Dynamic Correction Research Articles

Quantum delocalization, structural order, and density response of the strongly coupled electron liquid

We investigate the impact of electronic correlations and quantum delocalization onto the static structure factor and static density response function of the strongly coupled electron liquid. In contrast to a classical system, the density response of the electron liquid vanishes on small length scales due to quantum delocalization effects, which we rigorously quantify in terms of imaginary-time correlation functions and dynamic Matsubara response functions. This allows us to analyze the interplay of structural order and dynamic quantum effects as it manifests itself in the dynamic Matsubara local field correction. Finally, we identify an effective electronic attraction in the spin-offdiagonal static density response when the wavelength of the perturbation is commensurate with the average interparticle distance.

Measuring Tilt with an IMU Using the Taylor Algorithm

This article addresses the important problem of tilt measurement and stabilization. This is particularly important in the case of drone stabilization and navigation in underwater environments, multibeam sonar mapping, aerial photogrammetry in densely urbanized areas, etc. The tilt measurement process involves the fusion of information from at least two different sensors. Inertial sensors (IMUs) are unique in this context because they are both autonomous and passive at the same time and are therefore very attractive. Their calibration and systematic errors or bias are known problems, briefly discussed in the article due to their importance, and are relatively simple to solve. However, problems related to the accumulation of these errors over time and their autonomous and dynamic correction remain. This article proposes a solution to the problem of IMU tilt calibration, i.e., the pitch and roll and the accelerometer bias correction in dynamic conditions, and presents the process of calculating these parameters based on combined accelerometer and gyroscope records using a new approach based on measuring increments or differences in tilt measurement. Verification was performed by simulation under typical conditions and for many different inertial units, i.e., IMU devices, which brings the proposed method closer to the real application context. The article also addresses, to some extent, the issue of navigation, especially in the context of dead reckoning.

Dynamic Physics-Guided Deep Learning for Long-Term Production Forecasting in Unconventional Reservoirs

Summary Neural network predictive models are popular for production forecasting in unconventional reservoirs due to their ability to learn complex relationships between well properties and production responses from extensive field data. The intricate flow behavior in hydraulically fractured unconventional reservoirs, which remains poorly understood, makes these statistical models particularly useful. Various neural network variants have been developed for production prediction in these reservoirs, each offering predictive capability of varying levels of granularity, accuracy, and robustness against noisy and incomplete data. Neural network predictive models that integrate physical principles are especially useful for subsurface systems, as they provide predictions that adhere to physical laws. This work introduces a new dynamic physics-guided deep learning (DPGDL) model that incorporates physical functions into neural networks and employs residual learning to compensate for the imperfect description of the physics, under variable data support. The new formulation allows for dynamic residual correction, avoids unintended bias due to less-than-ideal input data, and provides robust long-term predictions. The DPGDL model improves upon a static formulation by utilizing a masked loss function to enable learning from wells with varying production lengths and by improving the results when partially-observed timesteps are present. In addition, a sequence-to-sequence residual model has been developed to correct additional biases in the long-term predictions from the physics-constrained neural networks. Several synthetic data sets with increasing complexity as well as a field data set from the Bakken are used to demonstrate the performance of the new DPGDL model.

The economics of home energy usage: Insights from urban economy

Recent regional investigations in the United States have revealed a thought-provoking perspective: household electricity consumption may act as an inferior commodity, displaying a negative correlation with wealth. This finding challenges the outcomes of various other econometric analyses and underscores the importance of scrutinizing power consumption patterns across different regional service areas. While it is commonly believed that home electricity usage decreases as income rises, this may not always hold true universally. This study focuses on power usage in Seattle, Washington households, a prominent urban economy in the Pacific Northwest. Employing dynamic error correction modeling techniques, the research demonstrates statistically significant fluctuations in domestic power usage attributed to variations in actual value, actual revenue, and cold weather. In the immediate future, energy for homes in this urban economy resembles any other commodity. However, investing in electricity for homes in Seattle may not be advisable in the long run. Home power usage in Seattle declines with each percentage point increase in actual per capita income over 1.2 %. This finding highlights the need for careful consideration and strategic planning in energy management policies for urban economies like Seattle.

