Reduce Time Cost Research Articles

Design of Connector Assembly Equipment for the Automotive Industry

The automotive industry is one of the most demanding sectors of all manufacturing industries due to its competitiveness. It is necessary to innovate through the implementation of automated and robotic equipment, leading to cycle time and labor cost reduction. This work aims to design semi-automatic equipment to assemble cabling connectors used in the automotive sector, replacing a manual process currently taking place in an automotive components company. In the proposed equipment, the operator places a connector in the equipment, and the components (pins and seals) are automatically inserted. A vision sensor with artificial intelligence then confirms the correct application. The equipment operation defined as Finite Element Method (FEM) was applied for structural verification; the materials and fabrication processes were detailed; the associated costs were calculated, and the equipment subsets were validated. The design was successfully accomplished, and the imposed requirements were fulfilled, with significant advantages over the current process, providing new knowledge on how semi-automatic systems can be deployed to enhance the productivity and quality of manufacturing processes. The design principles and insights gained from this work can be applied to other automation challenges, particularly where manual processes need to be replaced by more efficient semi-automatic or automatic systems. The modularity of the overall solution and the design concepts of the component inserter, component feeder, and assembly process allow for its use in different assembly scenarios beyond the automotive sector, such as electronics or aerospace, providing a contribution to increased competitiveness and survival in the global market.

The solution of super-scale DEA models based on preference character

The basic relationships and intrinsic laws in the data envelopment analysis (DEA) are the basis for designing fast solutions for big data DEA models. By delving into these relationships, it becomes feasible to address super-scale DEA problems more swiftly, aligning with the need for timeliness in real-world efficiency evaluations and reducing time costs. This paper leverages the intrinsic characteristics of DEA to formulate a DEA model scale reduction approach, offering reliable theoretical support for solving super- scale DEA problems. Initially, the partially ordered set theory to prove the relationship between the efficient decision making units (DMUs) of DEA and the maximal element of the partially ordered set. Building on this, a DEA model based on maximal element set (DEA-PS) is constructed based on the theory of partially ordered set theory, and the equivalence between DEA-PS model and the original DEA model is proved. Furthermore, it is proved that the dominant theorem exists in the solution of multiple classical DEA models, and a method of super-scale DEA models based on dominant theorem is given. The conclusions of this paper theoretically reveal the relationship between efficient DMUs and maximal elements, from this relationship, two key insights emerge: (1) Regardless of how huge the number of DMU is, the proposed method in this paper can realize the scale reduction of the DEA model quickly, (2) The algorithm has good compatibility with all current big data DEA model algorithms.

Simultaneous multinuclear MRI via a single RF channel

Magnetic resonance imaging (MRI) stands as one of the most powerful noninvasive and non-destructive imaging techniques, finding extensive utility in medical and industrial applications. Its ability to acquire signals from multiple nuclei grants it additional levels of strength by providing multi-dimensional datasets of the object under test. However, this typically requires dedicated hardware to detect each nucleus. In this paper, we report on the use of a digital lock-in amplifier to perform simultaneous multi-nuclear MRI with a single physical radio frequency (RF) channel. While we showcase this concept by demonstrating the results of fully parallel (TX and RX) 1H and 19F MRI images, we emphasize that it is not limited to two nuclei but can accommodate more nuclei with no extra cost on the hardware or scan time. The scalability is virtually unlimited, constrained only by the processing speed of the digital unit. Furthermore, we demonstrate that the quality of parallel imaging with SNR of 54 is comparable to the commercial single channel with SNR of 43. Thus with no reduction in imaging quality, the proposed concept promises a tremendous reduction in scan time, system complexity, and hardware costs.

