Ship detection performance using simulated dual-polarization RADARSAT constellation mission data

The RADARSAT Constellation Mission (RCM) planned to be launched in 2018 is designed to support maritime surveillance requirements in which ice, wind, oil pollution, and ships are to be monitored by providing wide swath beam modes. In this article, we introduce the first analysis of ship detection performance using simulated RCM data. We report ship detection performance using a likelihood ratio test (LRT) for three wide swath RCM imaging modes: Ship Detection (25 m), Low Resolution (100 m), and Medium Resolution (50 m). These beam modes were assessed for a number of dual-polarimetric (dual-pol) systems, including the standard linear polarizations as well as compact polarimetry (CP). These data were simulated from RADARSAT-2 Fine Quad (FQ) mode in the three RCM modes. Furthermore, the detection performance of the pseudo-quad data reconstructed from the simulated circular transmit, linear receive data is also investigated and compared to the other systems’ performance for the three RCM modes. The receiver operating characteristic curves are used in this study as the basic measure of detection performance for all beam modes and all detectors. We found that the compact polarimetric SAR detectors outperform the conventional linear dual-pol detectors at the three RCM modes for ship detection for medium to high incidence angles. At steep angles, the performance of the two polarization configurations was comparable. Our study confirmed that detection performance improved as incidence angle and spatial resolution increased. We also investigated the impact of the ship orientation with respect to the radar beam and found that detection performance was generally higher when ship was oriented perpendicular to the radar beam.     

Generalized additive neural network with flexible parametric link function: model estimation using simulated and real clinical data

Neural Computing and Applications - Artificial neural networks have been proposed in medical research as an alternative to some regression models such as the generalized linear models, being the...     

Monitoring urban sprawl using simulated PROBA-V data

Urbanization proceeds currently at a rapid pace and the impact on natural ecosystems cannot be neglected. Consequently, it is important to be able to monitor the expansion of urban areas. Yet the process of extracting them from satellite imagery is not trivial. Urban is a non-uniform class with spectral proximity to barren land. In this article, a method for extracting urban areas from medium-resolution Earth observation data is presented. The information source is simulated data of the PROBA-V sensor. Visual and near-infrared bands are classified by the adaptive neuro-fuzzy inference system (ANFIS) neuro-fuzzy classifier into urban and non-urban classes. The method can overcome the main difficulty in similar efforts, i.e. the extensive commission errors of barren to the class urban. The main novelty relies on exploiting annual spectral variability of each land-use class at the pixel level. The basic assumption is that urban and barren areas may have similar spectral values but they have different phenological cycles. The overall accuracy obtained by the classification is 91.57% with a Cohen’s kappa coefficient (khat) of 0.84. Sufficient correlation at the city level is also achieved. Change detection is also possible in terms of hot-spot identification, however marginally suitable for medium-sized cities.     

出租车数据的城市道路网路段通行时间估计方法

城市路段通行时间估计能够更好地运营和管理城市交通。针对包含起点-终点位置,行程时间和距离信息的GPS行程数据,提出了一种城市道路网短时通行时间的估计模型。首先将城市道路网按照交叉路口分解为多个路段,并基于k-最短路径搜索方法分析司机行进路线。然后针对每一个路段,提出了双车道通行时间多项式关联关系模型,既能提升道路网通行时间精细度,又能避免因训练数据不足导致的路网通行时间过拟合问题。最后以最小化行程期望时间和实际行程时间之间的均方误差为优化目标,拟合道路网通行时间。在纽约出租车数据集上的实验结果表明,所提模型及方法相对于传统单车道估计方法能够更准确地估计城市道路网路段的通行时间。     

Spatial event classification using simulated water quality data

This study deals with the integration of contamination simulations and a spatial event detection model. The simulation of contaminant intrusion includes detailed chemical-specific reactions within a multi-species water quality model. This set-up generates a scenario of contaminant distribution and produces a continuous multiple sensor stations database. Three organophosphates pesticides, Chlorpyrifos, Malathion, and Parathion, are modeled as possible contaminants. The event detection model comprises both local and spatial data analysis. The local model applies a previously developed single-sensor event detection model with a higher alert threshold that reduces false alarm rates. The spatial model considers upstream sensor datasets which are examined for their uniqueness and mutual resemblance in a sliding time window. The model utilizes outlier detection, data analyses, and network hydraulics for the detection of suspicious spatial trends. The proposed algorithm is capable of detecting events with low contamination signatures and spatial influence. Two case studies are explored and compared to the single sensor model. The proposed methodology resulted in a lower number of false alarms compared to the previous single sensor event detection modeling approach.     

