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Ai-Driven Innovation And Development In Manufacturing: An Overview Of Trends, Issues, And Suggestions, Rongping Mu, Jingjing Guo, Yuchen Li, Qiang Li, Chun Jiang, Ze Feng, Guowei Dong

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence technology has become a key force in driving innovation and development in global manufacturing industries, and its application in production automation, intelligent management, and other areas has become an important trend for innovation and development of manufacturing in all countries. This study comprehensively analyzes the innovation and development trend driven by artificial intelligence in global manufacturing, outlines the policies and measures of major countries in promoting the innovation and development of the manufacturing industries driven by artificial intelligence. It also points out the current situation and problems of the manufacturing’s innovation and development driven by artificial intelligence in …

Insights From Darpa’S Program Funding Layout In Artificial Intelligence, Zheng Su, Ning He, Qi Han, Qi Zhang, Xiaocheng Jiang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence, as a disruptor in the new wave of technological revolution and industrial transformation, had already become a focal point of strategic planning for the U.S. Defense Advanced Research Projects Agency (DARPA) as early as the 1960s. To explore the patterns of DARPA’s project funding allocation in the field of artificial intelligence, this study conducts an in-depth analysis of the agency’s budget reports from fiscal years from 2015 to 2025. The study identifies the following characteristics of DARPA’s project funding framework in the field of artificial intelligence: (1) distinctive application orientation; (2) highly flexible funding system; and (3) close …

Engineering Ecological Analysis Of Rise Of Huawei’S Harmonyos And Its Implications, Dazhou Wang, Yishi Lyu, Zhihuan Fu

Bulletin of Chinese Academy of Sciences (Chinese Version)

This study aims to explore the rise of the Harmony operating system from the perspective of engineering ecology. In the face of increasingly fierce global technological competition, Huawei, as a leading Chinese information technology enterprise, launched its self-developed HarmonyOS and has progressively built the Harmony ecosystem, providing foundational support for the development of all sectors. The development path of HarmonyOS can be divided into three main phases based on its strategic goals, technological and product characteristics, and ecological construction: the initial phase, the acceleration phase, and the transformation phase. It is revealed that, throughout this development, various construction strategies for …

Concept And Development Trend Of Novel E-Infrastructure Platform, Jing Xu, Chuan Tang, Kuangjunyu Yang, Juan Zhang, Ru Huang

Bulletin of Chinese Academy of Sciences (Chinese Version)

E-infrastructure platform has become a critical strategic asset for driving national scientific and technological innovation, and is the focus of strategic deployment by technologically advanced countries globally. This study, through the analysis of relevant strategy documents in the United States, European Union, and the United Kingdom, the relevant concepts and development changes of the novel E-infrastructure platform have been clarified. It also summarizes the planning process and development stages of e-infrastructure platform in the United States, Europe, and the United Kingdom, and analyzes that e-infrastructure platform will develop towards a new type of ecological, intelligent, diversified, and full process novel …

Challenges And Practices Of Deep Learning Model Reengineering: A Case Study On Computer Vision, Wenxin Jiang, Vishnu Banna, Naveen Vivek, Abhinav Goel, Nicholas Synovic, George K. Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Many engineering organizations are reimplementing and extending deep neural networks from the research community. We describe this process as deep learning model reengineering. Deep learning model reengineering — reusing, replicating, adapting, and enhancing state-of-the-art deep learning approaches — is challenging for reasons including under-documented reference models, changing requirements, and the cost of implementation and testing.

Assessment Of Web Security Vulnerabilities For Common Open Source Virtualization Software, Said Ally

Tanzania Journal of Engineering and Technology (TJET)

Open-source hypervisors have emerged as an integral technology for virtualizing server resources in cloud and data center computing. Hypervisor security efficiency is determined by virtual machine isolation, which is a de facto adoption factor in the selection process, as well as its ability to respond to web attacks. This paper assesses the security performance of Proxmox VE and XenServer for type 1 hypervisors, and Kernel Virtual Machine and Oracle Virtual Box for type 2 hypervisors. Security analysis was conducted using common exposures extracted from vulnerability databases and mapped against the OWASP 2013 and 2017 projects. For clarity, experiments were carried …

