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2021

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Articles 211 - 240 of 790

Full-Text Articles in Artificial Intelligence and Robotics

The Allocation Of Jamming Resources Based On Double Q-Learning Algorithm, Xingyuan Huang, Yanyi Li Aug 2021

The Allocation Of Jamming Resources Based On Double Q-Learning Algorithm, Xingyuan Huang, Yanyi Li

Journal of System Simulation

Abstract: In modern warfare, the multifunctional trend of radars, even multiple radars detecting targets together, enhances the anti-jamming capability of radars. However, the traditional jamming system still follows a fixed jamming strategy, and the real-time performance of decision-making facing large numbers of radars is poor. And the cognitive jamming study is urgent. The concept of reinforcement learning is explained and the difference between Q learning algorithm and double Q learning algorithm is compared. The reinforcement learning algorithm is used to establish a model based on cognitive electronic warfare to realize the allocation of radar jamming strategies. The simulation of the …

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Relationship Between Suspension Damping And Stability Of Vehicle Hunting Motion, Yan Yong, Zeng Jing, Kun Xu, Feiyan Zhao Aug 2021

Relationship Between Suspension Damping And Stability Of Vehicle Hunting Motion, Yan Yong, Zeng Jing, Kun Xu, Feiyan Zhao

Journal of System Simulation

Abstract: In order to avoid or restrain the primary hunting stability of rail vehicles, correlation between the primary hunting stability and suspension damping parameters under the damping ratio of 0 and 5% is calculated based on the analysis of suspension parameters on the vehicle modal frequency. The method to improve the stability of vehicle hunting motion by optimizing suspension parameters is obtained. The results show that when selecting different damping ratios to calculate the critical stability, the range of damping parameters varies greatly. The lateral damping parameter in a certain range or a larger vertical damping is beneficial to keep …

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A Signal Decomposition Method Based On Multi-Layer Iteration Structured Singular Spectrum Analysis, Yumiao Wei, Zhili Zhang, Hongguang Li, Shuqing Li Aug 2021

A Signal Decomposition Method Based On Multi-Layer Iteration Structured Singular Spectrum Analysis, Yumiao Wei, Zhili Zhang, Hongguang Li, Shuqing Li

Journal of System Simulation

Abstract: To process the complex signals with concentrated frequency distribution, an adaptive decomposition method based on singular spectrum analysis with multi-layer iteration structure is researched. The traditional singular spectrum analysis is improved by frequency band subdivision and iterative filtering approach. A high-precision decomposition algorithm base on recursive structure is therefore designed, solving the problems such as insufficient adaptive capability and unsatisfied decomposition. Simulation results show that adaptive decomposition capability of the proposed method is effectively enhanced. For the multi-mode vibration signal with 0.2% ratio of spitting frequency to center frequency, the components are all accurately extracted, and consistent well …

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Obstacle Avoidance Path Planning Of Bridge Crane Based On Improved Rrt Algorithm, Zhimei Chen, Li Min, Xuejuan Shao, Zhicheng Zhao Aug 2021

Obstacle Avoidance Path Planning Of Bridge Crane Based On Improved Rrt Algorithm, Zhimei Chen, Li Min, Xuejuan Shao, Zhicheng Zhao

Journal of System Simulation

Abstract: In view of the problem that the reasonable path can not be obtained quickly for bridge crane planning in complex environment, a rapidly exploring random tree (RRT) algorithm combined with particle swarm algorithm is proposed. According to the characteristics of the bridge crane operation, the RRT algorithm is improved. The two-way RRT algorithm is used to make the tree grow in the direction of the target according to the probability. When the path is generated, the particle swarm optimization algorithm is used to smooth the path to get a more suitable path for the operation of the bridge crane. …

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Short-Term Power Load Forecasting Based On Lstm Neural Network Optimized By Improved Pso, Tengfei Wei, Tinglong Pan Aug 2021

Short-Term Power Load Forecasting Based On Lstm Neural Network Optimized By Improved Pso, Tengfei Wei, Tinglong Pan

