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Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide (Karenia brevis)

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HARMFUL ALGAE 2010年第5期9卷 440-448页

作者： Carvalho, Gustavo A. Minnett, Peter J. Fleming, Lora E. Banzon, Viva F. Baringer, Warner Univ Miami Rosenstiel Sch Marine & Atmospher Sci Div Meteorol & Phys Oceanog Miami FL 33149 USA Univ Miami Rosenstiel Sch Marine & Atmospher Sci NSF NIEHS Oceans & Human Hlth Ctr Miami FL 33149 USA Univ Miami Miller Sch Med Dept Epidemiol & Publ Hlth Miami FL 33149 USA NOAA NESDIS Remote Sensing & Applicat Div Natl Climat Data Ctr Asheville NC USA

In a continuing effort to develop suitable methods for the surveillance of harmful algal blooms (HABs) of Karenia brevis using satellite radiometers, a new multi-algorithm method was developed to explore whether improvements in the remote-sensing detection of the Florida Red Tide was possible. A Hybrid Scheme was introduced that sequentially applies the optimized versions of two pre-existing satellite-based algorithms: an Empirical Approach (using water-leaving radiance as a function of chlorophyll concentration) and a Bio-optical Technique (using particulate backscatter along with chlorophyll concentration). The long-term evaluation of the new multi-algorithm method was performed using a multi-year MODIS dataset (2002-2006;during the boreal Summer-Fall periods - July-December) along the Central West Florida Shelf between 25.75 degrees N and 28.25 degrees N. algorithm validation was done with in situ measurements of the abundances of K. brevis: cell counts >= 1.5 x 10(4) cells I-1 defined a detectable HAB. Encouraging statistical results were derived when either or both algorithms correctly flagged known samples. The majority of the valid match-ups were correctly identified (similar to 80% of both HABs and non-blooming conditions) and few false negatives or false positives were produced (similar to 20% of each). Additionally, most of the HAB-positive identifications in the satellite data were indeed HAB samples (positive predictive value: similar to 70%) and those classified as HAB-negative were almost all non-bloom cases (negative predictive value: similar to 86%). These results demonstrate an excellent detection capability, on average similar to 10% more accurate than the individual algorithms used separately. Thus, the new Hybrid Scheme could become a powerful tool for environmental monitoring of K. brevis blooms, with valuable consequences including leading to the more rapid and efficient use of ships to make in situ measurements of HABs. (C) 2010 Elsevier B.V.

关键词： algorithm development Central West Florida Shelf (Gulf of Mexico) Detection Florida Red Tide (Karenia brevis) Harmful algal bloom (HAB) Hybrid Scheme Ocean color (MODIS-Aqua) Satellite remote sensing

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Process and alarm data integration under a two-stage Bayesian framework for fault diagnostics

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IFAC-PapersOnLine 2018年第24期51卷 1220-1226页

作者： Stief, Anna Ottewill, James R. Tan, Ruomu Cao, Yi ABB Corporate Research Center ul. Starowislna 13a Krakow31-038 Poland School of Water Energy and Environment Cranfield University CranfieldUK MK43 0AL United Kingdom College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China

Process and alarm data are usually available from industrial processes. It is common practice to use process data for process monitoring and diagnostics. In contrast, alarm data is typically used to determine the instantaneous health state of the process, often as part of a protection system. Alarm data is also used in alarm management systems and for alarm flood detection. Despite the fact that both data types perform similar, although not identical, functions in process monitoring, they are rarely used in combination. One of the main reasons for this is that the fusion between alarm and process data is not trivial: process data is sampled continually and is numerical, while alarm data is binary and appears at discrete times. The two data sources can contain complimentary information regarding the health state of the process, therefore their fusion is a promising direction for fault diagnostics algorithm development. A two-stage Bayesian framework is proposed to fuse alarm and process data on the decision level for fault diagnostics targeting industrial processes. Instead of the raw process data, the principal components of the process variables are used as the inputs of a naïve Bayes classifier. This step reduces the correlation between the process variables and reduces the dimension of the data. The alarm history is transformed into binary alarm features and input to a second, separate naïve Bayes classifier. The second stage of the method fuses the local classification results of the alarm and process data and provides the final classification result. The results show that the overall performance of the method fusing alarm and process data is superior when compared to the results of a similar single stage method using either alarm or process data. © 2018

关键词： Alarm systems Data fusion Data integration Fault detection Information management Plasma diagnostics Process control Process monitoring Alarms algorithm development Bayes method Classification results In process monitoring Industrial processs Principal Components Process monitoring and diagnostic

