What are the representational differences between situations that do and do not map well onto previous experiences? This research offers some answers to this question by having participants compare two narratives that were either reality or fantasy-based. Fantasy-based narratives, with their deviations from reality, were considered similar to situations that do not map well onto previous experience. The concept of systematicity, where high-order relations constrain low-order relations was used to describe such situations (Bowdle & Gentner, 1997). Compared to a reality-based narrative, extra processing is required to maintain a systematic representation of a fantasy-based narrative. One can reduce the amount of processing needed by grounding that fantasy-based narrative in a reality-based or another fantasy-based narrative. Comparative judgments were used to measure processing differences. In three studies, participants read two narratives and then performed a series of comparative judgments derived from retrospective duration judgment (Block, 1992), event-structure perception (Zacks & Tversky, 2001), and structure-mapping theory (Gentner, 1983) research. For example, one of the comparative judgments adopted from structure-mapping theory was the rating of directional similarity, or the similarity rating of the second-read narrative relative to the first-read narrative. Directional similarity was proposed to increase as the amount of processing associated with maintaining a systematic representation of the first and second-read narrative decreased. For Studies 1A-E, the directional similarity was higher for the RealityFirst condition (reality read first) than the FantasyFirst condition (fantasy read first). These results are interpreted as indicated that the increase in directional similarity for the RealityFirst conditions was due to structure lending from the first-read reality-based narrative and that the decrease in directional similarity for the FantasyFirst conditions was due to representational disruption from the first-read fantasy-based narrative. Results also indicated that comparing two reality-based narratives (Studies 2A-B) was similar to comparing two fantasy-based narratives (Studies 3A-B) for the directional similarity and directional duration judgments, but differed for the listing of commonalities and differences and the segmentation of the narrative event structure. According to the systematicity principle (Gentner, 1989), people prefer mappings between two representations that form coherent and highly interconnected structures. The results from Studies 1A-E demonstrate a clear directional preference for the RealityFirst conditions. The results, therefore, indicate that it was more difficult to utilize the inherent structure of the narratives for the FantasyFirst conditions then the RealityFirst conditions. Comparing the results across the final set of studies, the increase in segmentation and increase in word count for the commonalities and differences were clear indications that participants still had difficulties in utilizing the structure of the narratives when both narratives being compared were fantasy-based (Studies 3A-B). In operationalizing systematicity with fantasy and reality-based narratives, I have been able to extend our understanding of how structure-lending can occur between these two narrative types. The results, therefore, extend our understanding of the structural alignment approach to narrative comparisons. But, since this research also involves the theoretical integration of the structure alignment approach (directional similarity and listing of commonalities and differences) with theories of time estimation (directional duration), event structure representation (segmentation), the basic findings herein should be applicable to comparisons ranging from auditory narrative structures to simple lexical units (e.g., unicorns vs. horses) to visual depicted objects (e.g., aliens vs. humans), even if the current set of studies described in this article involved only the comparison of written narrative structure. / text

Travel time is one of the most important inputs in many Intelligent Transport Systems (ITS). As a result, this information needs to be accurate and dynamic in both spatial and temporal dimensions. For the estimation of travel time, data from fixed sensors such as Inductive Loop Detectors (ILD) and cameras have been widely used since the 1960 s. However, data from fixed sensors may not be sufficiently reliable to estimate travel time due to a combination of limited coverage and low quality data resulting from the high cost of implementing and operating these systems. Such issues are particularly critical in the context of Less Developed Countries, where traffic levels and associated problems are increasing even more rapidly than in Europe and North America, and where there are no pre-existing traffic monitoring systems in place. As a consequence, recent developments have focused on utilising moving sensors (i.e. probe vehicles and/or people equipped with GPS: for instance, navigation and route guidance devices, mobile phones and smartphones) to provide accurate speed, positioning and timing data to estimate travel time. However, data from GPS also have errors, especially for positioning fixes in urban areas. Therefore, map-matching techniques are generally applied to match raw positioning data onto the correct road segments so as to reliably estimate link travel time. This is challenging because most current map-matching methods are suitable for high frequency GPS positioning data (e.g. data with 1 second interval) and may not be appropriate for low frequency data (e.g. data with 30 or 60 second intervals). Yet, many moving sensors only retain low frequency data so as to reduce the cost of data storage and transmission. The accuracy of travel time estimation using data from moving sensors also depends on a range of other factors, for instance vehicle fleet sample size (i.e. proportion of vehicles equipped with GPS); coverage of links (i.e. proportion of links on which GPS-equipped vehicles travel); GPS data sampling frequency (e.g. 3, 6, 30, 60 seconds) and time window length (e.g. 5, 10 and 15 minutes). Existing methods of estimating travel time from GPS data are not capable of simultaneously taking into account the issues related to uncertainties associated with GPS and spatial road network data; low sampling frequency; low density vehicle coverage on some roads on the network; time window length; and vehicle fleet sample size. Accordingly this research is based on the development and application of a methodology which uses GPS data to reliably estimate travel time in real-time while considering the factors including vehicle fleet sample size, data sampling frequency and time window length in the estimation process. Specifically, the purpose of this thesis was to first determine the accurate location of a vehicle travelling on a road link by applying a map-matching algorithm at a range of sampling frequencies to reduce the potential errors associated with GPS and digital road maps, for example where vehicles are sometimes assigned to the wrong road links. Secondly, four different methods have been developed to estimate link travel time based on map-matched GPS positions and speed data from low frequency data sets in three time windows lengths (i.e. 5, 10 and 15 minutes). These are based on vehicle speeds, speed limits, link distances and average speeds; initially only within the given link but subsequently in the adjacent links too. More specifically, the final method draws on weighted link travel times associated with the given and adjacent links in both spatial and temporal dimensions to estimate link travel time for the given link. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-Berkeley s Mobile Century Project. The original GPS dataset which was broadcast on a 3 second sampling frequency has been extracted at different sampling frequencies such as 6, 30, 60 and 120 seconds so as to evaluate the performance of each travel time estimation method at low sampling frequencies. The results were then validated against reference travel time data collected from 4,126 vehicles by high resolution video cameras, and these indicate that factors such as vehicle sample size, data sampling frequency, vehicle coverage on the links and time window length all influence the accuracy of link travel time estimation.

