Related papers: Demand-Driven Asset Reutilization Analytics

Energy consumption from the selection, training, and deployment of deep learning models has seen a significant uptick recently. This work aims to facilitate the design of energy-efficient deep learning models that require less computational…

Networking data analytics is increasingly used for enhanced network visibility and controllability. We draw the similarities between the Software Defined Networking (SDN) architecture and the MapReduce programming model. Inspired by the…

Analytical tools often require real-time responses for highly concurrent parameterized workloads. A common solution is to answer queries using materialized subexpressions, hence reducing processing at runtime. However, as queries are still…

Software component reuse is the key to significant gains in productivity. However, the major problem is the lack of identifying and developing potentially reusable components. This paper concentrates on our approach to the development of…

Energy consumption and energy ananlytics has gained increased focus and consideration in industrial applications especially process lines to upgrade their performance and efficiency in the competitive world. A competent analytics method…

In this position paper, we propose an approach for sustainable data collection in the field of optimal mix design for marble sludge reuse. Marble sludge, a calcium-rich residual from stone-cutting processes, can be repurposed by mixing it…

As deep neural networks continue to expand and become more complex, most edge devices are unable to handle their extensive processing requirements. Therefore, the concept of distributed inference is essential to distribute the neural…

In our practice as educators, researchers and designers we have found that centering reverse engineering and reuse has pedagogical, environmental, and economic benefits. Design decisions in the development of new hardware tool-kits should…

The problem of efficient resource allocation has drawn significant attention in many scientific disciplines due to its direct societal benefits, such as energy savings. Traditional approaches in addressing online resource allocation…

Sustainability is the key concept in the management of products that reached their end-of-life. We propose that end-of-life products have -- besides their value as recyclable assets -- additional value for producer and consumer. We argue…

This article investigates how graph matching can be applied to process plant design data in order to support the reuse of previous designs. A literature review of existing graph matching algorithms is performed, and a group of algorithms is…

The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical…

In the context of global sustainability, buildings are significant consumers of energy, emphasizing the necessity for innovative strategies to enhance efficiency and reduce environmental impact. This research leverages extensive raw data…

Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings, which contribute a significant share of energy use, is therefore a key…

Predictive maintenance is used in industrial applications to increase machine availability and optimize cost related to unplanned maintenance. In most cases, predictive maintenance applications use output from sensors, recording physical…

Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge for implementation impede holistic implementation. Further,…

Recent years have seen an unprecedented growth in the use of sensor data to guide wind farm operations and maintenance. Emerging sensor-driven approaches typically focus on optimal maintenance procedures for single turbine systems, or model…

With the rapid increase in the research, development, and application of neural networks in the current era, there is a proportional increase in the energy needed to train and use models. Crucially, this is accompanied by the increase in…

Supply chain management has been concentrated on productive ways to manage flows through a sophisticated vendor, manufacturer, and consumer networks for decades. Recently, energy and material rates have been greatly consumed to improve the…

The growing renewable energy sources have posed significant challenges to traditional power scheduling. It is difficult for operators to obtain accurate day-ahead forecasts of renewable generation, thereby requiring the future scheduling…