Loop Approach Research Articles

A comparative analysis of intelligent energy modelling approaches for a grid-tied PVT system application

Renewable energy resources are a long-term answer to the current chronic energy dilemma. Solar photovoltaic (PV) systems are the most often used form of a practical solution for power supply and energy management in buildings. A PV-thermal (PVT) system is essential to lowering the demand for grid energy by capturing electrical and thermal energy from sunlight, optimising energy usage and encouraging sustainable energy practices. The most significant difficulty faced by developing countries, such as South Africa, is a lack of power supply to meet demand while limiting grid overload. An alternative source of energy is a critical component. This research compares two intelligent energy modelling methodologies: optimal control scheme-based open loop and model predictive control (MPC) in a PVT application. The primary goal of the modelling is to limit the electricity required from the grid while stressing the system’s energy efficiency and cost-effectiveness. The open loop approach uses mathematical optimisation techniques to establish the best control strategy for the PVT system. The ideal set-points for various system parameters are found by presenting the problem as an optimisation task to reduce grid power usage. The optimal control technique delivers a global optimisation solution based on the system dynamics and constraints. The MPC technique employs a predictive control technique of the PVT system to create optimal control actions in a receding horizon manner. The MPC algorithm anticipates future system behaviour and generates control actions that minimise grid power drawn by iteratively solving an optimisation problem at each time step. This robust online control scheme enables real-time modifications and responses to system disruptions that effectively and dynamically coordinate system behaviour.

Development and functional testing of battery management system algorithm for an electric bicycle using hardware in the loop testbench

An embedded Battery Management System (BMS) ensures the effective functionality and longevity of the vehicle battery systems. Testing the BMS using the Hardware in Loop (HIL) approach effectively increases safety and reduces product development time in the manufacturing sector. This research aims to develop an efficient battery management system with two-level protection for electric bicycles and test its functionality in the HIL configuration. The bicycle traction model and battery management system were first developed using a model-based design. Further, the model is tested and validated using battery emulators in the HIL methodology. The battery management system receives input data, such as current, voltage, and temperature, and monitors these parameters. The first level of protection involves the vehicle user receiving the warning when the monitoring parameters values exceed the given first safety threshold. As per the second safety threshold values, the BMS trips off the charging or discharging process when the voltage is 51V, the charge current is 5A, the discharge current is -5A and the IC temperature is 80 degrees. Also, BMS balances the cells in 3.84 min using passive balancing. Additionally, the BMS delivers a controlled current of 2.5A for safe battery charging. This methodology increases safety by reducing the potential risks of testing the BMS algorithms in the actual electric bicycle.

Topology classification in bi-anisotropic topological photonic crystals via the Wilson loop approach [Invited

Topological photonic crystals have attracted tremendous attention due to their promise of robust optical properties and great potential for applications in on-chip devices. Numerous successful experimental demonstrations have shown or proved their topological properties, however, many of them turn out to have a nature of fragile topological phases. Here, using theoretical methods of fragile topology, we analyze two cases of topological photonic crystals with preserved time reversal symmetry, which utilize (1), the intrinsic duality and bi-anisotropy, and (2), accidental duality and structural bi-anisotropy respectively to induce their topological order. Our results show that the former case belongs to a Wannier-obstructed type of topological phase, indicating strong topological protection in their edge states. However, the latter meta-waveguide designs with structural bi-anisotropy widely implemented in experiments are Wannierizable, implying the fragile properties of their topology and gapped edge spectra. Our results provide new insights into the topological properties of photonic crystals as well as other bosonic systems with time-reversal symmetry.

A Rapid Control Prototyping and Hardware-in-the Loop Approach for Upper Limb Robotic Exoskeletons Control

In the last decade, robotic-mediated rehabilitation has emerged as a potential solution to improve repetitive task training. Each device in this field has a unique development history shaped by engineers’ expertise in specific programming languages or platforms. In this work we adopt an approach that tries to abstract from the final implementation with the aim to make control logic more shareable and understandable. The authors will present the outcomes of the application of a Rapid Control Prototyping strategy to an upper-limb robotic exoskeleton. A model-based design approach implemented on a real-time target machine is presented. This modern design approach was explored with several control strategies and was used to test the exoskeleton’s performances. The proposed method highlights how it is possible to develop the entire control architecture in a single programming environment.

