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In this paper, we investigate the enforcement of opacity via supervisory control in the context of discrete-event systems. A system is said to be opaque if the intruder, which is modeled as a passive observer, can never infer confidentially that the system is at a secret state. The design objective is to synthesize a supervisor such that the closed-loop system is opaque even when the control policy is publicly known. In this paper, we propose a new approach for enforcing opacity using non-deterministic supervisors. A non-deterministic supervisor is a decision mechanism that provides a set of control decisions at each instant, and randomly picks a specific control decision from the decision set to actually control the plant. Compared with the standard deterministic control mechanism, such a non-deterministic control mechanism can enhance the plausible deniability of the controlled system as the online control decision is a random realization and cannot be implicitly inferred from the control policy. We provide a sound and complete algorithm for synthesizing a non-deterministic opacity-enforcing supervisor. Furthermore, we show that non-deterministic supervisors

Low power consumption, real-time computing, and high reliability are three key requirements/design objectives of real-time embedded systems. The standby-sparing technique can improve system reliability while it might increase the temperature of the system beyond safe limits. In this paper, we propose a thermal-aware standby-sparing (TASS) technique that aims at maximizing the Quality of Service (QoS) of soft real-time tasks, which is defined as a function of the finishing time of running tasks. The proposed technique tolerates permanent and transient faults for multicore real-time embedded systems while meeting the Thermal Safe Power (TSP) as the core-level power constraint, which avoids thermal emergencies in on-chip systems. Our TASS proposed method provides an opportunity to remove the overlaps of the execution of main and backup tasks to prevent extra power consumption due to applying the fault-tolerant technique. Meanwhile, in order to maximize the QoS, we employ a heterogeneous platform to execute the main tasks as soon as possible on high-performance cores with more power budget. Experiments show that our proposed method improves QoS up to 39.78% (on average by 18.40%) and reduces the peak power consumption and temperature by up to 40.21% and 15.47C (on average 28.31% and 13.60C), respectively, at runtime, while keeping the system reliability at the required level.

Modern physical design flows highly depend on design space exploration to find the commercial tools’ clock tree synthesis (CTS) parameters that lead to optimized clock trees. However, such exploration is often time-consuming and computationally inefficient. In this paper, we overcome this drawback by proposing a novel framework named GAN-CTS, which utilizes conditional generative adversarial network (GAN) to predict and optimize CTS outcomes. Our framework is built upon three sequential learning stages. First, to precisely characterize distinct designs, we leverage transfer learning to extract netlist features directly from placement images. Second, we perform regression learning using various methods to predict the target CTS outcomes and demonstrate that the proposed multitask learning approach achieves better accuracy than the metamodeling method adopted by previous works. To fully benefit from the predictions made by our framework, we further quantitatively interpret the importance of each CTS input parameter subject to various design objectives through attribution-based learning. Finally, generative adversarial learning is leveraged to optimize the target clock metrics with the guidance provided by the pre-trained regression model. To substantiate the generality of our framework, we perform validations on four unseen netlists that are not utilized in the training process. Experimental results conducted on real-world designs demonstrate that our framework (1) achieves an average prediction error of 3%, (2) improves the commercial tool’s auto-generated clock tree by 20.7% in clock power, 21.5% in clock wirelength, 36.1% in the worst skew, and (3) reaches an F1-score of 0.93 in the classification task of determining successful and failed CTS runs.

In Industrial Internet of Things (IIoT), a number of mutually untrusted devices with diverse computing resources and application requirements often need to work together to serve industrial production. When applying permissionless blockchain to process transactions among these devices, we are concerned with two types of fairness: mining fairness (i.e., miners should obtain commensurate rewards according to their respective invested computing power) and transaction-processing fairness (i.e., transaction packing and confirming is in a desired fair order). To address the two types of fairness simultaneously, this paper proposes GT-Chain, in which miners select transactions according to Geometric-distribution and propagate transactions with a Time-to-live limit for achieving transaction-processing fairness as well as balancing the processing efficiency and fairness. Besides, GT-Chain inherits properties of a famous blockchain called FruitChain for achieving mining fairness. We then conduct a probability analysis to quantify the efficiency of transaction processing and define a Euclidean-distance-based fair degree to measure the fairness of transaction processing. Extensive simulations verify that GT-Chain can well achieve our design objectives, and our theoretical model is accurate. This paper is helpful in better designing blockchain protocols for IIoT.

This paper proposes the tuning approach of the event-triggered controller (ETCTA) for the robotic system stabilization task where the reduction of the stabilization error and the data broadcasting of the control update are simultaneously considered. This approach is stated as a dynamic optimization problem, and the best controller parameters are obtained by using fourteen different bio-inspired optimization algorithms. The statistics results reveal that, among the tested bio-inspired optimization algorithms, the most reliable algorithm in the proposed tuning problem is the differential evolution variant DE/Best/1/Exp. The obtained result is validated both in numerical simulation as well as using a laboratory prototype. The simulation results indicate that the obtained control parameters can also deal with disturbances and reference changes not considered in the ETCTA’s optimization problem formulation without significantly worsening the control design objective. Experimental results disclose that the proposed event-triggered control tuning approach provides the best trade-off between the number of control signal updates and the position error among other tuning approaches, decreasing the data broadcasting of the control update by around 86.33% with a non-significant increase in the stabilization error of around 26.53%.

The design and control of drones remain areas of active research, and here we review recent progress in this field. In this article, we discuss the design objectives and related physical scaling laws, focusing on energy consumption, agility and speed, and survivability and robustness. We divide the control of such vehicles into low-level stabilization and higher-level planning such as motion planning, and we argue that a highly relevant problem is the integration of sensing with control and planning. Lastly, we describe some vehicle morphologies and the trade-offs that they represent. We specifically compare multicopters with winged designs and consider the effects of multivehicle teams.

