Numerical Optimization Method Research Articles

STRENGTH DESIGN OF REINFORCED CONCRETE SECTIONS BASED ON A NUMERICAL OPTIMIZATION METHOD

Reinforced concrete structures are widely used in construction of civil infrastructures. Classical methods are available for the analysis and design of reinforced concrete members, such as beams or columns, which are typically done by hand or using a spreadsheet program. In this study, an analysis and design approach of reinforced concrete sections is developed based on a numerical optimization method, and applied in accordance with the latest concrete design code. The basic idea of the proposed approach is to derive an optimization formulation in order to determine the minimum steel area required to support a given factored load combinations applied on a concrete section. A rigorously derived analysis and design framework of general beam sections is presented. For practical applications by civil and structural engineers, the readily-available optimization solver in Microsoft Excel spreadsheet is used. To illustrate the applicability of the approach, a numerical example with different analysis and design cases is studied. The proposed analysis and design approach using numerical optimization and spreadsheet solver works very well and is easy to implement in the Excel environment. The analysis and design results compare well with hand calculation results based on the classical method.

Numerical workspace optimization for a 3-DOF delta parallel robot on constrained geometric solid models

For the workspace optimization of Delta Parallel Robot (DPR), this paper employs Geometric Modeling Kernel (GMK) to construct DPR’s solid workspace model constrained by two categories of angles with non-toroidal boundaries. Leveraging slicing and interpolating techniques, the maximal inscribed regular space within the DPR’s workspace is obtained in conjunction with numerical optimization methods. With the objective of maximizing effective volume utilization (the volume ratio between the prescribed regular space to the DPR’s workspace) and a prescribed regular space dimension, the Hook-Jeeves direct search method is coupled with the Interior-Point method to attain the optimal geometric DPR parameters on the constrained solid model of workspace. The effectiveness of the algorithm is validated through a design case. This numerical optimization methodology possesses generality, intuitiveness, precision, and high efficiency, theoretically applicable to the workspace optimization for other parallel mechanisms.

ТЕОРЕТИЧЕСКИЕ ПРЕДПОСЫЛКИ ОПРЕДЕЛЕНИЯ РАЗМЕРА И ОБЪЁМА РАСПРОСТРАНЕНИЯ ОЧАГА ОЗОНА В ЗЕРНОВОМ ВОРОХЕ И ОПТИМИЗАЦИЯ ВРЕМЕНИ ОЗОНИРОВАНИЯ

One of the most promising methods for processing agricultural products today is the ozonation process. The effect of ozone on grain will allow it to be disinfected and stimulate the growth processes of the seed, without causing harmful emissions into the environment. The purpose of the research was to find the optimal time for ozonation of grain and seeds depending on the processing speed of a certain volume of grain material. To achieve it, numerical optimization methods were used, namely the patternsearch global search method, which will determine the most rational ozonation regime for given conditions, were used to achieve it. The source of ozone propagation in a grain heap during ozonation for 10...60 minutes is close in shape to an ellipsoid, but has a slightly different mathematical description. The mathematical models obtained by interpolation make it possible to determine the minimum ozonation time and the maximum speed of ozone treatment per unit volume of a grain heap. It was revealed that increasing the processed volume of grain due to the longer duration of the operation does not make sense, as it will contribute to an increase in energy and cost costs. Under given conditions, an ozonation time of 1270 seconds provides a maximum ozone treatment rate of 3059 mm3/s per unit volume of grain. Optimization will make it possible to ozone the maximum volume of a grain storage facility as quickly as possible at minimal cost. The shortest processing time per unit volume under these conditions was 0.000327 s/mm3. Other ozonation parameters will have different optimal values, therefore the proposed methodology is the basis of an appropriate computer program that will automatically determine the optimal ozonation regime for specific conditions.

