Operations Research, Systems Engineering and Industrial Engineering Commons^™

Incremental Image Dehazing Algorithm Based On Multiple Transfer Attention, Jinyang Wei, Keping Wang, Yi Yang, Shumin Fei

Journal of System Simulation

Abstract: In order to improve the processing ability of the depth-neural network dehazing algorithm to the supplementary data set, and to make the network differently process the image features of different importance to improve the dehazing ability of the network, an incremental dehazing algorithm based on multiple migration of attention is proposed. The teacher's attention generation network in the form of Encoder-Decoder extracts the multiple attention of labels and haze, which is used it as the label of the characteristic migration media network to constrain the network training to form the migration media attention as close as possible to the …

Hybrid Features Extraction For The Online Mineral Grades Determination In The Flotation Froth Using Deep Learning, Ahmed Bendaouia, El Hassan Abdelwahed, Sara Qassimi, Abdelmalek Boussetta, Intissar Benzakour, Abderrahmane Benhayoun, Oumkeltoum Amar, François Bourzeix, Karim Baïna, Mouhamed Cherkaoui, Oussama Hasidi

Manufacturing & Industrial Engineering Faculty Publications

The control of the froth flotation process in the mineral industry is a challenging task due to its multiple impacting parameters. Accurate and convenient examination of the concentrate grade is a crucial step in realizing effective and real-time control of the flotation process. The goal of this study is to employ image processing techniques and CNN-based features extraction combined with machine learning and deep learning to predict the elemental composition of minerals in the flotation froth. A real world dataset has been collected and preprocessed from a differential flotation circuit at the industrial flotation site based in Guemassa, Morocco. …

The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang

Theses and Dissertations

Deep neural networks and transfer learning show potential in addressing complex problems such as the Tower of Hanoi and knapsack problems. The primary aim is to examine how the use of deep neural networks and transfer learning can enhance the ability of artificial learning systems to generalize. Transfer learning plays a crucial role in machine learning, particularly in the domain of artificial neural networks, as it helps overcome the challenges associated with limited data, computational efficiency, and generalization. The methodology used in this research involves the creation of data sets for the Tower of Hanoi and knapsack problems. To predict …

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

Assessing Military Parking: A Deep Learning Approach To Evaluating Standards And Impacts, Ryan D. Lalonde

Theses and Dissertations

Current United States Department of Defense (DoD) standards require a minimum amount of parking for each building. This requirement defines how much off-street parking to construct. However, the impact of these requirements remains unclear. This study builds upon the emerging field of overhead imagery analytics by directly tying it to parking on military installations. Specifically, this study leverages a pretrained deep learning car detection model, Car Detection – USA, developed by Esri for use within ArcGIS, and couples it with open-access temporal imagery sourced from Google Earth Pro to assess selected parking lots across Area B, Wright-Patterson Air Force Base, …

Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil

Theses and Dissertations

This thesis investigates the impact of adjusting artificial intelligence explainability levels’ outputs on user perception. The overarching study extends within the Explainable Artificial Intelligence (XAI) domain. It examines a spectrum of variables, including performance, cognizance, familiarity, transparency, system bias, and the overall impact of AI, to understand their collective and individual effects that enable effective professional use in an organization. The study aims to illuminate the relationship between the degree of explainability provided by large language models such as ChatGPT, Bard, and Bing AI and the performance of these models when tasked with XAI adjustments.

Action Recognition Model Of Directed Attention Based On Cosine Similarity, Chen Li, Ming He, Chen Dong, Wei Li

Journal of System Simulation

Abstract: Aiming at the lack of directionality of traditional dot product attention, this paper proposes a directed attention model (DAM) based on cosine similarity. To effectively represent the direction relationship between the spatial and temporal features of video frames, the paper defines the relationship function in the attention mechanism using the cosine similarity theory, which can remove the absolute value of the relationship between features. To reduce the computational burden of the attention mechanism, the operation is decomposed from two dimensions of time and space. The computational complexity is further optimized by combining linear attention operation. The experiment is divided …

Artificial Intelligence For Enhanced Flotation Monitoring In The Mining Industry: A Convlstm-Based Approach, Ahmed Bendaouia, El Hassan Abdelwahed, Sara Qassimi, Abdelmalek Boussetta, Intissar Benzakour, Oumkeltoum Amar, Oussama Hasidi