Journal of System Simulation

Abstract: To improve the accuracy of short-term power load forecasting, a short-term power load forecasting model (ACMPSO-LSTM) based on long-short memory neural network (LSTM) optimized by adaptive Cauchy mutation particle swarm optimization (ACMPSO) is proposed. For the problem of difficult selection of LSTM model parameters, ACMPSO is used to optimize model parameters, and non-linear changing inertia weights are adopted to improve the global optimization ability and convergence speed of PSO algorithm. In the optimization process, a mutation operation based on genetic algorithm is added to reduce the risk of particles falling into local optimal solutions. The simulation results show that …

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Mpc Algorithm Design Based On Improved Macroscopic Traffic Flow Model, Hongguang Pan, Gao Lei, Wenyu Mi Aug 2021

Mpc Algorithm Design Based On Improved Macroscopic Traffic Flow Model, Hongguang Pan, Gao Lei, Wenyu Mi

Journal of System Simulation

Abstract: In order to obtain stable and orderly traffic flow, a model predictive control method based on improved macroscopic traffic flow model is proposed for highway system. Considering the uncertainty caused by the inflow and outflow of ramp, the method takes the traffic density and velocity of each section as the control target. Based on the traditional macroscopic traffic flow model, a macroscopic traffic flow state space model is improved. Aiming at the problem of multiple variables and control constraints, a traffic flow density and velocity controller based on model predictive control is designed to ensure better control effect. The …

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Method Of Battlefield Frequency Allocation Based On Chaotic Perturbation Mechanism Particle Swarm Optimization Algorithm, Niu Kan, Li Bing, Fu Qiang Aug 2021

Method Of Battlefield Frequency Allocation Based On Chaotic Perturbation Mechanism Particle Swarm Optimization Algorithm, Niu Kan, Li Bing, Fu Qiang

Journal of System Simulation

Abstract: In order to carry out the frequency allocation in the electromagnetic environment of battlefield and reduce the frequency equipment interference of various forces, a frequency allocation method based on chaotic perturbation mechanism particle swarm optimization algorithm is proposed. Which transforms battlefield frequency allocation into the optimal spectrum resource search and solution problem with constraints. The frequency allocation model with the lowest interference cost is built and the frequency allocation through the improved particle swarm optimization algorithm is carries out. The chaotic perturbation mechanism is introduced to improve the population diversity and the global optimization ability of the …

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Sar Imaging Seeker Interference Modeling & Evaluation And Simulation System Design, Xueping Luo, Yunhe Cao, Hu Qi, Shutao Chen, Shijie Yan, Cai Xi Aug 2021

Sar Imaging Seeker Interference Modeling & Evaluation And Simulation System Design, Xueping Luo, Yunhe Cao, Hu Qi, Shutao Chen, Shijie Yan, Cai Xi

Journal of System Simulation

Abstract: For the simulation, evaluation and verification of SAR imaging seeker interference, the modeling and evaluation of SAR imaging seeker interference and design of simulation system are discussed. The simulation system can be used to model and simulate the target environment, jamming and seeker at the signal level. The signal environment simulation, echo signal acquisition, signal processing and other processes can be simulated under the environment of multiple interference signals, and the interference effect can be evaluated at the same time. The validity and practicability of the simulation system are verified with simulation test. The design of this simulation system …

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Game Analysis Of Government Procurement Contract Financing Based On Blockchain Technology, Haitao Huang, Qinming Liu, Chunming Ye, Chen Xiang Aug 2021

Game Analysis Of Government Procurement Contract Financing Based On Blockchain Technology, Haitao Huang, Qinming Liu, Chunming Ye, Chen Xiang

Journal of System Simulation

Abstract: Abstract: In view of the financing difficulties of small and medium-sized enterprise and the existing credit problems of financial supply chain, the block chain technology is applied to the financing mode of government procurement contract of the Ministry of Finance. From the supply chain business aspect, the tripartite game model of government procurement departments, small and medium-sized enterprises and banks is built, and the decision of the main body is analyzed. From the block chain technology, the evolutionary game model is built and the selection of chain node is analyzed. By MATLAB, the simulation experiments are carried out to …

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Residential Demand Response Scheduling Optimization And Simulation Based On An Improved Pso Algorithm, Huazhen Li, Youquan Liu, Jiawei Zhu, Liao Qiang Aug 2021

Residential Demand Response Scheduling Optimization And Simulation Based On An Improved Pso Algorithm, Huazhen Li, Youquan Liu, Jiawei Zhu, Liao Qiang

