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Analysing the meta-interaction between pathways by gene set topological impact analysis

BMC GENOMICS 2020年第1期21卷 748-748页

作者： Yan, Shen Chi, Xu Chang, Xiao Tian, Mengliang Sichuan Agr Univ Coll Agron Chengdu 611130 Peoples R China Chinese Acad Sci Beijing Inst Genom Beijing 101300 Peoples R China China Natl Ctr Bioinformat Beijing 101300 Peoples R China Capital Med Univ Xuanwu Hosp Dept Dermatol & Venereal Dis Beijing 100053 Peoples R China

BackgroundPathway analysis is widely applied in transcriptome analysis. Given certain transcriptomic changes, current pathway analysis tools tend to search for the most impacted pathways, which provides insight into underlying biological mechanisms. Further refining of the enriched pathways and extracting functional modules by "crosstalk" analysis have been proposed. However, the upstream/downstream relationships between the modules, which may provide extra biological insights such as the coordination of different functional modules and the signal transduction flow have been *** quantitatively analyse the upstream/downstream relationships between functional modules, we developed a novel GEne Set Topological Impact Analysis (GESTIA), which could be used to assemble the enriched pathways and functional modules into a super-module with a topological structure. We showed the advantages of this analysis in the exploration of extra biological insight in addition to the individual enriched pathways and functional *** can be applied to a broad range of pathway/module analysis result. We hope that GESTIA may help researchers to get one additional step closer to understanding the molecular mechanism from the pathway/module analysis results.

关键词： Topological pathway analysis algorithm development Functional module

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A General Theory on First Passage Times with Applications to Adaptive Decision Making 21

A General Theory on First Passage Times with Applications to...

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Conference on Unmanned Systems Technology XXI

作者： Chen, Xinjia Northwestern State Univ Louisiana Dept Engn Technol Natchitoches LA 71497 USA

ISBN: (数字)9781510627086

ISBN: (纸本)9781510627086

Modern intelligent systems are expected to be able to learn from experience, making decisions on the basis of the available information and proceeding step by step to a desired goal. An important specification of such adaptive decision making method is the amount of time to accomplish a decision. In this paper, we propose a random walk model for such decision making method. The model involves random processes which have independent stationary increments. The decision times are formulated as first passage times dependent on the parameters of decision rules. Asymptotic and nonasymptotic results are developed for the analysis of first passage times.

关键词： Nickel Stochastic processes Intelligence systems Probability theory algorithm development Convex optimization Process modeling Machine learning Statistical inference Unmanned systems

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System Level Simulation Technique for Optimizing Battery Thermal Management System of an Electric Vehicle

System Level Simulation Technique for Optimizing Battery The...

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IEEE Transportation Electrification Conference (ITEC-India)

作者： Venugopal, Vipin K. Chandrasekaran, N. Sharma, Shrivatsal Mahindra Elect Mobil Ltd Energy Syst Adv Technol Bengaluru India North Carolina State Univ Dept Elect & Comp Engn Raleigh NC USA

ISBN: (纸本)9781728131696

Electric vehicles are powered by lithium-ion batteries which require an effective but low power consuming thermal management system. 1D and 3D models help us to understand the influence of different parameters on system response and optimizing them. Even though modelling using 3D tools gives a more accurate and precise result, complexity and simulation time of 3D modelling gives 1D modelling an upper hand. The ease of 1D modelling also allows it to be used during concept development and in parameter tuning to choose the best components at its best operating point. This paper presents 1D system level modelling for optimizing battery thermal management for different load cases. The system level 1D model discussed here consists of a driver model, vehicle model, equivalent circuit model, battery box model and refrigeration cycle model. After validating the model with test data, thermal algorithms have been tested for optimizing energy requirements.

关键词： Electric Vehicle Battery Thermal Management 1D Modelling System Level Modelling algorithm development Optimization Simscape Modelling

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Feature Extraction for Predicting the Probability of Detecting Buried Explosive Objects using GPR Data 24

Feature Extraction for Predicting the Probability of Detecti...

