Design Of Complex Systems Research Articles

Examination of AI Enhanced Distributed Systems and its Effects on Software Engineering

Distributed systems are groups of independent computers that appear to the system's users as a single coherent system. These systems involve several critical factors, including network communication, concurrency, and fault tolerance, which are vital in software engineering. For example, cloud computing platforms like Amazon Web Services (AWS) use distributed systems to offer scalable, on-demand resources to millions of users worldwide. The introduction of Artificial Intelligence (AI) into software engineering marks a transformative period that reshapes traditional development processes and breaks down the complexity of distributed systems. AI automates routine tasks and simplifies complex processes, serving as a digital collaborator that enables developers to focus on strategic thinking and creativity. One significant advantage of using AI in distributed systems within software engineering is enhanced resource optimization. AI can identify irregularities that might indicate hardware malfunctions and take preventative measures to address them, such as rerouting traffic to healthier servers. However, distributed systems also have challenges, including increased complexity in system design, difficulty ensuring data consistency, and potential security vulnerabilities. The intricate structure of these systems can result in problems with high startup costs, safety risks, and the accuracy of the data provided. Integrating AI into distributed systems offers both significant advantages and disadvantages. This study evaluates how AI impacts the efficiency and security of distributed systems in software engineering. By analyzing the advantages and disadvantages of AI-enhanced distributed systems, we can gain a comprehensive understanding of their overall effectiveness and implications in the field.

Brain-inspired biomimetic robot control: a review.

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.

Knowledge graph-based representation and recommendation for surrogate modeling method

Surrogate models have been widely used in engineering design for approximating a simulation system with high computational cost. Complex system design typically is a multi-stage and multi-discipline design problem, which requires a large number of surrogate models. The choice of surrogate modeling method (SMM) is critical as it directly impacts the performance of both the surrogate models and the designed systems. With the growing variety of SMMs, designers face challenges in selecting the appropriate methods for their specific applications. To address this, we propose a representation and recommendation framework for surrogate modeling methods based on knowledge graph. Firstly, we develop an ontology to formally represent core concepts involved in the recommendation for surrogate modeling methods, including surrogate modeling method, surrogate model, and data sets,etc. Secondly, we extract 460 samples from 46 benchmark functions using Latin hypercube sampling to construct a knowledge graph with 8,343 nodes and 16,100 relationships, which involves 7,820 surrogate models generated from 17 surrogate modeling methods. Finally, we propose a knowledge graph-based recommendation method for surrogate modeling method named KGRSMM to facilitate the selection of an appropriate surrogate modeling method. We test the efficacy of KGRSMM using examples of theoretical problems and engineering problems of hot rod rolling respectively. It is shown in the results that KGRSMM is capable of recommending surrogates with appropriate accuracy, robustness, and time to satisfy designers’ preferences.

Identification of required power for air transport system of the cotton-picking machine MX-2.4

Abstract This article addresses the issues in the air transport system of semi-suspended MX-1.8 and MX-2.4 cotton pickers with a vertical spindle. Given that semi-suspended machines currently pick cotton crops with 60 cm row spacing, the goal is to eliminate the shortcomings of these machines and optimize the technological parameters of this machine. The power required for fan operation is calculated, taking into account the additional aerodynamic resistance caused by the complexity of the air transport system design. The results of the calculations are presented in the form of a histogram.

A multi-image encryption scheme based on a new [formula omitted]-dimensional chaotic model and eight-base DNA

At present, multi-image encryption schemes usually require the type, quantity and size of images to be fixed, and there are many restrictions. Therefore, this paper proposes a multi-image encryption scheme based on a new n-dimensional(nD) chaotic model and eight-base DNA, which can encrypt any type, quantity and size of images. Firstly, an nD chaotic model is designed, which can generate different chaotic systems according to individual requirements, simplify the complexity of chaotic system design, and solve the problems of uneven chaotic trajectory and limited range. Secondly, some new eight-base algebraic operations are proposed, which opens up a new calculation method for DNA calculation and information processing. These algebraic operations are applied to the bidirectional diffusion process to achieve bidirectional DNA diffusion. Finally, multiple images are fused, and different keys are generated by the SHA-512 algorithm to ensure that each fused image has a unique key. Moreover, SCAN is utilized to scramble the fused image, which provides more flexibility and selectivity for image encryption and processing. Experimental simulation and security analysis verify that the proposed scheme has a superior effect and good anti-attack when encrypting multiple images with different types, quantities and sizes.