Journal of System Simulation

Abstract: Aiming at the problems of low utilization rate of household load energy and the potential damage to the power grid caused by the lack of systematic and efficient management of household power consumption, the power consumption characteristics of controllable equipment and the energy storage characteristics of electric vehicles are modeled respectively, and the scheduling optimization objective function of household equipment under time of use price is established, and the improved particle swarm optimization algorithm is used to solve the problem. Through the example simulation, the residential power dispatching under various scenarios is analyzed. The experimental results show that the …

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Self-Learning-Based Multiple Spacecraft Evasion Decision Making Simulation Under Sparse Reward Condition, Zhao Yu, Jifeng Guo, Yan Peng, Chengchao Bai Aug 2021

Self-Learning-Based Multiple Spacecraft Evasion Decision Making Simulation Under Sparse Reward Condition, Zhao Yu, Jifeng Guo, Yan Peng, Chengchao Bai

Journal of System Simulation

Abstract: In order to improve the ability of spacecraft formation to evade multiple interceptors, aiming at the low success rate of traditional procedural maneuver evasion, a multi-agent cooperative autonomous decision-making algorithm, which is based on deep reinforcement learning method, is proposed. Based on the actor-critic architecture, a multi-agent reinforcement learning algorithm is designed, in which a weighted linear fitting method is proposed to solve the reliability allocation problem of the self-learning system. To solve the sparse reward problem in task scenario, a sparse reward reinforcement learning method based on inverse value method is proposed. According to the task scenario, …

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Hybrid System Simulation Method Based On Quantized State, Zhihua Li, Jiang De, Hanwu Shen, Zhihua Fan Aug 2021

Hybrid System Simulation Method Based On Quantized State, Zhihua Li, Jiang De, Hanwu Shen, Zhihua Fan

Journal of System Simulation

Abstract: Hybrid system simulation and discontinuity processing have always been the difficulties of the time-discretized integration methods, while Quantized State System (QSS) is a new numerical integration method based on state variable discretization. Aiming at the hybrid systems simulation, a method of QSS+DEVS is proposed. The discrete part of hybrid system is represented as DEVS model, and the continuous part of hybrid system is discretized by QSS, which can also be represented as DEVS model. The simulation model of the whole hybrid system is obtained by coupling the two DEVS models. The accuracy, efficiency and simplicity of the QSS+DEVS method …

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Modeling On Anti-Uav System-Of-Systems Combat Ooda Loop Based On Netlogo, Zhao Zhu, Wang Yi, Ruifeng Fan, Liya Li, Qi Meng Aug 2021

Modeling On Anti-Uav System-Of-Systems Combat Ooda Loop Based On Netlogo, Zhao Zhu, Wang Yi, Ruifeng Fan, Liya Li, Qi Meng

Journal of System Simulation

Abstract: Aiming at the threats from unmanned aerial vehicle (UAV) or swarm, as well as the difficult problems of systems confrontation modeling, overall process design, and operational effectiveness evaluation in anti-UAV system-of-systems, the structure and operational process both of the UAV and anti-UAV systems are analyzed respectively, and the anti-UAV Observe-Orient-Decide-Act (OODA) system-of-systems combat model which based on multi-agent modeling platform NetLogo is proposed. Utilizing the emergence of agent role in this simulation model, the influence exerted by OODA on anti-UAV is studied by the multi-agent simulation. The results show that the OODA loop is one …

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Application Of Trust Driven Adaptive Cooperative Control Algorithm, Xindong Gui, Hongjiang Ji, Lingling Fan, Shida Liu Aug 2021

Application Of Trust Driven Adaptive Cooperative Control Algorithm, Xindong Gui, Hongjiang Ji, Lingling Fan, Shida Liu

Journal of System Simulation

Abstract: When a multi-manipulator performs cooperative task, it's end position is difficult to accurately track the target and ensure the consistency influenced by the nonlinear factors such as working environment, assembly condition and system disturbances, resulting in large errors during collaborative work. To solve multi-manipulators position coordinated control problem, a multi-agent based adaptive position coordination control algorithm is proposed by researching on the novel trust mechanism, including adaptive update mechanism of trust value (self-trust and mutual-trust) and Fisher information weighted (covariance) update mechanism. In automated collaborative assembly tasks, the precise consensus positioning of end-effector is achieved with improvements in the …

