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Off-design performance of micro-scale solar brayton cycle
No full textA novel methodology to design a micro-scale, solar-only, air-breathing, open Brayton cycle and assess its on- and off-design performance. The methodology is applied to generate and assess six thermodynamic layouts over a range of solar irradiation levels. All plants have the same on-design requirements to create a baseline to compare their off-design performance. PyCycle, a thermodynamic cycle modeling library to model jet engine performance, is revised to transform the jet engine performance modeling to solar thermal plant performance modeling and used to create a volumetric receiver component. A response surface surrogate model of the receiver is created for design optimization to maximize the component-level efficiency. The compressor and turbine maps are scaled for the balance of the plant. Off-design efficiency, mass flow rate, operation range, turbomachinery maps, and maximum power output are presented. Since the methodology can be adapted to all plant sizes, the results are normalized to on-design condition. The outcome of this study demonstrates the impact of the thermodynamic configuration on off-design performance and provides a methodology to design plants that are more robust across a range of solar irradiation levels and can be operated in a more flexible manner. Compared to single shaft configuration, solar radiation operation range is improved by 5%, with 6% less mass flow, and operates more efficiently than the benchmark case over 85% of the operating regime
Quality upshifting with auxiliary I-Frame splicing
No full textThis paper introduces the Auxiliary I-Frame Splicing method to reduce bandwidth waste in adaptive streaming. This method involves fetching a high-quality I-frame and splicing it into the already downloaded low-quality segment, resulting in a higher-quality rendering at a lower overhead than replacing the entire low-quality segment. In our experiments with three videos and four quantization parameters, the results show that the bandwidth can be saved up to 87% while still increasing the peak signal-to-noise ratio score by 20% and the video multi-method assessment fusion score by 73%. In the demo, we demonstrate the visual differences between the original and spliced videos
SUMO'da trafik çemberinin simülasyon modelinde artificial neural network tabanlı boşluk kabul modellerinin uygulanması
No full textThe impact of various operational and design alternatives at roundabouts and traffic circles can be evaluated using microscopic simulation tools. Most microscopic simulation softwares utilize default underlying models for this purpose, which may not be generalized to specific facilities. Since the effectiveness of traffic operations at traffic circles and roundabouts is highly affected by the gap rejection–acceptance behavior of drivers, it is essential to accurately model driver's gap acceptance behavior using location-specific data. The objective of this paper was to evaluate the feasibility of implementing an Artificial Neural Network (ANN)-based gap acceptance model in SUMO, using its application programming interface. A traffic circle in New Jersey was chosen as a case study. Separate ANN models for one stop-controlled and two yield-controlled intersections were trained based on the collected ground truth data. The output of the ANN-based model was then compared with the SUMO model, calibrated by modifying the default gap acceptance parameters to match the field data. Based on the analyses results it was concluded that the advantage of the ANN-based model lies not only in the accuracy of the selected output variables in comparison to the observed field values, but also in the realistic vehicle crossings at the uncontrolled intersections in the simulation model.Dönel kavşaklarda ve trafik dairelerinde çeşitli operasyonel ve tasarım alternatiflerinin etkisi, mikroskobik simülasyon yazılımları kullanılarak değerlendirilebilir. Çoğu mikroskobik simülasyon yazılımı, bu amaç için belirli tesislere genelleştirilemeyecek varsayılan temel modelleri kullanır. Trafik daireleri ve dönel kavşaklardaki trafik operasyonlarının etkinliği, sürücülerin boşluk reddetme-kabul davranışından büyük ölçüde etkilendiğinden, konuma özgü verileri kullanarak sürücünün boşluk kabul davranışını doğru bir şekilde modellemek esastır. Bu makalenin amacı, uygulama programlama arayüzünü kullanarak SUMO'da Artificial Neural Network (ANN)-tabanlı bir boşluk kabul modeli uygulamanın fizibilitesini değerlendirmektir. Örnek olay olarak New Jersey'deki bir trafik çemberi seçilmiştir. Bir durak kontrollü ve iki verim kontrollü kavşak için ayrı ANN modelleri, toplanan yer gerçeği verilerine dayalı olarak eğitilmiştir. ANN tabanlı modelin çıktısı daha sonra SUMO modeliyle karşılaştırıldı ve varsayılan boşluk kabul parametreleri saha verileriyle eşleşecek şekilde değiştirilerek kalibre edildi. Analiz sonuçlarına dayanarak, ANN-tabanlı modelin avantajı, seçilen çıktı değişkenlerinin gözlemlenen alan değerlerine göre doğruluğunda ve ayrıca simülasyon modelinde kontrolsüz kavşaklarda gerçekçi araç geçişlerinde yattığı sonucuna varılmıştır
Effect of phase change materials on the optical path of LEDs for opto-thermal enhancement
No full textA novel concept based on the encapsulation of transparent phase change materials (PCMs) into the optical packaging structure of light-emitting diodes (LEDs) is presented in this article. The concept was initiated by challenges of thermal management of photoluminescent particles in high-power optical systems. LEDs, white LEDs (WLEDs), and porous/network-based photoluminescent matrices can achieve improved thermal networks by embedding PCMs. In this article, paraffin is selected as a suitable PCM encapsulant, and aside from thermal perspectives, an unexpected optical benefit with melted paraffin after surface wetting of the chip was observed. Immersing an LED chip in a melted paraffin pool showed up to an 8% increase in light extraction efficiency and a 1.5% increase in power conversion efficiency (PCE). An accurate dynamic opto-electro-thermal monitoring of studied devices was used to support the proof of concept. This viable method can be integrated into current industrial packaging processes
Customer experience in five-star hotel businesses: is it an “experience” for customers?
