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24th International Conference on System Theory, Control and Computing (ICSTCC)

作者： Tiganasu, Alexandru Lazar, Corneliu Caruntu, Constantin Florin Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi Romania

ISBN: (纸本)9781728198095

In recent years, the problem of crowded public roads became more and more stringent. Some solutions for this problem may be represented by the individual advanced driver assistance systems (ADAS) that can make a vehicle more or less autonomous through a set of sensors, actuators and control algorithms. Depending on the level of automation, the driver needs to intervene or not in some traffic situations. Most of the ADAS systems treat two cases given by the movement direction of the vehicle: longitudinal or lateral. For the longitudinal direction the variable that must be controlled is the vehicle's velocity. In this paper, a cooperative vehicle following strategy is proposed based on predictive control. The movement of a leader and a follower together as a single entity is in focus for this case study. The leader is designed using a Cruise Control (CC) algorithm based on a generalized predictive controller (GPC). For the follower, a cooperative predictive adaptive controller (CPAC) with delay compensation is considered. Besides the feedback part, CPAC contains a feedforward element to counteract the negative effect of measurable disturbances that are represented by the velocities received from the leader. For this group of two vehicles implemented with the above-mentioned algorithms, a MATLAB/Simulink simulator was developed to analyze the obtained performance.

关键词： vehicle following cooperative control delay compensation predictive algorithm spacing/velocity adaptation inter-vehicle communications

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RBF neural network based predictive control of active power filter

RBF neural network based predictive control of active power ...

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IEEE Region 10 Conference on Analog and Digital Techniques in Electrical Engineering

作者： Wang, XH Xiao, JH Changsha Univ Sci & Technol Dept Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)0780385608

A RBF neural network based predictive control of active power filter is presented in this paper. RBF neural network is employed to predict future harmonic compensating current. In order to make the predictive model much simpler and tighter, an adaptive learning algorithm for RBF network is proposed. Based on the model output, branch-and-bound optimization method is adopted to produce proper value of control vector. This control vector is adequately modulated by means of a space vector PWM modulator which generates proper gating patterns of the inverter switches to maintain tracking of reference current. The RBF neural network based predictive algorithm is used in internal model control scheme to compensate for process disturbances, measurement noise and modeling errors. Experiment on an actual system is implemented. The results show the RBF neural network based predictive control eliminates supply current and voltage harmonics greatly and is more effective than PI control.

关键词： radial basis function networks active filters power harmonic filters learning systems adaptive control predictive control PI control pulse width modulation invertors switches power engineering computing optimisation noise measurement radial basis function RBF neural network active power filter adaptive learning algorithm branch-and-bound optimization method space vector PWM modulator gating patterns inverter switches predictive algorithm internal model control scheme voltage harmonics PI control

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predictive rental values model for low-income earners in slums: the case of Ijora, Nigeria

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INTERNATIONAL JOURNAL OF CONSTRUCTION MANAGEMENT 2023年第8期23卷 1426-1435页

作者： Iroham, Chukwuemeka O. Misra, Sanjay Emebo, Onyeka C. Okagbue, Hilary, I Covenant Univ Dept Estate Management Ota Ogun State Nigeria Atilim Univ Dept Comp Engn Ankara Turkey Covenant Univ Dept Elect & Informat Engn Ota Ogun State Nigeria Covenant Univ Dept Comp & Informat Sci Ota Ogun State Nigeria Covenant Univ Dept Math Ota Ogun State Nigeria

It is well known most often that values of properties tend to hike at the effluxion of time. This has necessitated the adoption of predictive models in interpreting outcomes in the property market in the future. Earlier studies have been oblivious of such models' outcomes as it affects any focal group, particularly the vulnerable. This present study focuses on the low-income earners found in the slum. The Ijora community in Lagos was the highlight of this study, particularly Ijora Badia and Ijora Oloye, regarded as slums according to the UNDP report. The entire fifty-two (52) local agents in the Ijora community were surveyed in cross-sectional survey research that entailed the questionnaire's issuance. The nexus of data collection, pre-processing, data analysis, algorithm application, and model evaluation resulted in retrieving rental values within the years 2010 and 2019 on two predominant residential property types of self-contain and one-bedroom flats found within the community. Three selected algorithms, Artificial Neural Network (ANN), Support Vector Machine, and Logistic Regression, were essentially used as classifiers but trained to predict the continuous values. These algorithms were implemented through the use of Python's SciKit-learn Library and RapidMiner. The findings revealed that though all three models gave accurate predictions, Logistic Regression was the highest with low error values. It was recommended that Logistic Regression be applied but with much data set of property values of low-income earners over much more period. This study will contribute to the Sustainable development goals(SDG) 11(Sustainable cities and communities) of the United Nations to benefit developing countries, especially in sub-Saharan Africa.