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Simulation Analysis Of Assessment Method For Missile Accuracy, Shuqing Li, Zhili Zhang, Yumiao Wei, Haitao Wang Aug 2021

Simulation Analysis Of Assessment Method For Missile Accuracy, Shuqing Li, Zhili Zhang, Yumiao Wei, Haitao Wang

Journal of System Simulation

Abstract: The missile accuracy of the falling points is one of the important performance indexes of missile systems, so the assessment method is very important. An improved algorithm for missile accuracy assessment is proposed. Accuracy assessment problem is simplified as a hypothesis check for probability circle, and the accuracy difference coefficients are defined to describe the accuracy difference between the real falling points and the expected situation. Based on the probability circle method, an improved risk assessment method is put forward to balance and minimize producer's risk and consumer's risk. According to sequential check method, this risk assessment …

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Cloud Model Pid Control Of Pmsm Based On Svm Inverse System, Li Hui, Yun Hao, Hongli Yue Aug 2021

Cloud Model Pid Control Of Pmsm Based On Svm Inverse System, Li Hui, Yun Hao, Hongli Yue

Journal of System Simulation

Abstract: Aiming at the problem of multivariable, nonlinearity and strong coupling of the permanent magnet synchronous motor(PMSM), a strategy of inverse system identification which is independent of precise mathematical model and parameters based on support vector machines(SVM) is proposed. The dynamic decoupling control of PMSM is researched based on multivariable nonlinear control inverse system theory. To deal with direct inverse control open-loop system with poor robustness and inverse modeling error of SVM, a parameter self-tuning PID(Proportional Integral Differential) closed-loop controller based on cloud model rule inference is designed. The simulation results confirm that the cloud model PID control based on …

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Combination Weighting-Based Comprehensive Evaluation For Discrete Workshop Production Plan, Zhangzhen Luo, Haifan Jiang, Jianlin Fu, Guofu Ding Aug 2021

Combination Weighting-Based Comprehensive Evaluation For Discrete Workshop Production Plan, Zhangzhen Luo, Haifan Jiang, Jianlin Fu, Guofu Ding

Journal of System Simulation

Abstract: Aiming at the lack of a general evaluation index system and a comprehensive evaluation method combining qualitative and quantitative for discrete workshop production planning, an evaluation index system is constructed from the economy, timeliness and adaptability and a combination weighting-based comprehensive evaluation method is proposed. A combination weight of subjective and objective significance is obtained by combining the extension of analytic hierarchy process, entropy value method and improved CRITIC (Criteria Importance Through Intercriteria Correlation) method, which improves the scientific evaluation of discrete workshop production planning. The verification results of examples show that the method is more sensitive to the …

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Actuator Fault Status Evaluation Based On Two-Class Nmf Network, Yinsong Wang, Tianshu Sun Aug 2021

Actuator Fault Status Evaluation Based On Two-Class Nmf Network, Yinsong Wang, Tianshu Sun

Journal of System Simulation

Abstract: In the feedback control loop, the adjustment ability of controller covers up the performance degradation of the actuator to some degree. A fault state evaluation algorithm based on a two-class non-negative matrix network is proposed to implement online fault state monitoring of the actuator, including fault classification and degradation assessment. The local static features of the samples are extracted, and a classifier model is established to form a network. The similarity is introduced to describe the dynamic characteristics between samples. To fulfill the actuator fault status assessment, the static distance and dynamic changes of the network output are merged …

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Small-Data Driven Modeling And Simulation Of High-Speed Train Running Time Under Limited Speeds, Xu Peng, Guoqi Feng, Xuewu Dai, Dongliang Cui, Qilong Wei, Baoxu Li, Jianming Li Aug 2021

Small-Data Driven Modeling And Simulation Of High-Speed Train Running Time Under Limited Speeds, Xu Peng, Guoqi Feng, Xuewu Dai, Dongliang Cui, Qilong Wei, Baoxu Li, Jianming Li

Journal of System Simulation

Abstract: In order to provide data support and evaluate the feasibility of high-speed train group scheduling optimization algorithm, a method of combining the mechanism model with the small-data drive is proposed. The train segment fitting model under speed limit is constructed and parameterized to reduce the number of parameters to be identified: In order to avoid the improper fitting, a parameter fitting algorithm based on the particle swarm optimization and the least square is proposed. “Location-Time-Speed” model for temporary speed limits together are proposed. The model is demonstrated on the simulation platform, and the train running time is simulated accurately …