No full textPurpose: Concerning the development of “experience” as an economic phenomenon, this study aims to analyse customers' evaluations of their experiences in five-star hotel businesses and to identify if the hospitality experience is evaluated as an “experience” by its specific aspects. Design/methodology/approach: Structural and thematic narrative analyses in a multi-dimensional setting were applied to stories from 107 participants who stayed in five-star hotel businesses. Findings: Customers evaluate their overall experience as an “experience” reflected by experiential statements. However, they demonstrate higher cognitive orientation at the sub-experience levels (food and beverage, rooms, etc.). Research limitations/implications: The paper sheds light on the fact that customers may evaluate their experiences with cognitive and experiential aspects. The study focuses on participants' lived experiences to understand the customer perspective with the “experience” concept leading to the memorability of customer experiences in hotel businesses. Further research is required with a larger sample group, mixed-methods implementation and longitudinal and comparable examination to understand seasonal, motivational and cultural differences. Practical implications: The paper reveals various aspects of customer experiences in five-star hotel businesses around the variety of their offerings evaluated by cognitive and experiential perceptions so that dedicated efforts of the managers will be enhanced with a better and strategic understanding of the “experience” concept to achieve business goals. Originality/value: The study offers insightful findings relating to customers’ service- and experience-based experiences and how “experience” is perceived by customers from various angles in the five-star hotel businesses
Deep learning based event recognition in aerial imagery
No full textIn this paper, we investigate event recognition for aerial surveillance. This is a significant task especially when we consider the growing popularity of UAVs. The main purpose of the paper is to detect events both at the clip level in aerial videos and also at the frame level in aerial images. To achieve this goal, novel deep learning models and training techniques are used. In this work, we propose new model architectures to detect events in both image and video domains. The developed models are tested on the ERA dataset. Results show that the proposed models achieve state-of-the-art performance on both single images and aerial video clips of the ERA dataset
Advancing home healthcare through machine learning: Predicting service time for enhanced patient care
No full textProviding healthcare services at home is crucial for patients who require long-term care or face mobility or other health-related constraints that prevent them from traveling to healthcare facilities. Effective data analysis techniques are needed to optimize these services to understand patient needs and allocate resources efficiently. Machine learning algorithms can analyze big datasets generated from home healthcare services to identify patterns, trends, and predictive factors. By utilizing these techniques, predictive models for service time can be developed, leading to improved patient outcomes, increased efficiency, and reduced costs. This study explores the significance of various features in predicting service time for home healthcare services by analyzing real-life data using data analysis techniques. By developing a correlation matrix, healthcare providers can examine the relationships between features as well as their connections with the target value, thereby providing valuable managerial insights into improving the quality of home healthcare services through enhanced predictions of service time
Doing good by sharing messages: An investigation of “You Share, We Donate” campaigns and how they can attain viral success
No full textWith the rise of social media, companies are engaging in a new type of cause-related marketing classified as "You Share, We Donate" (YSWD) campaigns. YSWD campaigns encourage consumers to share the company's campaign message, with the promise that the company will donate to a designated cause, and the total donation amount is linked to the number of "shares" the message gets. Thus, the success of these campaigns depends on the campaign's virality. In order to understand what organizations need to do to set up successful YSWD cam-paigns, we investigate the linkage between donation cap (non-)specification-an important design element-and YSWD campaign success. The results of the analyses of 574 real-world YSWD campaigns and four experiments show that donation cap non-specification encourages consumers to share the campaign message more than when a donation cap is specified. However, this effect diminishes if a company is perceived as highly credible
