Computer Engineering Commons^™

Kill Chain Efficiency Evaluation Model Based On Gray Dematel-Anp, Zejing Zhao, Junliang Shang, Yanpei Qin

Journal of System Simulation

Abstract: In modern conflict scenarios, the kill chain is integral to the comprehensive understanding, orchestration, and execution of military operations. Accurately appraising the efficiency of the kill chain is imperative for gaining insights into battle dynamics and strategically distributing military assets. However, traditional assessments of kill chain efficacy have been hampered by fragmented and isolated indicators that frequently overlook the interplay and influence among various segments of the kill chain. To address these limitations, based on the characteristics of each phase of the kill chain and the OODA loop theory, a new set of performance evaluation indices has been proposed. …

Benefit Distribution Optimization Model And Simulation For Multi-Mode Operation Of Industrial Software Platforms, Rongyu Guo, Xiaobin Li, Pei Jiang, Chuanjiang Li, Shanhui Liu, Jun Ma

Journal of System Simulation

Abstract: Industrial software service platforms, characterized by low-cost investment, customized services, and rapid application deployment, have been widely adopted in small and medium-sized industrial clusters. The benefit distribution mechanism under multi-mode operation is crucial to the sustainable development of such platforms. To address the current challenges of single-operation models and the difficulty in adapting to diverse service scenarios, this study focuses on two core stakeholders that users and software developers to analyze the core service components and cooperation mechanisms of industrial software service platforms in a multi-mode operational environment. By integrating the function point method, a multi-mode user demand quantification …

Optimization Method For Multi Agricultural Machinery Collaborative Operation Based On Genetic Algorithm And A* Algorithm, Yiran Yu, Huicheng Lai, Guxue Gao, Guo Zhang, Wangyinan Peng, Longfei Yang, Junhao Huang

Journal of System Simulation

Abstract: To address the uneven task distribution among multiple agricultural machines (referred to as farm machinery) and the high time cost due to numerous turning points at intersections, this paper proposes a task planning method that combines a pre-heat multi grouped genetic algorithm (PHMGA) with the turn A* algorithm (tA*). PHMGA allocates tasks to each piece of farm machinery based on the known environment, ensuring balanced workload through a cost objective function that considers travel, operation, and turning distances. It also designs various operators and strategies to search for nearoptimal solutions. The tA* algorithm is used to select paths …

Research On The Truth, Function And Common Principles Of Simulation, Haohua Xu, Bin Xiao, Yunhao Cui

Journal of System Simulation

Abstract: Simulation applications are becoming increasingly widespread and have a greater impact, while the theoretical foundation of simulation is relatively weak. This article provides a new definition of simulation by analyzing the common activities of simulation, which can include both virtual and real simulation forms; referring to Popper's three worlds theory, this paper discusses the objective authenticity of simulation from a philosophical perspective; From a methodological perspective, this paper elaborates on the methodological characteristics of simulation as an indirect cognitive object, revealing its significance in integrating human-machine intelligence and promoting knowledge evolution. It also discusses the common principles of simulation, …

A Model Combining Self-Attention And Weight Sharing For Human Activity Recognition, Lun Ma, Yue Yang, Daihe Wang, Guisheng Liao, Xing Li

Journal of System Simulation

Abstract: With the prevalence of wearable devices, human activity recognition based on wearable sensor data has garnered significant attention. The central issue in this field is how to extract effective behavioral information from raw sensor data to form corresponding feature vectors. Currently, convolutional neural networks and recurrent neural networks have been widely utilized for feature extraction from multisensory data. However, these networks struggle to globally capture the crucial temporal features inherent of human activity over time. To address this, a multi-CNN-BiLSTM-self attention (Multi-CBSA) model based on self-attention and weight sharing has been proposed, taking into consideration the logical correlations among …

Station Layout Optimization Method And Simulation For Non-Cooperative Target In Angle Of Arrival Positioning, Yida Ning, Jiongqi Wang, Juhui Wei, Zhenzu Bai, Zhangming He

Journal of System Simulation

Abstract: In the context of angle of arrival (AOA) positioning system for non-cooperative target tracking and positioning, accurately determining true location of the target poses a significant challenge. Conventional station deployment indicators like geometric dilution of precision (GDOP) fail to provide effective guidance for optimization station layout. To address the issue, this study introduces a novel indicator for station optimization and evaluation based on factors that influence positioning accuracy within an angle measurement system. These factors encompass angular differencing, baseline intersection angles, and the observer-target line distance. Moreover, this indicator encompasses the challenges associated with data conformity in "air to …