Demand-Oriented Optimization of Machine Tools: a Closed Loop Approach for Safe Exploration in Series Production

The resource- and energy-efficient operation of machine tools promises significant economic and ecological benefits. However, in the context of series production, optimization of the operating conditions can cause far-reaching consequences for the entire production chain. This paper presents a method for the safe exploration and optimization of new operating parameters on machine tools while ensuring process safety at all times. The method iteratively expands the allowable state space based on predictions of future Overall Equipment Effectiveness, while a Bayesian optimizer identifies the optimal operating points. A statistical verification of clear decision rules further safeguards the optimization and makes risks measurable. The method was tested by optimizing the energy demand on a grinding machine at Mercedes-Benz AG in series production, where it achieved 15% savings without compromising process safety at any point.

Rail integrity control system with closed loop approach

Rail systems have been used for transportation and carriage purposes since the beginning of the 19th century and are still an important means of transportation today. This transportation method is actively used in densely populated Asian countries, especially in America and European countries. According to data from the National Safety Council (NSC) in the United States, rail travel is indeed one of the safest modes of transportation, second only to air travel. NSC reported that in 2019 rail fatality rates were 0.31 deaths per billion passenger miles, while the death rate for air travel was only 0.061 deaths per billion passenger miles. However, this does not mean that serious accidents do not occur. Moreover, the cause of the majority of these accidents is derailment caused by line deterioration and breaking of rails. According to technical data from the Federal Railroad Administration (FRA), in 2020 there were 1,601 accidents caused by track issues, including rail cracks and deterioration. These statistics highlight the importance of regular road maintenance and inspection to ensure the integrity of the railway and prevent accidents caused by road problems. To date, many studies have been carried out to control and ensure the integrity of the rails. In our study, the main aim is to provide a system that will verify that there is no crack in the rails, and otherwise inform the machinist. Furthermore, this study does this verification by dividing the rail structure into small blocks and checking whether the circuit is completed with the electric current it provides over the train. Thanks to this easy-to-apply system, the deformations on the rails can be easily detected pointwise and the number of fatal accidents can be reduced.

Enhanced Scaling of Material Removal Rate with High Surface Quality Through Combined Laser Processes

In the field of ultrashort pulse laser processing, the effective utilization of average power provided by todays laser systems presents an almost unsolvable challenge. This study aims to address this challenge in the context of laser volume ablation. We propose a solution for utilization of high average power while producing a high-quality finish, even when starting from arbitrary surfaces. The approach combines closed loop laser volume ablation, laser cleaning and laser polishing in a subsequent process strategy. The closed loop approach enables ablative laser processes to be used to produce precise target geometries and surfaces, even if the initial geometry of the component is very different from the target geometry. One application example is the post-processing of components manufactured using additive processes such as laser powder bed fusion. Therefore, we demonstrate automated removal of support structures and finishing of a sample made of AlSi10Mg material by laser powder bed fusion. The combination of laser ablation and laser polishing made it possible to significantly increase the productivity of the post-processing and achieving a surface roughness of Ra = 0.3 µm.

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient’s cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p<0.01) and 0.91 (p<0.01), respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8–9 am, and 10–11 pm demonstrated to be the lowest RMSE values between the actual and predicted LVEF levels. The findings could potentially lead to the development of an automated screening system for patients with coronary artery disease (CAD) by using the best measurement timings during their circadian cycles.

Can Large Language Models Generate Outpatient Clinic Letters at First Consultation That Incorporate Complication Profiles From UK and USA Aesthetic Plastic Surgery Associations?

