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CRISP - DM Process in Smart Building Management

CRISP - DM Process in Smart Building Management

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international Conf on Electrical and Electronic Engineering (ICEEE)

作者： Mahir Dursun Moldir Sabyrzhan Dept. of Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Türkiye Department of Computer Science Graduate School of Informatics Gazi University Ankara Türkiye

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. this paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. the developed model will predict energy consumption in classrooms and laboratories based on weather conditions. the study is being conducted in two stages. the first stage describes how to collect data using electronic devices such as Internet of things (IoT) controllers of Smart Building Management systems. the second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM).

关键词： Heating systems Smart buildings Laboratories Energy conservation Decision making Process control Data models Planning Internet of things Data mining

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Effects of Phase-dependent Stimulation on Hippocampal Oscillations: A Computational Modeling Approach

Effects of Phase-dependent Stimulation on Hippocampal Oscill...

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international IEEE/EMBS conference on Neural Engineering, CNE

作者： Hsin-Pei Lee Heba Sattar William S Anderson Yousef Salimpour Department of Computer Science Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD US Department of Biomedical Engineering Johns Hopkins School of Medicine Baltimore MD US Department of Neurosurgery Functional Neurosurgery Laboratory Johns Hopkins School of Medicine Baltimore MD US

the important role of phase-amplitude coupling (PAC) between brain oscillations of different rhythms may imply possibly promising effects for phase-dependent neuromodulation techniques to treat neurological disorders. In particular, the theta-gamma PAC correlates with significant and complex cognitive functions. Computational models serve as convenient tools to overcome in vivo and in vitro limitations. Using a hippocampal computational model in the NEURON and Python environments, we delivered carefully timed current injections to the cells in the network at the harmonics of the network's peak theta band frequency. Each applied stimulus was categorized by its corresponding phase angle, and the stimulation effects on the network activity were analyzed. By analyzing the local field potential and bandpass power amplitudes, we demonstrated enhancement and depression of the theta band depending on the phase of the stimulus. Applying stimulation before the peak phase consistently amplified the the theta band while stimulating immediately after the peak phase consistently suppressed network activity. Further study is required to understand the continuous effects of phase-locked stimulation with a closed-loop stimulator and their implications for human and animal models of rhythmic neural activity. these results demonstrate the capacity of phase-dependent stimulation to modulate neuronal oscillations, which could allow for applications in the treatment of neurological disorders associated with abnormal oscillations, such as Alzheimer's disease. Clinical Relevance- Analyzing the origins of neuronal oscillations and developing a brain stimulation technique for modulating the level of oscillations can facilitate the development of novel treatment methods such as phase-dependent neuromodulation systems for neurological disorders associated with abnormal oscillations.

关键词： Neurological diseases Time-frequency analysis Computational modeling Neural activity Rodents Neural engineering Brain modeling

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Deep quantile regression based wind generation and demand forecasts 11th

Deep quantile regression based wind generation and demand fo...

引用

11th international conference on Soft Computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired Computing, NaBIC 2019

作者： Kirthika, N. Ramachandran, K.I. Kottayil, Sasi K. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783030493448

the widespread attention in the growth of clean energy for electricity production necessitates an accurate and reliable generation and demand forecasts. However, the decision-making process in electric power industry involves more uncertainty due to the transition towards distributed energy systems, which are not addressed in the conventional point forecasts. this paper proposes a probabilistic method termed as Deep Quantile Regression (DQR) for the construction of prediction intervals (PIs) that can potentially quantify uncertainty in the point forecasts of wind power generation and demand. the effectiveness of DQR is examined using the low and high seasonal wind and demand datasets. PIs with various confidence levels of 99%, 95% and 90% are estimated by constructing the appropriate quantiles using the proposed DQR method. the quantitative comparison of the quality in all the estimated PIs using the proposed method proves to outperform the other state-of-the-art methods. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Uncertainty analysis

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Anomalies Detecting in Medical Metrics Using Machine Learning tools 12

Anomalies Detecting in Medical Metrics Using Machine Learnin...

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12th international conference on Emerging Ubiquitous systems and Pervasive Networks, EUSPN 2021 / 11th international conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICth 2021

作者： Melnykova, Nataliia Kulievych, Roman Vycluk, Yaroslav Melnykova, Kateryna Melnykov, Volodymyr Department of Artificial Intelligence Lviv Polytechnic National University Kniazia Romana str. 5 Lviv79905 Ukraine Lviv Emergency Hospital Lviv79010 Ukraine Department of Transplantology Surgery Danylo Halytsky Lviv National Medical University Lviv79010 Ukraine

the article analyses the research related to the issue of detecting anomalies in current medical data. the work aims to develop an information system for detecting data anomalies in the format of time series, such as metrics, with the ability to visualize the results for expert evaluation. the process of detecting anomalies by machine learning tools for detecting anomalies in the flow data is investigated. A system for detecting anomalies in metrics using the HTM model is built. According to the model results, obtained satisfactory accuracy for detecting anomalies at standard network parameters, the estimation of anomalies differed clearly for different aggregation intervals. the OPF Client functionality was used to build the HTM model, which allowed to achieve speed and simplicity in its construction. the obtained results allow the expert to choose the best of the size of the intervals as well as the parameters of the models. this choice is necessary because the presence of the anomaly depends on expert knowledge in a particular field. © 2021 Elsevier B.V.. All rights reserved.

