- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Predicting detection performance on security X-ray images as a function of image quality
摘要： Developing methods to predict how image quality affects task performance is a topic of great interest in many applications. While such studies have been performed in the medical imaging community, little work has been reported in the security X-ray imaging literature. In this work, we develop models that predict the effect of image quality on the detection of improvised explosive device (IED) components by bomb technicians in images taken using portable X-ray systems. Using a newly developed NIST-LIVE X-Ray Task Performance Database, we created a set of objective algorithms that predict bomb technician detection performance based on measures of image quality. Our basic measures are traditional Image Quality Indicators (IQIs) and perceptually-relevant Natural Scene Statistics (NSS)-based measures that have been extensively used in visible light (VL) image quality prediction algorithms. We show that these measures are able to quantify the perceptual severity of degradations and can predict the performance of expert bomb technicians to identify threats. Combining NSS- and IQI-based measures yields even better task performance prediction than either of these methods independently. We also developed a new suite of statistical task prediction models that we refer to as Quality Inspectors of X-ray images (QUIX), which we believe to be the first NSS-based model for security X-ray images. We also show that QUIX can be used to reliably predict conventional IQI metric values on distorted X-ray images.
关键词： NSS,IQI prediction,IEEE/ANSI N42.55,Image Quality,Improvised explosive devices (IEDs),Task performance study,X-ray images
更新于2025-09-19 17:15:36
-
[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Efficiency Study on Single Pulse, Burst Mode and Multi Pulse Ultra-Short Pulsed Ablation of Pure Copper
摘要： Existing studies have contributed immensely to link prediction by identifying different types of network communities. In this paper, a new type of network community in online social networks (OSNs) is identified using the association between network nodes. This new network community is called ‘‘virtual community.’’ Virtual communities are based on either the real/ physical relationships of users that are connected to their constituency, social, and professional activities or their virtual interactions associated with their cognitive levels, choice selection, and ideology. Users belonging to the same virtual community exhibit similar behavior in linking to nodes of common interest. These nodes, which reflect the common interest of a community, are called ‘‘prime nodes.’’ Prime nodes are linked to the prediction problem in OSN completion and are generally recommended for OSN growth. Recent studies on ranking algorithms have shown that the incompleteness of OSNs contributes to the low accuracy of ranking algorithms in identifying top spreaders. Thus, in this paper, we propose an OSN completion method based on link prediction through association between prime nodes. An experiment on predicting new links in two real big data sets of two global OSNs, namely, Facebook and Twitter, is conducted. The effectiveness of the proposed method is also validated by applying prominent ranking algorithms to the newly predicted and original networks. Results show that the accuracy rates of the ranking algorithms are improved, thereby validating the importance of the proposed method in predicting vital links.
关键词： virtual community,Information diffusion,ranking algorithm,link prediction,network community,online social network,prime node
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID) - Miramar Beach, FL, USA (2019.8.19-2019.8.21)] 2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID) - Invited Talk: "High Resolution Space/Time Imaging of Shockwaves Generated by Remote Laser Plasmas Produced by Light Filaments"
摘要： In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.
关键词： PCA,dimensionality reduction,electric load prediction,smart grid,genetic algorithm,forecasting,echo state network,Time-series
更新于2025-09-19 17:13:59
-
Reliable energy prediction method for grid connected photovoltaic power plants situated in hot and dry climatic condition
摘要： This paper presents a mathematical model to predict the energy generation of photovoltaic power plant in hot and humid climatic condition. This model is based on meteorological data and laboratory tested solar module parameters with twenty-four inputs and one output. In addition the twenty-four inputs drive an equation to calculate final energy generation from photovoltaic power plant. Validation of the proposed model was done by comparing the results of predicted energy generation using proposed model and PVWATT software model for two existing PV power plants of India. Monthly and annual energy production and errors will be the main criteria for the selection of batter model. The result shows that in comparison with PVWATT software proposed model was found to be more efficient and accurate to predict energy generation and proposed model also reduces mean absolute percentage error and root mean square error significantly compared to PVWATT software for hot and humid climatic condition.
