Software-defined cloud manufacturing for industry 4.0

AbstractMany of the world's leading industrial nations have invested in national initiatives to foster advanced manufacturing, innovation, and design for the globalized world. Much of this investment has been driven by visions such as Industry 4.0, striving to achieve a future where intelligent factories and smart manufacturing are the norm. Within this realm, innovations such as the Industrial Internet of Things, Cloud-based Design and Manufacturing (CBDM), and Social Product Development (SPD) have emerged with a focus on capitalizing on the benefits and economies of scale provided by Internet Protocol (IP) communication technologies. Another emerging idea is the notion of software-defined systems such as software-defined networks, which exploit abstraction and inexpensive hardware advancements in an effort to build more flexible systems. Recently, the authors have begun considering how the notion of software-defined systems might be harnessed to achieve flexible cloud manufacturing systems. As a result, this paper introduces the notion of Software-Defined Cloud Manufacturing (SDCM). We describe a basic SDCM architecture based on leveraging abstraction between manufacturing hardware and cloud-based applications, services, and platforms. The goal of SDCM is to advance Cloud-Based Manufacturing and other Industry 4.0 pillars by providing agility, flexibility, and adaptability while also reducing various complexity challenges

Parametric Optimization of Artificial Neural Networks for Signal Approximation Applications

Artificial neural networks are used to solve diverse sets of problems. However, the accuracy of the network’s output for a given problem domain depends on appropriate selection of training data as well as various design parameters that define the structure of the network before it is trained. Genetic algorithms have been used successfully for many types of optimization problems. In this paper, we describe a methodology that uses genetic algorithms to find an optimal set of configuration parameters for artificial neural networks such that the network’s approximation error for signal approximation problems is minimized

Innovative Design Education in a Global Distance Learning Setting

A lot has been written about how much the world has changed since the advent of Globalization and that engineering education, in response to that needs to be addressed from a more holistic point of view. In this paper, we present an innovative approach to design education that represents a transformation from traditional in-class education to a globally distributed collaborative distance learning setting that mirrors real-world design experience

Educating Engineers for the Near Tomorrow

In this paper, we present an educational approach to facilitate Learning how to Learn, that is, to equip our students with competencies needed to become lifelong learners and succeed in the job market of the near tomorrow. Our approach is anchored in educational and instructional theory and closely tied to current professional practice. The approach is implemented in a graduate level engineering design course that is offered in a distributed collaborative distance learning setting

Architectures and Design Methodologies for Scalable and Sustainable Remote Laboratory Infrastructures

With the increasing demand for distance learning opportunities in the higher education sector, there isan ever-growing need for the design and deployment of remote laboratories, especially for engineering,science, and technology curricula. In order to accommodate the offering of entire degrees for distancelearning students whose curricula require remote laboratories, scalable information technology infrastructuresthat support the large scale use and deployment of these remote laboratories must exist. Thischapter provides a discussion of architectures and design methodologies using technology such as commandand control communications, Web 2.0, and cloud computing, which provide a scalable, manageable,and sustainable technological infrastructure-basis for large scale remote laboratory deployment

Towards data-driven cyber attack damage and vulnerability estimation for manufacturing enterprises

Defending networks against cyber attacks is often reactive rather than proactive. Attacks against enterprises are often monetary driven and are targeted to compromise data. While the best practices in enterprise-level cyber security of IT infrastructures are well established, the same cannot be said for critical infrastructures that exist in the manufacturing industry. Often guided by these best practices, manufacturing enterprises apply blanket cyber security in order to protect their networks, resulting in either under or over protection. In addition, these networks comprise heterogeneous entities such as machinery, control systems, digital twins and interfaces to the external supply chain making them susceptible to cyber attacks that cripple the manufacturing enterprise. Therefore, it is necessary to analyse, comprehend and quantify the essential metrics of providing targeted and optimised cyber security for manufacturing enterprises. This paper presents a novel data-driven approach to develop the essential metrics, namely, Damage Index (DI) and Vulnerability Index (VI) that quantify the extent of damage a manufacturing enterprise could suffer due to a cyber attack and the vulnerabilities of the heterogeneous entities within the enterprise respectively. A use case for computing the metrics is also demonstrated. This work builds a strong foundation for development of an adaptive cyber security architecture with optimal use of IT resources for manufacturing enterprises

Cannabis use and neurocognitive functioning in a non-clinical sample of users

OBJECTIVE: With the recent debates over marijuana legalization and increases in use, it is critical to examine its role in cognition. While many studies generally support the adverse acute effects of cannabis on neurocognition, the non-acute effects remain less clear. The current study used a cross-sectional design to examine relationships between recent and past cannabis use on neurocognitive functioning in a non-clinical adult sample. METHOD: One hundred and fifty-eight participants were recruited through fliers distributed around local college campuses and the community. All participants completed the Brief Drug Use History Form, the Structured Clinical Interview for DSM-IV Disorders, and neurocognitive assessment, and underwent urine toxicology screening. Participants consisted of recent users (n = 68), past users (n = 41), and non-users (n = 49). RESULTS: Recent users demonstrated significantly (p < .05) worse performance than non-users across cognitive domains of attention/working memory (M = 42.4, SD = 16.1 vs. M = 50.5, SD = 10.2), information processing speed (M = 44.3, SD = 7.3 vs. M = 52.1, SD = 11.0), and executive functioning (M = 43.6, SD = 13.4 vs. M = 48.6, SD = 7.2). There were no statistically significant differences between recent users and past users on neurocognitive performance. Frequency of cannabis use in the last 4 weeks was negatively associated with global neurocognitive performance and all individual cognitive domains. Similarly, amount of daily cannabis use was negatively associated with global neurocognitive performance and individual cognitive domains. CONCLUSIONS: Our results support the widespread adverse effects of cannabis use on neurocognitive functioning. Although some of these adverse effects appear to attenuate with abstinence, past users' neurocognitive functioning was consistently lower than non-users

Advancing cyber security with a semantic path merger packet classification algorithm

This dissertation investigates and introduces novel algorithms, theories, and supporting frameworks to significantly improve the growing problem of Internet security. A distributed firewall and active response architecture is introduced that enables any device within a cyber environment to participate in the active discovery and response of cyber attacks. A theory of semantic association systems is developed for the general problem of knowledge discovery in data. The theory of semantic association systems forms the basis of a novel semantic path merger packet classification algorithm. The theoretical aspects of the semantic path merger packet classification algorithm are investigated, and the algorithm's hardware-based implementation is evaluated along with comparative analysis versus content addressable memory. Experimental results show that the hardware implementation of the semantic path merger algorithm significantly outperforms content addressable memory in terms of energy consumption and operational timing.PhDCommittee Chair: Randal Abler; Committee Member: Dirk Schaefer; Committee Member: George Riley; Committee Member: Henry Owen; Committee Member: Raghupathy Sivakuma