关键词： Time series

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Re-defining Spatial Typologies of Humanitarian Housing Plans Using Machine Learning 11th

Re-defining Spatial Typologies of Humanitarian Housing Plans...

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11th international conference on Innovation in Urban and Regional Planning, INPUT 2021

作者： Pezzica, Camilla Cutini, Valerio de Souza, Clarice Bleil Chioni, Chiara Department of Energy Systems Territory and Construction Engineering University of Pisa Largo Lucio Lazzarino56122 Italy Welsh School of Architecture Cardiff University Bute Building King Edward VII Avenue CardiffCF10 3NB United Kingdom

ISBN: (纸本)9783030688233

the complexity of post-disaster recovery processes and the risks of implementing "one size fits all" solutions are now widely acknowledged by humanitarian actors, who are pushing for innovative approaches to better link housing recovery and urban development after urban disasters. However, there is currently little technical support for responsible local administrations tasked with selecting Temporary Housing (th) sites, to measure and visualise the complexity of their spatial configuration, illustrating patterns of urbanisation and transportation. the lack of time and appropriate decision-making support systems contribute to perpetuate unsuccessful practices of housing assistance provision, which detract resources from more important long-term investments. therefore, this paper proposes a computational analysis method which seeks to support the delivery of truly people-centred and performance-based th plans by enhancing administrations’ capacity to audit th proposals. It seeks to disclose non-trivial differences and trade-offs in candidate th plans, by exploiting unsupervised machine learning techniques to quantitatively define, starting from existing data, similar types of spatial arrangements. this research assumes that clustering algorithms can establish deep analogies between diachronic urban configurations, both real and projected. the proposed computational method can be scaled up so as to handle multiple dimensions according to changing strategic planning priorities and varying local needs. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Housing

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CBSOFT 2021 - Brazilian conference on Software;Proceedings - 15th Brazilian Symposium on Software Components, Architectures, and Reuse, SBCARS 2021

CBSOFT 2021 - Brazilian Conference on Software;Proceedings -...

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15th Brazilian Symposium on Software Components, Architectures, and Reuse, SBCARS 2021, held in conjunction with the Brazilian conference on Software:theory and Practice, CBSoft 2021

ISBN: (纸本)9781450384193

the proceedings contain 11 papers. the topics discussed include: on the configuration of multi-objective evolutionary algorithms for PLA design optimization;do critical components smell bad? an empirical study with component-based software product lines;guidelines for promoting software product line experiments;a domain-specific language to specify planned architectures of adaptive systems;microservice decomposition and evaluation using dependency graph and silhouette coefficient;are we speaking the industry language? the practice and literature of modernizing legacy systems with microservices;mining evidences of internet of robotic things (IoRT) software from open source projects;a comparative analysis between information flow control tools for Java-written systems;temporal analysis on pull request patterns: an approach with sliding window;and ModelGame: a quality model for gamifield software modeling learning.

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Arabic Sentiment analysis Based on Salp Swarm Algorithm with S-shaped Transfer Functions 11

Arabic Sentiment Analysis Based on Salp Swarm Algorithm with...

引用

11th international conference on Information and Communication systems (ICICS)

作者： Alzaqebah, Abdullah Smadi, Bushra Hammo, Bassam H. World Islamic Sci & Educ Univ Comp Sci Dept Amman Jordan Al Balqa Appl Univ Comp Sci Dept Ajloun Univ Coll Ajloun Jordan Univ Jordan Comp Informat Syst Dept Amman Jordan

ISBN: (纸本)9781728162270

Social media has played a significant role in marketing and advertising. Monitoring the attitude of customers and analyzing their written sentiments to evaluate their opinions toward a particular product, topic or situation becomes essential to improve the product quality and customer services. Due to the importance of sentiment analysis (SA), a plethora of tools and systems have been developed for analyzing the polarities of people's sentiments. However, SA is a difficult task, especially when dealing with massive data resources. Feature selection (FS) algorithms are needed for the machine learning (ML) process to reduce the high dimensionality space. In this paper, we propose an enhancement of a bio-inspired optimizer, called the salp swarm algorithm (SSA) designed for feature selection to solve the problem of Arabic sentiment analysis. Our proposed algorithm operates in two phases: the first phase employs a filtering technique based on the information gain (IG) metric to reduce the number of features. the second phase employs a wrapper technique which combines the basic SSA optimizer with four variants of S-shaped transfer functions. Experimental results show that the SSA combined with the S-shaped transfer functions outperformed the particle swarms optimizer (PSO) and the grey wolf optimizer (GWO) in term of classification accuracy.

