Engineering Commons^™

Element Grouping Faceted Fully Connected Network Based On Ris, Shunhu Hou, Shengliang Fang, Qingyao Zeng, Mengtao Wang

Journal of System Simulation

Abstract: In view of the over-fitting problem that caused by multiple parameters and high memory usage of the full connection layer of neural network in training, a RIS-based element grouping areal fully connected neural network (RGFCNN) is proposed for the first time based on the structural characteristics of reconfigurable intelligence surface (RIS). Based on the structural characteristics of RIS, the network is optimized on traditional FCNN. A novel transmission surface attention mechanism is designed for the effective feature extraction of data. Compared with the traditional FCNNs, the proposed network does not arrange the data in one-dimensional manner. Instead, a element …

Research On Theoretical Framework Of Simulative Experiment Evalution For Intelligent Unmanned Swarm Cooperation, Jipeng Wang, Xing Zhang, Hao Wu, Yu Gu, Huijie Yang

Journal of System Simulation

Abstract: As a typical representative of intelligent equipment, the technology, equipment and combat applications of intelligent unmanned swarm are being promoted globally. However, the research on experimental theory of unmanned swarm lags behind the technology and equipment in general. The emergence of swarm ability and complexity of confrontation of unmanned swarm, the nonrepeatability and non-generalization of swam experiment take great challenge to the basic theory and methods of unmanned swarm. The four experimental models including intelligent technology, intelligent equipment, intelligent swarm, and intelligent sos(system of systems) experiment from the perspective of system engineering and the whole life cycle of equipment …

Research On Dynamic Scene Slam Based On Improved Object Detection, Lanxi Shi, Wenxu Yan, Hongyu Ni, Feng Zhao

Journal of System Simulation

Abstract: Aiming at the epipolar constraint matching problem of monocular SLAM in dynamic scenes a dynamic feature point selection method based on object detection is proposed, in which the dynamic feature points in the front-end image frame of SLAM system is eliminated during feature extraction to improve the localization accuracy of SLAM. An improved target detection network is proposed to construct a loss function to describe the bounding box by using the overlap area, distance similarity and cosine similarity, which can achieve the accurate localization of target objects and obtain the range of object feature points in the current image …

Study On Forest Fire Visual Analysis Method For Extinguishing Command In Virtual Environment, Benrun Zhang, Weiqun Cao

Journal of System Simulation

Abstract: Aiming at the demand of forest fire fighting command, the information required for the command, such as geographical environment, meteorological conditions, forest resources and forest fire behavior are comprehensively analyzed, and the data visualization visual analysis method in the virtual forest fire environment is designed and realized. Wang Zhengfei-3D mixed cellular automata model is used to simulate the process of forest fire spread and the difference time method is adopted to predict the forest fire spreading behavior. The change of environmental data in different interest domains is captured in real time, and the multi-view panel and overlay layers are …

Asl-Catboost Method For Wind Turbine Fault Detection Integrated With Digital Twin, Hongtao Liang, Lingchao Kong, Guozhu Liu, Wenxuan Dong, Xiangyi Liu

Journal of System Simulation

Abstract: In view of the low visibility of the current wind farm status monitoring and insufficient realtime operation and maintenance, based on the concept of digital twin five-dimensional model, the framework of wind farm digital twin five-dimensional model is constructed. Aiming at the insufficient fault detection capability of traditional algorithms and unbalanced positive and negative samples in fan fault data set, the improved ASL-CatBoost algorithm is proposed to achieve the accurate detection of fan fault status. Based on the digital twinning platform, combined with MATLAB/Simulink, the simulation mathematical model of doubly-fed wind turbine under the condition of blade mass imbalance …

Predictive Ai Applications For Sar Cases In The Us Coast Guard, Joshua Nelson

Cybersecurity Undergraduate Research Showcase

This paper explores the potential integration of predictive analytics AI into the United States Coast Guard's (USCG) Search and Rescue Optimal Planning System (SAROPS) for deep sea and nearshore search and rescue (SAR) operations. It begins by elucidating the concept of predictive analytics AI and its relevance in military applications, particularly in enhancing SAR procedures. The current state of SAROPS and its challenges, including complexity and accuracy issues, are outlined. By integrating predictive analytics AI into SAROPS, the paper argues for streamlined operations, reduced training burdens, and improved accuracy in locating drowning personnel. Drawing on insights from military AI applications …

