Tri-Training Algorithm Based On Density Peaks Clustering,
2024
School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Tri-Training Algorithm Based On Density Peaks Clustering, Yuhang Luo, Runxiu Wu, Zhihua Cui, Yiying Zhang, Yeshen He, Jia Zhao
Journal of System Simulation
Abstract: Tri-training can effectively improve the generalization ability of classifiers by using unlabeled data for classification, but it is prone to mislabeling unlabeled data, thus forming training noise. Tritraining (Tri-training with density peaks clustering, DPC-TT) algorithm based on density peaks clustering is proposed. The DPC-TT algorithm uses the density peaks clustering algorithm to obtain the class cluster centers and local densities of the training data, and the samples within the truncation distance of the class cluster centers are identified as the samples with better spatial structure, and these samples are labeled as the core data, and the classifier is updated …
Deep Learning Based Local Path Planning Method For Moving Robots,
2024
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
Deep Learning Based Local Path Planning Method For Moving Robots, Zesen Liu, Sheng Bi, Chuanhong Guo, Yankui Wang, Min Dong
Journal of System Simulation
Abstract: In order to integrate visual information into the robot navigation process, improve the robot's recognition rate of various types of obstacles, and reduce the occurrence of dangerous events, a local path planning network based on two-dimensional CNN and LSTM is designed, and a local path planning approach based on deep learning is proposed. The network uses the image from camera and the global path to generate the current steering angle required for obstacle avoidance and navigation. A simulated indoor scene is built for training and validating the network. A path evaluation method that uses the total length and the …
Path Planning Of Unmanned Delivery Vehicle Based On Improved Q-Learning Algorithm,
2024
College of Engineering and Technology, Southwest University, Chongqing 400715, China
Path Planning Of Unmanned Delivery Vehicle Based On Improved Q-Learning Algorithm, Xiaokang Wang, Jie Ji, Yang Liu, Qing He
Journal of System Simulation
Abstract: To solve the traditional Q-learning algorithm for unmanned vehicle path planning suffers from the problems of low planning efficiency and slow convergence speed, for this reason, a path planning algorithm for unmanned delivery vehicles based on the improved Q-learning algorithm is proposed. Learning from the energy iteration principle of the simulated annealing algorithm, adjusts the greedy factor ε to make it change dynamically during the training process, so as to balance the relationship between exploration and utilization, and thus improve the planning efficiency. The reward value in the reward mechanism is changed from a discrete value to a continuous …
Research On Simulation Methods For Forest Fire Extinguishing Using Water Mist,
2024
Hulunbuir University, Hulunbuir 021008, China
Research On Simulation Methods For Forest Fire Extinguishing Using Water Mist, Bing Xiang, Xiaohong Dong, Yang Li
Journal of System Simulation
Abstract: As a highly hazardous natural disaster, the occurrence and spread of forest fires are usually affected by a variety of complex factors such as climate, terrain, vegetation, combustible materials, etc., which makes it difficult to accurately simulate the spread and extinguishing process of forest fires. The spread of forest fires and the process of water mist fire extinguishing are physically modele. The spread model adopts a tree module structure to simulate the pyrolysis reaction of tree burning, and considers the effects of temperature, wind field,mass loss rate and other factors on the spread of tree burning.In the fire extinguishing …
Time Slot Allocation Method Of Data Link Based On Improved Difference Algorithm,
2024
The 20th Research Institute, CETC, Xi'an 710068, China
Time Slot Allocation Method Of Data Link Based On Improved Difference Algorithm, Yuting Zhu, Huankun Su, Xiaodong Feng, Shijie Lei, Yanfang Fu
Journal of System Simulation
Abstract: Aiming at the problems of single algorithm, being prone to local optima, and weak generalization ability of the current strategies, based on an improved differential evolutionary algorithm, a chaos algorithm, an adaptive variational crossover algorithm, and a problem solution processing mechanism, a time slot allocation strategy is proposed. The chaos algorithm is used to initialize the population to increase the diversity and avoid the premature convergence. The selection probability parameter is then used to make the crossover and variation process more flexible, expanding the search range in early to increase the possibility of global optima in late. The experiment …
Research On Verification Method Of Missile Elastic Suppression Based On Frequency Compensation,
2024
Aerospace Jiangnan Group Co., Ltd, Guiyang 550009, China; College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