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Automatic determination of stream networks from DEMs by using road network data to locate culverts

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2019年第2期33卷 291-313页

作者： Makinen, Ville Oksanen, Juha Sarjakoski, Tapani Natl Land Survey Finland Finnish Geospatial Res Inst FGI Dept Geoinformat & Cartog Masala Finland

Determining stream networks automatically from digital elevation models is an issue that is actively being studied. The quality of elevation models has increased over time, but many hydrologically critical features, such as culverts, are often missing from the elevation data. To analyze the surficial water flow, one must either prepare a special elevation model or post-process an already-existing model. This study builds on the traditional, well-established method of determining the stream network from digital elevation models. We have extended the traditional method by locating culverts automatically, using road network data as an input. We show, by comparison to the reference data, that the culverts being most relevant for the stream network can be found with good accuracy. We demonstrate that by including the automatically located culverts in the automatic stream network determination, the quality of the generated network can be noticeably improved.

关键词： Hydrologic analysis digital elevation or terrain models algorithm development

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Simulation As A Tool In Designing And Evaluating A Robotic Apple Harvesting System

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IFAC-PapersOnLine 2018年第17期51卷 135-140页

作者： Wang, Heng Hohimer, Cameron J. Bhusal, Santosh Karkee, Manoj Mo, Changki Miller, John H. Washington State University Tri-Cities 2710 Crimson Way RichlandWA99354 United States Washington State University 24106 N. Bunn Rd ProsserWA99350 United States

During the past several years, significant effort has been made to develop a robotic system to harvest fresh market apples in a modern apple orchard at Washington State University (WSU). This system has gone through several modifications to achieve enhanced system accuracy, speed, robustness, and reduction of fruit damage and cost. However, due to the short window of apple picking each year and the challenging environment of the orchard, those modifications had limited success in improving the robotic systems performance even with extensive laboratory testing before each field trial. To further assess potential problems, better prepare for the field trial, and make best use of the limited time in the field, in silico testing is necessary in addition to laboratory testing. In this work, computer modeling and simulation were used to help identify potential problem in the robotic system, evaluate the effectiveness of the newly made modifications, and aid in both algorithm development and configuration design of the system. By doing simulations with field study data and workspace analysis of the system, we developed a novel algorithm to improve the reachability of the picking arm. We also concluded that the overlap of the arm's workspace and the fruit space is the key in deciding the reachability of the manipulator instead of the volume of arm's workspace. © 2018

关键词： Fruits algorithms Computer simulation Orchards Robotics Agricultural robot algorithm development Computer modeling and simulation Configuration designs Laboratory testing Potential problems Washington State University Workspace analysis

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Stratifying asthma severity in children using cough sound analytic technology

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JOURNAL OF ASTHMA 2021年第2期58卷 160-169页

作者： Swarnkar, Vinayak Abeyratne, Udantha Tan, Jamie Ng, Ti Wan Brisbane, Joanna M. Choveaux, Jennifer Porter, Paul Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia Joondalup Hlth Campus Dept Paediat Joondalup WA Australia Joondalup Hlth Campus Joondalup WA Australia Curtin Univ Sch Nursing Midwifery & Paramed Bentley WA Australia Perth Childrens Hosp Dept Emergency Med Nedlands WA Australia

Introduction: Asthma is a common childhood respiratory disorder characterized by wheeze, cough and respiratory distress responsive to bronchodilator therapy. Asthma severity can be determined by subjective, manual scoring systems such as the Pulmonary Score (PS). These systems require significant medical training and expertise to rate clinical findings such as wheeze characteristics, and work of breathing. In this study, we report the development of an objective method of assessing acute asthma severity based on the automated analysis of cough sounds. Methods: We collected a cough sound dataset from 224 children;103 without acute asthma and 121 with acute asthma. Using this database coupled with clinical diagnoses and PS determined by a clinical panel, we developed a machine classifier algorithm to characterize the severity of airway constriction. The performance of our algorithm was then evaluated against the PS from a separate set of patients, independent of the training set. Results: The cough-only model discriminated no/mild disease (PS 0-1) from severe disease (PS 5,6) but required a modified respiratory rate calculation to separate very severe disease (PS > 6). Asymptomatic children (PS 0) were separated from moderate asthma (PS 2-4) by the cough-only model without the need for clinical inputs. Conclusions: The PS provides information in managing childhood asthma but is not readily usable by non-medical personnel. Our method offers an objective measurement of asthma severity which does not rely on clinician-dependent inputs. It holds potential for use in clinical settings including improving the performance of existing asthma-rating scales and in community-management programs.

