Static Optimization Research Articles

A static robust energy management approach for modelling low emission multi-vectored energy hub including emission markets and power-to-gas units

This paper develops a robust and sustainable energy management framework to model a multi-vectored energy hub (MVEH) system. A max-min-based static robust optimization model is developed to counter the uncertainties. Furthermore, a flexible energy demand management program, battery electric vehicles (BEVs), and compressed air battery storage (CABS) are integrated to enhance MVEH's flexibility. Hybrid power-to-X units (electrolyser, methanization reactor and fuel cells) are modelled to reduce green energy spillage and improve operational independence, including heat pumps. Moreover, the emission (carbon cap and trade) market along with carbon capture storage and utilization units (CCS–U), are incorporated to establish a low-emission multi-energy model. Simulation results demonstrate that with flexible resources and power-to-X units, MVEH reduces the operating energy cost by 46.4 % and improves the system independence by 19.06 %. Besides, CCS-U units produce 92 % less emission while increasing the energy cost by 5.1 %. However, MVEH gains the right to sell carbon credits under the emission market. Thus, the multi-vectored system's economical, reliable, and environmental viability are ensured. Finally, sensitivity analysis proves the robustness of the proposed framework against natural gas and carbon price fluctuations.

Finite element analysis and optimization design of large vibrating screen based on equivalent static load method

Vibrating screens have wide applications in various industries, ranging from agriculture to coal mining. In recent years, improvement in production have put forward higher requirements on the efficiency of vibrating screens. However, these large screens are prone to fatigue damage, particularly crack formation, over time. The emergence of substantial dynamic inertial forces can be attributed to the considerable body mass of a large vibrating screen. This study analyzed the structural strength of the HZXZ200x300 large vibrating screen, employing finite element simulation to identify the maximum equivalent stress and primary stress distribution. The main frame model of the vibrating screen box was constructed by integrating the equivalent static load and submodule methods. The model transitioned from dynamic response to static optimization under equivalent static and dynamic loads, significantly reducing calculation scale and enhancing optimization efficiency. Equivalent static sub-models were employed for topology optimization, determining the optimal structure for material performance distribution. This process yielded an optimal conceptual model for reconstructing the actual model. The structural strength was further improved by comparing three-dimensional and dynamic local stress relationships post-topological optimization, followed by reinforcing the vibrating screen structure and introducing reinforced bars for increased stability. The study showed that lightweight topology optimization significantly decreased the stress levels and improved the fatigue durability of beams. Local strengthening, accomplished via topology optimization, effectively reduced the maximum equivalent stress to 76.487 MPa, a decrease of 46.8%. The vibrating screen mass also decreased by 225 kg, a reduction of 14.9%. In summary, this study employed structural analysis, topology optimization, and local reinforcement to mitigate stress levels, enhance the fatigue life of a vibrating screen, and reduce its weight. This study offers an effective solution to the dynamic optimization challenges of complex structures.

Estimation of Muscle Forces of Lower Limbs Based on CNN-LSTM Neural Network and Wearable Sensor System.

Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN-LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN-LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN-LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.

Power Generation Limits in Thermal, Chemical and Electrochemical Systems

Power generation limits are evaluated via optimization for various energy converters, such like thermal, solar, chemical, and electrochemical engines, in particular fuel cells. Thermodynamic analyses lead to converters’ efficiencies, which help to solve problems of optimal upgrading and downgrading of resources. While methods of static optimization, i.e. differential calculus and Lagrange multipliers, are sufficient for steady processes, dynamic optimization applies the variational calculus and dynamic programming for unsteady processes. In reacting systems chemical affinities constitute prevailing components of an overall efficiency, thus flux balances are applied to derive power in terms of active parts of chemical affinities. Methodological similarity is observed when treating power limits in flow thermal machines and fuel cells. The examples show power maxima in fuel cells and prove suitability of a thermal machine theory to chemical and electrochemical systems. The main novelty of contribution in the fuel cell context consists in introducing an effective change of Gibbs free energy between products p and reactants s which takes into account lowering of voltage and power caused by the incomplete conversion of the overall electrochemical reaction.

