2,569 research outputs found
WebAL Comes of Age: A review of the first 21 years of Artificial Life on the Web
We present a survey of the first 21 years of web-based artificial life (WebAL) research and applications, broadly construed to include the many different ways in which artificial life and web technologies might intersect. Our survey covers the period from 1994—when the first WebAL work appeared—up to the present day, together with a brief discussion of relevant precursors. We examine recent projects, from 2010–2015, in greater detail in order to highlight the current state of the art. We follow the survey with a discussion of common themes and methodologies that can be observed in recent work and identify a number of likely directions for future work in this exciting area
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
A Review of Platforms for the Development of Agent Systems
Agent-based computing is an active field of research with the goal of
building autonomous software of hardware entities. This task is often
facilitated by the use of dedicated, specialized frameworks. For almost thirty
years, many such agent platforms have been developed. Meanwhile, some of them
have been abandoned, others continue their development and new platforms are
released. This paper presents a up-to-date review of the existing agent
platforms and also a historical perspective of this domain. It aims to serve as
a reference point for people interested in developing agent systems. This work
details the main characteristics of the included agent platforms, together with
links to specific projects where they have been used. It distinguishes between
the active platforms and those no longer under development or with unclear
status. It also classifies the agent platforms as general purpose ones, free or
commercial, and specialized ones, which can be used for particular types of
applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference
PlantGL : a Python-based geometric library for 3D plant modelling at different scales
In this paper, we present PlantGL, an open-source graphic toolkit for the creation, simulation and analysis of 3D virtual plants. This C++ geometric library is embedded in the Python language which makes it a powerful user-interactive platform for plant modelling in various biological application domains. PlantGL makes it possible to build and manipulate geometric models of plants or plant parts, ranging from tissues and organs to plant populations. Based on a scene graph augmented with primitives dedicated to plant representation, several methods are provided to create plant architectures from either field measurements or procedural algorithms. Because they reveal particularly useful in plant design and analysis, special attention has been paid to the definition and use of branching system envelopes. Several examples from different modelling applications illustrate how PlantGL can be used to construct, analyse or manipulate geometric models at different scales
ControlPULP: A RISC-V On-Chip Parallel Power Controller for Many-Core HPC Processors with FPGA-Based Hardware-In-The-Loop Power and Thermal Emulation
High-Performance Computing (HPC) processors are nowadays integrated
Cyber-Physical Systems demanding complex and high-bandwidth closed-loop power
and thermal control strategies. To efficiently satisfy real-time multi-input
multi-output (MIMO) optimal power requirements, high-end processors integrate
an on-die power controller system (PCS).
While traditional PCSs are based on a simple microcontroller (MCU)-class
core, more scalable and flexible PCS architectures are required to support
advanced MIMO control algorithms for managing the ever-increasing number of
cores, power states, and process, voltage, and temperature variability.
This paper presents ControlPULP, an open-source, HW/SW RISC-V parallel PCS
platform consisting of a single-core MCU with fast interrupt handling coupled
with a scalable multi-core programmable cluster accelerator and a specialized
DMA engine for the parallel acceleration of real-time power management
policies. ControlPULP relies on FreeRTOS to schedule a reactive power control
firmware (PCF) application layer.
We demonstrate ControlPULP in a power management use-case targeting a
next-generation 72-core HPC processor. We first show that the multi-core
cluster accelerates the PCF, achieving 4.9x speedup compared to single-core
execution, enabling more advanced power management algorithms within the
control hyper-period at a shallow area overhead, about 0.1% the area of a
modern HPC CPU die. We then assess the PCS and PCF by designing an FPGA-based,
closed-loop emulation framework that leverages the heterogeneous SoCs paradigm,
achieving DVFS tracking with a mean deviation within 3% the plant's thermal
design power (TDP) against a software-equivalent model-in-the-loop approach.
Finally, we show that the proposed PCF compares favorably with an
industry-grade control algorithm under computational-intensive workloads.Comment: 33 pages, 11 figure
Exploring Cloud Adoption Possibilities for the Manufacturing Sector: A Role of Third-Party Service Providers
As the manufacturing sector strides towards digitalization under the influence of Industry 4.0, cloud services have emerged as the new norm, driving change and innovation in this rapidly transforming landscape. This study investigates the possibilities of cloud adoption in the manufacturing sector by developing a conceptual model to identify suitable cloud-based solutions and explores the role of third-party service providers in aiding manufacturers throughout their cloud adoption journey. The research methods consist of a comprehensive literature review of the manufacturing industry, digital transformation, cloud computing, etc., followed by qualitative analyses of industrial benchmarks case studies and an investigation into an application of the developed model to a hypothetical food manufacturing company as an example. This study indicates that cloud adoption can yield substantial benefits in the manufacturing sector, including operational efficiency, cost reduction, and innovation, etc. The study concludes that the developed conceptual model provides a practical framework to identify the most suitable cloud-based solutions during the cloud adoption process in the manufacturing context. In addition, third-party service providers like Capgemini are capable of not only filling the technical gaps but also consulting strategic directions and innovations for their client organizations, hence playing a vital role in driving the industrial digital transformation process. With an extensive mapping of their capabilities, a set of recommendations intended to assist Capgemini in enhancing capabilities and improving competitive performance in the market has been offered
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
Earth Observation Open Science and Innovation
geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc
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