关键词： algorithm development wheeze pulmonary score sound-analysis

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AI in radiology: From promise to practice - A guide to effective integration

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EUROPEAN JOURNAL OF RADIOLOGY 2024年 181卷 111798-111798页

作者： Katal, Sanaz York, Benjamin Gholamrezanezhad, Ali Los Angeles Gen Med Ctr Dept Radiol 1200 N State St Los Angeles CA 90033 USA St Vincents Hosp Melbourne Dept Med Imaging 41 Victoria Parade Fitzroy Vic 3065 Australia

While Artificial Intelligence (AI) has the potential to transform the field of diagnostic radiology, important obstacles still inhibit its integration into clinical environments. Foremost among them is the inability to integrate clinical information and prior and concurrent imaging examinations, which can lead to diagnostic errors that could irreversibly alter patient care. For AI to succeed in modern clinical practice, model training and algorithm development need to account for relevant background information that may influence the presentation of the patient in question. While AI is often remarkably accurate in distinguishing binary outcomes-hemorrhage vs. no hemorrhage;fracture vs. no fracture-the narrow scope of current training datasets prevents AI from examining the entire clinical context of the image in question. In this article, we provide an overview of the ways in which failure to account for clinical data and prior imaging can adversely affect AI interpretation of imaging studies. We then showcase how emerging techniques such as multimodal fusion and combined neural networks can take advantage of both clinical and imaging data, as well as how development strategies like domain adaptation can ensure greater generalizability of AI algorithms across diverse and dynamic clinical environments.

关键词： Artificial Intelligence Radiology algorithm development Workflow Diagnostic Imaging Reproducibility of Results

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Estimating CDOM Concentration in Highly Turbid Estuarine Coastal Waters

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JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS 2018年第8期123卷 5856-5873页

作者： Zhao, Jun Cao, Wenxi Xu, Zhantang Ai, Bin Yang, Yuezhong Jin, Guangzhen Wang, Guifen Zhou, Wen Chen, Yong Chen, Haiyun Sun, Zhaohua Sun Yat Sen Univ Sch Marine Sci Guangzhou Guangdong Peoples R China Chinese Acad Sci South China Sea Inst Oceanol State Key Lab Trop Oceanog Guangzhou Guangdong Peoples R China Hohai Univ Coll Oceanog Nanjing Jiangsu Peoples R China Chinese Acad Sci South China Sea Inst Oceanol Key Lab Ocean & Marginal Sea Geol Guangzhou Guangdong Peoples R China

We proposed a support vector machine (SVM) algorithm to retrieve colored dissolved organic matter (CDOM) concentration (using a(g)(443) as a proxy) in the highly turbid Pearl River estuary. Two band ratios, namely, R-rs(443)/R-rs(547) and R-rs(488)/R-rs(547), were used as inputs. Comparisons between the estimated and measured a(g)(443) illustrated high accuracy of the SVM algorithm, yielding R(2)s of 0.98 and 0.89, mean absolute percentage errors of 5.18% and 13.1%, and root-mean-square deviations of 0.012 and 0.034m(-1) for the training and validation data sets, respectively. The SVM algorithm was also evaluated against existing ones for the study area and gave the best accuracy with a R-2 of 0.9, a mean absolute percentage error of 10.23%, and a root-mean-square deviation of 0.025m(-1). The diurnal dynamics of CDOM in the Pearl River estuary was revealed showing complicated variations and influenced by the combined effects of wind, tide, circulation, and river discharge. As for remote sensing applications, the SVM-based CDOM product exhibited great potential to trace the Pearl River plume and the satellite-derived plume area agreed well with the FVCOM model simulation result. SVM is an accurate and fast tool for retrieving CDOM concentration, especially in highly turbid estuarine coastal waters, and thus, river plume dynamics can be traced. Plain Language Summary Colored dissolved organic matter is an important optically active component in the water column. Together with others, it determines the water color. In this study, we developed an algorithm to retrieve colored dissolved organic matter from remote sensing measurements in the turbid Pearl River estuary. The algorithm performs with high accuracy and outcompetes existing ones. Based on the algorithm, the diurnal dynamics of colored dissolved organic matter and the forcing factors were analyzed. The algorithm was also applied to a satellite scene. The characteristics of the Pearl River plume were elucidated.

