FRAM AHP approach to analyse offshore oil well drilling and construction focused on human factors

Cognition, Technology & Work - Since the beginning of the well-drilling activities of oil and gas industry, in the 19th century, these activities have presented specific risks that, over the...     

Employing resilience engineering in eliciting software requirements for complex systems: experiments with the functional resonance analysis method (FRAM)

Cognition, Technology & Work - Resilience engineering provides concepts and methods for assessing the ability of socio-technical systems to adjust their functioning before, during, or after...     

Timeliness and task specification in designing for human factors in railway operations

Human error is often related to flaws in system design which might have been avoided had greater attention been paid to human factors knowledge and methods as the system was developed. The paper considers the role of human factors in system design and argues that adopting an operational perspective in identifying human factors issues ensures that subsequent human factors advice is focused upon real needs and is consistent with how managers and engineers choose to manage their systems. It also considers the issue of timeliness of human factors input. Failure to consider human factors issues at a time when other design decisions are being taken often means that it is less straightforward to accommodate changes. Thus, managers and engineers may resist dealing effectively with potential human factors problems for reasons of cost and delay in meeting project milestones.     

Minimizing burr size in drilling using artificial neural network (ANN)-particle swarm optimization (PSO) approach

The burrs at the hole exit degrade the performance in precision part and affect the reliability of the product. Hence, it is essential to select the optimal process parameters for minimal burr size at the manufacturing stage so as to reduce the deburring cost and time. This paper illustrates the application of particle swarm optimization (PSO) to select the best combination values of feed and point angle for a specified drill diameter in order to simultaneously minimize burr height and burr thickness during drilling of AISI 316L stainless steel. The burr size models required for the PSO optimization were developed using artificial neural network (ANN) with the drilling experiments planned as per full factorial design (FFD). The PSO optimization results clearly indicate the importance of larger point angle for bigger drill diameter values in controlling the burr size.     

Analysis method for revealing human and organisational factors of occupational accidents with movement disturbance (OAMDs)

Abstract

Slips, trips and other movement disturbances account for 20 to 30% of recorded occupational accidents (OAs). The causal representations of these accidents hamper their prevention. An analysis method dedicated to occupational accidents with movement disturbance (OAMDs) has been developed to change these representations. In France, the causal tree method (CTM) is very commonly used for analysing OAs. An initial version of an OAMD analysis method, which overcomes the problems encountered when analysing these accidents using the CTM, has been developed. This OAMD analysis method was reviewed by six targeted prevention officers and as a result some proposals have been discarded and this initial version has been transformed into three additional CTM modules. The purpose of these modules is to identify human and organisational factors and provide a formal representation of damage caused, beyond bodily injuries.

Practitioner summary: A method for analysing occupational accidents triggered by a slip, a trip or any other movement disturbance has been developed in consideration of the practices and constraints in companies. In particular, this method allows us to highlight the human and organisational factors involved in the accident situation.

Abbreviations: OA: occupational accident; OAMD: occupational accident with movement disturbance; CTM: causal tree method     

Human error and commercial aviation accidents: an analysis using the human factors analysis and classification system

OBJECTIVE: The aim of this study was to extend previous examinations of aviation accidents to include specific aircrew, environmental, supervisory, and organizational factors associated with two types of commercial aviation (air carrier and commuter/ on-demand) accidents using the Human Factors Analysis and Classification System (HFACS). BACKGROUND: HFACS is a theoretically based tool for investigating and analyzing human error associated with accidents and incidents. Previous research has shown that HFACS can be reliably used to identify human factors trends associated with military and general aviation accidents. METHOD: Using data obtained from both the National Transportation Safety Board and the Federal Aviation Administration, 6 pilot-raters classified aircrew, supervisory, organizational, and environmental causal factors associated with 1020 commercial aviation accidents that occurred over a 13-year period. RESULTS: The majority of accident causal factors were attributed to aircrew and the environment, with decidedly fewer associated with supervisory and organizational causes. Comparisons were made between HFACS causal categories and traditional situational variables such as visual conditions, injury severity, and regional differences. CONCLUSION: These data will provide support for the continuation, modification, and/or development of interventions aimed at commercial aviation safety. APPLICATION: HFACS provides a tool for assessing human factors associated with accidents and incidents.     