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Conference on Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV

作者： Frigui, Hichem Khmaissia, Fadoua Karem, Andrew Univ Louisville Multimedia Res Lab CECS Dept Louisville KY 40292 USA

ISBN: (数字)9781510626904

ISBN: (纸本)9781510626904

Detection of buried explosive objects has been studied extensively and several sensors have been developed. In particular, ground penetrating radar (GPR) has proved to be one of the most successful modalities and many machine learning algorithms have been developed for buried threat detection using this sensor. Large scale experiments that involved multiple detection algorithms and very large data collections have indicated that the relative performance of different algorithms can vary significantly depending on the explosive objects, geographical site, soil and weather conditions, and burial depth. In fact, it is possible for an algorithm that performs well on training data to have low probability of target detection (PD), or high false alarm rate (FAR), on new data collected in a different environment. In this paper, we investigate the possibility of developing an algorithm that can predict the performance of a discrimination algorithm on GPR data collected in different environments. This can be used to select the optimal sensor/algorithm for a given location. It can also be used to select the optimal parameters of a given discriminator for a given site. Our approach combines predictive analysis with adequate feature selection methods to boost PD modeling and improve its prediction accuracy. Starting from raw GPR data, we extract and investigate a large set of potential descriptors that can quantify noise, surface roughness, and (implicit) soil properties. Our objectives are to: (i) Identify the optimal subset of features that can affect the target PDs of a given discriminator;and (ii) Learn a regression model for PD prediction. To validate our approach, we use data collected by a GPR sensor mounted on a vehicle. We extract over 50 different features from background regions and investigate feature selection and regression algorithms to learn a model that can predict the targets PD of a given discrimination algorithm for a given lane segment. We validate our result

关键词： Detection and tracking algorithms Feature selection Feature extraction Explosives Sensors algorithm development Explosives detection

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Design & Implementation of Micro Controller based Monitoring and Control System for Current, Voltage, Frequency and Phase of three phase AC supply 16

Design & Implementation of Micro Controller based Monitoring...

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16th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

作者： Zahid, Faisal Ilyas, Kashif Qazi, Hafeez CESAT Islamabad Islamabad Pakistan

ISBN: (纸本)9781538677292

This paper presents an advanced configuration for the development of monitoring & Control System for the measurement of voltage, frequency & identification of phase sequence based on AVR atmega microcontroller. Simulation of complete model is substantiated on Proteus software while designed hardware system has been endorsed with online AC mains 3phase supply. Its cardinal purpose is to supersede the conventional method, used for the detection of phase unbalance & identification of phase sequence, operate on induction principle. Notwithstanding, it infers alike zero crossing detection method, for developing an algorithm used for monitoring of 3 phase supply and control forthwith, if incorrect phase sequence is detected. With a few fundamental revamps, algorithm is simply modifiable for multi phase detection with wide range of frequencies and voltages as desired. Phase detection has its own significance in industrial applications like installation and commissioning of induction motor. This configuration allows the distant observer to control the output supply in case of any aberration. The monitoring unit is capable of highlighting the anomaly in input supply and its repercussion on output supply.

关键词： control system Phase sequence three phase AC Voltage level measurement frequency calculation algorithm development

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Archaeological Dating Using a Data Fusion Approach 28

Archaeological Dating Using a Data Fusion Approach

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

作者： Carlotto, Mark J. Independent Consultant (United States)

ISBN: (纸本)9781510627024

A new approach for dating archaeological sites is described. The method is inspired by Hapgood's hypothesis that patterns of glaciation and ice ages can be explained by shifts in the geographic location of the North Pole. We have identified over fifty archaeological sites throughout the world that could have once been aligned to north (i.e., to one of these past poles) when the sites were first established but are now misaligned due to subsequent pole shifts. An algorithm is described that fuses the location and orientation of these sites with Hapgood's original climate-dated pole locations to infer the date of construction of the associated sites. The results suggest that these sites may be far older than is currently thought.

