scholarly journals A Spatial-Contextual Indoor Trajectory Prediction Approach via Hidden Markov Models

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Peng Wang ◽  
Jing Yang ◽  
Jianpei Zhang

Unlike outdoor trajectory prediction that has been studied many years, predicting the movement of a large number of users in indoor space like shopping mall has just been a hot and challenging issue due to the ubiquitous emerging of mobile devices and free Wi-Fi services in shopping centers in recent years. Aimed at solving the indoor trajectory prediction problem, in this paper, a hybrid method based on Hidden Markov approach is proposed. The proposed approach clusters Wi-Fi access points according to their similarities first; then, a frequent subtrajectory based HMM which captures the moving patterns of users has been investigated. In addition, we assume that a customer’s visiting history has certain patterns; thus, we integrate trajectory prediction with shop category prediction into a unified framework which further improves the predicting ability. Comprehensive performance evaluation using a large-scale real dataset collected between September 2012 and October 2013 from over 120,000 anonymized, opt-in consumers in a large shopping center in Sydney was conducted; the experimental results show that the proposed method outperforms the traditional HMM and perform well enough to be usable in practice.

2021 ◽  
Vol 11 (16) ◽  
pp. 7685
Author(s):  
Alexandre Martins ◽  
Inácio Fonseca ◽  
José Torres Farinha ◽  
João Reis ◽  
António J. Marques Cardoso

The availability maximization is a goal for any organization because the equipment downtime implies high non-production costs and, additionally, the abnormal stopping and restarting usually imply loss of product’s quality. In this way, a method for predicting the equipment’s health state is vital to maintain the production flow as well as to plan maintenance intervention strategies. This paper presents a maintenance prediction approach based on sensing data managed by Hidden Markov Models (HMM). To do so, a diagnosis of drying presses in a pulp industry is used as case study, which is done based on data collected every minute for three years and ten months. This paper presents an approach to manage a multivariate analysis, in this case merging the values of sensors, and optimizing the observable states to insert into a HMM model, which permits to identify three hidden states that characterize the equipment’s health state: “Proper Function”, “Alert state”, and “Equipment Failure”. The research described in this paper demonstrates how an equipment health diagnosis can be made using the HMM, through the collection of observations from various sensors, without information of machine failures occurrences. The approach developed demonstrated to be robust, even the complexity of the system, having the potential to be generalized to any other type of equipment.


2016 ◽  
Author(s):  
Robert W. Rankin

AbstractThis study introduces statistical boosting for capture-mark-recapture (CMR) models. It is a shrinkage estimator that constrains the complexity of a CMR model in order to promote automatic variable-selection and avoid over-fitting. I discuss the philosophical similarities between boosting and AIC model-selection, and show through simulations that a boosted Cormack-Jolly-Seber model often out-performs AICc methods, in terms of estimating survival and abundance, yet yields qualitatively similar estimates. This new boosted CMR framework is highly extensible and could provide a rich, unified framework for addressing many topics in CMR, such as non-linear effects (splines and CART-like trees), individual-heterogeneity, and spatial components.


2020 ◽  
Vol 6 (12) ◽  
pp. eaay3050 ◽  
Author(s):  
C. C. Bultink ◽  
T. E. O’Brien ◽  
R. Vollmer ◽  
N. Muthusubramanian ◽  
M. W. Beekman ◽  
...  

Protecting quantum information from errors is essential for large-scale quantum computation. Quantum error correction (QEC) encodes information in entangled states of many qubits and performs parity measurements to identify errors without destroying the encoded information. However, traditional QEC cannot handle leakage from the qubit computational space. Leakage affects leading experimental platforms, based on trapped ions and superconducting circuits, which use effective qubits within many-level physical systems. We investigate how two-transmon entangled states evolve under repeated parity measurements and demonstrate the use of hidden Markov models to detect leakage using only the record of parity measurement outcomes required for QEC. We show the stabilization of Bell states over up to 26 parity measurements by mitigating leakage using postselection and correcting qubit errors using Pauli-frame transformations. Our leakage identification method is computationally efficient and thus compatible with real-time leakage tracking and correction in larger quantum processors.


Author(s):  
Imad Sassi ◽  
Samir Anter ◽  
Abdelkrim Bekkhoucha

<span lang="EN-US">Hidden </span><span lang="IN">M</span><span lang="EN-US">arkov models (HMMs) are one of machine learning algorithms which have been widely used and demonstrated their efficiency in many conventional applications. This paper proposes a modified posterior decoding algorithm to solve hidden Markov models decoding problem based on MapReduce paradigm and spark’s resilient distributed dataset (RDDs) concept, for large-scale data processing. The objective of this work is to improve the performances of HMM to deal with big data challenges. The proposed algorithm shows a great improvement in reducing time complexity and provides good results in terms of running time, speedup, and parallelization efficiency for a large amount of data, i.e., large states number and large sequences number.</span>


Sign in / Sign up

Export Citation Format

Share Document