The impact of administrative efficiency on SME Growth and Sustainability

enterprises (SMEs). This review examines the relationship between efficient administrative processes and the long-term success of SMEs, emphasizing how streamlined operations, resource management, and decision-making can enhance business performance. SMEs often struggle with limited resources, resulting in operational bottlenecks and inefficiencies that hinder their capacity to scale and sustain growth. By improving administrative efficiency through the adoption of technology, organizational restructuring, and optimized processes, SMEs can achieve greater productivity, reduce costs, and make informed decisions that promote sustainability. The review employs a case study approach, analyzing SMEs across different sectors—manufacturing, retail, and services to demonstrate the tangible benefits of administrative efficiency. The research includes qualitative interviews, operational data analysis, and performance evaluations before and after implementing efficiency-enhancing measures. Key findings highlight improvements in areas such as time management, cost reduction, and customer satisfaction, all contributing to accelerated growth and competitive advantage. Additionally, the study explores how administrative efficiency supports sustainable practices, such as resource optimization and risk management, leading to long-term business viability. Despite these advantages, challenges such as resistance to change, lack of skilled personnel, and financial constraints pose barriers to achieving administrative efficiency in SMEs. The review concludes by offering practical recommendations for overcoming these challenges, including investing in digital tools, employee training, and organizational restructuring. Ultimately, the research underscores the importance of administrative efficiency as a critical factor for SME growth and sustainability, providing insights for policymakers, business owners, and managers aiming to enhance the operational performance of small businesses in a competitive market.

An ensemble optimizer with a stacking ensemble surrogate model for identification of groundwater contamination source

The application of the simulation-optimization method for groundwater contamination source identification (GCSI) encounters two main challenges: the substantial time cost of calling the simulation model, and the limitations on the accuracy of identification results due to the complexity, nonlinearity, and ill-posed nature of the inverse problem. To address these issues, we have innovatively developed an inversion framework based on ensemble learning strategies. This framework comprises a stacking ensemble model (SEM), which integrates three distinct machine learning models (Extremely Randomized Trees, Adaptive Boosting, and Bidirectional Gated Recurrent Unit), and an ensemble optimizer (E-GKSEEFO), which combines two newly proposed swarm intelligence optimizers (Genghis Khan Shark Optimizer and Electric Eel Foraging Optimizer). Specifically, the SEM serves as a surrogate model for the groundwater numerical simulation model. Compared to the original simulation model, it significantly reduces time cost while maintaining accuracy. The E-GKSEEFO, functioning as the search strategy for the optimization model, greatly enhances the accuracy of the optimization results. We have verified the performance of the SEM-E-GKSEEFO ensemble inversion framework through two hypothetical scenarios derived from an actual coal gangue pile. The results are as follows. (1) The SEM exhibits improved fitting performance compared to single machine learning models when dealing with high-dimensional nonlinear data from GCSI. (2) The E-GKSEEFO achieves significantly higher accuracy in the identification results of GCSI than individual optimizers. These findings affirm the effectiveness and superiority of the proposed SEM-E-GKSEEFO ensemble inversion framework.

Optimization of the process of communication with customers when organizing sales on Internet platforms

Relevance. Currently, the field of online trading on Internet platforms is actively developing. Online resources allow customers to familiarize themselves with the assortment, read reviews based on them and personal preferences, make a choice and, in general, make purchases without leaving home. Marketplaces are becoming the main platform for online sales.The purpose of the research is to develop a model for optimizing the process of communication with customers when organizing sales on Internet platforms.Objectives: to study the theoretical aspects of the concept of "communication process in the marketplace"; to identify the key problems of the communication process between customers and the seller on the marketplace, to develop a project to optimize the business process of "Communication between customer and seller" in RTK; to form recommendations for optimizing the process of processing information from customers.Methodology. The research is aimed at implementing the tasks set and is based on a review of scientific research by national and international market participants and a statistical analysis of the functioning of online trading platforms.Results. During the analysis of the work of the communications department of RTK LLC, a method for automating the communication process between customers and the seller was proposed, namely, optimizing the process of processing customer feedback, while implementing an automated software product.Conclusions. There are a huge number of technologies that can be implemented into the work of brands engaged in sales on the marketplace, and now this is becoming the most relevant, as the number of sellers in the market increases and competition between them becomes stronger. Mastering new digital technologies will allow trading companies to radically change the business ecosystem, make it faster and more efficient in order to meet the changing needs of customers.Optimizing the process of communication with customers when organizing sales on Internet platforms will help reduce time costs and increase the efficiency of sales organizations.