This paper presents an optimization design method for a double-stator hybrid excited permanent magnet arc motor (DS-HE-PMAM). The proposed optimization method combining the machine learning algorithm random forest (RF) and the nondominated sorting genetic algorithm-II (NSGA-II) contributes to achieving high average torque, low torque ripple, high back electromotive force (EMF), and low total harmonic distortion of the back EMF. First, the motor structure and working principle of the DS-HE-PMAM are illustrated. The selection of parameters to be optimized are determined based on analytical model. Then, a variable importance measures-based new sensitivity analysis method is implemented to evaluate the influence of each structural parameter on the selected design objectives. The finite element analysis (FEA)-based DS-HE-PMAM model is developed to obtain the sample data regarding input structural parameters and output design objectives. Based on the sample data, a powerful machine learning algorithm called RF is employed to fit the function relationship between output design objectives and input structural parameters. After that, an intelligent search algorithm named NSGA-II is introduced to search the optimal solution to the structural parameters combination and obtain the optimal motor performances. Finally, the electromagnetic characteristics of the initial and optimized models of the DS-HE-PMAM are compared and analyzed, both FEA and prototype experiments verify the feasibility and superiority of the proposed optimization method.

A real-time high-performance transmission bandwidth-aware (TB-aware) coding bit-rate (CBR) controller design with low power consumption and low hardware complexity is presented in this article for H.265/high-efficiency video coding (HEVC) in a multiprocessor system-on-chip (MPSoC). Previous TB-aware motion estimation designs with CBR-control capability in video coding have focused on algorithm development with precise CBR models, which require a complicated algorithmic derivation according to the system on-demand CBR and are difficult to realize in very large scale integration (VLSI) due to their lack of consideration for hardware implementation and modeling. Consequently, we present a hardware-oriented CBR-control algorithm that uses simple CBR control functions instead of requiring root and exponential operations to realize the real-time low-power design objective for HEVC applications within a mobile MPSoC. Then, an adequate hardware architecture with low hardware complexity is exploited to accomplish a low-power and high-speed VLSI design of a CBR controller for our proposed algorithm. Using diverse video-sized sequences under on-demand system coding-bit-rate constraints, the experimental outcomes demonstrate that the introduced design is capable of low power consumption and high speeds and can utilize low-complexity hardware.

By leveraging the power of crowd, the prevalence of mobile devices in mobile crowd-sensing (MCS) networks helps and provides a wide range of sensing services through collecting and sharing sensing data. However, the diverse behaviours of mobile users affect the quality of sensing service. Unreliable or malicious users and platforms could provide untrusted data to gain illegal benefits, which deteriorates the practicability of MCS. Besides, due to individual selfishness, mobile users are reluctant to participant in sensing tasks. It is desirable to design a trust and incentive scheme to improve the service efficiency of MCS. In this paper, we propose a novel trust-driven contract incentive framework in MCS, which guarantees the service quality, while stimulating mobile users to join sensing tasks. The basic idea is to establish a trust evaluation and contract incentive scheme for mobile users and platforms. We first design a trust evaluation scheme between mobile users and sensing platforms based on the historical interactions to derive the reliability value of sensing platform. Then, the trust threshold is formulated to filter out malicious sensing platforms. By considering the privacy preferences of mobile users, we establish a contract incentive scheme to maximize the utility of both mobile users and sensing platforms. The design objective is to derive a set of optimal contracts under both discrete and continuous contract models. Meanwhile, the designed contracts guarantee the individual rationality (IR) and incentive compatibility (IC) properties. Finally, simulations are conducted to evaluate the effectiveness of the proposed trust-driven contract incentive scheme, and results demonstrate that the proposed scheme can jointly improve the quality of sensing service and maximize the utilities of mobile users and sensing platforms.

Intelligent reflecting surface (IRS) is a promising technology for achieving spectrum and energy-efficient wireless networks cost-effectively. Most existing works on IRS have focused on exploiting IRS to enhance the performance of wireless communication or wireless information transmission (WIT), while its potential for boosting the efficiency of radio frequency (RF) wireless energy transmission (WET) still remains largely open. Although IRS-aided WET shares similar characteristics with IRS-aided WIT, they differ fundamentally in terms of design objective, receiver architecture, practical constraints, and so on. In this article, we provide a tutorial overview on how to efficiently design IRS-aided WET systems as well as IRS-aided systems with both WIT and WET, namely, IRS-aided simultaneous wireless information and power transfer (SWIPT) and IRS-aided wireless powered communication network (WPCN), from a communication and signal processing perspective. In particular, we present state-of-the-art solutions to tackle the unique challenges in operating these systems, such as IRS passive reflection optimization, channel estimation, and deployment. In addition, we propose new solution approaches and point out important directions for future research and investigation.

This article presents a method for structured robust control design for systems with a mixture of parametric and dynamic uncertainty. The proposed method alternates between an analysis step and a synthesis step. Samples of the parametric uncertainty are computed during the analysis steps, thus yielding an array of uncertain systems containing only dynamic uncertainty. The controller is then synthesized on this array of uncertain models. This synthesis step itself involves an alternation between constructing a D-scale for each of the uncertain systems and tuning a single controller for the entire collection of scaled plants. The controller tuning is performed using structured control design techniques. The proposed method is utilized to design a flutter suppression controller for a flexible aircraft. The aircraft dynamics are described by both a high-fidelity and a reduced-order model. The design objectives for flutter suppression are to achieve robust stabilization in the presence of mixed uncertainty. The proposed structured design method yields a single, low-order, linear time-invariant (LTI) controller, which increases the flutter speed by 15%. Additional robustness analyses and high-fidelity simulations are provided to assess the controller performance.

