检索结果-内蒙古大学图书馆

static optimization of the Process of Separation of Marketable Ethylene from Pyrolysis Products in a Multi-Column Rectification Unit

引用

THEORETICAL FOUNDATIONS OF CHEMICAL ENGINEERING 2024年第2期58卷 480-487页

作者： Krivosheev, V. P. Tumaev, A. A. Efimov, I. M. Sitnik, P. V. Nakhodka Fertilizer Plant Nakhodka Russia Far Eastern Fed Univ Dept Oil Gas & Petrochem Ind Vladivostok Russia

In the article the method of stage-by-stage optimization of static regimes of rectification columns in five-column system of separation of marketable ethylene from pyrolysis products is considered. The essence of the method is to calculate each of the columns of the system by a private problem in the form of maximizing the flow rate of the product stream, carrying in ethylene of a given concentration. Concentrations of ethylene in product streams of columns, except for given concentration in outlet stream, have been determined by simulation to obtain convergence of static mode calculation of system in given range of supply rate variation. Upon completion of the calculation, the total heat consumption for all columns of the unit and the specific heat consumption for the finished product are calculated.

关键词： multi-column rectification unit static optimization highly concentrated ethylene heat-energy costs

来源：评论

学校读者我要写书评

暂无评论

Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking

引用

JOURNAL OF NEUROENGINEERING AND REHABILITATION 2024年第1期21卷 194页

作者： Ao, Di Fregly, Benjamin J. Northwestern Polytech Univ Inst Med Res Xian Shaanxi Peoples R China Rice Univ Dept Mech Engn Houston TX USA

BackgroundCalibrated electromyography (EMG)-driven musculoskeletal models can provide insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains *** study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing "gold standard" muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) applied to a scaled generic musculoskeletal model was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to "gold standard" muscle activations and *** average, compared to SO, SynX with simultaneous model calibration produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15, r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4

关键词： EMG-driven model Synergy extrapolation static optimization Model personalization Muscle force Muscle activation Stroke

来源：评论

学校读者我要写书评

暂无评论

Can static optimization detect changes in peak medial knee contact forces induced by gait modifications?

引用

JOURNAL OF BIOMECHANICS 2023年第1期152卷 111569-111569页

作者： Kaneda, Janelle M. Seagers, Kirsten A. Uhlrich, Scott D. Kolesar, Julie A. Thomas, Kevin A. Delp, Scott L. Stanford Univ Dept Bioengn Stanford CA 94305 USA Stanford Univ Dept Mech Engn Stanford CA USA Musculoskeletal Res Lab VA Palo Alto Healthcare Syst Palo Alto CA USA Stanford Univ Dept Biomed Data Sci Stanford CA USA Stanford Univ Dept Orthopaed Surg Stanford CA USA

Medial knee contact force (MCF) is related to the pathomechanics of medial knee osteoarthritis. However, MCF cannot be directly measured in the native knee, making it difficult for therapeutic gait modifications to target this metric. static optimization, a musculoskeletal simulation technique, can estimate MCF, but there has been little work validating its ability to detect changes in MCF induced by gait modifications. In this study, we quantified the error in MCF estimates from static optimization compared to measurements from instrumented knee re-placements during normal walking and seven different gait modifications. We then identified minimum mag-nitudes of simulated MCF changes for which static optimization correctly identified the direction of change (i.e., whether MCF increased or decreased) at least 70% of the time. A full-body musculoskeletal model with a multi-compartment knee and static optimization was used to estimate MCF. Simulations were evaluated using experimental data from three subjects with instrumented knee replacements who walked with various gait modifications for a total of 115 steps. static optimization underpredicted the first peak (mean absolute error = 0.16 bodyweights) and overpredicted the second peak (mean absolute error = 0.31 bodyweights) of MCF. Average root mean square error in MCF over stance phase was 0.32 bodyweights. static optimization detected the direction of change with at least 70% accuracy for early-stance reductions, late-stance reductions, and early-stance increases in peak MCF of at least 0.10 bodyweights. These results suggest that a static optimization approach accurately detects the direction of change in early-stance medial knee loading, potentially making it a valuable tool for evaluating the biomechanical efficacy of gait modifications for knee osteoarthritis.