Time and energy costs for stochastic synchronisation of multi-layer complex networks with noise

This paper explores time cost (TC) and energy cost (EC) for finite-time stochastic outer synchronisation (FTSS) of multi-layer complex networks in noisy environments. A novel switching controller is designed for achieving FTSS and optimising TC and EC, linking the advantages of finite-time control technologies and linear feedback. Sufficient criteria for achieving FTSS are established, and the estimates of TC and EC are further obtained. At last, the validity of theoretical findings is checked using numerical simulations. Especially, the theoretical analysis and simulating calculation show that a reduction in TC will lead to an increase in EC, in other words, there is a tradeoff between TC and EC.

Dynamic response and regular analysis of adjacent buildings under blasting construction across foundation pits of subway stations

To study the impact of cross-pit blasting on nearby buildings, a numerical model was used based on the case of a subway station excavation. The analysis focused on the vibration response patterns at monitoring points, considering different lengths, widths, depths, and positions of the adjacent foundation pit (AFP) and varying blasting foundation pit (BFP) depths. Additionally, a data-driven method was adopted to improve numerical calculation efficiency under multiple scenarios. In this method, leave-one-out cross-validation is used to enhance the accuracy of the XGBoost vibration reduction prediction model, and the model's hyperparameters are optimized using Bayesian algorithms. Furthermore, employing the established LOO-BO-XGBoost model in combination with the physical equations for charge control, the data-physical hybrid dynamic design method for cross-pit blasting charge is developed. The results indicate that, among the size parameters of the AFP, depth has the greatest impact on peak particle velocity (PPV), followed by length and width. When the size of the AFP is the same, the closer it is to the building, the more significant its vibration reduction effect. The deeper the excavation of the BFP, the lesser the impact of the blasting on the building. The LOO-BO-XGBoost model outperforms the XGBoost and ANN models, achieving higher accuracy (training dataset: R² = 0.93, RMSE = 0.023; testing dataset: R² = 0.90, RMSE = 0.021). Subsequently, the data-physical hybrid dynamic design method optimizes the charge in the cross-pit blasting case. As the AFP is excavated from 11 m to 15 m, the single-hole charge is increased from 9 kg to 10 kg according to this method. And the measured PPV still meets the requirements. Therefore, considering the vibration reduction effect of the AFP, it is important to expedite construction and reduce time costs. The research method proposed in this paper can serve as a valuable guide for similar projects.

Программный комплекс для дифференциальной оценки риска здоровью населения от воздействия шума железнодорожного и автомобильного транспорта

Objective: in the modern world, the number of vehicles on the roads is growing exponentially, and the infrastructure either remains unchanged or is changing, but it is not happening as fast as we would like [1]. Long-term effects of noise exposure, such as disorders of the cardiovascular system (high blood pressure, myocardial infarction, coronary heart disease, etc.), the nervous system (sleep disorder, cognitive impairment, vegetative vascular dystonia), diseases of the ear and mastoid process (conductive and sensorineural hearing loss), depression and other mental disorders [2, 3, 4], are more related to indicators that sum up the noise exposure over a long time. Both when conducting tests by accredited specialists and when choosing housing for buyers, you have to rely on an analytical or practical way to assess the noise pollution of a room. In order to provide a person with a simple and objective way to assess noise pollution, a software package has been developed that allows assessing the impact of noise pollution from transport on public health. Methods: quantitative risk assessment was carried out in accordance with the methodological recommendations of MP 2.1.10.0059-12 “Assessment of public health risk from exposure to traffic noise”. Results: in order to automate calculations, unify the evaluation of results and reduce time costs, the authors propose a software package for differential assessment of public health risks from exposure to rail and road transport. The end product is a user-friendly, cross-platform application for operational risk assessment in the field for employees of test centers who measure noise from known sources of automobile, railway or combined traf fic flows. Practical importance: convenience and optimization of risk processing by noise measurements by employees of noise measurement laboratories using the application.