Abstract Oxidized tannic acid (OTA) is a useful biomolecule with a strong tendency to form complexes with metals and proteins. In this study we open the possibility to further the application of OTA when assembled as supramolecular systems, which typically exhibit functions that correlate with shape and associated morphological features. We used machine learning (ML) to selectively engineer OTA into particles encompassing one-dimensional to three-dimensional constructs. We employed Bayesian regression to correlate colloidal suspension conditions (pH and pKa) with the size and shape of the assembled colloidal particles. Fewer than 20 experiments were found to be sufficient to build surrogate model landscapes of OTA morphology in the experimental design space, which were chemically interpretable and endowed predictive power on data. We produced multiple property landscapes from the experimental data, helping us to infer solutions that would satisfy, simultaneously, multiple design objectives. The balance between data efficiency and the depth of information delivered by ML approaches testify to their potential to engineer particles, opening new prospects in the emerging field of particle morphogenesis, impacting bioactivity, adhesion, interfacial stabilization, and other functions inherent to OTA. Impact statement Tannic acid is a versatile bio-derived material employed in coatings, surface modifiers, and emulsion and growth stabilizers, which also imparts mild anti-viral health benefits. Our recent work on the crystallization of oxidized tannic acid (OTA) colloids opens the route toward further valuable applications, but here the functional properties tend to depend strongly on particle morphology. In this study, we eschew trial-and-error morphology exploration of OTA particles in favor of a data-driven approach. We digitalized the experimental observations and input them into a Gaussian process regression algorithm to generate morphology surrogate models. These help us to visualize particle morphology in the design space of material processing conditions, and thus determine how to selectively engineer one-dimensional or three-dimensional particles with targeted functionalities. We extend this approach to visualize other experimental outcomes, including particle yield and particle surface-to-volume ratio, which are useful for the design of products based on OTA particles. Our findings demonstrate the use of data-efficient surrogate models for general materials engineering purposes and facilitate the development of next-generation OTA-based applications. Graphic abstract

This article is devoted to the development of an optimal speed profile for autonomous vehicles in order to cross a signalized intersection without stopping. The design objective is to achieve both a short travel time and low energy consumption by taking full advantage of the traffic light information based on vehicle-to-infrastructure communication. The eco-driving problem is formulated as an optimal control problem. For the case where the vehicles are in free-flow mode, we derive a real-time on-line analytical solution, distinguishing our method from most existing approaches based on numerical calculations. Under mild assumptions, the optimal eco-driving algorithm is readily extended to cases where the free-flow mode does not apply due to the presence of interfering traffic. Extensive simulations are provided to compare the performance of autonomous vehicles under the proposed speed profile and human-driven vehicles. The results show quantitatively the advantages of the proposed algorithm in terms of energy consumption and travel time.

Coupled walls with replaceable steel shear link coupling beams are used to minimize the repair cost and downtime of buildings after major earthquakes. In this study, the direct displacement-based design (DDBD) method for coupled walls with steel shear link coupling beams using inelastic displacement spectra is developed. For capacity design, a continuum model of the coupled walls that accounts for the stiffness degradation due to expected damage in the first mode is adopted, to obtain equations to estimate the shear forces and bending moments generated due to higher modes. The effectiveness of the DDBD design method for coupled walls with steel shear link coupling beams is assessed by comparing the drift demands with nonlinear response history analysis (NLRHA) results and for capacity design purposes the shear force and bending moment profiles obtained from NLRHA are compared with the results obtained from the equations derived in this study and the weighted capacity design (WCD) method. The results obtained for 5-, 10-, and 15-story coupled walls with steel shear link coupling beams, show that the proposed design methodology is suitable to achieve the design objectives.

In the development of hybrid electric vehicles (HEVs), a series hybrid powertrain is mainly utilized in tracked vehicles to reduce energy consumption. In order to achieve high energy efficiency while maintaining the required driving performance, key design parameters of traction systems, such as transmission ratio and motor torque and power, need to be optimized. With the aim of effectively analyzing a complex track system, this article proposes an equivalent inertia model, which collectively represents the motion of each component of the tracked vehicle. The equivalent inertia model showed that the inertial effect was 34.8% higher than when the total mass of the vehicle was considered exclusively. Based on this inertia model, design objectives, such as energy efficiency and driving performance, were defined as quantified functions. Because of the balanced relationships between the objective functions, this study formulated a multiobjective optimization problem that includes motor stack length and transmission gear ratio as design variables. Based on the multiobjective optimization results, a Pareto front was obtained, which illustrates the balanced relationships between the objective functions. Comparing the initial HEV design, the optimum designs can improve energy efficiency and driving performance as a maximum of 13.0% and 2.9%, respectively.

The impending spectrum congestion imposed by the emergence of new bandwidth-thirsty applications may be mitigated by the integration of radar and classic communications functionalities in a common system. Furthermore, the merger of a sensing component into wireless communication networks has raised interest in recent years and it may become a compelling design objective for 6G. This article presents the evolution of the hitherto separate radar and communication systems towards their amalgam known as a joint radar and communication (RADCOM) system. Explicitly, we propose to integrate a radio sensing component into 6G. We consider an ultra-dense network (UDN) scenario relying on an active multistatic radar configuration and on cooperation between the access points across the entire coverage area. The technological trends required to reach a feasible integration, the applications anticipated and the open research challenges are identified, with an emphasis on high-accuracy network synchronization. The successful integration of these technologies would facilitate centimeter-level resolution, hence supporting compelling high-resolution applications for next-generation networks, such as robotic cars and industrial assembly lines., 10 pages, 3 figures, accepted for publication in IEEE Vehicular Technology Magazine

