Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Automatic (data-based) learning of expressive indicators for machine failure detection : a proposal

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

Hide-and-seek in vegetation: time-to-detection is an efficient design for estimating detectability and occurrence

Ecology and conservation require reliable data on the occurrence of animals and plants. A major source of bias is imperfect detection, which, however, can be corrected for by estimation of detectability. In traditional occupancy models, this requires repeat or multi-observer surveys. Recently, time-to-detection models have been developed as a cost-effective alternative, which requires no repeat surveys and hence costs could be halved. We compared the efficiency and reliability of time-to-detection and traditional occupancy models under varying survey effort. Two observers independently searched for 17 plant species in 44100m(2) Swiss grassland quadrats and recorded the time-to-detection for each species, enabling detectability to be estimated with both time-to-detection and traditional occupancy models. In addition, we gauged the relative influence on detectability of species, observer, plant height and two measures of abundance (cover and frequency). Estimates of detectability and occupancy under both models were very similar. Rare species were more likely to be overlooked; detectability was strongly affected by abundance. As a measure of abundance, frequency outperformed cover in its predictive power. The two observers differed significantly in their detection ability. Time-to-detection models were as accurate as traditional occupancy models, but their data easier to obtain; thus they provide a cost-effective alternative to traditional occupancy models for detection-corrected estimation of occurrence.

Detecção de bordas em imagens digitais através de um processo de difusão anisotrópica não linear

The edges detection model by a non-linear anisotropic diffusion, consists in a mathematical model of smoothing based in Partial Differential Equation (PDE), alternative to the conventional low-pass filters. The smoothing model consists in a selective process, where homogeneous areas of the image are smoothed intensely in agreement with the temporal evolution applied to the model. The level of smoothing is related with the amount of undesired information contained in the image, i.e., the model is directly related with the optimal level of smoothing, eliminating the undesired information and keeping selectively the interest features for Cartography area. The model is primordial for cartographic applications, its function is to realize the image preprocessing without losing edges and other important details on the image, mainly airports tracks and paved roads. Experiments carried out with digital images showed that the methodology allows to obtain the features, e.g. airports tracks, with efficiency.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

Integrating detection probabilities in species distribution models of amphibians breeding in Mediterranean temporary ponds

Aim  The imperfect detection of species may lead to erroneous conclusions about species-environment relationships. Accuracy in species detection usually requires temporal replication at sampling sites, a time-consuming and costly monitoring scheme. Here, we applied a lower-cost alternative based on a double-sampling approach to incorporate the reliability of species detection into regression-based species distribution modelling.Location  Doñana National Park (south-western Spain).Methods  Using species-specific monthly detection probabilities, we estimated the detection reliability as the probability of having detected the species given the species-specific survey time. Such reliability estimates were used to account explicitly for data uncertainty by weighting each absence. We illustrated how this novel framework can be used to evaluate four competing hypotheses as to what constitutes primary environmental control of amphibian distribution: breeding habitat, aestivating habitat, spatial distribution of surrounding habitats and/or major ecosystems zonation. The study was conducted on six pond-breeding amphibian species during a 4-year period.Results  Non-detections should not be considered equivalent to real absences, as their reliability varied considerably. The occurrence of Hyla meridionalis and Triturus pygmaeus was related to a particular major ecosystem of the study area, where suitable habitat for these species seemed to be widely available. Characteristics of the breeding habitat (area and hydroperiod) were of high importance for the occurrence of Pelobates cultripes and Pleurodeles waltl. Terrestrial characteristics were the most important predictors of the occurrence of Discoglossus galganoi and Lissotriton boscai, along with spatial distribution of breeding habitats for the last species.Main conclusions  We did not find a single best supported hypothesis valid for all species, which stresses the importance of multiscale and multifactor approaches. More importantly, this study shows that estimating the reliability of non-detection records, an exercise that had been previously seen as a naïve goal in species distribution modelling, is feasible and could be promoted in future studies, at least in comparable systems.