Preparation of Thermochromic Vanadium Dioxide Films Assisted by Machine Learning.

In recent years, smart windows have attracted widespread attention due to their ability to respond to external stimuli such as light, heat, and electricity, thereby intelligently adjusting the ultraviolet, visible, and near-infrared light in solar radiation. VO2(M) undergoes a reversible phase transition from an insulating phase (monoclinic, M) to a metallic phase (rutile, R) at a critical temperature of 68 °C, resulting in a significant difference in near-infrared transmittance, which is particularly suitable for use in energy-saving smart windows. However, due to the multiple valence states of vanadium ions and the multiphase characteristics of VO2, there are still challenges in preparing pure-phase VO2(M). Machine learning (ML) can learn and generate models capable of predicting unknown data from vast datasets, thereby avoiding the wastage of experimental resources and reducing time costs associated with material preparation optimization. Hence, in this paper, four ML algorithms, namely multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were employed to explore the parameters for the successful preparation of VO2(M) films via magnetron sputtering. A comprehensive performance evaluation was conducted on these four models. The results indicated that XGB was the top-performing model, achieving a prediction accuracy of up to 88.52%. A feature importance analysis using the SHAP method revealed that substrate temperature had an essential impact on the preparation of VO2(M). Furthermore, characteristic parameters such as sputtering power, substrate temperature, and substrate type were optimized to obtain pure-phase VO2(M) films. Finally, it was experimentally verified that VO2(M) films can be successfully prepared using optimized parameters. These findings suggest that ML-assisted material preparation is highly feasible, substantially reducing resource wastage resulting from experimental trial and error, thereby promoting research on material preparation optimization.

Evaluating the Impact of ERP Implementation on Supply Chain Performance: A Case Study of Youngs Food in Pakistan

This study investigated the impact of Enterprise Resource Planning (ERP) implementation on supply chain performance at Youngs Food, a leading FMCG company in Pakistan. Utilizing a quantitative research methodology, the study aimed to assess how ERP systems influence various aspects of supply chain management, including process automation, lead time reduction, cost reduction, and delivery improvement. Data were collected from employees involved in the ERP transformation process through a structured questionnaire. The findings revealed that ERP implementation significantly enhances supply chain performance. Specifically, process automation emerged as the most influential factor, followed by cost reduction, improved deliveries, and lead time reduction. The study confirmed that ERP systems streamline operations, reduce operational costs, and improve efficiency and customer satisfaction. The comprehensive analysis underscores the critical role of ERP systems in modern supply chain management, advocating for their strategic adoption to achieve competitive advantage and operational excellence.

Production of dry extracts from sea buckthorn raw materials: Research results

Production of soluble dry extracts from sea buckthorn raw materials is relevant and promising for enterprises of the Republic of Buryatia. The authors have selected technological equipment for obtaining dry extracts: extractor, concentrator, dryer. The possibility of using vacuum-pulse action and the influence of technological factors on the yield of water-soluble substances from sea buckthorn leaves and shoots has been studied. The operating parameters of the technological process have been determined experimentally under production conditions: temperature 45–50 °C, total duration of extraction, concentration and drying 79–111 minutes. The process of obtaining dry extracts is carried out in three stages: water extraction; filtration and concentration; drying in a vacuum pulse dryer. The resulting experimental sample of dry extract from sea buckthorn leaves and shoots is a free-flowing crystalline powder with a moisture content of 4–6 %, highly soluble in water, having high organoleptic characteristics – a natural, well-defined aroma characteristic of sea buckthorn, light brown color. The use of low temperatures and vacuum-pulse modes at the stages of the technological process predicts high safety of thermolabile biologically active substances in the product. The process of obtaining dry extracts from sea buckthorn leaves and shoots has been studied. The results of the studies prove the possibility of producing a dry extract with high organoleptic characteristics using the developed by the authors' technology by equipment whose vacuum-pulse operating modes have a positive effect on the yield of extractives and help reduce time costs along with the intensification of heat and mass exchange processes in sea buckthorn raw materials.

