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Relevant bibliographies by topics / Optimized detection model

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Academic literature on the topic 'Optimized detection model'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "Optimized detection model"

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Huang, K. "An Optimized LightGBM Model for Fraud Detection." Journal of Physics: Conference Series 1651 (November 2020): 012111. http://dx.doi.org/10.1088/1742-6596/1651/1/012111.

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Hu, JianSheng, JunJie Ma, Bin Xiao, and Rui Zhang. "Improved Lightweight YOLOv3 model for Target Detection Algorithm." Journal of Physics: Conference Series 2370, no. 1 (November 1, 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2370/1/012029.

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When detecting small objects in interior situations, the classic object detection algorithm performs poorly in terms of real-time detection task and high precision detection task. This paper suggests an optimized tiny-YOLOv3-Shufflenetv2 light-weight model based on indoor scenes. The scheme adopts the fusion light-weight network which combines ShuffleNetv2 and YOLOv3, it reduces the complexity of the model to meet the lightweight requirements while ensuring good detection results for deployment to mobile robots. Also in this paper, an indoor small target object dataset, indoor-2022, is created to improve and optimize the model for the data images. YOLOv3, YOLOv3-Shufflenetv2, and tiny-YOLOv3-Shufflenetv2 are trained and tested on the indoor-2022 small target dataset in the Pytorch framework. The experimental findings indicate that in the indoor-2022 dataset. Compared with the single YOLOv3 model for object detection tasks, the fusion improved model used in this article improves the recognition ability of small objects in indoor images, With a 10-fold reduction in model size and a 4-fold increase in detection speed, only results in 1.6% reduction in the mean accuracy (mAP), and the comparison experiments with the current stage of traditional target detection algorithms validate the proposed tiny-YOLOv3-Shufflenetv2 model is verified to be superior and feasible. The optimized model in this article reduces mannequin parameters and model size while additionally ensuring the accuracy and velocity of inspection, and meets the requirements for deployment on indoor mobile robots.

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Boonsim, Noppakun, and Saranya Kanjaruek. "Optimized transfer learning for polyp detection." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 17, no. 1 (February 18, 2023): 73–81. http://dx.doi.org/10.37936/ecti-cit.2023171.250910.

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Early diagnosis of colorectal cancer focuses on detecting polyps in the colon as early as possible so that patients can have the best chances for success- ful treatment. This research presents the optimized parameters for polyp detection using a deep learning technique. Polyp and non-polyp images are trained on the InceptionResnetV2 model by the Faster Region Con- volutional Neural Networks (Faster R-CNN) framework to identify polyps within the colon images. The proposed method revealed more remarkable results than previous works, precision: 92.9 %, recall: 82.3%, F1-Measure: 87.3%, and F2-Measure: 54.6% on public ETIS-LARIB data set. This detection technique can reduce the chances of missing polyps during a pro- longed clinical inspection and can improve the chances of detecting multiple polyps in colon images.

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Behera, Bibhuti Bhusana, Binod Kumar Pattanayak, and Rajani Kanta Mohanty. "Deep Ensemble Model for Detecting Attacks in Industrial IoT." International Journal of Information Security and Privacy 16, no. 1 (January 1, 2022): 1–29. http://dx.doi.org/10.4018/ijisp.311467.

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In this research work, a novel IIoT attack detection framework is designed by following four major phases: pre-processing, imbalance processing, feature extraction, and attack detection. The attack detection is carried out using the projected ensemble classification framework. The projected ensemble classification framework encapsulates the recurrent neural network, CNN, and optimized bi-directional long short-term memory (BI-LSTM). The RNN and CNN in the ensemble classification framework is trained with the extracted features. The outcome acquired from RNN and CNN is utilized for training the optimized BI-LSTM model. The final outcome regarding the presence/absence of attacks in the industrial IoT is portrayed by the optimized BI-LSTM model. Therefore, the weight of BI-LSTM model is fine-tuned using the newly projected hybrid optimization model referred as cat mouse updated slime mould algorithm (CMUSMA). The projected hybrids the concepts of both the standard slime mould algorithm (SMA) and cat and mouse-based optimizer(CMBO), respectively.