Wingtip Docking Control Of Composite Aircraft Based On Adrc Theory, Chunlei Xie, Hongxia Hu, Weibo Han

Journal of System Simulation

Abstract: The process of wingtip docking in composite aircraft is challenged by significant unsteady vortex aerodynamic disturbances arising from the close-range coupling of wingtips, thereby posing considerable constraints on docking precision and flight safety. This study endeavors to address the intricate task of airborne wingtip docking control amidst wingtip vortex disturbances through a comprehensive investigation of airborne wingtip docking control technology, grounded in the tenets of active disturbance rejection control (ADRC) theory. Initially, a mathematical model encapsulating the dynamics of three-channel attitude/displacement during the docking operation, incorporating both the wingtip docking mechanism and the wingtip vortex model, is established. …

Solving The Vehicle Routing Problem Based On Deep Reinforcement Learning, Ming Jiang, Tao He

Journal of System Simulation

Abstract: The capacitated vehicle routing problem (CVRP) is a well-known combinatorial optimization challenge recognized as NP-hard due to its significant complexity. Building upon existing research, this paper introduces a novel end-to-end deep reinforcement learning approach based on a multi-pointer Transformer to tackle the CVRP. The proposed algorithm employs an invertible residual network in the encoder to encode input features, effectively reducing memory consumption. In the decoder, a multipointer network determines the probability distribution of solutions. To further enhance the performance of CVRP solutions, the algorithm leverages the symmetry in combinatorial optimization by implementing multi-trajectory parallel processing during both training …

Lane Detection In Dark Light Based On Instance Association, Yanji Jiang, Yingyang Zhang, Hao Dong, Xiaoguang Zhang, Meihui Wang

Journal of System Simulation

Abstract: In current research on lane detection, existing algorithms can efficiently detect lane lines under good lighting conditions. However, lane detection in low light still faces the challenge of a high false negative rate. A detection algorithm called Instance Association Net(IANet) is proposed to address this issue by utilizing the structural relationships between lane lines, which is helpful for low light conditions. The algorithm first generates unique masks for different lane lines using features at the starting points of the lane lines and a global feature map, achieving instance-level feature separation of the lane lines. It employs an instance-level attention …

Anylogic-Based Platform-Enterprise Collaborative Scheduling Simulation System For Cloud Manufacturing, Linxuan Wang, Yongkui Liu, Lin Zhang, Tingyu Lin, Lihui Wang

Journal of System Simulation

Abstract: Aiming at the lack of research on collaborative scheduling between a cloud manufacturing platform and associated enterprises, as well as the lack of simulation systems to simulate scheduling strategy combinations and to visualize dynamic scheduling processes, a simulation system that supports visualization of cloud manufacturing platform-enterprise collaborative dynamic scheduling processes is designed and developed. System requirements are analyzed in detail, and then a scalable platform-enterprise collaborative scheduling model and system functional architecture based on hierarchical multi-agents is proposed. Combined with a case of supply chain of industrial robots, considering random selection, time optimal strategy in the cloud manufacturing …

Digital Twin Modeling Method For Bulk Cargo Stacks Based On 2d Lidar, Houjun Lu, Yifei Zhu, Yanping Rong, Wanghui Zhang

Journal of System Simulation

Abstract: Due to the characteristics of large equipment, harsh working environment and time-varying shape of the material pile in bulk cargo terminal, there are some disadvantages such as low data accuracy and poor stability when building the storage yard model, which affects the unmanned and intelligent operation control. In this paper, we use two-dimensional laser radar combined with equipment mechanism motion to scan material pile point cloud data, present a digital twin modeling method for bulk storage yard, which includes static scene construction of storage yard and real-time modeling of material pile. Prefabricated models are used for the static scenes …

Research On Strong Real-Time Synchronisation Algorithm For Lvc Co-Simulation, Junhui Li, Songtao Sun, Fei Liu

Journal of System Simulation

Abstract: Live, virtual, and constructive(LVC) joint simulation has become a hot research topic of current military simulation; however, existing time management strategies usually fail to meet the needs of strict real-time performance of LVC. A LVC joint simulation synchronization algorithm is proposed that starts with a window sliding-based median smoothing strategy and real time drift rate-based clock compensation strategy for effective node synchronization. A novel hybrid timing strategy is introduced combining long and short cycles implemented in software, which balances precision and efficiency. A simulation catch-up strategy is proposed to address software delays, which combined with the highprecision timing strategy, …

Digital Imaging Simulation Of Complex Scene Of Space-Based Space Small Target, Pengfei Li, Wei Xu, Yongjie Piao, Yinghong Fang, Dunpan Shi