关键词： Musculoskeletal simulation static optimization Knee contact force Knee osteoarthritis

来源：评论

学校读者我要写书评

暂无评论

Envelope theorems for static optimization and Calculus of Variations

引用

JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS 2022年第2期509卷 125966-125966页

作者： Blot, Joel Yilmaz, Hasan Univ Paris 1 Pantheon Sorbonne Ctr PMF Lab SAMM EA4543 90 Rue Tolbiac F-75634 Paris 13 France Univ Paris Lab LPSM UMR 8001 Batiment Sophie Germain8 Pl Aurelie Nemours F-75013 Paris France

We establish differentiability properties of the value function of problems of static optimization in an abstract infinite dimensional setting and we apply that to problems of Calculus of Variations. We lighten the assumptions of existing results, notably by using Gateaux and Hadamard differentials. Moreover we use recently established Multipliers Rules.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Envelope theorem static optimization Calculus of Variations

来源：评论

学校读者我要写书评

暂无评论

A Valid Model for Prediction Tibiofemoral Force for Healthy People Based on static optimization

引用

IEEE ACCESS 2021年 9卷 88467-88477页

作者： Mohamed, Ali Mamdouh Sun, Ronglei Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Tanta Univ Fac Engn Mech Power Engn Dept Gharbeya 31511 Egypt

The musculoskeletal model plays an important role in the investigation of human lower limb diseases. Although different methods are used for musculoskeletal models, the prediction of the tibiofemoral and muscle forces still needs more improvements. This paper introduces a model for the lower limb;3-DOF hip, 1-DOF knee, and 3-DOF ankle. The model estimates the tibiofemoral and muscle forces based on static optimization. The shank and the foot are considered as one element to avoid the high-cost computation of the traditional inverse dynamic method. The direction of the tibiofemoral force is estimated based on the analytical method to tune the weight factors of the predicted force. Two subjects (A and B) performed walking at 1m/s for about 5 gait cycles with recording the kinematics, ground reaction force (GRF), and foot center of pressure (CoP) and electromyographic (EMG) signals. A static optimization technique was performed to predict the tibiofemoral and 6 lower limb muscles forces based on the 2(nd)-Newton law in three-dimensional (3D). Muscles moment arms were verified by compared to measured data in the literature. The validity of the model was guaranteed by testing the model on a subject of total knee replacement (TKR) and compared the predicted tibiofemoral force with the measured one. The predicted tibiofemoral forces had the root mean square error (RMSE) of 0.19, 0.56, and 0.43 BW for TKR subject, subject A and subject B, respectively. The predicted muscles force was validated by comparing it to EMG signals. The analysis and the validation of the model showed that it could be used for different activities like walking and running and assessment rehabilitation devices such as knee brace.

关键词： Muscles Force Knee Predictive models Mathematical model Kinematics optimization Musculoskeletal model of the lower limb tibiofemoral force prediction muscle force estimation static optimization

来源：评论

学校读者我要写书评

暂无评论

Two efficient static optimization algorithms that account for muscle-tendon equilibrium: approaching the constraint Jacobian via a constant or a cubic spline function

引用

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2020年第11期23卷 703-709页

作者： Michaud, Benjamin Begon, Mickael Univ Montreal Ecole Kinesiogie & Sci Act Phys Montreal PQ Canada

Despite recent advances in algorithms for estimating muscle activities, static optimization remains the most used. static optimization estimates muscle activations required to obtain a particular set of estimated kinematics. Although fast, static optimization may require considerable time for long trials. Improvements have been proposed in the past, but the current implementations are either accurate and slow (such as the most traditional implementation) or fast but less accurate (such as the linearized at maximal activations method used by OpenSim). Two innovative algorithms are proposed to improve both optimization time and accuracy of the static optimization. The first, designed to be fast, linearizes the constraint-i.e., constructing a constant constraint Jacobian-at the activations of the previous frame. The second, designed to be as accurate as possible, approaches the constraint Jacobian by cubic splines. Their performance and accuracy are compared to the traditional and OpenSim implementations. The linearized method performed as fast as the OpenSim implementation and was more accurate (0.3% of RMSE versus 5.9%). The spline method had excellent accuracy (0.1% of RMSE), but was 2X slower than the linearized approaches. Nevertheless, it was 100X faster than the traditional implementation. Our linearized method is therefore recommended when fast computation is needed, such as real-time applications, while the spline method is recommended otherwise.