Decoding The Future: Integration Of Artificial Intelligence In Web Development, Dhiraj Choithramani

Harrisburg University Dissertations and Theses

The thesis explores AI's profound impact on web development, particularly in front-end and back-end processes. AI revolutionizes UI prototyping by automating design creation, enhancing both efficiency and aesthetics. It also aids in code review, content generation, and process flow experimentation, streamlining development workflows. Through AI-driven tools like GitHub's Copilot and Wix ADI, developers benefit from coding assistance and innovative design capabilities. Despite some challenges, AI's evolving role promises to reshape web development, offering unprecedented efficiency and user-centric solutions.

Enhancing Cyber Resilience: Development, Challenges, And Strategic Insights In Cyber Security Report Websites Using Artificial Inteligence, Pooja Sharma

Harrisburg University Dissertations and Theses

In an era marked by relentless cyber threats, the imperative of robust cyber security measures cannot be overstated. This thesis embarks on an in-depth exploration of the historical trajectory and contemporary relevance of penetration testing methodologies, elucidating their evolution from nascent origins to indispensable tools in the cyber security arsenal. Moreover, it undertakes the ambitious task of conceptualizing and implementing a cyber security report website, meticulously designed to fortify cyber resilience in the face of ever-evolving threats in the digital realm.

The research journey commences with an insightful examination of the historical antecedents of penetration testing, tracing its genesis in …

The Role Of Attention Mechanisms In Enhancing Transparency And Interpretability Of Neural Network Models In Explainable Ai, Bhargav Kotipalli

Harrisburg University Dissertations and Theses

In the rapidly evolving field of artificial intelligence (AI), deep learning models' interpretability

and reliability are severely hindered by their complexity and opacity. Enhancing the

transparency and interpretability of AI systems for humans is the primary objective of the

emerging field of explainable AI (XAI). The attention mechanisms at the heart of XAI's work

are based on human cognitive processes. Neural networks can now dynamically focus on

relevant parts of the input data thanks to these mechanisms, which enhances interpretability

and performance. This report covers in-depth talks of attention mechanisms in neural networks

within XAI, as well as an analysis …

Enhancing Mobile App User Experience: A Deep Learning Approach For System Design And Optimization, Deepesh Haryani

Harrisburg University Dissertations and Theses

This paper presents a comprehensive framework for enhancing user experience in mobile applications through the integration of deep learning systems. The proposed system design encompasses various components, including data collection and preprocessing, model development and training, integration with mobile applications, dataset management service, model training service, model serving, hyperparameter optimization, metadata and artifact store, and workflow orchestration. Each component is meticulously designed with a focus on scalability, efficiency, isolation, and critical analysis. Innovative design principles are employed to ensure seamless integration, usability, and automation. Additionally, the paper discusses distributed training service design, advanced optimization techniques, and decision criteria for hyperparameter …

Human-Machine Communication: Complete Volume. Volume 7 Special Issue: Mediatization

Human-Machine Communication

This is the complete volume of HMC Volume 7. Special Issue on Mediatization

Artificial Sociality, Simone Natale, Iliana Depounti

Human-Machine Communication

This article proposes the notion of Artificial Sociality to describe communicative AI technologies that create the impression of social behavior. Existing tools that activate Artificial Sociality include, among others, Large Language Models (LLMs) such as ChatGPT, voice assistants, virtual influencers, socialbots and companion chatbots such as Replika. The article highlights three key issues that are likely to shape present and future debates about these technologies, as well as design practices and regulation efforts: the modelling of human sociality that foregrounds it, the problem of deception and the issue of control from the part of the users. Ethical, social and cultural …

Mediatization And Human-Machine Communication: Trajectories, Discussions, Perspectives, Andreas Hepp, Göran Bolin, Andrea L. Guzman, Wiebke Loosen

