System Design Task Research Articles

BASS: Safe Deep Tissue Optical Sensing for Wearable Embedded Systems.

In wearable optical sensing applications whose target tissue is not superficial, such as deep tissue oximetry, the task of embedded system design has to strike a balance between two competing factors. On one hand, the sensing task is assisted by increasing the radiated energy into the body, which in turn, improves the signal-to-noise ratio (SNR) of the deep tissue at the sensor. On the other hand, patient safety consideration imposes a constraint on the amount of radiated energy into the body. In this paper, we study the trade-offs between the two factors by exploring the design space of the light source activation pulse. Furthermore, we propose BASS, an algorithm that leverages the activation pulse design space exploration, which further optimizes deep tissue SNR via spectral averaging, while ensuring the radiated energy into the body meets a safe upper bound. The effectiveness of the proposed technique is demonstrated via analytical derivations, simulations, and in vivo measurements in both pregnant sheep models and human subjects.

Interpretable AI-Based Large-Scale 3D Pathloss Prediction Model for Enabling Emerging Self-Driving Networks

In modern wireless communication systems, radio propagation modeling to estimate pathloss has always been a fundamental task in system design and optimization. The state-of-the-art empirical propagation models are based on measurements in specific environments and limited in their ability to capture idiosyncrasies of various propagation environments. To cope with this problem, ray-tracing based solutions are used in commercial planning tools, but they tend to be extremely time-consuming and expensive. We propose a Machine Learning (ML)-based model that leverages novel key predictors for estimating pathloss. By quantitatively evaluating the ability of various ML algorithms in terms of predictive, generalization and computational performance, our results show that Light Gradient Boosting Machine (LightGBM) algorithm overall outperforms others, even with sparse training data, by providing a 65% increase in prediction accuracy as compared to empirical models and 13x decrease in prediction time as compared to ray-tracing. To address the interpretability challenge that thwarts the adoption of most ML-based models, we perform extensive secondary analysis using SHapley Additive exPlanations (SHAP) method, yielding many practically useful insights that can be leveraged for intelligently tuning the network configuration, selective enrichment of training data in real networks and for building lighter ML-based propagation model to enable low-latency use-cases.

Improving ship sustainability by re-using engineering simulators in multi-objective optimization

Alternatives for enhancing a cruise ship’s energy efficiency were investigated by introducing different waste heat recovery technologies and battery systems in the machinery. A ship designer’s in-house ship energy system simulator was applied in a cloud-based framework for simulation-based optimization. This multi-objective optimization with economic and environmental objectives used genetic algorithm for finding the best overall solution in a complex ship energy system design task. The results suggest that adding battery capacity alone contributes very moderately to reducing the case ship fuel consumption and, therefore, carbon emissions. Nevertheless, a combination of steam turbines and organic Rankine cycle units would offer the largest fuel saving potential with the lowest investment cost in the case setup. Also, the main engines’ running hours were of interest. The presented approach can bring significant added value for sustainable ship design with minimal additional effort.

Parametric modeling of radio engineering devices at the stage of system design

System design provides for solving the task of comparative analysis and choosing the best option for constructing a radio engineering device. Its main result should be a structural diagram of the product and an assessment of the effectiveness of its implementation. The number of possible structural options for their implementation is limited when designing radio engineering devices of a certain class, so the search for the best option is mainly determined by the costs of its implementation, provided that the requirements of the technical specifications are fully satisfied. The main way to reduce the costs of developing devices is to use high-availability modules as much as possible in the designed product, which means electronic modules that have passed certain stages of development or have at least working design documentation. Then, the task of system design is reduced to solving the task of covering the given structures of high-availability modules, and the efficiency of its solution depends to a large extent on the number of possible alternatives. The methodology of using parametric modeling to determine the best option of construction of a radio engineering device (system) is considered. It is proposed to use an information approach to assess the degree of coincidence of the parametric models of highavailability modules with the values of parameters specified in the technical specifications while maintaining a given level of reliability. The degree of coincidence is evaluated by three criteria: informativeness, adequacy, and accuracy. It is shown that the considered methodology makes it possible to solve the parametric synthesis task of the effective structure of a radio engineering device while maintaining its specified reliability. The resulting options are highly reliable because the cost estimation is performed by real developers. As information on the actual costs for possible modifications of high-availability modules is accumulated, the preliminary cost estimation procedure can be automated.