Fatigue life prediction for high-speed railway bridges by reconstructing monitoring-based dynamic stress

Abstract Bridge responses that are excited by high-speed trains have the characteristics of high amplitude, high cycle, and large dynamic effects, which greatly affect the fatigue bearing capacity of affected bridges. To achieve reliable analysis of the fatigue performance of high-speed railway bridges, this study developed a bridge fatigue life prediction method based on the reconstruction of the train-induced dynamic stress time history. First, the equations for solving the static stress time history under influence line virtual loading are derived, and then the dynamic stress time history reconstruction method based on two types of dynamic correction factors is proposed. The statistical characteristics of the train loads and dynamic correction factors are fit according to monitoring data, and bridge fatigue life prediction is realized by use of the reliability theory. Finally, the applicability and effectiveness of the proposed method are verified by using a train-bridge interaction model and monitoring data from a long-span high-speed railway bridge. The results show that the proposed method can greatly improve the accuracy of fatigue performance analysis and can effectively predict the fatigue life of high-speed railway bridges under complex loads. These results can provide an important reference for fatigue evaluation of high-speed railway bridges.

Large-Scale Land Cover Mapping Framework Based on Prior Product Label Generation: A Case Study of Cambodia

Large-Scale land cover mapping (LLCM) based on deep learning models necessitates a substantial number of high-precision sample datasets. However, the limited availability of such datasets poses challenges in regularly updating land cover products. A commonly referenced method involves utilizing prior products (PPs) as labels to achieve up-to-date land cover mapping. Nonetheless, the accuracy of PPs at the regional level remains uncertain, and the Remote Sensing Image (RSI) corresponding to the product is not publicly accessible. Consequently, the sample dataset constructed through geographic location matching may lack precision. Errors in such datasets are not only due to inherent product discrepancies, and can also arise from temporal and scale disparities between the RSI and PPs. In order to solve the above problems, this paper proposes an LLCM framework for generating labels for use with PPs. The framework consists of three main parts. First, initial generation of labels, in which the collected PPs are integrated based on D-S evidence theory and initial labels are obtained using the generated trust map. Second, for dynamic label correction, a two-stage training method based on initial labels is adopted. The correction model is pretrained in the first stage, then the confidence probability (CP) correction module of the dynamic threshold value and NDVI correction module are introduced in the second stage. The initial labels are iteratively corrected while the model is trained using the joint correction loss, with the corrected labels obtained after training. Finally, the classification model is trained using the corrected labels. Using the proposed land cover mapping framework, this study used PPs to produce a 10 m spatial resolution land cover map of Cambodia in 2020. The overall accuracy of the land cover map was 91.68% and the Kappa value was 0.8808. Based on these results, the proposed mapping framework can effectively use PPs to update medium-resolution large-scale land cover datasets, and provides a powerful solution for label acquisition in LLCM projects.

Multi-Modality Ensemble Distortion for Spatial Steganography With Dynamic Cost Correction

This paper tackles a recent challenge in designing an efficient steganographic distortion model, whose goal is to accurately measure the modification cost of a pixel and help design steganographic schemes with high undetectability. Existing distortion models mostly assume that different modification directions of a pixel have an identical cost value and that pixel modifications are independent. These assumptions, however, may not lead to good steganography design because the modification direction of neighbouring pixels may affect the cost measurement of the current pixel. To address this problem, we propose a new distortion calculation method using dynamic cost correction and multi-modality distortion ensemble. The proposed scheme first employs a given distortion model to generate the original cost map. The cost of each pixel is then dynamically adjusted with majority voting according to the modification directions of its neighbouring pixels. Furthermore, different distortion calculation models are integrated to make the final decision on the distortion of each pixel. Experimental results show that compared to existing additive distortion-based steganographic schemes and deep learning-based steganographic schemes, steganography using our proposed distortion model performs better when tested against state-of-the-art steganalysis methods.