2 DOF transformable wheel design based on geared 8 bar parallel linkage mechanism

This paper introduces a novel design and static optimization for a two-degrees-of-freedom transformable wheel based on a geared linkage mechanism. Overcoming obstacles, including stairs, with small wheels is a major challenge in the field of mobile robotics research. Among various robots, the transformable wheel, which can change the shape of the wheel to overcome steps and optimize the path, was presented and has undergone many improvements. Nevertheless, problems such as asymmetry and structural strength remain. Therefore, the design of this paper aims to address the structural inefficiencies identified in the previous research model, which were attributed to the asymmetric placement of the linear motion guide. Through the implementation of this mechanism, the linear motion of the lobe can be segregated, enabling each input motor to share the workload effectively. The optimization process focus on determining the optimal linkage length under static conditions, resulting in improved structural characteristics and force distribution of linkage within the designated workspace. As a result, asymmetry of motion is eliminated, required intervention angle of the driving motor and stress of linkage was reduced by 36.24% and 8.35%, respectively.

Predicted vs. measured paraspinal muscle activity in adolescent idiopathic scoliosis patients: EMG validation of optimization-based musculoskeletal simulations

Musculoskeletal (MSK) models offer great potential for predicting the muscle forces required to inform more detailed simulations of vertebral endplate loading in adolescent idiopathic scoliosis (AIS). In this work, simulations based on static optimization were compared with in vivo measurements in two AIS patients to determine whether computational approaches alone are sufficient for accurate prediction of paraspinal muscle activity during functional activities.We used biplanar radiographs and marker-based motion capture, ground reaction force, and electromyography (EMG) data from two patients with mild and moderate thoracolumbar AIS (Cobb angles: 21° and 45°, respectively) during standing while holding two weights in front (reference position), walking, running, and object lifting. Using a fully automated approach, 3D spinal shape was extracted from the radiographs. Geometrically personalized OpenSim-based MSK models were created by deforming the spine of pre-scaled full-body models of children/adolescents. Simulations were performed using an experimentally controlled backward approach. Differences between model predictions and EMG measurements of paraspinal muscle activity (both expressed as a percentage of the reference position values) at three different locations around the scoliotic main curve were quantified by root mean square error (RMSE) and cross-correlation (XCorr).Predicted and measured muscle activity correlated best for mild AIS during object lifting (XCorr’s ≥ 0.97), with relatively low RMSE values. For moderate AIS as well as the walking and running activities, agreement was lower, with XCorr reaching values of 0.51 and comparably high RMSE values.This study demonstrates that static optimization alone seems not appropriate for predicting muscle activity in AIS patients, particularly in those with more than mild deformations as well as when performing upright activities such as walking and running.

Replicating spine loading during functional and daily activities: An in vivo, in silico, in vitro research pipeline

Lifestyle heavily influences intervertebral disc (IVD) loads, but measuring in vivo loads requires invasive methods, and the ability to apply these loads in vitro is limited. In vivo load data from instrumented vertebral body replacements is limited to patients that have had spinal fusion surgery, potentially resulting in different kinematics and loading patterns compared to a healthy population. Therefore, this study aimed to develop a pipeline for the non-invasive estimation of in vivo IVD loading, and the application of these loads in vitro. A full-body Opensim model was developed by adapting and combining two existing models. Kinetic data from healthy participants performing activities of daily living were used as inputs for simulations using static optimisation. After evaluating simulation results using in vivo data, the estimated six-axis physiological loads were applied to bovine tail specimens. The pipeline was then used to compare the kinematics resulting from the physiological load profiles (flexion, lateral bending, axial rotation) with a simplified pure moment protocol commonly used for in vitro studies. Comparing kinematics revealed that the in vitro physiological load protocol followed the same trends as the in silico and in vivo data. Furthermore, the physiological loads resulted in substantially different kinematics when compared to pure moment testing, particularly in flexion. Therefore, the use of the presented pipeline to estimate the complex loads of daily activities in different populations, and the application of those loads in vitro provides a novel capability to deepen our knowledge of spine biomechanics, IVD mechanobiology, and improve pre-clinical test methods.

