2019 IEEE National Aerospace and Electronics Conference (NAECON)最新文献_第7页

High Speed Approximate Cognitive Domain Ontologies for Constrained Asset Allocation based on Spiking Neurons 基于峰值神经元的约束资产配置的高速近似认知领域本体

2019 IEEE National Aerospace and Electronics Conference (NAECON) Pub Date : 2019-07-01 DOI: 10.1109/NAECON46414.2019.9057909

C. Yakopcic, Nayim Rahman, Tanvir Atahary, T. Taha, Alex Beigh, Scott Douglass

{"title":"High Speed Approximate Cognitive Domain Ontologies for Constrained Asset Allocation based on Spiking Neurons","authors":"C. Yakopcic, Nayim Rahman, Tanvir Atahary, T. Taha, Alex Beigh, Scott Douglass","doi":"10.1109/NAECON46414.2019.9057909","DOIUrl":"https://doi.org/10.1109/NAECON46414.2019.9057909","url":null,"abstract":"Cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact at real time with their environment and are generally heavily power constrained. Thus, there is a strong need for a real time agent running on a low power platform. The agent examined is the Cognitively Enhanced Complex Event Processing (CECEP) architecture. This is an autonomous decision support tool that reasons like humans and enables enhanced agent-based decision-making. It has applications in a large variety of domains including autonomous systems, operations research, intelligence analysis, and data mining. One of the most time consuming and key components of CECEP is the mining of knowledge from a repository described as a Cognitive Domain Ontology (CDO). One problem that is often tasked to CDOs is asset allocation. Given the number of possible solutions in this allocation problem, determining the optimal solution via CDO can be very time and energy consuming. A grid of isolated spiking neurons is capable of generating solutions to this problem very quickly, although some degree approximation is required to achieve the speedup. The approximate spiking approach presented in this work was able to complete nearly all allocation simulations with greater than 98% accuracy. Our results in this work show that constraining the possible solution space by creating specific rules for a scenario can alter the quality of the allocation result. We present a study compares allocation score and computation time for three different constraint implementation cases. Given the vast increase in speed, as well as the reduction computational requirements, the presented algorithm is ideal for moving asset allocation to low power embedded hardware.","PeriodicalId":193529,"journal":{"name":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126448746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Utility Transformer Health Monitoring using a Single Chip Impedance Analyzer 使用单芯片阻抗分析仪监测公用事业变压器的运行状况

2019 IEEE National Aerospace and Electronics Conference (NAECON) Pub Date : 2019-07-01 DOI: 10.1109/NAECON46414.2019.9058239

B. Alemayehu, Akash Kota, Amy T. Neidhard-Doll, V. Chodavarapu, G. Subramanyam

引用次数: 1

CNN Optimization with a Genetic Algorithm 基于遗传算法的CNN优化

2019 IEEE National Aerospace and Electronics Conference (NAECON) Pub Date : 2019-07-01 DOI: 10.1109/NAECON46414.2019.9058307

Anthony Reiling, William Mitchell, Stefan Westberg, E. Balster, T. Taha

引用次数: 4

Towards a Heterogeneous Swarm for Object Classification 面向对象分类的异构群算法

2019 IEEE National Aerospace and Electronics Conference (NAECON) Pub Date : 2019-07-01 DOI: 10.1109/NAECON46414.2019.9058257

Ross D. Arnold, Benjamin Abruzzo, C. Korpela

引用次数: 11

In Situ Process Monitoring for Laser-Powder Bed Fusion using Convolutional Neural Networks and Infrared Tomography 基于卷积神经网络和红外层析成像的激光-粉末床融合现场过程监测

2019 IEEE National Aerospace and Electronics Conference (NAECON) Pub Date : 2019-07-01 DOI: 10.1109/NAECON46414.2019.9058251

