scholarly journals Poka Yoke Meets Deep Learning: a Proof of Concept for an Assembly Line Application

2021 ◽  
Author(s):  
Matteo Martinelli ◽  
Rita Gamberini ◽  
Marco Lippi
Keyword(s):  
Deep Learning ◽  
Lean Manufacturing ◽  
Assembly Line ◽  
Shop Floor ◽  
Optimal Arrangement ◽  
Whole Process ◽  
Critical Issues ◽  
Agricultural Applications ◽  
Definition Of ◽  
Assembly Error

Abstract In this paper we present the re-engineering process of an assembly line that features speed reducers and multipliers for agricultural applications. The product operates as an interface between an input torque, typically supplied by an agricultural vehicle, and an output torque, generally moving specific equipment placed on a trolley equipped with a tow hook. The "as-is'' (initial version) line was highly inefficient due to several critical issues, including the high age of the machines, a non-optimal arrangement of them in the shop floor, and the absence of controls and process standards. These critical issues were analysed with the tools offered by Lean Manufacturing, which made it possible to identify irregularities and operations that require effort (Mura), overload (Muri), and waste (Muda). The definition of the "to-be'' (new version) assembly line included actions to update the department layout, to modify the assembly process and to design the line feeding system in compliance with the well-known concepts of Golden Zone and Strike Zone. The whole process addressed, in particular, the problem of the incorrect assembly of the oil seals. The registered error was mainly caused by the difficulty in visually identifying the correct side of the assembled oil seal, and by the mental fatigue of operators at the end of the shift. The solution studied in this paper resulted in a Poka-Yoke solution, which, leveraging the modern technologies and methods of deep learning and computer vision, monitors the process flow of the operators through a camera, preventing its completion in the event of an assembly error.

scholarly journals Lean Companies Employed Assembly Workers Train with New Approches

2021 ◽  
Vol 12 (5) ◽  
pp. 574-579
Author(s):  
Somesh Dhamija
Keyword(s):  
Lean Manufacturing ◽  
Implementation Process ◽  
Shop Floor ◽  
Manufacturing Companies ◽  
Complex Activity ◽  
The Core ◽  
Production Processes ◽  
Effective Training ◽  
Manufacturing Activity ◽  
Definition Of

LM has proven itself the production system that enhances shop floor efficiency. Furthermore, the current environment for production firms is accelerating the pace at which LM is implemented. The manufacture of lean is not easy to introduce. It is constant and complex activity. Assembly workers in production processes are the core of lean manufacturing activity. Training is known as vehicle to aid the implementation process. While the importance of training is known so far, there are only a few options for organizing effective training. The results of the survey of questions conducted inside UK manufacturing companies are examined. This article illustrates the definition of lean production and worker requirements in lean environment.

The Rise of Partnerships: From Local to Global

2018 ◽  
Author(s):  
Barbara Gray ◽  
Jill Purdy
Keyword(s):  
Nongovernmental Organizations ◽  
Public Services ◽  
Civic Society ◽  
Robust Solutions ◽  
Essential Components ◽  
Critical Issues ◽  
Definition Of ◽  
Societal Problems ◽  
Shared Norms ◽  
Multistakeholder Partnerships

Multistakeholder partnerships (MSPs) are formed to tackle knotty societal problems, promote innovation, provide public services, expand governance capabilities, set standards for a field, or resolve conflicts that impede progress on critical issues. Partnerships are viewed as collaboration among four types of stakeholders: businesses, governments, nongovernmental organizations (NGOs), and civic society. The objective of collaboration is to create a richer, more comprehensive appreciation of the iss/problem than any of the partners could construct alone by viewing it from the perspectives of all the stakeholders and designing robust solutions. Such partnerships are necessary because few organizations contain sufficient knowledge and resources to fully analyze issues and take action on them unilaterally. Five essential components of a rigorous definition of collaboration are presented: interdependence among partners, emergence of shared norms, wrestling with differences, respect for different competencies, and assuming joint responsibility for outcomes. Several examples of MSPs are provided.

scholarly journals TOWARDS AN UNIFIED FRAMEWORK FOR VALIDATING DEEP LEARNING METHODS FOR COLORECTAL POLYPS: FIRST STEPS

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
L F Sánchez Peralta ◽  
J F Ortega Morán ◽  
Cr L Saratxaga ◽  
J B Pagador ◽  
A Picón ◽  
...  
Keyword(s):  
Deep Learning ◽  
Colorectal Polyps ◽  
Polyp Detection ◽  
Unified Framework ◽  
Endoscopic Image ◽  
Learning Techniques ◽  
Great Utility ◽  
Adenoma Detection ◽  
Definition Of ◽  
Basic Features

