Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entails that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific techniques to increase the forecast accuracy. As cyclone track is governed by a range of factors variations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earth' rotational force-the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cyclones and where human casualties are the highest.
Bangladesh has experienced several catastrophic Tropical Cyclones (TCs) during the last decades. Despite the efforts of disaster management organizations, as well as the Bangladesh Meteorological Department (BMD), there were lapses in the residents' evacuation behavior. To examine the processes of TC forecasting and warning at BMD and to understand the reasons for residents' reluctance to evacuate after a cyclone warning, we conducted an individual in-depth interview among the meteorologists at BMD, as well as a questionnaire survey among the residents living in the coastal areas. The results reveal that the forecasts produced by BMD are not reliable for longer than 12-hour. Therefore, longer-term warnings have to be based on gross estimates of TC intensity and motion, which renders the disseminated warning messages unreliable. Our results indicate that residents in the coastal areas studied, do not follow the evacuation orders due to mistrust of the warning messageswhich can deter from early evacuation; and insufficient number of shelters and poor transportation possibilities-which discourages late evacuation. Suggestions made by the residents highlight the necessity of improved warning messages in the future. These findings indicate the need for improved forecasting, and more reliable and more informative warning messages for ensuring a timely evacuation response from residents.
Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone events. To mitigate this damage, improved forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAA-AVHRR satellite images. A multi-layer neural network, resembling the human visual system, was trained to forecast the movement of cyclones based on satellite images. The trained network produced correct directional forecast for 98% of test images, thus showing a good generalization capability. The results indicate that multi-layer neural networks could be further developed into an effective tool for cyclone track forecasting using various types of remote sensing data. Future work includes extension of the present network to handle a wide range of cyclones and to take into account supplementary information, such as wind speeds, water temperature, humidity, and air pressure
Abstract-Advanced driver assistance systems are designed to make driving easier that is, to alleviate the driver's workload, and to increase traffic safety. However, traffic safety is affected by negative behavioral adaptation, meaning that drivers tend to increase speed and pay less attention to driving when supported by an advanced assistance system. We relate behavioral adaptation to reinforcement learning at a subconscious level, and propose that driver assistance is dynamically varied within predetermined safety limits. The aim of employing a dynamic assistance policy is to prevent the driver from noticing a constant improvement in vehicle handling. We conclude by describing ongoing work for empirically evaluating an improved lane departure warning system that uses a dynamic assistance policy.Index Terms-advanced driver assistance systems, lane departure warning systems, lane keeping assistance systems, negative behavioral adaptation, reinforcement learning, dynamic assistance policy I. INTRODUCTION he evolution of a new generation of advanced driver assistance systems is to a large extent propelled by advances in sensor technology, steadily increasing computing power and fast algorithms for analyzing in real-time multiple sources of sensor data [1]- [3]. Whereas much emphasis is put on the safety and reliability of the technical system, there is little concern of the human driver. However, the driver is of central importance for traffic safety. Empirical studies indicate that drivers have a tendency to adapt their driving style and misuse the increased safety margins created by advanced driver assistance systems, a phenomenon called negative behavioral adaptation [4]-[7]. Drivers may, for example, increase the driving speed and pay less attention to the driving task, to such an extent that the safety margins created by the driver assistance system are cancelled out [8], [9]. While this problem is widely acknowledged, to date no technically- based, engineering solutions have been proposed that could mitigate the adverse safety effects of negative behavioral adaptation.
Psychological experiments indicate that mental images are more difficult to reinterpret than physical drawings. This difficulty is often attributed to various limitations of the mental image and/or mental image fading. However, experiments indicate that additional, non-visual factors might be involved. In view of this, we propose a model of mental image reinterpretation which focuses on the interaction between conceptual and visual information in the cognitive system. Simulations of this model support our hypothesis that reinterpretations are inhibited when the presently held interpretation is kept within focus of attention. Also, it appears that the mental image itself can inhibit the reinterpretation process in cases when potential new interpretations do not match well with the mental image.
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