Research On Verification Method Of Missile Elastic Suppression Based On Frequency Compensation, Rixin Su, Ou Zhang
Journal of System Simulation
Abstract: For the elastic model of missile body in six degree of freedom mathematical simulation, the research on the verification method of elastic vibration suppression is carried out.. The notch filter used for elastic vibration suppression is introduced, the verification idea for filter design in boost-phase and passive-phase stages of missile flight is analyzed, and the problem currently existing in mathematical simulation verification is pointed out. Based on the frequency modulation phenomenon, an online verification method of frequency compensation for the notch filter is put forward, and it can be found that the function can be applied to any order …
Gradient-Based Deep Reinforcement Learning Interpretation Methods,
2024
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Science and Technology on Information Systems Engineering Laboratory, Nanjing 210014, China
Gradient-Based Deep Reinforcement Learning Interpretation Methods, Yuan Wang, Lin Xu, Xiaoze Gong, Yongliang Zhang, Yongli Wang
Journal of System Simulation
Abstract: The learning process and working mechanism of deep reinforcement learning methods such as DQN are not transparent, and their decision basis and reliability cannot be perceived, which makes the decisions made by the model highly questionable and greatly limits the application scenarios of deep reinforcement learning. To explain the decision-making mechanism of intelligent agents, this paper proposes a gradient based saliency map generation algorithm SMGG. It uses the gradient information of feature maps generated by high-level convolutional layers to calculate the importance of different feature maps. With the known structure and internal parameters of the model, starting from the …
Prediction Of Converter Gas Generation Based On Intermission Production Improved Elman,
2024
College of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Prediction Of Converter Gas Generation Based On Intermission Production Improved Elman, Jiajie Fei, Dinghui Wu, Junyan Fan, Jing Wang
Journal of System Simulation
Abstract: Aiming at large fluctuations of intermission and low prediction accuracy in iron and steel industry, based on the classification of intermission characteristics, a converter gas generation predicting model(CPSO-Elman) based on Elman neural network(ENN) optimized by chaotic PSO(CPSO) algorithm is proposed. The intermittent characteristics of converter gas generation time series are extracted and raw data is classified according to intermittent duration. The PSO algorithm improved by chaotic disturbance is introduced to optimize the initial weight and threshold of ENN and inertia weight of nonlinear updating is designed to balance global search ability and local search ability. Construct the combined prediction …
Improving Ethics Surrounding Collegiate-Level Hacking Education: Recommended Implementation Plan & Affiliation With Peer-Led Initiatives,
2024
University at Albany, SUNY
Improving Ethics Surrounding Collegiate-Level Hacking Education: Recommended Implementation Plan & Affiliation With Peer-Led Initiatives, Shannon Morgan, Dr. Sanjay Goel
Military Cyber Affairs
Cybersecurity has become a pertinent concern, as novel technological innovations create opportunities for threat actors to exfiltrate sensitive data. To meet the demand for professionals in the workforce, universities have ramped up their academic offerings to provide a broad range of cyber-related programs (e.g., cybersecurity, informatics, information technology, digital forensics, computer science, & engineering). As the tactics, techniques, and procedures (TTPs) of hackers evolve, the knowledge and skillset required to be an effective cybersecurity professional have escalated accordingly. Therefore, it is critical to train cyber students both technically and theoretically to actively combat cyber criminals and protect the confidentiality, integrity, …
Using Digital Twins To Protect Biomanufacturing From Cyberattacks,
2024
Washington State University
Using Digital Twins To Protect Biomanufacturing From Cyberattacks, Brenden Fraser-Hevlin, Alec W. Schuler, B. Arda Gozen, Bernard J. Van Wie
Military Cyber Affairs
Understanding of the intersection of cyber vulnerabilities and bioprocess regulation is critical with the rise of artificial intelligence and machine learning in manufacturing. We detail a case study in which we model cyberattacks on network-mediated signals from a novel bioreactor, where it is important to control medium feed rates to maintain cell proliferation. We use a digital twin counterpart reactor to compare glucose and oxygen sensor signals from the bioreactor to predictions from a kinetic growth model, allowing discernment of faulty sensors from hacked signals. Our results demonstrate a successful biomanufacturing cyberattack detection system based on fundamental process control principles.
