687 research outputs found
SLOTTING ALLOWANCES: EMPIRICAL EVIDENCE ON THEIR ROLE IN NEW PRODUCT LAUNCHES
No full textThe retail practice of charging a fee to stock new products is a relatively new but growing phenomenon. Termed a "slotting allowance", it has attracted considerable scrutiny because of uncertainty about its purposes and consequences. We propose and statistically test several hypotheses to assess the degree of empirical support for each of several extant explanations. Slotting allowances, we find, are charged by relatively large retailers who have an informational advantage over the manufacturer about the likely success of the new product. This result apparently contradicts theorizing about the "informational" content of slotting fees, as well as other pro- and anti-competitive explanations. We also find support for the claim that when retailers fear that manufacturers will not provide post-launch support, they pay relatively high wholesale prices.Industrial Organization, Marketing,
Architecture validation of VFP control for the WiNC2R platform
No full textA Cognitive Radio processing requires intelligent transceiver which can be easily programmed and reconfigured dynamically to support multiple protocols. The Winlab Network Centric Cognitive Radio (WiNC2R) platform is based on the concept of Virtual Flow Pipelining Paradigm. WiNC2R can support per packet protocol adaption through the reconfiguration of function sequencing. Since WiNC2R platform can be programmed by adding additional functions in software, and flow sequencing reprogramming architecturally supported in hardware, it can easily support future protocols. The latest version of WiNC2R has advanced shared VFP control unit, cluster based SoC architecture with all the processing engines in an 802.11a like OFDM transmitter flow. It is very important to characterize the VFP overhead with the realistic protocol processing examples to understand the performance and cost penalties of added flexibility, and establish the base for the comparison with Software Defined Radio approach. The performance analysis of the VFP will give detailed insight about the various latencies involved in the VFP processing. VFP Architecture is validated to see that the current implementation does meet the requirements of the WiNC2R platform. This performance analysis will help in characterizing VFP overhead under varying throughput requirements. Architectural validation of VFP will characterize certain parameters of the system programming, like reschedule period, guard time, etc.M.S.Includes bibliographical referencesby Akshay Jo
Content-based image retrieval of digitized histopathology via boosted spectral embedding (BoSE)
No full textContent-based image retrieval (CBIR) systems allow for retrieval of images from a database that are similar in visual content to a query image. This is particularly useful in scenarios such as digital pathology, where text-based descriptors alone might be inadequate to accurately describe image content. By representing images via a set of quantitative image descriptors, the similarity between a query image with respect to archived, annotated images in a database can be computed and the most similar images retrieved. Recently, non-linear dimensionality reduction (NLDR) methods have become popular for embedding high dimensional data into a reduced dimensional space while preserving local object adjacencies, thereby allowing for object similarity to be determined more accurately in the reduced dimensional space. However, most dimensionality reduction (DR) methods implicitly assume, in computing the reduced dimensional representation, that all features are equally important. Erroneous or noisy features could potentially result in dissimilar images being mapped close to each other in the reduced embedding space. In this work we present Boosted Spectral Embedding (BoSE), a variant of the traditional Spectral Embedding (SE) NLDR method, which unlike SE utilizes a boosted distance metric (BDM) to selectively weight individual features to subsequently map the data into a reduced dimensional space. In this work BoSE is evaluated against SE (which employs equal feature weighting) in the context of CBIR of digitized prostate and breast cancer histopathology images. Across 154 hematoxylin and eosin (H&E) stained histopathology images corresponding to benign and malignant prostate cancer biopsy images, low and high grade ER+ breast cancer studies, and HER2+ breast cancer H&E images, BoSE outperformed SE both in terms of CBIR-based (area under the precision recall curve) and classifier-based (classification accuracy) performance measures. Consistent trends were observed when embedding the data into spaces with different dimensions. Our results suggest that BoSE could serve as an important tool for CBIR and classification of high dimensional biomedical data.M.S.Includes bibliographical referencesIncludes vitaby Akshay Sridha
Weighted K-nearest neighbor algorithm as an object localization technique using passive RFID tags