关键词： Arabic sentiment analysis Information Gain Salp Swarm Algorithm Transfer Function Feature Selection

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Experimental Study on Dual parameter WeiBull Failure Model of Oil-Paper Insulation in Converter Transformers Considering the Influence of Harmonic Voltage

Experimental Study on Dual parameter WeiBull Failure Model o...

引用

international conference on High Voltage Engineering and Application, ICHVE

作者： Ning Zhang Jian Hao Jing Xu Jixiang Wang Houhe Chen Ruijin Liao Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing China

ISBN: (数字)9798350374988

ISBN: (纸本)9798350374995

the main causes of harmonic distortion in new power systems are high percentage renewable energy grid connections and high percentage power electronic equipment access. these factors pose a serious threat to the ultra-high voltage converter transformers’ ability to perform as oil-paper insulation, and little research has been done on the failure law of oil-paper insulation that takes the effects of harmonic voltage into account. this paper presents the design and construction of a typical oil-paper insulation structure breakdown test platform, as well as the oil-paper insulation breakdown voltage under various frequency and content of harmonic voltage superimposed on the fundamental voltage. the dual parameter WeiBull distribution model is used to investigate the oil-paper insulation failure law under the influence of various harmonic voltage components. the experimental results show that the breakdown voltage of oil-paper insulation significantly decreases relative to the breakdown voltage of power frequency under the action of harmonic voltages with different frequencies and contents, and all follow a dual parameter WeiBull probability distribution. Among them, the minimum $\boldsymbol{U}_{63.2 \%}$ is about 13.47 kV when the 7 th and 11th harmonic voltage are superimposed, and the minimum $U_{63.2 \%}$ is about 13 kV when the 5th and 13th harmonic voltage are superimposed, which is about $\mathbf{2 3 \%}$ lower than the power frequency. Secondly, the fitting distribution of test data under the action of harmonic voltage of different frequencies and contents is approximately parallel, indicating that the failure mechanism of oil-paper insulation is the same.

关键词： Breakdown voltage Renewable energy sources Power system harmonics Oil insulation Harmonic analysis Probability distribution Power electronics

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Simple Strategies in Multi-Objective MDPs 26th

Simple Strategies in Multi-Objective MDPs

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26th international conference on tools and algorithms for construction and analysis of systems (TACAS)

作者： Delgrange, Florent Katoen, Joost-Pieter Quatmann, Tim Randour, Mickael Rhein Westfal TH Aachen Aachen Germany UMONS Univ Mons Mons Belgium Vrije Univ Brussel Brussels Belgium

ISBN: (纸本)9783030451899;9783030451905

We consider the verification of multiple expected reward objectives at once on Markov decision processes (MDPs). this enables a trade-off analysis among multiple objectives by obtaining a Pareto front. We focus on strategies that are easy to employ and implement. that is, strategies that are pure (no randomization) and have bounded memory. We show that checking whether a point is achievable by a pure stationary strategy is NP-complete, even for two objectives, and we provide an MILP encoding to solve the corresponding problem. the bounded memory case is treated by a product construction. Experimental results using Storm and Gurobi show the feasibility of our algorithms.

关键词： Markov processes

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Development of Failure Modes and Effects analysis methodology using Model-Based Safety Assessment approach 11

Development of Failure Modes and Effects Analysis methodolog...

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11th international Scientific and Technical conference on Robotic and Intelligent Aircraft systems Improving Challenges, RIASIC 2020

作者： Savelev, A.S. Bersutskaya, O.D. Silin, N.D. Neretin, E.S. Moscow Aviation Institute National Research University 4 Volokolamskoe shosse Moscow125993 Russia

During the development of aircraft systems, the safety assessment process activities are carried out in parallel. Traditionally, safety professionals perform Functional Hazard Assessment, Preliminary System Safety Assessment, and System Safety Assessment. Despite the availability of many modern and advanced tools for performing calculations during the implementation of the System Safety Assessment, a great responsibility (and, accordingly, the risk of error) remains with the person. Model-Based Safety Assessment is a new and rapidly developing approach to evaluate the safety of airborne equipment worldwide, which improves existing methodologies for performing various analyzes, such as Failure Modes and Effects analysis (FMEA). Implementation of FMEA using this approach requires the development of special software tools that allow both to create a model of the analyzed onboard equipment and systems, and to provide an opportunity to use this model when performing FMEA. One such tool is ANSYS Medini Analyze. Model-oriented method for performing FMEA is proposed within this article. this method fully complies with the requirements of ARP-4761 and provides possibility to minimize the likelihood of human error. Model-oriented method for performing FMEA has been probated on the example of flight control system at various levels of abstraction (system, item, functional block and hardware/software level). © Published under licence by IOP Publishing Ltd.

关键词： Flight control systems

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