Research and Application of the Median Filtering Method in Enhancing the Imperceptibility of Perturbations in Adversarial Examples

In the field of object detection, the adversarial attack method based on generative adversarial network efficiently generates adversarial examples, thereby significantly reducing time costs. However, this approach overlooks the imperceptibility of perturbations in adversarial examples, resulting in poor visual performance and insufficient invisibility of the generated adversarial examples. To further enhance the imperceptibility of perturbations in adversarial examples, a method utilizing median filtering is proposed to address these generated perturbations. Experimental evaluations were conducted on the Pascal VOC dataset. The results demonstrate that, compared to the original image, there is an increase of at least 17.2% in the structural similarity index (SSIM) for generated adversarial examples. Additionally, the peak signal-to-noise ratio (PSNR) increases by at least 27.5%, while learned perceptual image patch similarity (LPIPS) decreases by at least 84.6%. These findings indicate that the perturbations in generated adversarial examples are more difficult to detect, with significantly improved imperceptibility and closer resemblance to the original image without compromising their high aggressiveness.

Fuzzy Logic-Based Fragility Curve Development for Steel Moment-Resisting Frames Considering Uncertainties in Seismic Response

In this paper, a comprehensive range of uncertainties is considered to assess the seismic abilities of a moment-resisting system. To incorporate the parameter of construction quality, which has a descriptive nature, a suitable fuzzy logic engine has been developed. This engine, for the first time, addresses the quantitative assessment of construction quality parameters based on linguistic variables, including map accuracy, worker skills, material quality, and site supervision conditions. Instead of using random selection, a self-organizing map (SOM) algorithm is employed to carefully select strong ground motion records, reducing time costs. By applying incremental dynamic analysis (IDA) results, analytical equations are derived for the response surface method. These equations determine the collapse fragility’s mean and standard deviation. The material quality is modeled using the fuzzy inference engine, with the coefficient of logarithm response surface. Collapse fragility curves are created by taking into account many of their material quality values and utilizing the fuzzy model to estimate the modeling parameter based on the logarithm regression coefficients. These curves take into consideration various sources of uncertainty. In countries with inadequate material quality control, it is important to take cognitive uncertainty into account when developing fragility curves. This will help improve the overall risk management strategy.

Fast prediction of optimal reaction conditions and dyeing effects of natural dyes on silk fabrics by lightweight integrated learning (XGBoost) models

AbstractThere is a lot of repetitive work involved in exploring the dyeing performance of natural dyes. To improve the experimental efficiency, save material, reduce time costs and shorten the research cycle, this study collects and analyses the literature data of 350 natural dye experiments to construct the Natural Dyes Dataset, and achieves rapid prediction of the optimal reaction conditions and dyeing effects of natural dyes using a lightweight integrated learning model. The size of the trained XGBoost model is only 562 KB; only the name of the dye and its approximate chemical composition need to be input to predict the results of the reaction environment pH, colour fastness to washing (CFW) and colour fastness to rubbing (CFR) of the natural dye on silk fabrics with the highest K/S in a very short time of 52 ms. The prediction accuracies for pH, CFW and CFR in the validation set are as high as 94.12%, 93.75% and 100%, respectively, and 77.78%, 91.67% and 83.33% for the real test set, with both validity and transferability. The integrated learning approach provides valuable guidance for exploring the dyeing performance of natural dyes with very small deployment costs and a very short inference time, expanding the possibilities of cross‐application of the disciplines of machine learning and textile dyeing.