Expedited re-design of multi-band passive microwave circuits using orthogonal scaling directions and gradient-based tuning

Geometry scaling of microwave circuits is an essential but challenging task. In particular, the employment of a given passive structure in a different application area often requires re-adjustment of the operating frequencies/bands while maintaining top performance. Achieving this necessitates the utilization of numerical optimization methods. Nonetheless, if the intended frequencies are distant from the ones at the starting point, local search procedures tend to fail, whereas global search algorithms are computationally expensive. As recently demonstrated, a combination of large-scale concurrent geometry parameter scaling with intermittent local tuning allows for dependable re-design of high-frequency circuits at low CPU costs. Unfortunately, the procedure is only applicable to single-band structures due to synchronized modifications of all operating bands under scaling. This article discusses a novel procedure that leverages a similar overall concept, but allows for independent control of all center frequencies. To achieve this goal, an automated decision-making procedure is developed in which a set of orthogonal scaling directions are determined based on their effect on individual circuit bands, and using auxiliary optimization sub-problems. The scaling range is then automatically computed by solving an appropriately-defined least-square design relocation problem. The methodology introduced in the work is illustrated using two planar passive devices. In both cases, wide-range operating frequency re-design has been demonstrated and favorably compared to conventional gradient-based tuning. Furthermore, the presented procedure has been shown to be computationally efficient. It is also easy to implement and integrate with a variety of gradient-based optimization procedures of a descent type.

Design of Quantum Gates Using Quantum Scattering Theory

Given two Hamiltonians \(H_0,H_1=H_0+V\) corresponding respectively to a free particle Hamiltonian and the free particle Hamiltonian plus its interaction energy with a scattering centre, we can compute the S-matrix, i.e., the matrix that determines the quantum mechanical amplitude of the process involving a particle coming from a free particle state at an infinite distance from the scattering centre at time \(t=-\infty\), entering into an initial scattered state, interacting with the scattering centre, entering into a final scattered state, and then escaping away to infinity at time \(t\rightarrow\infty\) to a final free particle state. Such amplitudes were first studied by Lippmann and Schwinger (Steven Weinberg, The Quantum Theory of Fields, Vol. 1, W.O. Amrein, Hilbert Space Methods in Quantum Mechanics) and then analyzed in a mathematically precise way by [T. Kato], [W. Amrein], and [K.B. Sinha]. In this paper, we first derive the Lippmann-Schwinger equation for the input and output scattered states in terms of the input and output free particle states using intuitive arguments originally due to Lippmann and Schwinger, and then, using rigorous operator theoretic arguments revolving around the spectral theorem for unbounded self-adjoint operators in a Hilbert space, we obtain an elegant formula for the scattering matrix elements in terms of the interaction potential and the free particle Hamiltonian between two free particle states corresponding to the same measure. We then discuss a computationally more efficient method based on the Dyson series expansion (Steven Weinberg, The Quantum Theory of Fields, Vol. 1), which works even in the case when the interaction Hamiltonian is time varying, in contrast to the Lippmann-Schwinger method, for calculating the S-matrix. We assume that the scattering potential, which can be time varying, can be controlled by incorporating control parameters and explain how the resulting unitary S-matrix can be made to approximate a given unitary gate in infinite-dimensional Hilbert space by optimizing the Hilbert-Schmidt/Frobenius norm distance between the given gate and the S-matrix gate, in which the latter is approximated by a truncated Dyson series. Of course, the S-matrix given by the Dyson series is a Taylor series functional expansion in the interaction potential and hence is a highly nonlinear function of the control parameters, and hence we propose numerical methods for such optimization. We then observe that if the control parameters are taken as random variables with joint moments, then the TPCP map involving statistical averaging of the adjoint action of the S-matrix acting on an initial mixed state w.r.t. the probability distribution of the control parameters will result in a final mixed state that is linear in the statistical moments of the control parameters, and hence optimization w.r.t. these moments can be easily carried out. Our method of TPCP map design is to optimize the sum of Frobenius distance squares between a sequence of desired output mixed states and the S-matrix based formula for the TPCP map acting on the corresponding input mixed states w.r.t. the statistical moments of the control parameters. We then generalize this method to include the case of TPCP maps obtained from the Hudson-Parthasarathy quantum noisy Schrödinger equation. In summary, we simulate the S-matrix gate for independent realizations of the control parameters having a probability distribution defined by their joint moments obtained by the above optimization procedure and apply the adjoint action of these independent S-matrices on a given input state, and take their ensemble average to obtain a good approximation to the action of the desired TPCP map on any given input state. This suggests that TPCP maps of arbitrarily large size can be realized using scattering experiments in the laboratory.