关键词： predictive algorithm rental values low-income earners slum Ijora sustainability SDG-11 Nigeria

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A predictive approach for dynamic replication of operators in distributed stream processing systems 34

A predictive approach for dynamic replication of operators i...

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34th IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Wladdimiro, Daniel Arantes, Luciana Sens, Pierre Hidalgo, Nicolas Sorbonne Univ LIP6 CNRS INRIA Paris France Univ Diego Portales Santiago Chile

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Stream Processing Systems (SPSs) can present significant fluctuation in input rate. To address this issue, some existing solutions propose reconfiguring the SPS by replicating its operators. However, such reconfiguration usually induces a high system downtime cost. Moreover, reconfiguration decisions are based only on resource utilization without balancing the load between replicas. We propose in this paper a predictive SPS that dynamically defines the necessary number of replicas of each operator based not only on the current resource utilization and input rate variation but also on the events that, due to the operator's overloading, could not be processed yet and are, thus, kept in the operator's queue. In addition, our SPS implements a load balancer that distributes incoming events more evenly among replicas of an operator. Our solution has been integrated into Storm. To avoid system reconfiguration downtime, our SPS preallocates a pool of replicas where each of them can be activated or deactivated based on per operator input load predictions. Using real traffic traces with different applications, we have conducted experiments on Google Cloud Platform (GCP), evaluating our SPS and comparing it with Storm and DABS-Storm.

关键词： Stream processing Adaptive SPS predictive algorithm Replication Google Cloud Platform

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Optimal Operation of Microgrid Employing predictive Control 15

Optimal Operation of Microgrid Employing Predictive Control

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15th International Conference on Development and Application Systems (DAS)

作者： Butaru, Florinel Carstea, Ciprian Lascu, Cristian Ancuti, Mihaela-Codruta Univ Politehn Timisoara Elect Engn Dept Timisoara Romania

ISBN: (纸本)9781728168708

The purpose of the present paper is to build a predictive algorithm based on the output voltage of the LC filter in order to achieve the AC Microgrids operation in the maximum power point tracking of the captured solar energy. Methods of regulating the microgrids input voltage are conventionally achieved by linear control loops. This type of control presents limitations like high sensitivity to parameter variations and slow transient response. The operation of the microgrids at the maximum power point is analyzed. The inverter's the optimal power values are determined based on the simulated data. The simulations were carried out in the MATLAB/Simulink simulation environment. Based on the vector control, the voltage drops on the inverter transistors are calculated in advanced for the next step, based on the phase diagram. A predictive algorithm is designed to ensure a stable operation in the maximum power point area, MPP, (i.e. the optimal area from the energy point of view).

关键词： predictive algorithm solar power plant energy efficiency maximum power point variable solar power

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Predictability of real temporal networks

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National Science Review 2020年第5期7卷 929-937页

作者： Disheng Tang Wenbo Du Louis Shekhtman Yijie Wang Shlomo Havlin Xianbin Cao Gang Yan School of Electronic and Information Engineering Beihang University Shanghai Institute of Intelligence Science and Technology Tongji University Network Science Institute Northeastern University Department of Physics Bar Ilan University CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation School of Physics Science and Engineering Tongji University

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and *** evidence has shown that the temporal nature of links in many real-world networks is not ***, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly,we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes.

关键词： temporal network predictability network entropy predictive algorithm

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Prediction-based protocol for mobile target tracking in wireless sensor networks

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Journal of Systems Engineering and Electronics 2011年第2期22卷 347-352页

作者： Liang Xue Zhixin Liu Xinping Guan institute of Electrical Engineering Yanshan University Qinhuangdao 066004 R R. China School of Electronic Information and Electrical Engineering Shanghai Jiaotong University Shanghai 200240 R R. China

Remote tracking for mobile targets is one of the most important applications in wireless sensor networks （WSNs）. A target tracking protoco–exponential distributed predictive tracking （EDPT） is proposed. To reduce energy waste and response time, an improved predictive algorithm–exponential smoothing predictive algorithm （ESPA） is presented. With the aid of an additive proportion and differential （PD） controller, ESPA decreases the system predictive delay effectively. As a recovery mechanism, an optimal searching radius （OSR） algorithm is applied to calculate the optimal radius of the recovery zone. The simulation results validate that the proposed EDPT protocol performes better in terms of track failed ratio, energy waste ratio and enlarged sensing nodes ratio, respectively.

关键词： wireless sensor network target tracking protocol predictive algorithm recovery mechanism.

