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Robust waste transfer station planning by stochastic programming

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Chemical engineering Transactions 2018年 70卷 889-894页

作者： Kudela, Jakub Šomplák, Radovan Nevrlý, Vlastimír Lipovský, Tomáš Institute of Mathematics Faculty of Mechanical Engineering Brno University of Technology-VUT Brno Technická 2896/2 Vc Brno616 69 Czech Republic Institute of Automation and Computer Science Faculty of Mechanical Engineering Brno University of Technology-VUT Brno Technická 2896/2 Brno616 69 Czech Republic Institute of Process Engineering Faculty of Mechanical Engineering Brno University of Technology-VUT Brno Technická 2896/2 Brno616 69 Czech Republic

Regarding the infrastructure planning in waste management, the future situation in the Czech Republic or in some other waste developing countries is unknown due to the undecided support from the government or the EU. Furthermore, the production of waste depends on many factors (e.g. separation rate) that are uncertain. The level of recycling will be influenced by technical progress while taking into account the compromise between maximum waste material recovery and the economic impact of the process. Nevertheless, it is important to plan the transportation infrastructure that will be able to support the realization of future projects/development. Such a feature might be the transfer station grid, which can decrease the total transportation costs/impact on the environment. The grid design should be robust with respect to all the possible technological solutions (establishment of waste treatment facilities). In this paper, the mathematical model for grid design of transfer stations is proposed. The model will be presented as a two-stage mixed-integer stochastic programming problem. The model will be tested through a case study on the current situation and possible legislation changes regarding waste management in the Czech Republic. The model scale will be on the micro-regional level proposing a robust transfer station grid design. The realization of these projects takes into consideration possible investments and decides about the capacity of the facility with regards to future government support and donations. The output in the form of recommendation will serve possible investors, municipalities and/or stakeholders from the field of waste management to plan more sustainable projects. Copyright © 2018, AIDIC Servizi S.r.l.

关键词： Stochastic programming

Building accurate models to determine the current CPU utilization of a host within a virtual machine allocated on it 49

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Building accurate models to determine the current CPU utiliz...

49th Summer computer Simulation Conference, SCSC 2017, Part of the 2017 Summer Simulation Multi-Conference, SummerSim 2017

作者： Briongos, Samira Malagón, Pedro Risco, José L. Moya, José M. Department of Computer Architecture and Automation Computer Science Faculty Complutense University of Madrid Spain LSI - Electronic Engineering Department CCS-Center for Computational Simulation Universidad Poliécnica de Madrid Madrid Spain

ISBN: (纸本)9781510838284

In cloud computing environments there are several virtual machines running in the same host. This fact opens the door for possible side channel-Attacks. Prior to perform an attack it is mandatory to determine coresidency with the victim. An synchronized variation in the CPU activity in the host is a possible indicator of the presence of neighboring processes. However, cloud providers do not give information about the hosts CPU load, so we have to figure out a way of estimating it. We estimate the host CPU load considering its impact on the performance of a virtual machine (VM) running on it. In this work, we show that it is possible to calculate the CPU load of the host by executing a reference process, measuring the time it takes to execute, and using this information as an input to generate the CPU load models. We explore regression methods and regression methods tuned with genetic algorithms for the model generation. As a result, considering a CPU load value between 0 (no load) and 100 (maximum load), we obtain models which compute the host load with a mean squared error of around 5%, 10% and 30% (depending on the host architecture) when estimating the load every second. © 2017 Society for Modeling & Simulation International (SCS).

关键词： Genetic algorithms

Machine learning for diagnosis of disease in plants using spectral data

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Machine learning for diagnosis of disease in plants using sp...

