A case-based reasoning approach for low volume, high added value electronics

This paper will report on the application of the Case-Based Reasoning (CBR) approach [1] to develop a defect prediction system to support the development of new printed circuit assembly (PCA) products. Using a CBR system, past PCA design specifications and manufacturing experiences including defect and yield results can be effectively stored and reapplied for future problem solving. For example, the CBR can then be used at design stage to amend designs or define process options to optimise the product yield and service reliability. A case study using a case-base provided by a PCA manufacturer is presented

Complex low volume electronics simulation tool to improve yield and reliability

Assembly of Printed Circuit Boards (PCB) in low volumes and a high-mix requires a level of manual intervention during product manufacture, which leads to poor first time yield and increased production costs. Failures at the component-level and failures that stem from non-component causes (i.e. system-level), such as defects in design and manufacturing, can account for this poor yield. These factors have not been incorporated in prediction models due to the fact that systemfailure causes are not driven by well-characterised deterministic processes. A simulation and analysis support tool being developed that is based on a suite of interacting modular components with well defined functionalities and interfaces is presented in this paper. The CLOVES (Complex Low Volume Electronics Simulation) tool enables the characterisation and dynamic simulation of complete design; manufacturing and business processes (throughout the entire product life cycle) in terms of their propensity to create defects that could cause product failure. Details of this system and how it is being developed to fulfill changing business needs is presented in this paper. Using historical data and knowledge of previous printed circuit assemblies (PCA) design specifications and manufacturing experiences, defect and yield results can be effectively stored and re-applied for future problem solving. For example, past PCA design specifications can be used at design stage to amend designs or define process options to optimise the product yield and service reliability

A case-based reasoning approach for low volume, high added value electronics

This paper will report on the application of the Case-Based Reasoning (CBR) approach [1] to develop a defect prediction system to support the development of new printed circuit assembly (PCA) products. Using a CBR system, past PCA design specifications and manufacturing experiences including defect and yield results can be effectively stored and reapplied for future problem solving. For example, the CBR can then be used at design stage to amend designs or define process options to optimise the product yield and service reliability. A case study using a case-base provided by a PCA manufacturer is presented

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Complex Low Volume Electronics Simulation Tool to Improve Yield and Reliability

Assembly of Printed Circuit Boards (PCB) in low volumes and a high-mix requires a level of manual intervention during product manufacture, which leads to poor first time yield and increased production costs. Failures at the component-level and failures that stem from non-component causes (i.e. system-level), such as defects in design and manufacturing, can account for this poor yield. These factors have not been incorporated in prediction models due to the fact that systemfailure causes are not driven by well-characterised deterministic processes. A simulation and analysis support tool being developed that is based on a suite of interacting modular components with well defined functionalities and interfaces is presented in this paper. The CLOVES (Complex Low Volume Electronics Simulation) tool enables the characterisation and dynamic simulation of complete design; manufacturing and business processes (throughout the entire product life cycle) in terms of their propensity to create defects that could cause product failure. Details of this system and how it is being developed to fulfill changing business needs is presented in this paper. Using historical data and knowledge of previous printed circuit assemblies (PCA) design specifications and manufacturing experiences, defect and yield results can be effectively stored and re-applied for future problem solving. For example, past PCA design specifications can be used at design stage to amend designs or define process options to optimise the product yield and service reliability

INVESTIGATING COLLABORATIVE EXPLAINABLE AI (CXAI)/SOCIAL FORUM AS AN EXPLAINABLE AI (XAI) METHOD IN AUTONOMOUS DRIVING (AD)