Research on Hedging Ratio of Stock Index Futures to ETF Fund

Exchange Traded Funds (ETF) can largely avoid non-systematic risk, but investors often cannot avoid systemic risk, and the hedging function of stock index futures can do this. Therefore, hedging ETF with stock index futures has become a good investment strategy. Based on the trading data of the China Securities Index (CSI) 300 stock index futures and CSI 300 Exchange-Traded Fund (ETF), this paper analyzes the optimal hedging ratio of CSI 300 stock index futures by Ordinary Least Squares (OLS) and dynamic Error Correction Model - Generalized Autoregressive Conditional Heteroskedasticity (ECM-GARCH) model and compares the hedging performance predicted by the model to avoid the systematic risk of ETF. The results show that the hedging effect of the dynamic hedging model is better than that of the static model, and the ability to avoid systemic risk is also better. The predictions of both models show that stock index futures hedge ETFs very well. The dynamic model is more able to reduce the heteroscedasticity of the transaction data.

Optimization of software-controlled machining of critical pipe fittings

The quality requirements for pipeline fittings for NPPs and oil and gas facilities have become more stringent recently, which is related to ensuring their safe operation life. When manufacturing parts using subtractive machining technologies, special importance is attached to accuracy indicators of linear-angular dimensions and surface shapes. This study examines ways to optimize dimensional turning using the example of a “separator” part on a multifunctional CNC turning and milling center MULTICUT 630/2000S. Operations of designing and optimizing the production of parts are increasingly determined by the results of simulation modelling, virtual models, which, in fact, are an integral part of technological processes. In this work, simulation modelling is performed on simulators with CNC Sinumerik 810/840D/Operate Turn. This procedure is a simplified version of the Weiler-Atherton clipping algorithm as applied to stock removal turning. In addition to the algorithm, additional modules for dynamic corrections of control programs are proposed in order to guarantee the requirements of design documentation for accuracy in the conditions of a dynamic SPID system: machine – fixture – tool – part. The cutting speed, depth of cut and tool feed in turning have the greatest influence on the stock removal process from the technological control parameters. Modules of dynamic corrections of control programmes allow to estimate quite accurately the influence of control factors in turning (cutting speed, depth of cut and feed) on cutting force and machining accuracy.

Dynamic off-resonance correction improves functional image analysis in fMRI of awake behaving non-human primates.

Use of functional MRI in awake non-human primate (NHPs) has recently increased. Scanning animals while awake makes data collection possible in the absence of anesthetic modulation and with an extended range of possible experimental designs. Robust awake NHP imaging however is challenging due to the strong artifacts caused by time-varying off-resonance changes introduced by the animal's body motion. In this study, we sought to thoroughly investigate the effect of a newly proposed dynamic off-resonance correction method on brain activation estimates using extended awake NHP data. We correct for dynamic B0 changes in reconstruction of highly accelerated simultaneous multi-slice EPI acquisitions by estimating and correcting for dynamic field perturbations. Functional MRI data were collected in four male rhesus monkeys performing a decision-making task in the scanner, and analyses of improvements in sensitivity and reliability were performed compared to conventional image reconstruction. Applying the correction resulted in reduced bias and improved temporal stability in the reconstructed time-series data. We found increased sensitivity to functional activation at the individual and group levels, as well as improved reliability of statistical parameter estimates. Our results show significant improvements in image fidelity using our proposed correction strategy, as well as greatly enhanced and more reliable activation estimates in GLM analyses.

Multi-stage perfect load-bearing behavior for tandem honeycomb by face-centered hierarchical strategy

Tandem design is promising to improve the undesired compressive behavior of honeycomb for the long-stroke demand. Therefore, a novel tandem honeycomb, named tandem face-centered hierarchical honeycomb (TFCHH), is developed to achieve the perfect load-bearing process. The deformation mechanism of TFCHH is revealed by the compressive experiments, which dissipates energy by progressive folding and demonstrates a multi-stage plateau response. The numerical research is conducted to explore the compressive properties of TFCHH. It is observed that the energy absorption and load-bearing stability of TFCHH is 23.04 % and 38.24 % higher than those of integrated face-centered hierarchical honeycomb. To approach the perfect load-bearing behavior of TFCHH, the matching strategy of hierarchy order and relative density is investigated to fully exploit the compressive potential of hierarchical honeycomb components. As a result, the optimal load-bearing capacities of hierarchical honeycomb components with the hierarchy order from 1 to 3 are obtained when the relative densities are 16.6 %, 15.0 %, and 11.2 %. The customized design on the load-bearing behavior of TFCHH demonstrates that altering the stacking sequence can control the deformation sequence of hierarchical honeycomb components, and adjusting the composition proportion can tailor the effective stroke of the desired plateau stress. By establishing the theoretical relationship between the plateau force and configuration parameters, it is found that the load-bearing behavior of TFCHH is accurately predicted with the dynamic correction coefficient of 1.4.