Detection, analysis and monitoring of slope movements by high-resolution digital elevation models

Summary Detection, analysis and monitoring of slope movements by high-resolution digital elevation modelsSlope movements, such as rockfalls, rockslides, shallow landslides or debris flows, are frequent in many mountainous areas. These natural hazards endanger the inhabitants and infrastructures making it necessary to assess the hazard and risk caused by these phenomena. This PhD thesis explores various approaches using digital elevation models (DEMs) - and particularly high-resolution DEMs created by aerial or terrestrial laser scanning (TLS) - that contribute to the assessment of slope movement hazard at regional and local scales.The regional detection of areas prone to rockfalls and large rockslides uses different morphologic criteria or geometric instability factors derived from DEMs, i.e. the steepness of the slope, the presence of discontinuities, which enable a sliding mechanism, and the denudation potential. The combination of these factors leads to a map of susceptibility to rockfall initiation that is in good agreement with field studies as shown with the example of the Little Mill Campground area (Utah, USA). Another case study in the Illgraben catchment in the Swiss Alps highlighted the link between areas with a high denudation potential and actual rockfall areas.Techniques for a detailed analysis and characterization of slope movements based on high-resolution DEMs have been developed for specific, localized sites, i.e. ancient slide scars, present active instabilities or potential slope instabilities. The analysis of the site's characteristics mainly focuses on rock slopes and includes structural analyses (orientation of discontinuities); estimation of spacing, persistence and roughness of discontinuities; failure mechanisms based on the structural setting; and volume calculations. For the volume estimation a new 3D approach was tested to reconstruct the topography before a landslide or to construct the basal failure surface of an active or potential instability. The rockslides at Åknes, Tafjord and Rundefjellet in western Norway were principally used as study sites to develop and test the different techniques.The monitoring of slope instabilities investigated in this PhD thesis is essentially based on multitemporal (or sequential) high-resolution DEMs, in particular sequential point clouds acquired by TLS. The changes in the topography due to slope movements can be detected and quantified by sequential TLS datasets, notably by shortest distance comparisons revealing the 3D slope movements over the entire region of interest. A detailed analysis of rock slope movements is based on the affine transformation between an initial and a final state of the rock mass and its decomposition into translational and rotational movements. Monitoring using TLS was very successful on the fast-moving Eiger rockslide in the Swiss Alps, but also on the active rockslides of Åknes and Nordnesfjellet (northern Norway). One of the main achievements on the Eiger and Aknes rockslides is to combine the site's morphology and structural setting with the measured slope movements to produce coherent instability models. Both case studies also highlighted a strong control of the structures in the rock mass on the sliding directions. TLS was also used to monitor slope movements in soils, such as landslides in sensitive clays in Québec (Canada), shallow landslides on river banks (Sorge River, Switzerland) and a debris flow channel (Illgraben).The PhD thesis underlines the broad uses of high-resolution DEMs and especially of TLS in the detection, analysis and monitoring of slope movements. Future studies should explore in more depth the different techniques and approaches developed and used in this PhD, improve them and better integrate the findings in current hazard assessment practices and in slope stability models.Résumé Détection, analyse et surveillance de mouvements de versant à l'aide de modèles numériques de terrain