Notice of Violation of IEEE Publication Principles: Scientific Workflow Partitioning and Data Flow Optimization in Hybrid Clouds

Notice of Violation of IEEE Publication Principles “Scientific Workflow Partitioning and Data Flow Optimization in Hybrid Clouds” by Rubing Duan, Rick Siow Mong Goh, Zheng Qin and Young Liu in Pre-Prints for IEEE Transactions on Cloud Computing After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper and it should not be used for research or citation The scheduling and execution of workflow applications are complex issues because of the dynamic and heterogeneous nature of distributed computing environments like hybrid clouds. While hybrid cloud computing environments provide good potential for achieving high performance and low economic cost, it also introduces a broad set of unpredictable overheads. This paper describes a novel approach to refine workflow structure and optimize intermediate data transfers without changing the scale of scheduled cloud service for large-scale scientific workflows containing thousands (or even millions) of tasks. These methods include pre- and post-partitioning of workflows, data flow optimization, static and dynamic optimization, as well as virtual single execution environment incorporated into a workflow management system.We demonstrate the effectiveness of our methods on the three real applications, Montage, Broadband, and WIEN2k, executed in a real hybrid cloud.

Static Analysis and Optimization Design of Bridge Structures under Different Load Conditions

The research is the optimization design of the bridge from the structure of the bridge, proper material utilization, and improving the performance of the bridge. First, the static model of the bridge structure was established, and then the finite analysis and rigid body mechanics models were utilized to separately detail the dead load, live load, and exceptional load. Based on the above analyses, optimization methods such as mathematical programming and genetic algorithms were used for optimizing the design of bridge structures. The tests demonstrated that the bridge’s weight can be significantly reduced by optimal design, the efficiency of usage in materials is enhanced, and the rigidity and stability of the structure advanced significantly. It is verified through checking the comparative analysis before and after optimization that the efficiency of the optimization method has been proved and the critical application value of optimization design in bridge engineering. The research conclusions provide new ideas and methods for bridge structure design, which has important theoretical and practical significance.

A Cooperative Evolutionary Computation Algorithm for Dynamic Multiobjective Multi-AUV Path Planning

Multiple autonomous underwater vehicles (AUVs) are popular for executing submarine missions, which involve multiple targets distributed in a large and complex underwater environment. The path planning of multiple AUVs is a significant and challenging problem, which determines the location of surface points for AUV launch and plans the paths of AUVs for target traveling. Most existing works model the problem as a single-objective static optimization problem. However, the target missions may change over time, and multiple optimization objectives are usually expected for decision making. Thus, this paper models the problem as a dynamic multiobjective optimization problem and proposes a cooperative evolutionary computation algorithm to provide diverse and high-quality solutions for decision makers. In the proposed method, solutions are represented using a bi-layer encode scheme, in which the first layer indicates the surface location points and the second layer represents the traveling sequences of target missions. Multiple populations for multiple objectives (MPMO) framework is adopted to efficiently solve the dynamic multiobjective AUV optimization problem. In addition, a recombination-based sampling strategy is developed to improve convergence by fusing the information of multiple populations. Once a change occurs, an incremental response strategy is adopted to generate high-quality solutions for population evolution. Based on the dataset of New Zealand bathymetry, six complex underwater scenarios are constructed with a size of 50 km×50 km×10 km and 400 target missions for tests. Experimental results show that the proposed method outperforms the state-of-the-art algorithms in terms of solution diversity and optimality.

Optimizing Network Reliability in Dynamic or Rapidly-Changing Topologies

With communication, power and transportation networks facing ever-greater dynamics, optimizing reliability poses significant modeling and computational challenges. Most existing reliability optimization research assumes static topologies with predefined failure probabilities and connectivity demands. This work puts forth a time-varying mathematical program to maximize expected network reliability under shifting topological uncertainties. Sets, parameters, decision variables and constraints are defined as functions of time to capture variability in links, capacities, risks and demands. The formulation adapts to detected changes through re-optimization triggered by model error thresholds. Approximation techniques based on constraint sampling and decomposition address solve efficiency for large fluid networks. Evaluations on simulated dynamic test cases demonstrate superior reliability versus periodic and static optimization approaches while fulfilling budget limits. Accuracy metrics assess model fidelity over increasing volatility levels. Implementation case studies exhibit optimized resilience in software-defined communication architectures, smart grid reconfiguration, and adaptive transportation maintenance scenarios. The mathematical programming foundation provides a pathway to achieve connectivity resilience for critical infrastructure networks facing intensifying dynamics. The integration of optimization, prediction and adaptive response provides a paradigm for decision making under modern uncertain conditions.