Predictive Filtering-Based Image Inpainting, Xiaoguang Li

Theses and Dissertations

Image inpainting is an important challenge in the computer vision field. The primary goal of image inpainting is to fill in the missing parts of an image. This technique has many real-life uses including fixing old photographs and restoring ancient artworks, e.g., the degraded Dunhuang frescoes. Moreover, image inpainting is also helpful in image editing. It has the capability to eliminate unwanted objects from images while maintaining a natural and realistic appearance, e.g., removing watermarks and subtitles. Disregarding the fact that image inpainting expects the restored result to be identical to the original clean one, existing deep generative inpainting methods …

Transforming Computer Science Pedagogy: An Exploration Of Self-Recorded Videos (Srv) As A Teaching And Evaluation Tool, Hussam Ghunaim

Computer Science Faculty Publications

This study aims to introduce Self-Recorded Videos (SRV) as a novel method to help improve students’ performance in coding assignments in computer science courses. To our best knowledge, this is the first time the SRV method is applied in the context of computer science classes. The study was conducted with a sample size of 41 students who were registered in the online CSCI 331 Operating Systems course at Fort Hays State University. These students were given specific instructions to create Self-Recorded Videos SRVs for every coding assignment they were tasked with. This approach was designed to encourage students to engage …

Object Classification, Detection And Tracking In Challenging Underwater Environment, Md Modasshir

Theses and Dissertations

The main contributions of this thesis is the applicability and architectural designs of deep learning algorithms in underwater imagery. In recent times, deep learning techniques for object classification and detection have achieved exceptional levels of accuracy that surpass human capabilities. However, the effectiveness of these techniques in underwater environments has not been thoroughly researched. This thesis delves into various research areas related to underwater environments, such as object classification, detection, semantic segmentation, pose regression, and semi-supervised retraining of detection models.

The first part of the thesis studies image classification and detection. Image classification is a fundamental process that involves assigning …

Learning And Planning In Stochastic Interactive Environments With The Presence Of Sparse Dependence Structures, Zihao Deng

McKelvey School of Engineering Graduate Student Theses & Dissertations

When interacting with an environment, an agent would want to make decisions on the fly based on the input of the dynamically changing environmental factors, so as to maximize the rewards or minimize the risks. In order to achieve that, one would want to learn the dynamics of the environment and employ efficient planning based on what has been learned about the environment. However, when the environment is large and convoluted, the planning and learning algorithms can easily become computationally intractable in general. In this thesis, we first quantitatively show that this type of problems is indeed hard to solve …

Role Of Vegfa In Intervertebral Disc Pathoanatomy And Low Back Pain, Ryan Potter

McKelvey School of Engineering Graduate Student Theses & Dissertations

Chronic low back pain (LBP) affects more than 80% of Americans and causes significant disability and staggering public health costs. Despite this astonishing prevalence, there are currently no disease modifying therapies. Intervertebral disc (IVD) degeneration accounts for a considerable proportion of LBP, but the precise mechanisms driving the painful pathoanatomy are not understood. The degenerating IVD exhibits a complex array of inflammatory and physiologic changes, and many are associated with the presentation of LBP. For example, the presence of nerves and blood vessels in degenerate IVDs have been identified in patients and animals presenting with LBP symptoms. However, no studies …

Automated Data-Flow Optimization For Digital Signal Processors, Madushan Thilina Abeysinghe

Theses and Dissertations

Digital signal processors (DSP), which are characterized by statically-scheduled Very-Long Instruction Word architectures and software-defined scratchpad memory, are currently the go-to processor type for low-power embedded vision systems, as exemplified by the DSP processors integrated into systems-on-chips from NVIDIA, Samsung, Qualcomm, Apple, and Texas Instruments. DSPs achieve performance by statically scheduling workloads, both in terms of data movement and instructions. We developed a method for scheduling buffer transactions across a data flow graph using data-driven performance models, yielding a 25% average reduction in execution time and a reduction of up to 85% DRAM utilization for randomly-generated data flow graphs. We …

Human-Human Vs Human-Ai Therapy: An Empirical Study, Mohammad Amin Kuhail, Nazik Alturki, Justin Thomas, Amal K. Alkhalifa, Amal Alshardan