HANDLING DYNAMIC NONLINEARITIES IN UAV AUTOMATIC FLIGHT CONTROL SYSTEMS

The linear or the nonlinear feature of systems being modelled or designed is a core property we should be familiar with before taking up any system design or analysis-related task. The dynamic model-based closed loop control design is widely used in control engineering when dynamic or static nonlinearities are considered to be present in the closed loop control system. This paper addresses both dynamical and static nonlinearities modelled and handled in the closed loop control system

Optimized selection of component models for photovoltaic and energy storage system simulations

Photovoltaic (PV) systems have become an integral and widespread part of renewable energy generation. In combination with energy storage, they offer a variety of advantages such as increased self-sufficiency or improved grid stability. However, geographic location, subsidies, energy tariffs, size of PV panels and battery, and other factors significantly influence the overall performance and profitability of the system. Solving this complex task of system design and component dimensioning is not easy. The diversity and multitude of parameters therefore usually requires the help of, more or less complex, computer simulations. This led and still leads to a high publication activity on techno-economic and other optimizations for such systems. However, the operating strategies and component models are often oversimplified, leading to results with significant errors. The goal of this paper is to provide in-depth insight into component modeling and parametrization for PV module, battery energy storage, and inverter, as well as giving suggestions on appropriate control strategies. The focus lies on the description of suitable simple-but-accurate models and their parametrization. In order to avoid the effort of having to conduct measurements for component characterization, an approach relying on values presented in product data sheets is considered. Furthermore, the effects of model simplifications, as they are found in many publications, are shown. Finally, the importance of an uncertainty analysis for the entire simulation is discussed in order to gain insight into the accuracy of the calculated simulation results and their dependency on model- and input-parameters.

Principals of digital system design debugging decomposition

Up-to-date digital integrated circuits become more and more complicated. The amount of its elements increases enormously, sizes of the elements become very small. That is why the task of digital system design debugging becomes more and more complicated. So, digital system design debugging decomposition is required. In the paper design debugging decomposition by simulation is considered. The classification of debugging decomposition methods and its algebraic models are presented. The following decomposition methods are described: vertical and horizontal structure decomposition, functional decomposition, decomposition based on error types.

Fluidic Topology Optimization with an Anisotropic Mixture Model

Fluidic devices are crucial components in many industrial applications involving fluid mechanics. Computational design of a high-performance fluidic system faces multifaceted challenges regarding its geometric representation and physical accuracy. We present a novel topology optimization method to design fluidic devices in a Stokes flow context. Our approach is featured by its capability in accommodating a broad spectrum of boundary conditions at the solid-fluid interface. Our key contribution is an anisotropic and differentiable constitutive model that unifies the representation of different phases and boundary conditions in a Stokes model, enabling a topology optimization method that can synthesize novel structures with accurate boundary conditions from a background grid discretization. We demonstrate the efficacy of our approach by conducting several fluidic system design tasks with over four million design parameters.

BASIC PARAMETERS OF LENS DESIGN

In this study, the basics of optical system design are presented. It contains a series of formulations ordered by subject areas or topics representing typical lens design tasks and optical system design tasks. Different aspects are covered, such as the definition of starting systems and the creation, evaluation, and optimization of lenses and systems. The formulas of the most critical lens design equations are given in detail in the content of the study. It also provides an overview of basic mathematical models for calculating simple optical components such as single lenses, systems, and achromatic pairs. Such components represent the foundation of any optical system. The literature study on this study determined that studies on lens design in national publications are limited. The study differs in that it clearly articulates the fundamentals of lens design. It is thought that this study will contribute to the literature.