Hamed Elwarfalli, Dimitri Papazoglou, D. Erdahl, Amy Doll, J. Speltz

{"title":"In Situ Process Monitoring for Laser-Powder Bed Fusion using Convolutional Neural Networks and Infrared Tomography","authors":"Hamed Elwarfalli, Dimitri Papazoglou, D. Erdahl, Amy Doll, J. Speltz","doi":"10.1109/NAECON46414.2019.9058251","DOIUrl":"https://doi.org/10.1109/NAECON46414.2019.9058251","url":null,"abstract":"Additive Manufacturing (AM) is a growing field for various industries of avionics, biomedical, automotive and manufacturing. The onset of Laser Powder Bed Fusion (LPBF) technologies for metal printing has shown exceptional growth in the past 15 years. Quality of parts for LPBF is a concern for the industry, as many parts produced are high risk, such as biomedical implants. To address these needs, a LPBF machine was designed with in-situ sensors to monitor the build process. Image processing and machine learning algorithms provide an efficient means to take bulk data and assess part quality, validating specific internal geometries and build defects. This research will analyze infrared (IR) images from a Selective Laser Melting (SLM) machine using a Computer Aided Design (CAD) designed part, featuring specific geometries (squares, circles, and triangles) of varying sizes (0.75–3.5 mm) on multiple layers for feature detection. Applying image processing to denoise, then Principal Component Analysis (PCA) for further denoising and applying Convolution Neural Networks (CNN) to identify the features and identifying a class which does not belong to a dataset, where a dataset are created from CAD images. Through this automated process, 300 geometric elements detected, classified, and validated against the build file through CNN. In addition, several build anomalies were detected and saved for end-user inspection.","PeriodicalId":193529,"journal":{"name":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","volume":"323 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133826740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Predicting Invasive Ductal Carcinoma in breast histology images using Convolutional Neural Network 应用卷积神经网络预测乳腺组织学图像中的浸润性导管癌

2019 IEEE National Aerospace and Electronics Conference (NAECON) Pub Date : 2019-07-01 DOI: 10.1109/NAECON46414.2019.9057822

Hesham Alghodhaifi, Abdulmajeed Alghodhaifi, Mohammed Alghodhaifi

{"title":"Predicting Invasive Ductal Carcinoma in breast histology images using Convolutional Neural Network","authors":"Hesham Alghodhaifi, Abdulmajeed Alghodhaifi, Mohammed Alghodhaifi","doi":"10.1109/NAECON46414.2019.9057822","DOIUrl":"https://doi.org/10.1109/NAECON46414.2019.9057822","url":null,"abstract":"Over the past ten years, there has been a rise in using deep learning for medical image analysis such as CNN. Deep learning is used extensively in the field of healthcare to identify patterns, classify and segment tumors and so on. The classification of breast cancer is a well-known problem that attracts the attention of many researchers in the field of healthcare because breast cancer is the second major cause of cancer-related deaths in women. The most common subtype of all breast cancers is the Invasive Ductal Carcinoma (IDC). There are many ways to identify this type of breast cancer such as a biopsy where tissue is removed from patient and studied under microscope. The biopsy is followed by a diagnosis which is based on the qualification of the pathologists, who will look for abnormal cells. The next task for pathologists is to assign an aggressiveness grade to a whole mount sample. To do this, pathologists focus on the region of interest which contain the IDC. Therefore, one of the popular pre-processing steps for automatic aggressiveness grading is to delineate the exact regions of IDC inside of a whole mount slide. In this paper, we have experimentally tested two CNN models using depthwise separable convolution and standard convolution to enhance the accuracy of the convolutional neural network. We tested different types of activation functions such as ReLU, Sigmoid, and Tanh. As well as applying gaussian noise to test the robustness of the two models. The results show convolutional neural networks outperformed the softmax classifier, with standard convolution and ReLU where we achieved ~87.5% classification accuracy, ~93.5% sensitivity, and ~71.5% specificity.","PeriodicalId":193529,"journal":{"name":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132527102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

High Speed-Low Power GNRFET based Digital to Analog Converters for ULSI applications 用于ULSI应用的高速低功率GNRFET数模转换器

2019 IEEE National Aerospace and Electronics Conference (NAECON) Pub Date : 2019-07-01 DOI: 10.1109/NAECON46414.2019.9057977