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Al-Sarem, Mohammed, Faisal Saeed, Zeyad Ghaleb Al-Mekhlafi, Badiea Abdulkarem Mohammed, Tawfik Al-Hadhrami, Mohammad T. Alshammari, Abdulrahman Alreshidi, and Talal Sarheed Alshammari. "An Optimized Stacking Ensemble Model for Phishing Websites Detection." Electronics 10, no. 11 (May 28, 2021): 1285. http://dx.doi.org/10.3390/electronics10111285.

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Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

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Ragab, Mahmoud, Khalid Eljaaly, Maha Farouk S. Sabir, Ehab Bahaudien Ashary, S. M. Abo-Dahab, and E. M. Khalil. "Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model." Computers, Materials & Continua 71, no. 3 (2022): 5751–64. http://dx.doi.org/10.32604/cmc.2022.024658.

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Vasavi, CH, and N. Divya Sruthi. "Detection of Lung Cancer Using Optimized SVM-CNN Model." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4608–13. http://dx.doi.org/10.22214/ijraset.2023.54496.

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Abstract: Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection is critical for effective treatment. Artificial Intelligence (AI) has shown great promise in improving the accuracy and speed of lung cancer detection. In this study, we present a review of recent research on lung cancer detection using AI, including the use of deep learning, and image analysistechnique. Neural networks have always been several a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human’s computational capabilities. The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using image processing methods.

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Ghaleb Al-Mekhlafi, Zeyad, Badiea Abdulkarem Mohammed, Mohammed Al-Sarem, Faisal Saeed, Tawfik Al-Hadhrami, Mohammad T. Alshammari, Abdulrahman Alreshidi, and Talal Sarheed Alshammari. "Phishing Websites Detection by Using Optimized Stacking Ensemble Model." Computer Systems Science and Engineering 41, no. 1 (2022): 109–25. http://dx.doi.org/10.32604/csse.2022.020414.

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Moukhafi, Mehdi, Khalid El Yassini, and Bri Seddik. "Intrusions detection using optimized support vector machine." International Journal of Advances in Applied Sciences 9, no. 1 (March 1, 2020): 62. http://dx.doi.org/10.11591/ijaas.v9.i1.pp62-66.

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<p><span>Computer network technologies are evolving fast and the development of internet technology is more quickly, people more aware of the importance of the network security. Network security is main issue of computing because the number attacks are continuously increasing. For these reasons, intrusion detection systems (IDSs) have emerged as a group of methods that combats the unauthorized use of a network’s resources. Recent advances in information technology, specially in data mining, have produced a wide variety of machine learning methods, which can be integrated into an IDS. This study proposes a new method of intrusion detection that uses support vector machine optimizing optimizing by a genetic algorithm. to improve the efficiency of detecting known and unknown attacks, we used a Particle Swarm Optimization algorithm to select the most influential features for learning the classification model.</span></p>

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Feng, Junzhe, Chenhao Yu, Xiaoyi Shi, Zhouzhou Zheng, Liangliang Yang, and Yaohua Hu. "Research on Winter Jujube Object Detection Based on Optimized Yolov5s." Agronomy 13, no. 3 (March 10, 2023): 810. http://dx.doi.org/10.3390/agronomy13030810.