Explainable AI (XAI) systems primarily focus on algorithms, integrating additional information into AI decisions and classifications to enhance user or developer comprehension of the system\u27s behavior. These systems often incorporate untested concepts of explainability, lacking grounding in the cognitive and educational psychology literature (S. T. Mueller et al., 2021). Consequently, their effectiveness may be limited, as they may address problems that real users don\u27t encounter or provide information that users do not seek. In contrast, an alternative approach called Collaborative XAI (CXAI), as proposed by S. Mueller et al (2021), emphasizes generating explanations without relying solely on algorithms. CXAI centers on enabling users to ask questions and share explanations based on their knowledge and experience to facilitate others\u27 understanding of AI systems. Mamun, Hoffman, et al. (2021) developed a CXAI system akin to a Social Question and Answer (SQA) platform (S. Oh, 2018a), adapting it for AI system explanations. The system successfully passed evaluation based on XAI metrics Hoffman, Mueller, et al. (2018), as implemented in a master’s thesis by Mamun (2021), which validated its effectiveness in a basic image classification domain and explored the types of explanations it generated. This Ph.D. dissertation builds upon this prior work, aiming to apply it in a novel context: users and potential users of self-driving semi-autonomous vehicles. This approach seeks to unravel communication patterns within a social QA platform (S. Oh, 2018a), the types of questions it can assist with, and the benefits it might offer users of widely adopted AI systems. Initially, the feasibility of using existing social QA platforms as explanatory tools for an existing AI system was investigated. The study found that users on these platforms collaboratively assist one another in problem-solving, with many resolutions being reached (Linja et al., 2022). An intriguing discovery was that anger directed at the AI system drove increased engagement on the platform. The subsequent phase leverages observations from social QA platforms in the autonomous driving (AD) sector to gain insights into an AI system within a vehicle. The dissertation includes two simulation studies employing these observations as training materials. The studies explore users\u27 Level 3 Situational Awareness (Endsley, 1995) when the autonomous vehicle exhibits abnormal behavior. These investigate detection rates and users\u27 comprehension of abnormal driving situations. Additionally, these studies measure the perception of personalization within the context of the training process (Zhang & Curley, 2018), cognitive workload (Hart & Staveland, 1988), trust, and reliance (Körber, 2018) concerning the training process. The findings from these studies are mixed, showing higher detection rates of abnormal driving with training but diminished trust and reliance. The final study engages current Tesla FSD users in semi-structured interviews (Crandall et al., 2006) to explore their use of social QA platforms, their knowledge sources during the training phase, and their search for answers to abnormal driving scenarios. The results reveal extensive collaboration through social forums and group discussions, shedding light on differences in trust and reliance within this domain

Δυναμικοί διάλογοι συστημάτων συστάσεων: εφαρμογή στην ηλεκτρονική διακυβέρνηση

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2010.Το πρόβλημα στο οποίο εντρυφήσαμε στην παρούσα εργασία είναι το πώς μπορούν οι αλγόριθμοι που χρησιμοποιούνται στα Συστήματα Συστάσεων ηλεκτρονικού εμπορίου για την σύσταση του καταλληλότερου προϊόντος να εφαρμοστούν και στις υπηρεσίες ηλεκτρονικής διακυβέρνησης προκειμένου να λάβει ο πολίτης πληροφορίες σχετικά με τις δημόσιες υπηρεσίες, όπως για παράδειγμα τα δικαιολογητικά που απαιτούνται κατά περίσταση. Μας απασχόλησε, λοιπόν, η εύρεση του καταλληλότερου αλγορίθμου για την δημιουργία ενός δυναμικού διαλόγου ανάμεσα στον πολίτη και στην υπηρεσία. Αυτό σημαίνει πως σκοπός ήταν η δημιουργία ενός δυναμικού συστήματος ερωτήσεων/απαντήσεων, όπου η επόμενη ερώτηση που θα γίνει στον χρήστη καθορίζεται στον χρόνο εκτέλεσης του προγράμματος σύμφωνα με την απάντησή του στην προηγούμενη ερώτηση. Στο τέλος του διαλόγου, η υπηρεσία θα πρέπει να έχει προσωποποιηθεί σύμφωνα με το προφίλ του πολίτη. Με βάση το πρόβλημα αυτό, υλοποιήθηκε στο NetBeans 6.7 η online εφαρμογή «Δήλωση Ακινήτου στο Κτηματολόγιο». Η εφαρμογή αυτή επιτρέπει στους χρήστες, απαντώντας ένα online ερωτηματολόγιο οι οποίοι απαντώντας το να πληροφορούνται για τα δικαιολογητικά και έγγραφα που πρέπει να καταθέσουν στο Γραφείο Κτηματογράφησης, ανάλογα με την κατηγορία που ανήκει το ακίνητο τους

An Incremental Retrieval Mechanism for Case-Based Electronic Fault Diagnosis

One problem with using CBR for diagnosis is that a full case description may not be available at the beginning of the diagnosis. The standard CBR methodology requires a detailed case description in order to perform case retrieval and this is often not practical in diagnosis. We describe two fault diagnosis tasks where many features may make up a case description but only a few features are required in an individual diagnosis. We evaluate an incremental CBR mechanism that can initiate case retrieval with a skeletal case description and will elicit extra discriminating information during the diagnostic process. Keywords: Case-based reasoning, case retrieval, electronic fault diagnosis. 2 1 Introduction The fact that human problem solving competence is often based on reasoning from examples supports the use of case-based reasoning (CBR) for developing knowledge-based systems. In particular, good performance in both technical and medical diagnosis is often dependent on remembering simi..