Probabilistic Time Research Articles

Robust consensus of nonlinear multi‐agent systems via reliable control with probabilistic time delay

In this paper, the consensus problem of uncertain nonlinear multi‐agent systems is investigated via reliable control in the presence of probabilistic time‐varying delay. First, the communication topology among the agents is assumed to be directed and fixed. Second, by introducing a stochastic variable which satisfies Bernoulli distribution, the information of probabilistic time‐varying delay is equivalently transformed into the deterministic time‐varying delay with stochastic parameters. Third, by using Laplacian matrix properties, the consensus problem is converted into the conventional stability problem of the closed‐loop system. The main objective of this paper is to design a state feedback reliable controller such that for all admissible uncertainties as well as actuator failure cases, the resulting closed‐loop system is robustly stable in the sense of mean‐square. For this purpose, through construction of a suitable Lyapunov–Krasovskii functional containing four integral terms and utilization of Kronecker product properties along with the matrix inequality techniques, a new set of delay‐dependent consensus stabilizability conditions for the closed‐loop system is obtained. Based on these conditions, the desired reliable controller is designed in terms of linear matrix inequalities which can be easily solved by using any of the effective optimization algorithms. Moreover, a numerical example and its simulations are included to demonstrate the feasibility and effectiveness of the proposed control design scheme. © 2016 Wiley Periodicals, Inc. Complexity 21: 138–150, 2016

The probabilistic time and cost risk analysis of a challenging part of an urban tunneling project

A predominant characteristic of geotechnical works and more particularly tunneling projects, is the uncertainty that surrounds the geologic medium in which the construction is to be performed. This paper is dealing with the risk analysis of both the construction time and cost of a challenging part of Tehran Metro Line 7, an urban tunneling project which is currently being under development. The Decision Aids for Tunneling (DAT) has been utilized for this assessment, concerning the risk analysis by taking into account different scenarios relating to the measures implemented to prevent surface subsidence problems. The results have shown that selection of different sorts of methods will help control the risk of escalating the project’s total construction cost and time to completion. This research delivers useful information to concurrently help the owner, the contractor as well as the designer of the project in making the most appropriate decisions under such a highly uncertain and challenging geological environment.

Frozen Soil Detection Based on Advanced Scatterometer Observations and Air Temperature Data as Part of Soil Moisture Retrieval

Surface soil moisture is one of the operational products derived from Advanced Scatterometer (ASCAT) data. The reliability of its estimation depends on the detection of predominantly frozen conditions of the landscape (including soil and vegetation) and the presence of wet snow, which would otherwise impede the estimation. As the robust determination of the freeze/thaw (F/T) state using exclusively scatterometer measurements on a global basis is complicated due to the myriad of different climatic and land cover conditions; we propose to support the retrieval using ERA Interim temperature data. The approach is based on a probabilistic time series model, whereby backscatter and temperature data are combined to estimate the freeze/thaw state. The method is assessed with proxy F/T states derived from modeled and in situ air and soil temperature data on a global basis. These analyses show an improved consistency compared to a previously published ASCAT F/T algorithm, with typical agreements between the external data and the results of the algorithm exceeding 80%. The quantitative interpretation of these comparisons is, however, hampered by discrepancies between the F/T state derived from temperature data and the one pertinent to radar remote sensing, as the former does not account for, e.g., wet snow conditions. The inclusion of the ERA Interim temperature data can improve the accuracy of the algorithm by more than 10 percentage points in regions where freezing conditions are rare.

Reverberant speech separation with probabilistic time–frequency masking for B-format recordings

Existing speech source separation approaches overwhelmingly rely on acoustic pressure information acquired by using a microphone array. Little attention has been devoted to the usage of B-format microphones, by which both acoustic pressure and pressure gradient can be obtained, and therefore the direction of arrival (DOA) cues can be estimated from the received signal. In this paper, such DOA cues, together with the frequency bin-wise mixing vector (MV) cues, are used to evaluate the contribution of a specific source at each time–frequency (T–F) point of the mixtures in order to separate the source from the mixture. Based on the von Mises mixture model and the complex Gaussian mixture model respectively, a source separation algorithm is developed, where the model parameters are estimated via an expectation–maximization (EM) algorithm. A T–F mask is then derived from the model parameters for recovering the sources. Moreover, we further improve the separation performance by choosing only the reliable DOA estimates at the T–F units based on thresholding. The performance of the proposed method is evaluated in both simulated room environments and a real reverberant studio in terms of signal-to-distortion ratio (SDR) and the perceptual evaluation of speech quality (PESQ). The experimental results show its advantage over four baseline algorithms including three T–F mask based approaches and one convolutive independent component analysis (ICA) based method.