Intra and inter-rater variability in the construction of patient-specific musculoskeletal model

BackgroundVariations observed in biomechanical studies might be attributed to errors made by operators during the construction of musculoskeletal models, rather than being solely attributed to patient-specific geometry. Research questionWhat is the impact of operator errors on the construction of musculoskeletal models, and how does it affect the estimation of muscle moment arms and hip joint reaction forces? MethodsThirteen independent operators participated in defining the muscle model, while a single operator performed 13 repetitions to define the muscle model based on 3D bone geometry. For each model, the muscle moment arms relative to the hip joint center of rotation was evaluated. Additionally, the hip joint reaction force during one-legged stance was assessed using static inverse optimization. ResultsThe results indicated high levels of consistency, as evidenced by the intra- rater and inter-rater agreement measured by the Intraclass Correlation Coefficient (ICC), which yielded values of 0.95 and 0.99, respectively. However, the estimated muscle moment arms exhibited an error of up to 16 mm compared to the reference musculoskeletal model. It was found that muscles attached to prominent anatomical landmarks were specified with greater accuracy than those attached over larger areas. Furthermore, the variability in estimated moment arms contributed to variations of up to 12% in the hip joint reaction forces. SignificanceBoth moment arm and muscle force demonstrated significantly lower variability when assessed by a single operator, suggesting the preference for employing a single operator in the creation of musculoskeletal models for clinical biomechanical studies.

The link between MiFID and Risk Appetite Framework as an application of best practices for wealth management and the entire value chain of the financial industry

After a short review of the MiFID regulations and the RAF, the paper identifies the link between them which allows to mitigate a balance sheet risk sustained by the financial intermediary and, at the same time, to improve its stability and value creation, through a maximization of customer loyalty. The client’s attitude towards risk can be summarized in these terms: "I don't like risk, but I like to win". Thus, a three-dimensional approach towards expected utility is suggested for estimating risk tolerance: risk aversion, loss aversion and reflection. In addition, a definition of the client's financial objectives is required, combined with greater disclosure - which allows the construction of a financial statement – to attest that risk-taking is indeed a luxury, as indicated by the metrics of the discretionary wealth ratio, and therefore, of the Standard of Living Risk (SLR). The next step consists in the determination of a set of portfolios along the efficient frontier where risk is represented by the expected shortfall, the determination of which belongs to a Generalized Extreme Value Theory logic. The client's objectives are described in terms of probability of success, where the latter is a function of an initial endowment, a potential positive contribution of financial resources over time as well as an expected return level. The above is expressed through a practical case that envisages the determination of a set of EGPF portfolios and the identification of the specific portfolio, obtained as a solution to a static and dynamic optimization problem, where the objectives have been formalized through the calculation of the associated utility

LDA-ID: An LDA-based framework for real-time network intrusion detection

Network intrusion poses a severe threat to the Internet. However, existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap. In addition, efficient real-time detection is an urgent problem. To address the two above problems, we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection (LDA-ID), consisting of static and online LDA-ID. The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection. Thus, the detection is based on the latent topic features. To achieve efficient real-time detection, we design an online computing mode for static LDA-ID, in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information. Furthermore, we design two matching mechanisms to accommodate the static and online LDA-ID, respectively. Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.

Adjustable Robust Optimization for Water Distribution System Operation Under Uncertainty

AbstractThe optimal operation of water distribution systems (WDS) is a paramount objective for water utilities due to the substantial energy consumption associated with pumping. A major challenge in optimizing WDS operation is addressing uncertainties such as those related to consumer demands. Real‐time operation under uncertainty necessitates a dynamic approach that can utilize the newly observed information and adjust the operational policy accordingly. This study presents an adjustable robust optimization (ARO) approach to tackle this challenge. Unlike static optimization methods, ARO generates a decision rule policy that is dynamically adjusted as new data becomes available and the operational horizon evolves, thereby ensuring adaptability to changing conditions. Furthermore, the study includes a quantitative analysis of typical demand uncertainty that supports the formulation of the ARO model. The proposed method is evaluated through two case studies and compared with traditional folding horizon approaches. The results indicate that the ARO method is competitive with traditional methods in terms of objective value and surpasses them in terms of robustness. An additional advantage of the method is its offline operation capability which enables it to produce decision rules independent of real‐time programs. This feature facilitates various practical applications such as what‐if analyses, maintenance work planning, and preparation for other special events.