2018 International Conference on Artificial Intelligence, ICAI 2018 at 2018 World Congress in computer science, computer engineering and Applied Computing, CSCE 2018

作者： Owomugisha, Godliver Melchert, Friedrich Mwebaze, Ernest Quinn, John A. Biehl, Michael University of Groningen Johann Bernoulli Institute for Mathematics and Computer Science P.O. Box 407 AK Groningen9700 Netherlands Fraunhofer Institute for Factory Operation and Automation IFF Sandtorstrasse 22 Magdeburg39106 Germany Makerere University School of Computing and Informatics Technology P.O. Box 7062 Kampala Uganda Busitema University Faculty of Engineering P. O. Box 236 Tororo Uganda

ISBN: (纸本)1601324804

Automating crop disease diagnosis is an important task, particularly for regions with few experts. Most current methods detect disease by analyzing leaf images, particularly for diseases that manifest on the aerial part of the plant. To train a good classifier one requires a huge image dataset and the appropriate methods to extract relevant features from the images that represent the disease unambiguously. Image data also tends to be prone to effects of occlusion that make consistent analysis of the data hard. In this paper we take a look at the use of spectral data collected from leaves of a plant. We analyse spectral data from visibly diseased parts of a leaf as well as parts that are visibly healthy. We employ prototype based classification methods and standard classification models in a three-class classification problem configuration. Results presented show significant improvement in performance when spectral data is used and the possibility of early detection of disease before the crops become visibly symptomatic, which for practical reasons is very important. CSREA Press ©.

关键词： Crops

Ensemble soft-margin softmax loss for image classification

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arXiv 2018年

作者： Wang, Xiaobo Zhang, Shifeng Lei, Zhen Liu, Si Guo, Xiaojie Li, Stan Z. CBSRandNLPR Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University School of Computer Software Tianjin University Tianjin China Faculty of Information Technology Macau University of Science and Technology Macau China

Softmax loss is arguably one of the most popular losses to train CNN models for image classification. However, recent works have exposed its limitation on feature discriminability. This paper casts a new viewpoint on the weakness of softmax loss. On the one hand, the CNN features learned using the softmax loss are often inadequately discriminative. We hence introduce a soft-margin softmax function to explicitly encourage the discrimination between different classes. On the other hand, the learned classifier of softmax loss is weak. We propose to assemble multiple these weak classifiers to a strong one, inspired by the recognition that the diversity among weak classifiers is critical to a good ensemble. To achieve the diversity, we adopt the Hilbert-Schmidt Independence Criterion (HSIC). Considering these two aspects in one framework, we design a novel loss, named as Ensemble soft-Margin Softmax (EM-Softmax). Extensive experiments on benchmark datasets are conducted to show the superiority of our design over the baseline softmax loss and several state-of-the-art alternatives. Copyright © 2018, The Authors. All rights reserved.

关键词： Image classification

PID2018 benchmark challenge: Learning feedforward control

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arXiv 2018年

作者： Zhao, Yang Dehghan, Sina Ates, Abdullah School of Control Science and Engineering Center for Robotics Shandong University Jinan Shandong250061 China University of California Merced MercedCA95340 United States School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey

The design and application of learning feedforward controllers (LFFC) for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge is presented, and its effectiveness is evaluated. The control system consists of two components: 1) a preset PID component and 2) a learning feedforward component which is a function approximator that is adapted on the basis of the feedback signal. A B-spline network based LFFC and a low-pass filter based LFFC are designed to track the desired outlet temperature of evaporator secondary flux and the superheating degree of refrigerant at evaporator outlet. Encouraging simulation results are included. Qualitative and quantitative comparison results evaluations show that, with little effort, a high-performance control system can be obtained with this approach. Our initial simple attempt of low-pass filter based LFFC and B-spline network based LFFC give J=0.4902 and J=0.6536 relative to the decentralized PID controller, respectively. Besides, the initial attempt of a combination controller of our optimized PI controller and low-pass filter LFFC gives J=0.6947 relative to the multi-variable PID controller. Copyright © 2018, The Authors. All rights reserved.

关键词： Feedforward control

Heuristic approaches to stochastic quadratic assignment problem: Var and cvar cases 23

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Heuristic approaches to stochastic quadratic assignment prob...