PPMGS: An efficient and effective solution for distributed privacy-preserving semi-supervised learning

Recently, distributed semi-supervised learning has attracted increasing research attention due to its tremendous practical value. A promising distributed semi-supervised learning method should not only achieve desirable classification performance but also protect data privacy in distributed scenarios. Existing approaches typically capture the similarities between data instances with privacy-preserving computations. This paradigm introduces extra computation and heuristic changes to the algorithm, resulting in sub-optimal solutions that are time-consuming. In current distributed semi-supervised learning, instance similarities are widely used to capture the underlying manifold or guide label propagation. This paper emphasizes that instance similarities are not necessary because the structure of data connections can be estimated using coarser-grained information. We propose a Privacy-preserving Mixture-distribution based Graph Smoothing (PPMGS) model for distributed privacy-preserving semi-supervised learning. Our motivation is to construct a graph based on a Gaussian mixture distribution instead of individual data instances, which better captures the underlying data distribution and improves model efficiency. PPMGS includes a privacy-preserving expectation-maximization (EM) phase to estimate the Gaussian mixture distribution depicting the input data and a mixture-distribution-based graph smoothing algorithm to learn a distribution-based classifier by fitting a few labeled samples. Experimental results show that the proposed PPMGS achieves 5%-10% higher accuracy and macro-F1 than state-of-the-art privacy-preserving semi-supervised learning methods. In terms of efficiency, it reduces time cost by 97% and communication cost by 96% in the most complex dataset. The numerical results demonstrate that our proposal outperforms state-of-the-art baselines in both efficiency and effectiveness.

Two improving approaches for faulty interaction localization using logistic regression analysis

Faulty Interaction Localization (FIL) is a process to identify which combination of input parameter values induced test failures in combinatorial testing. An accurate and fast FIL provides helpful information to fix defects causing the test failure. One type of conventional FIL approach, which analyzes test results of whole test cases and estimates the suspiciousness of each combination, has two main concerns; (1) the accuracy is not enough, (2) the huge time cost is sometimes needed. In this paper, we propose two novel approaches to improve those concerns. FROGa attempts to estimate suspiciousness more accurately using logistic regression analysis. FROGb attempts to estimate failure-inducing combinations at high speed by estimating the subsets of them using logistic regression analysis and exploring just their supersets. Through evaluation experiments using a large number of artificial test results based on several real software systems, we observed that FROGa has very high accuracy, and FROGb can drastically reduce time cost for targets that have been difficult to complete by the conventional method.

Effect of mail-in sperm cryopreservation solution on access to cryopreservation: A national demographic analysis.

