Prediction of Defect Propensity for the Manual Assembly of Automotive Electrical Connectors

Assembly for automotive production represents a significant proportion of total manufacturing cost, manufacturing time, and overall product cost. Humans remain a cost effective solution to adapt to the requirements of increasing product complexity and variety present in today\u27s flexible manufacturing systems. The human element present in the manufacturing system necessitates a better understanding of the human role in manufacturing complexity. Presented herein is a framework for enumerating assembly variables correlated with the potential for quality defect, presented in the design, process, and human factors domain. A case study is offered that illustrates on a manual assembly process the effect that complexity variables have on assembly quality

Using gamification to motivate occupants to energy efficiency in a social setting of a building automation system

UID/CEC/04516/2019The widespread use of IoTs, as cheap and immersive technology, is enabling a wide range of daily life systems where Humans play a central role. It is commonly accepted that Humans sometimes present non-reliable behaviour. On the other hand, Gamification is becoming a common technique in system development to integrate business logic and induce Humans to accomplish certain goals and enforce systems reliability. Due to human nature, the evolution of game logic becomes an essential aspect of such systems to keep users engaged and participative. In this paper, we illustrate and discuss evolution in the particular scenario of a running Office Automation System in our open space. Here, the Human plays different roles such as an actuator, source of system input, a controller (decision maker), or simply environment (Human-in-the-loop). The mentioned system is the result of partially retrofitting a room of a forty years old building. It runs with a dynamic context scenario (that motivates different setups) and is formed by a heterogeneous set of IoTs. Those types of equipment are integrated to mainly accomplish two, sometimes conflicting, main goals: energy efficiency and Human comfort. As we will describe next, given the complexity of our system, various system's requirements need to be fulfilled at the same time. Those will dynamically change during runtime to contribute to both efficiency and participants' engagement. Not only the game requirements of the system evolve, but also the participants' behaviour change. We have consulted the open space's occupants on their daily routines and their preferences towards Gamification and gamified systems, particularly considering their social settings. They were also consulted on their views towards achieving energy efficiency in the open space. The results of this assessment are presented in this paper. The major suggestions were integrated into the current design of the system. We considered those that could contribute to the system's efficiency and reliability according to the system's goals. Besides, we also considered those that led to the use of several game techniques for motivating and improving the Humans' participation.authorsversionpublishe

Moving from a "human-as-problem" to a "human-as-solution" cybersecurity mindset

Cybersecurity has gained prominence, with a number of widely publicised security incidents, hacking attacks and data breaches reaching the news over the last few years. The escalation in the numbers of cyber incidents shows no sign of abating, and it seems appropriate to take a look at the way cybersecurity is conceptualised and to consider whether there is a need for a mindset change.To consider this question, we applied a "problematization" approach to assess current conceptualisations of the cybersecurity problem by government, industry and hackers. Our analysis revealed that individual human actors, in a variety of roles, are generally considered to be "a problem". We also discovered that deployed solutions primarily focus on preventing adverse events by building resistance: i.e. implementing new security layers and policies that control humans and constrain their problematic behaviours. In essence, this treats all humans in the system as if they might well be malicious actors, and the solutions are designed to prevent their ill-advised behaviours. Given the continuing incidences of data breaches and successful hacks, it seems wise to rethink the status quo approach, which we refer to as "Cybersecurity, Currently". In particular, we suggest that there is a need to reconsider the core assumptions and characterisations of the well-intentioned human's role in the cybersecurity socio-technical system. Treating everyone as a problem does not seem to work, given the current cyber security landscape.Benefiting from research in other fields, we propose a new mindset i.e. "Cybersecurity, Differently". This approach rests on recognition of the fact that the problem is actually the high complexity, interconnectedness and emergent qualities of socio-technical systems. The "differently" mindset acknowledges the well-intentioned human's ability to be an important contributor to organisational cybersecurity, as well as their potential to be "part of the solution" rather than "the problem". In essence, this new approach initially treats all humans in the system as if they are well-intentioned. The focus is on enhancing factors that contribute to positive outcomes and resilience. We conclude by proposing a set of key principles and, with the help of a prototypical fictional organisation, consider how this mindset could enhance and improve cybersecurity across the socio-technical system

Towards a Complexity Framework for Transformative Evaluation

Background:  Complexity ideas originating in mathematics and the natural sciences have begun to inform evaluation practice. A new wave in evaluation history is about to break. A new mindset, new methods, and new evaluation processes are being summoned to explore and address the challenges of global pandemics, growing inequities, and existential environmental risks. This is part of a broader paradigm shift underway in science where interdisciplinarity has become the norm rather than the exception. Purpose: This article explores the utility of a complexity framework for a more effective evaluation function. It unearths the antecedents of complexity thinking; explores its relevance to evolving knowledge paradigms; provides a bird’s eye view of complexity concepts; uses the logic of complex adaptive systems to unpack the role of evaluation in society; and draws the implications of contemporary social challenges for evaluation policy directions. Setting: Not applicable. Intervention: Not applicable. Research design: Not applicable. Findings: The evaluation complexity challenge coincides with an urgent imperative: social transformation. The on-going pandemic has brought to light the disproportionate effects of health emergencies on disadvantaged groups and emphasized the urgency of improving the interface between humans and nature. It has also demonstrated the importance of modelling for policy making – as well as its limitations. Evaluation, a complex adaptive system, should be transformed to serve society. Keywords: complexity; computers; disciplines; emergence; modelling; paradigm, system