Characterizing Advanced Persistent Threats Through The Lens Of Cyber Attack Flows,
2024
University of Colorado, Colorado Springs (UCCS)
Characterizing Advanced Persistent Threats Through The Lens Of Cyber Attack Flows, Logan Zeien, Caleb Chang, Ltc Ekzhin Ear, Dr. Shouhuai Xu
Military Cyber Affairs
Effective cyber defense must build upon a deep understanding of real-world cyberattacks to guide the design and deployment of appropriate defensive measures against current and future attacks. In this abridged paper (of which the full paper is available online), we present important concepts for understanding Advanced Persistent Threats (APTs), our methodology to characterize APTs through the lens of attack flows, and a detailed case study of APT28 that demonstrates our method’s viability to draw useful insights. This paper makes three technical contributions. First, we propose a novel method of constructing attack flows to describe APTs. This abstraction allows technical audiences, …
Generative Machine Learning For Cyber Security,
2024
Washington State University
Generative Machine Learning For Cyber Security, James Halvorsen, Dr. Assefaw Gebremedhin
Military Cyber Affairs
Automated approaches to cyber security based on machine learning will be necessary to combat the next generation of cyber-attacks. Current machine learning tools, however, are difficult to develop and deploy due to issues such as data availability and high false positive rates. Generative models can help solve data-related issues by creating high quality synthetic data for training and testing. Furthermore, some generative architectures are multipurpose, and when used for tasks such as intrusion detection, can outperform existing classifier models. This paper demonstrates how the future of cyber security stands to benefit from continued research on generative models.
Machine Learning Security For Tactical Operations,
2024
Virginia Tech
Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu
Military Cyber Affairs
Deep learning finds rich applications in the tactical domain by learning from diverse data sources and performing difficult tasks to support mission-critical applications. However, deep learning models are susceptible to various attacks and exploits. In this paper, we first discuss application areas of deep learning in the tactical domain. Next, we present adversarial machine learning as an emerging attack vector and discuss the impact of adversarial attacks on the deep learning performance. Finally, we discuss potential defense methods that can be applied against these attacks.
Securing The Void: Assessing The Dynamic Threat Landscape Of Space,
2024
University at Albany
Securing The Void: Assessing The Dynamic Threat Landscape Of Space, Brianna Bace, Dr. Unal Tatar
Military Cyber Affairs
Outer space is a strategic and multifaceted domain that is a crossroads for political, military, and economic interests. From a defense perspective, the U.S. military and intelligence community rely heavily on satellite networks to meet national security objectives and execute military operations and intelligence gathering. This paper examines the evolving threat landscape of the space sector, encompassing natural and man-made perils, emphasizing the rise of cyber threats and the complexity introduced by dual-use technology and commercialization. It also explores the implications for security and resilience, advocating for collaborative efforts among international organizations, governments, and industry to safeguard the space sector.