Multi-modal Battle Royale optimizer

AbstractMultimodal optimization poses a challenging problem in the field of optimization as it entails the discovery of multiple local and global optima, unlike unimodal optimization, which seeks a single global solution. In recent years, the significance of addressing multimodal optimization challenges has grown due to the real-world complexity of many problems. While numerous optimization methods are available for unimodal problems, multimodal optimization techniques have garnered increased attention. However, these approaches often grapple with a common issue: the determination of the niching parameter, necessitating prior knowledge of the problem space. This paper introduces a novel multimodal optimization approach that circumvents the need for prior problem space knowledge and avoids the challenge of predefining the niching parameter. Building upon the Battle Royal Optimization (BRO) algorithm, this extended version formulates a multimodal solution by utilizing Coulomb's law to identify suitable neighbors. The incorporation of Coulomb's law serves the dual purpose of identifying potential local and global optima based on fitness values and establishing optimal distances from solution candidates. A comparison study was done between the MBRO and seven well-known multimodal optimization algorithms using 14 benchmark problems from the CEC 2013 and CEC 2015 competitions to see how well it worked. The experimental results underscore MBRO's proficiency in successfully identifying most, if not all, local and global optima, positioning it as a superior solution when compared to its competitors.

Adjustment of mutant vector elements in the differential evolution method for solving the problem of power shortage minimization in electric power systems

The article is aimed at increasing the effectiveness of numerical optimization methods in calculations of problems concerning power shortage minimization in electric power systems, specifically the differential evolution (DE) method and its variations–aDE and jDE. Experimental studies and testing of the proposed adjustments were carried out on complex electric power systems of different order. These systems are represented and realized by means of mathematical models of power shortage minimization with the possibility of their analysis using the developed software package as part of adequacy assessment. The performed analysis of the DE method elements and the existing variants of the mutation process revealed that the existing approaches can be further modified. This can subsequently increase the speed of problem-solving. It is shown that the main changes include an additional check that the mutant vectors meet the upper and lower bounds, and if they fail to do so, three adjustment options are considered. Existing approaches propose to generate new vector elements beyond the bounds by applying random numbers within the bounds. The present authors propose to use the “projections” of the found vector elements, i.e., to use the values of upper or lower bounds when they are exceeded for a particular element as mutant vector values. The implemented method involving the adjustment of mutant vector elements is shown to offer an advantage of a 47% reduction in problem-solving time over existing adjustment methods while maintaining the same accuracy. It is noted that aDE and jDE are the most effective variations for solving stated problems. The obtained results of experimental studies confirm the effectiveness and advantages of applying the proposed adjustment method in mutation process of in the form of “projections”, as well as using aDE and jDE variations of the DE method to solve the problems of power shortage minimization in electric power systems.

Optimization design and experimental research on cooling performance of U-shaped latticework cooling structure with perforations used for gas turbine blade

