Parameter Optimization Methodology Research Articles

Mechanistic regulation of planarian shape during growth and degrowth.

Adult planarians can grow when fed and degrow (shrink) when starved while maintaining their whole-body shape. It is unknown how the morphogens patterning the planarian axes are coordinated during feeding and starvation or how they modulate the necessary differential tissue growth or degrowth. Here, we investigate the dynamics of planarian shape together with a theoretical study of the mechanisms regulating whole-body proportions and shape. We found that the planarian body proportions scale isometrically following similar linear rates during growth and degrowth, but that fed worms are significantly wider than starved worms. By combining a descriptive model of planarian shape and size with a mechanistic model of anterior-posterior and medio-lateral signaling calibrated with a novel parameter optimization methodology, we theoretically demonstrate that the feedback loop between these positional information signals and the shape they control can regulate the planarian whole-body shape during growth. Furthermore, the computational model produced the correct shape and size dynamics during degrowth as a result of a predicted increase in apoptosis rate and pole signal during starvation. These results offer mechanistic insights into the dynamic regulation of whole-body morphologies.

An air-assisted mechanical hill-seeding device for foxtail millet (<em>Setaria italica</em>)

Setaria italica (foxtail millet) plays an important role in human nutrition, animal feed, and agriculture. However, the difficulty in realizing precision seeding introduces challenges to the planting and industrialization of foxtail millet. To address the challenge of suboptimal seeding effects for foxtail millet seeds, we engineered an air-assisted mechanical foxtail millet hole seeder equipped with a duckbill hole-forming device. Through bench testing, we investigated the influence of the wire diameter of the return spring on the seeding effect, as well as the interactive effects of the vacuum pressure of the seed-metering device and the rotational speed of the dibber wheel on seeding performance. The results show the following. (1) With the return spring having a major diameter of 44 mm, minor diameter of 2 mm, and length of 70 mm, wire diameters of 1.4, 1.6, and 1.8 mm were evaluated for conformity with standard requirements for the quality index. Given the reduced tearing extent with a smaller wire diameter, a wire diameter of 1.4 mm was selected. (2) The vacuum pressure of the seed-metering device and the rotational speed of the dibber wheel had a significant impact on the quality index. Using the response surface methodology (RSM) for parameter optimization, we aimed to maximize the qualification rate and minimize the missing and double seeding rates. Optimal parameters were found with an inlet vacuum pressure of −7.8 kPa, a dibber wheel speed of 75 r/min, and a seeder forward speed of 1.69 m/s (6.08 km/h). These settings achieved a quality index of 89.88%, a multiples index of 1.56%, and a miss index of 8.56%, in accordance with model predictions. Field tests were conducted with the determined parameters, revealing that at a seeder forward speed of 1.67 m/s (6 km/h) and a metering device vacuum pressure of −7.8 kPa, the average seed spacing was 16.51 mm, the hole spacing coefficient of variation was 2.55%, the mean seeding quality index was 88.46%, and the seedling pass rate was 97.77%. Thus, the designed air-suction-assisted mechanical foxtail millet hole seeder complies with the standard requirements and represents an advancement in agricultural machinery for small grain crops.

Optimization of production conditions, isolation, purification, and characterization of tannase from filamentous fungi.

Tannase-producing filamentous fungi residing alongside tannin-rich ambient in the Northwest Himalayas were isolated at laboratory conditions and further identified by 18S ribosomal RNA gene sequencing. Five most potent tannase producing strains (EI ≥ 2.0), designated Aspergillus fumigatus AN1, Fusarium redolens AN2, Penicillium crustosum AN3, Penicillium restrictum AN4, and Penicillium commune AN5, were characterized. The strain Penicillium crustosum AN3 exhibited a maximum zone dia (25.66mm ± 0.38). During solid-state fermentation, a maximal amount of tannase was attained withPenicillium crustosum AN3 usingpine needles (substrate) by adopting response surface methodology for culture parameter optimization. Gel filtration chromatography yielded 46.48% of the partially purified enzyme with 3.94-fold of tannase purification. We found two subunits in enzyme-117.76 KDa and 88.51 KDa, respectively, in the SDS-PAGE. Furthermore, the characterization of partially purified tannase revealed a maximum enzyme activity of 8.36 U/mL at 30°C using a substrate concentration (methyl gallate) of 10mM. To broaden the knowledge of crude enzyme application, dye degradation studies were subjected to extracellular crude tannase fromPenicillium crustosum AN3 where the maximumdegradation achievedat a low enzyme concentration (5ppm).