Reliable Mixed H∞ and Passivity-Based Control for Fuzzy Markovian Switching Systems With Probabilistic Time Delays and Actuator Failures.

This paper is concerned with the problem of reliable mixed H ∞ and passivity-based control for a class of stochastic Takagi-Sugeno (TS) fuzzy systems with Markovian switching and probabilistic time varying delays. Different from the existing works, the H∞ and passivity control problem with probabilistic occurrence of time-varying delays and actuator failures is considered in a unified framework, which is more general in some practical situations. The main aim of this paper is to design a reliable mixed H∞ and passivity-based controller such that the stochastic TS fuzzy system with Markovian switching is stochastically stable with a prescribed mixed H∞ and passivity performance level γ > 0 . Based on the Lyapunov-Krasovskii functional (LKF) involving lower and upper bound of probabilistic time delay and convex combination technique, a new set of delay-dependent sufficient condition in terms of linear matrix inequalities (LMIs) is established for obtaining the required result. Finally, a numerical example based on the modified truck-trailer model is given to demonstrate the effectiveness and applicability of the proposed design techniques.

Probabilistic Cost and Time Estimation of Rigless Plug and Abandonment

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 169203, ’An Investigation of Different Approaches for Probabilistic Cost and Time Estimation of Rigless Plug and Abandonment in a Subsea Multiwell Campaign,’ by Fatemeh Moeinikia and Kjell Kare Fjelde, SPE, University of Stavanger; Arild Saasen, SPE, Det Norske Oljeselskap ASA and the University of Stavanger; and Torbjorn Vralstad, SPE, SINTEF, prepared for the 2014 SPE Bergen One Day Seminar, Bergen, Norway, 2 April. The paper has not been peer reviewed. With a high demand for plug and abandonment (P&A) of subsea wells in the future on the Norwegian continental shelf, industry is challenged to find alternatives and rigless technologies that can make P&A operation more cost-effective and -efficient. Light-well-intervention vessels (LWIVs) can take over some parts of P&A operations and release semisubmersible rigs to function in the drilling and completing of new wells. This paper covers potential and experience of LWIVs in P&A. Introduction The P&A work of a single well can be separated into three distinct phases: reservoir abandonment, intermediate abandonment, and removal of the wellhead and the casing strings a few meters below seabed (Oil and Gas UK 2011). Different vessels and technologies will be applied for these phases, which are also affected by the complexity of the well. It is also expected that these P&A phases to some extent will be performed in multiwell campaigns in which issues such as learning effects and correlations have to be considered. The deterministic approach for time and cost estimation is a simple, traditional method, and the results can be transferred easily. There are some limitations associated with the deterministic approach, such as the yielding of biased results and, sometimes, the inability to capture the full range of outcomes. On the other hand, the probabilistic approach has been recognized recently as a common and preferred technique for time and cost estimation, compared with the deterministic approach. This approach presents a nonbiased technique for time and cost forecasting and allows for inclusion of uncertainty into the model, covering the correct range of possible outcomes. Oil and Gas UK (2011) provides a guideline for well-abandonment-cost estimation, aiming to provide a simple and structured approach for estimating cost and duration of field abandonment. As mentioned previously, the P&A work of a well can be divided into three distinct phases according to these guidelines. In addition, the complexity of each phase is reflected in the types of vessels and technologies that are required to perform the work scope of that specific phase. Five categories are used for this classification. P&A operations will mostly be performed in multiwell campaigns, and different vessel technologies are used to complete distinct operations or phases. Because P&A of the wells can typically involve using the same vessels and technologies for distinct phases, performance of P&A in multiwell campaigns shares mobilization and demobilization time and cost as well as transit time from mobilization point to field and transit time among different wells.

