Computer Sciences Commons^™

Intelligent Path Planning For Mobile Robots Based On Sac Algorithm, Laiyi Yang, Jing Bi, Haitao Yuan

Journal of System Simulation

Abstract: Aiming at the high dimension, slow convergence and complex modelling of traditional path planning algorithms for mobile robots, a new intelligent path planning algorithm is proposed, which is based on deep reinforcement learning soft actor-critic (SAC) algorithm to save the poor performance of robot in complicated environments with static and dynamic obstacles. An improved reward function is designed to enable mobile robots to quickly avoid obstacles and reach targets by using state dynamic normalization and priority experience pool techniques. To evaluate the performance, a pygame-based simulation environment is constructed. Compared with proximal policy optimization(PPO) algorithm, experimental …

Intelligent Air Defense Task Assignment Based On Assignment Strategy Optimization Algorithm, Jiayi Liu, Gang Wang, Qiang Fu, Xiangke Guo, Siyuan Wang

Journal of System Simulation

Abstract: Aiming at the insufficient solving speed of assignment strategy optimization algorithm in largescale scenarios, deep reinforcement learning is combined with Markov decision process to carry out the intelligent large-scale air defense task assignment. According to the characteristics of large-scale air defense operations, Markov decision process is used to model the agent and a digital battlefield simulation environment is built. Air defense task assignment agent is designed and trained in digital battlefield simulation environment through proximal policy optimization algorithm. The feasibility and advantage of the method are verified by taking a large-scale ground-to-air countermeasure mission as an example.

Real-Time Simulation Method Of Ultra-High-Definition Video Texture, Yangyang Liu, Gangyi Ding, Dapeng Yan, Tong Xue

Journal of System Simulation

Abstract: With the development and promotion of ultra-high-definition video technology, how to quickly simulate ultra-high-definition video texture has gradually become an important research issue. Aiming at the completeness and high efficiency of simulation, a real-time simulation method of ultra-high-definition video texture is proposed to improve the video texture quality and display frequency simultaneously. A fast generation method of video texture based on GPU parallel is designed, which solves the time-consuming problem of decoding and transcoding. An efficient data transmission method based on shared texture is proposed. On the basis of the simulation engine, the real-time simulation system of ultra-high-definition video …

Research On Nested Named Entity Recognition In Missile Field Text, Jingwen Guan, Xiao Song, Xiaoqing Li, Tong Yang, Junhua Zhou

Journal of System Simulation

Abstract: Compared with the text recognition in conventional fields, it is difficult to recognize the large number of nested named entities in professional terms. This is also one of the care challenges in building the knowledge graph in aerospace field. For the named entity recognition technologies, bidirectional long short-term memory network plus conditional random field (BiLSTM-CRF) is often used to identify entities, which is difficult to distinguish the complex relationships such as nesting and intersection of terms in missile field. In order to solve the problem, based on the nested entity labeling of domain text, a nested named entity recognition …

Robot Path Planning By Fusing Particle Swarm Algorithm And Improved Grey Wolf Algorithm, Menglong Cao, Wenbin Zhao, Zhiqiang Chen

Journal of System Simulation

Abstract: Aiming at the long paths and slow convergence speed of GWO algorithm in robot path planning, a hybrid PSO-GWO algorithm based on PSO algorithm and the improved GWO algorithm is proposed. By running PSO algorithm for many times, the initial wolf group size and initial fitness value are determined. A nonlinear convergence factor is introduced to balance the exploration and development capabilities of GWO algorithm, and a dynamic inertia weight factor is proposed to ensure the leadership system of alpha wolf and to promote the population communication. Levy flight and greedy strategy are used to effectively avoid the local …

Monitoring Method Research On Passenger Behavior On Escalator Based On Digital Twin, Nan Lü, Qibing Wang, Lu Jiawei, Juntong Chen, Gang Xiao

Journal of System Simulation

Abstract: In order to solve the problems that the traditional escalator cannot be monitored and analyzed in real time during operation, the management and maintenance only on escalator equipment side, and the lack of monitoring passenger dangerous behavior, a monitoring method of passenger behavior on escalator based on digital twin is proposed. By constructing the digital twin of escalators, a visual interface is designed to map the escalator running status and passenger behavior data. Through passenger video surveillance, the improved OpenPose posture recognition algorithm is used to obtain the key point data of human body. Posture recognition is classified to …

A Compliant Robot Control Based On Extended Social-Force Model For Human-Following And Obstacle Avoidance, Jianwei Peng, Zhelin Liao, Hanchen Yao, Zhiyu Wan, Liqi Zhu, Houde Dai