Journal of System Simulation

Abstract: In response to the universal demand for space target detection technology research in space image data sources, this study focuses on the problems of insufficient training data for intelligent algorithms and the use of single data for traditional algorithms, with the goal of generating dynamic digital sequence images of small space targets in complex scenes. A visible light digital imaging simulation system based on a space observation platform is designed. A small target imaging model is proposed, which is based on two-dimensional shape feature point description and imaging analysis model to carry out digital modeling and imaging simulation of …

Control Strategy For Uav Cluster Formation Rendezvous Based On Lde-Maddpg Algorithm, Wei Xiao, Jiabo Gao, Xueliang Ke

Journal of System Simulation

Abstract: To solve the problem of difficulty in UAV cluster formation rendezvous based on MADDPG algorithm, an autonomous collaborative control strategy based on LDE-MADDPG algorithm is proposed. To address the issues of weak generalization, poor scalability, and slow cluster training process of MADDPG algorithm, LDE-MADDPG algorithm was proposed by designing a state feature learning network and a decoupled Critical network. By integrating LDE-MADDPG algorithm with strategy generation elements such as the decoupled reward function, cluster state space, and UAV action space, a control strategy for UAV cluster formation endezvous that can adapt to diverse formations and varying quantities has been …

Robot Path Planning Based On Improved A-Ddqn Algorithm, Peilong Ni, Pengjun Mao, Ning Wang, Mengjie Yang

Journal of System Simulation

Abstract: An improved A-DDQN algorithm is proposed to address the challenges of reward sparsity and the inability to distinguish sample importance in traditional DQN algorithms during robot path planning. Building on the original DQN, an enhancement is made by incorporating the Double-DQN approach, which updates the predictive Q-value network based on actions selected by the Q network, rather than directly using the predicted Q-values for action selection, thereby mitigating overestimation issues. Secondly, the concept of artificial potential field (APF) is introduced to design specific rewards for each step of the robot's movement, guiding the robot and addressing the problem of …

Uncalibrated Visual Servoing For Spatial Under-Constrained Cable-Driven Parallel Robots, Jarrett-Scott K. Jenny, Matt Marshall

Faculty Articles

Cable-driven parallel robots (CDPRs) offer large workspaces with minimal infrastructure, but their control becomes difficult when the platform is under-constrained and sensing is limited. This paper investigates uncalibrated visual servoing (UVS) with a single monocular camera, asking whether simple global static Jacobians (GSJ) can be sufficient and how an adaptive Jacobian estimator behaves. Two platforms are evaluated: a three-cable (3C) platform and a redundant six-cable du-al-plane platform (RC). Motion-capture (MoCap) validation shows that redundancy improves stability and tracking by reducing platform tilt and making image errors correspond more directly to Cartesian motions. Across static and low-speed tracking tasks, GSJ proved …

Comparison Of Liu-Type Estimator For Multicollinearity In Fuzzy Logistic Regression Model, Ayad Habib Shemail, Ahmed Razzaq Al-Lami, Amal Hadi Rashid

Iraqi Journal for Computer Science and Mathematics

This article addresses the fuzzy logistic regression model under conditions of multicollinearity, which causes instability and inflated variance in parameter estimation. In this model, both the response variable and parameters are represented as fuzzy triangular numbers. To overcome the multicollinearity problem, various Liu-type estimators were employed: Fuzzy Maximum Likelihood Estimators (FMLE), Fuzzy Logistic Ridge Estimators (FLRE), Fuzzy Logistic Liu Estimators (FLLE), Fuzzy Logistic Liu-type Estimators (FLLTE), and Fuzzy Logistic Liu-type Parameter Estimators (FLLTPE). Through simulations with various sample sizes and application to real fuzzy data on kidney failure, model performance was evaluated using mean square error (MSE) and goodness of …

Software Engineering Approach To Enhancing Privacy Protection: Automated Face Blurring Using Deep Learning In Arab Social Media, Yasmin Makki Mohialden, Nadia Mahmood Hussien, Mostafa Abdulghafoor Mohammed

Iraqi Journal for Computer Science and Mathematics

In the age of digital media, securing personal identities in shared material, especially on social media, has become a significant challenge. This research leverages software engineering to automate face blurring in photographs of Arab social media personalities. It proposes a system that integrates sophisticated deep-learning algorithms with standard image processing within a robust software architecture. This modular system is scalable, maintainable, and compatible with digital media platforms. Gaussian blur is applied to protect privacy once convolutional neural networks (CNNs) identify faces. The system’s efficiency and accuracy are enhanced by OpenCV and NumPy. In experiments, this system consistently identifies and blurs …

