Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Presently, artificial intelligence (AI) is playing a leading role in our contemporary world via numerous applications. Despite its many advantages, analytical frameworks highlighting the implications of AI applications are still evolving. Particularly, in manufacturing and industrial production where novel technologies are continuously being harnessed. Consequently, AI and the implications of its applications have relatively remained a gray area for many engineering managers who are key players in the gravitation of manufacturing and industrial production toward the fourth industrial revolution and more recently, the fifth industrial revolution, generally termed as Industry 4.0 (I4.0) and Industry 5.0 (I5.0), respectively. In this study, the implications of AI applications in the general context of manufacturing and industrial production, are presented to provide insight for engineering managers. These implications are discussed via political, economic, social, and technological (PEST) considerations of the broad impact of the adoption of AI techniques in manufacturing and industrial production systems. A new engineering management model has not been proposed in this article. Rather, a discussion aimed at serving as a tool for the appraisal of the implications of the general applications of AI by engineering managers, who may not be AI specialists or data science experts is presented

AI-Driven Design of Microwave Antennas with Case Studies

With the advent of artificial intelligence (AI), the design of microwave devices such as antennas has been expedited in terms of throughput and time-to-market. This is chiefly because design automation via optimization has replaced the use of time intensive manual design techniques which premise on trial and error without any guarantee of successful outcomes. For the rapid design of antennas via optimization, surrogate model-based optimization (SBO) methods tend to be at the forefront due to their efficiency improvement in terms of computational cost. The surrogate model assisted differential evolution for antenna synthesis (SADEA) algorithm family are a class of state-of-the-art SBO methods. In this paper, the use and advantages of the SADEA algorithm family is demonstrated using two cases of real-world antenna design problems as examples. The antenna design problems are the optimization of: a multi-layered compact multiple-input and the multiple-output (MIMO) antenna array for wireless communications and a microwave imaging antenna for ultra wide band (UWB) body-centric applications. For both examples, the SADEA algorithm family obtained very good design solutions within an affordable time and the quality of the obtained solutions are validated by the close agreement which exits between the simulated and measured results of the fabricated and ready-to-use prototypes of the antennas. In one of the cases (the microwave imaging antenna), the performance of the SADEA algorithm family when compared to 2019 Computer Simulation Technology - Microwave Studio (CST-MWS) optimizers (trust region framework (TRF) and particle swarm optimisation (PSO)) is reported. Results from the comparisons show that the SADEA algorithm family obtains very satisfactory design solutions in all runs using an affordable optimization time in each, whereas the alternative optimizers failed in all runs by not meeting the design requirements and/or generating designs with geometric incongruities

Behavioral Study of Software-Defined Network Parameters Using Exploratory Data Analysis and Regression-Based Sensitivity Analysis

To provide a low-cost methodical way for inference-driven insight into the assessment of SDN operations, a behavioral study of key network parameters that predicate the proper functioning and performance of software-defined networks (SDNs) is presented to characterize their alterations or variations, given various emulated SDN scenarios. It is standard practice to use simulation environments to investigate the performance characteristics of SDNs, quantitatively and qualitatively; hence, the use of emulated scenarios to typify the investigated SDN in this paper. The key parameters studied analytically are the jitter, response time and throughput of the SDN. These network parameters provide the most vital metrics in SDN operations according to literature, and they have been behaviorally studied in the following popular SDN states: normal operating condition without any incidents on the SDN, hypertext transfer protocol (HTTP) flooding, transmission control protocol (TCP) flooding, and user datagram protocol (UDP) flooding, when the SDN is subjected to a distributed denial-of-service (DDoS) attack. The behavioral study is implemented primarily via univariate and multivariate exploratory data analysis (EDA) to characterize and visualize the variations of the SDN parameters for each of the emulated scenarios, and linear regression-based analysis to draw inferences on the sensitivity of the SDN parameters to the emulated scenarios. Experimental results indicate that the SDN performance metrics (i.e., jitter, latency and throughput) vary as the SDN scenario changes given a DDoS attack on the SDN, and they are all sensitive to the respective attack scenarios with some level of interactions between them

Hybrid Single and Multiobjective optimization for Engineering Design without Exact Specifications

A challenge in engineering design optimization is that sufficient information may not be available to define the exact specifications beforehand. While iterative trial optimization using different specifications is widely used in industry, multiobjective optimization is attracting much attention in the academic field. However, off-the-shelf methods in both categories are time-consuming due to the involved computationally expensive simulations. In this paper, the characteristics of the targeted problem are summarized; the gap between off-the-shelf methods and the practical need is then analyzed. A simple yet effective framework, called two-stage multi-fidelity surrogate model-assisted optimization (TMSO), is proposed to improve efficiency. TSMO is implemented by two state-of-the-art optimization algorithms and two real-world design cases demonstrate its effectiveness in practice. The research topics in multiobjective optimization and surrogate model-assisted optimization inspired by the TSMO framework is finally discussed