Collaborative Task Execution in Mobile Cloud Computing Under a Stochastic Wireless Channel

This paper investigates collaborative task execution between a mobile device and a cloud clone for mobile applications under a stochastic wireless channel. A mobile application is modeled as a sequence of tasks that can be executed on the mobile device or on the cloud clone. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically offloading tasks to the cloud. We formulate the collaborative task execution as a constrained shortest path problem. We derive a one-climb policy by characterizing the optimal solution and then propose an enumeration algorithm for the collaborative task execution in polynomial time. Further, we apply the LARAC algorithm to solving the optimization problem approximately, which has lower complexity than the enumeration algorithm. Simulation results show that the approximate solution of the LARAC algorithm is close to the optimal solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline, which is transformed to hard deadline by Markov inequality. Moreover, compared to the local execution and the remote execution, the collaborative task execution can significantly save the energy consumption on the mobile device, prolonging its battery life.

Adaptive-Velocity Time-Geographic Density Estimation for Mapping the Potential and Probable Locations of Mobile Objects

Time-geographic density estimation (TGDE) provides a method for generating probability density surfaces for mobile objects. The technique operates by fitting distance-weighted geo-ellipses to each pair of points in a tracking dataset and combining them to create a final density surface. The sizes of the geo-ellipses, and thus the amount of smoothing, are determined by a velocity parameter which specifies how fast the object can travel. In prior formulations of TGDE this maximum-velocity parameter was treated as constant or fixed. This can be problematic if an object displays variable movement patterns, such as alternating between movements and stops. In this research we develop a more robust formulation of time-geographic density estimation, termed adaptive-velocity TGDE, which allows the maximum-velocity parameter to vary for each segment of the space-time path. First, the new mathematical formulation is demonstrated and compared with fixed-TGDE through an application to synthetic pedestrian tracking data. Second, both methods are evaluated in the context of accurately mapping the movement patterns of a homing pigeon tracked by GPS. The results of both applications demonstrate that adaptive-velocity TGDE has the potential to more accurately delineate the trajectories of mobile objects than the fixed-velocity version. Implications of these results in the context of developing a probabilistic time geography for general mobile objects are also discussed.

Fault-tolerant sampled-data control of flexible spacecraft with probabilistic time delays

In this paper, fault-tolerant sampled-data control for flexible spacecraft in the presence of external disturbances, partial actuator failures and probabilistic time delays is investigated. In particular, unlike the common assumptions on continuous-time information on control input, a more realistic sampled-data communication strategy is proposed with probabilistic occurrence of time-varying delays which is modeled by introducing Bernoulli distributed sequences. The main purpose of this paper is to derive fault-tolerant sampled-data control law which makes the closed-loop system robustly asymptotically stable with a prescribed upper bound of the cost function about its equilibrium point for all possible actuator failures. More precisely, by constructing an appropriate Lyapunov–Krasovskii functional involving the lower and upper bound of the probabilistic time delay, a new set of sufficient conditions are derived in terms of linear matrix inequalities for achieving the required result. Numerical simulations are presented by taking the real parameters to the considered aircraft model, which is not only highlighting the ensured closed-loop performance by the proposed control law, but also illustrates its superior fault tolerance, fast convergence and robustness in the presence of external disturbances and actuator faults when compared with the conventional controller. The simulation result reveals the effectiveness and potential of the proposed new design techniques.

Transit tomography using probabilistic time geography: planning routes without a road map

Vehicle routing usually depends on a road map, and road maps are expensive to create and maintain. While crowdsourcing road maps from logged GPS data has proven effective, the limited availability of GPS data limits their coverage area. To overcome this limitation, we show how to use location data from geotagged tweets, which cover much of the world, to compute routes directly without making a road map. We compensate for the wide spacing of tweets' latitude/longitude points by using probabilistic time geography, which explicitly models the uncertain location of someone traveling between measured locations. In our formulation, each pair of temporally adjacent tweets contributes an estimate of the driving time along hypothesised roads in a regular grid. We show how to compute these estimates as expected values based on probabilistic Brownian bridges. We can compute routes on this regular grid using traditional A* search. Our experiments demonstrate that our computed routes match well with routes computed on the actual road network using a commercial router. Furthermore, we show that our computed routes vary sensibly with changes in traffic between rush hour and weekends. We also apply the same technique to compute reasonable airplane routes.

Consensus of Euler-Lagrange systems networked by sampled-data information with probabilistic time delays.

This paper investigates the consensus problem of multiple Euler-Lagrange systems under directed topology. Unlike the common assumptions on continuous-time information exchanges, a more realistic sampled-data communication strategy is proposed with probabilistic occurrence of time-varying delays. Both of the sampling period and the delays are assumed to be time-varying, which is more general in some practical situations. In addition, the relative coordinate derivative information is not required in the distributed controllers such that the communication network burden can be further reduced. In particular, a distinct feature of the proposed scheme lies in the fact that it can effectively reduce the energy consumption. By employing the stochastic analysis techniques, sufficient conditions are established to guarantee that the consensus can be achieved. Finally, a numerical example is provided to illustrate the applicability and benefits of the theoretical results.