All Works

In many nations, demand for mental health services currently outstrips supply, especially in the area of talk-based psychological interventions. Within this context, chatbots (software applications designed to simulate conversations with human users) are increasingly explored as potential adjuncts to traditional mental healthcare service delivery with a view to improving accessibility and reducing waiting times. However, the effectiveness and acceptability of such chatbots remains under-researched. This study evaluates mental health professionals’ perceptions of Pi, a relational Artificial Intelligence (AI) chatbot, in the early stages of the psychotherapeutic process (problem exploration). We asked 63 therapists to assess therapy transcripts between a human …

Scene Text Detection And Recognition Via Discriminative Representation, Liang Zhao

Theses and Dissertations

Scene texts refer to arbitrary text presented in an image captured by a camera in the real world. The tasks of scene text detection and recognition from complex images play a crucial role in computer vision, with potential applications in scene understanding, information retrieval, robotics, autonomous driving, etc. Despite the notable progress made by existing deep-learning methods, achieving accurate text detection and recognition remains challenging for robust real-world applications. The challenges in scene text detection and recognition stem from: 1) diverse text shapes, fonts, colors, styles, layouts, etc.; 2) countless combinations of characters with unfixed attributes for complete detection, coupled …

On Parallelization Of Graph Algorithms, Performance Modelling And Autonomous 3d Printable Object Synthesis, Shams-Ul-Haq Syed

Theses and Dissertations

The degree of hardware level parallelism offered by today’s GPU architecture makes it ideal for problem domains with massive inherent parallelism potential, fields such as computer vision, image processing, graph theory and graph computations. We have identified three problem areas for purpose of this research dissertation, under the umbrella of performance improvement by harnessing the power of GPUs for novel applications. The first area is concerned with k-vertex connectivity in graph theory, the second area deals performance evaluation using extended roofline models for GPU parallel applications and finally the third problem area is related to synthesis 3D printable objects from …

Interventional Radiology's Exploration Into Artificial Intelligence, Raymond Nguyen

Master's Projects and Capstones

Background: Artificial intelligence (AI) has become more prominent in our daily lives in recent years. This includes various aspects of healthcare. Interventional radiology (IR) is one of these specialties that has taken strides in understanding how AI can be leveraged for patient care. This literature review aims to understand what areas will be most impacted by AI in IR and how it will influence both the patient and interventional radiologist.

Methods: Twenty-six publications from 2019-2024 were selected from PubMed and Scopus. Publications were sourced through a combination of keywords, subject headings (MeSH terms), and citation searching.

Results: This literature review …

Parallax-Tolerant Image Stitching With Geometric Structure Protection For Unmanned Ship Visual Perception, Zhilin Yang, Yong Yin, Rukai Zhang, Qianfeng Jing, Sen Jiang, Wenfeng Zhu

Journal of System Simulation

Abstract: In order to address the artifacts caused by misalignment in maritime image stitch with low texture and large parallax, a geometric structure parallax-tolerant image stitch algorithm based on point-line feature registration and optimal seam fusion is proposed. Line segment features are introduced into the traditional homography transformation based on point features, and potential coplanar local line segments are merged into global line segments to provide accurate alignment conditions for seam line fusion. In the image fusion stage, the energy function of seam cutting method is designed by using the color difference and gradient difference of tanh measure and introducing …

An Improved Cat Swarm Optimization For Heterogeneous Multiple Mobile Robots, Liang Kang, Yi Du, Lihua Yin

Journal of System Simulation

Abstract: At present, it is difficult to achieve the real homogeneity of the members of the multiple mobile robots. The existing swarm intelligence algorithm is also difficult to accommodate the heterogeneity of the team. Focusing on the heterogeneous cooperation of multiple mobile robots, the concept of mother and child robots is proposed. In order to realize the application of swarm intelligence algorithm in heterogeneous multiple mobile robots, the basic cat swarm algorithm is improved. The subdomain and neighborhood of cat swarm are defined, and eight improvements of cat swarm algorithm are proposed, including priority of search direction, extended trajectory tracking …

Research On Path Optimization Algorithm In Dynamic Routing Environment, Xin Xie, Xiaobing Hu, Hang Zhou