Biomechanical evaluation of 3D-printed joint-type orthosis for hallux valgus.

Orthoses play an important role in the conservative treatment of hallux valgus (HV) with different therapeutic effects. In this study, a new HV orthosis was developed using three-dimensional (3D) printing technology. In addition, its kinematic effect was evaluated using motion analysis. Seventeen participants with an HV angle of >20° were included in the study. The first metatarsophalangeal abduction angle before and after the orthosis was measured statically. Subsequently, dynamic first metatarsophalangeal abduction, dorsiflexion angle and ground reaction force with and without the orthosis were recorded and calculated during walking using a Vicon motion analysis system and force plates. The patients' comfort scales were determined after the motion analysis. The angular corrections of the orthosis in the first metatarsophalangeal abduction were 14.6° and 6.3° under static and dynamic conditions, respectively. Reduced hallux dorsiflexion was observed with the orthosis in the early stance phase. However, no significant changes in ground reaction forces were observed. The results of our study confirm the potential of the 3D-printed HV orthosis in the static and dynamic correction of deformities while ensuring patient comfort with minimal impact on hallux kinematics, suggesting the potential of our design for long-term use.

ОКРЕМІ АСПЕКТИ ЗАБЕЗПЕЧЕННЯ БЕЗПЕКИ В УСТАНОВАХ ВИКОНАННЯ ПОКАРАНЬ

The article discusses the necessity of implementing admitted by the world community and enshrined in relevant international documents principles and norms of dynamic security as one of the forms of ensuring the security of penitentiary institutions and its implementation into the national legislation of Ukraine. In particular, the main aspects of comprehensive application of principles and forms of physical, procedural, and dynamic security are defined with the aim of transitioning from a punitive method of dealing with convicts to a rehabilitative one. The authors believe that during the study of these issues, it is necessary to outline the following questions: regarding physical security, it is a matter concerning modern approaches to equipping penitentiary institutions, modernization of perimeter security systems and other electronic security systems, physical barriers, means for localization and isolation, access control systems, means of identification and verification of people, video surveillance systems, the use of metal detectors, X-ray machines, and other detection devices; regarding procedural security, it is a matter concerning the regime as the main means of educational influence on convicts, especially related to the study of world practice regarding the organization of admission regime, search work, application of incentive and punitive measures, actions of personnel in case of emergencies while respecting the rights, freedoms, and legitimate interests of convicts; regarding dynamic security, it is a matter related to the introduction into the activities of penitentiary institution personnel of its principles and approaches in working with convicts, establishing trustful and positive relationships with them, forming interpersonal skills in personnel, and their training to resist manipulation by convicts, and so on. Key words: Penitentiary system, State Criminal and Executive Service of Ukraine, penitentiary institution, correctional colony, pre-trial detention center, security, physical, procedural, and dynamic security, convict, correction and resocialization, labor, convict labor, labor relations, ordinary and managerial personnel, discipline, responsibility, selection, conditions and terms of service.

Revolutionizing the Application of Automatic Inspection System for Industrial Parts Using AI Machine Vision Technology

This paper addresses the challenges of large data management, prolonged operation times, and low detectionefficiency encountered in automatic detection systems. To overcome these issues, we propose a novel research and application method utilizing AI machine vision technology. The methodology employs the Pulse-Coupled Neural Network (PCNN) algorithm for analyzing machine control points, enhancing system reliability and detection efficiency. Furthermore, a three-stage sliding table mechanism is implemented to facilitate seamless operation restarts. Notably, our approach significantly reduces the time required for key operations, such as feeding, imaging, decision-making, on-site inspection, and re-inspection, all within 5 seconds and 1 meter distance. It supports high-precision dynamic identification, detection, and correction of errors during high-speed movement, thereby enhancing overall system performance. The experimental results demonstrate exceptional accuracy, particularly in detecting small parts measuring 28.87 mm in length and 12.36 mm in width, achieving an impressive precision of 0.04 mm. Additionally, our system boasts meticulous hardware selection, robust software stability, and high-performance capabilities, culminating in improved detection efficiency and accuracy. This research not only contributes novel ideas and results but also holds significant commercial value in industrial applications. Overall, our proposed methodology represents a noteworthy advancement in automatic inspection systems, offering superior performance and reliability compared to existing approaches.

