Design Problem Research Articles

Basic Problems in Stability and Design of Switched Systems [25 Years Ago

Basic Problems in Stability and Design of Switched Systems [25 Years Ago

An adaptive gravitational search algorithm for optimizing mechanical engineering design and machining problems

The gravitational search algorithm (GSA) is widely used for solving optimization problems because it performs in a superior manner as compared to various competing evolutionary and swarm-based metaheuristics. However, GSA frequently gets trapped in local optima due to a lack of solution diversity. Although chaotic gravitational search algorithm (CGSA) can resolve this issue to some extent but its degraded exploitation rate and convergence speed may not result in desired outcome. To this end, diversity-based chaotic GSA (DCGSA) has exhibited its capability to resolve the issues encountered with GSA and CGSA to a certain extent for unconstrained problems. DCGSA achieves this characteristic through the use of an adaptive gravitational constant, which varies according to the diversity values of the population. Since most real-world problems are subjected to some constraints, it is prudent to improve the performance of GSA in solving constrained optimization problems. The present study integrates the enhanced search capability of DCGSA with a generalized constraint handling mechanism to improve the performance of GSA in solving constrained problems. It is observed that DCGSA significantly outperforms GSA on both CEC (Congress on evolutionary computation) 2006, 2010 and 2017 functions and competes strongly with CGSA. Diversity analysis shows that the capability to balance exploration and exploitation rates is enhanced using CGSA and DCGSA. Furthermore, DCGSA algorithms outperform GSA and CGSA on real-world machining and CEC 2020 mechanical design problems. Comparison with state-of-the-art algorithms is made to analyze the performance of the algorithms from a larger perspective.

Control problems in the coefficients and the domain for linear elliptic equations

In the present work we are interested in an optimal design problem for a linear elliptic state equation with a homogeneous boundary Dirichlet condition. The control variables correspond to the coefficients of the diffusion term and the open set where the equation is posed. From the application point of view these variables represent the layout of the materials composing the corresponding domain and its shape. We obtain a relaxed formulation of the problem, the optimality conditions, and we provide a numerical algorithm to solve it. Some numerical simulations are also carried out.

Chaotic artificial hummingbird algorithm applied to elementary machine design problems

Chaotic artificial hummingbird algorithm applied to elementary machine design problems

Wind farm power optimization using system identification

The wake effect reduces the total power production of wind farms. This paper presents a method for wind farm power optimization through wake effect reduction. The proposed method optimizes the yaw angle offsets and de-rating settings of all turbines to maximize total power generation. The optimization approach is gradient-based, with gradients at each iteration obtained through system identification using field test data, eliminating the need for physical models. In system identification, test signal design, model estimation and model validation problems are solved in a systematic manner; in the gradient-based optimization, in order to achieve fast convergence, methods for initial value and initial step-size determination, variable step-size iteration and iteration termination are developed. The method is verified using the FLORIS wind farm model developed by National Renewable Energy Laboratory (NREL), USA. The studied wind farm consists of 80 wind turbines configured similarly to the Horns Rev I offshore wind farm in Denmark. The result of the developed optimization method is highly consistent with those obtained using FLORIS's built-in optimization tool.

A Novel Tourist Trip Design Problem with Stochastic Travel Times and Partial Charging for Battery Electric Vehicles

This study proposes a novel mathematical model for the Multi-Day Tourist Trip Design Problem with Stochastic Travel Time and Partial Charging for Battery Electric Vehicle (MD-TTDP-STT-PCBEV). To the best of our knowledge, no prior study has fully incorporated the use of BEVs into TTDP models. Given the limited driving range of BEVs, the model requires decisions regarding the locations and policy for recharging the vehicle’s battery. The problem also incorporates real-world uncertainty by considering travel time as a random variable subjected to normal distribution. The model is formulated using chance-constraint programming, aiming to find optimal tourist routes for BEVs that maximize tourist satisfaction. Numerical experiments were conducted to compare solutions between stochastic and deterministic environments. Computational experiments using the LINGO optimization solver demonstrated that the total rating scores obtained from the stochastic model with chance-constraint programming were generally lower than those from the deterministic model due to travel time uncertainties. These results highlight the importance of incorporating real-world uncertainty and variability to achieve more accurate and reliable planning.