No full textTechnologies using identification by radio frequencies (RFID) are experiencing rapid development and healthcare is a major application area benefiting from it. Highly pervasive RFID enables remote identification, tracking and localization of the medical staff, patients, medications and equipment, thus increasing safety, optimizing in real-time management and providing support for new ambient-intelligent services. This thesis describes and evaluates an algorithm that enables object localization and tracking using passive RFID tags. This thesis also describes scenarios of how this technology can be used as a part of building a smart trauma resuscitation room by tracking the equipments. The main contribution of this thesis is the adaptation of the Weighted K-Nearest Neighbor Algorithm as a localization technique to track objects in a confined and crowded space by using passive RFID tags. The input parameter to the algorithm is the received signal strength indicator (RSSI), which gives a measure of back-scattered radio frequencies from passive tags. While using RFID technology special attention has to be given to the placement of antennas to get the optimum result. Therefore, we analyzed various antenna placement configurations with mean error and error consistency as the two performance parameters. The detection of multiple tags and human occlusion are two major concerns while tracking tags in a confined space with many team members collaborating on solving a problem. The RF signal can be interrupted by people walking around randomly and holding multiple (tagged) instruments at the same time. While the algorithm worked fine when tracking multiple tags, we had to modify the experimental set-up and attach an antenna onto the ceiling (which we call a vertical antenna), so that even if all the wall antennas are blocked we get at least one input parameter to base our localization decision on. We evaluated the algorithm for different combinations of configurations and number of neighbors, and achieved the following results. The best results were obtained for the 3 antennae (placed orthogonally) configuration considering the 4 nearest neighbors wherein a mean error rate of 15% of the maximum possible error was achieved under ideal conditions. We tested the algorithm for different human occlusion scenarios i.e. blocking 1 or 2 wall antennas, standing in random positions and then roaming in the field area randomly. The mean error rate for the standing scenario was measured as 20% of the maximum possible error and 18% in the case of roaming configuration. The error was found to be consistently within our defined maximum error for 100% of the recorded readings. The results obtained were found to be satisfactory for our application where, more than the exact location of the object, knowing whether the object is within a particular region is good enough for the users to know what task is being carried out in the trauma bay. Also the algorithm holds good in an indoor environment having a lot of factors and materials which affect the RF signal disrupting accurate calculation of the location co-ordinates. The algorithm does not require extensive data collection prior to implementation which makes it easily deployable in any environment. Apart from the problems mentioned there are some other factors like materials on which the tags are attached and orientation of tags which were found to be potential hindrances for accurate localization. Acceptable solutions to these problems form a part of our future work.M.S.Includes bibliographical referencesby Akshay Shett
Determining the effects of different market mechanisms on the power flow of a prosumer building: An analysis of feed-in tariff, capacity mechanism and frequency regulation on the power flow of a building with its own generation and storage
No full textWith distributed energy sources becoming prominent, it is expected that new market mechanisms would become necessary to overcome the issues caused by the intermittency of those sources. This research determines the effect of certain market mechanisms, which have been proposed to either incentivize the distributed generation or to reduce their undesirable effects, on power flow of a prosumer household. The prosumer household was assumed to consist of PV generation system, an Electric Vehicle which was capable of V2G and a household battery. The market mechanisms used were Feed-in tariffs, Capacity Mechanism and Frequency Containment Reserve. An Energy Management System which determines both, the optimal system size and the optimal power flow by minimizing the operational costs was used to achieve this. It was observed that different level of feed in tariff affected the optimal system size and the power flow. At high feed-in tariff, the system size was the largest and high grid peak power was observed. Furthermore, the introduction of the capacity mechanism in the form of capacity tariffs per kW led to reduction in grid power consumption and feeding in. Finally, it was observed that the energy management system was able to reserve power for the frequency regulation market. Compared to an uncontrolled case, a reduction of 70.26% in total costs was achieved when the EMS control was introduced. A cost reduction of 52.02% compared to the uncontrolled case was achieved when an additional capacity tariff was also introduced to the control. Finally, introduction of the frequency regulation mechanism in the EMS control led to even further reduction in costs with a drop of 1205.07% compared to the uncontrolled case
The Future of Additive Manufacturing of Spare Parts
No full textThe master thesis project explores the spare parts business due to the evidence available on how well it contributes to the revenues and profits of firms. With increasing product variety, shorter product lifecycles and market competition, it is necessary for firms to supply spare parts to customers for keeping the products functional. Forecasting for the spare parts aftermarket is difficult due to fluctuating demand rates. These issues make spare parts management challenging. Moreover, the covid-19 pandemic has complicated spare parts management and increased its importance. Therefore, the spare parts business presents opportunities to utilize Additive Manufacturing (AM) technology to solve issues related to spare parts management, which is the focus of this master thesis project. The thesis project has been carried out at Atos SE in the manufacturing consulting domain. Atos aims to identify the technical and economic criteria for selecting spare parts to be produced by AM technology and explore various business