Manufacturing & Industrial Engineering Faculty Publications

In the mining industry, accurate monitoring of the elemental composition in the flotation froth is crucial for efficient minerals separation. The hybrid deep learning algorithms offer powerful computational intelligence for real-time monitoring of froth quality in flotation processes. This soft sensor tool can provide valuable information for process control, including predictions of the elemental chemical composition. In this study, we propose a novel approach based on a Convolutional Long Short-Term Memory (ConvLSTM) neural network for real-time monitoring of chemical composition grades in flotation froth. The proposed model effectively extracts spatial and temporal patterns from video data, providing a better …

Integrative Machine Learning Approaches For Enhanced Classification Of Genomic Sequences: A Next-Generation Sequencing Perspective, Sujatha Alla, Nagesh Bheesetty, Sai Gireesh Komaragiri, Prasanthi Chidipudi, Joshit Mohanty, Sathish Kumar Chintala, Jubin Thomas, Jayapal Vummadi, Hemanth Volikatla, Navin Kamuni

Engineering Management & Systems Engineering Faculty Publications

The advent of Next-Generation Sequencing (NGS) techniques has revolutionized genomic research by enabling the rapid sequencing of DNA and RNA. This data can be used for various applications, including genome sequencing, transcriptome profiling, metagenomics, and epigenetics studies. For this study, DNA classifier dataset was extracted from UCI repository of machine learning databases. This vast amount of genomic data necessitates the development of sophisticated machine learning (ML) models for effective classification and analysis. This study presents a comprehensive comparison of various ML models, including Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NNs), approaches, in classifying genomic data. We …

Integrating Generative Artificial Intelligence With Systems Architecting Diagram Creation: Advancement, Challenges, Opportunities And Future Perspectives, Cansu Yalim, Holly H. Handley

Engineering Management & Systems Engineering Faculty Publications

Generative AI (GenAI) serves as a powerful tool that can create a wide range of content, including but not limited to text, speech, images, code, videos, and 3D models. ChatGPT stands out as a particularly appealing Generative Pretrained Transformer (GPT) model that offers supplementary capabilities through GPTs and plugins. These extensions enable users to engage with the chatbot and improve its functionality, surpassing mere content generation. Our study delves into the potential of ChatGPT, specifically GPT-4, to expedite the creation of diagrams to support the system architecting process. To this end, we explored the use of ChatGPT's Diagrams Show Me …

Probabilistic Multivariate Time Series Forecasting And Robust Uncertainty Quantification With Applications In Electricity Price Prediction, Jie Han

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Electricity price forecasting (EPF) is a crucial task for market participants seeking informed decisions in day-ahead electricity markets. The increasing penetration of stochastic renewable energy and the deregulation of electricity markets pose challenges to electricity price forecasting. Given the dependence of electricity prices on stochastic factors such as weather conditions, market dynamics, and customer behaviors, deterministic forecasting methods offer limited insight into the potential future states of energy prices in highly stochastic markets. In this study, a transformer-based electricity price forecasting (TDEPF) model was developed, utilizing a two-step training process and demonstrating superior performance compared to typical RNN models. Subsequently, …

An Improved Saliency Map With Trustworthiness For Localizing Abnormalities In Medical Imaging, Nolan C. Skelly

Theses and Dissertations

Saliency maps are a widely used methodology to make deep learning models more interpretable. They provide post-hoc explanations through identification of the most pertinent areas of an input medical image. These techniques have been assessed based on 1) localization utility, 2) sensitivity to model weight randomization, 3) repeatability, and 4) reproducibility. Ten saliency map techniques will be tested, Grad, Smooth Grad, Integrated Gradients, Smooth Integrated Gradients, XRAI, Grad-CAM, Guided Backpropagation, Guided GradCAM, GradCAM++, and ScoreCAM. The neural networks used to predict and read medical information require a reliable solution to provide medical practitioners intelligible results. Using the information of two, …

Style Transfer Network For Generating Opera Makeup Details, Fengquan Zhang, Duo Cao, Xiaohan Ma, Baijun Chen, Jiangxiao Zhang

Journal of System Simulation

Abstract: To address the problem of the loss of local style details in cross-domain image simulation, a ChinOperaGAN network framework suitable for opera makeup is designed from the perspective of protecting the excellent traditional culture. In order to solve the style translation of differences in two image domains, multiple overlapping local adversarial discriminators are proposed in the generative adversarial network. Since paired opera makeup data are difficult to obtain, a synthetic image is generated by combining the source image makeup mapping to effectively guide the transfer of local makeup details between images. In view of the characteristics of opera makeup …

Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historicalvalue models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep timeindex models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a …

Inducing Sparsity Within High-Dimensional Remote Sensing Modalities For Lightning Prediction, Grace E. Metzgar

Theses and Dissertations

The uncertainty of lightning constantly threatens many weather-sensitive fields where the slightest presence of lightning can endanger valuable personnel and assets. The consequences of delaying operations have incited the research of methods that can accurately predict the location of future lightning strikes from the current weather conditions. High-dimensional remote sensing modalities contain information capable of detecting significant patterns and intensities within storms that could indicate the presence of lightning. This thesis induces sparsity into convolutional neural networks (CNNs) and remote sensing modalities through a combination of regularization and tensor decomposition techniques to call attention to sparse features that are most …

Modulation Recognition Method Of Mixed Signal Based On Intelligent Analysis Of Cyclic Spectrum Section, Yu Du, Xinquan Yang, Jianhua Zhang, Suchun Yuan, Huachao Xiao, Jingjing Yuan

Journal of System Simulation

Abstract: Aiming at the problems of low intelligence and poor adaptability for the existing mixed signal recognition methods, an intelligent recognition method based on cyclic spectral cross section and deep learning is proposed. For common mixed communication signals, the characteristics of zero frequency cross section of cyclic spectrum are theoretically deduced and analyzed. Two new pre-processing methods, nonlinear segmental mapping and directional pseudo-clustering are proposed, which can effectively improve the adaptability and consistency of cross section features. The pre-processed feature graph is combined with the residual network (ResNet), and the deep learning network is used to mine and analyze the …

Research On Intelligent Prediction Method Of Wargaming Air Mission, Dayong Zhang, Jingyu Yang, Xi Wu

Journal of System Simulation

Abstract: The efficient, accurate and automatic judgment of the combat mission or intention of the enemy's air targets in the battlefield is the basis of situation awareness and the key to the allocation of auxiliary combat resources. Combined with the calculation characteristics of feed forward deep neural network and long-term and short-term memory network model, two targeted basic index learners are designed, and then the weighted combination is carried out according to the cross entropy of the basic index, which can be used to further train the evaluation index of the learner. It can not only effectively prevent the model …

An Effective Transfer Learning Based Landmark Detection Framework For Uav-Based Aerial Imagery Of Urban Landscapes, Bishwas Praveen, Vineetha Menon, Tathagata Mukherjee, Bryan Mesmer, Sampson Gholston, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

Aerial imagery captured through airborne sensors mounted on Unmanned Aerial Vehicles (UAVs), aircrafts, satellites, etc. in the form of RGB, LiDAR, multispectral or hyperspectral images provide a unique perspective for a variety of applications. These sensors capture high-resolution images that can be used for applications related to mapping, surveying, and monitoring of crops, infrastructure, and natural resources. Deep learning based algorithms are often the forerunners in facilitating practical solutions for such data-centric applications. Deep learning-based landmark detection is one such application which involves the use of deep learning algorithms to accurately identify and locate landmarks of interest in images captured …

A Wind Turbine Fault Diagnosis Method Based On Siamese Deep Neural Network, Jiarui Liu, Guotian Yang, Xiaowei Wang

Journal of System Simulation

Abstract: In order to effectively extract the fault features of time series data in supervisory control and data acquisition (SCADA), considering the advantages of one-dimensional convolutional neural network (1-D CNN) for extracting local time series features and the advantages of long-term memory (LSTM) which can extract long-term dependent features, a method for fault diagnosis of wind turbines based on 1-D CNN-LSTM is proposed. To solve the problem of the scarcity of fault samples of wind turbines based on the siamese network architecture, a wind fault diagnosis method based on siamese 1-D CNN-LSTM is proposed. The proposed siamese 1-D CNN-LSTM …

Fatigue Detection Method Based On Facial Features And Head Posture, Rongxiu Lu, Bihao Zhang, Zhenlong Mo

Journal of System Simulation

Abstract: Aiming at the of the single fatigue characteristics, low robustness and inability to customize fatigue thresholds for different drivers of fatigue detection methods, a method based on facial features and head posture is proposed. In face detection and face key point positioning HOG feature operator and regression tree algorithm are used. In head posture estimation, head posture Euler angle is estimated by combining the face key points with the coordinate system transformation. In fatigue feature extraction, a deep residual neural network model is established to extract the eye fatigue features, which the eye, mouth aspect ratio and head posture …