Abstract The importance of written communication between clinicians and patients, especially in the wake of the Supreme Court case of Montgomery vs Lanarkshire, has led to a shift toward patient-centric care in the United Kingdom. This study investigates the use of large language models (LLMs) like ChatGPT and Google Bard in enhancing clinic letters with gold-standard complication profiles, aiming to improve patients’ understanding and save clinicians’ time in aesthetic plastic surgery. The aim of this study is to assess the effectiveness of LLMs in integrating complication profiles from authoritative sources into clinic letters, thus enhancing patient comprehension and clinician efficiency in aesthetic plastic surgery. Seven widely performed aesthetic procedures were chosen, and complication profiles were sourced from the British Association of Aesthetic Plastic Surgeons (BAAPS) and the American Society of Plastic Surgeons (ASPS). We evaluated the proficiency of the ChatGPT4, ChatGPT3.5, and Google Bard in generating clinic letters which incorporated complication profiles from online resources. These letters were assessed for readability using an online tool, targeting a recommended sixth-grade reading level. ChatGPT4 achieved the highest compliance in integrating complication profiles from BAAPS and ASPS websites, with average readability grades between eighth and ninth. ChatGPT3.5 and Google Bard showed lower compliance, particularly when accessing paywalled content like the ASPS Informed Consent Bundle. In conclusion, LLMs, particularly ChatGPT4, show promise in enhancing patient communications in aesthetic plastic surgery by effectively incorporating standard complication profiles into clinic letters. This aids in informed decision making and time saving for clinicians. However, the study underscores the need for improvements in data accessibility, search capabilities, and ethical considerations for optimal LLM integration into healthcare communications. Future enhancements should focus on better interpretation of inaccessible formats and a Human in the Loop approach to combine Artifical Intelligence capabilities with clinician expertise. Level of Evidence: 3

A nested optimization approach for robot gripper multi-objective optimization problem

Robot Gripper Design Optimization Problem is a well-known multi-objective optimization problem explored by various approaches in previous studies over the last few decades. The continuous parameter z plays an important role in determining a robot gripper's minimum and maximum gripping forces. However, previous studies have utilized a simple loop approach in which the value of z is iteratively increased by a constant value from 0 to 50 to determine the gripping forces of a solution. This approach limits the solution space by treating the continuous z parameter as a discrete variable. To overcome this limitation, we propose a nested optimization approach in which a single-objective optimizer is embedded within the fitness function of the main optimizer to determine the optimal value of z. The proposed approach is evaluated using a single-optimizer Simulated Annealing (SA) nested within multi-objective optimizers such as Multi-Objective Gray Wolf Optimizer (MOGWO), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Artificial Algae Algorithm (MOAAA), and Non-Dominated Sorting Genetic Algorithm (NSGA-II). Experimental results demonstrate that our approach significantly outperforms previous studies and achieves the best-known performance. The findings of the study indicate that this method can present a novel framework for researchers in tackling design optimization challenges, encouraging a reevaluation and possible improvement of conventional techniques.

Analysis of cerium substitution in Co–Sr based spinel ferrites for high frequency application

Spinel ferrites exhibit exceptional magnetic properties, making them a distinctive class of magnetic materials. The sol–gel technique was utilized for the synthesis of spinel ferrites with the chemical formula Co0.6Sr0.4CexFe2–xO4. Following that, a comprehensive X-ray diffraction analysis unveiled the crystalline cubic structure of the synthesized materials. Through the utilization of the M-H loop approach, the ferromagnetic attributes of ferrites were assessed, and the assimilation of rare earth elements led to substantial enhancements in saturation magnetization, remanence, and coercivity. Spinel ferrites with a high concentration of rare earth elements have improved direct current resistivity and activation energy. The logarithm of a material's resistance increased from 5.29 to 8.12 Ω⋅cm as cerium is added. With a change in the amount of cerium, the activation energy goes up from 0.19 to 0.29. By changing the frequency from 5.5 to 9.5 GHz, the dielectric characteristics were determined. As the frequency goes up, the dielectric constant goes down. Spinel ferrites that have been made better in every way can be used in high-frequency applications.