Human-Machine Communication

As research fields, mediatization and Human-Machine Communication (HMC) have distinct historical trajectories. While mediatization research is concerned with the fundamental interrelation between the transformation of media and communications and cultural and societal changes, the much younger field of HMC delves into human meaning-making in interactions with machines. However, the recent wave of “deep mediatization,” characterized by an increasing emphasis on general communicative automation and the rise of communicative AI, highlights a shared interest in technology’s role within human interaction. This introductory article examines the trajectories of both fields, demonstrating how mediatization research “zooms out” from overarching questions of societal and …

A Spatial Data Framework For Indoor Positioning Using Machine Learning Techniques, Venkateswari P

Theses and Dissertations

The last few years have seen an increase in interest in indoor positioning and localization as potential research and development areas. WiFi is a strong substitute that supports positioning based on indoor floor plans. In this thesis, the Principal Featured - Kohonen Deep Structure (PF-KDS) model is developed to position WiFi devices more accurately and efficiently for indoor floor planning. Initially, spatial data analysis is conducted using the Principal Feature Enhanced Auto-Encoder algorithm, extracting principal features for dimensionality reduction.

Following this, the Kohonen Self- Organizing Deep Structured Learning technique is devised for precise position estimation by considering a new path …

Energy-Aware Vehicle Routing Problem, Derek Redmond

Student Research Symposium

Outline

• Motivation
• The Vehicle Routing Problem (VRP)
• Solution Techniques
• Ant Colony Optimization
• Current Work
• Future Work

Multi Base Station Energy Efficient Cluster-Aware Routing For Wireless Sensor Networks With Realtime Data Backup, Martinaa M

Theses and Dissertations

Wireless Sensor Networks (WSNs) is created, stemming from their applications in distinct areas. This research focuses on implementing an efficient clustering and routing protocols to maximize the lifespan of the WSN by proposing a novel method known as the Energy Efficient Cluster-aware Routing Protocol (EECR). The proposed method comprises of three steps: cluster formation, cluster head (CH) selection, and multi-hop data transmission. The factors needed are residual energy, the minimum distance to the base station (BS), and the minimum Load Count as given in the Energy and Distance CH selection algorithm. The shortest pathway is estimated by the Energy Route …

Stiffness And Strength-Based Concurrent Shape And Fiber Path Optimization Of Continuous Fiber Composites, Zhelong He, Ali Tamijani

Publications

A computer-implemented method of optimizing a computer model including a shape and a fiber path for a continuous fiber composite can include initializing a fixed finite element mesh. The method can also include creating an updated version of the shape and the fiber path by iterating updates of the shape and the fiber path using the fixed finite element mesh. The method can also include initializing an adapted finite element mesh on the updated version of the shape and the fiber path. The method can also include creating an optimized version of the shape and the fiber path by iterating …

Trend Tour, Aaron Ancheta, Khushboo Gupta, Xiomara Quinonez

Computer Science and Engineering Senior Theses

Choosing outfits as a college student can prove challenging – especially for new college students and students new to the area where their college is. Trend Tour, our web-based application, displays what the fashion trends are in the user's college and near their area. This helps the user with ideas on how to dress. Furthermore, the user can change the color, silhouette, and/or accessories of outfits they want to see at that college and near their area. This web application aims to help college students feel more confident and comfortable.

First-Year Engineering Students And Genai: Experience, Attitudes, Trust, And Ethics., Elisabeth Thomas, Cenetria Crockett, Campbell Rightmyer Bego

Undergraduate Research Events

Generative AI (GenAI) has the potential to benefit student learning by offering personalized feedback, idea generation, research, and analysis support, writing aid, and administrative support (Chan and Hu, 2023; Zhang, 2023). However, if used inappropriately, the same tools can lead to false/biased content creation and reduced ethical awareness leading to possible academic dishonesty and privacy issues (Schwartz, 2016; Wu, 2023). At this early stage, ethical standards and professorial guidance are unavailable, so it is important to understand what students are thinking about the recent technologies (Shen et al., 2013). Spring 2023 survey results revealed that some students used ChatGPT, a …

Towards A Wearable Device For Measuring Impedance Plethysmography Of The Radial Artery, Pritom Chowdhury

Dartmouth College Master’s Theses

Recent advancements in bioimpedance technology have demonstrated significant promise in the application of cardiac health monitoring. This research explores the design and development of a forearm-based wearable bioimpedance device for non-invasive measurement of heart rate and respiratory rate at an accuracy level comparable to medical-grade monitors. It utilizes a tetrapolar electrode configuration to analyze bioimpedance changes in the radial artery due to blood flow.