Inductive power transfer (IPT) is an effective energy supply solution for maglev trains that depend on current collectors to obtain enough electricity. This article proposed a system optimization that combines the compensation parameter and coupler optimization. The Pareto theory is employed to optimize an IPT system with a single-transmitter coupled multireceiver for maglev trains. The circuit model of the multipickup system and the finite element model of the coupler are given. A scale-down 1-kW prototype is built as the benchmark for the optimization. Then, the proposed system optimization is presented. The efficiency, pickup power density, and system cost are selected as the design objectives, and an optimal solution is selected from the Pareto fronts. A further coupler optimization is presented, and the optimization result is discussed and compared with other Maglev IPT coupler designs. Finally, an 8.5-kW IPT prototype based on the final optimized solution is built, and it is compared with other two different solutions to validate the method.

Purpose It is widely recognized that interior office space can affect health in several ways. Strategic and evidence-based design, including explicit design objectives, well-chosen design solutions and evaluation of results, aid realization of desired health effects. Therefore, this paper aims to identify possibly effective interior design strategies and accompanying design solutions and to provide examples of effectiveness measures. Design/methodology/approach A literature sample of 59 peer-reviewed papers published across disciplines was used to collect examples of workplace design features that have positively influenced workers’ well-being. The papers were grouped by their health objective and design scope successively and their theoretical assumptions, measures and findings were analyzed. Findings Four main workplace design strategies were identified. Design for comfort aims at reducing or preventing health complaints, discomfort and stress, following a pathogenic approach. It has the longest tradition and is the most frequently addressed in the included papers. The other three take a salutogenic approach, promoting health by increasing resources for coping with demands through positive design. Design for restoration supports physical and mental recovery through connections with nature. Design for social well-being facilitates social cohesion and feelings of belonging. Design for healthy behavior aims at nudging physical activity in the workplace. Originality/value By drawing complementary perspectives and offering examples of design solutions and effectiveness measures, this paper encourages workplace designers, managers and researchers to take a transdisciplinary and evidence-based approach to healthy workplaces. It also serves as a starting point for future empirical research.

Designing a nanogrid involves intricate considerations. Its primary system components, including PV systems, inverter type and control, batteries, and diesel generator, always offer a trade-off among conflicting design objectives – the cost of electricity and reliability, for example. This research proposes a synergistic Parallel Multiobjective PSO-based approach (PMOPSO), a merger of four optimization methods to optimally design a hybrid photovoltaic/diesel/battery nanogrid. The merged approaches are the Speed-Constrained Multiobjective Particle Swarm Optimization (SMPSO), MultiObjective Particle Swarm Optimization Algorithm Based on Decomposition (MPSO-D), Novel multiobjective particle swarm optimization (NMPSO), and Competitive Mechanism-Based Multiobjective Particle Swarm Optimizer (CMPSO). The developed approach allows the designer/operator to test multiple component models based on cost and reliability and choose the design that gives the best-suited solution. The four combined algorithms are run in parallel, and the obtained solutions are aggregated together in an archive pool where only non-dominated solutions are kept. A desert camp in the sub-urban area of Hafr Al-Batin city, situated in the Western region of Saudi Arabia, is used as a test case. The approach obtains a well-spread and large Pareto Front (PF), offering many options (solutions) to the designer/operator in a single run. The results achieved a superior set of solutions than those obtained by using each of the four combined PSO-based algorithms individually. Therefore, the developed technique provides improved and viable design solutions for a hybrid nanogrid.

The advancements in analysis and design approaches considering the seismic loads have helped safeguard buildings against structural failure and collapse. However, design objectives should also consider the associated effects and performance evaluation incorporating the sequential effects. Improving the performance of buildings at both the asset and community levels is now of major interest in the field of structural engineering. This research addresses the advancement toward this goal by incorporating post-earthquake fire and aftershock to multi-hazard design methodology. A set of mid-rise buildings – 3-, 9-, and 20-story moment resisting steel frames, characteristic of those found in rapidly growing metropolitan areas of US, are selected for the study considering the relative vulnerability to post-earthquake fire. The analyses of the responses are based on advanced finite element simulation methods/techniques and corresponding approaches. ABAQUS/CAE is primarily employed for the simulation and analysis. The effects of fire are thoroughly studied from the literature and considered explicitly, and the analysis is performed for a complete cycle of main shock (full-intensity) –fire- aftershock (reduced intensity) earthquake. The research commences with the investigation of the effects of modeling approach-- using beam element and using solid element with slab, on Post Earthquake Fire (PEF) study of a single room model. At the end of a complete sequence, the model developed using 3D solid elements are found to underestimate the stiffness loss by around 15% compared to those developed using 2D beam elements. The behaviors of selected steel moment resisting frame buildings then simulated with PEF are investigated to quantify the overall performance behavior of the building system. Inter-story Drift Ratio (IDR) and Stiffness Degradation over the complete sequence of events are chosen as the damage measures. The same approach is applied to a real building in California and the performance behavior is studied. All the building systems studied show the significant damages and change in behavior after incorporating the fire and aftershock. In all building sets, the stiffness loss (global) due to mainshock alone (2% to 10% in the cases studied) is very less as compared to the stiffness loss due to fire alone (6% to 15%) and aftershock (10% to 25%) alone.