Road freight transport travel time estimation is an important task in fleet management and traffic planning. Goods often must be delivered in a predefined time window and any deviation may lead to serious consequences. It is possible to improve travel time estimation by considering more factors that may affect it. In this thesis work we identify factors that may affect travel time, find possible sources of information about them, propose a model for estimating travel time of heavy goods vehicles, and verify this model on real data. As results, the experiments showed that considering time related and weather related factors, it is possible to improve accuracy of travel time estimation. Also, it was shown that the influence of a particular factor on travel time depended on the considered road segment. Furthermore, it was shown that different data mining algorithms should be applied for different road segments in order to get the best estimation.

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Indiana University-Purdue University Indianapolis (IUPUI) / Accurate estimation of State of Charge (SOC) is crucial. With the ever-increasing usage of batteries, especially in safety critical applications, the requirement of accurate estimation of SOC is paramount. Most current methods of SOC estimation rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation as the battery ages or under different operating conditions. This work aims at exploring the real-time estimation and optimization of SOC by applying Particle Swarm Optimization (PSO) to a detailed electrochemical model of a single cell. The goal is to develop a single cell model and PSO algorithm which can run on an embedded device with reasonable utilization of CPU and memory resources and still be able to estimate SOC with acceptable accuracy. The scope is to demonstrate the accurate estimation of SOC for 1C charge and discharge for both healthy and aged cell.

京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第24064号 / 地環博第227号 / 新制||地環||43(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)准教授 西川 完途, 教授 瀬戸口 浩彰, 教授 本川 雅治 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DGAM

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Reverse engineering, defined as extracting information about a product from the product itself, is a common industry practice for gaining insight into innovative products. Both the original designer and those reverse engineering the original design can benefit from estimating the time and barrier to reverse engineer a product. This thesis presents a set of metrics and parameters that can be used to calculate the barrier to reverse engineer any product as well as the time required to do so. To the original designer, these numerical representations of the barrier and time can be used to strategically identify and improve product characteristics so as to increase the difficulty and time to reverse engineer them. One method for increasing the time and barrier to reverse engineer a product – presented in this thesis – is to treat material microstructures (crystallographic grain size, orientation, and distribution) as continuous design variables that can be manipulated to identify unusual material properties and to design devices with unexpected mechanical performance. A practical approach, carefully tied to proven manufacturing strategies, is used to tailor material microstructures by strategically orienting and laminating thin anisotropic metallic sheets. This approach, coupled with numerical optimization, manipulates material microstructures to obtain desired material properties at designer-specified locations (heterogeneously) or across the entire part (homogeneously). As the metrics and parameters characterizing the reverse engineering time and barrier are also quantitative in nature, they can also be used in conjunction with numerical optimization techniques, thereby enabling products to be developed with a maximum reverse engineering barrier and time – at a minimum development cost. On the other hand, these quantitative measures enable competitors who reverse engineer original designs to focus their efforts on products that will result in the greatest return on investment. While many products were analyzed in an empirical study demonstrating that the characterization of the time to reverse engineer a product has an average error of 12.2%, we present the results of three different products. Two additional examples are also presented showing how microstructure manipulation leads to product hardware with unexpected mechanical performance effectively increasing reverse engineering time and barrier.

This work examines the effect of participants' gender and handedness on the perception of short intervals of time. The time estimation task consisted of an empty production procedure with forty trials at each of four intervals of one, three, seven, and twenty seconds. The four target intervals represent a natural logarithmic progression and a series that bracket important temporal thresholds. The order of presentation of those intervals was randomized across participants but yoked across the sexes in each of the respective dominant hand groups. The two between-subject factors, with two levels each, were sex and handedness. Participants produced forty estimates at each of the required intervals, which was the first within-subject factor, estimated interval being the other. T-tests were conducted on the dependent measures, the time estimates in terms of their variability and their central tendency with respect to the target duration. If handedness plays a significant role in timing, this may indicate differences between hemispheric functioning as a possible causal mechanism. If there is cerebral asymmetry in time perception, namely if one hemisphere is more competent regarding time perception, accuracy in judging duration should be higher for the contralateral hand. The results of the present study indicated that there are no significant differences in performance between right-handed and left-handed participants, or between male and female participants, in the estimation of short intervals of time.