Working practices in a perchery system, using the OVAKO Working posture Analysing System (OWAS)

Stockworkers should be able to easily manage alternative systems for commercial egg production. Such production systems can be analysed in terms of human welfare, based on ergonomic criteria. Work-related postures can cause discomfort and strain to workers (Stoffert, 1985). The OVAKO Working posture Analysing System (OWAS) developed in 1974 (Karhu et al, 1977) defines body positions during working practices and scores them according to the strain caused. This technique has so far not been used to assist in the design of working systems for the poultry industry. This pilot study was carried out to determine if such a technique could be easily applied to a perchery system and whether it could lead to an improved design. The stockworker was video recorded performing normal duties within the perchery and the positions of the body were assessed, using the OWAS system. It was found that manual collection of floor eggs and reaching into the middle of the perches from the litter side put the most strain on the stockworker. Modification of the perchery, in light of these results, will establish an improved perchery system for the stockworkers.     

Probabilistic sensitivity analysis for novel second-order reliability method (SORM) using generalized chi-squared distribution

Reliability-based design optimization (RBDO) requires evaluation of sensitivities of probabilistic constraints. To develop RBDO utilizing the recently proposed novel second-order reliability method (SORM) that improves conventional SORM approaches in terms of accuracy, the sensitivities of the probabilistic constraints at the most probable point (MPP) are required. Thus, this study presents sensitivity analysis of the novel SORM at MPP for more accurate RBDO. During analytic derivation in this study, it is assumed that the Hessian matrix does not change due to the small change of design variables. The calculation of the sensitivity based on the analytic derivation requires evaluation of probability density function (PDF) of a linear combination of non-central chi-square variables, which is obtained by utilizing general chi-squared distribution. In terms of accuracy, the proposed probabilistic sensitivity analysis is compared with the finite difference method (FDM) using the Monte Carlo simulation (MCS) through numerical examples. The numerical examples demonstrate that the analytic sensitivity of the novel SORM agrees very well with the sensitivity obtained by FDM using MCS when a performance function is quadratic in U-space and input variables are normally distributed. It is further shown that the proposed sensitivity is accurate enough compared with FDM results even for a higher order performance function.     

Brain processing of vocal sounds in advertising: A functional magnetic resonance imaging (fMRI) study

Using functional magnetic resonance imaging (fMRI), this study aimed at investigating the neural mechanisms associated with human and non-human sounds’ perception in advertising. The study employed a block design paradigm in which participants heard human versus non-human sounds in different sets of advertisements. The results showed that, compared to nonhuman sounds, human sounds elicited greater activation in several areas in or around the primary auditory cortex (t > 5.16, p < 0.001). This result suggests that different types of sounds are processed in different functional brain pathways. The existence of voice-selective areas in the brain lends strong support to the face perception neurocognitive model which proposes that visual, affective and linguistic information are processed in different cortical regions in the brain.     

Observations of working postures in garages using the Ovako Working posture Analysing System (OWAS) and consequent workload reduction recommendations

The working postures of mechanics (n = 84) in 42 garages were observed using the Ovako Working posture Analysis System (OWAS). During observation, both working postures and work activities were recorded. A computer program was developed for the data analyses. Using this program it is possible to calculate the working posture load for each work activity and the contribution of a specific activity to the total working posture load. This is a substantial extension of the original OWAS method. Five out of 19 observed postures of the body members were classified as Action Category 2, which suggests they were slightly harmful to the musculoskeletal system and likely to cause discomfort. Of the so-called typical working postures, 31.9% was classified in Action Category 2, suggesting that during a substantial part of the working day typical working postures occur which are at least slightly harmful to the musculoskeletal system. Moreover, those work activities principally causing the workload to fall in OWAS' higher Action Categories were identified. For each of these three work activities an alternative work method was observed. The data show that in all three work activities the use of a vehicle lift reduces the number of poor working postures thereby reducing the load on the musculoskeletal system.     

Relational mountain (density) clustering method and web log analysis

The mountain clustering method and the subtractive clustering method are useful methods for finding cluster centers based on local density in object data. These methods have been extended to shell clustering. In this article, we propose a relational mountain clustering method (RMCM), which produces a set of (proto) typical objects as well as a crisp partition of the objects generating the relation, using a new concept that we call relational density. We exemplify RMCM by clustering several relational data sets that come from object data. Finally, RMCM is applied to web log analysis, where it produces useful user profiles from web log data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 375–392, 2005.     

