13,545 research outputs found
Adjacent LSTM-Based Page Scheduling for Hybrid DRAM/NVM Memory Systems
Recent advances in memory technologies have led to the rapid growth of hybrid systems that combine traditional DRAM and Non Volatile Memory (NVM) technologies, as the latter provide lower cost per byte, low leakage power and larger capacities than DRAM, while they can guarantee comparable access latency. Such kind of heterogeneous memory systems impose new challenges in terms of page placement and migration among the alternative technologies of the heterogeneous memory system. In this paper, we present a novel approach for efficient page placement on heterogeneous DRAM/NVM systems. We design an adjacent LSTM-based approach for page placement, which strongly relies on page accesses prediction, while sharing knowledge among pages with behavioral similarity. The proposed approach leads up to 65.5% optimized performance compared to existing approaches, while achieving near-optimal results and saving 20.2% energy consumption on average. Moreover, we propose a new page replacement policy, namely clustered-LRU, achieving up to 8.1% optimized performance, compared to the default Least Recently Used (LRU) policy
A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching
An efficient team is essential for the company to successfully complete new
projects. To solve the team formation problem considering person-job matching
(TFP-PJM), a 0-1 integer programming model is constructed, which considers both
person-job matching and team members' willingness to communicate on team
efficiency, with the person-job matching score calculated using intuitionistic
fuzzy numbers. Then, a reinforcement learning-assisted genetic programming
algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP
adopts the ensemble population strategies. Before the population evolution at
each generation, the agent selects one from four population search modes
according to the information obtained, thus realizing a sound balance of
exploration and exploitation. In addition, surrogate models are used in the
algorithm to evaluate the formation plans generated by individuals, which
speeds up the algorithm learning process. Afterward, a series of comparison
experiments are conducted to verify the overall performance of RL-GP and the
effectiveness of the improved strategies within the algorithm. The
hyper-heuristic rules obtained through efficient learning can be utilized as
decision-making aids when forming project teams. This study reveals the
advantages of reinforcement learning methods, ensemble strategies, and the
surrogate model applied to the GP framework. The diversity and intelligent
selection of search patterns along with fast adaptation evaluation, are
distinct features that enable RL-GP to be deployed in real-world enterprise
environments.Comment: 16 page
Approaches to Improving the Accuracy of Machine Learning Models in Requirements Elicitation Techniques Selection
Selecting techniques is a crucial element of the business analysis approach
planning in IT projects. Particular attention is paid to the choice of
techniques for requirements elicitation. One of the promising methods for
selecting techniques is using machine learning algorithms trained on the
practitioners' experience considering different projects' contexts. The
effectiveness of ML models is significantly affected by the balance of the
training dataset, which is violated in the case of popular techniques. The
paper aims to analyze the efficiency of the Synthetic Minority Over-sampling
Technique usage in Machine Learning models for elicitation technique selection
in case of the imbalanced training dataset and possible ways for positive
feature importance selection. The computational experiment results confirmed
the effectiveness of using the proposed approaches to improve the accuracy of
machine learning models for selecting requirements elicitation techniques.
Proposed approaches can be used to build Machine Learning models for business
analysis activities planning in IT projects
A framework for experimental-data-driven assessment of Magnetized Liner Inertial Fusion stagnation image metrics
A variety of spherical crystal x-ray imager (SCXI) diagnostics have been
developed and fielded on Magnetized Liner Inertial Fusion (MagLIF) experiments
at the Sandia National Laboratories Z-facility. These different imaging
modalities provide detailed insight into different physical phenomena such as
mix of liner material into the hot fuel, cold liner emission, or reduce impact
of liner opacity. However, several practical considerations ranging from the
lack of a consistent spatial fiducial for registration to different
point-spread-functions and tuning crystals or using filters to highlight
specific spectral regions make it difficult to develop broadly applicable
metrics to compare experiments across our stagnation image database without
making significant unverified assumptions. We leverage experimental data for a
model-free assessment of sensitivities to instrumentation-based features for
any specified image metric. In particular, we utilize a database of historical
and recent MagLIF data including image plate scans
gathered across different experiments to assess the
impact of a variety of features in the experimental observations arising from
uncertainties in registration as well as discrepancies in signal-to-noise ratio
and instrument resolution. We choose a wavelet-based image metric known as the
Mallat Scattering Transform for the study and highlight how alternate metric
choices could also be studied. In particular, we demonstrate a capability to
understand and mitigate the impact of signal-to-noise, image registration, and
resolution difference between images. This is achieved by utilizing multiple
scans of the same image plate, sampling random translations and rotations, and
applying instrument specific point-spread-functions found by ray tracing to
high-resolution datasets, augmenting our data in an effectively model-free
fashion.Comment: 17 pages, 14 figure
Anuário cientÃfico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021
É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário CientÃfico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa cientÃfica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção cientÃfica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo cientÃfico de elevada qualidade e partilhada com a Sociedade na forma de livros, capÃtulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos especÃficos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa cientÃfica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o inÃcio dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade cientÃfica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio
Decoding spatial location of attended audio-visual stimulus with EEG and fNIRS
When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location in the presence of background noises and irrelevant visual objects. The ability to decode the attended spatial location would facilitate brain computer interfaces (BCI) for complex scene analysis. Here, we tested two different neuroimaging technologies and investigated their capability to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. For functional near-infrared spectroscopy (fNIRS), we targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. We found that fNIRS provides robust decoding of attended spatial locations for most participants and correlates with behavioral performance. Moreover, we found that FEF makes a large contribution to decoding performance. Surprisingly, the performance was significantly above chance level 1s after cue onset, which is well before the peak of the fNIRS response.