Multiple parameter optimization methodology by integrating a game theory principle into priority-based decision making

Mathematical model development for multivariate decision-making problems (i.e., multiple parameter and response optimization problems) has received significant attention and has been investigated by many researchers and practitioners. Although many different optimization models and methods, including priority and weight allocation problems, have been proposed in the literature, there is significant room for improvement. The majority of existing optimization methods may have difficulties in determining reliable priorities and weights for practical industrial situations. In addition, because they often focus on tradeoffs only among multiple output responses, these methods may not incorporate the multiple priority and/or weight effects directly from input factors to output responses (i.e., quality characteristics). To address these problems, the primary objective of this research is to propose a new multi-objective optimization approach by integrating a game theory principle (i.e., the Stackelberg leadership game) into a robust parameter design model to determine the optimal factor settings. First, a multi-response robust design optimization (RDO) problem was formulated using a mean squared error model and response surface methodology. A Stackelberg leadership game is then integrated into this RDO problem, where multiple responses play the role of game participants. Second, the integrated RDO model using the Stackelberg game-based multi-response (SGMR) formulation approach is analyzed by decomposition into various sequential optimization models in terms of different leader–follower relationships. Finally, non-dominated solutions are obtained from the proposed model by evaluating the overall quality loss (OQL). A pharmaceutical numerical example was performed to demonstrate the proposed integrated model and to examine whether this model can determine the most efficient solutions when the relative importance among multiple responses is unidentified. In addition, a comparative study associated with the conventional multi-objective optimization methods and the newly proposed RDO model is presented, and the proposed RDO model provided significantly better solutions than the conventional methods in the relevant comparative study.

Parameter Optimization and Tuning Methodology for a Scalable E-Bus Fleet Simulation Framework: Verification Using Real-World Data from Case Studies

This study presents the optimization and tuning of a simulation framework to improve its simulation accuracy while evaluating the energy utilization of electric buses under various mission scenarios. The simulation framework was developed using the low fidelity (Lo-Fi) model of the forward-facing electric bus (e-bus) powertrain to achieve the fast simulation speeds necessary for real-time fleet simulations. The measurement data required to verify the proper tuning of the simulation framework is provided by the bus original equipment manufacturers (OEMs) and taken from the various demonstrations of 12 m and 18 m buses in the cities of Barcelona, Gothenburg, and Osnabruck. We investigate the different methodologies applied for the tuning process, including empirical and optimization. In the empirical methodology, the standard driving cycles that have been used in previous studies to simulate various use case (UC) scenarios are replaced with actual driving cycles derived from measurement data from buses traversing their respective routes. The key outputs, including the energy requirements, total cost of ownership (TCO), and impact on the grid are statistically compared. In the optimization scenario, the assumptions for the various vehicle and mission parameters are tuned to increase the correlation between the simulation and measurement outputs (the battery SoC profile), for the given scenario input (the velocity profile). Improved simple optimization (iSOPT) was used to provide a superfast optimization process to tune the passenger load in the bus, cabin setpoint temperature, battery’s age as relative capacity degradation (RCD), SoC cutoff point between constant current (CC) and constant voltage charging (CV), charge decay factor used in CV charging, charging power, and cutoff in initial velocity during braking for which regenerative braking is activated.