Additional damping ratio (ADR), which plays a significant role in damped structure design, is broadly utilized to evaluate damping effect of the dampers. In current method, the ratio of energy dissipated by the dampers in one complete cycle to maximum strain energy of the structural system is adopted as the value of ADR. In the computation of damper energy dissipation, the bilinear model is commonly used to characterize hysteresis behavior of displacement-dependent dampers such as BRB. However, the accuracy of the bilinear model is not precise enough. In the light of the Bouc-Wen model can accurately characterize hysteresis behavior of various dampers, the analytical solution of hysteresis energy is derived. The ADR calculation methods based on the strain energy and the energy dissipated by the inherent damping of the main structure are proposed, respectively. The bi-objective design method of the RC frame with BRBs is put forward. The damping effect of the dampers is included in the design objectives, which makes up the deficiency of single objectives with the story drift ratio. The design objectives are that the story drift ratio of the frame does not exceed the limit, and the damping ratio reaches the expected value. The layout and force of the dampers are taken as design variables. Based on the finite element method, a RC frame with BRBs is designed iteratively. The reasonable time period length of time-varying energy is determined. Meanwhile, the strain energy method and modal damping energy dissipation method are compared and analyzed. As a result, the calculation result of strain energy method is larger than that of modal damping energy dissipation method. The strain energy method is hazardous to structural safety, and the modal damping energy dissipation method is suggested. The bi-objective design method is effective, which can further improve the structural performance.

PurposeThis research presents a multi-objective optimization approach to integrate spatial planning measures in open-plan office environments in order to lower the risk of a workplace contagion. These measures were gathered, formalized, parameterized, and coded and integrated into a digital tool.Design/methodology/approachTo demonstrate the research's approach, a simple design problem was designed, explored, and the results were evaluated. The researchers assumed an empty open office space, with the windows and doors (as exits and/or as access to amenities) already in place (Figure 1). The aim is to optimize the space planning, with the following objectives in mind: maximize the number of employees in a floor while maintaining physical distancing recommendations for avoiding infections; no face-to-face or back-to-back seating positions are allowed; maximize physical access to windows for natural ventilation; minimizing areas with potential “congestions” in the space, i.e. areas susceptible to overlapping foot traffic from numerous employees, which increases the potential for close encounters and minimizing the travel distance from the employee's desk to all neighbouring desks, hence reducing the foot traffic in the space. In the experiment, the following was assumed: the workspace layout is rectangular, the workstation desks are rectangular, the seating area, windows, and access to exits and amenities are well-defined.FindingsIt was found that configurations with desks parallel to the longer side of the space provided more employee capacity; however, they usually performed poorer in terms of the buzz score. On the other hand, configurations with desks perpendicular to the longer side of the space had, on average, better buzz scores, usually at the cost of the reduction of the number of potential employees. There was however one alternative in the latter set of configurations, which achieved above-average buzz and adjacency scores, and the potential to accommodate 56 employees, one of the highest capacities for employees in the solution space (the highest being 60). Designers could explore the design space further to make sure it complies with these basic spatial rules for mitigating the spread of infections, while experimenting with the workspace layout.Research limitations/implicationsIt is important to note that in order for a designer to handle any given design problem even with the aid of a computer system, it is important to provide a set of initial conditions and assumptions and a set of variables. In the universe of all possible variables, the designer can pick a number of variations of the initial conditions and run parallel experiments to compare their outcomes. In the experiment demonstrated here the following was assumed. The workspace layout is rectangular with predefined entrances/exits. Free flow of employees is allowed. No pre-set one-way paths. The workstation desks are rectangular. The seating area windows and access to amenities are well-defined.Originality/valueThis research presented a digital optimization approach to enhance the spatial planning process in open-plan office spaces, with the aim of mitigating the risks of infectious diseases' transmission. Spatial design considerations were gathered from literature and formalized as design objectives and constraints, then further parameterized and represented as numerical values and scores for objective evaluation. The design parameters, constraints and calculations to derive the scores for the designated design objectives were coded into a digital tool that can receive a building information model (BIM) model of an office space and provide preliminary furniture plans using a multi-objective optimization (MOO) approach. It is obvious that the furniture layouts that can be considered “acceptable”, based on this approach, are not considered “ready-to-implement” solutions, because designers need to integrate a multitude of other design factors in their design. This approach can still, however, be useful to help the designer integrate spatial considerations for slowing down a contagion.

During Hurricane Katrina in 2005 and the events thereafter, failures of levees with I-walls caused extensive flooding and damage. The geological background in the New Orleans area and associated uncertainties contributed significantly to the failures. To increase the robustness of the I-walls and levee system and reduce the associated risk of failure, the uncertainties of the system must be incorporated into the design procedures, especially in a geological environment mainly composed of sand deposits. This paper presents a robust optimization procedure to identify optimal designs for the stability of an I-walls and levee system supported on sandy foundation soil in the face of flood hazards. The uncertainties associated with the I-walls and levee system, including the strength parameters of levee and foundation soils and the height of the floodwater behind the I-walls, were considered in a systematic manner. The wall embedded depth, levee crown width, and slope ratio of the levee in the landside were considered as the design parameters. For the robust optimization, the construction cost of the I-walls and levee system and the standard deviation of the failure probability were considered as the design objectives. Finally, the multi-objective optimization resulted in a set of acceptable designs that were presented in a graphical form called Pareto front, which is combined with the knee point concept to provide useful decision aids for selecting the most preferred design that meets both the economics and performance requirements.

Incorporating 3D virtual environments into psychological experiments offers an innovative solution for balancing experimental control and ecological validity. Their flexible application to virtual navigation experiments, however, has been limited because accessible development tools best support only a subset of desirable task design features. We created OpenMaze, an open-source toolbox for the Unity game engine, to overcome this barrier. OpenMaze offers researchers the ability to conduct a wide range of first-person spatial navigation experiment paradigms in fully customized 3D environments. Crucially, because all experiments are defined using human-readable configuration files, our toolbox allows even those with no prior coding experience to build bespoke tasks. OpenMaze is also compatible with a variety of input devices and operating systems, broadening its possible applications. To demonstrate its advantages and limitations, we review and contrast other available software options before providing an overview of our design objectives and walking the reader through the process of building an experiment in OpenMaze.