Mounica Patnala, T. Ytterdal, M. Rizkalla

引用次数: 0

Radar-based Object Classification Using An Artificial Neural Network 基于雷达的人工神经网络目标分类

2019 IEEE National Aerospace and Electronics Conference (NAECON) Pub Date : 2019-07-01 DOI: 10.1109/NAECON46414.2019.9058319

Dajung Lee, Colman Cheung, Dan Pritsker

{"title":"Radar-based Object Classification Using An Artificial Neural Network","authors":"Dajung Lee, Colman Cheung, Dan Pritsker","doi":"10.1109/NAECON46414.2019.9058319","DOIUrl":"https://doi.org/10.1109/NAECON46414.2019.9058319","url":null,"abstract":"Radar-based object detection becomes a more important problem as such sensor technology is broadly adopted in many applications including military, robotics, space exploring, and autonomous vehicles. However, the existing solution for classification of radar echo signal is limited because its deterministic analysis is too complicated to describe various object features. It needs a more sophisticated approach that is capable of identifying them. In this paper, we intend to solve this problem using a state-of-art machine learning approach to read object features or patterns in their micro-Doppler signatures in radar reflection data. In spectrogram analysis, we observe unique patterns of objects, which should be recognizable by a well-trained machine learning algorithm. We train our AlexNet-inspired convolutional neural network model to see these patterns over their radar signal spectrogram and design an intelligent waveform detection system. We demonstrate our proposed system on an Intel® Xeon CPU and an Intel® Arria 10 FPGA using Intel® Open VINO toolkit, a unified framework to import deep learning algorithms in different platforms and achieve a real-time system for automated object classification with over 90% of accuracy on a given radar dataset.","PeriodicalId":193529,"journal":{"name":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130769058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Real-Time 3-D Segmentation on An Autonomous Embedded System: using Point Cloud and Camera 基于点云和相机的自主嵌入式系统实时三维分割

2019 IEEE National Aerospace and Electronics Conference (NAECON) Pub Date : 2019-07-01 DOI: 10.1109/NAECON46414.2019.9057988

Dewant Katare, M. El-Sharkawy

{"title":"Real-Time 3-D Segmentation on An Autonomous Embedded System: using Point Cloud and Camera","authors":"Dewant Katare, M. El-Sharkawy","doi":"10.1109/NAECON46414.2019.9057988","DOIUrl":"https://doi.org/10.1109/NAECON46414.2019.9057988","url":null,"abstract":"Present day autonomous vehicle relies on several sensor technologies for it’s autonomous functionality. The sensors based on their type and mounted-location on the vehicle, can be categorized as: line of sight and non-line of sight sensors and are responsible for the different level of autonomy. These line of sight sensors are used for the execution of actions related to localization, object detection and the complete environment understanding. The surrounding or environment understanding for an autonomous vehicle can be achieved by segmentation. Several traditional and deep learning related techniques providing semantic segmentation for an input from camera is already available, however with the advancement in the computing processor, the progression is on developing the deep learning application replacing traditional methods. This paper presents an approach to combine the input of camera and lidar for semantic segmentation purpose. The proposed model for outdoor scene segmentation is based on the frustum pointnet, and ResNet which utilizes the 3d point cloud and camera input for the 3d bounding box prediction across the moving and non-moving object and thus finally recognizing and understanding the scenario at the point-cloud or pixel level. For real time application the model is deployed on the RTMaps framework with Bluebox (an embedded platform for autonomous vehicle). The proposed architecture is trained with the CITYScpaes and the KITTI dataset.","PeriodicalId":193529,"journal":{"name":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132407118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Toward the Development of a Cognitive Agent for Wide-Area Search 面向广域搜索的认知代理的开发

2019 IEEE National Aerospace and Electronics Conference (NAECON) Pub Date : 2019-07-01 DOI: 10.1109/NAECON46414.2019.9058210

Ben Purman, J. Messing, J. Crossman

引用次数: 0