Search results for "working"

showing 10 items of 2747 documents

Julkisen sektorin palveluprosessit kevyiksi lean-tuotantomalleilla - mutta kevyttuotteet eivät aina laihduta?

2019

Pääkirjoituslcsh:HD4801-8943lcsh:Labor. Work. Working classlean-ajattelulcsh:Businesslcsh:HF5001-6182julkiset palveluttuotantokustannukset
researchProduct

Back to work! : työ ja työelämän tutkimus kutsuvat vapaajakson jälkeen

2019

Pääkirjoituslcsh:HD4801-8943lomahenkilöstölcsh:Labor. Work. Working classtutkimuspääkirjoituksetlcsh:Businesstyöelämälcsh:HF5001-6182yliopistotvapaa-aika
researchProduct

Lehden toimitus Jyväskylään – jatkuvuuksia ja uudistumista

2014

Pääkirjoitus nonPeerReviewed

Pääkirjoituslcsh:HD4801-8943toimitustyölcsh:Labor. Work. Working classlcsh:Businesslcsh:HF5001-6182Työelämän tutkimus
researchProduct

Tutkimusetiikka yhä tärkeämpää työelämän tutkimuksessa

2019

Pääkirjoituslcsh:HD4801-8943tutkimusetiikkatutkimuspääkirjoituksetlcsh:Labor. Work. Working classtyöelämälcsh:Businesslcsh:HF5001-6182Työelämän tutkimus
researchProduct

Työtilajärjestelyjen monet merkitykset

2015

Pääkirjoituslcsh:HD4801-8943työtilattyöhyvinvointitilasuunnittelulcsh:Labor. Work. Working classlcsh:Businesslcsh:HF5001-6182
researchProduct

Nuoret, työ ja tulevaisuus poikkeusaikana

2020

Pääkirjoitusnuoretlcsh:HD4801-8943poikkeusolotCOVID-19lcsh:Labor. Work. Working classlcsh:Businesstyöelämänuorisotyöttömyyslcsh:HF5001-6182
researchProduct

A Context-Aware System for Ambient Assisted Living

2017

In the near future, the world's population will be characterized by an increasing average age, and consequently, the number of people requiring for a special household assistance will dramatically rise. In this scenario, smart homes will significantly help users to increase their quality of life, while maintaining a great level of autonomy. This paper presents a system for Ambient Assisted Living (AAL) capable of understanding context and user's behavior by exploiting data gathered by a pervasive sensor network. The knowledge inferred by adopting a Bayesian knowledge extraction approach is exploited to disambiguate the collected observations, making the AAL system able to detect and predict…

QA75Computer sciencemedia_common.quotation_subjectPopulationAmbient Assisted LivingContext (language use)02 engineering and technologyTheoretical Computer ScienceDynamic Bayesian NetworkKnowledge extractionQuality of lifeRule-based reasoningHuman–computer interactionHome automation0202 electrical engineering electronic engineering information engineeringContext awarenesseducationmedia_commonSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionieducation.field_of_studyMulti-sensor data fusionbusiness.industryComputer Science (all)Context awarene020206 networking & telecommunicationsRule-based system020201 artificial intelligence & image processingbusinessWireless sensor networkAutonomy
researchProduct

An Ambient Intelligence System for Assisted Living

2017

Nowadays, the population's average age is constantly increasing, and thus the need for specialized home assistance is on the rise. Smart homes especially tailored to meet elderly and disabled people's needs can help them maintaining their autonomy, whilst ensuring their safety and well-being. This paper proposes a complete context-aware system for Ambient Assisted Living (AAL), which infers user's actions and context, analyzing its past and current behavior to detect anomalies and prevent possible emergencies. The proposed system exploits Dynamic Bayesian Networks to merge raw data coming from heterogeneous sensors and infer user's behavior and health conditions. A rule-based reasoner is ab…

QA75ExploitComputer sciencemedia_common.quotation_subjectPopulationAmbient Assisted Living02 engineering and technologyAmbient Assisted Living; Multi-sensor data fusion; Dynamic Bayesian Networks; Context awareness; Rule-based ReasoningDynamic Bayesian NetworkHome automationHuman–computer interaction0202 electrical engineering electronic engineering information engineeringeducationDynamic Bayesian networkmedia_commonSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionieducation.field_of_studyAmbient intelligenceMulti-sensor data fusionbusiness.industryRule-based ReasoningContext awarene020206 networking & telecommunicationsSemantic reasoner020201 artificial intelligence & image processingbusinessRaw dataAutonomy
researchProduct

A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing

2019

Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by applicati…

QA75General Computer ScienceComputer scienceDistributed computingenergy efficient offloading02 engineering and technologyVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 42001 natural sciencesuser equipmentComputational offloadingServer0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Mobile edge computingbusiness.industryDeep learning010401 analytical chemistryGeneral Engineeringdeep learning020206 networking & telecommunicationsEnergy consumption0104 chemical sciencesUser equipmentArtificial intelligencemobile edge computinglcsh:Electrical engineering. Electronics. Nuclear engineeringbusinesslcsh:TK1-9971Efficient energy useIEEE Access
researchProduct

Energy-Efficiency and Coverage Quality Management for Reliable Diagnostics in Wireless Sensor Networks

2020

International audience; The processing of data and signals provided by sensors aims at extracting rnrelevant features which can be used to assess and diagnose the health state rnof the monitored targets. Nevertheless, Wireless Sensor Networks (WSNs) present rna number of shortcomings that have an impact on the quality of the gathered rndata at the sink level, leading to imprecise diagnostics rnof the observed targets. To improve data accuracy, two main critical and related issues, namely the energy consumption and coverage quality, need to be considered. The goal is to maximize the network lifetime while guaranteeing the complete coverage of all the targets. Unfortunately, these performance…

Quality managementComputer scienceComputer Networks and CommunicationsReal-time computingCorrectness proofs020206 networking & telecommunicationsEnergy consumption02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE][INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationComputer Science Applications[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]Distributed algorithmControl and Systems Engineering[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Data accuracy0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processing[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Electrical and Electronic Engineering[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Wireless sensor networkEfficient energy use
researchProduct