Journal of System Simulation

Abstract: In a real dynamic routing environment, static path optimization (SPO) and traditional dynamic path optimization (DPO) tend to encounter issues such as detours, reversals and high computational complexity due to frequent real-time optimization calculation. To address these problems, a novel restart co-evolutionary path optimization (RCEPO) method based on the ripple-spreading algorithm (RSA) is proposed. This method integrates the path optimization process with the dynamic changes of the routing network environment to enhance the effectiveness of path optimization. Moreover, the path reoptimization calculation is performed only when the dynamic changes in the routing environment exceed the predicted range, thereby reducing …

Parametric Identification Of Ship Maneuvering Motion Response Model Based On Square Root Cubature Kalman Filtering, Qinghao Li, Junsheng Ren, Yan Hua

Journal of System Simulation

Abstract: The system identification algorithm, based on the square root cubature Kalman filter (SRCKF) is proposed to address issues such as low accuracy, poor robustness, and weak generalization ability encountered by the extended Kalman filter (EKF) algorithm in parameters identification of ship maneuvering motion models. This algorithm, within the framework of CKF, replaces the original covariance matrix with its root mean square and utilizes triangular decomposition for prediction and update to enhance identification stability. The EKF is used as a comparison algorithm to identify the parameters of the second-order nonlinear response model of a ship with rudder angles that comply …

A Method Based On Deep Learning For Assisting Sins/Dvl Integrated Navigation, Xinghong Kuang, Aowei Huang

Journal of System Simulation

Abstract: The navigation and positioning accuracy of an Autonomous Underwater Vehicle (AUV) affects the efficiency of the AUV to a certain extent, and since GNSS cannot be used underwater, the integrated navigation system of Strapdown Inertial Navigation System/ Doppler Velocity Log (SINS/DVL) has been widely favored. However, DVL will fail in some cases, and if DVL is isolated directly, the system will become a pure inertial navigation system, which seriously affects the accuracy of navigation and positioning. In order to cope with the situation that DVL is missing in some beams, a DLinear-Informer assisted integrated navigation algorithm is proposed. Through …

Dynamic Data Driven Simulation: An Overview, Xu Xie, Xiaogang Qiu, Yizheng Bao, Kai Xu

Journal of System Simulation

Abstract: Dynamic data driven simulation is a simulation paradigm which integrates simulation and data together. This paradigm continuously feeds real-time data into the simulation, enabling the simulation be dynamically adjusted by the data, which thus improves the simulation-based estimation and prediction capability. Due to this integration, the dynamic data driven simulation can estimate system states and predict future state evolution more accurately. This paper reviews the origins and basic concept of dynamic data driven simulation, and introduces several simulation paradigms originated from the idea of "integrating models with data", and identifies the linkages and differences among them. The particle filterbased …

Study On Robust Chance Constrained Optimization Of Multi-Energy Supply System Based On Wind And Solar Power Combined Output Simulation, Zhe Bao, Wei Li, Xiaofang Zhang, Zongyuan An, Ye Xu

Journal of System Simulation

Abstract: In order to effectively avoid potential imbalance between supply and demand caused by the uncertainty of wind and solar power outputs, and promote the sustained development of the multi-energy supply system, a robust chance-constrained optimization model is developed for identifying optimal operation strategies under complexities and uncertainties through incorporating Copula theory, chance-constrained programming, and robust programming within a general framework. The results show that this model can not only accurately characterize the distribution probability of combined outputs of wind and solar power and formulate the operational strategies under low default risk conditions, but also reduce the proportion of highrisk …

Research On Digital Twins Technology In Cyberspace Security, Qiankun Ren, Xinli Xiong, Jingju Liu, Qian Yao

Journal of System Simulation

Abstract: Combined with digital twins and cyber-space modeling and simulation, the network digital twins (NDT) technology with in-deep research can enable the development of diverse techniques of cyber security. The basic concept and research history of NDT are summarized, and a taxonomy is proposed to survey applications of NDT. A cyber security-oriented network digital twin model (CyS-NDT) is concluded through the literature. The relationship between the internal security problem of NDT and the method of enabling network security technology is discussed to prospect further challenges and opportunities.