e13524 Background: There are many circumstances under which someone may choose to bank their sperm, but forthcoming cancer treatment is among the most common. The advent of services offering mail-in semen analysis and cryopreservation has expanded access to sperm banking, especially for those living in rural areas, far away from traditional andrology labs. This study summarized demographic characteristics and reasons for cryopreservation among those using one such US-based mail-in service, with a focus on patients banking sperm prior to cancer treatment. Methods: Patients >18 years who banked sperm using a mail-in cryopreservation solution developed by Fellow Health, Inc. from March 2021 to January 2024 were included. Test kits sold for research purposes were excluded. To categorize each patient by reason for cryopreservation, we made use of a patient-provided reason, supplied during test kit registration, where available. For the remaining patients, reason was inferred using several data sources including medical history, medications, and clinic and physician information. Patients who could not be reliably classified were omitted from this portion of the analysis. Descriptive statistics of key demographics and reason for cryopreservation were reported under an IRB-approved protocol. Results: 2,154 patients met our inclusion criteria and 725 (33.7%) provided demographic information. Average age was 34.7 (SD=9.2) years and 11.9% identified as a trans female. The majority of the population identified as white (79.5%) and non-Hispanic (84.8%). Over half of the population had a college degree or greater (59.3%), was married/partnered (57.1%), and had private insurance (84.9%). Among the 756 patients whose reason for cryopreservation was discernible, the majority, 450 (59.5%), were doing so in the context of fertility treatment, with only 46 (6.1%) indicating cancer treatment. Other reasons included pre-vasectomy storage (n=137, 18.4%) and gender affirming hormone therapy (n=90, 11.9%). Of the patients who indicated cancer treatment as their reason for cryopreservation, 50.0% lived greater than a 25-mile drive of the nearest National Cancer Institute (NCI)-designated Cancer Center. Conclusions: Surprisingly, few patients utilizing a mail-in service for cryopreservation did so prior to beginning cancer treatment, with most originating from the fertility or pre-vasectomy context. Use was disproportionately high among trans women initiating hormone replacement therapy (HRT), perhaps demonstrating their preference for at-home sample collection to avoid the unique challenges they encounter in a clinical setting. Especially for oncology patients who are admitted to a hospital and cannot leave for fertility preservation, there is an opportunity to further integrate mail-in cryopreservation services within oncology to improve access and reduce time costs.

An Adversarial Approach for Automated Pokémon Team Building and Metagame Balance

Meta-game balance is a crucial task in game development, and automation of this process could assist game developers by vastly reducing time costs. We explore and evaluate a meta-game balance model over the recently proposed VGC AI Competition Framework. We propose an adversarial model where team builder agents try to maximize their win rate by narrowing to the most optimal team configurations, resulting in a reduction of the diversity of Pokémon employed, while a balancing agent re-adapts the Pokémon inner attributes to incentive the team builder agents to incorporate a greater variety of Pokémon into their teams increasing the meta-game's overall diversity and balance. one Furthermore, we developed multiple team builder agents divided into two groups: the first group assumes that individual Pokémon advantages are the primary factor to determine the outcome of game matches; the second group also exploits the implicit synergy between teammates. These agents make use of meta-gaming, linear optimization, and evolutionary search to find strong combinations against the current meta-game. The strongest team builder is faced against the team meta-game balance agent for its evaluation. Deep learning is also employed to predict the outcome of matches and recommend constructive elements of teams.

Dynamic Magnetic Field Analysis and Delivery Strategy Optimization for Scanning Magnet in Proton Therapy System

In pencil beam scanning proton therapy, the regulation and stabilization of the scanning magnetic field between two spots should be completed as quickly as possible in order to reduce treatment time. Because of the eddy current effect, the dynamic magnetic field lags behind the excitation current. It is significant to analyze the dynamic field and reduce the field stability time to minimize the delivery time and improve the therapy efficiency. In this paper, dynamic magnetic field simulation is carried out with a full lamination model of the scanning magnet in the Huazhong University of Science and Technology Proton Therapy Facility. In addition, a single lamination model instead of a full lamination model is explored to reduce time cost and memory for lamination of no more than 1-mm thickness. The eddy current diffusion trend and the influence of lamination on the eddy current are investigated. Moreover, the effect of lamination thickness (ranging from 5 to 0.1 mm) and current ramp rate (ranging from 20 to 100 A/ms) on the magnetic field stability time is studied. In addition, the characteristic of magnetic stability time for various spot steps is analyzed. Considering two spot patterns with discrete or clustered spots, an optimized delivery strategy with various scanning dead times according to the step is presented. When the lamination is 1 mm, the scanning time can be reduced by 39.2% for a clustered pattern and 38.4% for a discrete pattern using a genetic algorithm based on the different scanning dead-time strategy instead of the fixed dead-time strategy. With a thinner 0.1-mm lamination, the scanning time can be reduced by 49.8% for the clustered pattern and 43.3% for the discrete pattern, compared to that of the 1-mm lamination.