A dynamic correction method for the optimal value settings of the solution purification process at multiple time scales

The solution purification process includes multiple continuous reactors. Setting the key technical indicators of each reactor through global optimization is the prerequisite for realizing the optimal operation of the entire process. Affected by fluctuations in inlet conditions, adjustments of operating parameters, and random disturbances, the operating status of the solution purification process will change accordingly, causing the optimal value settings based on global optimization to become no longer applicable. To ensure the applicability of the optimal value settings as the process changes and considering that the production data collected at different time scales contain different process information, this study proposes a dynamic correction method for the optimal value settings of the solution purification process at multiple time scales. First, considering the low-frequency testing data that can reflect the operation effect, the low-frequency correction is realized by combining mechanism knowledge and expert experience. Second, based on the characteristic that the high-frequency detection data can reflect the changing operating status in time, a supervised self-organizing map method is proposed to classify the changing trends in the operating status. Finally, an integrated, spatiotemporal, just-in-time learning method (with multiple changing trends in the operating status) is proposed to realize high-frequency correction. The experimental results show that the proposed method can dynamically correct the optimal value settings and reduce resource consumption while ensuring product quality.

Improvement of High-Resolution Daytime Fog Detection Algorithm Using GEO-KOMPSAT-2A/Advanced Meteorological Imager Data with Optimization of Background Field and Threshold Values

This study aimed to improve the daytime fog detection algorithm GK2A_HR_FDA using the GEO-KOMPSAT-2A (GK2A) satellite by increasing the resolution (2 km to 500 m), improving predicted surface temperature by the numerical model, and optimizing some threshold values. GK2A_HR_FDA uses numerical model prediction temperature to distinguish between fog and low clouds and evaluates the fog detection level using ground observation visibility data. To correct the errors of the numerical model prediction temperature, a dynamic bias correction (DBC) technique was developed that reflects the geographic location, time, and altitude in real time. As the numerical model prediction temperature was significantly improved after DBC application, the fog detection level improved (FAR: −0.02–−0.06; bias: −0.07–−0.23) regardless of the training and validation cases and validation method. In most cases, the fog detection level was improved due to DBC and threshold adjustment. Still, the detection level was abnormally low in some cases due to background reflectance problems caused by cloud shadow effects and navigation errors. As a result of removing navigation errors and cloud shadow effects, the fog detection level was greatly improved. Therefore, it is necessary to improve navigation accuracy and develop removal techniques for cloud shadows to improve fog detection levels.

Tailored and Externally Corrected Coupled Cluster with Quantum Inputs.

We propose to use wave function overlaps obtained from a quantum computer as inputs for the classical split-amplitude techniques, tailored and externally corrected coupled cluster, to achieve balanced treatment of static and dynamic correlation effects in molecular electronic structure simulations. By combining insights from statistical properties of matchgate shadows, which are used to measure quantum trial state overlaps, with classical correlation diagnostics, we can provide quantum resource estimates well into the classically no longer exactly solvable regime. We find that rather imperfect wave functions and remarkably low shot counts are sufficient to cure qualitative failures of plain coupled cluster singles doubles and to obtain chemically precise dynamic correlation energy corrections. We provide insights into which wave function preparation schemes have a chance of yielding quantum advantage, and we test our proposed method using overlaps measured on Google's Sycamore device.

