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7th international conference on Future Buildings and Districts - Energy Efficiency from Nano to Urban Scale (CISBAT)

作者： Mavromatidis, Georgios Orehounig, Kristina Carmeliet, Jan Swiss Fed Inst Technol Chair Bldg Phys Zurich Switzerland Empa Lab Urban Energy Syst Dubendorf Switzerland Empa Lab Multiscale Studies Bldg Phys Dubendorf Switzerland

this paper examines the design of autonomous Distributed Energy Systems (DES) under energy demand and solar radiation uncertainty. A two-stage stochastic program is developed that seeks cost-optimal DES designs considering probabilistic scenarios for the uncertain parameters to represent possible operating conditions. then, energy autonomy constraints are imposed to each individual scenario, ensuring the autonomy robustness of the DES. the model is applied to a Swiss office building and results reveal that the most cost-effective DES solutions achieve an electrical autonomy of 20% relying on renewable PV electricity generation. Higher autonomy levels require additional electricity from a CHP engine, while a 100% autonomous system is achievable but requires significant amounts of thermal and electrical storage. Finally, comparing the stochastic DES designs against deterministic ones reveal significant differences, illustrating the importance of uncertainty considerations in the design of autonomous DES. (C) the Authors. Published by Elsevier Ltd.

关键词： Distributed Energy System Energy hub Energy autonomy Uncertainty Stochastic programming

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NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis

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BMC SYSTEMS BIOLOGY 2017年第4-Sup期11卷 61-74页

作者： Sun, Duanchen Liu, Yinliang Zhang, Xiang-Sun Wu, Ling-Yun Chinese Acad Sci Acad Math & Syst Sci Inst Appl Math Beijing 100190 Peoples R China Chinese Acad Sci Natl Ctr Math & Interdisciplinary Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

Background: High-throughput experimental techniques have been dramatically improved and widely applied in the past decades. However, biological interpretation of the high-throughput experimental results, such as differential expression gene sets derived from microarray or RNA-seq experiments, is still a challenging task. Gene Ontology (GO) is commonly used in the functional enrichment studies. the GO terms identified via current functional enrichment analysis tools often contain direct parent or descendant terms in the GO hierarchical structure. Highly redundant terms make users difficult to analyze the underlying biological processes. Results: In this paper, a novel network-based probabilistic generative model, NetGen, was proposed to perform the functional enrichment analysis. An additional protein-protein interaction (PPI) network was explicitly used to assist the identification of significantly enriched GO terms. NetGen achieved a superior performance than the existing methods in the simulation studies. the effectiveness of NetGen was explored further on four real datasets. Notably, several GO terms which were not directly linked with the active gene list for each disease were identified. these terms were closely related to the corresponding diseases when accessed to the curated literatures. NetGen has been implemented in the R package CopTea publicly available at Github (http://***/wulingyun/CopTea/). Conclusion: Our procedure leads to a more reasonable and interpretable result of the functional enrichment analysis. As a novel term combination-based functional enrichment analysis method, NetGen is complementary to current individual term-based methods, and can help to explore the underlying pathogenesis of complex diseases.

关键词： Gene ontology Enrichment analysis Network-based probabilistic generative model Integer programming Complex diseases

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8th international conference on Information Science and Applications, ICISA 2017

8th International Conference on Information Science and Appl...

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8th international conference on Information Science and Applications, ICISA 2017

ISBN: (纸本)9789811041532

the proceedings contain 96 papers. the special focus in this conference is on Ubiquitous Computing, Information Systems, Multimedia, Visualization, Privacy, Artificial Intelligence, Web Technology and 7th international Workshop on ICT Convergence. the topics include: Improving performance and energy efficiency for OFDMA systems using adaptive antennas and CoMP;a study on OPNET state machine model based IoT network layer test;indoor positioning solely based on user’s sight;a highly robust and secure digital image encryption technique;saliency based object detection and enhancements in static images;design of OpenGL SC 2.0 rendering pipeline;saliency detection via foreground and background seeds;information visualization for mobile-based disability test applications;an augmented reality learning system for programming concepts;MBR image automation analysis techniques utilizing emulab;timing side channel attack on key derivation functions;known bid attack on an electronic sealed-bid auction scheme;secret sharing deniable encryption technique;improved 3D mesh steganalysis using homogeneous kernel map;enhanced database security using homomorphic encryption;design and evaluation of chaotic iterations based keyed hash function;a case-based approach to colorectal cancer detection;deep learning based recommendation;a network-based approach on big data for the comorbidities of urticaria;calculate deep convolution neural network on cell unit;smart learner-centric learning systems;static testing using different types of CRUD matrices and piezoelectric voltage monitoring system using smartphone.

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7th JSAI international Symposium on Artificial Intelligence, JSAI-isAI 2015

7th JSAI International Symposium on Artificial Intelligence,...