Using blockchain technology to maximize supply chain and logistics management in north America

This study explores the application of blockchain technology in optimizing supply chain and logistics management within North America. Through qualitative thematic analysis, three key objectives were addressed: identifying benefits, examining implementation challenges, and assessing barriers to adoption. The analysis highlights significant benefits of blockchain adoption, including enhanced trust, transparency, automation, traceability, and data security, supported by findings from various studies. However, implementation challenges such as system design complexity, resistance to change, and inadequate IT staff pose obstacles to adoption. Furthermore, barriers to widespread adoption, such as the absence of a proper legal framework and the capital-intensive nature of blockchain adoption, were identified. These barriers underscore the need for collaborative efforts to address regulatory uncertainties and financial constraints. It was suggested that further studies could explore the long-term impacts of blockchain technology on supply chain resilience and sustainability, as well as investigate strategies to overcome implementation challenges and maximize the technology's potential across diverse industries and regions.

Multi-fidelity design framework integrating compositional kernels to facilitate early-stage design exploration of complex systems

Abstract Early-stage design of complex systems is considered by many to be one of the most critical design phases because that is where many of the major decisions are made. The design process typically starts with low-fidelity tools, such as simplified models and reference data, but these prove insufficient for novel designs, necessitating the introduction of high-fidelity tools. This challenge can be tackled through the incorporation of multi-fidelity models. The application of MF models in the context of design optimization problems represents a developing area of research. This study proposes incorporating compositional kernels into the autoregressive scheme (AR1) of Multi-Fidelity Gaussian Processes, aiming to enhance the predictive accuracy and reduce uncertainty in design space estimation. The effectiveness of this method is assessed by applying it to 5 benchmark problems and a simplified design scenario of a cantilever beam. The results demonstrate significant improvement in the prediction accuracy and a reduction in the prediction uncertainty. Additionally, the paper offers a critical reflection on scaling up the method and its applicability in early-stage design of complex engineering systems, providing valuable insights into its practical implementation and potential benefits.

Smart socio-technological infrastructures: cyber security risks and the human factor

Smart socio-technological infrastructure is a new approach to the design and creation of complex systems, based on the integration of technological and social elements. Currently, smart sociotechnological infrastructures are applied in all spheres of life, from business and industry to healthcare and medical facilities. The implementation of these infrastructures creates new opportunities for the development of various fields. However, it causes a number of problems. The reasons for new information security problems arising from the characteristics of smart sociotechnological infrastructures may include the increase in the number of devices (the number of devices interacting with each other increases, which expands the potential attack plane), the complexity of integration (the integration of social and technological components leads to the creation of new vulnerabilities), data heterogeneity (where the processing and storage of various types of information, including confidential information, make them an attractive target for cybercriminals), the dynamism of the environment (smart socio-technological infrastructures are constantly evolving and adapting, which makes it difficult to ensure their security). This article examines information security problems of smart socio-technological infrastructures. New threats arising from the introduction of these infrastructures are classified. The development of information security culture is justified as one of the main factors of combating these threats, and recommendations are given on the principles and methods of its formation.

LEVERAGING MBSE USAGE THROUGH MODEL CHECKERS

AbstractSystem modeling is an essential part of the systems engineering process, helping with the design, analysis, and communication of complex systems. The usage of modeling languages like SysML and UML has become increasingly prevalent in this domain. To ensure the “3C” of validation of these models (correctness, completeness and consistency), model checkers play an important aspect.This paper discusses the role of model checkers in the validation of system models, and their importance in adopting MBSE approach with a quick benefit to system engineers. Different possible implementations are presented including one based on an ontology able to take advantage of semantic analyses. Finally, in order to deal with the number of issues due to the complexity of our models, we suggest correcting them regarding the goal of the model and the project milestones. This last point implies the need of an issue acknowledgement feature making it possible to justify the temporary or definitive rejection of these issues.