PARIBHAASHA

As in world increasingly reliant on digital communication, effective writing skills are paramount. However, for “Hindi” language users, finding tools that take their unique linguistic needs presents a challenge. This abstract will come with a novel solution: a Grammarly-like platform precisely designed to empower Hindi writers by offering advanced language correction and enhancement features tailored specifically for the nuances of the Hindi language. At the core of our platform lies a sophisticated text analysis engine, powered by state-of-the-art natural language processing algorithms meticulously trained on vast corpora of Hindi text. Our platform offers a seamless user experience, with a user-friendly interface designed to facilitate effortless interaction in single time of corrections. Users can simply input their “Hindi” text into the platform, and within moments, receive comprehensive feedback and suggestions aimed at refining their writing to perfection. In addition to its high dynamic language correction capabilities, our platform also features advanced plagiarism detection functionality. Leveraging cutting-edge algorithms that is particular focusing on platform thoroughly scans user-submitted text against an extensive database of online sources, academic papers, and published works to ensure originality and integrity, vulnerable and fully secured. Furthermore, our platform goes beyond mere error correction to offer personalized writing insights and guidance. Users can set custom writing goals based on their intended audience, style preferences, and communication objectives. Our platform then provides tailored suggestions and recommendations aimed at helping users achieve their desired writing outcomes, whether it be crafting formal business documents, creative literature, or casual social media posts. This engine enables our platform to accurately identify and rectify a myriad of grammatical errors, spelling mistakes, and stylistic inconsistencies commonly encountered in Hindi writing.

Development of the two-stage SCR control strategy to satisfy ultra-low NOX emission regulation for heavy-duty diesel engine

The emission regulations for heavy-duty diesel engines regarding nitrogen oxide (NOX) are becoming increasingly stringent, particularly in relation to cold start cycles. While the two-stage selective catalytic reduction (SCR) has the potential to achieve ultra-low NOX emissions, several challenges remain, including the accurate prediction of ammonia (NH3) storage mass and the co-control of the two-stage SCR. The first step in this study involved the establishment of a rapid control prototype platform to facilitate the development and validation of a two-stage SCR control strategy. Secondly, an initial method for predicting the NH3 storage based on the mass conservation law was proposed, which was subsequently improved by filling and emptying experiments. The third step involved the development of a two-stage SCR co-control strategy, including obtaining the steady-state NH3 storage target value, dynamic correction for NH3 storage target value, regulation of NH3 storage, and control of the close-coupled SCR urea injector state. Finally, the two-stage SCR urea injection control strategy was certified under the world harmonized transient cycle (WHTC). The results demonstrate that the composite value of engine outlet NOX emissions under cold and hot start WHTC cycles is 13 g/(kW·h). Meanwhile, the composite value of tailpipe NOX emissions under cold and hot start WHTC cycles is 0.065 g/(kW·h), representing only 14% of the EU VI limit value of 0.46 g/(kW·h). Thus, the findings demonstrate that integrating an accurate NH3 storage prediction method with the two-stage SCR co-control function is crucial for heavy-duty diesel engines to achieve ultra-low NOX emissions.

Direct reconstruction of tissue temperature fields during thermal therapy by dynamic response matching and steady-state correction

In the field of oncology thermotherapy technology, online and real-time reconstruction of biological tissue temperature field is a fundamental work with important practical needs. In this paper, we proposed a new method for direct reconstruction of biological tissue temperature field by dynamic response feature matching and steady-state correction. Based on the bioheat transfer equation, the step response model of tissue temperature field during thermal therapy is established, and the dynamic response eigenvectors of biological tissue temperature and the steady-state gain of temperature response are determined offline. According to the optimal matching principle between the temperature dynamic response of the observation point and the internal space point of tissue, the optimal matching matrix of the dynamic response features between the observation point temperature and the internal temperature field of tissue is determined with the use of the dynamic response eigenvectors. Further, through the weighted synthesis of observation point temperature response by using the optimal matching matrix of dynamic response features, the tissue transient temperature field pre-estimation model is established. Finally, by correcting the tissue temperature field pre-estimation model with steady-state gain correction factor, the direct reconstruction scheme of temperature field based on dynamic response feature matching and steady-state gain correction is established. In this work, the non-destructive reconstruction of the internal temperature field of biological tissues during laser-induced thermal therapy is achieved based on the above direct reconstruction scheme. Numerical simulation experiments are performed to investigated the effects of pre-estimation time domain, the number of measurement points, laser irradiation form and measurement error on the temperature field reconstruction results.