Multiple Parameter Optimization Methodology by Integrating a Game Theory Principle into Priority-Based Decision Making

Multiple Parameter Optimization Methodology by Integrating a Game Theory Principle into Priority-Based Decision Making

Parameter Optimization of Large-Size High-Speed Cam-Linkage Mechanism for Kinematic Performance

The cam-linkage mechanism is a typical transmission mechanism in mechanical science and is widely used in various automated production equipment. However, conventional modeling methods mainly focus on the design and dimensional synthesis of the cam-linkage mechanism in the slow-speed scenario. The influence of component dimensions is not taken into consideration. As a result, the model accuracy dramatically falls when analyzing large-size cam-linkage mechanisms, especially in high-speed environments. The kinematic aspects of cam design have been investigated, but there are few studies discussing the motion characteristic and accuracy analysis models of the large-size cam-linkage mechanism under high-speed scenarios. To handle such issues, this paper proposes a parameter optimization methodology for the design analysis of the large-size high-speed cam-linkage mechanism considering kinematic performance. Firstly, the mathematical model of the cam five-bar mechanism is presented. The cam curve and motion parameters are solved forward with linkage length and output speed. Then, a particle swarm-based multi-objective optimization method is developed to find the optimal structure parameters and output speed curve to minimize cam pressure angle and roller acceleration and maximize linkage mechanism drive angle. A Monte Carlo-based framework is put forward for the reliability and sensitivity analysis of kinematic accuracy. Finally, a transverse device of a sanitary product production line is provided to demonstrate the applicability of the proposed method. With the parameter optimization, the productivity of the transverse device is doubled, from 600 pieces per minute (PPM) to 1200 PPM.

TensorRT-Based Framework and Optimization Methodology for Deep Learning Inference on Jetson Boards

As deep learning inference applications are increasing in embedded devices, an embedded device tends to equip neural processing units (NPUs) in addition to a multi-core CPU and a GPU. NVIDIA Jetson AGX Xavier is an example. For fast and efficient development of deep learning applications, TensorRT is provided as the SDK for high-performance inference, including an optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. Like most deep learning frameworks, TensorRT assumes that the inference is executed on a single processing element, GPU or NPU, not both. In this article, we present a TensorRT-based framework supporting various optimization parameters to accelerate a deep learning application targeted on an NVIDIA Jetson embedded platform with heterogeneous processors, including multi-threading, pipelining, buffer assignment, and network duplication. Since the design space of allocating layers to diverse processing elements and optimizing other parameters is huge, we devise a parameter optimization methodology that consists of a heuristic for balancing pipeline stages among heterogeneous processors and fine-tuning the process for optimizing parameters. With nine real-life benchmarks, we could achieve 101%~680% performance improvement and up to 55% energy reduction over the baseline inference using a GPU only.

Forecasting COVID-19 Chile’ second outbreak by a generalized SIR model with constant time delays and a fitted positivity rate

The COVID-19 disease has forced countries to make a considerable collaborative effort between scientists and governments to provide indicators to suitable follow-up the pandemic’s consequences. Mathematical modeling plays a crucial role in quantifying indicators describing diverse aspects of the pandemic. Consequently, this work aims to develop a clear, efficient, and reproducible methodology for parameter optimization, whose implementation is illustrated using data from three representative regions from Chile and a suitable generalized SIR model together with a fitted positivity rate. Our results reproduce the general trend of the infected’s curve, distinguishing the reported and real cases. Finally, our methodology is robust, and it allows us to forecast a second outbreak of COVID-19 and the infection fatality rate of COVID-19 qualitatively according to the reported dead cases.

Removal of Iron from Groundwater by Ozonation: The Response Surface Methodology for Parameter Optimization

This research studied the possibility of using ozone to remove iron from groundwater. The optimum conditions were investigated using a Box-Behnken experiment design with statistical analysis by response surface technique. The three parameters investigated, pH (6.0-8.0), hardness (300-500 mg/L as CaCO3) and removal time (10 to 60 min) were independent parameters of iron removal. Data was examined for optimal conditions and included main effects and their interactions. Analysis of variance indicated that the proposed quadratic model successfully interpreted the experimental data with a coefficient of determination (R2) of 98.83% and adjusted R2 of 96.72%. Through this model, it could predict the iron removal efficiency under variable conditions. Furthermore, the optimum conditions were pH 6.99, hardness of 300 mg/L as CaCO3, and 10 min of reaction time. The predicted iron removal efficiency obtained from the model under the optimum conditions was 99.00%. The experiment confirmed that the optimum condition which validated the model’s accuracy of iron removal efficiency was 98.45%. The results showed that ozone can remove iron from groundwater.