基于模拟退火的多标记数据特征选择

多标记数据有很多的冗余特征和数据,为了解决多标记数据中冗余和无关特征,提高多标记学习算法的泛化能力。提出一个基于模拟退火的卷积式特征选择方法——SAML(simulated annealing based feature selection for multi-label data),已有的算法只是使用了遗传算法来进行优化,新算法采用模拟退火来寻找最优子集,其效果在已有的工作中表现出比前者遗传算法更好的效果。在用于公开评测的Yahoo网页分类数据集上的实验结果表明,SAML算法的性能优于新近提出的一些流行的多标记特征选择方法。     

Comparative study of model updating methods using simulated experimental data

The inverse eigensensitivity method and the response function method of analytical model updating have become relatively more popular among other methods and have been successfully applied to the practice of analytical model improvement. This paper gives a detailed comparison of these two approaches of model updating on the basis of computer simulated experimental data with the objective of studying the convergence of the two methods and the accuracy with which they predict the corrections required in a finite element model. The effect of the amount of experimental data used in the process of model improvement on the quality of an updated model is also studied. The test cases of complete, incomplete and noisy experimental data are considered. The updated models are compared on the basis of some error indices constructed to quantify error in the predicted natural frequencies, mode shapes and response functions.     

Experimenting with local networks using an integrated circuit data link controller

There has been considerable discussion recently about the implementation of local networks. Much of this discussion centres around techniques requiring special hardware which is often too expensive for the application or not yet available. There is, however, an immediate need for local networks of a more modest price and performance. This paper describes the use of an integrated circuit data link controller to implement an inexpensive low performance local network.     

Cognition and the temporal arts: Investigating audience response to dance using PDAs that record continuous data during live performance

If artists and art explore organization of the brain [Zeki, S., Lamb, M., 1994. The neurology of kinetic art. Brain 117, 607–636], then investigation of response to artistic performance holds promise as a window to perceptual and cognitive processes. A new instrument for recording real-time audience response – the portable Audience Response Facility (pARF) – is described. Twenty, hand-held, Personal Digital Assistants (PDAs) collect responses on customizable skin interfaces. The pARF server transmits the customizable options, synchronizes devices and collects data for export. We report two studies using the pARF that demonstrate respondent agreement of perceived emotion during particular sections of two dance works. Greater agreement was evident in continuous ratings of arousal than valence; arousal appears to be related to surface features of the dance work. Future applications of the pARF to studies of multi-modal perception and cognition are discussed.     

Investigating haptic distance-to-break using linear and nonlinear materials in a simulated minimally invasive surgery task

Accurate detection of mediated haptic information in minimally invasive surgery (MIS) is critical for applying appropriate force magnitudes onto soft tissue with the aim of minimising tissue trauma. Force perception in MIS is a dynamic process, with surgeons’ administration of force into tissue revealing information about the remote surgical site which further informs the surgeons’ haptic interactions. The relationship between applied force and material deformation rate provides biomechanical information specifying the deformation distance remaining until a tissue will fail: which is termed distance-to-break (DTB). The current study demonstrates that observers can detect DTB while deforming simulated tissues and stop before reaching the tissues’ failure points. The design of training simulators, control devices and automated robotic systems for applications outside of MIS is discussed.

Practitioner Summary: In MIS, haptic information is critical for applying appropriate forces onto soft tissue to minimise tissue trauma. Observers used force information to detect how far they could deform a virtual tissue before it would break. The design of training simulators, control devices and automated robotic systems is discussed.     