Electrical Resistance Tomography And Flow Pattern Identification Method Based On Deep Residual Neural Network, Weiguo Tong, Shichao Zeng, Lifeng Zhang, Zhe Hou, Jiayue Guo

Journal of System Simulation

Abstract: Aiming at the low accuracy of inverse problem imaging and flow pattern recognition in electrical resistance tomography (ERT), a two-phase flow electrical resistance tomography and flow pattern recognition method based on the deep residual neural network is proposed. The finite element method is used to model the ERT forward problem to construct the "boundary voltage-conductivity distribution-flow pattern category" dataset of various gas-liquid two-phase flow distributions. The residual neural network for ERT image reconstruction and flow pattern identification of gas-liquid two-phase flow is built and trained. The two outputs of the residual neural network are processed respectively to obtain …

Deep Learning Applications In Industrial And Systems Engineering, Winthrop Harvey

Graduate Theses and Dissertations

Deep learning - the use of large neural networks to perform machine learning - has transformed the world. As the capabilities of deep models continue to grow, deep learning is becoming an increasingly valuable and practical tool for industrial engineering. With its wide applicability, deep learning can be turned to many industrial engineering tasks, including optimization, heuristic search, and functional approximation. In this dissertation, the major concepts and paradigms of deep learning are reviewed, and three industrial engineering projects applying these methods are described. The first applies a deep convolutional network to the task of absolute aerial geolocalization - the …

Aerial Target Threat Assessment Method Based On Deep Learning, Huimin Chai, Yong Zhang, Xinyue Li, Yanan Song

Journal of System Simulation

Abstract: Due to many factors of aerial target threat assessment and the lack of self-learning ability of current assessment methods, a deep neural network model for aerial target threat assessment is established using deep learning theory. In order to improve the fitting effect of the model training, a symmetric pre-training method is given. The hidden layers of the model are pre-trained layer by layer, and finally the whole model is trained. Sample data and air to air simulation scene experiments are carried out respectively. The experiments results show that the accuracy of the model using the symmetric pre-training method is …

Exploring Deep Learning In Finance, Abhijit Anand Anand Deshpande

Industrial, Manufacturing, and Systems Theses - Archive

Financial market analysis is process of analyzing market closely and predict the next move of market whether it will go up or down using historical data. Financial market is stochastic and has rapid changes over time, therefore it is very difficult to predict. The main goal of this work is to understand novel approaches of machine learning in finance, data parsing techniques, labelling the financial data. Furthermore, understand state of art Transformer model and implement and compare results with other traditional machine learning algorithms. Experiment carried out in python along with pytorch.

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

Radar Remote Sensing Data Augmentation Method Based On Generative Adversarial Network, Xu Kang, Xiaofeng Zhang

Journal of System Simulation

Abstract: In the research field of radar remote sensing, both the completeness and diversity of radar data samples cannot meet the requirement of effective training of deep learning models, and the models are prone to over-fitting, which significantly limits the wide application of deep learning techniques in this field. Targeting on the needs of intelligent application in radar remote sensing, a microwave imaging radar suited data augmentation method is proposed to solve the issue of insufficient radar data samples by leveraging the general framework of generative adversarial network. Aiming at the features of radar samples being not obvious, the label …

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …

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.

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, …

Variety Recognition Based On Deep Learning And Double-Sided Characteristics Of Maize Kernel, Feng Xiao, Zhang Hui, Zhou Rui, Qiao Lu, Wei Dong, Dandan Li, Yuyao Zhang, Guoqing Zheng

Journal of System Simulation

Abstract: In order to construct a maize kernel variety recognition model with high recognition accuracy and suitable for mobile phone application, a mobile phone is used to obtain maize kernel double-sided (embryonic and non-embryonic) images. Based on the lightweight convolutional neural network MobileNetV2 and transfer learning, a maize kernel image variety recognition model is constructed. In view of the existing research methods are mainly for single-sided recognition of maize kernel variety, the performance of single-sided and double-sided characteristics modeling and recognition is compared. The results show that the double-sided recognition accuracy of maize kernel double-sided characteristics modeling is 99.83%, which …