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Research And Application Of Simulation Support Platform For System-Of-Systems Combat, Xiaodong Huang, Kongshu Xie, Li Ni, Xuefeng Yan, Yali Zhao Aug 2021

Research And Application Of Simulation Support Platform For System-Of-Systems Combat, Xiaodong Huang, Kongshu Xie, Li Ni, Xuefeng Yan, Yali Zhao

Journal of System Simulation

Abstract: Based on the requirement of developing high-precision, high-reliability, and high-fidelity SoS (System-of-Systems) combat simulation system efficiently, the overall structure of SoS combat simulation platform is designed with the implementation process of the SoS simulation development as the starting point. The key methods such as the parameterized & serviced SoS simulation framework, SoS combat oriented multi-view collaborative modeling, extensible high-performance distributed parallel simulation, and intelligent simulation evaluation based on large data & deep learning are emphatically put forward and implemented. A simulation platform is developed to support the weapon equipment SoS combat simulation deduction and evaluation in complex environment. Applications …

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The Research And Implementation Of Film Virtual Photography Harware-In-The-Loop Simulation, Baihong Lu, Jianjun Zhao, Gesan Liu Aug 2021

The Research And Implementation Of Film Virtual Photography Harware-In-The-Loop Simulation, Baihong Lu, Jianjun Zhao, Gesan Liu

Journal of System Simulation

Abstract: Film virtual photography is an important part of the film virtual Previs. Aiming at the problems of high cost, poor user experience, and bad simulation of existing virtual photography, a hardware-in-the-loop simulation system for film virtual photography is proposed. It uses a hardware-in-the-loop simulation virtual photography module that is consistent with the operation method of the film creator in real shooting for virtual photography, which solves the problems of difficult virtual photography operations and not in line with the real operating habits in the past. It provides a virtual photography method that is more in line with movie …

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Two-Point Joint Cpa Attacks Against Aes And Its Simulation, Tong Yu, Jingwen Cai Aug 2021

Two-Point Joint Cpa Attacks Against Aes And Its Simulation, Tong Yu, Jingwen Cai

Journal of System Simulation

Abstract: Aiming at the problems of large sampling amount and low utilization rate of attack information in single-point power analysis attack, a method of two-point joint power analysis attack for AES (Advanced Encryption Standard) is proposed. This method selects two power leakage points for power analysis according to the correlation between the power leakage points and the key in the AES. By constructing a power leakage model of intermediate variables, an intermediate value joint function is established which means, the method can be used to recover the key of AES. The simulation results demonstrate that the attack time of …

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Passenger Flow Sensitivity Analysis Of Evacuation Time For Standard Subway Station, Guoao Zhang, Ma Si, Wang Lin Aug 2021

Passenger Flow Sensitivity Analysis Of Evacuation Time For Standard Subway Station, Guoao Zhang, Ma Si, Wang Lin

Journal of System Simulation

Abstract: Underground two-level island platform stations widely existed in urban rail transit system. In order to analyze the relationship between the evacuation time and the character of passenger flow, the capacity of main evacuation facilities and the evacuation bottleneck of stations are studied, and groups of sensitivity analysis experiment are designed. The variability of the evacuation process is analyzed by comparing the output of each simulation model in a group and between groups. The simulation result shows that the station evacuation time increases within a certain limit at peak hour. And the station evacuation time is mainly affected by the …

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Teaching Machine Learning For The Physical Sciences: A Summary Of Lessons Learned And Challenges, Viviana Acquaviva Aug 2021

Teaching Machine Learning For The Physical Sciences: A Summary Of Lessons Learned And Challenges, Viviana Acquaviva

Publications and Research

This paper summarizes some challenges encountered and best practices established in several years of teaching Machine Learning for the Physical Sciences at the undergraduate and graduate level. I discuss motivations for teaching ML to physicists, desirable properties of pedagogical materials, such as accessibility, relevance, and likeness to real-world research problems, and give examples of components of teaching units.