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A multi-energy system optimisation software for advanced process control using hypernetworks and a micro-service architecture

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ENERGY REPORTS 2021年 7卷 167-175页

作者： Jones, Sean Charlesworth, Richard Naik, Kevin Charlesworth, Thomas O'Dwyer, Edward Ianakiev, Anton Johnson, Jeff Boukhanouf, Rabah Gillott, Mark Sellwood, Victor Aloor, Joy Siemens Sir William Siemens House Manchester Lancs England Nottingham Trent Univ Nottingham England Imperial Coll London London England Open Univ Milton Keynes Bucks England Univ Nottingham Nottingham England

This paper describes a multi-energy system optimisation software, "Sustainable Energy Management System" (SEMS), developed as part of a Siemens, Greater London Authority and Royal Borough of Greenwich partnership in collaboration with the University of Nottingham, Nottingham Trent University and Imperial College London. The software was developed for application at a social housing estate in Greenwich, London, as part of the Borough's efforts to retrofit the energy systems and building fabric of its housing stock. Its purpose is to balance energy across vectors and networks through day-ahead forecasting and optimisations that can be interpreted as control outputs for energy plant such as a water source heat pump, district heating pumps and values, power switchgear, gas boilers, a thermal store, electric vehicle chargers and a photovoltaic array. The optimisation objectives are to minimise greenhouse gas emissions and operational cost. The tool uses Hypernetwork Theory based orchestration coupled with a microservice architecture. The distributed nature of the design ensures flexibility and scalability. Currently, microservices have been programmed to forecast domestic heating demand, domestic electricity demand, electric vehicle demand, solar photovoltaic generation, ground temperature, and to run a day-ahead energy balance optimisation. This paper presents the results from both domestic heat and electricity demand forecasting, as well as the overall design and integration of the software with a physical system. The works build on that of O'Dwyer, et al. (2020) who developed a preliminary energy management software and digital twin. Their work acts as a foundation for this real-world commercialisation-ready program that integrates with physical assets. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Multi-energy systems Energy optimisation predictive algorithm Hypernetwork theory Microservice architecture

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A digital power factor correction (PFC) control strategy optimized for DSP

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2004年第6期19卷 1474-1485页

作者： Zhang, WF Guang, F Liu, YF Bin, W Queens Univ Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada

A predictive algorithm for digital control power factor correction (PFC) is presented in this paper. Based on this algorithm, all of the duty cycles required to achieve unity power factor in one half line period are calculated in advance by digital signal processors (DSPs). A Boost converter controlled by these precalculated duty cycles can achieve sinusoidal current waveform. One main advantage is that the digital control PFC implementation based on this control strategy can operate at a high switching frequency which is not directly dependent on the processing speed of DSP. Input voltage feed-forward compensation makes the output voltage insensitive to the input voltage variation and guarantees sinusoidal input current even if the input voltage is distorted. A prototype of Boost PFC controlled by a DSP evaluation board was set up to implement the proposed predictive control strategy. Both the simulation and experimental results show that the proposed predictive strategy for PFC achieves near unity power factor.

关键词： digital control power factor correction (PFC) predictive algorithm

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Beyond the Limits of Predictability in Human Mobility Prediction: Context-Transition Predictability

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第5期35卷 4514-4526页

作者： Zhang, Chao Zhao, Kai Chen, Meng Shandong Univ Sch Software Jinan 250100 Peoples R China Georgia State Univ Robinson Coll Business Atlanta GA 30302 USA

Urban human mobility prediction is forecasting how people move in cities. It is crucial for many smart city applications including route optimization, preparing for dramatic shifts in modes of transportation, or mitigating the epidemic spread of viruses such as COVID-19. Previous research propose the maximum predictability to derive the theoretical limits of accuracy that any predictive algorithm could achieve on predicting urban human mobility. However, existing maximum predictability only considers the sequential patterns of human movements and neglects the contextual information such as the time or the types of places that people visit, which plays an important role in predicting one's next location. In this paper, we propose new theoretical limits of predictability, namely Context-Transition Predictability, which not only captures the sequential patterns of human mobility, but also considers the contextual information of human behavior. We compare our Context-Transition Predictability with other kinds of predictability and find that it is larger than these existing ones. We also show that our proposed Context-Transition Predictability provides us a better guidance on which predictive algorithm to be used for forecasting the next location when considering the contextual information. Source code is at https://***/zcfinal/ContextTransitionPredictability.

关键词： Prediction algorithms Urban areas predictive models Trajectory COVID-19 Entropy Context modeling Limits of predictability entropy location prediction predictive algorithm human mobility

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