Latticework cooling structure provides great structural strength and well heat transfer enhancement commonly used for internal cooling of turbine blades. However, traditional rectangle latticework cooling structure raises friction loss badly and reduces cooling efficiency of turbine blades due to the increase of wetted surface area. A new latticework cooling structure with U-shaped sub-channel and perforations on raffles was proposed, aiming to promote heat transfer and reduce friction loss for latticework cooling structure. Numerical simulation, optimization method and experimental test are conducted to investigate heat transfer and fluid flow characteristics of this new latticework cooling structure. Optimization design based on response surface approximation model and non-dominated sorting genetic algorithms-Ⅱ was conducted and objectives were to maximize heat transfer and minimize friction loss. The optimal U-shaped latticework cooling structure with perforations was obtained and experimental research was conducted. Characteristics of heat transfer and friction loss between the optimal U-shaped latticework cooling structure with perforations and the rectangle latticework cooling structure were compared and analyzed. The experiment results showed that the overall heat transfer and thermal–hydraulic performances of the optimal U-shaped latticework cooling structure with perforations are 21.6–37.0% and 27.9–46.7% respectively higher than those of the rectangle latticework cooling structure while friction loss performance of the former is 16.3–29.2% lower than that of the latter as Reynolds number increases from 5000 to 50000. Moreover, empirical formulas for the optimal U-shaped latticework cooling structure with perforations and the rectangle latticework cooling structure are obtained. The present study indicates that the U-shaped latticework cooling structure with perforations not only maintains the superior heat transfer performance, but also reduces friction loss, which is mainly due to the perforation ribs reduce flow distance for part of coolant and also provide an impingement effect for the heated wall and enhance flow interactions and flow mixing.

Design and Characterization of Hallow Porous Floating Microspheres of Favipiravir

The present investigation deals with the design and characterization of favipiravir floating microspheres for gastro retentive drug delivery system by employing 3² factorial design and also to investigate the main effects of different independent variables on microspheres that float. The emulsion solvent diffusion method was used to manufacture floating microspheres with Eudragit S 100 as the polymer. These microspheres will extend the release of the medicine, reducing the frequency of administration and the harmful effects caused by fluctuations in plasma concentration with conventional dosage forms. The main effect of independent variables like polymer Eudragit S 100 concentrations (50, 100, 150 mg) and stirring time (1, 2 and 3 hours) on the performance of microspheres. Formulated microspheres were characterized for responses including drug release, particle size, and entrapment efficiency and floating time. Based on the results of the responses, the optimized composition was arrived using the response surface method graphical and numerical optimization method using design of experiments (DoE) software. Then, the optimized formulation (OFES) was prepared and evaluated for the four responses compared with predicted values to find the validity of the selected model. The optimized formulation was further analyzed for drug-excipient compatibility by fourier-transform infrared (FTIR), differential scanning calorimetry (DSC), and also analyzed by X-ray diffraction (XRD), scanning electron microscope (SEM) analysis. Further zeta potential and micrometric properties were also observed. The results for the formulation FES 1 to 9 found were, that particles size ranged from 0.192 to 0.277 μm, entrapment efficiency ranged from 68.65 ± 1.9 to 76.25 ± 3.2%, percentage drug release range was from 89.25 ± 0.24 to 93.68 ± 0 .25%, and floating time range was from 12 to 23 hours. As per the design, OFES an optimized formulation fitted with the concentration of Eudragit S 100 of 139.1 mg and stirring time of 1-hour. Regarding the kinetic release, the responses were best fitted with Higuchi model of R² value of 0.9795 with kinetic mechanism of non-fickian diffusion with R² value of 0.9499, indicating good linearity. The combination of different variables and their effects on responses were investigated well using factorial design, and an optimized formulation was developed for favipiravir.

Optimizing the Finishing Treatment of Potato Harvester Transmission Shafts

The potato harvester’s distinctive specifications include the spatially segregated drive of its units from an external engine, operating under stochastic conditions. This setup complicates the loading of its components, including transmission shafts, and creates the prerequisites for decreased durability, leading to an upsurge in fatigue failures. To counteract this process, transmission shafts undergo a special treatment aimed at mitigating concentrated defects. (Research purpose) The purpose of this research is to formulate an objective function for controlling the finishing treatment of transmission shafts in a potato harvester. This function is designed to maximize potato yield while monitoring concentrated defects. (Materials and methods) The implementation of the method requires equipment capable of being controlled through a technical vision channel. The control scheme employs mathematical tools for describing discrete and continuous random variables, Markov processes with discrete time and continuous state space, the maximum likelihood method, and methods of numerical optimization in multifactor space. (Results and discussion) The research has yielded formulas for calculating the probabilities of successful and unsuccessful outcomes of finishing treatment based on the criterion of concentrated defect presence. The paper provides the results of testing the proposed algorithm for predicting the yield of viable products using the proposed method based on interim measurements of defect characteristics during the treatment process. Additionally, a scheme is proposed for integrating the obtained results into the production process of manufacturing transmission shafts for potato harvesters. (Conclusions) An algorithm has been proposed to control the finishing treatment of the transmission shaft based on a predictive assessment of the probability of manufacturing a product that adheres to the specified technical criteria regarding geometry and surface cleanliness. The predictive probability of achieving a surface with the required cleanliness without exceeding permissible defect limits is considered as the objective function. The solution to the problem is achieved through the selection of appropriate technological parameters.

