People with visual impairments who need to make bank transfers use talking Automatic Teller Machines （ATMs）. The aim of our study is to determine user-friendly methods for making bank transfers using the numerical keypad of a talking ATM, whose procedures can be difficult for visually impaired customers to complete. Our experiment investigates four operational patterns consisting of combinations that assign three operations （multitap, cursor movement, and number inputs） to three procedures: financial institution selection, input of the initial three letters of the name of the financial institute, and a final selection from among five candidates. Twenty blindfolded-sighted and nine blind participants utilized the four operational patterns to conduct a bank transfer using a simulator. The results show that cursor movement input in the first and third operational stages and multitap input in second stage were the most useable methods for both blindfolded-sighted and blind participants.

The warning sound of the driver assistance system has become more and more important for safe driving. However, the auditory stimuli response time of the warning sound with a sound expression may be longer while using the voice activation system. The purpose of this study is to clarify the parameters for estimating the auditory stimuli response times of warning sounds with sound expressions in the case of using the voice activation system while driving and verifying the effects of the visual presentation of predicted auditory stimuli response times. As a result of our two experiments using a driving simulator, the first frequency standard deviation of the steering angle estimated the auditory stimuli response time for the warning sound. Moreover, the auditory stimuli response time was shown to be closer to the results of concentrating on driving conditions with the visual stimuli color change based on predicted auditory stimuli response times and the sense of anxiety when driving.

The purpose of this paper is to provide learning model to improve proactive action or risk management for hazard which occurs due to human errors. The paper first considers most of accidents are caused by stochastic coincidence of various situations and malfunctions. So complex system approaches could be applied instead of root cause analysis methodology. Then, authors propose a learning model using Self-Organization Maps methodology. Proposed model contain human error expediencies occurred in aircraft maintenance works specified such as “Age”, “Day of duty”, “Experience” or so on. The first reason using Self-Organization Maps is this method is considered to be suitable to non-linear system description as is actions or intentions like as human being. The second reason is to have cluster retrieval function which makes possible to extract similar events. The last reason is to use in a visual way. The appreciation of this learning model could indicate us some characteristics of possible risk about human error which is emerging consequence if nobody take measure. So applying this model, most likely characteristics could be suggested and avail the necessary actions previously. This learning model with many near miss accidents or minor affairs enough to contribute prevention of accidents.