Journal of System Simulation

Abstract: Human-robot coexisting is an essential feature of the next generation mobile robot. A compliant robot control strategy based on the extended social-force model for human-following and obstacle avoidance in coexisting-cooperative-cognitive environment is presented. The human-following controller based on impedance control can simultaneously adjust human-robot interaction force and position deviation to carry out the compliant human-following of mobile robots. Considering humanrobot- obstacle interactions, based on the extended social-force model and proxemics, a control strategy for human-friendly compliant human-following and obstacle avoidance is designed to solve the obstacle avoidance problem of robot and ensure the human comfort and improving the social …

Toward An Interoperable And Scalable Iot-Driven Smart City Solution Using Blockchain, Nada Abdulaziz A Alasbali

Student Works (2020-2029)

A smart city is primarily an urban region that makes effective use of contemporary technology to manage its resources, assets, and services. Internet of Things (IoT) devices and sensors are installed throughout the smart city in order to facilitate remote monitoring and data collection in relation to a variety of key infrastructures. The data is then utilised to plan the necessary maintenance tasks to ensure that citizens receive high-quality services. Even though IoT devices make it possible to collect data in real-time in a smart city, most sensors and IoT devices do not have a lot of storage space or …

Paradigm Review Of Data Localization In India And Its Implications For China, Ying Fan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data localization is a focal point of global data governance and its impact on global data governance is no longer confined to a single country. Over the years, India has followed a unique policy framework in terms of cross-border data flows and data localization, and its insistence on data sovereignty reflects its position in the international arena. This study uses the Indian data localization paradigm as a research base to discuss the common phenomenon of disconnect between policy motivations and practical effects of data localization, and as an entry point to introduce the latest Indian research findings in this area. …

On Digital Productivity Base Of Policies For Cross-Border Data Flows Between Rcep Parties And Its Influences—Taking Digital Integration Index As A Reference, Gui Huang, Ru Tao

Bulletin of Chinese Academy of Sciences (Chinese Version)

This study reviews the newest legislation and policies of Regional Comprehensive Economic Partnership (RCEP) participating countries on cross-border data flow, and then categorized them according to the ban on data transfer, local storage of data, permission-based regulation, and standards-based regulation. By referring to the indexes in the ASEAN Digital Integration Index, the subject and object factors of digital productivity in RCEP parities are sorted out, as well as the status quo of digital economy. Through the introduction of data value chain theory, the decisive impact of digital productivity factors on the policy formulation of cross-border data flow is expounded; by …

Research On Multi-Source Heterogeneous Big Data Fusion Based On Wsr, Aihua Li, Weijia Xu, Yong Shi

Bulletin of Chinese Academy of Sciences (Chinese Version)

In the era of multi-source heterogeneous big data, big data presents new features such as cross, diversity and variability. The applications of big data in a wider range of fields have new requirements for data fusion. Under this background, the connotation of data fusion is enriched and expanded. The generalized data fusion includes the fusion of data resources, the fusion of model methods, and the fusion of decision-makers' knowledge and experience. This study analyzes the characteristics of multi-source heterogeneous data fusion at three different fusion levels: data level, information level and decision level, and discusses challenges for data fusion in …

Threads, Buckets, And Impact: A Framework For Tool Accelerated Machine Learning Courses, Jonathan Adam Niemirowski

Doctoral Dissertations

Artificial intelligence and machine learning (ML) have exploded in use, accessibility, and awareness in the past few years, particularly with the release of ChatGPT in late 2022. Advances in end-user ML tools are accelerating the development of ML applications, lowering the technical barrier of entry for users outside of the computer science (CS) community. Access to ML education within STEM is mostly limited to upper-level computer science courses that have deep pre-requisite requirements or to introductory workshops that yield limited ML skills. Despite the critical need for ML education, there is a lack of guidance in instructional design for applied …

Don't Fear The Artificial Intelligence: A Systematic Review Of Machine Learning For Prostate Cancer Detection In Pathology, Aaryn Frewing, Alexander B. Gibson, Richard Robertson, Paul Urie, Dennis Della Corte

Faculty Publications

The adoption of whole slide image (WSI) scanners in clinical practice was accelerated by US Food and Drug Administration approval in 2017, which allowed primary pathologic diagnoses to be made on scanned images. Images in the digital domain allow the application of pathology artificial intelligence (AI), including clinical decision support with algorithms performing specific diagnoses.1,2 These algorithms, if trained properly, could go beyond the ability of human observation to detect and quantify features that are not recognizable by human perception.1,3,4

Autonomous Shipwreck Detection & Mapping, William Ard

LSU Master's Theses

This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …

Ranking And Analysis The Strategies Of Crowd Management To Reduce The Risks Of Crushes And Stampedes In Crowded Environments And Ensure The Safety Of Passengers, Waleed Tawfiq Al-Nami