Abstract INTRODUCTION Deep learning techniques have significantly contributed to the field of medical imaging analysis. In case of colorectal cancer, they have shown a great utility for increasing the adenoma detection rate at colonoscopy, but a common validation methodology is still missing. In this study, we present preliminary efforts towards the definition of a validation framework. MATERIAL AND METHODS Different models based on different backbones and encoder-decoder architectures have been trained with a publicly available dataset that contains white light and NBI colonoscopy videos, with 76 different lesions from colonoscopy procedures in 48 human patients. A computer aided detection (CADe) demonstrator has been implemented to show the performance of the models. RESULTS This CADe demonstrator shows the areas detected as polyp by overlapping the predicted mask on the endoscopic image. It allows selecting the video to be used, among those from the test set. Although it only present basic features such as play, pause and moving to the next video, it easily loads the model and allows for visualization of results. The demonstrator is accompanied by a set of metrics to be used depending on the aimed task: polyp detection, localization and segmentation. CONCLUSIONS The use of this CADe demonstrator, together with a publicly available dataset and predefined metrics will allow for an easier and more fair comparison of methods. Further work is still required to validate the proposed framework.

scholarly journals Beneficial and harmful explanatory machine learning

2021 ◽  
Author(s):  
Lun Ai ◽  
Stephen H. Muggleton ◽  
Céline Hocquette ◽  
Mark Gromowski ◽  
Ute Schmid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Beneficial Effect ◽  
Empirical Evidence ◽  
Task Performance ◽  
Human Performance ◽  
Human Learning ◽  
Science Literature ◽  
Definition Of ◽  
Self Learning

AbstractGiven the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the potential harmfulness of machine’s involvement for human comprehension during learning. This paper investigates the explanatory effects of a machine learned theory in the context of simple two person games and proposes a framework for identifying the harmfulness of machine explanations based on the Cognitive Science literature. The approach involves a cognitive window consisting of two quantifiable bounds and it is supported by empirical evidence collected from human trials. Our quantitative and qualitative results indicate that human learning aided by a symbolic machine learned theory which satisfies a cognitive window has achieved significantly higher performance than human self learning. Results also demonstrate that human learning aided by a symbolic machine learned theory that fails to satisfy this window leads to significantly worse performance than unaided human learning.

scholarly journals Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 52
Author(s):  
Thomas Lee ◽  
Susan Mckeever ◽  
Jane Courtney
Keyword(s):  
Deep Learning ◽  
Research Work ◽  
Learning Approaches ◽  
Clear Definition ◽  
Top Down ◽  
Research Activity ◽  
Comprehensive Overview ◽  
The Past ◽  
Computer Vision Applications ◽  
Definition Of

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the Society of Automotive Engineers, to specific drone tasks in order to create a clear definition of autonomy when applied to drones. A top–down examination of research work in the area is conducted, focusing on drone navigation tasks, in order to understand the extent of research activity in each area. Autonomy levels are cross-checked against the drone navigation tasks addressed in each work to provide a framework for understanding the trajectory of current research. This work serves as a guide to research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating key works and areas of opportunity for development of this area in the future.

Application of Deep Learning Technique to Predict Downhole Pressure Differential in Eccentric Annulus of Ultra-Deep Well

2021 ◽  
Author(s):  
Shwetank Krishna ◽  
Syahrir Ridha ◽  
Suhaib Umer Ilyas ◽  
Scott Campbell ◽  
Uday Bhan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pressure Gradient ◽  
Field Data ◽  
Activation Function ◽  
Pressure Differential ◽  
Optimal Arrangement ◽  
Eccentric Annulus ◽  
Learning Framework ◽  
Deep Well ◽  
Deep Wells

Abstract Accurate prediction of downhole pressure differential (surge/swab pressure gradient) in the eccentric annulus of ultra-deep wells during tripping operation is a necessity to optimize well geometry, reduction of drilling anomalies, and prevention of hazardous drilling accidents. Therefore, a new predictive model is developed to forecast surge/swab pressure gradient by using feed-forward and backpropagation deep neural networks (FFBP-DNN). A theoretical-based model is developed that follows the physical and mechanical aspects of surge/swab pressure generation in eccentric annulus during tripping operation. The data generated from this model, field data, and experimental data are used to train and test the FFBP-DNN networks. The network is developed used Keras’s deep learning framework. After testing the models, the most optimal arrangement of FFBP-DNN is the ReLU algorithm as an activation function, 4-hidden layers, the learning rate of 0.003, and 2300 of training numbers. The optimum FFBP-DNN model is validated by comparing it with field data (Wells K 470 and K 480, North Sea). It shows an excellent argument between predicted data and field data with an error range of ±7.68 %.

