Engineering Commons^™

Inverse Kinematics 3d Human Modeling Simulation Based On Multi-View Vision, Guoyu Fang, Yanze Li, Kai Chen, Xiaodong Zhao, Zizhuo Hu, Mingshi Yang, Wanqing Wu, Zichen Wang, Wenkai Guo

Journal of System Simulation

Abstract: In autonomous driving simulation and industrial virtual reality simulation, there is a high demand for accuracy and robustness in 3D human body modeling. However, current joint-based human modeling approaches suffer from issues such as continuous modeling jitter, local distortion, and poor adaptability to occlusion, which degrade model quality and limit the development of practical applications such as intelligent driving and digital factories. To address these challenges, this paper proposes a multi-view vision-based inverse kinematics 3D human modeling method using a vector quantized variational autoencoder(IK-VQ-VAE). By integrating joint training with an automatic variational gradient descent approach, the proposed method achieves …

Pdr-Stgcn: An Enhanced Stgcn With Multi-Scale Periodic Fusion And A Dynamic Relational Graph For Traffic Forecasting, Jie Hu, Bingbing Tang, Langsha Zhu, Yiting Li, Jianjun Hu, Guanci Yang

Faculty Publications

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and …

Military Metaverse: Conceptual Connotation, Construction And Application Framework, Key Issues, Dayong Liu, Zhiming Dong, Jiancheng Gao

Journal of System Simulation

Abstract: Based on the analysis of the concept of the metaverse, the military metaverse concept model is established and compared with virtual-real fusion systems such as the digital twin battlefield, analyzing its core characteristics and construction significance. To accelerate the construction of the military metaverse, an overall logical architecture for the construction and application of the military metaverse is designed, the concept of military metaverse primitives is proposed, and the technical architecture is designed. The main application directions of the military metaverse are analyzed, and the construction stage division and overall thinking are provided. The key issues in construction and …

Virtual-Real Fusion Simulation Technology And Application Research For Industrial Control Systems Cybersecurity Of Process Manufacturing, Xinwei Wang, Jinjiang Wang, Zheng Wang, Laibin Zhang

Journal of System Simulation

Abstract: Aiming at the problem that the industrial control system in the process manufacturing industry lacks an effective attack and defense drill platform when facing network attacks, it is difficult to truly simulate the attack situation, verify the protective measures, and accurately evaluate the impact of attacks on the physical system, an industrial control cybersecurity simulation technology based on virtual-real fusion is proposed to build an efficient attack and defense drill range. The industrial control cybersecurity simulation architecture based on virtual-real fusion is designed, and the consistency analysis of virtual-real fusion data is carried out. At the same time, …

Spatio-Temporal Swin Transformer-Based Flow-Solid Coupling Interaction Sequence Image Prediction Network, Changjun Zou, Zhiyu Ge, Chenxi Zhong

Journal of System Simulation

Abstract: To address limitations in modeling long-term dependencies and multi-scale features in fluidstructure interaction scenarios, a spatiotemporal deep learning model (SwinLSTM) integrating ConvLSTM and Swin Transformer is proposed. The model employs a gated spatiotemporal attention mechanism that dynamically embeds Swin Transformer's window-based multi-head self-attention into ConvLSTM's output gate, enabling adaptive temporal-spatial feature coupling, and designs a multi-level ConvLSTM framework to hierarchically capture complex spatiotemporal correlations. Experiments on a self-built fluid-interaction dataset show that our method achieves the highest PSNR and leading SSIM scores, with superior performance in preserving vortex details and boundary consistency. This work provides an efficient solution …

Pl-Mamba: A 3d Point Cloud Semantic Segmentation Network Based On Bimodal Fusion, He Zhu, Feng Zhou, Mengxiao Zhu, Ju Dai

Journal of System Simulation

Abstract: To enhance the semantic discrimination capability in point cloud semantic segmentation, a 3D point cloud semantic segmentation network named PL-Mamba is proposed, which is centered on the fusion of point cloud (P) and language (L) dual modalities. This method takes PointMamba as the backbone network, leveraging its excellent long-sequence modeling and global perception capabilities. It introduces a language prompt mechanism and uses a pretrained language model BERT to encode the context of category labels, obtaining semantically rich text features. The text information serves as a language guided token and is deeply integrated with point cloud features through cross modal …

Dehpr: A Diffusion-Based End-To-End Hand Pose Reconstruction Network, Guoqiong Liao, Longjie Huang, Qingxin Li, Jiajun Zhang, Kefan Chen

Journal of System Simulation

Abstract: Traditional methods such as convolutional neural networks (CNNs) and Transformers suffer from strong dependence on large-scale annotated data and limited generalization capability when dealing with hand pose reconstruction in complex scenarios. To address these issues, a diffusion-based end-to-end hand pose reconstruction network (DEHPR) is proposed. This method employs a diffusion model to directly generate and refine 3D predictions, thereby reducing spatial uncertainties inherent in 2D-to-3D modeling paradigms. By incorporating an end-to-end framework that reprojects multiple 3D candidate predictions to select optimal joint positions, the approach ultimately produces accurate hand pose estimations. Comprehensive evaluations conducted on HO3D V2, DexYCB, …

Cross-Domain Crowd Counting Model Based On Frequency Domain Enhancement, De Zhang, Zishan Liang, Ningning Liu

