Ecodriving and Carbon Footprinting: Understanding How Public Education Can Reduce Greenhouse Gas Emissions and Fuel Use

Ecodriving is a collection of changes to driving behavior and vehicle maintenance designed to impact fuel consumption and greenhouse gas (GHG) emissions in existing vehicles. Because of its promise to improve fuel economy within the existing fleet, ecodriving has gained increased attention in North America. One strategy to improve ecodriving is through public education with information on how to ecodrive. This report provides a review and study of ecodriving from several angles. The report offers a literature review of previous work and programs in ecodriving across the world. In addition, researchers completed interviews with experts in the field of public relations and public message campaigns to ascertain best practices for public campaigns. Further, the study also completed a set of focus groups evaluating consumer response to a series of websites that displayed ecodriving information. Finally, researchers conducted a set of surveys, including a controlled stated-response study conducted with approximately 100 University of California, Berkeley faculty, staff, and students, assessing the effectiveness of static ecodriving web-based information as well as an intercept clipboard survey in the San Francisco Bay Area. The stated-response study consisted of a comparison of the experimental and control groups. It found that exposure to ecodriving information influenced people’s driving behavior and some maintenance practices. The experimental group’s distributional shift was statistically significant, particularly for key practices including: lower highway cruising speed, driving behavior adjustment, and proper tire inflation. Within the experimental group (N = 51), fewer respondents significantly changed their maintenance practices (16%) than the majority that altered some driving practices (71%). This suggests intentionally altering driving behavior is easier than planning better maintenance practices. While it was evident that not everyone modifies their behavior as a result of reviewing the ecodriving website, even small shifts in behavior due to inexpensive information dissemination could be deemed cost effective in reducing fuel consumption and emissions

A preliminary approach to intelligent x-ray imaging for baggage inspection at airports

Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted

The songwriting coalface: where multiple intelligences collide

This paper investigates pedagogy around songwriting professional practice. Particular focus is given to the multiple intelligence theory of Howard Gardner as a lens through which to view songwriting practice, referenced to recent songwriting‐specific research (e.g. McIntyre, Bennett). Songwriting education provides some unique challenges; firstly, due to the qualitative nature of assessment and the complex and multi‐faceted nature of skills necessary (lyric writing, composing, recording, and performing), and secondly, in some less‐tangible capacities beneficial to the songwriter (creative skills, and nuanced choice‐making). From the perspective of songwriting education, Gardner’s MI theory provides a ‘useful fiction’ (his term) for knowledge transfer in the domain, especially (and for this researcher, surprisingly) in naturalistic intelligence

Optimising the Loading Diversity of Rail Passenger Crowding using On-Board Occupancy Data

Crowded conditions on trains can lead to lower passenger satisfaction, discourage rail travel, result in negative economic impacts and are a factor in a number of health and safety hazards. In the UK there is an annual survey of rail passenger crowding, although the measures used do not reflect coach-by-coach variations, nor do they reflect variations across the peak period. In this MPhil thesis I investigated the application of weight-based automatic passenger counting data to deliver more even loadings on trains through the provision of new real-time and static solutions. In addition I investigated the potential benefits of such solutions in terms of reduced dwell times and reduced crowding. The overall concept proposed was to make the most of the existing available capacity; for example, so that no-one is standing when seats are available. Through analysing a large sample of air suspension data, I identified station-specific trends where some coaches were over capacity while others had spare capacity. I also conducted a critical review of academic research into on-train crowding and solutions that seek to optimise ‘loading diversity’. This study contributes to this emerging subject area in several ways: I propose two new metrics to describe inter-coach loading diversity that, unlike existing metrics, contain information relative to the capacity; I have revealed a link between the inter-coach loading diversity metrics and estimated boarding times, with trains classified as ‘very uneven’ on departure typically having dwell times of approximately five to ten seconds greater than services that were classified as being ‘even’ with a similar total number of passengers on board; and finally I have applied classification supervised learning techniques to predict the load factor for a given service and these predictors were an improvement over taking the historical average

DATASET2050 D5.2 - Assessment execution

Over recent years there has been an increasing effort to enhance European door-to-door mobility. Several initiatives have focused on improving the seamlessness, effectiveness and predictability of the European transport system through improving the related systems, technologies, concepts or processes. In an effort to establish a concrete methodology for assessing the system's current performance, this document describes a data-driven model centred on the current and future performance of European mobility. Included in this study, but not restricted to, is data and insight related to the Flightpath 2050 goal that states "90% of travellers within Europe [will be] able to complete their journey, door-to-door within four hours" where this journey includes at least one leg by air. In this report, the current door-to-door times and prices are quantified, dis-aggregated by passenger profile, door-to-door phase (door-kerb-gate-gate-kerb-door) and airport considered. In addition, major bottlenecks are identified that are hindering the 4-hour goal