Primary Care: The Future for Pediatric Education

PEDIATRICS ◽  
1990 ◽  
Vol 86 (5) ◽  
pp. 653-659
Author(s):  
Joel J. Alpert
Keyword(s):  
Primary Care ◽  
Rural Areas ◽  
Primary Care Physicians ◽  
Geographic Location ◽  
Pediatric Care ◽  
Past Century ◽  
Inner Cities ◽  
Critical Issues ◽  
Definition Of ◽  
Care Problems

There is a continuing crisis in primary care, characterized by inadequate numbers of appropriately trained primary care physicians and the failure to mount an effective and consistent graduate educational program for primary care. This paper reviews the history of the primary care crisis; revisits the definition of primary care; and, through identification of critical issues, presents a primary care educational agenda for the 1990s. Pediatrics is at a crossroads regarding primary care, as powerful social and economic forces are impacting on today's major pediatric care problems. Before the second World War there were more than 300 primary care physicians available for each 100 000 of our population. Today the ratio is 75 for 100 000. This is despite the fact that a shortage of 50 000 physicians 10 years ago no longer exists. The majority view is that a physician surplus of 70 000 will be present by the early 1990s.1 Whether there is a surplus is subject to interpretation and the surplus may end up as nonexistent. Moreover, the availability of primary care physicians varies with geographic location, and even a single figure for this nation provides a distorted picture. The shortage is especially serious in inner cities and in many rural areas. In addition, the use of overall numbers assumes that all primary care physicians are appropriately trained in the general disciplines. For the past century, physicians have cared for patients usually as family physicians. Today, however, the generalist has been replaced by the specialist. Is this a function of financial rewards and society's needs and values or the educational experience?

scholarly journals Student access to and skills in using technology in an open and distance learning context

2012 ◽  
Vol 13 (4) ◽  
pp. 250 ◽  
Author(s):  
Hanlie Liebenberg ◽  
Yuraisha Chetty ◽  
Paul Prinsloo
Keyword(s):  
Higher Education ◽  
Distance Learning ◽  
Digital Divide ◽  
South African ◽  
Learning Context ◽  
Access To Technology ◽  
Critical Issues ◽  
Information And Communication ◽  
Definition Of ◽  
South African Higher Education

<p>Amidst the different challenges facing higher education, and particularly distance education (DE) and open distance learning (ODL), access to information and communication technology (ICT) and students’ abilities to use ICTs are highly contested issues in the South African higher education landscape. While there are various opinions about the scope and definition of the digital divide, increasing empirical evidence questions the uncritical use of the notion of the digital divide in South African and international higher education discourses.</p><p>In the context of the University of South Africa (Unisa) as a mega ODL institution, students’ access to technology and their functional competence are some of the critical issues to consider as Unisa prepares our graduates for an increasingly digital and networked world.</p><p>This paper discusses a descriptive study that investigated students’ access to technology and their capabilities in using technology, within the broader discourse of the “digital divide.” Results support literature that challenges a simplistic understanding of the notion of the “digital divide” and reveal that the nature of access is varied.</p>

Critical Issues in the Evaluation of Comorbidity of Psychiatric Disorders

1996 ◽  
Vol 168 (S30) ◽  
pp. 9-16 ◽  
Author(s):  
Hans-Ulrich Wittchen
Keyword(s):  
Interdisciplinary Approach ◽  
Epidemiological Studies ◽  
Comorbid Conditions ◽  
Substantial Variation ◽  
Research And Practice ◽  
Cognitive Markers ◽  
Potential Value ◽  
Critical Issues ◽  
Definition Of ◽  
Study Designs

Comorbidity has become an increasingly popular theme in psychiatry and clinical psychology, although its heuristic value was recognised long ago. Frequently used in research and practice, no definition of comorbidity is uniformly accepted and it has no comprehensive and coherent theoretical framework. These factors have led to substantial variation in the magnitude of comorbidity across studies. The variability in the definition, assessment and design of comorbidity studies has led to an increasingly complex and confusing picture about the potential value of this concept. The full exploration of mechanisms of comorbidity requires an interdisciplinary approach to investigating nosology, assessment, and underlying models of comorbidity, as well as experimental study designs beyond the scope of clinical and epidemiological studies. A more precise specification of comorbidity patterns might help identify common biochemical and cognitive markers relevant in the aetiology of specific mental disorders as well as comorbid conditions. Critical issues that might help us understand and explain the variability of findings are described.

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