关键词： CDOM remote sensing turbid coastal waters algorithm development SVM FVCOM

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Analytics of Heterogeneous Process Data: Multiphase Flow Facility Case Study

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IFAC-PapersOnLine 2018年第18期51卷 363-368页

作者： Stief, Anna Tan, Ruomu Cao, Yi Ottewill, James R. ABB Corporate Research Center ul. Starowislna 13a Krakow31-038 Poland School of Water Energy and Environment Cranfield University CranfieldMK43 0AL United Kingdom College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China

Improvements in sensing, connectivity and computing technologies mean that industrial processes now generate a vast amount of data from a variety of disparate sources. Data may take a number of different forms, from different time-domain signals, sampled at different rates using various types of sensors, through to more disparate sources such as alarm and event logs. New process and condition monitoring techniques are needed to be developed to tackle the new challenges of big and heterogeneous data. Although there are a few publicly available benchmark studies, e.g. the Tennessee Eastman process plant (Ricker, 1995), a multiphase flow benchmark case for statistical process monitoring (Ruiz-Cárcel et al., 2015), they provide only standard process data. This work presents a benchmark case on an industrial scale multiphase flow facility. Various operational conditions were tested under normal operating modes as well as with seeded faults. Heterogeneous data was collected from various sources, including process data, alarm data and high frequency ultrasonic and pressure data. Two different fault detection algorithms are applied to the data, a multivariate PCA-enhanced Canonical Variate Analysis (CVA) and a probabilistic Bayesian method. This benchmark case study with data from disparate sources can be used for algorithm development and validation for fault detection, fault identification, fault classification, fault severity detection, monitoring of fault evolution and prognostics. © 2018

关键词： Fault detection Bayesian networks Condition monitoring Data Analytics Experiments Multiphase flow Process monitoring Statistical process control Time domain analysis Ultrasonic applications algorithm development Benchmark case studies Canonical variate analysis Fault detection algorithm Operational conditions Process and condition monitoring Statistical process monitoring Tennessee Eastman process

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Machine-learning for biopharmaceutical batch process monitoring with limited data

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IFAC-PapersOnLine 2018年第18期51卷 126-131页

作者： Tulsyan, Aditya Garvin, Christopher Undey, Cenk Digital Integration & Predictive Technologies Amgen Inc. 360 Binney Street CambridgeMA02141 United States Digital Integration & Predictive Technologies Amgen Inc. 40 Technology Way West GreenwichRI02817 United States Digital Integration & Predictive Technologies Amgen Inc. One Amgen Center Drive Thousand OaksCA91320 United States

Commercial biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. This article addresses the problem of real-time statistical batch process monitoring (BPM) for biopharmaceutical processes with limited production history; herein, referred to as the ‘Low-N’ problem. In this article, we propose an approach to transition from a Low-N scenario to a Large-N scenario by generating an arbitrarily large number of in silico batch data sets. The proposed method is a combination of hardware exploitation and algorithm development. To this effect, we propose a Bayesian non-parametric approach to model a batch process, and then use probabilistic programming to generate an arbitrarily large number of dynamic in silico campaign data sets. The efficacy of the proposed solution is elucidated on an industrial process. © 2018

关键词： Batch data processing Learning systems Process control Process monitoring algorithm development Batch process monitoring Efficient monitoring Industrial processs Low N problem Nonparametric approaches Probabilistic programming Statistical batch process monitoring

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Deep learning application engine (DLAE): development and integration of deep learning algorithms in medical imaging

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SOFTWAREX 2019年 10卷 100347页

作者： Sanders, Jeremiah W. Fletcher, Justin R. Frank, Steven J. Liu, Ho-Ling Johnson, Jason M. Zhou, Zijian Chen, Henry Szu-Meng Venkatesan, Aradhana M. Kudchadker, Rajat J. Pagel, Mark D. Ma, Jingfei Univ Texas MD Anderson Canc Ctr Dept Imaging Phys 1515 Holcombe BlvdUnit 1472 Houston TX 77030 USA MD Anderson Canc Ctr UTHlth Grad Sch Biomed Sci Med Phys Grad Program 1515 Holcombe BlvdUnit 1472 Houston TX 77030 USA Odyssey Syst Consulting LLC 550 Lipoa Pkwy Maui HI USA Univ Texas MD Anderson Canc Ctr Dept Radiat Oncol 1515 Holcombe BlvdUnit 1422 Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Diagnost Radiol 1515 Holcombe BlvdUnit 1473 Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Radiat Phys 1515 Holcombe BlvdUnit 1420 Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Canc Syst Imaging 1515 Holcombe BlvdUnit 1907 Houston TX 77030 USA

Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Several example applications using clinical images were developed and tested to demonstrate the capabilities of DLAE. Additionally, a model deployment example was demonstrated in which DLAE was used to integrate two trained models into a commercial clinical software package. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Medical imaging Software Deep learning algorithm development

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