Close roof-mounted system temperature estimation for compliance to IEC TS 63126

When photovoltaic (PV) modules are installed on rooftops, the module temperature depends primarily on the geographic location and the mounting configuration. If the mounting structure does not provide sufficient airflow in a hot environment, the 98th percentile temperature will exceed 70 °C, which according to IEC TS 63126 ED. 1, requires higher levels of thermal stability testing. However, there is no clear way to determine the temperature level needed for a particular location and system design. In this work, we identify a relationship between the module standoff to the rooftop and the module temperature and propose methods to describe a minimum standoff for typical PV modules in a simple mounting configuration installed in a given location. For more complex system designs, we show how to determine an equivalent “effective standoff” that can be applied to generic calculations. Lastly, we show measurements and calculations from several systems to demonstrate how this method could work.

Risk based design optimization via scenario generation and genetic programming under hybrid uncertainties

Abstract The design of complex systems often requires the incorporation of uncertainty optimization strategies to mitigate system failures resulting from multiple uncertainties during actual operation. Risk-based design optimization, as an alternative methodology that accounts for the balance between design cost and performance, has garnered significant attention and recognition. This paper proposes a risk design optimization method for tackling hybrid uncertainties via scenario generation and genetic programming. The hybrid uncertainties are quantified through scenario generation method to obtain risk assessment indicators. The genetic programming method is used to simulate the real output of the objective or constraints. To drive the optimization process, the sample-based discrete gradient expression is constructed, and the optimal scheme aligning the risk requirements is obtained. Three calculation examples of varying computing complexity are presented to verify the efficacy and usability of the suggested approach.

Development of Static Test Equipment and a System for Lever-Loaded Air Springs

In light of the heavy load applied by traditional air spring test equipment and its complex structural system design, a lever-type torque loading air spring test system is designed. It adopts the principle of Chinese scales to apply the load on the air spring in the form of proportional amplification, which can apply a simulated load on the air spring of 500–800 kg, using the vertical sliding shaft as the transverse limit to make the air spring elongate and compress by 280 mm in the longitudinal direction. The measurement and control system of the test equipment is then developed based on the LabVIEW platform, and the required sensors are selected and installed. The system can achieve real-time data acquisition of the air pressure, load, height and other parameters of the air spring and air spring charging and discharging control. Following the debugging of the function of the test equipment, the function of each subsystem is normal and able to meet the requirements of air spring characteristic and pressure tightness tests. For small spaces, such as laboratories, by reducing the installation of hydraulic and other oil source systems, avoiding the use of large mass blocks to simulate the loading of air spring loads and optimizing the complex installation and debugging process, this miniaturized design for air spring test equipment has benefits for practical applications.

Application of computer information management system in universities in the information age

The application of computer information management system (IMS for short here) in university management faces problems such as incomplete system software and complex system design. Applying clustering algorithms (CA for short here) to computer student IMS can help optimize the system’s overall effectiveness. This article constructed a computer student IMS based on computer technology and applied it to the management of college students. This article also combined CA to conduct relevant effectiveness tests on the system, in order to optimize the overall effectiveness of the system. Under the algorithm in this article, the average connection speed for each user accessing the system was 9.17 Mbps. The average reaction time was 0.34 seconds, the average security level was 92.47%, and the highest memory usage rate of the system was 34.27%; Under the decision tree algorithm, the average connection speed of each user accessing the system was 8.82 Mbps, and the average reaction time reached 0.64 s. The average security level was 88.41%, and the highest memory usage rate was 42.58%. Under the artificial neural network algorithm, the average connection speed of the system was 8.47 Mbps, the average response time was 0.86 s, and the highest memory usage rate was 45.97%. Analyzing the data reveals that the algorithm introduced in this paper significantly enhances system connection speed and reduces reaction time. This improvement not only enhances security measures but also minimizes memory usage, effectively optimizing the overall efficiency of the system.

Dynamic Near‐Infrared Circularly Polarized Luminescence Encoded by Transient Supramolecular Chiral Assemblies