Wireless Sensor Network (WSN) has critical design objectives required to consider in sensor node design like power consumption, fault tolerance and security. FPGA is considered as preferred solution to design the sensor node. Thus the work in this paper is focused on soft core built custom component based paradigm to meet the above mentioned objectives with desired flexibility. It includes new custom component based model for WSN nodes with three customization approaches. In first approach, a unique isolated customization is done for fault tolerance, security, reduction in the size of node affecting power consumption. Second approach deals with design and implementation of selection index based extended custom block, which helps to reduce the overall power and time required for execution. The WSN cooperative custom module is designed in third approach which mitigates to achieve all objectives in single module. A sensor node processing unit for acceleration of complex algorithms is sufficed by the use of NIOS II soft-core processor. The work performance of customized soft core based sensor node is compared to the microcontroller based execution. The results represent the significance of soft core based implementation by enhancement in performance of clock cycle counts. They are required for typical processing unit design objectives built as custom instructions. The customization reduces the overhead of soft core processor, thus the notion of custom instruction is compared with software only implementation of respective processing unit tasks. The results show considerable improvement in performance with energy consumption reduction.

In seismic design based on performance, seismic performance level is determined based on failure state of the building and seismic design objective is set according to the importance of the buildings. In many countries, they calculate the seismic reaction of the buildings with the use of structural design programs to check the aseismic performance through the nonlinear static analysis method. In this paper, we established seismic performance levels and aseismic design objective to design on the basis of design objective according to the three levels in Seismic Design Code of Building, DPR Korea, 2010.

In this article, a variable-saliency-ratio permanent magnet motor is designed and optimized considering driving cycles. To satisfy the demands of multi-operation conditions of EVs, a variable resistance is adopted and a new variable-saliency-ratio design concept is proposed to enhance output torque, widen speed range and lower irreversible demagnetization risk effectively. A multiobjective optimization considering driving cycles is proposed, where the design objective of saliency ratio is firstly selected and optimized with multiobjective genetic algorithm method. Then, the operation performances of the motor in different operation conditions are investigated in detail. Finally, a prototype motor is built and tested. Both the simulation and experimental results verify the validity of the proposed method.

Purpose The purpose of the study is to optimise the cross-sectional shape of passively cooled horizontally mounted pin-fin heat sink for higher cooling performance and lower material usage. Design/methodology/approach Multi-objective shape optimisation technique is used to design the heat sink fins. Non-dominated sorting genetic algorithm (NSGA-II) is combined with a geometric module to develop the shape optimiser. High-fidelity computational fluid dynamics (CFD) is used to evaluate the design objectives. Separate optimisations are carried out to design the shape of bottom row fins and middle row fins of a pin-fin heat sink. Finally, a computational validation was conducted by generating a three-dimensional pin-fin heat sink using optimised fin cross sections and comparing its performance against the circular pin-fin heat sink with the same inter-fin spacing value. Findings Heat sink with optimised fin cross sections has 1.6% higher cooling effectiveness than circular pin-fin heat sink of same material volume, and has 10.3% higher cooling effectiveness than the pin-fin heat sink of same characteristics fin dimension. The special geometric features of optimised fins that resulted in superior performance are highlighted. Further, Pareto-optimal fronts for this multi-objective optimisation problem are obtained for different fin design scenarios. Originality/value For the first time, passively cooled heat sink’s cross-sectional shapes are optimised for different spatial arrangements, using NSGA-II-based shape optimiser, which makes use of CFD solver to evaluate the design objectives. The optimised, high-performance shapes will find direct application to cool power electronic equipment.

Engine size and weight optimization have always been high-priority design objectives for designers. Compressors occupy a relatively large part of the gas turbine engine. Owing to the adverse pressure gradient in the compressor, achieving the required pressure ratio within fewer stages has been a challenging task for compressor designers. Tandem blading is one of the novel concepts, which could be used to increase the pressure ratio by means of higher flow turning through the blade passages. This paper presents the performance characteristics of a tandem stage based on results from experiments and numerical analyses. The investigation is further extended to analyze the effect of a radial hub and tip distortion on the performance of the tandem stage. The experimental results are very well supported with some interesting numerical results, particularly near the hub and tip region. It is observed that the tandem stage demonstrates higher pressure rise and stall margin under clean inflow. The tandem stage is also observed to be more sensitive to radial distortion leading to a significant loss in the total pressure and the stall margin.

This paper deals with the observer design problem for semi-Markov jump systems with incremental quadratic constraints. Based on the design objective that the state estimation error is stochastically stable, the sufficient conditions formulated by linear matrix inequalities are presented. To reduce the conservatism of sufficient conditions as well as the computational burden, the relaxation method with slack variable is employed. Finally, a simulation example verifies the effectiveness and superiority of the method studied in this paper.

In this work, we investigate an optimal assessment strategy to measure student learning in the first-year undergraduate engineering course at X-Department at X University. Specifically, we evaluate and compare challenge-based and competency-based assessment strategies. In the challenge-based approach, the students are required to design a C++-based application that meet the required design objectives. The competency-based assessment involves assessing learning by asking a variety of pointed questions pertaining to a single or a small group of concepts. After studying the performance of 207 students, we found that in the challenge-based assessment, due to the complex nature of the questions that assess numerous concepts simultaneously, students who are not very thorough with even one or two concepts fared very poorly since they were unable to finish the challenge and present a functional prototype of the program. On the other hand, the competency-based assessment allowed for a more balanced approach in which the students’ learning was reflected more accurately by their performance in the various assessments.