CBM drilling technical parameter optimization methodology and software development: a case study of LUAN mining area.

Based on the leakage of the coalbed methane (CBM) drilling engineering practice of Luan mining area in China, the author determines the safe drilling fluid density range for the stable borehole wall based on borehole wall collapse and fracture pressure. Such parameters as the drilling fluid hydraulic parameters (including pump pressure, pump power and displacement, nozzle diameter, bit pressure drop, bit hydraulic horsepower, circulation pressure drop, impact force and jet velocity) and drilling parameters (including weight-on-bit, drilling rotary speed, bit tooth wear) in each borehole section are optimized. Taking the lowest drilling cost as the controlling target, the drilling parameter optimization model is designed and solved by the genetic algorithm. Furthermore, a software named "CBM borehole wall stability parameter design and optimization" characterized by visualization and applicable for drilling formation condition, which can be used to design and optimize the borehole drilling technological parameters, is developed. This program includes such modules as drilling fluid density prediction, drilling technology design, database management, user management and help. The developed software is proven to solve the drilling leakage effectively in the No.67 borehole practice, which can help drilling engineers to optimize CBM drilling technological parameters safely and quickly.

Enhancing as-built microstructural integrity and tensile properties in laser powder bed fusion of AlSi10Mg alloy using a comprehensive parameter optimization procedure

Abstract The importance of a thorough and systematic approach to parameter optimization in the laser powder bed fusion (LPBF) of AlSi10Mg alloy is demonstrated, particularly in the event of varying the physical attributes of the input powder (i.e., upon changing from one powder source to another). The proposed approach involves the effective use of prevalent energy density/enthalpy models complimented by an in-depth microstructural and mechanical characterization. Using a formulation derived from the functioning characteristics of a pulsed-wave laser system, a reliable volumetric energy density (VED) model is developed, from which a trend in VED-porosity relationship is identified. Particularly, it is shown that a multi-stage optimization through porosity minimization, followed by a microstructural screening step, can successfully result in achieving excellent tensile properties; In the first and second screening steps, using a VED/enthalpy approach, the as-built porosity content is correlated with VED, and then a number of optimized parameter sets are identified; In the third screening step, the number of potentially optimized parameter sets is reduced via a microstructural screening for the least amount of lack of fusion and lowest density of melt pool boundaries; In the fourth screening step, through tensile testing, the final parameter set representing the “best optimized” condition is identified (i.e., exhibiting an excellent combination of ultimate tensile strength and elongation-to-fracture of ~461 MPa and ~11.5%, respectively). Furthermore, it is demonstrated that the optimized VED value is a strong function of the physical attributes of the powder source, e.g., the particle morphology and size distribution (the effect of which will be thoroughly investigated in a separate study). The novel parameter optimization methodology described here represents a standard practice that can be potentially adopted in the LPBF processing of many metallic alloys.

Integration of knowledge-based seismic inversion and sedimentological investigations for heterogeneous reservoir

Abstract Conventional geological modelling methods are not capable to provide precise and comprehensive model of the subsurface structures, when dealing with insufficient data. Knowledge based methods employing rule bases techniques are found vast applications in geoscience studies. These methods are applicable for petroleum reservoir geological modelling and characterizations, specifically for geologically complex structures. In this study, we present a knowledge based seismic acoustic impedance inversion method which employs rule based method for porosity estimation. The back propagation algorithm and the fuzzy neural network are also used in the methodology for parameter optimization and definition of nonlinear relationship between seismic attributes and porosity of the reservoir rock. The methodology initiates by seismic acoustic impedance inversion, followed by conventional porosity estimation. Subsequently, a knowledgebase was designed by investigation on more than 24 published case studies. This knowledgebase was used for definition of rules and optimization number of rules and improve efficiency of the inference engine. The porosity model obtained by conventional method in previous step would be used for primary evaluation of the rules. The extracted rules and optimized number rules then would be used for rule-based porosity estimation. The methodology was applied on a petroleum field containing two heterogeneous reservoir formations. Result of application of the proposed approach was evaluated with core analysis, thin sections and drilling data. Consistency of result obtained by the proposed method with geological data has shown its capability to resolve problem of insufficient data in reservoir geological modelling.