Examining The Impact Of Customer Rfp Characteristics On Award Compliance,
2024
University of Arkansas, Fayetteville
Examining The Impact Of Customer Rfp Characteristics On Award Compliance, Laasya Ravipati
Data Science Undergraduate Honors Theses
In the context of intermodal transportation, understanding the dynamics of award compliance holds significant importance for operational efficiency and strategic decision-making. Award compliance refers to the percentage of awarded freight volume that is realized, indicating the extent to which contractual agreements are fulfilled. This analysis delves into the intricate relationship between customer characteristics and award compliance, aiming to provide valuable insights into the variability and predictability of compliance rates. By analyzing Request for Pricing (RFP) data and primary awarded freight volumes, the study seeks to address the need for more accurate volume estimations, crucial for sales planning, revenue projections, and …
The Impact Of Using Standard Quality Measures Such As Iso 9001:2015 On Emerging Software Companies Created With Agile Methodologies.,
2024
Harrisburg University of Science and Technology
The Impact Of Using Standard Quality Measures Such As Iso 9001:2015 On Emerging Software Companies Created With Agile Methodologies., Elisa Orillac
Harrisburg University Dissertations and Theses
This qualitative research aimed to analyze the impact of applying quality measure standards, specifically ISO 9001:2015, in emerging software companies that have decided to manage their projects through Agile methodologies. While Agile methodologies are well known for their continuous delivery of software, the quality that results from these projects is not always guaranteed. This research aimed to evaluate what benefits in terms of cost, timely delivery of working software, and production have companies who already applied QMS had. Or, if applying the QMS had a negative result, why was this the case? The hypothesis for the research was that those …
Intelligent Optimization Method Of Cloud Manufacturing Swarm Based On Incomplete Information Game,
2024
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Intelligent Optimization Method Of Cloud Manufacturing Swarm Based On Incomplete Information Game, Kunpeng Zhang, Yan Wang, Zhicheng Ji
Journal of System Simulation
Abstract: In the process of cloud manufacturing, the incomplete information status and the mutual competition and restriction relationship between cloud platform operator and demander lead to the difficult choice of manufacturing services. A cloud manufacturing swarm intelligent optimization method based on incomplete information game model is proposed. A static game model based on incomplete information is established for the interest competition between demand-side and cloud platform, with the goal of rationally pursuing the maximization of their own revenue function. The competition rules between demand-side and cloud platform are proposed, which are introduced into nature through Harsanyi transformation and converted into …
Hyper-Heuristic Approach With K-Means Clustering For Inter-Cell Scheduling,
2024
School of Mathematics and Computer Science, Yan'an University, Yan'an 716000, China
Hyper-Heuristic Approach With K-Means Clustering For Inter-Cell Scheduling, Yanlin Zhao, Yunna Tian
Journal of System Simulation
Abstract: According to the actual production situation of China's manufacturing industry, a hyperheuristic algorithm based on K-means clustering is proposed for inter-cell scheduling problem of flexible job-shop. K-means clustering is applied to group entities with similar attributes into the corresponding work cluster decision blocks, and the ant colony algorithm is used to select heuristic rules for each decision block. The optimal scheduling solutions are generated by using corresponding heuristic rules for scheduling of entities in each decision block. Computational results show that, the computational granularity is properly increased by the form of decision blocks, and the computational efficiency of the …
Distributed Energy Management Strategy Of Microgrid Based On Master-Slave Game,
2024
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Distributed Energy Management Strategy Of Microgrid Based On Master-Slave Game, Miaomiao Ma, Hao Wang, Lipeng Dong, Xiangjie Liu
Journal of System Simulation
Abstract: Under the operation mode of power market, based on two-layer master-slave game, a distributed energy management strategy for the microgrid is proposed to tackle the conflict between the overall optimal operation of renewable microgrid and the maximum profit of each investor. To fully consider the balance between energy supply and demand, the concept of power trading agent is introduced, and an integrated demand response strategy based on consumer satisfaction and adjustable load is proposed on the user side. Considering the initiative and decision-making ability of power supply and load, the decision-making game model is established with power trading agent …
Optimization Of Urban Traffic Microsimulation Model For Carbon Emission Reduction,
2024
Beijing City Interface Technology Co., Ltd., Beijing 100045, China; School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Optimization Of Urban Traffic Microsimulation Model For Carbon Emission Reduction, Bo Liu, Jianxin Lin, Yini Liu, Dong Zhang
Journal of System Simulation
Abstract: To assess the environmental benefits of transportation management or control strategies, a method to effectively integrate the micro-traffic simulation model and the micro-vehicle emission model is proposed. VISSIM platform is used to build a case micro traffic simulation model. K-means clustering method is used to divide the driving behavior into 4 types based on the acceleration and deceleration characteristics of different speed intervals of the trajectory data, and the global parameters of the simulation model are calibrated based on the driving characteristics, which quantitatively describe the total sensitivity of the parameters and the sensitivity of the interactions between the …