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7th JSAI international Symposium on Artificial Intelligence, JSAI-isAI 2015

ISBN: (纸本)9783319509525

the proceedings contain 39 papers. the special focus in this conference is on Artificial Intelligence. the topics include: Towards a probabilistic analysis for conditionals and unconditionals;an inference problem set for evaluating semantic theories and semantic processing systems for Japanese;applicative abstract categorial grammars in full swing;scope parallelism in coordination in dependent type semantics;discourse particles as CCP-modifiers: German doch and ja as context filters;tracking down disjunction;the meaning and use of the Japanese counter-expectational adverbs;evaluative predicates and evaluative uses of ordinary predicates;strong permission in prescriptive causal models;truth as a logical connective;abductive logic programming for normative reasoning and ontologies;a belief revision technique to model civil code updates;using ontologies to model data protection requirements in workflows;utilization of multi-word expressions to improve statistical machine translation of statutory sentences;argumentation support tool with reliability-based argumentation framework;applying a convolutional neural network to legal question answering;lexical-morphological modeling for legal text analysis;on the issue of argumentation and informedness;on the interpretation of assurance case arguments;learning argument acceptability from abstract argumentation frameworks;designing intelligent sleep analysis systems for automated contextual exploration on personal sleep-tracking data;a comparative study of similarity measures for time series classification;extracting propagation patterns from bacterial culture data in medical facility;aggregating and analyzing articles and comments on a news website;feasibility of collaborative learning and work between robots and children with autism spectrum disorders.

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Finding a Nash Equilibrium and an Optimal Sharing Policy for Multiagent Network Expansion Game

Finding a Nash Equilibrium and an Optimal Sharing Policy for...

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7th international Network Optimization conference (INOC)

作者： Chaabane, Nadia Briand, Cyril Huguet, Marie-Jose Agnetis, Alessandro Univ Toulouse CNRS INSA LAASUPS Siena Italy Univ Siena Dipartimento Ingn Informaz & Sci Matemat Siena Italy

In this work, a multiagent network flow problem is addressed, aiming at characterizing the properties of stable flows and allowing their computation. Two types of agents are considered: transportation-agents, that carry a flow of products on a given network and another agent, either a producer or a customer, who is willing to ship (receive, respectively), products. Every transportation-agent controls a set of arcs, each having a capacity that can be increased up to a certain point at a given cost. the other agent (i.e., the customer/producer) is interested in maximizing the flow transshipped through the network. To this aim, we assume it offers the transportation-agents a reward that is proportional to the realized flow value. this particular multiagent framework is referred to as a Multiagent network expansion game. We characterize and find particular stable strategies (i.e., Nash equilibria) that are of interest for this game. We particularly focus on the problem of finding a Nash Equilibrium and a sharing policy that maximize the value of the total flow. We prove that this problem is NP-hard in the strong sense and show how such a strategy can be characterized considering paths in specific auxiliary graphs. We also provide a mixed integer linear programming formulation to solve the problem. Computational experiments are provided to prove the effectiveness of our approach and derive some insights for practitioners. (C) 2016 Wiley Periodicals, Inc.

关键词： multiagent network flow Nash equilibria complexity network expansion game mixed integer linear programming

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An Economic Optimization for BESS Sizing in a Hybrid PV and Wind Power Plant 7

An Economic Optimization for BESS Sizing in a Hybrid PV and ...

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7th IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)

作者： Yang, Yuqing Bremner, Stephen Menictas, Chris Meng, Ke Dong, Zhao Yang Kay, Merlinde UNSW Australia Sch Photovolta & Renewable Energy Engn Sydney NSW Australia UNSW Australia Sch Mech & Mfg Engn Sydney NSW Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia UNSW Australia Sch Elect Engn & Telecommun Sydney NSW Australia

ISBN: (纸本)9781538649503

this paper presents an analytical sizing strategy for a Battery Energy Storage System (BESS) in a Hybrid PV and Wind Power Plant (HPP), aiming at maximizing the operating profits of the HPP. this goal was achieved mainly through applying the battery to compensate for forecasting errors, i.e., reducing renewable energy curtailment and ancillary service requirement under the electricity market operation rules. A novel reformulation for the optimization problem was proposed to convert the original nonlinear programming problem into a linear programming problem. Two preliminary forecasting methods were applied as comparable studies to verify the effectiveness of installing the BESS. the numerical simulation results have showed the detailed operating profits in each scenario with the corresponding BESS power and energy capacity.

关键词： Battery Energy Storage System Hybrid PV and Wind Power Plant Forecasting Errors Operating Profits

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Guided installation of basic linear algebra routines in a cluster with manycore components

Guided installation of basic linear algebra routines in a cl...

