Radar Emitter Signal Identification Via Distance Features,
2022
1. College of Electrical Engineering, Southwest Jiao tong University, Chengdu 610031, China; ;
Radar Emitter Signal Identification Via Distance Features, Yingkun Huang, Weidong Jin, Yan Kang, Jiehao Zhu
Journal of System Simulation
Abstract: Aiming at the problem that traditional recognition methods of radar emitter signal have low accuracy in low signal to noise ratio (SNR) environment, and are usually suitable for only several specific radar signals, an identification approach of radar signal based on distance features is proposed. Several cluster centers are extracted via the k-means algorithm, and the Dynamic Time Warping (DTW) values between the radar signal and the cluster center are calculated respectively, which are combined as the input features of k-Nearest Neighbor (k-NN) algorithm. The simulation results show that when the SNR is 3 dB, the identification rate …
Study On Prediction Of Crystal Properties Based On Deep Learning,
2022
1. College of Information Engineering, Yangzhou University, Yangzhou 225127, China; ;
Study On Prediction Of Crystal Properties Based On Deep Learning, Buwei Wang, Wang Min, Fan Qian, Ya'nan Wang, Hanwen Zhang, Yunliang Yue
Journal of System Simulation
Abstract: Predicting crystal properties using traditional machine learning methods requires complex feature engineering. In order to bypass time-consuming feature engineering, element network (ElemNet), representation learning from stoichiometry (Roost), compositionally-restricted attention-based network (CrabNet) and crystal graph convolution neural network (CGCNN) based on deep learning technology are used to simulate the formation energy, total energy per atom, band gap, and Fermi energy of crystal. The residual learning is introduced into CGCNN, and a crystal graph convolution residual neural network (CGCRN) is proposed. In the CGCRN, the number of hidden layers and the number of nodes in the hidden layers are increased, …
Improved Ant Colony Optimization Algorithm For Jamming Resource Allocation,
2022
1. Beijing Information Technology Co, Ltd, Beijing 100094, China; ;
Improved Ant Colony Optimization Algorithm For Jamming Resource Allocation, Qingyun Wang, Dezhong Jiao, Shi Shuo, Genyan Peng, Junhua Sun, Yuxin Duan
Journal of System Simulation
Abstract: Ant Colony Optimization (ACO) is a new intelligence optimization algorithm. When applied to jamming resource allocation, the velocity of convergence in optimization process is slow and the probability of obtaining the global optimal solution is low. In order to raise the efficiency of jamming resource allocation and the probability of getting global optimal solution, the attenuation factor is improved to a variable that changes according to the exponential function in optimization process. The attenuation factor is taken as a relatively small value in the initial search phase, and increases monotonically and exponentially as the number of iterations increases. Simulation …
Brief Review On Applying Reinforcement Learning To Job Shop Scheduling Problems,
2022
1. Beihang University, Beijing 100191, China; ;2. Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing 100191, China;
Brief Review On Applying Reinforcement Learning To Job Shop Scheduling Problems, Xiaohan Wang, Zhang Lin, Ren Lei, Kunyu Xie, Kunyu Wang, Ye Fei, Chen Zhen
Journal of System Simulation
Abstract: Reinforcement Learning (RL) achieves lower time response and better model generalization in Job Shop Scheduling Problem (JSSP). To explain the current overall research status of JSSP based on RL, summarize the current scheduling framework based on RL, and lay the foundation for follow-up research, the backgrounds of JSSP and RL are introduced. Two simulation techniques commonly used in JSSP are analyzed and two commonly used frameworks for RL to solve JSSP are given. In addition, some existing challenges are pointed out, and related research progress is introduced from three aspects: direct scheduling, feature representation-based scheduling, and parameter search-based scheduling.