Cascading Integration of Electrofermentation and Photosynthesis—Low‐Carbon Biorefinery in Closed Loop Approach

AbstractSingle‐chambered electrofermentation (EF) bioreactors are operated using spent wash to evaluate the effect of applied voltage (EF/AV; −0.6 V) against closed‐circuit (EF/CC; 100 Ω) and control (EF/C; no voltage/resistance) operations using deoiled microalgae biomass‐derived biochar electrodes (anode) on increasing acidogenesis rate/efficiency. Higher total volatile fatty acids (VFA) production (mg L−1) is observed in EF/AV (2866), depicting 31% and 57% increments than EF/CC (1984) and EF/C (1224), respectively. The VFA profiles (C2–C4) in EF/AV are higher with acetic/butyric/propionic acids (1922/704/240 mg L−1) and biogas co‐generation [H2 (22%) and CH4 (3%)] than EF/CC and EF/C. The applied voltage (EF/AV) helps in electron flux regulation for increasing substrates conversion and VFA generation. Residual VFA‐rich effluents from EF process is utilized as substrate to mixotrophic microalgae cultivation (nutrient‐ and stress‐phase) that resulted in biomass production with simultaneous wastewater treatment. EF/AV depicts higher biomass growth (0.72 g L−1; 4th day) and substrate removal (63%) than other operations. Integrations of electrofermentation with photosynthetic processes operated with the residual resources in effluents towards product valorization and wastewater polishing, accounts for low‐carbon economy.

Evaluating and optimising formic acid treatment against varroa mites on honey bees with MOx-sensors and a control loop

In many parts of the world beekeepers report high numbers of honey bee colony losses, which is both an environmental and an economical problem. Varroa mites (Varroa destructor) are a major factor of honey bee colony losses. Many beekeepers use formic acid to fight varroa, but treatments show low efficacy when weather conditions are suboptimal or the acid is not handled correctly. To monitor different treatment approaches, a sensor setup with commercially available MOx gas sensors has been implemented and used for comparative measurements. With the sensors installed in the bee hive, the acid level can be measured continuously - which also allows to set up a control system. Together with adequate hardware for automated acid application and a closed loop approach, the varroa treatment can be made more stable. It also allows for optimization of its efficacy, since target values for acid concentration and duration of the treatment can be adjusted and tested in a reproducible way. We provide a thorough discussion of advantages and disadvantages of the MOx technology.

Dual hormone fully closed loop in type 1 diabetes: a randomised trial in the Netherlands – study protocol

IntroductionThe management of type 1 diabetes (T1DM) has undergone significant advancements with the availability of novel technologies, notably continuous and flash glucose monitoring (CGM and FGM, respectively) and hybrid closed...

Application of fully automated centralized voltage regulation in transmission system operation management

The influence of wind farms, inclusion of distributed energy and the energy market has a large impact on the voltage profile in a transmission system. The Croatian Transmission System Operator HOPS has implemented VVC (Volt Var Control) system which was one of the subprojects included in the EU co-founded Sincro.Grid smart grid project that was financed by the EU CEF fund. The main goal of the project is to raise voltage quality in the transmission network. VVC system is an optimal power flow-based application which calculates the optimal solution regarding the desired objective function, available control variables and a defined set of constraints. To achieve the calculated optimal power system state, control of field devices is included in the process of optimization by shifting tap changer positions or changing setpoint values of reactive power injection. Voltage and reactive power constraints are set accordingly to available regulating devices included in the optimization.
 The lack of automatic coordination of reactive power resources motivated this work for implementing an advanced VVC for real time control of reactive power in partially automatic and fully automatic (closed loop) mode using the optimal power flow (OPF) algorithm. Due to its complexity, the closed loop approach of reactive power regulation is rarely used in the TSO community and the HOPS is one of the first TSOs that implemented regulation in such manner. Except fully automatic mode of operation, VVC can be used in semi-auto or manual mode. For a successful implementation several safety algorithms are implement to avoid many unwanted situations in the power system such as voltage breakdown, overloading of regulating equipment and similar