23rd International Conference on Soft Computing, MENDEL 2017

作者： Matoušek, Radomil Popela, Pavel Kudela, Jakub Brno University of Technology Faculty of Mechanical Engineering Institute of Automation and Computer Science Technická 2896/2 Brno616 69 Czech Republic Brno University of Technology Faculty of Mechanical Engineering Institute of Mathematics Technická 2896/2 Brno616 69 Czech Republic

The goal of this paper is to continue our investigation of the heuristic approaches of solving the stochastic quadratic assignment problem (StoQAP) and provide additional insight into the behavior of difierent formulations that arise through the stochastic nature of the problem. The deterministic Quadratic Assignment Problem (QAP) belongs to a class of well-known hard combinatorial optimization problems. Working with several real-world applications we have found that their QAP parameters can (and should) be considered as stochastic ones. Thus, we review the StoQAP as a stochastic program and discuss its suitable deterministic reformulations. The two formulations we are going to investigate include two of the most used risk measures-Value at Risk (VaR) and Conditional Value at Risk (CVaR). The focus is on VaR and CVaR formulations and results of test computations for various instances of StoQAP solved by a genetic algorithm, which are presented and discussed. © 2017 Brno University of Technology. All rights reserved.

关键词： Genetic algorithms

Study of tau neutrinos and non-unitary neutrino mixing with the first six detection units of KM3NeT/ORCA

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arXiv 2025年

作者： Aiello, S. Albert, A. Alhebsi, A.R. Alshamsi, M. Garre, S. Alves Ambrosone, A. Ameli, F. Andre, M. Aphecetche, L. Ardid, M. Ardid, S. Aublin, J. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Becherini, Y. Gualandi, F. Benfenati Benhassi, M. Bennani, M. Benoit, D.M. Berbee, E. Bertin, V. Biagi, S. Boettcher, M. Bonanno, D. Bouasla, A.B. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Breuhaus, M. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Clark, R. Cocimano, R. Coelho, J.A.B. Coyle, P. Creusot, A. Wasseige, G. De Decoene, V. Rosso, I. Del Mauro, L.S. Di Palma, I. Di Díaz, A.F. Diego-Tortosa, D. Distefano, C. Donzaud, C. Drakopoulou, E. Drouhin, D. Ducoin, J.-G. Duverne, P. Eberl, T. Eckerová, E. Eddymaoui, A. Eff, M. Bojaddaini, I. El Hedri, S. El Ellajosyula, V. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gagliardini, S. Gal, T. Méndez, J. García Soto, A. Garcia Oliver, C. Gatius Geißelbrecht, N. Genton, E. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Gracia, R. Guidi, C. Guillon, B. Gutiérrez, M. Haack, C. van Haren, H. Heijboer, A. Hennig, L. Hernández-Rey, J.J. Idrissi, A. Ibnsalih, W. Idrissi Illuminati, G. Joly, D. de Jong, M. de Jong, P. Jung, B.J. Kalaczynski, P. Kikvadze, V. Kistauri, G. Kopper, C. Kouchner, A. Kovalev, Y.Y. Krupa, L. Kueviakoe, V. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Lamoureux, M. Larosa, G. Lastoria, C. Lazar, J. Lazo, A. Le Stum, S. Lehaut, G. Lemaître, V. Leonora, E. Lessing, N. Levi, G. Clark, M. Lindsey Magnani, F. Majumdar, J. Malerba, L. Mamedov, F. Manfreda, A. Manousakis, A. Marconi, M. Margiotta, A. Marinelli, A. Markou, C. Martin, L. Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Khalifa University of Science and Technology Department of Physics PO Box 127788 Abu Dhabi United Arab Emirates Aix Marseille Univ CNRS IN2P3 CPPM Marseille France c/Catedrático José Beltrán 2 Valencia Paterna46980 Spain INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain Subatech IMT Atlantique IN2P3-CNRS Nantes Université 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Università di Genova Via Dodecaneso 33 Genova16146 Italy INFN Sezione di Genova Via Dodecaneso 33 Genova16146 Italy LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia INFN Sezione di Bologna v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università di Bologna Dipartimento di Fisica e Astronomia v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università degli Studi della Campania"Luigi Vanvitelli" Dipartimento di Matematica e Fisica viale Lincoln 5 Caserta81100 Italy LPC Campus des Cézeaux 24 av