Advanced Numerical and Analytical Methods for Assessing Concrete Sewers and Their Remaining Service Life

Pipelines are extensively used engineering structures which convey fluid from one place to another. Most of the time, pipelines are placed underground and are encumbered by soil weight and traffic loads. Corrosion of pipe material is the most common form of pipeline deterioration and should be considered in both the strength and serviceability analysis of pipes. The study in this research focuses on concrete pipes in sewage systems (concrete sewers). This research firstly investigates how to involve the effect of corrosion as a time dependent process of deterioration in the structural and failure analysis of this type of pipe. Then three probabilistic time dependent reliability analysis methods including the first passage probability theory, the gamma distributed degradation model and the Monte Carlo simulation technique are discussed and developed. Sensitivity analysis indexes which can be used to identify the most important parameters that affect pipe failure are also discussed. The reliability analysis methods developed in this paper contribute as rational tools for decision makers with regard to the strengthening and rehabilitation of existing pipelines. The results can be used to obtain a cost-effective strategy for the management of the sewer system.

On the Design of Delay based Admission Control in Hierarchical Networks

Today, as the hierarchical cellular system is getting more attention than before, some recent studies introduce delay based admission control (AC) scheme which delays the admission to the macro-embedded small cell for a relatively short time to prevent unnecessary handover caused by the short-term visitors of the small cell area. In such delay based ACs, when we use improper delay parameter, the system frequently makes incorrect handover decisions such as where unnecessary handover is allowed due to too short delaying, or where necessary handover is denied due to too long delaying. In order to avoid these undesirable situations as much as possible, we develop a new delay parameter decision method based on probabilistic cell residence time approximations. By the extensive numerical and analytical evaluations, we determine the proper delay parameter which prevents the incorrect handover decision as much as possible. We expect our delay parameter decision method can be useful system administration tips in hierarchical cellular system where delay based AC is adopted.

Probabilistic Time Forecasting Using Beta Distribution

The main objective of this study is to help the project managers for making better informed decisions in the form of corrective and/or preventive actions through developing a probabilistic time forecasting model, which is generated on the basis of the beta distribution as a curve fitting technique, and to provide a better basis for the schedule performance control and for the risk management of on-going projects. Four projects has been generated and used to validate the time prediction generated from the all models through the different periods of actual completion. The beta forecasting model (BFM) has been programmed in a graphical user interface (GUI) for Matlab (R2009a) and it can be implemented on all types of projects. A comparative study reveals that the BFM provides much more accurate forecasts than those are generated from the conventional methods, forecasts the completion dates on the basis of analyzing the summary of project-level data, and has accurate forecasts as the critical path method (CPM) does.

Exact likelihood computation in Boolean networks with probabilistic time delays, and its application in signal network reconstruction

For biological pathways, it is common to measure a gene expression time series after various knockdowns of genes that are putatively involved in the process of interest. These interventional time-resolved data are most suitable for the elucidation of dynamic causal relationships in signaling networks. Even with this kind of data it is still a major and largely unsolved challenge to infer the topology and interaction logic of the underlying regulatory network. In this work, we present a novel model-based approach involving Boolean networks to reconstruct small to medium-sized regulatory networks. In particular, we solve the problem of exact likelihood computation in Boolean networks with probabilistic exponential time delays. Simulations demonstrate the high accuracy of our approach. We apply our method to data of Ivanova et al. (2006), where RNA interference knockdown experiments were used to build a network of the key regulatory genes governing mouse stem cell maintenance and differentiation. In contrast to previous analyses of that data set, our method can identify feedback loops and provides new insights into the interplay of some master regulators in embryonic stem cell development. The algorithm is implemented in the statistical language R. Code and documentation are available at Bioinformatics online. duemcke@mpipz.mpg.de or tresch@mpipz.mpg.de Supplementary Materials are available at Bioinfomatics online.