AbstractCircularly polarized luminescence (CPL) is promising for applications in many fields. However, most systems involving CPL are within the visible range; near‐infrared (NIR) CPL‐active materials, especially those that exhibit high glum values and can be controlled spatially and temporally, are rare. Herein, dynamic NIR‐CPL with a glum value of 2.5×10−2 was achieved through supramolecular coassembly and energy‐transfer strategies. The chiral assemblies formed by the coassembly between adenosine triphosphate (ATP) and a pyrene derivative exhibited a red CPL signal (glum of 10−3). The further introduction of sulfo‐cyanine5 resulted in a energy‐transfer process, which not only led to the NIR CPL but also increased the glum value to 10−2. Temporal control of these chiral assemblies was realized by introducing alkaline phosphatase to fabricate a biomimetic enzyme‐catalyzed network, allowing the dynamic NIR CPL signal to be turned on. Based on these enzyme‐regulated temporally controllable dynamic CPL‐active chiral assemblies, a multilevel information encryption system was further developed. This study provides a pioneering example for the construction of dynamic NIR CPL materials with the ability to perform temporal control via the supramolecular assembly strategy, which is expected to aid in the design of supramolecular complex systems that more closely resemble natural biological systems.

Dynamic Near-Infrared Circularly Polarized Luminescence Encoded by Transient Supramolecular Chiral Assemblies.

Circularly polarized luminescence (CPL) is promising for applications in many fields. However, most systems involving CPL are within the visible range; near-infrared (NIR) CPL-active materials, especially those that exhibit high glum values and can be controlled spatially and temporally, are rare. Herein, dynamic NIR-CPL with a glum value of 2.5×10-2 was achieved through supramolecular coassembly and energy-transfer strategies. The chiral assemblies formed by the coassembly between adenosine triphosphate (ATP) and a pyrene derivative exhibited a red CPL signal (glum of 10-3). The further introduction of sulfo-cyanine5 resulted in a energy-transfer process, which not only led to the NIR CPL but also increased the glum value to 10-2. Temporal control of these chiral assemblies was realized by introducing alkaline phosphatase to fabricate a biomimetic enzyme-catalyzed network, allowing the dynamic NIR CPL signal to be turned on. Based on these enzyme-regulated temporally controllable dynamic CPL-active chiral assemblies, a multilevel information encryption system was further developed. This study provides a pioneering example for the construction of dynamic NIR CPL materials with the ability to perform temporal control via the supramolecular assembly strategy, which is expected to aid in the design of supramolecular complex systems that more closely resemble natural biological systems.

THE METHOD OF RANKING EFFECTIVE PROJECT SOLUTIONS IN CONDITIONS OF INCOMPLETE CERTAINTY

The subject of research in the article is the process of ranking options in support systems for project decision-making under conditions of incomplete certainty. The goal of the work is to increase the efficiency of technologies for automated design of complex systems due to the development of a combined method of ranking effective options for building objects in conditions of incomplete certainty of input data. The following tasks are solved in the article: analysis of the current state of the problem of ranking options in support systems for project decision-making; decomposition of problems of system optimization of complex design objects and support of project decision-making; development of a variant ranking method that combines the procedures of lexicographic optimization and cardinal ordering in conditions of incomplete certainty of input data. The following methods are used: systems theory, utility theory, optimization, operations research, interval and fuzzy mathematics. Results. According to the results of the analysis of the problem of supporting project decision-making, the existence of the problem of correctly reducing subsets of effective options for ranking, taking into account factors that are difficult to formalize and the experience of the decision-maker (DM), was established. Decomposition of the problems of system optimization of complex design objects and support for project decision-making was carried out. For the case of ordinalistic presentation of preferences between local criteria, an estimate of the size of the rational reduction of subsets of optimal and suboptimal options for each of the indicators is proposed. Its use allows for one approach to obtain a subset of effective variants of a given capacity for analysis and final selection of the DM. A method of transforming the ordinalistic presentation of preferences between local criteria to their quantitative presentation in the form of weighting coefficients is proposed. Conclusions. The developed methods expand the methodological foundations of the automation of processes supporting the adoption of multi-criteria project decisions. They make it possible to correctly reduce the set of effective alternatives in conditions of incomplete certainty of the input data for the final choice, taking into account factors that are difficult to formalize, knowledge and experience of ODA. The practical use of the obtained results will allow to reduce the time and capacity complexity of the procedures for supporting project decision-making, and due to the use of the technology of selection of subsets of effective options with intervally specified characteristics - to guarantee the quality of project decisions and to provide a more complete assessment of them.