Tpms kafes yapılarının hibridizasyon yoluyla optimizasyonu ve kafes morfolojilerinin değerlendirilmesi
No full textOwing to its excellent mechanical properties, triply periodic minimum surfaces (TPMS) lattice structures have recently gained more interest in engineering applications. The superior properties of these structures make it easier to achieve engineering design goals such as strength and weight. Thanks to recent developments in additive manufacturing, the fabrication of the lattices are easier compared to the traditional methods. Therefore, their usage in the designs are more popular in recent application. However, technological advancements compel the designer to enhance the traditional TPMS design qualities. This thesis covers two approaches to enhance the design's mechanical performance by infilling the design domain with the optimal lattice design parameters. Initially, homogenization-based topology (HMTO) and free-size optimization-based graded lattice generation (FOGLG) methods are studied to obtain optimum lattice thickness distribution. The optimization methods are conducted for the modal characterization of a sandwiched structure. In the second study, a new hybrid optimization framework in which genetic algorithm (GA) and homogenization-based topology optimization are used to enhance the mechanical performance of the design. The method initially selects suitable lattice mythologies via GA and then grades them by topology optimization. In addition, the graded multi-morphology design is reconstructed by a novel blending algorithm in the study. The results of the studies clearly show that the proposed methods enable the designer to improve the mechanical performance of the designs. The proposed methods are also experimentally validated to assess their accuracy.Mükemmel mekanik özelliklerinden dolayı üçlü periyodik minimum yüzeyler (TPMS) kafes yapıları son zamanlarda mühendislik uygulamalarında daha fazla ilgi görmeye başlamıştır. Bu yapıların üstün özellikleri, mukavemet ve ağırlık gibi mühendislik tasarım hedeflerine ulaşmayı kolaylaştırır. Eklemeli imalattaki son gelişmeler sayesinde, kafeslerin imalatı geleneksel yöntemlere göre daha kolaydır. Bu nedenle tasarımlarda kullanımları son dönemde daha popülerdir. Bununla birlikte, teknolojik gelişmeler tasarımcıyı geleneksel TPMS tasarım niteliklerini geliştirmeye zorlamaktadır. Bu tez, tasarım alanını optimal kafes tasarım parametreleriyle doldurarak tasarımın mekanik performansını geliştirmek için iki yaklaşımı kapsar. İlk olarak, optimum kafes kalınlığı dağılımını elde etmek için homojenleştirme tabanlı topoloji (HMTO) ve serbest boyutlu optimizasyon tabanlı kademeli kafes oluşturma (FOGLG) yöntemleri incelenmiştir. Sandviçlenmiş bir yapının modal karakterizasyonu için optimizasyon yöntemleri gerçekleştirilir. İkinci çalışmada, tasarımın mekanik performansını artırmak için genetik algoritma (GA) ve homojenizasyon tabanlı topoloji optimizasyonunun kullanıldığı yeni bir hibrit optimizasyon çerçevesi. Yöntem ilk olarak GA aracılığıyla uygun kafes mitolojilerini seçer ve daha sonra bunları topoloji optimizasyonu ile derecelendirir. Ek olarak, kademeli çoklu morfoloji tasarımı, çalışmada yeni bir harmanlama algoritması ile yeniden oluşturulmuştur. Çalışmaların sonuçları, önerilen yöntemlerin tasarımcının tasarımların mekanik performansını iyileştirmesini sağladığını açıkça göstermektedir. Önerilen yöntemler, yöntemlerin doğruluğunu değerlendirmek için deneysel olarak da doğrulanmıştır
A machine learning approach to deal with ambiguity in the humanitarian decision-making
No full textOne of the major challenges for humanitarian organizations in response planning is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in postdisaster settings may involve missing elements and inconsistencies, which can hamper effective humanitarian decision-making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision-makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision-making. To the best of our knowledge, the integration of ambiguous information into decision-making by combining a cluster machine learning method with stochastic optimization has not been done before. We illustrate the proposed approach on a realistic case study that focuses on locating shelters to serve internally displaced people (IDP) in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates in making shelter location decisions. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively. With the newly proposed methodology, the decision-maker is able to analyze the degree of ambiguity in the data and the degree of consensus between different data sources to ultimately make better decisions for delivering humanitarian aid.Publisher versio