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Winter jujube is a popular fresh fruit in China for its high vitamin C nutritional value and delicious taste. In terms of winter jujube object detection, in machine learning research, small size jujube fruits could not be detected with a high accuracy. Moreover, in deep learning research, due to the large model size of the network and slow detection speed, deployment in embedded devices is limited. In this study, an improved Yolov5s (You Only Look Once version 5 small model) algorithm was proposed in order to achieve quick and precise detection. In the improved Yolov5s algorithm, we decreased the model size and network parameters by reducing the backbone network size of Yolov5s to improve the detection speed. Yolov5s’s neck was replaced with slim-neck, which uses Ghost-Shuffle Convolution (GSConv) and one-time aggregation cross stage partial network module (VoV-GSCSP) to lessen computational and network complexity while maintaining adequate accuracy. Finally, knowledge distillation was used to optimize the improved Yolov5s model to increase generalization and boost overall performance. Experimental results showed that the accuracy of the optimized Yolov5s model outperformed Yolov5s in terms of occlusion and small target fruit discrimination, as well as overall performance. Compared to Yolov5s, the Precision, Recall, mAP (mean average Precision), and F1 values of the optimized Yolov5s model were increased by 4.70%, 1.30%, 1.90%, and 2.90%, respectively. The Model size and Parameters were both reduced significantly by 86.09% and 88.77%, respectively. The experiment results prove that the model that was optimized from Yolov5s can provide a real time and high accuracy small winter jujube fruit detection method for robot harvesting.

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Dissertations / Theses on the topic "Optimized detection model"

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Montes, Martin Alejandro. "Monte carlo simulations as a tool to optimize target detection by AUV/ROV laser line scanners." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001295.

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Mohan, Rathish. "Algorithmic Optimization of Sensor Placement on Civil Structures for Fault Detection and Isolation." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353156107.

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Chauhan, Ajay Singh. "Financial statement fraud detection Model based on Hybrid data mining methods: Proposing an optimized Detection model." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/17200.

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Cheng-YuanChen and 陳正元. "Facial Feature Point Detection using Shape Optimized Search and Tracking using Inverse Compositional Active Appearance Models." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/08075334818920959853.

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碩士
國立成功大學
資訊工程學系碩博士班
101
Understanding human action is one of the most important issues of computer vision. In recent years, non-contact human action analysis system using camera has become more and more popular because the price of camera devices are getting lower. In human action analysis systems, facial feature points can tell a lot about the motion of human head such as gazing direction, drowsy and facial expression. In this thesis, a real-time system is proposed to detect the facial feature points and track these points with high accuracy and low computational cost. In the feature point detection algorithm of our system, approximately face position will be detected and compared with each feature template to create the location probability table for each point. After that, we can extract the feature points from the face by maximizing the sum of each feature point's location probability with suitable shape constraint. The feature point tracking algorithm of our system can be deliberated into three major steps. The first step is warping the face to frontal view by estimating current feature point positions. The second step is to estimate the current warping error by comparing the warped face with the trained frontal face template. The third step is updating the feature point positions by analyzing current warping error. System can track the feature points with high accuracy by iteratively updating the current feature point positions.

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HAIKL, Petr. "Ověření funkce bezkontaktního snímače hladiny paliva v palivové nádrži." Master's thesis, 2013. http://www.nusl.cz/ntk/nusl-156178.

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This work inkluse measurement principles for measuring liquid level in tanks with a focus on fuel. Are described most frequently used types of liquid level sensors, focusing on fuel. The work inkluse design chosen technical solutions fuel level sensor and its technical implementation, including the technical implementation and verification of its functionality and calibration.

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Books on the topic "Optimized detection model"

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Basu, Sanjay. Modeling Public Health and Healthcare Systems. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190667924.001.0001.

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This book aims to empower readers to learn and apply engineering, operations research, and modeling techniques to improve public health programs and healthcare systems. Readers will engage in in-depth study of disease detection and control strategies from a “systems science” perspective, which involves the use of common engineering, operations research, and mathematical modeling techniques such as optimization, queuing theory, Markov and Kermack-McKendrick models, and microsimulation. Chapters focus on applying these techniques to classical public health dilemmas such as how to optimize screening programs, reduce waiting times for healthcare services, solve resource allocation problems, and compare macroscale disease control strategies that cannot be easily evaluated through standard public health methods such as randomized trials or cohort studies. The book is organized around solving real-world problems, typically derived from actual experiences by staff at nongovernmental organizations, departments of public health, and international health agencies. In addition to teaching the theory behind modeling methods, the book aims to confer practical skills to readers through practice in model implementation using the statistical software R.