Voxel-based probabilistic space-time prisms for analysing animal movements and habitat use

Time-geographic analysis has been limited in the past by its capacity to model only potential locations for moving objects, without sufficiently evaluating which locations are more probable. This paper expands upon existing research in probabilistic time geography by accomplishing two main tasks. First, a new geocomputational approach is presented for generating probabilistic space-time prisms. Here, probabilistic space-time prisms are represented as three-dimensional rasters of volume elements, or voxels, that record the probability that an object was located at any location at any time. After describing the geocomputational approach, its utility is illustrated through a detailed analysis of tracking data collected for a Muscovy duck (Cairina mochata). Specifically, probabilistic space-time prisms are used to map the duck’s fine-scale movement patterns over five complete days of global positioning system (GPS)-tracking. Then, the space-time prisms are used in conjunction with a detailed habitat map of the study area in order to quantify the duck’s habitat usage over the course of each day. This application highlights the utility of probabilistic space-time prisms for understanding the movements and activities of animals at fine temporal and spatial scales.

Toward a kinetic-based probabilistic time geography

Time geography represents a powerful framework for the quantitative analysis of individual movement. Time geography effectively delineates the space–time boundaries of possible individual movement by characterizing movement constraints. The goal of this paper is to synchronize two new ideas, probabilistic time geography and kinetic-based time geography, to develop a more realistic set of movement constraints that consider movement probabilities related to object kinetics. Using random-walk theory, the existing probabilistic time geography model characterizes movement probabilities for the space–time cone using a normal distribution. The normal distribution has a symmetric probability density function and is an appropriate model in the absence of skewness – which we relate to an object’s initial velocity. Moving away from a symmetric distribution for movement probabilities, we propose the use of the skew-normal distribution to model kinetic-based movement probabilities, where the degree and direction of skewness is related to movement direction and speed. Following a description of our model, we use a set of case-studies to demonstrate the skew-normal model: a random walk, a correlated random walk, wildlife data, cyclist data, and athlete movement data. Our results show that for objects characterized by random movement behavior, the existing model performs well, but for object movement with kinetic properties (e.g., athletes), the proposed model provides a substantial improvement. Future work will look to extend the proposed probabilistic framework to the space–time prism.

Robust stability analysis for discrete-time genetic regulatory networks with probabilistic time delays

This paper investigates the robust stability problem of a class of discrete-time genetic regulatory networks (GRNs) with probabilistic time delays. Different from the previous works, at each instant the feedback regulation delay and the translation delay are assumed to take values in two given finite sets with deterministic probability distributions. By utilizing a class of indicator functions and discrete-time Jensen inequality, delay-probability-distribution-dependent sufficient conditions are obtained in terms of linear matrix inequalities (LMIs) such that the discrete-time GRNs are robustly asymptotically stable in the mean-square sense for all admissible uncertainties and random delays. Three numerical examples are given to demonstrate the effectiveness of our theoretical results.

Prediction accuracy and sloppiness of log-periodic functions

We show that log-periodic power-law (LPPL) functions are intrinsically very hard to fit to time series. This comes from their sloppiness, the squared residuals depending very much on some combinations of parameters and very little on other ones. The time of singularity that is supposed to give an estimate of the day of the crash belongs to the latter category. We discuss in detail why and how the fitting procedure must take into account the sloppy nature of this kind of model. We then test the reliability of LPPLs on synthetic AR(1) data replicating the Hang Seng 1987 crash and show that even this case is borderline regarding the predictability of the divergence time. We finally argue that current methods used to estimate a probabilistic time window for the divergence time are likely to be over-optimistic.

Evaluating Beat Structure and Truck Allocation for the Tarrant County, Texas, Courtesy Patrol

Nonrecurring congestion poses a significant concern for drivers on urban freeways. Effective incident management relies on many tools to prevent the delays caused by crashes, road debris, and stalled or disabled vehicles. Many urban areas have adopted freeway service patrol (FSP) programs that search the freeway network for incidents, provide aid to motorists, and assist with incident management and clearance. FSP management must consider the patrols' beat structure and the fleet allocation. Both deterministic and probabilistic response time estimates for each beat are used to assess fleet allocations. The goal is to consider whether the urban network should be segmented into as many beats as possible, with individual trucks assigned to each beat, or whether additional trucks should be allocated to fewer beats. The Tarrant County, Texas, Courtesy Patrol (CP) is used as a case study to explore the truck allocation problem with field data. The Tarrant County CP typically uses a one-beat, two-beat, or three-beat configuration with a single truck allocated to each beat. The merits of a two-beat configuration are examined. The study indicates that the incident distribution must be considered during both beat and tour design.