Attentional models of multitask pilot performance using advanced display technology

In the first part of the reported research, 12 instrument-rated pilots flew a high-fidelity simulation, in which air traffic control presentation of auditory (voice) information regarding traffic and flight parameters was compared with advanced display technology presentation of equivalent information regarding traffic (cockpit display of traffic information) and flight parameters (data link display). Redundant combinations were also examined while pilots flew the aircraft simulation, monitored for outside traffic, and read back communications messages. The data suggested a modest cost for visual presentation over auditory presentation, a cost mediated by head-down visual scanning, and no benefit for redundant presentation. The effects in Part 1 were modeled by multiple-resource and preemption models of divided attention. In the second part of the research, visual scanning in all conditions was fit by an expected value model of selective attention derived from a previous experiment. This model accounted for 94% of the variance in the scanning data and 90% of the variance in a second validation experiment. Actual or potential applications of this research include guidance on choosing the appropriate modality for presenting in-cockpit information and understanding task strategies induced by introducing new aviation technology.     

Evaluation of global fire detection algorithms using simulated AVHRR infrared data

This paper provides a comparison of selected algorithms that have been proposed for global active fire monitoring using data from the NOAA Advanced Very High Resolution Radiometer (AVHRR). A simple theoretical model was used to generate scenes of AVHRR infrared channel 3 and channel 4 data containing fires of various sizes and temperatures in a wide range of terrestrial biomes and climates. Three active fire detection algorithms were applied to the simulated AVHRR images and their performance was characterized in terms of probability of fire detection and false alarm as functions of fire temperature and area, solar and viewing geometry, visibility, season and biome. Additional comparisons were made using AVHRR imagery. Results indicate that while each algorithm has a comparable probability of detecting large (1000m2) fires in most biomes, substantial differences exist in their ability to detect small fires, their tolerance of smoke and neighbouring fires, the number of false alarms, and their overall suitability for global application. An improved automatic algorithm is finally presented. It offers enhanced active fire detection with comparable or reduced false alarm rates in most biomes.     

Knowledge acquisition from questionnaire data using simulated breeding and inductive learning methods

Marketing decision making tasks require the acquisition of efficient decision rules from noisy questionnaire data. Unlike popular learning-from-example methods, in such tasks, we must interpret the characteristics of the data without clear features of the data nor pre-determined evaluation criteria. The problem is how domain experts get simple, easy-to-understand, and accurate knowledge from noisy data.

This paper describes a novel method to acquire efficient decision rules from questionnaire data using both simulated breeding and inductive learning techniques. The basic ideas of the method are that simulated breeding is used to get the effective features from the questionnaire data and that inductive learning is used to acquire simple decision rules from the data. The simulated breeding is one of the Genetic Algorithm based techniques to subjectively or interactively evaluate the qualities of offspring generated by genetic operations.

The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data: the acquired rules are simpler than the results from the direct application of inductive learning; a domain expert admits that they are easy to understand; and they are at the same level on the accuracy compared with the other methods.

Furthermore, we address three variations of the basic interactive version of the method: (i) with semiautomated GA phases, (ii) with the relatively evaluation phase via AHP, and (iii) with an automated multiagent learning method.     

Fault detection for non-condensing boilers using simulated building automation system sensor data

Building performance has been shown to degrade significantly after commissioning, resulting in increased energy consumption and associated greenhouse gas emissions. Fault Detection and Diagnosis (FDD) protocols using existing sensor networks and IoT devices have the potential to minimize this waste by continually identifying system degradation and re-tuning control strategies to adapt to real building performance. Due to its significant contribution to greenhouse gas emissions, the performance of gas boiler systems for building heating is critical. A review of boiler performance studies has been used to develop a set of common faults and degraded performance conditions, which have been integrated into a MATLAB/Simscape emulator. This resulted in a labeled dataset with approximately 10,000 simulations of steady-state performance for each of 14 non-condensing boilers. The collected data is used for training and testing fault classification using K-nearest neighbour, Decision tree, Random Forest, and Support Vector Machines. The results show that the Decision Tree, Random Forest, and Support Vector Machines method provide high prediction accuracy, consistently exceeding 95%, and generalization across multiple boilers is not possible due to low classification accuracy.     