models to print and deliver spare parts to customers. The thesis objective has been achieved by conducting a market study on AM technologies & spare parts, and developing a support process using the technical and economic criteria along with the business models. The data and information have been collected using literature study and semi-structured interviews. Through the market study, it was found that Powder Bed Fusion (PBF) is the most industry ready AM technology due to its high processing speed, material compatibility, high strength and mechanical properties and the non - requirement of support structures. The commonly used AM materials are Nylon PA 11 and PA 12, ABS, PLA, Aluminium, Titanium and Stainless Steel. The cost factors driving AM adoption in spare parts were found to be machine, materials, post – processing, labour and energy. The challenges to AM adoption were found to be technology awareness, intellectual property (IP) issues, costs and return on investment (ROI), strength and physical properties of AM produced parts. Following the market study, the criteria identified for selecting spare parts to be AM produced are increased responsiveness, minimized supply disruption, cost optimization, part complexity and sustainability. Furthermore, to select spare parts for AM, multi-criteria decision-making Tools (MCDM) such as AHP (Analytical Hierarchy Process) and PROMETHEE (Preference Ranking Organisation Method for Enrichment Evaluation) have been used and validated. To print the spare parts, four business models have been described in the thesis, using the make-to-stock, make-to-order and engineer-to-order approaches. The study limitations, recommendations, discussions and implications have been enclosed in the master thesis.Management of Technology (MoT
A Global Analysis on Microgrids through the PESTEL Framework
No full textMicrogrids enable distribution of electricity with higher shares of variable renewables, higher power quality, greater reliability and higher efficiency. There are a large number of factors in addition to the technology, which affect their shift towards market competitiveness and widespread adoption. The PESTEL framework, covering Political, Economic, Social, Technical, Environmental and Legislative factors, is used to identify and describe the drivers and barriers for microgrid development at the global level. The framework enables a broader approach to describe potential for microgrid applications. The results aim to provide engineers, project developers and microgrid specialists with an overview of the prospects for microgrid deployment.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Energy Technolog
Personalized Energy Services: A Data-Driven Methodology towards Sustainable, Smart Energy Systems
No full textThe rapid pace of urbanization has an impact on climate change and other environmental issues. Currently, 54% of the global population lives in cities accounting for two-thirds of global energy demand. Sustainable energy generation and consumption is the top humanity’s problem for the next 50 years. Faced with rising urban population and the need to achieve energy efficiency, urban planners are focusing on sustainable, smart energy systems. This has led to the development of Smart Grids (SG) that employs intelligent monitoring, control and communication technologies to enhance efficiency, reliability and sustainability of power generation and distribution networks. While energy utilities are optimizing energy generation and distribution, consumers play a key role in sustainable energy usage. Several energy services are provided to the consumers to know households' hourly energy consumption, estimate monthly electricity cost and recommendations to reduce energy consumption. Furthermore, advanced services such as demand response, can now control and influence energy demand at the consumer-end to reduce the overall peak demand and re-shape demand profiles. The effectiveness and adoption of these services highly depend on the consumers’ awareness, their participation and engagement. Current energy services seldomly consider consumer preferences such as their daily behavior, comfort level and energy-consumption pattern. In this thesis, we investigate development of personalized energy services that strive to achieve a balance between efficient-energy consumption and user comfort.Personalization refers to tailoring energy services based on individual consumers’ characteristics, preferences and behavior. To develop effective personalized energy services a set of challenges need to be tackled. First, fine-grained data collection at user and appliance level is required (data collection challenge). Mechanisms should be devised to collect fine-grained data at various levels in a non-intrusive way with minimal sensors. Second, personalized energy services require detailed user preferences such as their thermal comfort level, appliance usage behavior and daily habits (user preference challenge). Accurate learning models to derive user preferences with minimal training and intrusion are required. Third, energy services developed needs to be easily scalable, from one household to tens and thousands of households (scalability challenge). Mechanisms should be developed to tackle the deluge of data and support distributed storage and processing. Fourth, energy services should deliver real-time feedback or recommendations so that users can promptly act upon it (real time challenge). This calls for development of distributed and low complexity algorithms. This thesis moves away from traditional SG services -- which hardly consider consumer preferences and comfort -- and proposes a novel approach to develop effective personalized energy services. The proposed energy services provide actionable feedback, raise awareness and promote energy-saving behavior