A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design Optimization

Optimization efficiency is a major challenge for electromagnetic (EM) device, circuit, and machine design. Although both surrogate model-assisted evolutionary algorithms (SAEAs) and parallel computing are playing important roles in addressing this challenge, there is little research that investigates their integration to benefit from both techniques. In this paper, a new method, called parallel SAEA for electromagnetic design (PSAED), is proposed. A state-of-the-art SAEA framework, surrogate model-aware evolutionary search, is used as the foundation of PSAED. Considering the landscape characteristics of EM design problems, three differential evolution mutation operators are selected and organized in a particular way. A new SAEA framework is then proposed to make use of the selected mutation operators in a parallel computing environment. PSAED is tested by a micromirror and a dielectric resonator antenna as well as four mathematical benchmark problems of various complexity. Comparisons with state-of-the-art methods verify the advantages of PSAED in terms of efficiency and optimization capacity

Efficient Global Optimisation of Microwave Antennas Based on a Parallel Surrogate Model-assisted Evolutionary Algorithm

Computational efficiency is a major challenge for evolutionary algorithm (EA)-based antenna optimisation methods due to the computationally expensive electromagnetic simulations. Surrogate model-assisted EAs considerably improve the optimisation efficiency, but most of them are sequential methods, which cannot benefit from parallel simulation of multiple candidate designs for further speed improvement. To address this problem, a new method, called parallel surrogate model-assisted hybrid differential evolution for antenna optimisation (PSADEA), is proposed. The performance of PSADEA is demonstrated by a dielectric resonator antenna, a Yagi-Uda antenna, and three mathematical benchmark problems. Experimental results show high operational performance in a few hours using a normal desktop 4-core workstation. Comparisons show that PSADEA possesses significant advantages in efficiency compared to a state-of-the-art surrogate model-assisted EA for antenna optimisation, the standard parallel differential evolution algorithm, and parallel particle swarm optimisation. In addition, PSADEA also shows stronger optimisation ability compared to the above reference methods for challenging design cases

Design Considerations and Data Communication Architecture for National Animal Identification and Traceability System in Nigeria

Wireless communication systems and their supporting infrastructure continue to play a vital role in contemporary daily activities. Due to the unprecedented levels of interconnectivity achieved between wireless devices in recent times, new insights and paradigms for the robust deployment and better utilization of wireless communication systems are always of interest to many countries for socio-economic development. Present-day Nigeria is faced with the challenge of insurgencies whose financing has been linked to proceeds from livestock theft or rustling according to many scholarly works and news reports. To mitigate rustling and the sales of stolen livestock via identification and traceability from ‘herds to markets to homes’, the design considerations and data communication architecture for national animal identification and traceability system in Nigeria (NAITS) is proposed in this paper for safer and improved livestock farming and production. Particularly, technical insight into the co-use of radio frequency identification (RFID) and fifth-generation new radio (5G NR) technologies for the implementation of NAITS are highlighted and discussed in this paper for a prospective technological policy plan and development in Nigeria

GUI Design Exploration Software for Microwave Antennas

Optimizers in commercial electromagnetic (EM) simulation software packages are the main tools for performing antenna design exploration today. However, these general purpose optimizers are facing challenges in optimization efficiency, supported optimization types and usability for antenna experts without deep knowledge on optimization. Aiming to fill the gaps, a new antenna design exploration tool, called Antenna Design Explorer (ADE), is presented in this paper. The key features are: (1) State-of-the-art antenna design exploration methods are selected and embedded, addressing efficient antenna optimization (critical but unable to be solved by existing tools) and multiobjective antenna optimization (not available in most existing tools); (2) Human-computer interaction for the targeted problem is studied, addressing various usability issues for antenna design engineers, such as automatic algorithmic parameter setting and interactive stopping criteria; (3) Compatibility with existing tools is studied and ADE is able to co-work with existing EM simulators and optimizers, combining advantages. A case study verifies the advantages of ADE

An Energy Efficient Service Composition Mechanism Using a Hybrid Meta-heuristic Algorithm in a Mobile Cloud Environment

By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms

Detection and Classification of DDoS Flooding Attacks on Software-Defined Networks: A Case Study for the Application of Machine Learning

Software-defined networks (SDNs) offer robust network architectures for current and future Internet of Things (IoT) applications. At the same time, SDNs constitute an attractive target for cyber attackers due to their global network view and programmability. One of the major vulnerabilities of typical SDN architectures is their susceptibility to Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding attacks can render SDN controllers unavailable to their underlying infrastructure, causing service disruption or a complete outage in many cases. In this paper, machine learning-based detection and classification of DDoS flooding attacks on SDNs is investigated using popular machine learning (ML) algorithms. The ML algorithms, classifiers and methods investigated are quadratic discriminant analysis (QDA), Gaussian Naïve Bayes (GNB), k -nearest neighbor (k-NN), and classification and regression tree (CART). The general principle is illustrated through a case study, in which, experimental data (i.e. jitter, throughput, and response time metrics) from a representative SDN architecture suitable for typical mid-sized enterprise-wide networks is used to build classification models that accurately identify and classify DDoS flooding attacks. The SDN model used was emulated in Mininet and the DDoS flooding attacks (i.e. hypertext transfer protocol (HTTP), transmission control protocol (TCP), and user datagram protocol (UDP) attacks) have been launched on the SDN model using low orbit ion cannon (LOIC). Although all the ML methods investigated show very good efficacy in detecting and classifying DDoS flooding attacks, CART demonstrated the best performance on average in terms of prediction accuracy (98%), prediction speed ( 5.3×105 observations per second), training time (12.4 ms), and robustness