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Taillefer, Raymond, and Frans J. Th Wackers. Kinetics of Conventional and New Cardiac Radiotracers. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0004.

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The kinetics of radiotracers, that is the mode of uptake, retention and release from the myocardium, are relevant for designing and implementing optimized nuclear cardiac imaging protocols. This chapter addresses the kinetics of commonly used radiotracers for imaging myocardial perfusion, sympathetic neuronal function and cardiac metabolism, both with SPECT and PET cardiac imaging. The optimal timing of imaging after injection either at stress or at rest is determined by rate of uptake in the heart and adjacent organs, as well as the residence time of radiotracers within the myocytes. The efficiency of myocardial extraction over a wide range myocardial blood flows is relevant for reliable detection of obstructive coronary artery disease and absolute quantification of regional myocardial blood flow. For each cardiac imaging agent the cellular mechanism of uptake and its release or retention are discussed with an emphasis on the clinical impact of these parameters.

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Book chapters on the topic "Optimized detection model"

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Abid, Nesrine, Kais Loukil, Walid Ayedi, Ahmed Chiheb Ammari, and Mohamed Abid. "Optimized Parallel Model of Covariance Based Person Detection." In Image Analysis and Processing — ICIAP 2015, 287–98. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23234-8_27.

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Sinhmar, Abhinav, Vinamra Malhotra, R. K. Yadav, and Manoj Kumar. "Spam Detection Using Genetic Algorithm Optimized LSTM Model." In Computer Networks and Inventive Communication Technologies, 59–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3728-5_5.

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Park, ByoungGun, Ji Su Park, and YounSoon Shin. "Optimized Vehicle Fire Detection Model Based on Deep Learning." In Advances in Computer Science and Ubiquitous Computing, 685–91. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1252-0_92.

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Liu, Lianye, Jinping Liu, Juanjuan Wu, Jiaming Zhou, and Meiling Cai. "Novelty Detection-Based Automated Anomaly Identification via Optimized Deep Generative Model." In Big Data, 117–34. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9709-8_9.

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Bodavarapu, Pavan Nageswar Reddy, P. V. V. S. Srinivas, Pragnyaban Mishra, Venkata Naresh Mandhala, and Hye-jin Kim. "Optimized Deep Neural Model for Cancer Detection and Classification Over ResNet." In Smart Technologies in Data Science and Communication, 267–80. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1773-7_22.

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Reddy, Mure Vamsi Kalyan, Prithvi K. Murjani, Sujatha Rajkumar, Thomas Chen, and V. S. Ajay Chandrasekar. "Optimized CNN Model with Deep Convolutional GAN for Brain Tumor Detection." In Third Congress on Intelligent Systems, 409–25. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9225-4_31.

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Al-Mekhlafi, Zeyad Ghaleb, and Badiea Abdulkarem Mohammed. "Using Genetic Algorithms to Optimized Stacking Ensemble Model for Phishing Websites Detection." In Communications in Computer and Information Science, 447–56. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8059-5_27.

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Abid, Nesrine, Tarek Ouni, Kais Loukil, A. Chiheb Ammari, and Mohamed Abid. "Optimized Parallel Model of Human Detection Based on the Multi-Scale Covariance Descriptor." In Parallel Processing and Applied Mathematics, 423–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32149-3_40.

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Matei, Alexander, and Stefan Ulbrich. "Detection of Model Uncertainty in the Dynamic Linear-Elastic Model of Vibrations in a Truss." In Lecture Notes in Mechanical Engineering, 281–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_22.