Hyperspectral Rx Anomaly Detection Method Based On The Fusion Of Spatial And Spectral Feature,
2022
Rocket Force University of Engineering, Xi'an 710025, China;
Hyperspectral Rx Anomaly Detection Method Based On The Fusion Of Spatial And Spectral Feature, Liu Xuan, Xiangyang Li, He Fang, Jianwei Zhao, Fenggan Zhang
Journal of System Simulation
Abstract: To address the problem that the hyperspectral anomaly detection algorithm does not make full use of the spatial information of the hyperspectral image and the detection accuracy is limited, a FSSRX (Fusing Spatial and Spectral Reed-Xiaol) anomaly detection algorithm that fuses spatial and spectrum information is proposed to improve the accuracy of hyperspectral anomaly detection. In FSSRX algorithm, the spatial feature of hyperspectral images is firstly extracted by the EMAP(Extended Multi-attribute Profile) method and the abnormal score of each pixel in spatial features is then calculated with RX detector. Meanwhile, RX anomaly detection is carried out directly on the …
Research On Model Reuse Technology Based On Semantic Matching And Composition,
2022
1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; ;
Research On Model Reuse Technology Based On Semantic Matching And Composition, Xingyu Tian, Guangxun Zeng, Yunbo Gao, Lili Ye, Guanghong Gong, Li Ni
Journal of System Simulation
Abstract: In order to solve the data barriers between the conceptual model and the simulation scenario of the combat system, the intelligent mapping and model reuse technology of the simulation scenario is researched. The conceptual model is analyzed using DOM technology. Based on the ontology theory, the knowledge base of the combat domain is constructed and the web crawler is customized to build the domain thesaurus. Through the SWRL rule library, the reasoning engine is called to realize the relational reasoning at the semantic level. An intelligent matching algorithm is designed to map the semantic relationship to the combination relationship …
Artificial Intelligence Framework Identifies Candidate Targets For Drug Repurposing In Alzheimer’S Disease,
2022
Cleveland Clinic Foundation
Artificial Intelligence Framework Identifies Candidate Targets For Drug Repurposing In Alzheimer’S Disease, Jiansong Fang, Pengyue Zhang, Quan Wang, Chien Wei Chiang, Yadi Zhou, Yuan Hou, Jielin Xu, Rui Chen, Bin Zhang, Stephen J. Lewis, James B. Leverenz, Andrew A. Pieper, Bingshan Li, Lang Li, Jeffrey Cummings, Feixiong Cheng
Brain Health Faculty Research
Background: Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer’s disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. Methods: To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein–protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, …
Can Lethal Autonomous Weapons Be Just?,
2022
College of Saint Benedict/Saint John's University
Can Lethal Autonomous Weapons Be Just?, Noreen L. Herzfeld, Robert H. Latiff
Computer Science Faculty Publications
In 2018 the United States Department of Defense (DoD) created a new Joint Artificial Intelligence Center to study the adoption of AI by the military. Their strategy, outlined in a document entitled, “Harnessing AI to Advance Our Security and Prosperity,” proposes to accelerate the adoption of AI in the military by fostering a culture of experimentation and calculated risk taking, noting that AI will change the character of the future battlefield and, even more, the pace of battle. Is there any way to ensure that this future battlefield will be just? Can the age-old precepts of just warfare help guide …
Explainabilityaudit: An Automated Evaluation Of Local Explainability In Rooftop Image Classification,
2022
Kennesaw State University
Explainabilityaudit: An Automated Evaluation Of Local Explainability In Rooftop Image Classification, Duleep Rathgamage Don, Jonathan Boardman, Sudhashree Sayenju, Ramazan Aygun, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, Dan Sullivan, Girish Modgil
Published and Grey Literature from PhD Candidates
Explainable Artificial Intelligence (XAI) is a key concept in building trustworthy machine learning models. Local explainability methods seek to provide explanations for individual predictions. Usually, humans must check these explanations manually. When large numbers of predictions are being made, this approach does not scale. We address this deficiency for a rooftop classification problem specifically with ExplainabilityAudit, a method that automatically evaluates explanations generated by a local explainability toolkit and identifies rooftop images that require further auditing by a human expert. The proposed method utilizes explanations generated by the Local Interpretable Model-Agnostic Explanations (LIME) framework as the most important superpixels of …
An Attention-Based Resnet Architecture For Acute Hemorrhage Detection And Classification: Toward A Health 4.0 Digital Twin Study,
2022
COMSATS University Islamabad
An Attention-Based Resnet Architecture For Acute Hemorrhage Detection And Classification: Toward A Health 4.0 Digital Twin Study, Aftab Hussain, Muhammad Usman Yaseen, Muhammad Imran, Muhammad Waqar, Adnan Akhunzada, Mohammad Al-Ja'afreh, Abdulmotaleb El Saddik
Computer Vision Faculty Publications
Due to the advancement of digital twin (DT) technology, Health 4.0 applications have become reality and starting to take roots. In this article, we focus on intracranial hemorrhage (ICH) which is a life-threatening emergency that needs immediate diagnosis and treatment. ICH is caused by bleeding inside the skull or brain. Radiologists typically examine computed tomography (CT) scans of the patients to determine the ICH and its subtype. But the manual assessment of the CT scan is a complex and time-consuming task. The existing pre-trained convolutional neural network (CNN) models are state-of-the-art for ICH classification. However, they employ poor feature extraction …
Module 1: Introduction To Technology Foresight, Risk Management, And Insurtech,
2022
Old Dominion University
Module 1: Introduction To Technology Foresight, Risk Management, And Insurtech, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar
Developing Technology Foresight: Case Study of AI in InsurTech
Instructional Module 1 for course, Developing Technology Foresight: Case Study of AI in InsurTech.