Nanophotonic structure inverse design for switching application using deep learning

Switching functionality is pivotal in advancing communication systems, serving as a paramount mechanism. Despite numerous innovations in this field, optical switch design, fabrication, and characterization have traditionally followed an iterative approach. Within this paradigm, the designer formulates an informed conjecture regarding the switch's structural configuration and subsequently resolves Maxwell's equations to ascertain its performance. Conversely, the inverse problem, which entails deriving a switch geometry to achieve a targeted electromagnetic response, continues to pose formidable challenges and necessitates substantial time and effort, particularly under the constraints of specific assumptions. In this work, we propose a deep neural network-based method to approximate the spectral transmittance of all-optical switches. The findings substantiate the efficacy of deep learning in the design of all-optical plasmonic switches, which are renowned as the fastest switches at the nanoscale. The nonlinear Kerr effect in square resonators is leveraged to demonstrate the switching performance. Juxtaposed with conventional simulations, the proposed model showcases a remarkable improvement in computational efficiency. Furthermore, deep learning can resolve nanophotonic inverse design problems without reliance on trial-and-error or empirical strategies. Compared to simulations, the mean squared error for both forward and inverse models is meager, with values of around 0.03 and 0.02, respectively. The deep learning-proposed switches exhibit excellent suitability for integration into photonic integrated circuits, substantially influencing the progression of all-optical signal processing.

Using a simulator of Minigrid modes to solve design problems

Using a simulator of Minigrid modes to solve design problems

Chirped apodized fiber Bragg gratings inverse design via deep learning

Overcoming inverse design problems in fiber Bragg gratings (FBGs) can be challenging due to the significant nonlinearity of the problem and the intricate relationship between structural properties and optical characteristics. Here, we present a novel artificial intelligence-based approach that effectively addresses these challenges. We introduce a methodology centered on applying deep learning (DL) to estimate the reflective spectrum of FBGs. The results highlight DL’s exceptional capability in designing chirped apodized FBGs, with our model demonstrating significantly enhanced computational efficiency relative to traditional numerical simulations. Notably, our DL-based approach exhibits the remarkable ability to tackle the inverse design challenges of FBGs, thereby eliminating the reliance on trial-and-error or empirical methodologies. The predictive losses for both the forward and inverse models are impressively minimal, with low loss values of 2.2 × 10-2 and 1.6 × 10-2, respectively. The FBG configurations derived via DL are ideally suited for optical communications, heralding significant advancements in all-optical signal processing.

Service Designing Together for Better International Employee Experiences : An example from health and social care services in Finland

The World Health Organization (WHO) estimates that the world needs 4.5 million new nurses by 2030.1 Nurses are a critical touchpoint of health and social care services. Without them, the services could not exist as we know them. The nurse shortage is a vicious service design problem.

SparkDWM: a scalable design of a Data Washing Machine using Apache Spark

Data volume has been one of the fast-growing assets of most real-world applications. This increases the rate of human errors such as duplication of records, misspellings, and erroneous transpositions, among other data quality issues. Entity Resolution is an ETL process that aims to resolve data inconsistencies by ensuring entities are referring to the same real-world objects. One of the main challenges of most traditional Entity Resolution systems is ensuring their scalability to meet the rising data needs. This research aims to refactor a working proof-of-concept entity resolution system called the Data Washing Machine to be highly scalable using Apache Spark distributed data processing framework. We solve the single-threaded design problem of the legacy Data Washing Machine by using PySpark's Resilient Distributed Dataset and improve the Data Washing Machine design to use intrinsic metadata information from references. We prove that our systems achieve the same results as the legacy Data Washing Machine using 18 synthetically generated datasets. We also test the scalability of our system using a variety of real-world benchmark ER datasets from a few thousand to millions. Our experimental results show that our proposed system performs better than a MapReduce-based Data Washing Machine. We also compared our system with Famer and concluded that our system can find more clusters when given optimal starting parameters for clustering.