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Euro-Par conference / 7th international Workshop on programming Models and Applications for Multicores and Manycores (PMAM) held in conjunction with the 21st SIGPLAN Symposium on Principles and Practice of Parallel programming (PPoPP)

作者： Cuenca, J. Garcia, L. P. Gimenez, D. Herrera, F. J. Univ Murcia Dept Engn & Technol Comp Murcia Spain Tech Univ Cartagena Serv Support Technol Res Murcia Spain Univ Murcia Dept Comp & Syst Murcia Spain

Computational systems are nowadays composed of basic computational components that share multiprocessors and coprocessors of different types, typically several graphics processing units (GPUs) or many integrated cores (MICs), and those computational components are combined in heterogeneous clusters of nodes with different characteristics, including coprocessors of different types, with varying numbers of nodes at different speeds. the software previously developed and optimized for simpler system needs to be redesigned and reoptimized for these new, more complex systems. the adaptation to hybrid multicore+multiGPU and multicore+multiMIC of autotuning techniques for basic linear algebra routines is analyzed. the matrix-matrix multiplication kernel, which is optimized for different computational system components through guided experimentation, is studied. the routine is installed for each node in the cluster, and the information generated from individual installations may be used for a hierarchical installation in a cluster. the basic matrix-matrix multiplication may, in turn, be used inside higher level routines, which delegate their efficient execution to the optimization of the lower level routine. Experimental results are satisfactory in different multicore+multiGPU and multicore+multiMIC systems. So the guided search of execution configurations for satisfactory execution times proves to be a useful tool for heterogeneous systems, where the complexity of the system means a correct use of highly efficient routines and libraries is difficult.

关键词： autotuning heterogeneous computing hybrid programming parallel linear algebra manycore

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Interval T-S fuzzy modeling based on minimizing 1-norm on approximation error 7

Interval T-S fuzzy modeling based on minimizing 1-norm on ap...

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7th international conference on Computer Engineering and Networks, CENet 2017

作者： Liu, Xiaoyong Zhou, Shufang Xiong, Zhonggang College of Engineering and Technology Zunyi Normal College Zunyi563006 China Affiliated Hospital of Zunyi Medical College Zunyi563003 China

As the obtained data in many practical applications tend to be uncertain or inaccurate, the conventional modeling methods characterized by the deterministic model for this type of data have become undesirable. Taking linear programming, the T-S fuzzy model and some ideas from 1-norm minimization into consideration, a novel method identifying interval fuzzy model (INFUMO) consisted of the upper and lower T-S fuzzy model (referred to as f U and f L ) has been studied in this paper. In order to solve the INFUMO, optimization problems based on minimizing 1-norm with respect to the approximation error corresponding to f U and f L are constructed. Finally, the optimization problems are solved by the linear programming and INFUMO is thus constructed. To demonstrate its effectiveness, the proposed method is applied to identify the interval T-S model of static and dynamic nonlinear model with noise. the proposed method can not only deal with uncertain data to be usually modeled as the deterministic model, but also has better robustness. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Linear programming

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Riccati-Regularized Precision Matrices for Neuroimaging 1

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25th Biennial international conference on Information Processing in Medical Imaging (IPMI)

作者： Honnorat, Nicolas Davatzikos, Christos Univ Penn Dept Radiol 3700 Hamilton WalkRichards Bldg7th Floor Philadelphia PA 19104 USA

ISBN: (数字)9783319590509

ISBN: (纸本)9783319590509;9783319590493

the introduction of graph theory in neuroimaging has provided invaluable tools for the study of brain connectivity. these methods require the definition of a graph, which is typically derived by estimating the effective connectivity between brain regions through the optimization of an ill-posed inverse problem. Considerable efforts have been devoted to the development of methods extracting sparse connectivity graphs. the present paper aims at highlighting the benefits of an alternative approach. We investigate low-rank L2 regularized matrices recently introduced under the denomination of Riccati regularized precision matrices. We demonstrate their benefits for the analysis of cortical thickness map and the extraction of functional biomarkers from resting state fMRI scans. In addition, we explain how speed and result quality can be further improved with random projections. the promising results obtained using the Human Connectome Project dataset, as well as, the numerous possible extensions and applications suggest that Riccati precision matrices might usefully complement current sparse approaches.

关键词： rs-fMRI Precision Sparse inverse covariance

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Automatic Weld Path Generation for Mesh Objects

Automatic Weld Path Generation for Mesh Objects

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IEEE international conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Nathan Larkin Andrew Short Zengxi Pan Stephen van Duin Defence Materials Technology Centre University of Wollongong Australia School of Mechanical Materials and Mechatronics Engineering University of Wollongong Australia

ISBN: (纸本)9781538604915

Automated Offline programming (AOLP) is an emerging programming paradigm where industrial robot programs are generated from computer models with minimal human effort. Arc welding, such as Gas Metal Arc Welding (GMAW) or Tungsten Inert Gas (TIG), is particularly suited for this application due to the unique retooling requirements. A key aspect of the AOLP process is the determination of accurate welding paths. We present a novel algorithm to extract accurate welding paths for regular or irregular 3D bodies. Our process is computationally inexpensive with processing times less than a second for typical applications. We detail three examples with a range of complexity and examine the accuracy of the results and computational performance.

关键词： Welding Wires programming Barium Service robots Solid modeling

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