Two-Factor Authentication Software For Bluetooth Pairing Between Mobile And Pc Operating Systems, Sundos A. Hameed Alazawi, Abbas A. Abdulhameed, Mostafa Abdulghafoor Mohammed, Thekra Abbas

Iraqi Journal for Computer Science and Mathematics

Bluetooth devices actively broadcast software when pairing to connect. Even during the connection process, the connection can be monitored to view information about the transmission. Using this information, anyone can hijack your existing connection and steal data. Bluetooth connections can be single or multiple. Thus, while connecting to a device, the same device could be simultaneously connected to another device. To avoid this problem, a new software is proposed to support the ID-based authentication process for paired devices by integrating an authentication method based on the biometric features of the device owner. The proposed two-factor security authentication system for pairing …

Retracted: Evaluating The Performance Of The Dbscan Algorithm's Using Number Of External Scores Measures, Rajaa Hasan Abbas, Huda Qusay Hashim, Huda Karem Nasser

Iraqi Journal for Computer Science and Mathematics

The emergence of more informative clustering methods than classical representations is important, so the density-based spatial clustering for applications with noise (DBSCAN) technique can yield an accurate statistical idea of clusters. DBSCAN is becoming more and more popular. On the other hand, if we are aware of actual datasets so that we can make comparisons with these datasets, we aim to determine the accuracy with which the partitioning is estimated using the density-based method. Therefore, in order to compare the success of the partitioning found by the density-based approach under different models, some external scores measures (Adjusted Rand, F-measure, and …

Ontology Features-Based Arabic Text Augmentation Using Word2vec, Enas Tariq Khudair, Onsa Lazzez, Mourad Zaied, Tarek M. Hamdani, Ahmed T. Sadiq, Habib Chabchoub, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

Text augmentation plays a major role when data is scarce. In this context, there are few Arabic news texts for specific purposes, and hence, there is a dire need to generate Arabic text, especially news. This paper presents an enhanced approach to Arabic text augmentation based on Arabic ontology features. The Arabic part of speech, particularly adjectives, verbs, and prepositions, and the ontology properties regarding such parts to create new texts, make up the first stage of the system, which has multiple stages. Word2Vector (Word2Vec) plays a pivotal role in giving Arabic ontology features to the specific Arabic Part of …

Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Valuing Fruit Tree Lease Contracts Under Uncertainty: A Probabilistic Framework For Fair Pricing, Abdurakhman Abdurakhman, Agus Sihabuddin, Kurniawan Chandra Wijaya, Evita Purnaningrum, Di Asih I Maruddani

Iraqi Journal for Computer Science and Mathematics

Fruit tree lease contracts are a prevalent economic practice in Indonesia, especially within rural communities. This study addresses the challenge of establishing equitable contract prices for both lessees and tree owners, specifically by integrating the inherent uncertainties associated with crop yield and fruit price fluctuations. To achieve this, we develop and employ two distinct models: Fixed-Time Discount Model (FTD) and the Dynamic-Time Discount Model (DTD). Each model is mathematically formulated, leveraging a Poisson distribution to capture yield uncertainty and a Uniform distribution to represent fruit price variability. Through computations, we evaluate the impact of key parameters - average yield ( …

Ada-Application Of Decision Analysis For Developing A Healthcare System To Predict Fetal Health, Melfi Alrasheedi, Theyazn.H.H Aldhyani

Iraqi Journal for Computer Science and Mathematics

A fatal health condition involves an unborn baby that persists throughout the embryonic stage until delivery. The fetus grows and develops during each trimester of pregnancy. Obstetricians may detect fetal anomalies and select medical interventions based on cardiotocogram (CTG) data. However, the obstetrician's visual assessment of CTG data can sometimes be subjective or inaccurate. Therefore, automated analysis using machine learning approaches for CTG data is essential. This research employs decision analysis techniques, including decision trees (DT), gradient boosting (GB), and type-2 fuzzy neural networks (FNN), for prenatal analysis and prediction. The system was tested using a standard dataset consisting of …

Retracted: Hotspot Issue Handling And Reliable Data Forwarding Technique For Ocean Underwater Sensor Networks, Omar Adil Mahdi, Yusor Rafid Bahar Al-Mayouf, Sameer Sami Hassan Al-Obaidi, Bourair Al-Attar, Hamed Balogun, Suleman Khan

Iraqi Journal for Computer Science and Mathematics

Underwater Wireless Sensor Networks (UWSNs) have emerged as a promising technology for a wide range of ocean monitoring applications. The UWSNs suffer from unique challenges of the underwater environment, such as dynamic and sparse network topology, which can easily lead to a partitioned network. This results in hotspot formation and the absence of the routing path from the source to the destination. Therefore, to optimize the network lifetime and limit the possibility of hotspot formation along the data transmission path, the need to plan a traffic-aware protocol is raised. In this research, we propose a traffic-aware routing protocol called PG-RES, …