Hybrid Evolutionary Multi-Objective Optimization Algorithm For Vehicle Routing Problem With Simultaneous Delivery And Pickup, Wenqiang Zhang, Xiaomeng Wang, Xiaoxiao Zhang, Guohui Zhang

Journal of System Simulation

Abstract: In order to provide reasonable and effective decision support for logistics enterprises in vehicle distribution route planning, a hybrid evolutionary multi-objective optimization algorithm combining a multi-region mixed-sampling strategy for global search and a local search based on individual route sequence differences is proposed for the problem. A reasonable mathematical model is constructed and the global search strategy is used to make the population individuals to converge quickly to the Pareto front from multiple directions, and the local search strategy is employed to guide the poorly performing individuals in the population to evolve towards the direction of better performing individuals, …

A Platform For Integrating Internet Of Things, Machine Learning, And Big Data Practicum In Electrical Engineering Curricula, Nandana Jayachandran, Atef Abdrabou, Naod Yamane, Anwer Al-Dulaimi

All Works

The integration of the Internet of Things (IoT), big data, and machine learning (ML) has pioneered a transformation across several fields. Equipping electrical engineering students to remain abreast of the dynamic technological landscape is vital. This underscores the necessity for an educational tool that can be integrated into electrical engineering curricula to offer a practical way of learning the concepts and the integration of IoT, big data, and ML. Thus, this paper offers the IoT-Edu-ML-Stream open-source platform, a graphical user interface (GUI)-based emulation software tool to help electrical engineering students design and emulate IoT-based use cases with big data analytics. …

Estimation Of The Berthing Parameter Of Unmanned Surface Vessels Based On 3d Lidar, Haichao Wang, Yong Yin, Qianfeng Jing, Lin Cong

Journal of System Simulation

Abstract: Accurate estimation of berthing parameters is a prerequisite for unmanned surface vessel autonomous berthing. A method for berthing parameter estimation is proposed based on shipborne 3D LiDAR. The method consists of two main modules: ship pose estimation and berthing state estimation. In the berthing position estimation module, raw point cloud data undergoes preprocessing algorithms aims at downsampling and removing outliers. Point cloud registration algorithms are employed to determine the vessel's position during the berthing process. The berthing state estimation module extracts berth boundary information by using the MSAC algorithm, and on the basis of this information, calculates the berthing …

Research On Virtual Simulation Testing Technology For Intelligent Navigation Collision Avoidance Decision-Making And Planning, Jialun Liu, Fan Yang, Lingli Xie, Shijie Li, Tengfei Wang

Journal of System Simulation

Abstract: This paper studies virtual simulation testing technology for intelligent navigation collision avoidance decision-making and planning. The application requirements of intelligent navigation in cargo ships are introduced, and the current research status of collision avoidance decision-making strategies, path planning algorithms and decision-making planning testing technology are analyzed. For the intelligent navigation collision avoidance decision-making and planning capabilities of cargo ships, an intelligent navigation collision avoidance decision-making and planning algorithm is proposed based on the encounter situation division in the collision avoidance rules, combined with the quaternary ship field and Bezier curve interpolation theory. A simulation testing method for decision-making planning …

Digital Twin Method Of Stress Field Of Deep Submersible Spherical Shell Based On Simulation Database, Yu Cao, Jie Li, Fang Wang, Zhixiang Liu, Xueliang Wang

Journal of System Simulation

Abstract: This paper presents a method for predicting the stress field of deep diving spherical shells based on simulation databases and digital twin technology. By establishing simulation databases of stress field distribution of pressure-resistant spherical shells under different scales and loads, virtual sensing monitoring of stress states in other parts of the vessel is realized through finite sensor layout of pressureresistant shells on the submersible. Based on the DT(digital twin) technology, a three-level virtual structure layer is constructed. The Level-1 DT layer realizes the spatial mapping and cloud image display from the finite element simulation model to the digital model. …

Section Point Cloud Denoising Method Based On Enhanced Dbscan And Distance Consensus Evaluation, Chengpeng Ge, Dong Zhao, Rui Wang, Qinghua Ma

Journal of System Simulation

Abstract: A denoising method based on the improved DBSCAN(density-based spatial clustering of applications with noise) algorithm is proposed to address the problem of removing noise points in point cloud data. The statistical filtering method is applied to pre-screen isolated outliers and remove largescale noise from the point cloud. The DBSCAN algorithm is optimized to reduce computational time complexity and achieve adaptive parameter adjustment, thereby dividing the point cloud into normal clusters, suspected clusters and abnormal clusters, and immediately removing abnormal clusters. Distance consensus assessment is applied, and suspect clusters are further evaluated. By calculating the distance between the suspected point …