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Panoramic Learning With A Standardized Machine Learning Formalism, Zhiting Hu, Eric P. Xing Aug 2021

Panoramic Learning With A Standardized Machine Learning Formalism, Zhiting Hu, Eric P. Xing

Machine Learning Faculty Publications

Machine Learning (ML) is about computational methods that enable machines to learn concepts from experiences. In handling a wide variety of experiences ranging from data instances, knowledge, constraints, to rewards, adversaries, and lifelong interplay in an ever-growing spectrum of tasks, contemporary ML/AI research has resulted in a multitude of learning paradigms and methodologies. Despite the continual progresses on all different fronts, the disparate narrowly-focused methods also make standardized, composable, and reusable development of learning solutions difficult, and make it costly if possible to build AI agents that panoramically learn from all types of experiences. This paper presents a standardized ML …

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Machine Learning In Complex Scientific Domains: Hospitalization Records, Drug Interactions, Predictive Modeling And Fairness For Class Imbalanced Data, Arghya Datta Aug 2021

Machine Learning In Complex Scientific Domains: Hospitalization Records, Drug Interactions, Predictive Modeling And Fairness For Class Imbalanced Data, Arghya Datta

McKelvey School of Engineering Graduate Student Theses & Dissertations

Machine learning has demonstrated potential in analyzing large, complex datasets and has become ubiquitous across many fields of scientific research. As machine learning is actively deployed in many complex and critical domains, it is essential for machine learning to engage with domain expertise to aid in knowledge discovery as well as address challenges in predictive modeling in complex domains. Domain expertise represents an essential and elaborate collection of knowledge that is often under-utilized when applying machine learning in complex domains. In this dissertation, I have addressed existing challenges regarding knowledge discovery in complex domains via engagement with domain expertise, particularly …

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A Neuromorphic Machine Learning Framework Based On The Growth Transform Dynamical System, Ahana Gangopadhyay Aug 2021

A Neuromorphic Machine Learning Framework Based On The Growth Transform Dynamical System, Ahana Gangopadhyay

McKelvey School of Engineering Graduate Student Theses & Dissertations

As computation increasingly moves from the cloud to the source of data collection, there is a growing demand for specialized machine learning algorithms that can perform learning and inference at the edge in energy and resource-constrained environments. In this regard, we can take inspiration from small biological systems like insect brains that exhibit high energy-efficiency within a small form-factor, and show superior cognitive performance using fewer, coarser neural operations (action potentials or spikes) than the high-precision floating-point operations used in deep learning platforms. Attempts at bridging this gap using neuromorphic hardware has produced silicon brains that are orders of magnitude …

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Classification Of Explainable Artificial Intelligence Methods Through Their Output Formats, Giulia Vilone, Luca Longo Aug 2021

Classification Of Explainable Artificial Intelligence Methods Through Their Output Formats, Giulia Vilone, Luca Longo

Articles

Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial search on Google Scholar with the keywords “explainable artificial intelligence”; “explainable machine learning”; and “interpretable machine learning”. …

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Multi-Modal Data Fusion, Image Segmentation, And Object Identification Using Unsupervised Machine Learning: Conception, Validation, Applications, And A Basis For Multi-Modal Object Detection And Tracking, Nicholas Lahaye Aug 2021

Multi-Modal Data Fusion, Image Segmentation, And Object Identification Using Unsupervised Machine Learning: Conception, Validation, Applications, And A Basis For Multi-Modal Object Detection And Tracking, Nicholas Lahaye

Computational and Data Sciences (PhD) Dissertations

Remote sensing and instrumentation is constantly improving and increasing in capability. Included within this, is the increase in amount of different instrument types, with various combinations of spatial and spectral resolutions, pointing angles, and various other instrument-specific qualities. While the increase in instruments, and therefore datasets, is a boon for those aiming to study the complexities of the various Earth systems, it can also present a large number of new challenges. With this information in mind, our group has set our aims on combining datasets with different spatial and spectral resolutions in an effective and as-general-as-possible way, with as little …

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Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra, Joshua D. Ellis Aug 2021

Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra, Joshua D. Ellis

Graduate Theses/Dissertations

Fourier-transform infrared (FTIR) spectra of organic compounds can be used to compare and identify compounds. A mid-FTIR spectrum gives absorbance values of a compound over the 400-4000 cm-1 range. Spectral matching is the process of comparing the spectral signature of two or more compounds and returning a value for the similarity of the compounds based on how closely their spectra match. This process is commonly used to identify an unknown compound by searching for its spectrum’s closes match in a database of known spectra. A major limitation of this process is that it can only be used to identify …

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