In this work, we consider the problem of joint sparse array antenna design and phase-only beampattern shaping with reconfigurability. Our design objective is to reduce the cost of the antenna system for the unmanned aerial vehicle (UAV) radar with a large-aperture array. We formulate this co-design problem as a sparse optimization problem with angular response and binary-like modulus constraints. To solve the resultant NP-hard nonconvex problem, we propose a consensus alternating direction method of multipliers (ADMM) based algorithm. This method transforms the original problem into a higher-dimensional one by introducing two categories of auxiliary variables. By doing so, the problem can be effectively tackled via alternately solving several subproblems, meanwhile the tricky binary modulus constraints can be well handled by utilizing the geometric property of the subproblem. We further prove that the proposed algorithm converges to the KKT points of the original problem. In addition, we extend our methodology to the discrete phase case to handle the practical finite-bit phase quantization. The devised algorithm is capable of generating phase-only weight vector meanwhile automatically selecting appropriate antenna elements according to the specific beam shape. Numerical examples are given to demonstrate the effectiveness of the proposed approach.

Design is a fundamental component in various engineering curricula. Programs across the nation teach this component via design projects in different courses, culminating in a capstone design course in the final year. While these projects may achieve the goal of teaching the use of mathematics and sciences to meet design objectives and constraints, they are limited in terms of the team size, scope of the project, and diversity of team members.

Pull-in potential of electrostatic actuation ohmic cantilever beam type RF MEMS switch is lowered by reducing stiffness constant of its cantilever beam. However, this design objective is highly int...

Superhydrophobic surfaces have been largely achieved through various surface topographies. Both empirical and numerical simulations have been reported to help understand and design superhydrophobic surfaces. Many such successful surfaces have also been achieved using bioinspired and biomimetic designs. Despite this, identifying the right surface texture to meet the requirements of specific applications is not a straightforward task. Here, we report a hybrid approach that includes experimental methods, numerical simulations, and machine learning (ML) algorithms to create design maps for superhydrophobic polymer topographies. Two design objectives to investigate superhydrophobic properties were the maximum water contact angle (WCA) and Laplace pressure. The design parameters were the geometries of an isotropic pillar structure in micrometer and sub-micrometer length scales. The finite element method (FEM) was validated by the experimental data and employed to generate a labeled dataset for ML training. Artificial neural network (ANN) models were then trained on the labeled database for the topographic parameters (width W, height H, and pitch P) with the corresponding WCA and Laplace pressure. The ANN models yielded a series of nonlinear relationships between the topographic design parameters and the WCA and Laplace pressure and substantial differences between the micrometer and sub-micrometer length scales. Design maps that span the topography design parameters provide optimal design or tradeoff parameters. This research demonstrates the potential of ANN as a rapid design tool for surface topography exploration.

A membrane reactor (MR) combines reaction and separation phenomena in a single unit and offers an energy-efficient, cost-effective, compact, modular, and sustainable design compared to conventional designs. A systematic design framework can yield such benefits of MRs and increase their adoption in the chemical process industry. To this end, we present SPICE_MARS (synthesis and process intensification of chemical enterprises involving membrane-assisted reactive separations), a software prototype for conceptual design, simulation, synthesis, and optimization of MRs for different process applications. At the conceptual level, we can determine whether MR is desired or not and select which species to convert/separate. At the equipment level, we obtain optimal MR configurations considering different flow arrangements, intensification strategies, membrane types, sweep gases, reactor lengths, membrane areas, and catalyst amounts. Additionally, we can generate rank-ordered lists of optimal reactor configurations for different design objectives. These enabling capabilities are demonstrated using two case studies involving methanol synthesis and methane partial oxidation. In both cases, novel MR designs—achieving drastic improvement compared to current industrial practice—are found.

A supercapacitor (SC)-based interior permanent magnet synchronous motor (IPMSM) drive including the speed tracking of a specific velocity profile and the charging of the SC is developed in this study to emulate the operation of an urban light rail vehicle (LRV). In the SC-based IPMSM drive, the motoring mode to emulate the LRV speed tracking control and the charging mode for the charging of the SC are both designed. In the motoring mode, a field-oriented controlled (FOC) IPMSM drive system is developed to emulate the speed control of an LRV. In the charging mode, the constant current and constant voltage (CC–CV) charging strategy is developed for the charging of the SC. Moreover, the above two modes use the same inverter and coordinate transformations to reduce the design complexity. Furthermore, in order to test the performance of SC, the speed command of the emulated LRV is obtained using a specific testing driving cycle. The design objective is for fast charging of SC being able to provide enough energy for the emulated LRV to operate a full testing driving cycle. In addition, to improve the transient speed response of the emulated LRV, a Chebyshev fuzzy neural network (CheFNN) intelligent speed controller is proposed. Finally, the simulation and experimental results are given to demonstrate the effectiveness of the developed CC–CV charging strategy for the SC and the proposed CheFNN speed controller for the emulated LRV.

Three closely related polynomial‐based group key pre‐distribution schemes have recently been proposed, aimed specifically at wireless sensor networks. The schemes enable any subset of a predefined set of sensor nodes to establish a shared secret key without any communications overhead. It is claimed that these schemes are both secure and lightweight, that is, making them particularly appropriate for network scenarios where nodes have limited computational and storage capabilities. Further studies have built on these schemes, for example, to propose secure routing protocols for wireless sensor networks. Unfortunately, as shown by the author, all three schemes are completely insecure; whilst the details of their operation vary, they share common weaknesses. In two cases, we show that an attacker equipped with the information built into just one sensor node can compute all possible group keys, including those for which the attacked node is not a member; this breaks a fundamental design objective. In the other case, an attacker equipped with the information built into at most two sensor nodes can compute all possible group keys. In the latter case, the attack can also be achieved by an attacker armed with the information from a single node together with a single group key to which this sensor node is not entitled. Repairing the schemes appears difficult, if not impossible. The existence of major flaws is not surprising given the complete absence of any rigorous proofs of security for the proposed schemes. A further recent work proposes a group membership authentication and key establishment scheme based on one of the three key pre‐distribution schemes analysed here; as the author demonstrates, this scheme is also insecure, as the attack we describe on the corresponding pre‐distribution scheme enables the authentication process to be compromised.