Neutrosophic Systems with Applications

Public gatherings, transit hubs, stadiums, and crowded retail malls are just a few examples of places where crowd management has become an urgent issue in recent years. Effective crowd management strategies have been required due to the increasing population, urbanization, and frequency of large-scale meetings. These strategies are used in dynamic, sometimes chaotic, circumstances to protect people and facilitate their free movement. The purpose of this study is to analyze and rank various strategies for crowd management to reduce the risks of crushes and stampedes, improve security, and facilitate smoother traffic flow. This study used the single-valued neutrosophic set to …

Ranking And Analysis The Strategies Of Crowd Management To Reduce The Risks Of Crushes And Stampedes In Crowded Environments And Ensure The Safety Of Passengers, Waleed Tawfiq Al-Nami

Neutrosophic Systems with Applications

Public gatherings, transit hubs, stadiums, and crowded retail malls are just a few examples of places where crowd management has become an urgent issue in recent years. Effective crowd management strategies have been required due to the increasing population, urbanization, and frequency of large-scale meetings. These strategies are used in dynamic, sometimes chaotic, circumstances to protect people and facilitate their free movement. The purpose of this study is to analyze and rank various strategies for crowd management to reduce the risks of crushes and stampedes, improve security, and facilitate smoother traffic flow. This study used the single-valued neutrosophic set to …

Static Malware Family Clustering Via Structural And Functional Characteristics, David George, Andre Mauldin, Josh Mitchell, Sufiyan Mohammed, Robert Slater

SMU Data Science Review

Static and dynamic analyses are the two primary approaches to analyzing malicious applications. The primary distinction between the two is that the application is analyzed without execution in static analysis, whereas the dynamic approach executes the malware and records the behavior exhibited during execution. Although each approach has advantages and disadvantages, dynamic analysis has been more widely accepted and utilized by the research community whereas static analysis has not seen the same attention. This study aims to apply advancements in static analysis techniques to demonstrate the identification of fine-grained functionality, and show, through clustering, how malicious applications may be grouped …

Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney

Theses and Dissertations

Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …

Ide-Based Learning Analytics For Assessing Introductory Programming Skill, Phyllis J. Beck

Theses and Dissertations

Providing a sufficient level of personalized feedback on students' current level of strategic knowledge within the context of the natural programming environment through IDE-based learning analytics would transform learning outcomes for introductory programming students. However, providing sufficient insight into the programming process was previously inaccessible due to the need for more complex and scalable data collection methods and metrics with a wider variety for understanding programming metacognition and the full programming process.

This research developed a custom-built web-based IDE and event compression system to investigate two of the five components of a five-dimensional model of cognition for programming skill estimation …

New Statistical Methodology For Capacitor Data Analysis Via Lcr Meter, Usama Afzal, Muhammad Aslam

Neutrosophic Systems with Applications

This research work introduces a novel methodology to establish the relationship between capacitance and resistance when dealing with imprecise data obtained from LCR meters. The proposed relationship is based on the principles of neutrosophic statistics, enabling the utilization of interval data of resistance or capacitance without losing the indeterminacy of the intervals. By employing this relationship, we can accurately determine capacitance values from interval data of resistance, thereby generating more flexible and informative graphs. Additionally, we have applied the neutrosophic analysis method to the interval data of resistance to further enhance our findings. The comparative analysis demonstrates the superiority of …

New Types Of Soft Sets” Hypersoft Set, Indetermsoft Set, Indetermhypersoft Set, And Treesoft Set”: An Improved Version, Florentin Smarandache

Neutrosophic Systems with Applications

This is an improved paper of [10], where we recall the definitions together with practical applications of the Soft Set and its extensions to HyperSoft Set, IndetermSoft Set, IndetermHyperSoft Set, and TreeSoft Set.

New Statistical Methodology For Capacitor Data Analysis Via Lcr Meter, Usama Afzal, Muhammad Aslam

Neutrosophic Systems with Applications

This research work introduces a novel methodology to establish the relationship between capacitance and resistance when dealing with imprecise data obtained from LCR meters. The proposed relationship is based on the principles of neutrosophic statistics, enabling the utilization of interval data of resistance or capacitance without losing the indeterminacy of the intervals. By employing this relationship, we can accurately determine capacitance values from interval data of resistance, thereby generating more flexible and informative graphs. Additionally, we have applied the neutrosophic analysis method to the interval data of resistance to further enhance our findings. The comparative analysis demonstrates the superiority of …

New Types Of Soft Sets” Hypersoft Set, Indetermsoft Set, Indetermhypersoft Set, And Treesoft Set”: An Improved Version, Florentin Smarandache

Neutrosophic Systems with Applications

This is an improved paper of [10], where we recall the definitions together with practical applications of the Soft Set and its extensions to HyperSoft Set, IndetermSoft Set, IndetermHyperSoft Set, and TreeSoft Set.