Memf-Net: A Mega-Ensemble Of Multi-Feature Cnns For Classification Of Breast Histopathological Images, Alaa Hussein Abdulaal, Ali H. Abdulwahhab, Aqeel Majeed Breesam, Zahra Hasan Oleiwi, Riyam Ali Yassin, Morteza Valizadeh, Saja Nafea Mohsin

Iraqi Journal for Computer Science and Mathematics

Pathological anatomical images play a pivotal role in diagnosing diseases, notably breast cancer, which affects women globally. These images, obtained through biopsies or post-mortem examinations, are preserved to maintain their structural integrity. Software tools, like computer-aided diagnosis, aid doctors in early detection and treatment planning, contributing to reduced mortality rates. In this context, convolutional neural networks (CNNs) have emerged as valuable tools for diagnosing benign and malignant breast cancers. This paper introduces a Mega Ensemble Net method, leveraging multi-scale combination features on the breast histopathology dataset. Three fine-tuned deep learning models, namely ResNet-18, ResNet-34, and ResNet-50, are integrated into this …

Retracted: Capsule Network Model For Detecting Spoofing Attack In The Internet Of Medical Things (Iomt), Mohammad A. Alsharaiah, Mohammed Amin Almaiah, Mansour Obeidat, Rami Shehab

Iraqi Journal for Computer Science and Mathematics

The Internet of Medical Things (IoMT) has transformed healthcare delivery through real-time monitoring and data exchange. However, this integration of smart medical devices has also introduced critical cybersecurity threats, particularly spoofing attacks, which can compromise patient safety and system reliability. Conventional Intrusion Detection Systems (IDS) often fail to address IoMT-specific challenges such as class imbalance, computational constraints, and the need for real-time adaptability. This study proposes a Capsule Network (CapsNet)-based IDS that leverages spatial dependency modeling and hierarchical feature relationships to detect spoofing attacks in IoMT environments. Using the CICIoMT2024 dataset, we implemented a binary classification framework where spoofing instances …

Application Of Neural Networks For Intelligent Processing Of Sensor Signals In The Control Of Technological Process Parameters, N.R. Yusupbekov, Yu.Sh. Avazov, G.Kh. Rashidov

Chemical Technology, Control and Management

This scientific article investigates the problem of analyzing technological process parameters in the fields of chemistry, energy, and metallurgy based on sensor data and applying intelligent signal processing methods. The main objective is to evaluate the effectiveness of artificial intelligence and deep learning models for intelligent analysis, forecasting, and anomaly detection of data obtained from sensors. Time-series data collected from industrial sensors were analyzed using LSTM (Long Short-Term Memory) and Autoencoder neural networks, as well as the Kalman filter. At the first stage of the study, sensor signals were denoised and their true state was estimated using the Kalman filter. …

Evaluation Of Deep Learning Techniques In Road Sign Recognition, Latafat Abbas Gardashova, Haji Fakhraddin Hajiyev

Chemical Technology, Control and Management

Deep learning has transformed the computer vision field and greatly improved the performance and efficiency of road sign recognition systems. This research compares different deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, in terms of their ability to effectively detect and classify road signs under various conditions. The study compares performance measures such as accuracy, processing speed, and robustness to environmental conditions like low lighting, occlusion, and adverse weather. The results show that CNN-based methods, especially those with transfer learning and ensemble techniques, have better performance in real-time scenarios. Problems like computational …

Retracted: Automated Diagnosis Of Orthopedic Patients With Vertebral Column Disorders Using Advanced Mathematical Modeling, Chen Feng, Zhenhua Sun, Xinheng Dai, Hongli Wen

Iraqi Journal for Computer Science and Mathematics

Orthopedic disorders are multifactorial, making accurate diagnosis a significant challenge. This study introduces a novel method for classifying patients into three categories—normal, disc herniation, and spondylolisthesis—using biomechanical parameters derived from diagnostic datasets. To enhance classification accuracy, two meta-heuristic optimization algorithms—the Zebra Optimization Algorithm (ZOA) and Chaos Game Optimization (CGO)—are integrated with Adaptive Boosting (ADAC) and Light Gradient Boosting Machine (LGBM) classifiers. The experimental results reveal that ZOA significantly improves model performance, particularly in the ADAC classifier. The baseline ADAC model achieved a mean accuracy of 0.916, which increased to 0.952 after optimization with ZOA (referred to as the ADZO model). …