For electroencephalography (EEG), while there are several successful EEG-based algorithms, to date, all of them focused exclusively on auditory modality where eye-related artifacts are minimized or controlled. Successful integration into a more ecological typical usage requires careful consideration for eye-related artifacts which are inevitable. We showed that fast and reliable decoding can be done with or without ocular-removal algorithm. Our results show that EEG and fNIRS are promising platforms for compact, wearable technologies that could be applied to decode attended spatial location and reveal contributions of specific brain regions during complex scene analysis
Network Transmission Flags Data Affinity-based Classification by K-Nearest Neighbor
Abstract—This research is concerned with the data generated during a network transmission session to understand how to extract value from the data generated and be able to conduct tasks. Instead of comparing all of the transmission flags for a transmission session at the same time to conduct any analysis, this paper conceptualized the influence of each transmission flag on network-aware applications by comparing the flags one by one on their impact to the application during the transmission session, rather than comparing all of the transmission flags at the same time. The K-nearest neighbor (KNN) type classification was used becauseit is a simple distance-based learning algorithm that remembers earlier training samples and is suitable for taking various flags withtheir effect on application protocols by comparing each new sample with the K-nearest points to make a decision. We used transmission session datasets received from Kaggle for IP flow with 87 features and 3.577.296 instances. We picked 13 features from the datasets and ran them through KNN. RapidMiner was used for the study, and the results of the experiments revealed that the KNN-based model was not only significantly more accurate in categorizing data, but it was also significantly more efficient due to the decreased processing costs
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process
Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine).
In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model.
AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development.
Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models.
In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
Annals [...].
Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin
Innovative Hybrid Approaches for Vehicle Routing Problems
This thesis deals with the efficient resolution of Vehicle Routing Problems (VRPs).
The first chapter faces the archetype of all VRPs: the Capacitated Vehicle Routing Problem (CVRP). Despite having being introduced more than 60 years ago, it still remains an extremely challenging problem. In this chapter I design a Fast Iterated-Local-Search Localized Optimization algorithm for the CVRP, shortened to FILO. The simplicity of the CVRP definition allowed me to experiment with advanced local search acceleration and pruning techniques that have eventually became the core optimization engine of FILO. FILO experimentally shown to be extremely scalable and able to solve very large scale instances of the CVRP in a fraction of the computing time compared to existing state-of-the-art methods, still obtaining competitive solutions in terms of their quality.
The second chapter deals with an extension of the CVRP called the Extended Single Truck and Trailer Vehicle Routing Problem, or simply XSTTRP. The XSTTRP models a broad class of VRPs in which a single vehicle, composed of a truck and a detachable trailer, has to serve a set of customers with accessibility constraints making some of them not reachable by using the entire vehicle. This problem moves towards VRPs including more realistic constraints and it models scenarios such as parcel deliveries in crowded city centers or rural areas, where maneuvering a large vehicle is forbidden or dangerous. The XSTTRP generalizes several well known VRPs such as the Multiple Depot VRP and the Location Routing Problem. For its solution I developed an hybrid metaheuristic which combines a fast heuristic optimization with a polishing phase based on the resolution of a limited set partitioning problem. Finally, the thesis includes a final chapter aimed at guiding the computational evaluation of new approaches to VRPs proposed by the machine learning community
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