de haute résolutionDes mouvements de versant, tels que des chutes de blocs, glissements de terrain ou laves torrentielles, sont fréquents dans des régions montagneuses et mettent en danger les habitants et les infrastructures ce qui rend nécessaire d'évaluer le danger et le risque causé par ces phénomènes naturels. Ce travail de thèse explore diverses approches qui utilisent des modèles numériques de terrain (MNT) et surtout des MNT de haute résolution créés par scanner laser terrestre (SLT) ou aérien - et qui contribuent à l'évaluation du danger de mouvements de versant à l'échelle régionale et locale.La détection régionale de zones propices aux chutes de blocs ou aux éboulements utilise plusieurs critères morphologiques dérivés d'un MNT, tels que la pente, la présence de discontinuités qui permettent un mécanisme de glissement ou le potentiel de dénudation. La combinaison de ces facteurs d'instabilité mène vers une carte de susceptibilité aux chutes de blocs qui est en accord avec des travaux de terrain comme démontré avec l'exemple du Little Mill Campground (Utah, États-Unis). Un autre cas d'étude - l'Illgraben dans les Alpes valaisannes - a mis en évidence le lien entre les zones à fort potentiel de dénudation et les sources effectives de chutes de blocs et d'éboulements.Des techniques pour l'analyse et la caractérisation détaillée de mouvements de versant basées sur des MNT de haute résolution ont été développées pour des sites spécifiques et localisés, comme par exemple des cicatrices d'anciens éboulements et des instabilités actives ou potentielles. Cette analyse se focalise principalement sur des pentes rocheuses et comprend l'analyse structurale (orientation des discontinuités); l'estimation de l'espacement, la persistance et la rugosité des discontinuités; l'établissement des mécanismes de rupture; et le calcul de volumes. Pour cela une nouvelle approche a été testée en rétablissant la topographie antérieure au glissement ou en construisant la surface de rupture d'instabilités actuelles ou potentielles. Les glissements rocheux d'Åknes, Tafjord et Rundefjellet en Norvège ont été surtout utilisés comme cas d'étude pour développer et tester les diverses approches. La surveillance d'instabilités de versant effectuée dans cette thèse de doctorat est essentiellement basée sur des MNT de haute résolution multi-temporels (ou séquentiels), en particulier des nuages de points séquentiels acquis par SLT. Les changements topographiques dus aux mouvements de versant peuvent être détectés et quantifiés sur l'ensemble d'un glissement, notamment par comparaisons des distances les plus courtes entre deux nuages de points. L'analyse détaillée des mouvements est basée sur la transformation affine entre la position initiale et finale d'un bloc et sa décomposition en mouvements translationnels et rotationnels. La surveillance par SLT a démontré son potentiel avec l'effondrement d'un pan de l'Eiger dans les Alpes suisses, mais aussi aux glissements rocheux d'Aknes et Nordnesfjellet en Norvège. Une des principales avancées à l'Eiger et à Aknes est la création de modèles d'instabilité cohérents en combinant la morphologie et l'agencement structural des sites avec les mesures de déplacements. Ces deux cas d'étude ont aussi démontré le fort contrôle des structures existantes dans le massif rocheux sur les directions de glissement. Le SLT a également été utilisé pour surveiller des glissements dans des terrains meubles comme dans les argiles sensibles au Québec (Canada), sur les berges de la rivière Sorge en Suisse et dans le chenal à laves torrentielles de l'Illgraben.Cette thèse de doctorat souligne le vaste champ d'applications des MNT de haute résolution et particulièrement du SLT dans la détection, l'analyse et la surveillance des mouvements de versant. Des études futures devraient explorer plus en profondeur les différentes techniques et approches développées, les améliorer et mieux les intégrer dans des pratiques actuelles d'analyse de danger et surtout dans la modélisation de stabilité des versants.

