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

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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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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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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Cy-preds: An algorithm and a web service for the analysis and prediction of cysteine reactivity

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2016年第2期84卷 278-291页

作者： Soylu, Inanc Marino, Stefano M. Akdeniz Univ Dept Agr Biotechnol TR-07058 Antalya Turkey

Cysteine (Cys) is a critically important amino acid, serving a variety of functions within proteins including structural roles, catalysis, and regulation of function through post-translational modifications. Predicting which Cys residues are likely to be reactive is a very sought after feature. Few methods are currently available for the task, either based on evaluation of physicochemical features (e.g., pKa and exposure) or based on similarity with known instances. In this study, we developed an algorithm (named HAL-Cy) which blends previous work with novel implementations to identify reactive Cys from nonreactive. HAL-Cy present two major components: (i) an energy based part, rooted on the evaluation of H-bond network contributions and (ii) a knowledge based part, composed of different profiling approaches (including a newly developed weighting matrix for sequence profiling). In our evaluations, HAL-Cy provided significantly improved performances, as tested in comparisons with existing approaches. We implemented our algorithm in a web service (Cy-preds), the ultimate product of our work;we provided it with a variety of additional features, tools, and options: Cy-preds is capable of performing fully automated calculations for a thorough analysis of Cys reactivity in proteins, ranging from reactivity predictions (e.g., with HAL-Cy) to functional characterization. We believe it represents an original, effective, and very useful addition to the current array of tools available to scientists involved in redox biology, Cys biochemistry, and structural bioinformatics.

关键词： cysteine biochemistry redox biology prediction of reactivity algorithm development web service structural bioinformatics

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Special Section Guest Editorial: Computed tomography (CT) at 50 years

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JOURNAL OF MEDICAL IMAGING 2021年第5期8卷 052101页

作者： La Riviere, Patrick J. Fahrig, Rebecca Pelc, Norbert J. Univ Chicago Dept Radiol Chicago IL 60637 USA Siemens Healthineers Innovat Adv Therapies Forchheim Bavaria Germany Friedrich Alexander Univ Dept Comp Sci 5 Erlangen Germany Stanford Univ Dept Radiol Stanford CA 94305 USA

Guest editors Patrick La Riviere, Rebecca Fahrig, and Norbert Pelc introduce the JMI Special Section Celebrating X-Ray Computed Tomography at 50.

关键词： X-ray computed tomography Computed tomography Scanners Image quality algorithm development Artificial intelligence Evolutionary algorithms Lanthanum Radiology Reconstruction algorithms

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Evaluation of ECG algorithms designed to improve detect of transient myocardial ischemia to minimize false alarms in patients with suspected acute coronary syndrome

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JOURNAL OF ELECTROCARDIOLOGY 2018年第2期51卷 288-295页

作者： Pelter, Michele M. Xu, Yuan Fidler, Richard Xiao, Ran Mortara, David W. Hu, Xiao Univ Calif San Francisco Dept Physiol Nursing 2 Koret Way San Francisco CA 94143 USA

Background: Patients hospitalized for suspected acute coronary syndrome (ACS) are at risk for transient myocardial ischemia. During the "rule-out" phase, continuous ECG ST-segment monitoring can identify transient myocardial ischemia, even when asymptomatic. However, current ST-segment monitoring software is vastly underutilized due to false positive alarms, with resultant alarm fatigue. Current ST algorithms may contribute to alarm fatigue because;(I) they are not designed with a delay (minutes), rather alarm to brief spikes (i.e., turning, heart rate changes), and (2) alarm to changes in a single ECG lead, rather than contiguous leads. Purpose: This study was designed to determine sensitivity, and specificity, of ST algorithms when accounting for;ST magnitude (100 mu V vs 200 mu V), duration, and changes in contiguous ECG leads (i.e., aVL, I, aVR, II, aVF, III;V1, V2, V3, V4, V5, V6, V6, I). Methods: This was a secondary analysis from the COMPARE Study, which assessed occurrence rates for transient myocardial ischemia in hospitalized patients with suspected ACS using 12-lead Holter. Transient myocardial ischemia was identified from Holter using >100 mu V ST-segment up arrow or down arrow, in >1 ECG lead, > I min. algorithms tested against Holter transient myocardial ischemia were done using the University of California San Francisco (UCSF) ECG algorithm and included: (1)100 mu V vs 200 mu V any lead during a 5-min ST average;(2)100 mu V vs 200 mu V any lead >5 min, (3) 100 mu V vs 200 mu V any lead during a 5-min ST average in contiguous leads, and (4) 100 mu V vs 200 mu V > 5 min in contiguous leads (Table below). Results: In 361 patients;mean age 63 + 12 years, 63% male, 56% prior CAD, 43 (11%) had transient myocardial ischemia. Of the 43 patients with transient myocardial ischemia, 17 (40%) had ST-segment elevation events, and 26 (60%) ST-segment depression events. A higher proportion of patients with ST segment depression has missed ischemic events. [GRAPHICS]

关键词： Myocardial Ischemia ST Monitoring Acute Coronary Syndrome algorithm development Hospital ECG Monitoring

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