Analytical Modeling of Human Choice Complexity in a Mixed Model Assembly Line Using Machine Learning-Based Human in the Loop Simulation

Despite the recent advances in manufacturing automation, the role of human involvement in manufacturing systems is still regarded as a key factor in maintaining higher adaptability and flexibility. In general, however, modeling of human operators in manufacturing system design still considers human as a physical resource represented in statistical terms. In this paper, we propose a human in the loop (HIL) approach to investigate the operator???s choice complexity in a mixed model assembly line. The HIL simulation allows humans to become a core component of the simulation, therefore influencing the outcome in a way that is often impossible to reproduce via traditional simulation methods. At the initial stage, we identify the significant features affecting the choice complexity. The selected features are in turn used to build a regression model, in which human reaction time with regard to different degree of choice complexity serves as a response variable used to train and test the model. The proposed method, along with an illustrative case study, not only serves as a tool to quantitatively assess and predict the impact of choice complexity on operator???s effectiveness, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput

Complex concept lattices for simulating human prediction in sport

In order to address the study of complex systems, the detection of patterns in their dynamics could play a key role in understanding their evolution. In particular, global patterns are required to detect emergent concepts and trends, some of them of a qualitative nature. Formal concept analysis (FCA) is a theory whose goal is to discover and extract knowledge from qualitative data (organized in concept lattices). In complex environments, such as sport competitions, the large amount of information currently available turns concept lattices into complex networks. The authors analyze how to apply FCA reasoning in order to increase confidence in sports predictions by means of detecting regularities from data through the management of intuitive and natural attributes extracted from publicly available information. The complexity of concept lattices -considered as networks with complex topological structure- is analyzed. It is applied to building a knowledge based system for confidence-based reasoning, which simulates how humans tend to avoid the complexity of concept networks by means of bounded reasoning skills.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606

Network Training for Continuous Speech Recognition

Spoken language processing is one of the oldest and most natural modes of information exchange between humans beings. For centuries, people have tried to develop machines that can understand and produce speech the way humans do so naturally. The biggest problem in our inability to model speech with computer programs and mathematics results from the fact that language is instinctive, whereas, the vocabulary and dialect used in communication are learned. Human beings are genetically equipped with the ability to learn languages, and culture imprints the vocabulary and dialect on each member of society. This thesis examines the role of pattern classification in the recognition of human speech, i.e., machine learning techniques that are currently being applied to the spoken language processing problem. The primary objective of this thesis is to create a network training paradigm that allows for direct training of multi-path models and alleviates the need for complicated systems and training recipes. A traditional trainer uses an expectation maximization (EM)based supervised training framework to estimate the parameters of a spoken language processing system. EM-based parameter estimation for speech recognition is performed using several complicated stages of iterative reestimation. These stages typically are prone to human error. The network training paradigm reduces the complexity of the training process while retaining the robustness of the EM-based supervised training framework. The hypothesis of this thesis is that the network training paradigm can achieve comparable recognition performance to a traditional trainer while alleviating the need for complicated systems and training recipes for spoken language processing systems

Energy Flows in Low-Entropy Complex Systems

Nature's many complex systems--physical, biological, and cultural--are islands of low-entropy order within increasingly disordered seas of surrounding, high-entropy chaos. Energy is a principal facilitator of the rising complexity of all such systems in the expanding Universe, including galaxies, stars, planets, life, society, and machines. A large amount of empirical evidence--relating neither entropy nor information, rather energy--suggests that an underlying simplicity guides the emergence and growth of complexity among many known, highly varied systems in the 14-billion-year-old Universe, from big bang to humankind. Energy flows are as centrally important to life and society as they are to stars and galaxies. In particular, the quantity energy rate density--the rate of energy flow per unit mass--can be used to explicate in a consistent, uniform, and unifying way a huge collection of diverse complex systems observed throughout Nature. Operationally, those systems able to utilize optimal amounts of energy tend to survive and those that cannot are non-randomly eliminated.Comment: 12 pages, 2 figures, review paper for special issue on Recent Advances in Non-Equilibrium Statistical Mechanics and its Application. arXiv admin note: text overlap with arXiv:1406.273

Self-directedness, integration and higher cognition

In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm

Contrasting Views of Complexity and Their Implications For Network-Centric Infrastructures

There exists a widely recognized need to better understand and manage complex “systems of systems,” ranging from biology, ecology, and medicine to network-centric technologies. This is motivating the search for universal laws of highly evolved systems and driving demand for new mathematics and methods that are consistent, integrative, and predictive. However, the theoretical frameworks available today are not merely fragmented but sometimes contradictory and incompatible. We argue that complexity arises in highly evolved biological and technological systems primarily to provide mechanisms to create robustness. However, this complexity itself can be a source of new fragility, leading to “robust yet fragile” tradeoffs in system design. We focus on the role of robustness and architecture in networked infrastructures, and we highlight recent advances in the theory of distributed control driven by network technologies. This view of complexity in highly organized technological and biological systems is fundamentally different from the dominant perspective in the mainstream sciences, which downplays function, constraints, and tradeoffs, and tends to minimize the role of organization and design