circuit fault location in rail transit auxiliary inverter is high, and the dynamic estimation ability of inverter output voltage deviation is strong.

Analytical model of reliability of the freight rail transportation forecasting system

The purpose of the study: to develop an analytical model of the reliability of the freight transportation forecasting system by railway transport. Methods: economic and mathematical methods and models of transportation forecasting adapted to the peculiarities of railway freight transportation –models based on “Bottom/Top” approaches, methods of predictive adjustment by types of cargo, methods of scenario forecasting. Results: an analytical model of the reliability of the freight rail transportation forecasting system has been developed, the distinctive feature of which is the integration of cargo transportation evaluation indicators, characteristics of scenario methods and parameters of the forecasting process, a forecast has been formed for the volume and structure of freight traffic following through the Oktyabrskaya railway landfill for the future up to 2030 in the category of main types of cargo.

Adjustment and testing method of large aperture mirror Bipod support structure

Adjustment and testing method of large aperture mirror Bipod support structure

Evaluation of Moving Grid Adjustment (MGA) Method in Field Variation Control

Evaluation of Moving Grid Adjustment (MGA) Method in Field Variation Control

Forecasting Monthly Export of Readymade Garments by Removing Seasonal Impact

Various Bangladeshi authorities release time series data that takes seasonal effects into account. However, no adjusted series is offered. The developed world has completely different conditions since they broadcast seasonally adjusted series. Many seasonal adjustment techniques, such as classical and X-based techniques, are available; however they cannot be used in practice in accordance with Bangladesh's seasonal time series. So here we have executed X-11 and X-12-ARIMA which are known as X-based seasonal adjustment methods and some classical methods like SARIMA and MA to seasonal time series data collected from secondary source as economic trend revealed by Bangladesh Bank. We will use export of readymade garments which are monthly data and have seasonal impacts. The entire data collection must first be divided into training and test data. Next, the data was de-seasonalized, and future values were predicted using training and test data sets, respectively, to compute various forecasting errors such as MAPE, PMAD, MAD, and RMSE. We utilize several X-based approaches and traditional methods to compare the errors. We conclude that the seasonal adjustment strategy performs better and has fewer forecasting mistakes. In conclusion, we suggest the optimal seasonal adjustment technique for ready-to-wear exports and project certain future values based on that technique.

Improved Adjustment Method Applied in Transformation of Laser Tracker

Improved Adjustment Method Applied in Transformation of Laser Tracker

Multiplatform Bundle Adjustment Method Supported by Object Structural Information

Multiplatform Bundle Adjustment Method Supported by Object Structural Information

Depth of field adjustment method on 3D raster display based on 3D point clouds

Depth of field adjustment method on 3D raster display based on 3D point clouds

Adjustment Method of Optimum Breakdown Charge during Impulse Voltage Conditioning Process for Vacuum Interrupter

Adjustment Method of Optimum Breakdown Charge during Impulse Voltage Conditioning Process for Vacuum Interrupter

Deep Reinforcement Learning for Nash Equilibrium of Differential Games.

Nash equilibrium is a significant solution concept representing the optimal strategy in an uncooperative multiagent system. This study presents two deep reinforcement learning (DRL) algorithms for solving the Nash equilibrium of differential games. Both algorithms are built upon the distributed distributional deep deterministic policy gradient (D4PG) algorithm, which is a one-sided learning method. We modified it to a two-sided adversarial learning method. The first is D4PG for games (D4P2G), which directly applies an adversarial play framework based on the D4PG. A simultaneous policy gradient descent (SPGD) method is employed to optimize the policies of the players with conflicting objectives. The second is the distributional deep deterministic symplectic policy gradient (D4SPG) algorithm, which is our main contribution. More specifically, it newly designs a minimax learning framework that combines the critics of the two players and proposes a symplectic policy gradient adjustment method to find a better policy gradient. Simulations show that both algorithms converge to the Nash equilibrium in most cases, but D4SPG can learn the Nash equilibrium more accurately and efficiently, especially in Hamiltonian games. Moreover, it can handle games with complex dynamics, which is challenging for traditional methods.