Inverse-designed taper configuration for the enhancement of integrated 1 × 4 silicon photonic power splitters

Abstract Once light is coupled to a photonic chip, its efficient distribution in terms of power splitting throughout silicon photonic circuits is very crucial. We present two types of 1 × 4 power splitters with different splitting ratios of 1:1:1:1 and 2:1:1:2. Various taper configurations were compared and analyzed to find the suitable configuration for the power splitter, and among them, parabolic tapers were chosen. The design parameters of the power splitter were determined by means of solving inverse design problems via incorporating particle swarm optimization that allows for overcoming the limitation of the intuition-based brute-force approach. The front and rear portions of the power splitters were optimized sequentially to alleviate local minima issues. The proposed power splitters have a compact footprint of 12.32 × 5 μm2 and can be fabricated through a CMOS-compatible fabrication process. Two-stage power splitter trees were measured to enhance reliability in an experiment. As a result, the power splitter with a splitting ratio of 1:1:1:1 exhibited an experimentally measured insertion loss below 0.61 dB and an imbalance below 1.01 dB within the bandwidth of 1,518–1,565 nm. Also, the power splitter with a splitting ratio of 2:1:1:2 showed an insertion loss below 0.52 dB and a targeted imbalance below 1.15 dB within the bandwidth of 1,526–1,570 nm. Such inverse-designed power splitters can be an essential part of many large-scale photonic circuits including optical phased arrays, programmable photonics, and photonic computing chips.

Toward A Comprehensive Framework for Accessibility Measures for Movement-Challenged Persons

Access is one of the most important rights for persons with disabilities (PWDs), particularly for movement-challenged persons (MCPs). To adopt appropriate policy measures that ensure this right for MCPs, it is necessary to evaluate accessibility appropriately. There are, however, very few accessibility measures applicable to MCPs. The current study proposes a framework for a novel comprehensive accessibility measure for MCPs (CAMMCP) by integrating parameters of universal design and the impact of mode-specific challenges faced by MCPs with existing accessibility measures following an origin-to-destination approach. The study presents a literature review of existing accessibility measures and suggests approaches to modify measures to suit mobility challenges faced by MCPs. Modified accessibility measures have been integrated with other parameters related to universal design and mode-specific problems. Universal-design-related parameters will include ramp slope and curb cuts, which can be measured through physical survey. Information on the impact of mode-specific challenges to MCP accessibility can be collected through questionnaires and participatory survey methods. The study also suggests approaches to modifying existing accessibility measures that do not differentiate between mobility constraints for MCPs and non-MCPs when evaluating accessibility. CAMMCP will be helpful for transportation policymakers taking the initiative to improve built-environment accessibility for MCPs as well as for evaluating projects through the lens of transport equity. It can be particularly helpful in determining walkability and transit-service accessibility. Hopefully, it will help build better road networks and transportation modes to develop a sustainable and inclusive transportation system.

Approximations for the Steiner Multicycle problem

The Steiner Multicycle problem consists in, given a complete graph, a weight function on its edges, and a collection of pairwise disjoint non-unitary sets called terminal sets, finding a minimum weight collection of vertex-disjoint cycles in the graph such that, for every terminal set, all of its vertices are in a same cycle of the collection. This problem generalizes the Traveling Salesman problem and, therefore, is hard to approximate in general. On the practical side, it models a collaborative less-than-truckload problem with pickup and delivery locations. Using an algorithm for the Survivable Network Design problem and T-joins, we obtain a 3-approximation for the metric case, improving on the previous best 4-approximation. Furthermore, we present an (11/9)-approximation for the particular case of the Steiner Multicycle in which each edge weight is 1 or 2. This algorithm can be adapted to obtain a (7/6)-approximation when every terminal set contains at least four vertices. Finally, we devise an O(lg⁡n)-approximation algorithm for the asymmetric version of the problem.