Nested loop approach for topology and shape optimization of crash-loaded deep-drawn components using contact forces for the inner loops

In this paper a new method for the optimization of deep-drawn shell components in crash by using substitute load cases in established sensitivity based static optimization is presented. The optimization process is split into an inner and an outer loop. In the outer loop the contact forces during crash are determined. These contact forces are used as substitute load cases in a static topology optimization, which builds the inner loop of the method. The presented method was inspired by the Equivalent Static Loads Method, but differs primarily in two aspects. These are the use of the contact forces as alternative to equivalent static loads in the linear static topology optimization and the extraction of a shell model that represents the voxel result obtained from topology optimization. A manufacturing constraint is applied in the topology optimization so that the results are shell components. This manufacturing constraint is enabling the mentioned extraction of a shell model. Two examples are presented where the method is applied to minimize the intrusion. The first example is a cantilever, the second example is a structure that is twisted. In both examples a rigid body impacts the structure. The method has shown to be numerical stable and suitable for the shown examples when the intrusion is the objective of the optimization. The intrusion was reduced significantly in both examples. The key novelty of the presented method is the ability to perform a static topology optimization of a voxel design space for a non-linear dynamic optimization model with the result being a shell model that can be analyzed for crash.

Online auto-tuning of multiresonant current controller with nature-inspired optimization algorithms and disturbance in the loop approach

This paper presents a novel tuning method of a complex multiresonant current controller (MRCC). An online auto-tuning process is proposed to provide optimal coefficients suitable for the real-time operation of a grid-connected power converter. The time-consuming and inaccurate simulation stage based on the model of the plant is omitted. A novel function of constraints has been introduced into the optimization scheme to ensure the desired behavior of the control system. In this solution, the noise level of a control signal and the rise time of controlled currents are directly defined. To the author’s best knowledge, it is the first time when such a solution is proposed. Next, the most commonly used meta-heuristic optimization algorithms were applied to solve this particular optimization problem. Another original concept presented in the paper is the application of the disturbance-in-the-loop (DiL) approach. In this method, the distorted phase voltages are emulated during the tuning stage, and optimal coefficients of MRCC are selected in the off-grid operation of the power converter. The proposed solution ensures safe and efficient online tuning of the MRCC for an extremely simple performance index. As a result, the high-performance operation of the grid-connected inverter is achieved. A brief stability analysis based on Lyapunov’s stability theory is included. Experimental results obtained for grid-connected inverter indicate superior disturbance attenuation and current tracking. Advantages of the proposed approach are exhibited when compared to two analytical reference solutions.

Iterative design development of a reusable micro-launcher within the RRTB project

The Recovery and Return-To-Base European Reusable Micro-Launcher project, part of the European Commission’s H2020 programme, intends to develop a novel reusable micro launcher system to bring independent and cost-effective access-to-space for the European small satellite market. The multi-disciplinary project focuses on the optimization of aerospike tail-first atmospheric re-entry of the MESO vehicle, together with the design and manufacturing of reusable cryogenic tanks, and the effort is divided into two design loops, where each design loop starts from establishing early design decisions and assumptions so that dedicated subsystem work may progress immediately. The multiple design loop approach allows for unsuitable design decisions, or risky and speculative assumptions to be revised or excluded at the project half-way point, thereby acting as a form of risk management. Throughout the work performed in the first design loop several conclusions were reached. The design presented both novel design features, but also critical issues which became better understood as the fidelity of the subsystem designs and mission analysis increased. These issues ultimately lead to the overall system design and CONOPS to be updated for the second loop, with several of the early design choices being modified, especially through the removal of the EDF landing system, based on the results of the first loop. For the second design loop, the design is updated to alleviate the most critical problem drivers, and to improve the system’s ability to fulfil the top-level requirements and objectives. The updated design uses a Mid-Air Catch approach, and leverages on the heritage of the previous design loop, implementing tailored changes based on the outcome of the trade-offs performed at mission and system levels.