among consumers. In this thesis, we follow a bottom-up data-driven methodology to develop personalized energy services at various scales -- (i) nano: individual households, (ii) micro: buildings and spaces, and (iii) macro: neighborhoods and cities. To this end, we present our approach -- physical analytics for sustainable, smart energy systems -- that combines IoT data, physical modeling and data analytics to develop intelligent, personalized energy services. Physical analytics fuses data from various Internet of Things (IoT) devices such as smart meters, smart phones and smart watches, along with physical information such as household type, demographics and occupancy to infer energy-usage patterns, user behavior and discover hidden patterns. This approach is used to learn and model user preferences and energy usage, subsequently, employed to develop personalized energy services. This thesis is organized into three parts. Part I describes how to derive fine-grained information with minimal sensors and intrusion. We present two novel algorithms viz., LocED and PEAT that derive fine-grained information from appliance and user level, respectively. This real-time information is used to raise awareness on energy-usage behavior among occupants. Part II presents personalized energy services targeted at households and buildings. We develop services that shift and/or reduce energy consumption and cost by considering individual consumers’ preferences and comfort. These energy services are aimed at providing actionable feedback to occupants towards sustainable energy usage. Part III presents energy services targeted at neighborhood and city level. These energy services aim to identify target consumers in a neighborhood based on their energy-usage pattern and preferences for various DR programs. Finally, we present data-processing architectures that investigate how to cope with the overwhelming data generated from smart meters towards design and development of sustainable, smart energy systems.This thesis advocates that the design and development of energy services should follow personalized approach with consumer preferences and comfort given paramount importance. Results show that the personalized energy services developed has significant potential to raise awareness, reduce energy consumption and improve user comfort in smart -- homes, buildings and neighborhoods
How does trade impact agricultural productivity?
No full textThe student, Akshay Pandit, submitted this Thesis for approval on 2020-07-22 at 15:29.This Thesis was approved for publication on 2020-07-23 at 10:50.DSpace SAF Submission Ingestion Package generated from Vireo submission #15729 on 2020-10-02 at 15:34:07Made available in DSpace on 2020-10-07T22:44:48Z (GMT). No. of bitstreams: 2
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Previous issue date: 2020-07-23"Agricultural production has faced increased demands over the last half century from an expanding economy and population. We live in a globalized world, in which agriculture is deeply intertwined in international markets and trade. In this paper, we address the overarching research question: ""What is the impact of trade on agricultural productivity?''. To this end, we present a comprehensive statistical and econometric analysis on the relationship between international trade and agricultural production. We use national-scale data on crop yield, area harvested, production, and trade for the last half century (1961-2016) from the Food and Agricultural Organization of the United Nations. We introduce novel weighting and decomposition analyses to explore the relationship between trade and crop productivity. To determine the causal impact of trade on agriculture we implement instrumental variable (IV) econometric methods. We find that trade has led to an increase in global agricultural productivity over time (e.g. through increased productivity, the intensive margin). Global productivity gains have accrued primarily through the participation of more countries in global trade (e.g. expanding the area of contribution, the extensive margin). Additionally, we find that trade has enabled global crop consumption to increase. These findings indicate that trade openness leads to greater productivity in agriculture in general. This work highlights that trade can help to achieve productivity gains in agriculture and potentially help the world to address remaining yield gaps."Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-08-01The student, Akshay Pandit, accepted the attached license on 2020-07-22 at 15:28.Embargo set by: Seth Robbins for item 116267
Lift date: 2022-10-07T22:44:53Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Onl
Behavioral data collection and simulation
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 45).On-demand ridesharing services, such as Uber and Lyft, and autonomous vehicles are significantly changing the landscape of transportation and mobility. In light of these disruptions, we aim to determine consumer preferences with regards to transportation and use this data to simulate and analyze the urban effects of smart mobility solutions. We collect behavioral data using Future Mobility Sensing (FMS), a smartphone and prompted-recall-based integrated activity-travel survey, and create simulations using the data with SimMobility, a simulation platform that integrates various mobility-sensitive behavioral models with state-of-the-art scalable simulators to predict the impact of mobility demands on transportation networks, intelligent transportation services, and vehicular emissions. Enhancing these projects with on-demand preferences, individual patterns, and incentives as inputs, we aim to simulate and analyze a wide range of viable smart mobility solutions.by Akshay Padmanabha.M. Eng
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