GSO-Net: Grid Surface Optimization via Learning Geometric Constraints

In the context of surface representations, we find a natural structural similarity between grid surface and image data. Motivated by this inspiration, we propose a novel approach: encoding grid surfaces as geometric images and using image processing methods to address surface optimization-related problems. As a result, we have created the first dataset for grid surface optimization and devised a learning-based grid surface optimization network specifically tailored to geometric images, addressing the surface optimization problem through a data-driven learning of geometric constraints paradigm. We conduct extensive experiments on developable surface optimization, surface flattening, and surface denoising tasks using the designed network and datasets. The results demonstrate that our proposed method not only addresses the surface optimization problem better than traditional numerical optimization methods, especially for complex surfaces, but also boosts the optimization speed by multiple orders of magnitude. This pioneering study successfully applies deep learning methods to the field of surface optimization and provides a new solution paradigm for similar tasks, which will provide inspiration and guidance for future developments in the field of discrete surface optimization. The code and dataset are available at https://github.com/chaoyunwang/GSO-Net.

Advanced Optimal Control Problem and Numerical Method for its Solving

The advanced statement of the optimal control problem is presented. The difference between the extended setting of the problem and the classical one is that the model of the control object consists of two subsystems, a reference model, which generates an optimal motion path and a dynamic model of the control object with a system for stabilizing movement along the optimal trajectory. In the problem, it is necessary to find a program control function whose argument is time and a stabilization system function whose argument is the deviation of the state vector of the control object from the optimal program trajectory. The task has many initial conditions, one of which is used in the search for software control, and the rest for the search for a stabilization system. The control quality criterion is defined as the sum of the original quality criterion for all specified initial conditions. The procedure for trans forming the classical setting of the optimal control problem to an extended setting based on the refinement of the problem for its practical implementation is presented. To solve the extended optimal control problem, a universal numerical method is proposed based on a piecemeal linear approximation of the control function using evolutionary algorithms and symbol regression methods for structurally parametric optimization of the stabilization system function. An example of solving an extended optimal control problem for spatial motion by a quadcopter, which should conduct reconnaissance of a given territory in a minimum time, is given.

Efficient Algorithm Set Forming for the Computing Resources Distribution in Heterogeneous Dynamic Computational Environments Based on the Ontology Usage

Purpose of research. The purpose of this research is to develop an ontology structure as the basis of a database/knowledge base for selecting effective metaheuristic algorithms for solving the problem of load distribution in heterogeneous distributed dynamic computing environments, taking into account the overhead of data transmission over the network.Methods. The main scientific methods used in this study are domain analysis, methods for constructing subject ontologies, numerical optimization methods and computer modeling.Since the literature does not present resource allocation planning models that would take into account geographic distribution, the presence of intermediate data transmission routes, the dynamics of topologies and load, as well as system heterogeneity in terms of criteria for assessing the quality of load distribution, this article proposes a new model that takes into account these features. The complexity of solving a planning problem becomes one of the variable parameters, which has a significant impact on the planning result: with a decrease in the complexity of calculations, the result deteriorates accordingly. Therefore, a greedy strategy is proposed as a solution method: from the optimization methods to be considered, select the least labor-intensive one that would allow obtaining the best result in the allotted time. Test runs of simulated annealing algorithms demonstrate different effectiveness under different initial conditions of the problem; therefore, it is advisable for selected classes of problems to choose algorithms that are effective in terms of solution quality and labor intensity.Results. The result of the study is the structure of the ontology of effective algorithms. Also, the results are instances of simulated annealing algorithms and tasks included in the ontology, related by the “efficiency” relation.Conclusion. This article proposes the structure of an ontology of effective optimization algorithms and an approach to solving the problem of distributing the computational load, taking into account the complexity of the distribution procedure through the “greedy” selection of the most effective optimization algorithms.