Multi-criteria evaluation of smart product-service design concept under hesitant fuzzy linguistic environment: A novel cloud envelopment analysis approach

Design concept evaluation is an essential but challenging task in smart product service system (SPSS) design. It is critical to select the design concept with better smart experience to ensure the success of SPSS design. Meanwhile, SPSS design concept evaluation is an expert knowledge-based decision process with scant data and a short time window under a hesitant, ambiguous, and subjective environment, which would lead to low decision-making efficiency and inaccurate evaluation results. Hence, this paper proposes a novel cloud envelopment analysis method to evaluate SPSS design concept with respect to smart experience criteria. The proposed method integrates hesitant fuzzy linguistic terms (HFLTs), cloud model and data envelopment analysis (DEA) method to accurately evaluate SPSS design concepts with handling the hesitancy, fuzziness and randomness in qualitative and subjective information. A hybrid model of normal cloud and trapezium cloud is used to quantify hybrid-length HFLT variables to avoid decision information loss and distortion. A novel cloud non-linear programming model is constructed to extend the classical DEA into cloud environment. Finally, a case study and some comparison analyses illustrate the effectiveness and advantages of proposed method.

Reliability optimization of dynamic k-out-of-n systems with competing failure modes

Designing a reliable system under different working conditions is a challenging task for system designers. This paper introduces different approaches to cope with the uncertainty of working conditions in redundancy allocation problems. A novel dynamic k-out-of-n system is developed, in which possible working conditions are modelled using discrete scenarios. The proposed dynamic strategy takes advantage of changing the value of k as a response to the different working condition. The decision making process consists of two stages: in the first stage, when the working condition is uncertain, the total number and the type of the redundant components are determined. In the second stage, after realizing the working condition, the decision about the value of k is made. The proposed strategy is capable of modelling the systems consisting of components with two competing failure modes: open and short mode. To make the model more realistic, the possibility of mixing the redundancy choices as well as the effect of each failure mode at the system level are considered. The model is solved using a genetic algorithm for a fire detection system. It is demonstrated that the model outperforms the classic approaches and provides a higher level of system reliability without imposing extra cost on the system.

Dynamic power management based on model predictive control for hybrid-energy-storage-based grid-connected microgrids

In this study, an efficient and reliable dynamic power management system (PMS) is proposed for microgrids (μGs) based on hybrid energy storage systems. Owing tothe differences in the response times of the different components (i.e., the battery, supercapacitor, and fuel cell) of the μG, efficiently allocating the power between the different devices is a challenging task for system designers. To address this issue and optimize the operation of the μG, a dynamic PMS is necessary. The contribution of this study is the application of model predictive control (MPC) for operating and controlling such μGs. The MPC method is used to manage power electronic devices such as DC-DC converters (for the battery, fuel cell, and supercapacitor) and grid-connected inverters. After the proposed PMS is successfully implemented in simulations, itsreal-time performance is validatedunder different operating scenarios that could occur in actual situations. The proposed PMS is found to be effective in controlling the control and operation of the μG.It can regulate the DC bus voltage during transient power imbalances, control the fuel cell current slope as per the requirement, and smoothen the operation between isolated and grid-connected modes.

Toward flexible and persistent UAV service: Multi-period and multi-objective system design with task assignment for disaster management

Usability of lightweight unmanned aerial vehicles (UAVs) limited in a variety of activities in disaster management due to two fundamental drawbacks: limited flight duration, and limited loading capacity. With the proper system design and task assignment methodologies, these limitations can be overcome to enable long-duration UAV missions over large areas. The present study set out to determine the optimal unmanned aerial system (UAS) system design and UAV task assignment for such applications. Based on a proposed multi-objective mathematical model, exact Pareto optimal solutions were derived using the ε–constraint method. To find approximate Pareto solutions, an approximate two-phase (ATP) approach was developed, which is computationally inexpensive compare to the optimal solution approach. Through numerical experiments based on a realistic case study, it was shown that the ATP approach results in high accuracy and a two-orders-of-magnitude reduction in computation time. Additionally, for prompt application of our study to continuously changing situations, a rolling-horizon UAS approach herein is suggested.