Accelerating Innovation Through Analogy Mining

The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.Comment: KDD 201

Helicopter landing procedures and landing manual in M/V Island Intervention

Summary This bachelor thesis defines the helicopter operations onboard the ship Island Intervention. All existing manuals are very hard to read, due to the amount of theoretical information concerning helicopter operations. This is the reason why I wanted to write a more practical synopsis of the operations. The thesis is written to be a summary of the operations and safety aspects in a more practical point of view. The thesis starts with a theoretical part of the offshore business and helicopter operations, as it is an important part of the business. The theoretical part of the thesis handles in brief the construction of the helideck, the requirements and Island Intervention`s helideck’s safety equipment. The thesis is meant to be more practical than theoretical. Therefore, detailed theory and legal issues have been limited. To obtain more content and practical ideas, I have used interviews as my research method. All interviewed personnel have been trained in helideck procedures during the helicopter operations and they all have several years of experience in offshore business and helideck operations. To gain answers to my thesis problem, I used a questionnaire as information gaining method. The questionnaire was given to ten crewmembers, which all have HLO (Helicopter Landing Officer) training. The questionnaire consisted of five open questions. Thus, they were meant to be answered in the personnel’s own words. An open questionnaire was chosen, for the reason that it would allow the participants to answer freely and express his/her own thoughts on their personal experiences. Therefore, the answers were not tied up to numeral multiple choice answers. According to all examinee, helicopter operations are restively safe and in good standard. However, all of the participants agreed that the existing manuals onboard are too abstract and needed to be simplified. This observation supported my basic idea for this thesis.Tiivistelmä Tämä opinnäytetyö käsittelee helikopteri operaatiota laivalla jossa työskentelen. Nykyiset laivoilla olevat manuaalit ovat hyvin vaikealukuisia sisältäen paljon teoreettista informaatiota helikopterioperaatiosta. Tästä syystä halusin rakentaa käytännönläheisemmän paketin. Opinnäytetyö on rakennettu työkaluksi, josta on helppo ja nopea käydä läpi operaation pääkohdat ja turvallisuusaspektit. Työn alkuun olen rakentanut teoriaosuuden, jossa annetaan lukijalle selvennys itse offshore toiminnasta ja siihen läheisesti liittyvästä helikopteri toiminnasta. Tässä osuudessa käsitellään lyhyesti myös helikopterikannen vaatimuksia ja Island Interventionin turvallisuusvälineitä. Koska työ on tarkoituksenmukaisesti ajateltu olevan painoltaan käytännönläheinen työkalu, rajasin pois turhaa teoreettista puolta ja juridista puolta. Jotta työhön saatiin enemmän sisältöä ja käytännölliseen puoleen eri ammattilaisten ajatuksia, päätin käyttää yhtenä tiedonkeruumenetelmänä haastatteluja. Haastattelut suoritettiin Island Interventionin miehistölle, jotka ovat koulutettuja toimimaan helikopterikannella operaation aikana. Heillä kaikilla on jo vuosien kokemus öljypuolen laivatyöskentelystä ja helikopteri operaatiosta Itse ongelman ratkaisussa päätin käyttää kyselyä tutkimusmetodina. Kysely teetettiin 10 miehistönjäsenelle, eli kaikille saatavilla oleville HLO (Helicopter landing officer) kurssin käyneille. Kysely pitää sisällään 5 kysymystä joihin kaikkiin haluttiin sanallinen vastaus. Tämän tyylinen kysely antoi vastaajalle mahdollisuuden kertoa lyhyesti omista ajatuksistaan sitomatta liikaa vastauksia numeraalisiin monivalinta vastauksiin. Vastauksista kävi selville, että turvallisuus helikopteri operaatioissa on hyvällä tasolla. Kävi myös selville, että vastaajat ovat yhtä mieltä kanssani mitä tulee manuaalien vaikeaselkoisuuteen. Monet kaipasivat selkeyttä manuaaleihin ja tämä tukee tätä opinnäytetyön tarpeellisuutta

Neural Baby Talk

We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image. Our approach reconciles classical slot filling approaches (that are generally better grounded in images) with modern neural captioning approaches (that are generally more natural sounding and accurate). Our approach first generates a sentence `template' with slot locations explicitly tied to specific image regions. These slots are then filled in by visual concepts identified in the regions by object detectors. The entire architecture (sentence template generation and slot filling with object detectors) is end-to-end differentiable. We verify the effectiveness of our proposed model on different image captioning tasks. On standard image captioning and novel object captioning, our model reaches state-of-the-art on both COCO and Flickr30k datasets. We also demonstrate that our model has unique advantages when the train and test distributions of scene compositions -- and hence language priors of associated captions -- are different. Code has been made available at: https://github.com/jiasenlu/NeuralBabyTalkComment: 12 pages, 7 figures, CVPR 201