Analyzing Motorcycle Accident Frequency Using Generalized Poisson Distributions

Motorcycle accidents in East Java are more common than accidents in other modes of transportation. In addition to the many motorcycle users today, human, environmental, and road factors are considered the highest causes of these accidents. The study's goal is to find the best model of the Generalized Poisson Family Distribution (GPR), namely Lagrangian Poisson Regression (LPR) and to construct a model that will quantify the frequency of motorcycle accidents in East Java. Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) criteria are the model comparison methods used in this research. The selection was also made based on the model's exponential coefficient with a 95% CI to further deepen the selection results obtained. In addition, the paired samples test was performed to determine the degree of dissimilarity between the outcomes produced by the developed model and the actual data. The best performance model is applied to identify the characteristics or factors highly involved in motorcycle accidents. The research uses secondary data from related agencies, namely the East Java Regional Police, especially the traffic accident unit, and East Java BPS, for 38 cities and districts in 2021. The numerical optimization method used is the iteratively reweighted least squares (IRLS) algorithm, assisted by R Studio software. The study findings show that LPR is the most efficient and exact approach for modeling the frequency of motorcycle accidents. Meanwhile, the percentage of teenagers (X1), the frequency of motorized vehicles (X3), and the average annual rainfall (X5) have a considerable impact on accident occurrence. This research has an important contribution, especially in the field of transportation modeling and designing appropriate strategies to reduce the frequency of motorcycle accidents.

Optimal constrained design of control charts using stochastic approximations

In statistical process monitoring, control charts typically depend on a set of tuning parameters besides its control limit(s). Proper selection of these tuning parameters is crucial to their performance. In a specific application, a control chart is often designed for detecting a target process distributional shift. In such cases, the tuning parameters should be chosen such that some characteristic of the out-of-control (OC) run length of the chart, such as its average, is minimized for detecting the target shift, while the control limit is set to maintain a desired in-control (IC) performance. However, explicit solutions for such a design are unavailable for most control charts, and thus numerical optimization methods are needed. In such cases, Monte Carlo-based methods are often a viable alternative for finding suitable design constants. The computational cost associated with such scenarios is often substantial, and thus computational efficiency is a key requirement. To address this problem, a two-step design based on stochastic approximations is presented in this paper, which is shown to be much more computationally efficient than some representative existing methods. A detailed discussion about the new algorithm’s implementation along with some examples are provided to demonstrate the broad applicability of the proposed methodology for the optimal design of univariate and multivariate control charts. Computer codes in the Julia programming language are also provided in the supplemental material.

Static and Dynamic Analysis of Coupled Vibration of Suspension Bridge Structure Under Earthquake Action

This article investigates the coupled vibration problem of suspension bridge structures under earthquake action, and conducts static and dynamic analysis using computer algorithms. In terms of static analysis, complex structures are divided into many small elements, and the stress and deformation of each element are calculated through finite element analysis, thereby obtaining the static characteristics of the entire structure. In terms of dynamic analysis, the seismic response analysis method based on time history considers seismic action as a time-varying external excitation, and calculates and simulates the dynamic response of the suspension bridge structure. In order to achieve efficient and accurate calculations, this article also adopts optimization algorithms and numerical calculation methods. Through a comprehensive study of statics and dynamics analysis, the key characteristics and regularities of coupled vibration of suspension bridge structures under earthquake action have been obtained. These research results not only provide important theoretical guidance and technical support for related engineering practices, but also provide useful references and inspirations for dynamic analysis and optimization design of similar structures. Meanwhile, using computer algorithms and simulation software for static and dynamic analysis can significantly reduce analysis costs and time, improve analysis accuracy and efficiency, and have broad application prospects.