BORS/BJR TRAVELLING FELLOWSHIP ABSTRACT: MOTION CAPTURE OF NEONATAL INFANT KICKING MOVEMENTS CAN PROVIDE AN EARLY PREDICTION OF CEREBRAL PALSY

AbstractObjectivesNeonatal motor development transitions from initially spontaneous to later increasingly complex voluntary movements. A delay in transitioning may indicate cerebral palsy (CP). The general movement optimality score (GMOS) evaluates infant movement variety and is used to diagnose CP, but depends on specialized physiotherapists, is time-consuming, and is subject to inter-observer differences. We hypothesised that an objective means of quantifying movements in young infants using motion tracking data may provide a more consistent early diagnosis of CP and reduce the burden on healthcare systems. This study assessed lower limb kinematic and muscle force variances during neonatal infant kicking movements, and determined that movement variances were associated with GMOS scores, and therefore CP.MethodsElectromagnetic motion tracking data (Polhemus) was collected from neonatal infants performing kicking movements (min 50° knee extension-flexion, <2 seconds) in the supine position over 7 minutes. Tracking data from lower limb anatomical landmarks (midfoot inferior, lateral malleolus, lateral knee epicondyle, ASIS, sacrum) were applied to subject-scaled musculoskeletal models (Gait2354_simbody, OpenSim). Inverse kinematics and static optimisation were applied to estimate lower limb kinematics (knee flexion, hip flexion, hip adduction) and muscle forces (quadriceps femoris, biceps femoris) for isolated kicks. Functional principal component analysis (fPCA) was carried out to reduce kicking kinematic and muscle force waveforms to PC scores capturing ‘modes’ of variance. GMOS scores (lower scores = reduced variety of movement) were collected in parallel with motion capture by a trained operator and specialised physiotherapist. Pearson's correlations were performed to assess if the standard deviation (SD) of kinematic and muscle force waveform PC scores, representing the intra-subject variance of movement or muscle activation, were associated with the GMOS scores.ResultsThe study compared GMOS scores, kinematics, and muscle force variances from a total of 26 infants with a mean corrected gestational age of 39.7 (±3.34) weeks and GMOS scores between 21 and 40. There was a significant association between the SD of the PC scores for knee flexion and the GMOS scores (PC1: R = 0.59, p = 0.002; PC2: R = 0.49, p = 0.011; PC3: R = 0.56, p = 0.003). The three PCs captured variances of the overall flexion magnitude (66% variance explained), early-to-late kick knee extension (20%), and continual to biphasic kicking (6%). For hip flexion, only the SD of PC1 correlated with GMOS scores (PC1: R = 0.52, p = 0.0068), which captured the variance of the overall flexion magnitude (81%). For the biceps femoris, the SD of PC1 and PC3 associated with GMOS scores (PC1: R = 0.50, p = 0.002; PC3: R = 0.45, p = 0.03), which captured the variance of the overall bicep force magnitude (79%) and early-to-late kick bicep activation (8%).ConclusionsInfants with reduced motor development as scored in the GMOS displayed reduced variances of knee and hip flexion and biceps femoris activation across kicking cycles. These findings suggest that combining objectively measured movement variances with existing classification methods could facilitate the development of more consistent and accurate diagnostic tools for early detection of CP.Declaration of Interest(b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.

Clinical Validation of Estimated Muscle Activations during Phases of Elderly Gait

This study validated muscle activation estimations generated by OpenSim during the gait of elderly fallers. Ten healthy elderly participants walked on an instrumented treadmill, monitored by motion capture, force platforms, and 12 surface EMG sensors. Static optimization was used to calculate muscle activations, evaluated through cosine similarity, comparing them with EMG signals from 12 muscles of the right leg. Findings revealed varied similarity levels across muscles and gait phases. During stance phase, tibialis anterior (TIBA), peroneus longus (PERL), soleus (SOL), gastrocnemius lateralis (GASL), semitendinosus (SEMI), tensor fasciae latae (TFL), and rectus femoris (RECF) demonstrated poor similarity (cosim < 0.6), while gluteus medius (GMED), biceps femoris long head (BFLH), and vastus lateralis (VL) exhibited moderate similarity (0.6 ≤ cosim ≤ 0.8), and gluteus maximus (GMAX) and vastus medialis (VASM) displayed high similarity (cosim > 0.8). During the swing phase, only SOL demonstrated inadequate similarity, while GASL, GMAX, GMED, BFLH, SEMI, TFL, RECF, and VASL exhibited moderate similarity, and TIBA, PERL, and VASM showed high similarity. Comparing the different 10% intervals of the gait cycle generally produced more favorable similarity results. For most of the muscles and intervals, good agreement was found. Moderate agreement was estimated in the cases of TIBA (0–10%), PERL (60–70%), GASL (60–70%), TFL (10–20%), RECF (0–10%, 80–100%), and GMED (50–60%). Bad agreement was found in the cases of SOL (60–70%), GASL (0–10%), and TFL (0–10%). In conclusion, the study’s validation outcomes were acceptable in most cases, underlining the potential for user-friendly musculoskeletal modeling routines to study muscle output during elderly gait.