关键词： Climatology Climate change Data fusion Sun algorithm development Solar radiation models

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Ensembles of Sparse Classifiers for Osteoporosis Characterization in Digital Radiographs

Ensembles of Sparse Classifiers for Osteoporosis Characteriz...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Zheng, Keni Jennane, Rachid Makrogiannis, Sokratis Delaware State Univ Div Phys & Computat Sci 1200 N DuPont Highway Dover DE 19901 USA Univ Orleans I3MTO Lab F-45067 Orleans France

ISBN: (数字)9781510625488

ISBN: (纸本)9781510625488

The analysis and characterization of imaging patterns is a significant research area with several applications to biomedicine, remote sensing, homeland security, social networking, and numerous other domains. In this paper we study and develop mathematical methods and algorithms for disease diagnosis and tissue characterization. The central hypothesis is that we can predict the occurrence of diseases with a certain level of confidence using supervised learning techniques that we apply to medical imaging datasets that include healthy and diseased subjects. We develop methods for calculation of sparse representations to classify imaging patterns and we explore the advantages of this technique over traditional texture-based classification. We introduce integrative sparse classifier systems that utilize structural block decomposition to address difficulties caused by high dimensionality. We propose likelihood functions for classification and decision tuning strategies. We performed osteoporosis classification experiments on the TCB challenge dataset. TCB contains digital radiographs of the calcaneus trabecular bone of 87 healthy and 87 osteoporotic subjects. The scans of healthy and diseased subjects show little or no visual differences, and their density histograms have significant overlap. We applied 30-fold cross-validation to evaluate the classification performances of our methods, and compared them to a texture based classification system. Our results show that ensemble sparse representations of imaging patterns provide very good separation between groups of healthy and diseased subjects and perform better than conventional sparse and texture-based techniques.

关键词： Image classification Bone Radiography Classification systems algorithm development

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A Comparative Analysis of the SVM and K-NN to Detect Buried Explosive Objects using Edge Histogram Features from GPR Data 24

A Comparative Analysis of the SVM and K-NN to Detect Buried ...

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Conference on Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV

作者： Karem, Andrew Frigui, Hichem Univ Louisville Multimedia Res Lab CECS Dept Louisville KY 40292 USA

ISBN: (数字)9781510626904

ISBN: (纸本)9781510626904

The Edge Histogram Detector (EHD) is an algorithm for buried threat detection (BTD) using sensor data generated by ground penetrating radar (GPR). It has been tested extensively and has demonstrated excellent performance on large real-world data sets. It has been implemented in real-time versions in vehicle mounted GPR. The EHD captures the spatial distribution of the edges within a 3D GPR volume. To keep the computation simple, 2D edge operators are used, and two types of edge histograms are computed. The first one, called EHDDT, is obtained by fixing the cross-track dimension and extracting edges in the (time, down-track) B-scans. The second edge histogram, called EHDCT, is obtained by fixing the down-track dimension and extracting edges in the (time, cross-track) B-Scans. For confidence assignment, it uses either a possibilistic K-Nearest Neighbors (p-KNN) or a Support Vector Machine (SVM) classifier. In this paper, we first propose an improvement to the EHD by adding a new feature component extracted from (down-track, cross-track) C-Scans. We show that this feature can improve the probability of detection while reducing the false alarm rate. Second, we design a large-scale experiment to compare the performance of the p-KNN and SVM classifiers and investigate their risk of over-fitting the training data. We use large datasets accumulated across multiple dates and multiple test sites by a vehicle mounted mine detector (VMMD) using a GPR sensor. The data includes a diverse set of buried explosive objects consisting of varying shapes, metal content, and underground burial depths. Performance of the EHD with the different features and classification methods are analyzed using receiver operating characteristics (ROC). To study the potential over-fitting problem, we use two different cross validation methods.