On the Modeling of the Avalanche Multiplication Coefficient in SiGe HBTs

This paper focuses on the modeling of the avalanche multiplication coefficient in state-of-the-art SiGe:C HBTs suffering from radical self-heating and impact-ionization effects. Experimental data are measured in a wide current range on devices fabricated during the DOTFIVE project and used as a reference. A comparative analysis of the available empirical descriptions for the avalanche coefficient is made at low current density; the best model is then extended to account for medium/high-current effects by resorting to a suitable parameter optimization methodology. The same procedure is adopted to examine the accuracy of physics-based formulations implemented in advanced transistor models for circuit design.

Validation of a 5-zone-car-cabin model to predict the energy saving potentials of a battery electric vehicle’s HVAC system

This paper presents a 5-zone-car-cabin model which is able to simulate the car cabin’s thermal condition depending on several influencing parameters. This includes the solar radiation, to which special attention is paid in this paper. In addition, a generic methodology for parameter optimization considering measurements on a reference vehicle is presented. Thus, a very high degree of determination of the model was achieved. This paper is motivated by the impact of auxiliary loads on overall energy consumption in battery electric vehicles. The further use of the model is intended to calculate the energy saving potentials of the heating, ventilation, and air conditioning system by reducing or increasing the target interior temperature. This is necessary for a predictive control of secondary consumers.

Real-time parameter optimization based on neural network for smart injection molding

The manufacturing industry has been facing several challenges, including sustainability, performance and quality of production. Manufacturers attempt to enhance the competitiveness of companies by implementing CPS (Cyber-Physical Systems) through the convergence of IoT(Internet of Things) and ICT(Information & Communication Technology) in the manufacturing process level. Injection molding process has a short cycle time and high productivity. This features have been making it suitable for mass production. In addition, this process is used to produce precise parts in various industry fields such as automobiles, optics and medical devices. Injection molding process has a mixture of discrete and continuous variables. In order to optimized the quality, variables that is generated in the injection molding process must be considered. Furthermore, Optimal parameter setting is time-consuming work to predict the optimum quality of the product. Since the process parameter cannot be easily corrected during the process execution. In this research, we propose a neural network based real-time process parameter optimization methodology that sets optimal process parameters by using mold data, molding machine data, and response data. This paper is expected to have academic contribution as a novel study of parameter optimization during production compare with pre - production parameter optimization in typical studies.

Sulfite assisted rotating disc electrocoagulation on cadmium removal: Parameter optimization and response surface methodology

Abstract Redox conditions is an extraordinary parameter as it controlled the generation of functional products in iron–electrocoagulation process. In the present study, a newly designed rotating disc electrocoagulation system (RDEC) on cadmium removal from wastewater assisted by sodium sulfite is investigated. The oxidation and reduction environment are adjusted and controlled with rotate speed and sodium sulfite, respectively. In the first stage, single effect factors such as initial pH (pHi), current density (j), electrode distance (d), rotate speed (n), Na2SO3 dosage (CS), and initial Cd2+ concentration (C0) in RDEC process are evaluated using iron disc as sacrificial electrodes. Results indicate that initial pH, current density, and Na2SO3 dosage have a positive influence on Cd2+ removal, whereas rotate speed and electrode distance exhibit an opposite affect. The increase of Cd2+ removal efficiency at low rotate speed or high Na2SO3 dosage might be attributed to the generation of Fe(II)–Fe(III) layered double hydroxides (LDHs), which is identified to be an vital intermediatein on the removal of cadmium because of an oxygen–free solution environment is maintained. Finally, Response Surface Methodology (RSM) is carried out to investigate the interactional influences between current density, rotate speed, and Na2SO3 dosage and the optimal parameters based on the result of single factor experiments. The actual removal efficiency of Cd2+ and energy consumption (EEC) are respectively calculated as 98.11% and 2.98 kW h/m3 at the optimum operating conditions: C0 25 mg/L, pHi 7, j 10.85 mA/cm2, CCl 1000 mg/L, CS 114.5 mg/L, d 20 mm, and n 44.16 rpm.