An Introduction To Bipolar Pythagorean Refined Sets, R. Janani, A. Francina Shalini

Neutrosophic Systems with Applications

The aim of this paper is to introduce the new concept of a Bipolar Pythagorean refined set by combining the two notions called a Bipolar Pythagorean set and a Pythagorean refined set. Also, the basic operations and algebraic properties of the Bipolar Pythagorean refined set are discussed with suitable examples.

An Introduction To Bipolar Pythagorean Refined Sets, R. Janani, A. Francina Shalini

Neutrosophic Systems with Applications

The aim of this paper is to introduce the new concept of a Bipolar Pythagorean refined set by combining the two notions called a Bipolar Pythagorean set and a Pythagorean refined set. Also, the basic operations and algebraic properties of the Bipolar Pythagorean refined set are discussed with suitable examples.

Moving Lab-Based In-Person Training To Online Delivery: The Case Of A Continuing Engineering Education Program, Catherine Maware, David M. Parsley Ii, Kun Huang, Gerry M. Swan, Nelson Akafuah

Institute of Research for Technology Development Faculty Publications

Background: Online learning has recently surged due to the COVID-19 global pandemic. Because of the pandemic, many universities were forced to move to online learning, and various online teaching and learning environments emerged, both asynchronous and synchronous.

Objective: This study explores how a large university in the Southeastern United States of America converted an in-person Lean Manufacturing professional course into synchronous online learning for industry participants.

Method: The study analysed the performance of 212 in-person and 43 online industry participants and examined the views of online participants about the training. Paired t-tests, one-way multivariate analysis of covariance (MANCOVA), and univariate …

Controllable Language Generation Using Deep Learning, Rohola Zandie

Electronic Theses and Dissertations

The advent of deep neural networks has sparked a revolution in Artificial Intelligence (AI), notably with the creation of Transformer models like GPT-X and ChatGPT. These models have surpassed previous methods in various Natural Language Processing (NLP) tasks. As the NLP field evolves, there is a need to further understand and question the capabilities of these models. Text generation, a crucial part of NLP, remains an area where our comprehension is limited while being critical in research.

This dissertation focuses on the challenging problem of controlling the general behaviors of language models such as sentiment, topical focus, and logical reasoning. …

Activation Of The Renin–Angiotensin–Aldosterone System Is Attenuated In Hypertensive Compared With Normotensive Pregnancy, Robin C. Shoemaker, Marko Poglitsch, Hong Huang, Katherine Vignes, Aarthi Srinivasan, Cynthia Cockerham-Morris, Aric Schadler, John Anthony Bauer, John O'Brien

UK CARES Faculty Publications

Hypertension during pregnancy increases the risk of adverse maternal and fetal outcomes, but the mechanisms of pregnancy hypertension are not precisely understood. Elevated plasma renin activity and aldosterone concentrations play an important role in the normal physiologic adaptation to pregnancy. These effectors are reduced in patients with pregnancy hypertension, creating an opportunity to define the features of the renin–angiotensin–aldosterone system (RAAS) that are characteristic of this disorder. In the current study, we used a novel LC-MS/MS-based methodology to develop comprehensive profiles of RAAS peptides and effectors over gestation in a cohort of 74 pregnant women followed prospectively for the development …

Terrain And Adversary-Aware Autonomous Robot Navigation, Aniekan Ufot Inyang

Electronic Theses and Dissertations

In autonomous robot navigation, the robot is able to understand the environment around it for intelligent navigation. From its world model of this environment, it generates a global plan for navigation from a position to a goal based on different factors. This research aims to implement autonomous robot navigation by learning terrain affordances: traversability (moving quickly) and concealment (staying hidden from an adversary) using the Preference-based Inverse Reward Learning (PbIRL) methodology. The PbIRL methodology reduces the barrier of generating initial demonstration data to learn the terrain affordances by using a human expert’s preferences to learn individual weights over the terrain …

Generalized Double Statistical Convergence Sequences On Ideals In Neutrosophic Normed Spaces, Jeyaraman M., Iswariya S., Pandiselvi R.

Neutrosophic Systems with Applications

In this present research, having view in the Neutrosophic norm (u, v, w), which we presented I2-lacunary statistical convergence and I2-lacunary convergence strongly, looked into interactions between them, and made a few findings regarding the respective categories. At least went further to look at how both of such case approaches relate to I2-statistical convergence within the relevant Neutrosophic normed space.