关键词： Sensors Explosives Feature extraction Detection and tracking algorithms Explosives detection algorithm development

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Evaluation of algorithms for Fake News Identification 28

Evaluation of Algorithms for Fake News Identification

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

作者： Kalvoda, Brian Stoick, Brandon Snell, Nicholas Straub, Jeremy North Dakota State Univ Dept Comp Sci Fargo ND 58105 USA

ISBN: (纸本)9781510627024

Today, information is spread quickly throughout communities by means of simple messaging, group chats, and social media platforms. Because of the ease of use that these services provide, misinformation has become a common trend. The term 'fake news' has emerged as being a way to refer to all information shared in a manner that is meant to mislead a reader into thinking something is a true statement when it is not. Combating fake news has become a major topic, and many are attempting to find a way of detecting when something is real or made up. In this paper, we look at a database of news articles that have been classified as either real or fake and apply machine learning to automatically determine if something is deliberately misleading. algorithms have been developed to make judgements, classify articles in a database and judge new articles based on learned knowledge. This model combines multiple factors that may raise or lower confidence in the article being legitimate or illegitimate and provides a single confidence metric. This paper presents the development of these algorithms for assessing articles. It discusses the efficacy of using this approach and compares it to other classification approaches. It then presents the results of using the system to classify numerous presented articles and discusses the sufficiency of system accuracy for multiple applications. Finally, it discusses next steps in the fake news detection project and how these algorithms fit within them.

关键词： fake news classification systems algorithm development databases machine learning web 2.0 technologies

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Status of Second VIIRS Reanalysis (RAN2) 11

Status of Second VIIRS Reanalysis (RAN2)

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Conference on Ocean Sensing and Monitoring XI

作者： Jonasson, O. Ignatov, A. NOAA STAR College Pk MD 20740 USA Global Sci & Technol Inc GST Greenbelt MD 20770 USA

ISBN: (数字)9781510626942

ISBN: (纸本)9781510626942

NOAA produces operational sea surface temperature (SST) data products from two VIIRS sensors, flown onboard NPP (launched in Oct 2011) and NOAA-20 (N20, aka. JPSS-1 prior to launch;launched in Nov 2017). These first two satellites in the US new generation Joint Polar Satellite System (JPSS) series will be followed by JPSS-2 to -4, planned for launch in 2022, 2026, and 2031, respectively. The goal of VIIRS SST Reanalyses (RANs) is to generate stable, accurate and consistent data products, which currently include Level 2P (L2P;swath projection;26 Gb/day) and gridded 0.02 degrees L3U (0.45 Gb/day). The RAN comprises multiples steps, including 1) RDR-to-SDR conversion, i.e., reprocessing of all available VIIRS L0 ("Raw Data Records", RDRs) into improved L1b ("Sensor Data Records", SDRs), using latest and most accurate sensor calibration, 2) pre-processing, which includes destriping the radiances, resampling VIIRS imagery (to minimize the effect of bow-tie deletions and distortions), and aggregating original 86-sec granules into 10-min SDR granules;3) feeding those into the latest NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) enterprise SST code (currently, version 2.61), and producing L2P;4) gridding L2P data and producing L3U;5) matching L2P/3U data with several accurate Level 4 (L4) analyses, and with quality controlled in situ SSTs from the NOAA in situ SST Quality Monitor, iQuam, system;6) calculation of the corresponding performance statistics - global daily mean biases and standard deviations, SDs, of various paired SST differences,.Ts, stratified by day and night, and displaying them in the NOAA SST Quality Monitor (SQUAM) web-based system;6) generation of SST imagery over similar to 30 regional targets and routine monitoring in another NOAA system, ACSPO Regional Monitor for SST (ARMS);7) calculation of brightness temperature (BT) differences between measured BTs and those simulated using the fast Community Radiative Transfer Model (CRTM), as a part of AC

关键词： Satellites Sensors algorithm development Data conversion Atmospheric modeling Statistical analysis Calibration Error analysis Pattern recognition Stars

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