Investigation in optimization of process parameters in turning of mild steel using response surface methodology and modified deep neural network

The turning operation is a traditional and conventional process for machining cylindrical materials to achieve a desired shape. During machining in the turning process, the accuracy and quality of the end product mainly depend on its process parameters. Therefore, this study attempts to achieve a quality product from a conventional lathe machine by optimizing its process parameters for En2-BS970 mild steel. The experiments were designed with the help of response surface methodology (RSM). The most influential turning parameters, such as feed rate, spindle speed, cutting fluid flow rate, and cutting angle, are investigated experimentally. In addition, the machining responses, namely material removal rate (MRR), surface roughness (SR), cutting force (CF), and cutting time (CT), have been optimized using the RSM numerical optimization method. Furthermore, a deep neural network (DNN) machine learning prediction model based on the whale optimization algorithm (WOA) is developed for this experiment in order to predict turning performances for En2-BS970 material. The predicted DNN results showed an accuracy of about 90% compared to experimental results, indicating that the implementation of WOA significantly optimized the DNN weights during training. The RSM optimized responses are obtained as 14.608 mm3/min for MRR, 0.7504 µm for SR, 442.94 N for CF, and 2.48 seconds for CT during the turning operation with input settings of 0.24 mm/rev, 466.535 rpm, 0.075 ml/s and 60.18° for feed rate, spindle speed, cutting fluid flow rate and cutting edge angle respectively.

Design and Analysis of Optomechanical Micro-Gyroscope for Angular-Vibration Detection

Micro-gyroscopes based on the Coriolis principle are widely employed in inertial navigation, motion control, and vibration analysis applications. Conventional micro-gyroscopes often exhibit limitations, including elevated noise levels and suboptimal performance metrics. Conversely, the advent of cavity optomechanical system technology heralds an innovative approach to micro-gyroscope development. This method enhances the device’s capabilities, offering elevated sensitivity, augmented precision, and superior resolution. This paper presents our main contributions which include a novel dual-frame optomechanical gyroscope, a unique photonic crystal cavity design, and advanced numerical simulation and optimization methods. The proposed design utilizes an optical cavity formed between dual oscillating frames, whereby input rotation induces a measurable phase shift via optomechanical coupling. Actuation of the frames is achieved electrostatically via an interdigitated comb-drive design. Through theoretical modeling based on cavity optomechanics and finite element simulation, the operating principle and performance parameters are evaluated in detail. The results indicate an expected angular rate sensitivity of 22.8 mV/°/s and an angle random walk of 7.1 × 10−5 °/h1/2, representing superior precision to existing micro-electromechanical systems gyroscopes of comparable scale. Detailed analysis of the optomechanical transduction mechanism suggests this dual-frame approach could enable angular vibration detection with resolution exceeding state-of-the-art solutions.

Distributed Swarm Trajectory Planning for Autonomous Surface Vehicles in Complex Sea Environments

In this paper, a swarm trajectory-planning method is proposed for multiple autonomous surface vehicles (ASVs) in an unknown and obstacle-rich environment. Specifically, based on the point cloud information of the surrounding environment obtained from local sensors, a kinodynamic path-searching method is used to generate a series of waypoints in the discretized control space at first. Next, after fitting B-spline curves to the obtained waypoints, a nonlinear optimization problem is formulated to optimize the B-spline curves based on gradient-based local planning. Finally, a numerical optimization method is used to solve the optimization problems in real time to obtain collision-free, smooth and dynamically feasible trajectories relying on a shared network. The simulation results demonstrate the effectiveness and efficiency of the proposed swarm trajectory-planning method for a network of ASVs.

Vertical Parking Trajectory Planning With the Combination of Numerical Optimization Method and Gradient Lifting Decision Tree

Vertical Parking Trajectory Planning With the Combination of Numerical Optimization Method and Gradient Lifting Decision Tree