Oscillations of atmospheric muon and electron neutrinos produce tau neutrinos with energies in the GeV range, which can be observed by the ORCA detector of the KM3NeT neutrino telescope in the Mediterranean Sea. First measurements with ORCA6, an early subarray corresponding to about 5% of the final detector, are presented. A sample of 5828 neutrino candidates has been selected from the analysed exposure of 433 kton-years. The ντ normalisation, defined as the ratio between the number of observed and expected tau neutrino events, is measured to be Sτ = 0.48+0−0.335 . This translates into a ντ charged-current cross section measurement of στmeas = (2.5+2−1.68) × 10−38 cm2 nucleon−1 at the median ντ energy of 20.3 GeV. The result is consistent with the measurements of other experiments. In addition, the current limit on the non-unitarity parameter affecting the τ-row of the neutrino mixing matrix was improved, with α33 > 0.95 at the 95% confidence level. Copyright © 2025, The Authors. All rights reserved.

关键词： Neutrons

Multi-view hybrid embedding: A divide-and-conquer approach

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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

Probing invisible neutrino decay with the first six detection units of KM3NeT/ORCA

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arXiv 2025年

作者： Aiello, S. Albert, A. Alhebsi, A.R. Alshamsi, M. Garre, S. Alves Ambrosone, A. Ameli, F. Andre, M. Aphecetche, L. Ardid, M. Ardid, S. Aublin, J. Badaracco, F. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Becherini, Y. Bendahman, M. Gualandi, F. Benfenati Benhassi, M. Bennani, M. Benoit, D.M. Berbee, E. Bertin, V. Biagi, S. Boettcher, M. Bonanno, D. Bouasla, A.B. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Breuhaus, M. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Clark, R. Cocimano, R. Coelho, J.A.B. Coleiro, A. Condorelli, A. Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. Dallier, R. De Benedittis, A. De Wasseige, G. Decoene, V. Deguire, P. Del Rosso, I. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Drakopoulou, E. Drouhin, D. Ducoin, J.-G. Duverne, P. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. El Mentawi, S. Ellajosyula, V. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gagliardini, S. Gal, T. Méndez, J. García Soto, A. Garcia Oliver, C. Gatius Geißelbrecht, N. Genton, E. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goswami, P. Gozzini, S.R. Gracia, R. Guidi, C. Guillon, B. Gutiérrez, M. Haack, C. van Haren, H. Heijboer, A. Hennig, L. Hernández-Rey, J.J. Idrissi, A. Ibnsalih, W. Idrissi Illuminati, G. Joly, D. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Kikvadze, V. Kistauri, G. Kopper, C. Kouchner, A. Kovalev, Y.Y. Krupa, L. Kueviakoe, V. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Lamoureux, M. Larosa, G. Lastoria, C. Lazar, J. Lazo, A. Le Stum, S. Lehaut, G. Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Khalifa University of Science and Technology Department of Physics PO Box 127788 Abu Dhabi United Arab Emirates Aix Marseille Univ CNRS IN2P3 CPPM Marseille France c/Catedrático José Beltrán 2 Paterna Valencia46980 Spain INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain Subatech IMT Atlantique IN2P3-CNRS Nantes Université 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Università di Genova Via Dodecaneso 33 Genova16146 Italy INFN Sezione di Genova Via Dodecaneso 33 Genova16146 Italy LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia INFN Sezione di Bologna v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università di Bologna Dipartimento di Fisica e Astronomia v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università degli Studi della Campania "Luigi Vanvitelli" Dipartimento di Matematica e Fisica viale Lincoln 5 Caserta81100 Italy LPC Campus des Cézeaux 24 a

In the era of precision measurements of neutrino oscillation parameters, it is necessary for experiments to disentangle discrepancies that may indicate physics beyond the Standard Model in the neutrino sector. KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study the oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on the initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photosensors. A high-purity neutrino sample was extracted during 2020 and 2021, corresponding to an exposure of 433 kton-years. This sample is analysed following a binned log-likelihood approach to search for invisible neutrino decay, in a three-flavour neutrino oscillation scenario, where the third neutrino mass state ν3 decays into an invisible state, e.g. a sterile neutrino. The resulting best fit of the invisible neutrino decay parameter is (formula presented), corresponding to a scenario with θ23 in the second octant and normal neutrino mass ordering. The results are consistent with the Standard Model, within a 2.1 σ interval. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmic ray detectors