WinLight: A WiFi-based occupancy-driven lighting control system for smart building

The minimum velocity required to prevent sediment deposition in open channels is examined in this study. The parameters affecting transport are first determined and then categorized into different dimensionless groups, including "movement," "transport," "sediment," "transport mode," and "flow resistance." Six different models are presented to identify the effect of each of these parameters. The feed-forward neural network (FFNN) is used to predict the densimetric Froude number (Fr) and the extreme learning machine (ELM) algorithm is utilized to train it. The results of this algorithm are compared with back propagation (BP), genetic programming (GP) and existing sediment transport equations. The results indicate that FFNN-ELM produced better results than FNN-BP, GP and existing sediment transport methods in both training (RMSE = 0.26 and MARE = 0.052) and testing (RMSE = 0.121 and MARE = 0.023). Moreover, the performance of FFNN-ELM is examined for different pipe diameters.

Extreme learning machine assessment for estimating sediment transport in open channels

With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is the key point to maintaining good positioning accuracy. To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (Stacked Denoising Autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal. We also leverage the strong representation ability of MLP (Multi-Layer Perceptron) to build a regression model, which maps the extracted features to the corresponding location. To fully evaluate the performance of our proposed algorithm, three datasets are applied, which represent three different scenarios, namely, spacious area with time interval, no time interval, and complex area with large time interval. The experimental results confirm the validity of our proposed SDAE-based feature extraction method, which can accurately reflect Wi-Fi signals in corresponding locations. Compared with other regression models, our proposed regression model can better map the extracted features to the target position. The average positioning error of our proposed algorithm is 4.24 m when there is a 52-day interval between training dataset and testing dataset. That confirms that the proposed algorithm outperforms other state-of-the-art positioning algorithms when there is a large time interval between training dataset and testing dataset.

https://www.mdpi.com/2072-4292/11/11/1293/pdf

A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.

https://www.mdpi.com/1424-8220/19/10/2397/pdf

Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization.

Author(s): Zhou, Yuxun | Advisor(s): Spanos, Costas J | Abstract: Recent advancements in the study of cyber-physical systems (CPS) have addressed the combination of computation, networking, physical processes, and human involvement as an overall system to improve adaptability, autonomy, eciency, functionality, reliability, safety, and usability. Among other lines of CPS research, machine learning (ML) has emerged as an indispensable component for state estimation, prediction, diagnosis, structure identication, operation specication, event detection, etc. This work particularly discusses three learning tasks that are commonly encountered in CPS sensing and operation applications. The primary motivations underlying this dissertation are (1) to incorporate the unique characteristics of the data generated from system measurement, and (2) to facilitate the integration of ML into other components of CPS, such as sensing and control subsystems.More specically, we rst consider learning interaction structures for sparse sensing. With the generic directed information maximization as the learning objective, we discuss two subset selection problems and provide performance guarantees for greedy algorithms by extending the notion of submodularity. Practically, the proposed learning framework can be applied broadly to streaming feature selection, causality mining, sensor placement, as well as the construction of causal graphs. The second learning task discussed in this work is focused on the detection of outliers or novelties from multiple correlated time series data generated from CPS measurement. The key issue being addressed is the utilization of the correlation information in the smoothing process of multiple sequences. Two methods, one based on a multi-task extension of non-parametric time series modeling and the other based on merging hidden Markov model with matrix factorization, are established and analyzed. Lastly, we discuss the task of learning system requirement for agile operation and optimal control. The classical ML paradigm is modied with \shape constraints" to facilitate its usage for optimal control or to capture class imbalance for event detection. While developing new learning formulations, we also propose a novel global optimization procedure, namely parametric dual maximization, that is able to solve a class of modied machine learning problems having non-convex objectives.

/pdf/statistical-learning-for-sparse-sensing-and-agile-operation-1c9t6dve0c.pdf

Statistical Learning for Sparse Sensing and Agile Operation

We propose two kinds of robust extreme learning machines (RELMs) based on the close-to-mean constraint and the small-residual constraint respectively to solve the problem of noisy measurements in indoor positioning systems (IPSs). We formulate both RELMs as second order cone programming problems. The fact that feature mapping in ELM is known to users is exploited to give the needed information for robust constraints. Real-world indoor localization experimental results show that, the proposed algorithms can not only improve the accuracy and repeatability, but also reduce the deviations and worst case errors of IPSs compared with basic ELM and OPT-ELM based IPSs.

Robust extreme learning machine for regression problems with its application to wifi based indoor positioning system

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Robust extreme learning machine for regression problems with its application to wifi based indoor positioning system