关键词： PV power plant,Climatic condition,India,Mathematical method,Prediction model,Energy generation
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA) - Chengdu, China (2019.11.13-2019.11.15)] 2019 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA) - A 10 Gb/s, 150 mA Laser Diode Driver with Active Back-Termination in 0.13-?μm SOI CMOS Technology
摘要： To facilitate software quality assurance, defect prediction metrics, such as source code metrics, change churns, and the number of previous defects, have been actively studied. Despite the common understanding that developer behavioral interaction patterns can affect software quality, these widely used defect prediction metrics do not consider developer behavior. We therefore propose micro interaction metrics (MIMs), which are metrics that leverage developer interaction information. The developer interactions, such as file editing and browsing events in task sessions, are captured and stored as information by Mylyn, an Eclipse plug-in. Our experimental evaluation demonstrates that MIMs significantly improve overall defect prediction accuracy when combined with existing software measures, perform well in a cost-effective manner, and provide intuitive feedback that enables developers to recognize their own inefficient behaviors during software development.
关键词： software metrics,Defect prediction,developer interaction,software quality,Mylyn
更新于2025-09-19 17:13:59
-
[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Resonance of the Annihilation Channel of a Laser-Assisted Electron-Positron Scattering
摘要： Data missing in collections of time series occurs frequently in practical applications and turns out to be a major menace to precise data analysis. However, most of the existing methods either might be infeasible or could be inefficient to predict the missing values in large-scale coevolving time series. Also, the evolving of time series needs to be handled properly to adapt to the temporal characteristic. Furthermore, more massive volume of data is generated in many areas than ever before. In this paper, we have taken up the challenge of missing data prediction in coevolving time series by employing temporal dynamic matrix factorization techniques. First, our approaches are optimally designed to largely utilize both the interior patterns of each time series and the information of time series across multiple sources to build an initial model. Based on the idea, we have imposed hybrid regularization terms to constrain the objective functions of matrix factorization. Then, temporal dynamic matrix factorization is proposed to effectively update the initial already trained models. In the process of dynamic matrix factorization, batch updating and fine-tuning strategies are also employed to build an effective and efficient model. Extensive experiments on real-world data sets and synthetic data set demonstrate that the proposed approaches can effectively improve the performance of missing data prediction. Even when the missing ratio reaches as high as 90%, our proposed methods still show low prediction errors. Dynamic performance demonstrates that the methods can obtain satisfactory effectiveness and efficiency. Furthermore, we have also demonstrated how to take advantage of the high processing power of Apache Spark to perform missing data prediction in large-scale coevolving time series.
关键词： time series,missing data prediction,Apache Spark,Matrix factorization
更新于2025-09-19 17:13:59
-
[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Resonant Production of an Ultrarelativistic Electron-Positron Pair by a Gamma Quantum in the Field of a Nucleus and a Laser Wave
摘要： When and why people change their mobile phones are important issues in mobile communications industry, because it will impact greatly on the marketing strategy and revenue estimation for both mobile operators and manufactures. It is a promising way to take use of big data to analyze and predict the phone changing event. In this paper, based on mobile user big data, ?rst through statistical analysis, we ?nd that three important probability distributions, i.e., power-law, log-normal, and geometric distribution, play an important role in the user behaviors. Second, the relationships between eight selected attributes and phone changing are built, for example, young people have greater intention to change their phones if they are using the phones belonging to the low occupancy phones or feature phones. Third, we veri?ed the performance of four prediction models on phone changing event under three scenarios. Information gain ratio was used to implement attribute selection and then sampling method, cost-sensitive together with standard classi?ers were used to solve imbalanced phone changing event. Experiment results show our proposed enhanced backpropagation neural network in the undersampling scenario can attain better prediction performance.