Exploring the effects of different achievement goals on contributor participation in crowdsourcing

PurposeThis study examines how contributors with different achievement goals participate under the influence of two common motivators/demotivators on crowdsourcing platforms, namely system design features and task nature.Design/methodology/approachA free simulation experiment was conducted among undergraduate students with the use of a crowdsourcing platform for two weeks.FindingsThe results indicate that contributors with a strong performance-approach goal get better scores and participate in more crowdsourcing tasks. Contributors with a strong mastery-avoidance goal participate in fewer heterogeneous tasks.Research limitations/implicationsContributors with different achievement goals participate in crowdsourcing tasks to different extents under the influence of the two motivators/demotivators. The inclusion of the approach-avoidance dimension in the performance-mastery dichotomy enables demonstrating the influence of motivators/demotivators more specifically. This article highlights differentiation between the quality and the quantity of heterogeneous crowdsourcing tasks.Practical implicationsManagement is advised to approach performance-approach people if a leaderboard and a point system are incorporated into their crowdsourcing platforms. Also, management should avoid offering heterogeneous tasks to mastery-avoidance contributors. System developers should take users' motivational goals into consideration when designing the motivators in their systems.Originality/valueThe study sheds light on habitual achievement goals, which are relatively stable in comparison to contributors' motives and states. The relationships between achievement goals and motivators/demotivators are more persistent across time. This study informs system designers' decisions to include appropriate motivators for sustained contributor participation.

Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks

Abstract In engineering systems design, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are essential to the development of new algorithms embedded with human intelligence to augment the computational design. In this paper, we develop a deep learning-based approach to model and predict designers’ sequential decisions in the systems design context. The core of this approach is an integration of the function-behavior-structure (FBS) model for design process characterization and the long short-term memory unit (LSTM) model for deep leaning. This approach is demonstrated in two case studies on solar energy system design, and its prediction accuracy is evaluated benchmarking on several commonly used models for sequential design decisions, such as the Markov Chain model, the Hidden Markov Chain model, and the random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to rely on both short-term and long-term memory of past design decisions in guiding their future decision-making in the design process. Our approach can support human–computer interactions in design and is general to be applied in other design contexts as long as the sequential data of design actions are available.

Wireless Characterization and Assessment of an UWB-Based System in Industrial Environments

The advent of Indsutrial Internet of Things is one of the main drivers for the implementation of Industry 4.0 scenarios and applications, in which wireless communication systems play a key role in terms of flexibility, mobility and deployment capabilities. However, the integration of wireless communication systems poses challenges, owing to variable path loss conditions and interference impact. In this work, an Ultra-Wideband (UWB) system for indoor location in very large, complex industrial scenarios is presented. Precise wireless channel characterization for the complete volume of a logistical plant is performed, based on 3D hybrid ray launching approximation, in order to aid network node design process. Wireless characterization, implementation and measurement results are obtained for both 4 GHz and 6 GHz frequency bands, considering different densities of scatterers within the scenario under test. Time domain estimation results have been obtained and compared with time of flight measurement results, showing good agreement. The proposed methodology enables to perform system design and performance tasks, analyzing the impact of variable object density conditions in wireless channel response, providing accurate time of flight estimations without the need of complex channel sounder systems, aiding in optimal system planning and implementation.

Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit

The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed. An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced. Design of robust knowledge bases is performed using a developed computational intelligence – quantum / soft computing toolkit (QC/SCOptKBTM). The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described. The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described. The general design methodology of a generalizing control unit based on the physical laws of quantum computing (quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal) is considered. The modernization of the pattern recognition system based on stereo vision technology presented. The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system.