关键词： static optimization linearizing constraint constraint Jacobian

来源：评论

学校读者我要写书评

暂无评论

InverseMuscleNET: Alternative Machine Learning Solution to static optimization and Inverse Muscle Modeling

引用

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2021年 15卷 759489页

作者： Nasr, Ali Inkol, Keaton A. Bell, Sydney McPhee, John Univ Waterloo Dept Syst Design Engn Waterloo ON Canada

InverseMuscleNET, a machine learning model, is proposed as an alternative to static optimization for resolving the redundancy issue in inverse muscle models. A recurrent neural network (RNN) was optimally configured, trained, and tested to estimate the pattern of muscle activation signals. Five biomechanical variables (joint angle, joint velocity, joint acceleration, joint torque, and activation torque) were used as inputs to the RNN. A set of surface electromyography (EMG) signals, experimentally measured around the shoulder joint for flexion/extension, were used to train and validate the RNN model. The obtained machine learning model yields a normalized regression in the range of 88-91% between experimental data and estimated muscle activation. A sequential backward selection algorithm was used as a sensitivity analysis to discover the less dominant inputs. The order of most essential signals to least dominant ones was as follows: joint angle, activation torque, joint torque, joint velocity, and joint acceleration. The RNN model required 0.06 s of the previous biomechanical input signals and 0.01 s of the predicted feedback EMG signals, demonstrating the dynamic temporal relationships of the muscle activation profiles. The proposed approach permits a fast and direct estimation ability instead of iterative solutions for the inverse muscle model. It raises the possibility of integrating such a model in a real-time device for functional rehabilitation and sports evaluation devices with real-time estimation and tracking. This method provides clinicians with a means of estimating EMG activity without an invasive electrode setup.

关键词： recurrent neural network (RNN) muscle modeling electromyography static optimization inverse problems solution

来源：评论

学校读者我要写书评

暂无评论

static optimization underestimates antagonist muscle activity at the glenohumeral joint: A musculoskeletal modeling study

引用

JOURNAL OF BIOMECHANICS 2019年 97卷 109348-000页

作者： Kian, Azadeh Pizzolato, Claudio Halaki, Mark Ginn, Karen Lloyd, David Reed, Darren Ackland, David Univ Melbourne Dept Biomed Engn Parkville Vic 3010 Australia Griffith Univ Menzies Hlth Inst Queensland GCORE Griffith NSW Australia Griffith Univ Sch Allied Hlth Sci Griffith NSW Australia Univ Sydney Fac Hlth Sci Discipline Exercise & Sport Sci Camperdown NSW Australia Univ Sydney Fac Med & Hlth Discipline Anat & Histol Camperdown NSW Australia

static optimization is commonly employed in musculoskeletal modeling to estimate muscle and joint loading;however, the ability of this approach to predict antagonist muscle activity at the shoulder is poorly understood. Antagonist muscles, which contribute negatively to a net joint moment, are known to be important for maintaining glenohumeral joint stability. This study aimed to compare muscle and joint force predictions from a subject-specific neuromusculoskeletal model of the shoulder driven entirely by measured muscle electromyography (EMG) data with those from a musculoskeletal model employing static optimization. Four healthy adults performed six sub-maximal upper-limb contractions including shoulder abduction, adduction, flexion, extension, internal rotation and external rotation. EMG data were simultaneously measured from 16 shoulder muscles using surface and intramuscular electrodes, and joint motion evaluated using video motion analysis. Muscle and joint forces were calculated using both a calibrated EMG-driven neuromusculoskeletal modeling framework, and musculoskeletal model simulations that employed static optimization. The EMG-driven model predicted antagonistic muscle function for pectoralis major, latissimus dorsi and teres major during abduction and flexion;supraspinatus during adduction;middle deltoid during extension;and subscapularis, pectoralis major and latissimus dorsi during external rotation. In contrast, static optimization neural solutions showed little or no recruitment of these muscles, and preferentially activated agonistic prime movers with large moment arms. As a consequence, glenohumeral joint force calculations varied substantially between models. The findings suggest that static optimization may under-estimate the activity of muscle antagonists, and therefore, their contribution to glenohumeral joint stability. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Shoulder Deltoid Rotator cuff EMG Neuromusculoskeletal static optimization Biomechanical model