Blind channel estimation and data detection using hidden Markov models theory

In this correspondence, we propose applying the hiddenMarkov models (HMM) theory to the problem of blind channel estimationand data detection. The Baum–Welch (BW) algorithm, which is able toestimate all the parameters of the model, is enriched by introducingsome linear constraints emerging from a linear FIR hypothesis on thechannel. Additionally, a version of the algorithm that is suitable for timevaryingchannels is also presented. Performance is analyzed in a GSMenvironment using standard test channels and is found to be close to thatobtained with a nonblind receiver.

Detection of the Vulnerable Atherosclerotic Plaque. Pre-Clinical Evaluation of Novel PET Imaging Probes With Experimental Models

Atherosclerosis is a life-long vascular inflammatory disease and the leading cause of death in Finland and in other western societies. The development of atherosclerotic plaques is progressive and they form when lipids begin to accumulate in the vessel wall. This accumulation triggers the migration of inflammatory cells that is a hallmark of vascular inflammation. Often, this plaque will become unstable and form vulnerable plaque which may rupture causing thrombosis and in the worst case, causing myocardial infarction or stroke. Identification of these vulnerable plaques before they rupture could save lives. At present, in the clinic, there exists no appropriated, non-invasive method for their identification. The aim of this thesis was to evaluate novel positron emission tomography (PET) probes for the detection of vulnerable atherosclerotic plaques and to characterize, two mouse models of atherosclerosis. These studies were performed by using ex vivo and in vivo imaging modalities. The vulnerability of atherosclerotic plaques was evaluated as expression of active inflammatory cells, namely macrophages. Age and the duration of high-fat diet had a drastic impact on the development of atherosclerotic plaques in mice. In imaging of atherosclerosis, 6-month-old mice, kept on high-fat diet for 4 months, showed matured, metabolically active, atherosclerotic plaques. [18F]FDG and 68Ga were accumulated in the areas representative of vulnerable plaques. However, the slow clearance of 68Ga limits its use for the plaque imaging. The novel synthesized [68Ga]DOTA-RGD and [18F]EF5 tracers demonstrated efficient uptake in plaques as compared to the healthy vessel wall, but the pharmacokinetic properties of these tracers were not optimal in used models. In conclusion, these studies resulted in the identification of new strategies for the assessment of plaque stability and mouse models of atherosclerosis which could be used for plaque imaging. In the used probe panel, [18F]FDG was the best tracer for plaque imaging. However, further studies are warranted to clarify the applicability of [18F]EF5 and [68Ga]DOTA-RGD for imaging of atherosclerosis with other experimental models.

Deep generative models for facial keypoints detection

A new area of machine learning research called deep learning, has moved machine learning closer to one of its original goals: artificial intelligence and general learning algorithm. The key idea is to pretrain models in completely unsupervised way and finally they can be fine-tuned for the task at hand using supervised learning. In this thesis, a general introduction to deep learning models and algorithms are given and these methods are applied to facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. In experiments, we pretrained deep belief networks (DBN) and finally performed a discriminative fine-tuning. We varied the depth and size of an architecture. We tested both deterministic and sampled hidden activations and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than publicly available benchmarks for the dataset.

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Partial discharge in power distribution electrical systems: pulse propagation models and detection optimization

Investigation on impulsive signals, originated from Partial Discharge (PD) phenomena, represents an effective tool for preventing electric failures in High Voltage (HV) and Medium Voltage (MV) systems. The determination of both sensors and instruments bandwidths is the key to achieve meaningful measurements, that is to say, obtaining the maximum Signal-To-Noise Ratio (SNR). The optimum bandwidth depends on the characteristics of the system under test, which can be often represented as a transmission line characterized by signal attenuation and dispersion phenomena. It is therefore necessary to develop both models and techniques which can characterize accurately the PD propagation mechanisms in each system and work out the frequency characteristics of the PD pulses at detection point, in order to design proper sensors able to carry out PD measurement on-line with maximum SNR. Analytical models will be devised in order to predict PD propagation in MV apparatuses. Furthermore, simulation tools will be used where complex geometries make analytical models to be unfeasible. In particular, PD propagation in MV cables, transformers and switchgears will be investigated, taking into account both irradiated and conducted signals associated to PD events, in order to design proper sensors.

Automated detection of the osseous acetabular rim using three-dimensional models of the pelvis

An automated algorithm for detection of the acetabular rim was developed. Accuracy of the algorithm was validated in a sawbone study and compared against manually conducted digitization attempts, which were established as the ground truth. The latter proved to be reliable and reproducible, demonstrated by almost perfect intra- and interobserver reliability. Validation of the automated algorithm showed no significant difference compared to the manually acquired data in terms of detected version and inclination. Automated detection of the acetabular rim contour and the spatial orientation of the acetabular opening plane can be accurately achieved with this algorithm.