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AbstractDynamic processes have always been of profound interest for scientists and engineers alike. Often, the mathematical models used to describe and predict time-variant phenomena are uncertain in the sense that governing relations between model parameters, state variables and the time domain are incomplete. In this paper we adopt a recently proposed algorithm for the detection of model uncertainty and apply it to dynamic models. This algorithm combines parameter estimation, optimum experimental design and classical hypothesis testing within a probabilistic frequentist framework. The best setup of an experiment is defined by optimal sensor positions and optimal input configurations which both are the solution of a PDE-constrained optimization problem. The data collected by this optimized experiment then leads to variance-minimal parameter estimates. We develop efficient adjoint-based methods to solve this optimization problem with SQP-type solvers. The crucial test which a model has to pass is conducted over the claimed true values of the model parameters which are estimated from pairwise distinct data sets. For this hypothesis test, we divide the data into k equally-sized parts and follow a k-fold cross-validation procedure. We demonstrate the usefulness of our approach in simulated experiments with a vibrating linear-elastic truss.

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Shead, T. M., I. K. Tezaur, W. L. Davis IV, M. L. Carlson, D. M. Dunlavy, E. J. Parish, P. J. Blonigan, J. Tencer, F. Rizzi, and H. Kolla. "A Novel In Situ Machine Learning Framework for Intelligent Data Capture and Event Detection." In Lecture Notes in Energy, 53–87. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_3.

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AbstractWe present a novel framework for automatically detecting spatial and temporal events of interest in situ while running high performance computing (HPC) simulations. The new framework – composed from signature, measure, and decision building blocks with well-defined semantics – is tailored for parallel and distributed computing, has bounded communication and storage requirements, is generalizable to a variety of applications, and operates in an unsupervised fashion. We demonstrate the efficacy of our framework on several cases spanning scientific domains and applications of event detection: optimized input/output (I/O) in computational fluid dynamics simulations, detecting events that can lead to irreversible climate changes in simulations of polar ice sheets, and identifying optimal space-time subregions for projection-based model reduction. Additionally, we demonstrate the scalability of our framework using a HPC combustion application on the Cori supercomputer at the National Energy Research Scientific Computing Center (NERSC).

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Conference papers on the topic "Optimized detection model"

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Dongdong, Liu, Dou Hongtao, Han Bo, and Niu Lei. "An optimized network intrusion detection model." In 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2022. http://dx.doi.org/10.1109/imcec55388.2022.10019952.

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Mohapatra, Saumendra Kumar, Abhishek Das, and Mihir Narayan Mohanty. "An Optimized Ensemble Model for COVID Detection." In 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS). IEEE, 2022. http://dx.doi.org/10.1109/mlcss57186.2022.00044.

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Hatua, S., H. Bose, and D. N. Ray. "Dynamic and Optimized Model for Stairs Detection." In 2020 National Conference on Emerging Trends on Sustainable Technology and Engineering Applications (NCETSTEA). IEEE, 2020. http://dx.doi.org/10.1109/ncetstea48365.2020.9119939.

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Thomas, Jaison Shajan, Sania Ejaz, Zaheeruddin Ahmed, and Sushma Hans. "Optimized Car Damaged Detection using CNN and Object Detection Model." In 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). IEEE, 2023. http://dx.doi.org/10.1109/iccike58312.2023.10131804.

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Tang, Kuan, Fei Yan, and Wenbo Liu. "Pedestrian-Vehicle Detection Model Based on Optimized YOLOv3." In 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9327192.

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Shukla, Ganesh, Bhargav Desai, Parth Mehta, and Sunil Karamchandani. "Holistic Siamese Model Optimized for Aged Face-Sketch Similarity Detection." In 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2020. http://dx.doi.org/10.1109/gucon48875.2020.9231260.

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Thaseen, I. Sumaiya, and Ch Aswani Kumar. "Intrusion detection model using fusion of PCA and optimized SVM." In 2014 International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2014. http://dx.doi.org/10.1109/ic3i.2014.7019692.

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Wu, Zheng Yi. "Model-Based Leakage Hotspot Detection with Optimized Pressure Logger Placement." In World Environmental and Water Resources Congress 2015. Reston, VA: American Society of Civil Engineers, 2015. http://dx.doi.org/10.1061/9780784479162.074.