Reinforced Lyapunov controllers for low-thrust lunar transfers

Future missions to the Moon and beyond are likely to involve low-thrust propulsion technologies due to their propellant efficiency. However, these still present a difficult trajectory design problem, owing to the near continuous thrust, lack of control authority and chaotic dynamics. Lyapunov control laws can generate sub-optimal trajectories for such missions with minimal computational cost and are suitable for feasibility studies and as initial guesses for optimisation methods. In this work a Reinforced Lyapunov Controller is used to design optimal low-thrust transfers from geostationary transfer orbit towards lunar polar orbit. Within the reinforcement learning (RL) framework, a dual-actor network setup is used, one in each of the Earth- and Moon-centred inertial frames respectively. A key contribution of this paper is the demonstration of a forwards propagated trajectory, removing the need to define a patch point a priori. This is enabled by an adaptive patch distance and extensive initial geometry exploration during the RL training. Results for both time- and fuel-optimal transfers are presented, along with a Monte Carlo analysis of the robustness to disturbances for such transfers. Phasing is introduced where necessary to aid rendezvous with the Moon. The results demonstrate the potential for such techniques to provide a basis for the design and guidance of low-thrust lunar transfers.

An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems

Aiming at the problems of insufficient ability of artificial COA in the late optimization search period, loss of population diversity, easy to fall into local extreme value, resulting in slow convergence and lack of exploration ability; In this paper, an improved COA algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy and alert updating strategy is proposed to enhance the performance of COA (shorted as TNTWCOA). The algorithm introduces chaotic sequence mechanism to initialize the position. The position distribution of the initial solution is more uniform, the high quality initial solution is generated, the population richness is increased, and the problem of poor quality and uneven initial solution of the Coati Optimization Algorithm is solved. In exploration phase, the nonlinear inertial weight factor is introduced to coordinate the local optimization ability and global search ability of the algorithm. In the exploitation phase, adaptive T-distribution variation is introduced to increase the diversity of individual population under low fitness value and improve the ability of the algorithm to jump out of the local optimal value. At the same time, the alert update mechanism is proposed to improve the alert ability of COA algorithm, so that it can search within the optional range. When Coati is aware of the danger, Coati on the edge of the population will quickly move to the safe area to obtain a better position, while Coati in the middle of the population will randomly move to get closer to other Coatis. IEEE CEC2017 with 29 classic test functions were used to evaluate the convergence speed, convergence accuracy and other indicators of TNTWCOA algorithm. Meanwhile, TNTWCOA was used to verify 4 engineering design optimization problems, such as pressure vessel optimization design and welding beam design. The results of IEEE CEC2017 and engineering design Optimization problems are compared with Improved Coati Optimization Algorithm (ICOA), Coati Optimization Algorithm (COA), Golden Jackal Optimization Algorithm (GJO), Osprey Optimization Algorithm (OOA), Sand Cat Swarm Optimization Algorithm (SCSO), Subtraction-Average-Based Optimizer (SABO). The experimental results show that the improved TNTWCOA algorithm significantly improves the convergence speed and optimization accuracy, and has good robustness. Three‑bar truss design problem, The Gear Train Design Problem, Speed reducer design problem shows a strong solution advantage. The superior optimization ability and engineering practicability of TNTWCOA algorithm are verified.

Design of Vibration and Noise Reduction for Ultra-Thin Cemented Carbide Circular Saw Blades in Woodworking Based on Multi-Objective Optimization

Cemented carbide circular saw blades are widely used for wood cutting, but they often suffer from vibration and noise issues. This study presents a multi-objective optimization method that integrates ANSYS and MATLAB to optimize the design of noise reduction slots in circular saw blades. A mathematical model was developed to correlate the emitted sound power with the overall vibration intensity. A multi-objective optimization model was then formulated to map the slot shape parameters to the deformation, equivalent stress, and vibration intensity during sawing. The ABAQUS thermal–mechanical coupling analysis was used to determine the sawing force and temperature field. The NSGA-II algorithm was applied on the ANSYS–MATLAB platform to iteratively compute slot shape parameters and conduct optimization searches for a globally optimal solution. Circular saw blades were fabricated based on the optimization results, and experimental results showed a significant reduction in sawing noise by 2.4 dB to 3.0 dB on average. The noise reduction effect within the specified frequency range closely agreed with the simulation results, validating the method’s efficiency. This study provides a feasible and cost-effective solution to the multi-objective optimization design problem of noise reduction slots for circular saw blades.