VisuaLeague: Player performance analysis using spatial-temporal data

In recent years, the phenomenon of eSports has been a growing trend and consequently, in addition to players, other groups of users, including coaches and analysts, took an interest in online video games and the data extracted from them. Among many types of video games, one of the most widely played is the MOBA (Multiplayer Online Battle Arena) League of Legend (LoL) game. Similary to traditional sports, players and coaches/analysts analyse all game events, such as, players’ movements, to understand how they play to define new strategies and improve their performance. Our main goal is to get a better understanding of which visualizations techniques are more adequate to handle this type of spatio-temporal information data, associated to player performance analysis in video games. To address this goal, we inquired players to identify the analytical questions they need to support for performance analysis and designed the VisuaLeague prototype for the visualization of in-game player trajectories, using animated maps, and events during a LoL match. This paper presents a user study to evaluate the adequacy of animated maps and the analytical strategies followed by players when using spatio-temporal data to analyse player performance. The results support the adequacy of using the animated maps technique to convey information to users in this context. Moreover, they also point out towards a high degree of importance given to the spatio-temporal components of the data for player performance analysis.

     

Bayesian estimation of survival and capture probabilities using logit link and photoidentification data

We develop a series of Bayesian statistical models for estimating survival of wild populations monitored using capture-recapture experiments and photoidentification data. The proposed methodology is based on Cormack-Jolly-Seber model [Cormack, R.M., 1964. Estimates of survival from the sighting of marked animals. Biometrika 51, 429-438; Jolly, G.M., 1965. Explicit estimates from capture-recapture data with both death and immigration—stochastic model. Biometrika 52, 225-247; Seber, G.A.F., 1965. A note on the multiple recapture census. Biometrika 52, 249-259]. Besides time effects in capture probabilities, the proposed models allow taking into account heterogeneity in capture probability caused by the existence of different groups of individuals in the population. For that purpose, the capture probabilities are fitted using a logistic model. Additionally, it is also possible to estimate group-specific survival rates. Goodness of fit is evaluated using Bayes factor methodology. The models are applied to an 11-year photoidentification capture-recapture experiment for bowhead whales, Balaena mysticetus. The best model provides an estimate close to the one obtained by Zeh et al. [2002. Survival of bowhead whales, Balaena mysticetus, estimated from 1981-1998 photoidentification data. Biometrics 58, 832-840] using the Jolly-Seber model, but accounting for heterogeneity in capture probabilities improves precision.     

Bayesian estimation of survival and capture probabilities using logit link and photoidentification data

We develop a series of Bayesian statistical models for estimating survival of wild populations monitored using capture–recapture experiments and photoidentification data. The proposed methodology is based on Cormack–Jolly–Seber model [Cormack, R.M., 1964. Estimates of survival from the sighting of marked animals. Biometrika 51, 429–438; Jolly, G.M., 1965. Explicit estimates from capture–recapture data with both death and immigration—stochastic model. Biometrika 52, 225–247; Seber, G.A.F., 1965. A note on the multiple recapture census. Biometrika 52, 249–259]. Besides time effects in capture probabilities, the proposed models allow taking into account heterogeneity in capture probability caused by the existence of different groups of individuals in the population. For that purpose, the capture probabilities are fitted using a logistic model. Additionally, it is also possible to estimate group-specific survival rates. Goodness of fit is evaluated using Bayes factor methodology. The models are applied to an 11-year photoidentification capture–recapture experiment for bowhead whales, Balaena mysticetus. The best model provides an estimate close to the one obtained by Zeh et al. [2002. Survival of bowhead whales, Balaena mysticetus, estimated from 1981–1998 photoidentification data. Biometrics 58, 832–840] using the Jolly–Seber model, but accounting for heterogeneity in capture probabilities improves precision.     

A high speed computer-to-computer data link

The ICL 1905E at Queen Mary College, London and the CDC 6600 at the University of London Computer Centre, have been linked by a 48 kilobaud Post Office line. Jobs can be transmitted from the 1905E to the 6600, and resuts returned to the 1905E. A modified CDC data terminal (the QSE 2815) is installed at QMC, and is connected to the 1905E through a peripheral channel via a British Standard Interface converter. Standard CDC data transmission procedures are used; the software system is based on the CDC IMPORT/EXPORT package, with a program in the 1905E simulating the operation of IMPORT The 1905E software was written in two parts: an off-lining package which writes batches of programs to magnetic tape, and subsequently prints off-line output from tape, and a link control program which reads program batches from tape for transmission to the 6600, and writes to tape the output received from the link. This off-lining technique ensures that the full capacity of the high speed line is realized The link system began operation at the beginning of 1970, and is now used regularly to transmit jobs to ULCC and other sites in the network. Work is continuing on the provision of network facilities, and an integration of the link system with the operating system in the 1905E.