Development and Validation of Machine Learning and Electronic Medical Records-Based Characterization of Stiff Person Syndrome (P5-8.001)

<h3>Objective:</h3> To develop and validate an objective framework in identifying medical history items that best characterize the diagnosis of Stiff Person Syndrome (SPS). <h3>Background:</h3> SPS is a rare neurologic disorder with characteristic muscle spasms and painful rigidity. While electronic medical records may contain items that can help refine its diagnostic criteria, the rarity of SPS leads to statistical underpowering in conventional tests. A machine learning approach that leverages underutilized electronic medical records may reveal clinical features that are strongly associated with the diagnosis of SPS. <h3>Design/Methods:</h3> This retrospective cohort study assessed 23 patients carrying SPS diagnoses and 25 controls who were all anti-GAD positive and were treated at Dartmouth Hitchcock Medical Center. After binarizing the 319 clinical features, we employed an iterative machine-learning-based feature selection approach (Contribution Selection Algorithm) to identify variables that best discriminate between patients with SPS vs. anti-GAD positive controls. Each iteration generated SHapley Additive exPlanation (SHAP) values for each binarized feature. Classifier accuracy, AUC, and Matthews Correlation Coefficients (MCC) for the Support Vector Machine models were calculated using repeated stratified 4-fold cross-validation. <h3>Results:</h3> The feature selection algorithm identified depression, pain in joint, hypothyroidism, and GERD as the top 4 predictors of SPS, as the SVM model that employed these features achieved the best predictive performance with a binary classification accuracy of 0.775 (95%CI, 0.759–0.790), an AUC of 0.808 (95%CI, 0.791–0.825), and a MCC of 0.565 (95%CI, 0.534–0.597). <h3>Conclusions:</h3> Our machine learning-based architecture identified features that are strongly associated with SPS. By circumventing the limitations of statistical underpowering and focusing on identifying associations through induction, this approach may act as a powerful tool for generating new hypotheses for rare disorders. This framework complements hypothesis-driven investigations, together forming a closed loop approach to aid diagnostic challenges in neurology. <b>Disclosure:</b> Mr. Park has nothing to disclose. Dr. Song has nothing to disclose.

Fuel performance uncertainty quantification and sensitivity analysis in the presence of epistemic and aleatoric sources of uncertainties

Fuel performance modeling and simulation includes many uncertain parameters from models to boundary conditions, manufacturing parameters and material properties. These parameters exhibit large uncertainties and can have an epistemic or aleatoric nature, something that renders fuel performance code-to-code and code-to-measurements comparisons for complex phenomena such as the pellet cladding mechanical interaction (PCMI) very challenging. Additionally, PCMI and other complex phenomena found in fuel performance modeling and simulation induce strong discontinuities and non-linearities that can render difficult to extract meaningful conclusions form uncertainty quantification (UQ) and sensitivity analysis (SA) studies. In this work, we develop and apply a consistent treatment of epistemic and aleatoric uncertainties for both UQ and SA in fuel performance calculations and use historical benchmark-quality measurement data to demonstrate it. More specifically, the developed methodology is applied to the OECD/NEA Multi-physics Pellet Cladding Mechanical Interaction Validation benchmark. A cold ramp test leading to PCMI is modeled. Two measured quantities of interest are considered: the cladding axial elongation during the irradiations and the cladding outer diameter after the cold ramp. The fuel performance code used to perform the simulation is FAST. The developed methodology involves various steps including a Morris screening to decrease the number of uncertain inputs, a nested loop approach for propagating the epistemic and aleatoric sources of uncertainties, and a global SA using Sobol indices. The obtained results indicate that the fuel and cladding thermal conductivities as well as the cladding outer diameter uncertainties are the three inputs having the largest impact on the measured quantities. More importantly, it was found that the epistemic uncertainties can have a significant impact on the measured quantities and can affect the outcome of the global sensitivity analysis.