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Pan, Wen-Tsao, and Sheng-Chu Su. "Construction of the optimized production performance detection model using data mining." In 2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2014. http://dx.doi.org/10.1109/iciea.2014.6931491.

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Zhang, Yan, Hongmei Zhang, Xiangli Zhang, and Dongsheng Qi. "Deep Learning Intrusion Detection Model Based on Optimized Imbalanced Network Data." In 2018 IEEE 18th International Conference on Communication Technology (ICCT). IEEE, 2018. http://dx.doi.org/10.1109/icct.2018.8600219.

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Reports on the topic "Optimized detection model"

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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.

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Alders, George. L51630A In-Line Detection and Sizing of Stress Corrosion Cracks Using EMAT Ultrasonics - Phase II. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), November 1991. http://dx.doi.org/10.55274/r0011370.

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This report covers that part of a Phase II effort that was completed by Magnasonics, Inc. prior to its liquidation by an adverse court ruling. The Phase I program investigated several configurations of Electromagnetic Acoustic Transducers (EMAT5) to arrive at an optimum approach to the problem of detecting and sizing stress corrosion cracks (SCC) in operating pipelines. Phase II was designed to optimize the most promising configuration by applying it to as many pipe samples as possible. Three pipe samples that contained several colonies of 5CC were made available to Magnasonics and a computerized data collection apparatus was assembled to collect ultrasonic data on the cracked areas. In agreement with the Phase I findings, high order Lamb wave modes were found to reflect strongly from the stress corrosion cracks and thus provide a sensitive detection method. In order to develop sizing capacitates, the crack depths in the colonies of SCC were measured with an eddy current technique and by a new surface acoustic wave method. Good qualitative correlation between the severity of cracking and the amplitude of the reflected ultrasonic energy was observed but real quantitative comparisons must wait on destructive measurement of the actual crack depths. Since only 30% of the funding has been used to date, it is hoped that additional measurements can be made on even more samples in the future.

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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research first attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage, chlorophyll and nitrogen status. In addition to that, an adaptable, multispectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status was developed. Spectral characteristics of corn leaves reflectance were investigated in order to estimate the nitrogen status of the plants, using a commercial laboratory spectrometer. Statistical models relating leaf N and reflectance spectra were developed for both greenhouse and field plots. A basis was established for assessing nitrogen status using spectral reflectance from plant canopies. The combined effect of variety and N treatment was studied by measuring the reflectance of three varieties of different leaf characteristic color and five different N treatments. The variety effect on the reflectance at 552 nm was not significant (a = 0.01), while canonical discriminant analysis showed promising results for distinguishing different variety and N treatment, using spectral reflectance. Ambient illumination was found inappropriate for reliable, one-beam spectral reflectance measurement of the plants canopy due to the strong spectral lines of sunlight. Therefore, artificial light was consequently used. For in-field N status measurement, a dark chamber was constructed, to include the sensor, along with artificial illumination. Two different approaches were tested (i) use of spatially scattered artificial light, and (ii) use of collimated artificial light beam. It was found that the collimated beam along with a proper design of the sensor-beam geometry yielded the best results in terms of reducing the noise due to variable background, and maintaining the same distance from the sensor to the sample point of the canopy. A multispectral sensor assembly, based on a linear variable filter was designed, constructed and tested. The sensor assembly combined two sensors to cover the range of 400 to 1100 nm, a mounting frame, and a field data acquisition system. Using the mobile dark chamber and the developed sensor, as well as an off-the-shelf sensor, in- field nitrogen status of the plants canopy was measured. Statistical analysis of the acquired in-field data showed that the nitrogen status of the com leaves can be predicted with a SEP (Standard Error of Prediction) of 0.27%. The stage of maturity of the crop affected the relationship between the reflectance spectrum and the nitrogen status of the leaves. Specifically, the best prediction results were obtained when a separate model was used for each maturity stage. In-field assessment of the nitrogen status of corn leaves was successfully carried out by non contact measurement of the reflectance spectrum. This technology is now mature to be incorporated in field implements for on-line control of fertilizer application.