来源：评论

学校读者我要写书评

暂无评论

Muscle Synergy Constraints Do Not Improve Estimates of Muscle Activity From static optimization During Gait for Unimpaired Children or Children With Cerebral Palsy

引用

FRONTIERS IN NEUROROBOTICS 2019年 13卷 102页

作者： Shuman, Benjamin R. Goudriaan, Marije Desloovere, Kaat Schwartz, Michael H. Steele, Katherine M. Univ Washington Dept Mech Engn Seattle WA 98195 USA Vrije Univ Amsterdam Fac Behav & Movement Sci Dept Human Movement Sci Amsterdam Netherlands Katholieke Univ Leuven Dept Rehabil Sci Leuven Belgium Univ Hosp Leuven Pellenberg Clin Mot Anal Lab Lubbeek Belgium Gillette Childrens Specialty Healthcare James R Gage Ctr Gait & Mot Anal St Paul MN USA Univ Minnesota Dept Biomed Engn Orthopaed Surg Minneapolis MN USA

Neuromusculoskeletal simulation provides a promising platform to inform the design of assistive devices or inform rehabilitation. For these applications, a simulation must be able to accurately represent the person of interest, such as an individual with a neurologic injury. If a simulation fails to predict how an individual recruits and coordinates their muscles during movement, it will have limited utility for informing design or rehabilitation. While inverse dynamic simulations have previously been used to evaluate anticipated responses from interventions, like orthopedic surgery or orthoses, they frequently struggle to accurately estimate muscle activations, even for tasks like walking. The simulated muscle activity often fails to represent experimentally measured muscle activity from electromyographic (EMG) recordings. Research has theorized that the nervous system may simplify the range of possible activations used during dynamic tasks, by constraining activations to weighted groups of muscles, referred to as muscle synergies. Synergies are altered after neurological injury, such as stroke or cerebral palsy (CP), and may provide a method for improving subject-specific models of neuromuscular control. The aim of this study was to test whether constraining simulation to synergies could improve estimated muscle activations compared to EMG data. We evaluated modeled muscle activations during gait for six typically developing (TD) children and six children with CP. Muscle activations were estimated with: (1) static optimization (SO), minimizing muscle activations squared, and (2) synergy SO (SynSO), minimizing synergy activations squared using the weights identified from EMG data for two to five synergies. While SynSO caused changes in estimated activations compared to SO, the correlation to EMG data was not higher in SynSO than SO for either TD or CP groups. The correlations to EMG were higher in CP than TD for both SO (CP: 0.48, TD: 0.36) and SynSO (CP: 0.46, TD:

关键词： electromyography muscle synergies musculoskeletal modeling cerebral palsy static optimization

来源：评论

学校读者我要写书评

暂无评论

static optimization in PHP 7 2017

Static Optimization in PHP 7

引用

26th International Conference on Compiler Construction (CC)

作者： Popov, Nikita Cosenza, Biagio Juurlink, Ben Stogov, Dmitry Tech Univ Berlin Berlin Germany Zend Technol Moscow Russia

ISBN: (纸本)9781450352338

PHP is a dynamically typed programming language commonly used for the server-side implementation of web applications. Approachability and ease of deployment have made PHP one of the most widely used scripting languages for the web, powering important web applications such as WordPress, Wikipedia, and Facebook. PHP's highly dynamic nature, while providing useful language features, also makes it hard to optimize statically. This paper reports on the implementation of purely static byte-code optimizations for PHP 7, the last major version of PHP. We discuss the challenge of integrating classical compiler optimizations, which have been developed in the context of statically-typed languages, into a programming language that is dynamically and weakly typed, and supports a plethora of dynamic language features. Based on a careful analysis of language semantics, we adapt static single assignment (SSA) form for use in PHP. Combined with type inference, this allows type-based specialization of instructions, as well as the application of various classical SSA-enabled compiler optimizations such as constant propagation or dead code elimination. We evaluate the impact of the proposed static optimizations on a wide collection of programs, including micro-benchmarks, libraries and web frameworks. Despite the dynamic nature of PHP, our approach achieves an average speedup of 50% on micro-benchmarks, 13% on computationally intensive libraries, as well as 1.1% (MediaWiki) and 3.5% (WordPress) on web applications.

关键词： PHP static optimization SSA form

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：