A 3D chip geometry driven predictive method for heat-loading performance of hob tooth in high-speed dry hobbing

High-speed dry hobbing is an emerging green technique for gear hobbing owing to its high productivity and environmental friendliness. However, severe friction occurs and heat instantaneously accumulates in the cutting region due to the adoption of high cutting speed and the lack of metalworking fluids, which causes serious hob wear. This investigation is concerned with the thermal performance and heat-loading quantitative method of hob cutter in high-speed dry hobbing. According to the multiple-edge and intermittent cutting behavior, the temporal dynamic characteristics of hob cutter are analyzed. Based on the complex 3D chip geometry produced in high-speed dry hobbing, the calculation method for heat-production (the amount of generated cutting heat) of hob tooth is proposed. Considering the coupling effects of heat conduction, heat convection, and heat radiation, the prediction model for heat-loading quantity (the bearing capacity of cutting heat) of hob tooth is developed. With the help of the developed method, the interrelation between the thermal performance of hob cutter and material removal volume, cutting conditions, coating material, respectively, are investigated. The predictive method developed in this work provides a methodology for process parameter optimization and hob cutter manufacturing of high-speed dry hobbing.

Electromechanical Sigma–Delta Modulators (<inline-formula> <tex-math notation="LaTeX">$\Sigma \Delta {\mathrm{ M}}$ </tex-math> </inline-formula>) Force Feedback Interfaces for Capacitive MEMS Inertial Sensors: A Review

Analog-to-digital converters based on sigma–delta modulators ( $\Sigma \Delta {\mathrm{ M}}$ ) are a popular choice for high resolution conversion from the analog to the digital domain. With relatively small modifications, they can also be used as electromechanical $\Sigma \Delta {\mathrm{ M}}$ (EM- $\Sigma \Delta {\mathrm{ M}}$ ) force feedback interfaces for capacitive micro-electromechanical systems (MEMSs) inertial sensors. Such interfaces are able to combine the benefits of force feedback and analog-to-digital conversion at relatively modest circuit cost. This paper provides a comprehensive review of EM- $\Sigma \Delta {\mathrm{ M}}$ interfaces for capacitive MEMS inertial sensors. The principle and the design methodology of EM- $\Sigma \Delta {\mathrm{ M}}$ interfaces are introduced. A classification of EM- $\Sigma \Delta {\mathrm{ M}}$ accelerometers and gyroscopes is presented, and a detailed analysis of different EM- $\Sigma \Delta {\mathrm{ M}}$ architectures is given. The most representative EM- $\Sigma \Delta {\mathrm{ M}}$ inertial sensors systems are discussed and compared with regard to their performance characteristics. In particular, the properties of various discrete and continuous-time techniques and a system parameter optimization methodology are illustrated through specific examples. Finally, current challenges and future development trends of EM- $\Sigma \Delta {\mathrm{ M}}$ interfaces for inertial sensors have been identified.

Experimental DC Extraction of the Base Resistance of Bipolar Transistors: Application to SiGe:C HBTs

This paper presents an improved variant of a dc method to experimentally evaluate the base resistance of a bipolar transistor. The technique relies on a device model associated with a simple parameter optimization methodology, and is suited for modern technologies wherein self-heating and impact-ionization effects play a relevant role. The approach is successfully applied to state-of-the-art SiGe:C heterojunction bipolar transistors for high-frequency applications, although it can in principle be exploited for any bipolar device. The accuracy of the method is verified by numerical and experimental procedures.