关键词： imbalance problem,attribute selection,phone changing prediction,machine learning,Mobile big data
更新于2025-09-19 17:13:59
-
[IEEE 2019 International Symposium on Electromagnetic Compatibility - EMC EUROPE - Barcelona, Spain (2019.9.2-2019.9.6)] 2019 International Symposium on Electromagnetic Compatibility - EMC EUROPE - Comparison Between Impulsive Noise Models Considering a Time Structure of LED Noise
摘要： The main objective of this study was to investigate complex human socioeconomic infrastructure interactions and information on past human adverse events (AE) in an active war theater in order to predict future AE in a given geographical region. Human AE were defined as those security-related events that threatened human lives. Human socioeconomic infrastructure development data were derived by integrating three different datasets from different sources based on the United States Agency for International Development database. Using empirical data obtained from the country of Afghanistan from 2002 to 2010, we applied evolving self-organizing maps (ESOM) to forecast future patterns of such AE. Records from 2003–2009 were used as training data, while records from year 2010 were used to test the efficacy of ESOM in predicting AE. The socioeconomic data, dates, and geographical location information was used as input for the trained model. ESOM algorithm with supervised learning was effective in understanding future patterns of AE in a war region. The results also showed the possibility of predicting future AE based on the incomplete information pertaining to the geographical location, recent history of AE in the specific region of the country, and relevant socioeconomic infrastructure development data. The differences in applying the classical self-organizing maps and ESOM approaches for modeling of complex human socioeconomic infrastructure interactions were also discussed.
关键词： modeling and prediction,human life,socioeconomic system interactions,self-organizing maps (SOM),security threats,complexity,Adverse events (AE),infrastructure development,evolving self-organizing maps (ESOM)
更新于2025-09-19 17:13:59
-
[IEEE 2019 22nd International Conference on Electrical Machines and Systems (ICEMS) - Harbin, China (2019.8.11-2019.8.14)] 2019 22nd International Conference on Electrical Machines and Systems (ICEMS) - Research on maximum power point contrast tracking of photovoltaic system based on improved particle swarm algorithm
摘要： Measurement procedures for determining the radiated disturbances from electronic equipment are described in several IEC/CISPR standards and are well probed for frequencies up to 1 GHz. Above that frequency, radiation pattern of EUTs evolve complex forms so that the direction and magnitude of the maximum directivity is not known by design. Hence, standardized sampling approaches might underestimate the “true” maximum of the radiated emission. In this paper, an extension of these measurement procedures is proposed. The method uses a stochastic approach for estimating the maximum directivity based on the electrical size of the EUT. This is combined with a total radiated power measurement for a reduced sampling procedure to predict the maximum free-space, far-zone electric field. For validation purposes, an extensive 3-D scan of the radiation pattern of a generic EUT is performed. Different subsampling approaches are then investigated while the new prediction method is applied. It can be shown that the accuracy of the measurement procedures can be increased due to the proposed method.
关键词： prediction,sampling approach,unintentional electromagnetic radiator,Emission measurement,maximum directivity
更新于2025-09-19 17:13:59
-
[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Generating Maximal Entanglement between Spectrally Distinct Solid-State Emitters
摘要： Data missing in collections of time series occurs frequently in practical applications and turns out to be a major menace to precise data analysis. However, most of the existing methods either might be infeasible or could be inefficient to predict the missing values in large-scale coevolving time series. Also, the evolving of time series needs to be handled properly to adapt to the temporal characteristic. Furthermore, more massive volume of data is generated in many areas than ever before. In this paper, we have taken up the challenge of missing data prediction in coevolving time series by employing temporal dynamic matrix factorization techniques. First, our approaches are optimally designed to largely utilize both the interior patterns of each time series and the information of time series across multiple sources to build an initial model. Based on the idea, we have imposed hybrid regularization terms to constrain the objective functions of matrix factorization. Then, temporal dynamic matrix factorization is proposed to effectively update the initial already trained models. In the process of dynamic matrix factorization, batch updating and fine-tuning strategies are also employed to build an effective and efficient model. Extensive experiments on real-world data sets and synthetic data set demonstrate that the proposed approaches can effectively improve the performance of missing data prediction. Even when the missing ratio reaches as high as 90%, our proposed methods still show low prediction errors. Dynamic performance demonstrates that the methods can obtain satisfactory effectiveness and efficiency. Furthermore, we have also demonstrated how to take advantage of the high processing power of Apache Spark to perform missing data prediction in large-scale coevolving time series.
关键词： missing data prediction,time series,Apache Spark,Matrix factorization
更新于2025-09-19 17:13:59