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Fluhr, Robert, and Maor Bar-Peled. Novel Lectin Controls Wound-responses in Arabidopsis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7697123.bard.

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Innate immune responses in animals and plants involve receptors that recognize microbe-associated molecules. In plants, one set of this defense system is characterized by large families of TIR–nucleotide binding site–leucine-rich repeat (TIR-NBS-LRR) resistance genes. The direct interaction between plant proteins harboring the TIR domain with proteins that transmit and facilitate a signaling pathway has yet to be shown. The Arabidopsis genome encodes TIR-domain containing genes that lack NBS and LRR whose functions are unknown. Here we investigated the functional role of such protein, TLW1 (TIR LECTIN WOUNDRESPONSIVE1). The TLW1 gene encodes a protein with two domains: a TIR domain linked to a lectin-containing domain. Our specific aim in this proposal was to examine the ramifications of the TL1-glycan interaction by; A) The functional characterization of TL1 activity in the context of plant wound response and B) Examine the hypothesis that wounding induced specific polysaccharides and examine them as candidates for TL-1 interactive glycan compounds. The Weizmann group showed TLW1 transcripts are rapidly induced by wounding in a JA-independent pathway and T-DNA-tagged tlw1 mutants that lack TLW1 transcripts, fail to initiate the full systemic wound response. Transcriptome methodology analysis was set up and transcriptome analyses indicates a two-fold reduced level of JA-responsive but not JA-independent transcripts. The TIR domain of TLW1 was found to interact directly with the KAT2/PED1 gene product responsible for the final b-oxidation steps in peroxisomal-basedJA biosynthesis. To identify potential binding target(s) of TL1 in plant wound response, the CCRC group first expressed recombinant TL1 in bacterial cells and optimized conditions for the protein expression. TL1 was most highly expressed in ArcticExpress cell line. Different types of extraction buffers and extraction methods were used to prepare plant extracts for TL1 binding assay. Optimized condition for glycan labeling was determined, and 2-aminobenzamide was used to label plant extracts. Sensitivity of MALDI and LC-MS using standard glycans. THAP (2,4,6- Trihydroxyacetophenone) showed minimal background peaks at positive mode of MALDI, however, it was insensitive with a minimum detection level of 100 ng. Using LC-MS, sensitivity was highly increased enough to detect 30 pmol concentration. However, patterns of total glycans displayed no significant difference between different extraction conditions when samples were separated with Dionex ICS-2000 ion chromatography system. Transgenic plants over-expressing lectin domains were generated to obtain active lectin domain in plant cells. Insertion of the overexpression construct into the plant genome was confirmed by antibiotic selection and genomic DNA PCR. However, RT-PCR analysis was not able to detect increased level of the transcripts. Binding ability of azelaic acid to recombinant TL1. Azelaic acid was detected in GST-TL1 elution fraction, however, DHB matrix has the same mass in background signals, which needs to be further tested on other matrices. The major findings showed the importance of TLW1 in regulating wound response. The findings demonstrate completely novel and unexpected TIR domain interactions and reveal a control nexus and mechanism that contributes to the propagation of wound responses in Arabidopsis. The implications are to our understanding of the function of TIR domains and to the notion that early molecular events occur systemically within minutes of a plant sustaining a wound. A WEB site (http://genome.weizmann.ac.il/hormonometer/) was set up that enables scientists to interact with a collated plant hormone database.

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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The clustering algorithm can complete the classification without labeled data, but it is limited by the low accuracy. In this paper, the optimized VMD is used to decompose and standardize the vibration signal. Then the correlation-based feature selection method is implemented to obtain the feature results after dimensionality reduction. Finally, the results are input into the classifier combined by K-means and genetic algorithm (GA). By introducing and optimizing the genetic algorithm, the number of classes can be selected automatically, and the accuracy is significantly improved. This method can carry out adaptive multiple fault detection of a diesel engine without labeled data. Compared with many supervised learning algorithms, the proposed method also has high accuracy.

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