An ongoing aspect of this work involves the preliminary development of an embedded framework intended to integrate signal generation, acquisition, and processing within the device to achieve compact and efficient system design, anticipated to contribute to …

Exploring Lstm-Based Attention Mechanisms With Pso And Grid Search Under Different Normalization Techniques For Energy Demands Time Series Forecasting, Andri Pranolo, Xiaofeng Zhou, Yingchi Mao, Bambang Widi Pratolo, Aji Prasetya Wibawa, Agung Bella Putra Utama, Abdoul Fatakhou Ba, Abdullahi Uwaisu Muhammad

Knowledge Engineering and Data Science

Advanced analytical approaches are required to accurately forecast the energy sector's rising complexity and volume of time series data. This research aims to forecast the energy demand utilising sophisticated Long Short-Term Memory (LSTM) configurations with Attention mechanisms (Att), Grid search, and Particle Swarm Optimization (PSO). In addition, the study also examines the influence of Min-Max and Z-Score normalization approaches in the preprocessing stage on the accuracy performances of the baselines and the proposed models. PSO and Grid Search techniques are used to select the best hyperparameters for LSTM models, while the attention mechanism selects the important input for the LSTM. …

Hybrid Method For User Review Sentiment Categorization In Chatgpt Application Using N-Gram And Word2vec Features, Husna Luthfiatun Nisa, Atina Ahdika

Knowledge Engineering and Data Science

The rapid development of Artificial Intelligence (AI) has significantly influenced nearly all aspects of life. One AI product widely used by people worldwide is the Chat Generative Pre-Training Transformer (ChatGPT), which can respond to questions conversationally. Although data indicates that the use of ChatGPT in Indonesia is less widespread than in other countries, a Populix survey reveals that half of the respondents have utilized ChatGPT, using AI more than once a month. This indicates its crucial role among the Indonesian population. ChatGPT is not limited to browsers; it is also available as a downloadable application on the Google Play Store. …

Convolutional Neural Network In Motion Detection For Physiotherapy Exercise Movement, Dika Fikri Laistulloh, Anik Nur Handayani, Rosa Andrie Asmara, Phillip Taw

Knowledge Engineering and Data Science

Physiotherapy focuses on movement and optimal utilization of the patient's potential. Exercise Therapy is a physiotherapy procedure that specifically focuses exercises on active and passive movements. Cerebral Palsy (CP) patients are one of the sufferers of motor disorders of the upper extremities. Cerebral Palsy (CP) patients suffer from disorders in motor functions of the upper extremities. Physiotherapy Exercise Movement has 4 categories of movement exercises for the therapy of people with upper extremity body disorders: Elbow flexor strengthening in sitting using free weights, lifting an object up, reaching diagonally in sitting, and reaching from a low surface to a high …

Optimizing Malaria Control: Granular And Cost-Effective Mosquito Habitat Index In Endemic Areas Through Satellite Imagery, Nur Ainun Daulay, Salwa Rizqina Putri, Arie Wahyu Wijayanto, Ika Yuni Wulansari

Knowledge Engineering and Data Science

Malaria, classified as a tropical disease under the Sustainable Development Goals (SDGs) indicator 3.3, remains a significant global health challenge. In this study, by taking advantage of multiple spectral composite indexes of multisource satellite imagery to capture various geospatial features relevant to the suitability of marsh mosquito habitat, we introduced the Mosquito Habitat Suitability Index (MHSI) to assess potential Anopheles mosquito breeding sites in terms of the vegetation density, water bodies, environment temperature, and humidity in any particular areas. The MHSI integrates the publicly accessible granular level of the normalized difference vegetation index, water index, land surface temperature, and moisture …