Multivoxel analysis for functional magnetic resonance imaging (fMRI) based on time-series and contextual information: relationship between maternal love and brain regions as a case study

This study explores the relationship between maternal love and brain regions by using functional magnetic resonance imaging (fMRI). Also, a novel pattern analysis for fMRI based on the discovered brain regions is proposed in this work. Firstly, to identify which region responds to stimuli, a statistical t-test is used after the scan. Based on these preliminary regions of interest, this study develops discriminant features extracted from multivoxels for cognitive modeling. In total, five parameters are used in the time-series and contextual analysis, including the proposed blood-oxygen-level-dependent (BOLD) contrast edge, BOLD contrast centroid, activated voxels, mean, and variance. Furthermore, this study also proposes a test function for examining voxel activation based on variance, so that insignificant voxels and irrelevant outliers can be removed from the features. After the feature extraction from brain regions of interest, the analysis subsequently uses Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for reducing the feature size. Lastly, this study adopts a computer-aided pattern recognizer, the Support Vector Machine (SVM), to facilitate automation of the proposed analysis. A dataset consisting of brain-scanning images from 22 subjects was used for evaluation. The statistical result shows that the neural circuitry associated with maternal bonds indeed appears in the relevant brain regions as indicated by the other research. Such regions are subsequently used for assessment of the proposed analysis. Classification result shows that the proposed approach can effectively identify activated samples. Besides, our system achieves an accuracy rate of as high as 83.33 %. A comparison among different systems reveals that the proposed system is superior to the others and establishes its feasibility.     

An analysis method to detect the presence of DNA using electrochemical impedance spectroscopy (EIS) for real-time PCR

Microsystem Technologies - This paper reports a technique for detecting the presence of DNA in real-time PCR using label-free electrochemical impedance spectroscopy. The change in the real and...     

A priority queue in which initialization and queue operations takeO(loglogD) time

Many computer algorithms have embedded in them a subalgorithm called a priority queue which produces on demand an element of extreme priority among elements in the queue. Queues on unrestricted priority domains have a running time of (nlogn) for sequences ofn queue operations. We describe a simple priority queue over the priority domain {1,,N} in which initialization, insertion, and deletion takeO(loglogD) time, whereD is the difference between the next lowest and next highest priority elements in the queue. In the case of initialization,D=(N). Finding a least element, greatest element, and the neighbor in priority order of some specified element take constant time. We also consider dynamic space allocation for the data structures used. Space can be allocated in blocks of size (N ^1/p ), for small integerp. This research was supported by the National Science Foundation under grants MCS 77-21092 and MCS 80-002684.     

Modeling and analysis of project team formation factors in a project-oriented virtual organization (ProVO)

In this era of rapid changes in the project-oriented R&D organization’s environment, some are actively pursuing joint research to gain a leading edge over other R&D organizations. The condition for joint research is the knowledge that an organization needs from other organizations and the capability of collaboration. This study presents a ProVO model using the concept of virtual organization and project team formation based on knowledge and collaboration. In this model, VO is represented by the capability of carrying out a project and the cost of employment. Capability consists of knowledge competence (KC) and collaboration competence (CC). KC, in turn, consists of individual knowledge and collective knowledge from social network, while CC consists of density, degree centrality, and closeness centrality. To verify the presented model, we conducted a case study on a research institute. The analysis results show that all five project team formation factors of KC and CC are statistically significant. A prototype was also developed for selecting project team members using the binary logistics regression model. The proposed ProVO model can assist quantitative decision making on the selection of project team members by a project-oriented R&D organization from the aspects of knowledge and collaboration.     

Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China)

Infectious diarrhea is an important public health problem around the world. Meteorological factors have been strongly linked to the incidence of infectious diarrhea. Therefore, accurately forecast the number of infectious diarrhea under the effect of meteorological factors is critical to control efforts. In recent decades, development of artificial neural network (ANN) models, as predictors for infectious diseases, have created a great change in infectious disease predictions. In this paper, a three layered feed-forward back-propagation ANN (BPNN) model trained by Levenberg–Marquardt algorithm was developed to predict the weekly number of infectious diarrhea by using meteorological factors as input variable. The meteorological factors were chosen based on the strongly relativity with infectious diarrhea. Also, as a comparison study, the support vector regression (SVR), random forests regression (RFR) and multivariate linear regression (MLR) also were applied as prediction models using the same dataset in addition to BPNN model. The 5-fold cross validation technique was used to avoid the problem of overfitting in models training period. Further, since one of the drawbacks of ANN models is the interpretation of the final model in terms of the relative importance of input variables, a sensitivity analysis is performed to determine the parametric influence on the model outputs. The simulation results obtained from the BPNN confirms the feasibility of this model in terms of applicability and shows better agreement with the actual data, compared to those from the SVR, RFR and MLR models. The BPNN model, described in this paper, is an efficient quantitative tool to evaluate and predict the infectious diarrhea using meteorological factors.