A dual‐mode integration framework and application to agile feedback design of launch vehicles

AbstractIn recent decades, the design of complex systems like launch vehicles in the aerospace industry has presented engineers with challenges that go beyond system complexity. Issues such as time‐to‐market pressures and intricate industrial processes have underscored the increasing significance of agile design methodologies. Agile design is derived from the simplification of the design process and enhancing cross‐domain data transmission and feedback. While methods based on model‐based system engineering have improved iteration times in system architecture design, challenges persist in cross‐domain data transmission. Due to the diversity of complex system models and data, a single‐mode integration method is difficult to realize the data link construction of all tools used. To address this challenge, this paper proposes a dual‐mode data integration framework with expansibility, universality, and cost‐efficiency which leverages the benefits of Remote Procedure Call and Intermediate Exchange Module, addressing the challenge of constructing cross‐domain data links under single‐mode integration. In this study, two critical requirements of the first‐ and second‐stage separation systems, namely, weight and minimum separation gap, are selected for data feedback. A Modelica‐based multiphysics simulation model is developed in MWorks; visualization and computation of the minimum gap are carried out in CoppeliaSim. To bridge the gap between domain‐specific tools, Matlab and Functional Mock‐up Unit modules are introduced as middleware, facilitating data feedback linkage. The entire simulation process is orchestrated using activity diagrams in the MagicDraw tool. The study delves into the influence of critical design parameters, such as the initial angular velocity of separation and the thrust of the retro rocket, on the minimum separation gap. It provides an analysis of minimum separation gap variations under uncertain operating conditions and examines design margins. Significantly, the paper highlights the significance of controlling the initial angular velocity during separation and the reliability of the retro rocket, providing essential decision supports and valuable insights to agile the process of system design.

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

РОЗРОБКА FRAMEWORK ДЛЯ КОНЦЕПТУАЛЬНОГО ПРОЕКТУВАННЯ СИСТЕМ РЕАЛЬНОГО ЧАСУ (FCD_RTS)

The paper proposes new results in improving the CoDeCS framework for the conceptual design of complex systems. A new architecture consisting of a subsystem for generating variants of enterprise information architectures (GEntA) and a subsystem for conceptual analytics (ConAn) for characterisation of real-time computer systems (RTSCS) is considered. Both subsystems rely on a common intellectual knowledge bank consisting of a base of facts, a base of production rules and a base of goals formed on the basis of the known experience of conceptual design of complex information-management computer systems. The paper describes the information-technological structures of formalised production lines and presents the first results of subsystems development.

THE ROLE OF BIG DATA IN SYSTEMS ENGINEERING: A REVIEW

This research review explores the transformative intersection of Big Data and Systems Engineering (SE), a convergence that leverages the vast capabilities of Big Data analytics to enhance the design, analysis, and management of complex systems. As the digital era ushers in unprecedented volumes of data, traditional systems engineering approaches are challenged to adapt, necessitating a paradigm shift towards integrating Big Data technologies. This review draws on recent scholarly contributions to highlight the implications, challenges, and innovative solutions emerging at this intersection. The integration of Big Data into Systems Engineering introduces significant opportunities for innovation. By processing and analyzing vast datasets, systems engineers can uncover hidden patterns and insights, leading to more efficient, reliable, and adaptable systems. However, this integration is not without its challenges. Information security emerges as a paramount concern, with the risk of insider attacks necessitating the development of new architectural solutions. Furthermore, the benchmarking and evaluation of Big Data systems present unique challenges due to the diversity of data and workloads involved. To address these challenges, this review examines several key contributions to the field. It discusses a formal framework for designing information systems that handle heterogeneous data, emphasizing the role of ontological models in separating system architecture from its implementation. The review also highlights the importance of security in Big Data systems, proposing a novel architecture for detecting insider attacks through data replication. Additionally, it explores the development of BigDataBench, a benchmark suite that facilitates the comprehensive evaluation of Big Data systems and architectures. Moreover, the review delves into the design of Big Data analytics architectures, focusing on goal-oriented modeling and the resolution of obstacles to quality goal achievement. It also introduces datar, a unified framework for Big Data Management Systems, showcasing a solution that manages Big Data in a pluggable, automatic, and intelligent manner. In conclusion, the intersection of Big Data and Systems Engineering heralds a new era of system design and management. By addressing the inherent challenges and leveraging innovative solutions, this convergence holds the potential to significantly enhance the capabilities of Systems Engineering, driving forward the development of complex, data-driven systems.
 Keywords: Big Data, Systems Engineering, Information Security, Data Management Systems, Benchmarking Big Data Systems.