Module 2: Case Studies Of Ai And Insurtech,
2022
Old Dominion University
Module 2: Case Studies Of Ai And Insurtech, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar
Developing Technology Foresight: Case Study of AI in InsurTech
Instructional Module 2 for course, Developing Technology Foresight: Case Study of AI in InsurTech.
Module 3: Technology Foresight And Insurtech,
2022
Old Dominion University
Module 3: Technology Foresight And Insurtech, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar
Developing Technology Foresight: Case Study of AI in InsurTech
Instructional Module 3 for course, Developing Technology Foresight: Case Study of AI in InsurTech.
Insurtech And Distribution,
2022
Old Dominion University
Insurtech And Distribution, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar
Developing Technology Foresight: Case Study of AI in InsurTech
Questions regarding InsurTech and distribution.
Insurtech And Actuarial,
2022
Old Dominion University
Insurtech And Actuarial, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar
Developing Technology Foresight: Case Study of AI in InsurTech
Questions regarding InsurTech and actuarial work.
Introduction To The Course,
2022
Old Dominion University
Introduction To The Course, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar
Developing Technology Foresight: Case Study of AI in InsurTech
This PDF document describes the course, Developing Technology Foresight: Case Study of AI in InsurTech, and includes learning outcomes and a course outline.
Insurtech And Underwriting,
2022
Old Dominion University
Insurtech And Underwriting, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar
Developing Technology Foresight: Case Study of AI in InsurTech
Questions regarding InsurTech and underwriting work.
Insurtech And Claims,
2022
Old Dominion University
Insurtech And Claims, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar
Developing Technology Foresight: Case Study of AI in InsurTech
Questions related to InsurTech and claims.
Nlp@Vcu: Crop Characteristic Extraction Framework,
2022
Macalester College
Nlp@Vcu: Crop Characteristic Extraction Framework, Cora Lewis, Bridget Mcinnes, Getiria Onsongo
Summer REU Program
We developed a crop characteristic extraction framework. Starting from a custom SpaCy named entity recognition model, we added pre-trained word embeddings and a part-of-speech based entity expansion post-processing step. Then, we implemented an evaluation framework that functioned as a 5-fold cross validation wrapper for SpaCy custom training. Preliminary results showed improvement in the extraction framework after these additions.
Multi-Agent Pathfinding In Mixed Discrete-Continuous Time And Space,
2022
University of Denver
Multi-Agent Pathfinding In Mixed Discrete-Continuous Time And Space, Thayne T. Walker
Electronic Theses and Dissertations
In the multi-agent pathfinding (MAPF) problem, agents must move from their current locations to their individual destinations while avoiding collisions. Ideally, agents move to their destinations as quickly and efficiently as possible. MAPF has many real-world applications such as navigation, warehouse automation, package delivery and games. Coordination of agents is necessary in order to avoid conflicts, however, it can be very computationally expensive to find mutually conflict-free paths for multiple agents – especially as the number of agents is increased. Existing state-ofthe- art algorithms have been focused on simplified problems on grids where agents have no shape or volume, and …