Exploring the impact of body mass change on fatigue and activity of the muscular system during daily routine.

Correct body weight is one of the factors that allows you to maintain a properly functioning body. Abnormal body weight can cause muscle tissue remodelling, affecting activity and muscle fatigue. Changes in the muscular system can cause occurrence of functional limitations. To determine the effect of weight change on fatigue and activity of the muscular system during daily activities. The evaluation of musculoskeletal functioning was based on the results of computer simulations conducted in the AnyBody Modeling System. The following activities were analysed: standing, sitting down and getting up from a chair, holding and lifting an object, and walking. The simulations of the activities were carried out using averaged kinematic data, and by changing body mass in the range of 50 kg to 100 kg by increments of 2 kg, to map different nutritional status from excessive thinness to extreme obesity. Identification of loads in the musculoskeletal system was based on solving an inverse dynamics problem and then the estimation of muscle force values using static optimization. The simulation results allowed to determine the value of muscle fatigue and the level of muscle activity. For activities (i.e., standing, walking, sitting down and getting up from a chair) it was observed that the value of muscle fatigue increases with increasing body mass. However, for activities that cause more load on the musculoskeletal system, i.e. lifting and holding an object, the highest value of muscle fatigue was observed in underweight individuals. The change in body weight alters the functioning of the muscular system and thus the ability to perform activities. It was shown that in case of underweight, overweight or obese people, abnormal body weight can be the reason for occurrence of difficulties in performing the activities of lifting and holding a 20 kg object, as well as walking.

Kirigami pattern design for buckling-induced assembly 3D structures via topology optimization

Complex three-dimensional (3D) mesostructures are attracting an increasing interest due to their wide applications in microelectronics, biomedicine and other fields. A buckling-induced 3D structure can be assembled by buckling of a 2D plane precursor rapidly and precisely at low cost. However, the inverse design problem of finding 2D precursor configurations corresponding to the predetermined 3D mesostructures has not been well solved yet. In the present work, an inverse method is developed for buckling-induced assembly 3D structure design. Unlike the existing methods, the Moving Morphable Void (MMV) -based topology optimization approach is adopted for driving the inverse design. The voids in MMV are defined as kirigami cuts on the 2D precursor with explicit geometry parameters to generate complex buckling deformation. To cope with the costly non-linear optimization, the Equivalent Static Loads (ESL) method is incorporated, with which the non-linear optimization process can be transformed into a linear static optimization process. Therefore, the layout of the pattern of cuts is sought on the 2D precursor with linear sensitivity calculation. Some self-assembly numerical examples demonstrate the effectiveness and efficiency of the proposed approach.

Steady-state real-time optimization using transient measurements and approximated Hammerstein dynamic model: A proof of concept in an experimental rig

An attractive method for steady-state real-time optimization (RTO) using transient measurements consists of a persistent parameter adaptation throughout a dynamic parameter estimation followed by a static economic optimization. The method is called RTO with persistent parameter adaptation (ROPA) or Hybrid RTO (HRTO). Although such a method avoids the long steady-state wait to trigger the optimizations, it adds extra complexity to the RTO project by including the requirement of developing a rigorous dynamic model, which can hinder the industrial-scale applicability of the methodology. To overcome this requirement, a Hammerstein dynamic approximation model has been proposed to replace the rigorous dynamic model in a Hammerstein ROPA (HROPA) framework. Previous work has demonstrated that the proposed model is adequate for use in RTO since it preserves the parameter observability property of the static model. In this paper, two HROPA approaches are implemented in an experimental rig and compared to a previously implemented ROPA approach. The experimental results confirmed that HROPA presents a similar economic performance to ROPA with much less effort required in the modeling stage. Finally, a comprehensive literature review of the topic is presented, and general guidelines for the Hammerstein dynamic matrix identification are provided.