Study on the design of gas extraction drilling angle of crossing coal layer drilling in the floor rock roadway considering the boreholes superposition effect

To solve the problem of the borehole design for extracting gas of crossing coal layer drilling in the floor rock roadway and to improve the efficiency of drilling arrangement as much as possible under the premise of eliminating the gap zone of extraction, the formula for calculating the optimal arrangement angle of crossing coal layer drilling in the floor rock roadway was put forward, and the difference of the superposition effect of the holes on the radius of the effective extraction area under the different arrangement modes and spacings was analyzed through numerical simulation. The experimental results show that the optimal angle, volume, and other calculation formulas for drilling arrangement are consistent with the CAD calculation values, verifying the correctness of these calculation formulas. The study discusses the coefficients of variation of effective extraction radius (R) for three types of arrangement, different drill spacing, and the time to reach the two coefficients of variation, 1.05 and 1.03, as a criterion for whether or not to consider the effect of borehole superposition. The results of the tests can provide borehole designers with calculation formulas and provide theoretical references for the R differences that need to be considered in different scenarios.

Developing Surveys on Questionable Research Practices: Four Challenging Design Problems

AbstractThe exposure of scientific scandals and the increase of dubious research practices have generated a stream of studies on Questionable Research Practices (QRPs), such as failure to acknowledge co-authors, selective presentation of findings, or removal of data not supporting desired outcomes. In contrast to high-profile fraud cases, QRPs can be investigated using quantitative, survey-based methods. However, several design issues remain to be solved. This paper starts with a review of four problems in the QRP research: the problem of precision and prevalence, the problem of social desirability bias, the problem of incomplete coverage, and the problem of controversiality, sensitivity and missing responses. Various ways to handle these problems are discussed based on a case study of the design of a large, cross-field QRP survey in the social and medical sciences in Sweden. The paper describes the key steps in the design process, including technical and cognitive testing and repeated test versions to arrive at reliable survey items on the prevalence of QRPs and hypothesized associated factors in the organizational and normative environments. Partial solutions to the four problems are assessed, unresolved issues are discussed, and tradeoffs that resist simple solutions are articulated. The paper ends with a call for systematic comparisons of survey designs and item quality to build a much-needed cumulative knowledge trajectory in the field of integrity studies.

A modified slime mold algorithm for parameter identification of hydrogen-powered proton exchange membrane fuel cells

In the quest for sustainable and efficient energy solutions, hydrogen fuel cells emerge as a beacon of hope, offering a promising pathway towards a greener future. Accurate Identification of the ungiven parameters of proton exchange membrane fuel cell (PEMFC) mathematical models is indispensable for designing, managing, and simulating the practical PEMFC. In order to identify the parameters of PEMFC punctually, this paper presents a modified version of the slime mould algorithm (MSMA). In order to increase capability of the MSMA in the exploitation phase, both locally and globally, the sine-cosine technique has been utilized to boost the search capabilities. To assess the performance of MSMA, MSMA is first utilized to address ten well-known benchmark functions. The obtained results confirm that MSMA outperforms SMA on all benchmark functions. Then, MSMA is employed to solve the optimization problem of different mechanical design problems and also the MSMA provides superior performance over the standard SMA. Finally, the MSMA is used to identify the unknown parameters of four typical PEMFCs: 250W PEMFC, BCS 500W PEMFC, AVISTA SR-12 model, and the Temasek 1 kW PEMFC model. Experimental results boost the supremacy of MSMA in the PEMFC parameters extraction by comparing it with the original SMA and well-known potent optimization techniques. Furthermore, MATLAB/Simulink is employed for advanced dynamic PEMFC modeling, facilitating a comprehensive assessment of fuel cell parameters. The validation of this dynamic PEMFC model, using MSMA-optimized parameters, establishes its practical utility in system analysis and real-world fuel cell operation, marking a significant advancement in PEMFC technology management and simulation.