Semantic Support for Log Analysis of Safety-Critical Embedded Systems

Testing is a relevant activity for the development life-cycle of Safety Critical Embedded systems. In particular, much effort is spent for analysis and classification of test logs from SCADA subsystems, especially when failures occur. The human expertise is needful to understand the reasons of failures, for tracing back the errors, as well as to understand which requirements are affected by errors and which ones will be affected by eventual changes in the system design. Semantic techniques and full text search are used to support human experts for the analysis and classification of test logs, in order to speedup and improve the diagnosis phase. Moreover, retrieval of tests and requirements, which can be related to the current failure, is supported in order to allow the discovery of available alternatives and solutions for a better and faster investigation of the problem.Comment: EDCC-2014, BIG4CIP-2014, Embedded systems, testing, semantic discovery, ontology, big dat

Real-time Thermal Error Compensation Module for Intelligent Ultra Precision Turning Machine (iUPTM)

AbstractAccuracy & precision are 1he main requirements for ultra precision machine tools. Many factors affect 1he performance of 1he system 1hat in turns affect 1he product quality. Among all sources of errors, the thermo mechanical deformation errors are the main contributor for 1he overall geometrical errors. This paper mainly aims at establislunent of methodology to compensate thermal deformation errors in real-time for ultra precision machine tools. The real-time thermal error compensation module has been developed and integrated to intelligent Ultra Precision Turning machine. The module includes temperatures as inputs, neural network algorithm for computing the thermal deformations errors, ‘C’ programming for real-time calculations and integration with open architecture CNC controller. The module runs in silent mode which avoids human intervention for correction of thermal deformation errors

Analyzing human errors in flight mission operations

A long-term program is in progress at JPL to reduce cost and risk of flight mission operations through a defect prevention/error management program. The main thrust of this program is to create an environment in which the performance of the total system, both the human operator and the computer system, is optimized. To this end, 1580 Incident Surprise Anomaly reports (ISA's) from 1977-1991 were analyzed from the Voyager and Magellan projects. A Pareto analysis revealed that 38 percent of the errors were classified as human errors. A preliminary cluster analysis based on the Magellan human errors (204 ISA's) is presented here. The resulting clusters described the underlying relationships among the ISA's. Initial models of human error in flight mission operations are presented. Next, the Voyager ISA's will be scored and included in the analysis. Eventually, these relationships will be used to derive a theoretically motivated and empirically validated model of human error in flight mission operations. Ultimately, this analysis will be used to make continuous process improvements continuous process improvements to end-user applications and training requirements. This Total Quality Management approach will enable the management and prevention of errors in the future

Automated attendance management and alert system

“Automated Attendance Management and Alert System (AAMAS)” was developed to help UiTM lecturers and Academic Affairs Department in monitoring students’ absenteeism and improving the absenteeism record management. AAMAS provides various functions, from managing and recording students’ attendance record, to sending automatic alerts to students with high absenteeism via short messaging system (SMS) and email. The system is also able to track the number of alerts sent. Through AAMAS, a significant amount of time and money can be saved, for instance time needed to fill out forms and issue notification letters manually can be minimized significantly. Besides, message interception, human resources and human errors can also be reduced. AAMAS which was tailored to UiTM could be also enhanced and custom-made to cater other learning institutions’ requirements throughout Malaysia.Keywords: automated system; attendance management; system development

Transparency Enhancing Technologies to Make Security Protocols Work for Humans

As computer systems are increasingly relied on to make decisions that will have significant consequences, it has also become important to provide not only standard security guarantees for the computer system but also ways of explaining the output of the system in case of possible errors and disputes. This translates to new security requirements in terms of human needs rather than technical properties. For some context, we look at prior disputes regarding banking security and the ongoing litigation concerning the Post Office’s Horizon system, discussing the difficulty in achieving meaningful transparency and how to better evaluate available evidence

The REPAIRER Reporting System for Integrating Human Factors into SMS in Aviation Maintenance

Acknowledging the FAA’s well-known PEAR model, and the influence of the dirty dozen in aviation maintenance, the authors examine a tracking and reporting system that fulfills FAA requirements for safety management systems in aviation maintenance organizations. Implications and suggestions for a robust safety management system which encompasses human factors and ORM, applicable to an aviation maintenance environment are presented, with the inclusion of specific risk hazards. The resulting safety reporting system proposed addresses both consistency and reliability challenges, unique to the aviation maintenance environment. Using the four pillars of safety as a foundation, the REPAIRER strategy procedures serves as the safety policy pillar, through the examination and rating of potential risk hazards, based on the dirty dozen. The resulting reporting system leverages aviation maintenancespecific factors to identify and correct for human errors, improving the reliability of maintenance procedures, enhancing safety practices, and ultimately creating a greater state of operational readiness

Design and Implementation of Poka-Yoke System in Stationary Spot-Welding Production Line Utilizing Internet-of-Things Platform

This paper presents the design and implementation of a poka-yoke system in a stationary spot welding (SSW) production line. The human-based SSW production process in a local automotive component company was considered in this study. Due to the repetitive and fast cycle-time of the production process, human errors are inevitable. Such errors lead to customer claims with the subject company. Based on the data of customer claims, there were three major quality issues (missing nuts, wrong-size nuts, asymmetrical spot weld marks). Due to the production line being manual, control of planned production and actual production was poor, leading to delivery issues (delayed delivery). Together these major issues contributed to 34.7% of customer claims on average from May to December 2018. The objective of this study was to solve the issues in the subject company through design and implementing a poka-yoke system utilizing the internet-of-things (IoT) platform to ensure data acquisition and information storage, and production progress monitoring and data analysis to meet user requirements. The combined approach of the poka-yoke system utilizing IoT in the SSW production line yielded satisfactory results with reduced customer claims to 5.3% for the stated problems from February to May 2019. Hence, the design objective was achieved

Towards a Framework for Visual Intelligence in Service Robotics:Epistemic Requirements and Gap Analysis

A key capability required by service robots operating in real-world, dynamic environments is that of Visual Intelligence, i.e., the ability to use their vision system, reasoning components and background knowledge to make sense of their environment. In this paper, we analyse the epistemic requirements for Visual Intelligence, both in a top-down fashion, using existing frameworks for human-like Visual Intelligence in the literature, and from the bottom up, based on the errors emerging from object recognition trials in a real-world robotic scenario. Finally, we use these requirements to evaluate current Knowledge Basesfor Service Robotics and to identify gaps in the support they provide for Visual Intelligence.These gaps provide the basis of a research agenda for developing more effective knowledge representations for Visual Intelligence

Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets.

BackgroundBioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis.Main textWe present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google's Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others.ConclusionsKeemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes

Modelling Security of Critical Infrastructures: A Survivability Assessment

Critical infrastructures, usually designed to handle disruptions caused by human errors or random acts of nature, define assets whose normal operation must be guaranteed to maintain its essential services for human daily living. Malicious intended attacks to these targets need to be considered during system design. To face these situations, defence plans must be developed in advance. In this paper, we present a Unified Modelling Language profile, named SecAM, that enables the modelling and security specification for critical infrastructures during the early phases (requirements, design) of system development life cycle. SecAM enables security assessment, through survivability analysis, of different security solutions before system deployment. As a case study, we evaluate the survivability of the Saudi Arabia crude-oil network under two different attack scenarios. The stochastic analysis, carried out with Generalized Stochastic Petri nets, quantitatively estimates the minimization of attack damages on the crude-oil network