The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the sDA300/50% by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the UDI300-2000 lx increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model.

Through extenics-based case reasoning, this research puts forward a method to configure the modules of complex machine products. Firstly, the knowledge on the process of product design was represented, and the convergence modes of design plans were analyzed, with the aid of product semantics and the knowledge representation model of the primitives. Next, the semantic correlation function was constructed, and a priority degree evaluation method for product family was created, in order to obtain the optimal configuration plan. In this way, the excellent design genes in product plans with relatively low correlations will be preserved. Finally, the proposed method was verified on the design of computer numerically controlled (CNC) roll grinder. Specifically, the quantitative and qualitative user needs were converted into the design objectives, producing the set of primitives (design objectives). Based on sematic correlations and priority degrees, the product/product family that best match the conceptual product was searched for in the case library, and the optimal configuration plan was prepared, in the light of semantic correlation network and local correlation evaluation. The results show that the local correlations of the bed and workbench module, headstock module, grinding system, measuring system, and support of MK8420 with design objectives were 0.298, 0.450, 0.600, 0.500, and 0.298, respectively, indicating that the configuration plan is in line with user needs and suitable for case-based reasoning. The proposed method has been successfully applied in actual production, laying a good basis for the intelligent design of products.

Product design and supply chain design are two key determinants of company competitiveness. However, they follow different design objectives and thus require a systematic trade-off. Although methodologies for product design and supply chain design are well established within each domain in research and industry, an integrated methodology that bridges both design domains is still lacking. Based on a recently introduced concurrent product and supply chain design process, we contribute to this underdeveloped research area with a generic approach towards exploring design tradespace. We introduce a detailed operational process for the concurrent design of product and supply chain architectures. To apply this generic process to the specific trade-off between the product-related objective of modularity and the supply-chain-related objective of sourcing flexibility, we also develop new methods for key steps of the process. We demonstrate the application of the process and the developed methods using an industrial case study of a new product (electric-vehicle battery module). The case shows that our methodology was able to structure the concurrent design process. It hereby ensured an efficient trade-off and led to high-quality designs.

This article proposes a multi-objective optimization method for the optimization design of a new dual-stator arc permanent magnet machine (DS-APMM) which can be applied on the direct-drive scanning systems with limited angular movement, such as radar, large telescope. The proposed optimization method integrates light gradient boosting machine (LightGBM) with differential evolution algorithm (DEA) to achieve optimal design objectives of high back electromotive force, low total harmonic distortion, high average torque, and low torque ripple at different rotor speeds. The machine topology and analytical model of DS-APMM are firstly presented to determine the structural parameters to be optimized. The sensitivity of each structural parameter to the optimization objectives is analyzed based on the SHAP (SHapley Additive exPlanations) value. Then, a finite-element analysis (FEA)-based DS-APMM model is developed to acquire sample data. Based on the acquired sample data, a machine learning algorithm, LightGBM, is introduced to establish surrogate model that can fit the function relationship between design objectives and structural parameters. Subsequently, an intelligent search algorithm named DEA is adopted to search optimal combination of the structural parameters and hence obtain optimal machine performances of DS-APMM. Finally, the electromagnetic characteristics of the initial model, middle model and optimal model of DS-APMM are compared and analyzed, both FEA and prototype experiments verify the feasibility and superiority of the proposed multi-objective optimization method.

In the standard linear, time-invariant feedback system, controllers that achieve diagonal decoupling and closed-loop stability exist if and only if the plant satisfies the diagonal denominator condition or has no coinciding poles and zeros in the region of instability. A simple and systematic decoupling design procedure is presented under each of these conditions. The closed-loop poles can be placed at any desired points, and free parameters are included for satisfying additional design objectives. The designs are also extended to provide integral action in order to track step inputs with zero steady-state error.

In the recent years, the displacement-based seismic design (DBD) method of structures has been recognized as the alternative design method to the conventional force-based seismic design (FBD) method. The problems inherited in the FBD method are mostly overcome by the DBD method as it better correlates the structural damage with displacement-based quantities. In this study, the seismic response of multi-span bridges with varying number of spans and unequal pier heights was evaluated using the DBD method and compared with the conventional FBD method. Two approaches were employed in the DBD method, such as the traditional direct displacement-based seismic design (DDBD) approach and the proposed approach in this study entitled as alternative-to-direct displacement-based seismic design (ADBD). For seismic evaluation of the bridge structures with the FBD method, acceleration response spectra as suggested in the Bangladesh National Building Code were used; while in the DBD method, displacement response spectra for different damping ratios were developed keeping consistency with the above-mentioned design acceleration spectra. Five bridge models were used in the analysis to compare the seismic response in both the longitudinal and transverse directions taking into account the number of spans and height irregularity in the bridge models. Numerical analysis showed very obvious results that the base shear calculated by both approaches of the DBD method is found to be smaller than the FBD method indicating that an economic member section be attained for meeting the same design objective. More specifically, the ADBD approach provides the design professionals a more conservative design approach in compared with the DDBD approach keeping an economic design approach comparing with the FBD method.