Docker Optimization Of An Automotive Sector Virtual Server Infrastructure, Leonel Hernandez, Carlos Eduardo Uc Rios

Knowledge Engineering and Data Science

Server virtualization is a powerful strategy for optimizing network infrastructure. It allows multiple virtual servers to run on a single physical server, maximizing resource utilization and improving efficiency. Deploying server virtualization using Docker technology offers a lightweight and flexible approach to optimizing network infrastructure. Docker contains package applications and their dependencies, enabling consistent and efficient deployment across various environments. Specifically, optimizing virtual server infrastructure using Docker Technology in the automotive sector focuses on improving the efficiency and management of the company's virtual server resources. By implementing Docker technology, a container platform that allows the packaging and running of applications in …

Timbre Style Transfer For Musical Instruments Acoustic Guitar And Piano Using The Generator-Discriminator Model, Widean Nagari, Joan Santoso, Esther Irawati Setiawan

Knowledge Engineering and Data Science

Music style transfer is a technique for creating new music by combining the input song's content and the target song's style to have a sound that humans can enjoy. This research is related to timbre style transfer, a branch of music style transfer that focuses on using the generator-discriminator model. This exciting method has been used in various studies in the music style transfer domain to train a machine learning model to change the sound of instruments in a song with the sound of instruments from other songs. This work focuses on finding the best layer configuration in the generator- …

Implementation Of Path Planning Methods To Detect And Avoid Gps Signal Degradation In Urban Environments, Ayush Raminedi

Doctoral Dissertations and Master's Theses

In the modern world, various missions are being carried out under the assistance of autonomous flight vehicles due to their ability to operate in a wide range of flight conditions. Regardless, these autonomous vehicles are prone to GPS signal loss in urban environments due to obstructions that cause scintillation, multi-path, and shadowing. These effects that decrease the GPS functionality can deteriorate the accuracy of GPS positioning causing losses in signal tracking leading to a decrease in navigation performance. These effects are modeled into the simulation environment and are used as part of the path planning algorithm to provide better navigation …

State Omniscience For Cooperative Local Catalog Maintenance Of Close Proximity Satellite Systems, Chris Hays

Doctoral Dissertations and Master's Theses

Resiliency in multi-agent system navigation is reliant on the inherent ability of the system to withstand, overcome, or recover from adverse conditions and disturbances. In large part, resiliency is achieved through reducing the impact of critical failure points to the success and/or performance of the system. In this view, decentralized multi-agent architectures have become an attractive solution for multi-agent navigation, but decentralized architectures place the burden of information acquisition directly on the agents themselves. In fact, the design of distributed estimators has been a growing interest to enable complex multi-sensor/multi-agent tasks. In such scenarios, it is important that each local …

Autonomous Humanoid Soccer Robot With Ml Applications, Zichen Huang, Wesley Tu

Computer Science and Engineering Senior Theses

As the sports industry increasingly incorporates technological advancements, robotics has found a unique application in the field of soccer, traditionally dominated by human players. This transition has sparked significant interest in examining the performance and dynamics of robots in athletic contexts. In this senior design project, we focus on the development of autonomous movement, ball control, and game strategy algorithms for the NAO humanoid to play soccer. Utilizing an array of sensors and actuators, the robot is designed to perform fundamental soccer skills such as dribbling and shooting. The robot is able to identify both the goal and the ball …

Reducing Bias In Cyberbullying Detection With Advanced Llms And Transformer Models, Dahana Moz Ruiz, Annaliese Watson, Anjana Manikandan, Zachary Gordon

Center for Cybersecurity

This paper delved into a comprehensive exploration of the inherent biases present in Large Language Models (LLMs) and various Transformer models, with a focus on their role in identifying and addressing instances of cyberbullying. The objective was to refine and enhance the accuracy and fairness of these models by mitigating the biases deeply ingrained in their structures. This was crucial because language models could inadvertently perpetuate and amplify existing biases present in the data they were trained on.