52,665 research outputs found
The application of ANFIS prediction models for thermal error compensation on CNC machine tools
Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.
A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system
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Measuring the features sensitivity of fusion sensor using neural network in milling operation
Rapid design of tool-wear condition monitoring systems for turning processes using novelty detection
Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the key technologies that provide the competitive advantage in many manufacturing environments. It is capable of providing an essential means to reduce cost, increase productivity, improve quality and prevent damage to the machine or workpiece. Turning operations are considered one of the most common manufacturing processes in industry. It is used to manufacture different round objects such as shafts, spindles and pins. Despite recent development and intensive engineering research, the development of tool wear monitoring systems in turning is still ongoing challenge. In this paper, force signals are used for monitoring tool-wear in a feature fusion model. A novel approach for the design of condition monitoring systems for turning operations using novelty detection algorithm is presented. The results found prove that the developed system can be used for rapid design of condition monitoring systems for turning operations to predict tool-wear
GSO: Designing a Well-Founded Service Ontology to Support Dynamic Service Discovery and Composition
A pragmatic and straightforward approach to semantic service discovery is to match inputs and outputs of user requests with the input and output requirements of registered service descriptions. This approach can be extended by using pre-conditions, effects and semantic annotations (meta-data) in an attempt to increase discovery accuracy. While on one hand these additions help improve discovery accuracy, on the other hand complexity is added as service users need to add more information elements to their service requests. In this paper we present an approach that aims at facilitating the representation of service requests by service users, without loss of accuracy. We introduce a Goal-Based Service Framework (GSF) that uses the concept of goal as an abstraction to represent service requests. This paper presents the core concepts and relations of the Goal-Based Service Ontology (GSO), which is a fundamental component of the GSF, and discusses how the framework supports semantic service discovery and composition. GSO provides a set of primitives and relations between goals, tasks and services. These primitives allow a user to represent its goals, and a supporting platform to discover or compose services that fulfil them
Graphene Quantum Dot-Based Electrochemical Immunosensors for Biomedical Applications
In the area of biomedicine, research for designing electrochemical sensors has evolved over the past decade, since it is crucial to selectively quantify biomarkers or pathogens in clinical samples for the efficacious diagnosis and/or treatment of various diseases. To fulfil the demand of rapid, specific, economic, and easy detection of such biomolecules in ultralow amounts, numerous nanomaterials have been explored to effectively enhance the sensitivity, selectivity, and reproducibility of immunosensors. Graphene quantum dots (GQDs) have garnered tremendous attention in immunosensor development, owing to their special attributes such as large surface area, excellent biocompatibility, quantum confinement, edge effects, and abundant sites for chemical modification. Besides these distinct features, GQDs acquire peroxidase (POD)-mimicking electro-catalytic activity, and hence, they can replace horseradish peroxidase (HRP)-based systems to conduct facile, quick, and inexpensive label-free immunoassays. The chief motive of this review article is to summarize and focus on the recent advances in GQD-based electrochemical immunosensors for the early and rapid detection of cancer, cardiovascular disorders, and pathogenic diseases. Moreover, the underlying principles of electrochemical immunosensing techniques are also highlighted. These GQD immunosensors are ubiquitous in biomedical diagnosis and conducive for miniaturization, encouraging low-cost disease diagnostics in developing nations using point-of-care testing (POCT) and similar allusive techniques.TU Berlin, Open-Access-Mittel - 201
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
Sensing as a Service Model for Smart Cities Supported by Internet of Things
The world population is growing at a rapid pace. Towns and cities are
accommodating half of the world's population thereby creating tremendous
pressure on every aspect of urban living. Cities are known to have large
concentration of resources and facilities. Such environments attract people
from rural areas. However, unprecedented attraction has now become an
overwhelming issue for city governance and politics. The enormous pressure
towards efficient city management has triggered various Smart City initiatives
by both government and private sector businesses to invest in ICT to find
sustainable solutions to the growing issues. The Internet of Things (IoT) has
also gained significant attention over the past decade. IoT envisions to
connect billions of sensors to the Internet and expects to use them for
efficient and effective resource management in Smart Cities. Today
infrastructure, platforms, and software applications are offered as services
using cloud technologies. In this paper, we explore the concept of sensing as a
service and how it fits with the Internet of Things. Our objective is to
investigate the concept of sensing as a service model in technological,
economical, and social perspectives and identify the major open challenges and
issues.Comment: Transactions on Emerging Telecommunications Technologies 2014
(Accepted for Publication
License to chill!: how to empower users to cope with stress
There exists today a paucity of tools and devices that empower people to take control over their everyday behaviors and balance their stress levels. To overcome this deficit, we are creating a mobile service, Affective Health, where we aim to provide a holistic approach towards health by enabling users to make a connection between their daily activities and their own memories and subjective experiences. This construction is based upon values detected from certain bodily reactions that are then visualized on a mobile phone. Accomplishing this entailed figuring out how to provide real-time feedback without making the individual even more stressed, while also making certain that the representation empowered rather than controlled them. Useful design feedback was derived from testing two different visualizations on the mobile in a Wizard of Oz study. In short, we found that a successful design needs to: feel alive, allow for interpretative openness, include short-term history, and be updated in real-time. We also found that the interaction did not increase our participants stress reactions
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