A NARX Model Reference Adaptive Control Scheme: Improved Disturbance Rejection Fractional-Order PID Control of an Experimental Magnetic Levitation System

Real control systems require robust control performance to deal with unpredictable and altering operating conditions of real-world systems. Improvement of disturbance rejection control performance should be considered as one of the essential control objectives in practical control system design tasks. This study presents a multi-loop Model Reference Adaptive Control (MRAC) scheme that leverages a nonlinear autoregressive neural network with external inputs (NARX) model in as the reference model. Authors observed that the performance of multi-loop MRAC-fractional-order proportional integral derivative (FOPID) control with MIT rule largely depends on the capability of the reference model to represent leading closed-loop dynamics of the experimental ML system. As such, the NARX model is used to represent disturbance-free dynamical behavior of PID control loop. It is remarkable that the obtained reference model is independent of the tuning of other control loops in the control system. The multi-loop MRAC-FOPID control structure detects impacts of disturbance incidents on control performance of the closed-loop FOPID control system and adapts the response of the FOPID control system to reduce the negative effects of the additive input disturbance. This multi-loop control structure deploys two specialized control loops: an inner loop, which is the closed-loop FOPID control system for stability and set-point control, and an outer loop, which involves a NARX reference model and an MIT rule to increase the adaptation ability of the system. Thus, the two-loop MRAC structure allows improvement of disturbance rejection performance without deteriorating precise set-point control and stability characteristics of the FOPID control loop. This is an important benefit of this control structure. To demonstrate disturbance rejection performance improvements of the proposed multi-loop MRAC-FOPID control with NARX model, an experimental study is conducted for disturbance rejection control of magnetic levitation test setup in the laboratory. Simulation and experimental results indicate an improvement of disturbance rejection performance.

Polynomial Controller Design and Its Application: Experimental Validation on a Laboratory Setup of Nonideal DC–DC Buck Converter

The transient response control is one of the most important tasks in the control system design. The transient response has characterized based on the relationship between characteristic ratios and time domain specifications. The characteristic ratios are the design parameters of the characteristic ratio assignment (CRA) approach. This article presents a novel polynomial controller for all types of linear second-order systems. The proposed controller is designed using CRA. The important features of this controller are that it has simple design steps, set-point tracking, and disturbance rejection capabilities. Initially, the proposed controller is designed for an unstable nonminimum-phase second-order system to demonstrate the performance and robustness. Subsequently, the presented controller is analyzed and validated on the nonideal dc–dc buck converter. The buck converter is highly sensitive to the suddenly changing load conditions. The major issue of output voltage control in the dc–dc buck converter is addressed in this article. In addition, the robustness and performance of the proposed controller are verified for the sudden change in supply input voltage, the sudden change in the reference voltage, the parametric uncertainty in inductor ( $L$ ) and capacitor ( $C$ ), and the sudden change in load. Furthermore, the efficacy of the proposed controller is evaluated by comparing it with the recently published control schemes for this converter. The proposed controller is implemented using dSPACE 1104 real-time controller. Applicability of the presented controller is corroborated experimentally on the nonideal dc–dc buck converter. The simulation as well as hardware results recommend that the proposed controller is suited for the nonideal dc–dc buck converter under such conditions.

Development of Decision Support System for Solving the Machine Selection Problem in an Intermittent Manufacturing System Design

Capacity planning, which is integral to resource selection and allocation, is an important task in manufacturing system design. As budget is limited, investment in resources must be done carefully and appropriately in order to achieve desired capacity and system efficiency. One of the most expensive and valuable resources in the manufacturing system is machinery. Therefore, selecting an appropriate machine is of high significance. Among many types of manufacturing systems, intermittent manufacturing system is complex, yet flexible. The intermittent manufacturing system enables manufacturers to produce multiple products using multiple processes and multiple machines. The system comprises a network of multiple types of multi-process machines. A machine selection problem in the context of the intermittent manufacturing system is a delicate process, as it must correspond appropriately with production complexities. To alleviate this problem, we develop a decision support system for solving the machine selection problem in an intermittent manufacturing system design. We study several manufacturing systems producing multiple types of products with various multi-process machines to lay out the framework. This decision support system consists of four main parts, namely; data entry, data conversion, optimization model and report. To solve the machine selection problem, an optimization technique is used to identify the optimal number of machines and the allocation of production process of each machine. The objective of this optimization problem is to minimize total production costs (machine cost and operating cost) while maintaining production capacity and other production constraints. The decision support system is developed for and validated with the manufacturing company.