Journal of System Simulation

Abstract: Crowd counting takes video surveillance data as input and can be applied to the construction of city digital twin platforms, virtual city modeling and smart city management, etc. However, when there are data domain differences between the application scenario and training scenario, counting performance often significantly decreases. A cross-domain crowd counting model based on frequency domain enhancement is proposed. To alleviate the distribution differences between domains, a frequency domain feature enhancement module and a domain invariant frequency domain adapter module are constructed: the former uses discrete cosine transform to extract key statistical features to enhance spatial representation ability, while …

Research On Real-Time Animatable Human Avatar Generation Via 3d Gaussian Splatting, Yuyou Zhong, Xukun Shen, Yong Hu

Journal of System Simulation

Abstract: Real-time animatable 3D human avatar generation technology hold significant application value in fields such as virtual reality and remote collaboration. To address the limitations of existing methods in detail modeling, real-time performance, and robustness under novel pose driving, an efficient human avatar generation and driving method based on 3D Gaussian splatting (3DGS) is proposed. This method integrates optimized parametric human reconstruction, tri-plane feature encoding, and dynamic offset prediction to achieve efficient modeling from monocular video input. By introducing a skeleton binding and visibility analysis strategy, while designing a multi-scale regularization loss to address the overfitting problem. Simulation experiments demonstrate …

Fatigue Crack Length Estimation Using Acoustic Emissions Technique-Based Convolutional Neural Networks, Asaad Migot, Ahmed Saaudi, Roshan Joseph, Victor Giurgiutiu

Faculty Publications

Fatigue crack propagation is a critical failure mechanism in engineering structures, requiring meticulous monitoring for timely maintenance. This research introduces a deep learning framework for estimating fatigue fracture length in metallic plates through acoustic emission (AE) signals. AE waveforms recorded during crack growth are transformed into time-frequency images using the Choi–Williams distribution. First, a clustering system is developed to analyze the distribution of the AE image-based dataset. This system employs a CNN-based model to extract features from the input images. The AE dataset is then divided into three categories according to fatigue lengths using the K-means algorithm. Principal Component Analysis …

Acknowledgement Of Reviewers

Journal of Sustainable Mining

No abstract provided.

A Critical Appraisal On The Injury Susceptibility Of Underground Metalliferous Mine Workers: Application Of Logistic Regression Model, Sudip Das, Falguni Sarkar, P.S. Paul, B.K. Pal

Journal of Sustainable Mining

The aim of this study is to analyze the occupational injury data of Indian underground metalliferous mines for scrutinizing the injury proneness of different groups of mine workers. In this context, injury records from 2011 to 2022 were obtained from underground metalliferous mines situated at Eastern part of India. The data were characterized and segregated based on different individual and workplace level variables. The workplace injury is categorized as ‘no injury’ and ‘all injury’. Subsequently, Frequency and Classification Based analysis (FCBA), Standardized Injury Rate (SIR) analysis and Logistic Regression Model (LRM) analysis were performed sequentially (FCBA-SIR-LRM) to predict the susceptibility …

Factors Impacting Elementary School Students’ Engineering Design And Optimization Decisions, Elaine Silva Mangiante, Ilana Haliwa

Journal of Pre-College Engineering Education Research (J-PEER)

This mixed methods study explored possible factors that could impact elementary school students’ engineering design and optimization decisions. Data were collected from six teachers and 117 students in six fourth grade classrooms that implemented the Engineering is Elementary geotechnical engineering unit, A Stick in the Mud: Evaluating a Landscape. Students were to recommend to villagers their site decision of where to build a TarPul bridge based on four properties: soil type, villager preference, amount of compaction needed, and location less prone to erosion. Data sources included a pre-and post-assessment question, students’ documentation of property choices for their first and optimized …

Assessing The Student Awareness To Age Friendly Design: The Case Of Ain Shams University- Architecture Students, Shorouk Saleh Ali Kabel, Samah Mohamed Elsayed Atteia Elkhateb, Rowaida M. O. Mohamed Rashed, Wesam M. El-Bardisy

HBRC Journal

With Egypt’s rapid urbanization and projected demographic shift, where individuals aged 60 and above are expected to constitute more than 15% of the population by 2050, addressing the needs of older adults in urban design has become increasingly urgent. Despite global recognition of the importance of age-friendly design, its integration into Egyptian architectural education remains limited, particularly concerning accessibility, safety, and inclusivity.

This study evaluates the level of awareness of Age-Friendly Design (AFD) principles among architecture students at Ain Shams University using a mixed-methods approach. Quantitative data were collected through an online questionnaire assessing students’ familiarity with the concept, while …

Performance-Based Seismic Design And Retrofitting Via Drift And Strain Criteria, Tharwat A. Sakr, Hanaa E. Abd-El-Mottaleb, Shrouk S. Kamel

HBRC Journal

In recent decades, the demand for seismic code updates has significantly increased. Performance-based seismic design (PBSD) is a modern approach to earthquake-resistant building construction. By using this technique, the designers can establish performance targets that satisfy the owner. Most codes rely on strength design that utilizes a design response spectrum, highlighting serviceability without addressing performance levels. In this paper, an appraisal for the incorporation of PBSD into the Egyptian Code is proposed using nonlinear static and dynamic analysis. At first, response spectrum charts for 72, 475, and 2475 years return periods (RP) were developed based on the Egyptian seismicity information. …

Place Personality And User Behaviour: Insights From Cairo’S Streets, Sara Sabahy, Zeina Elzein, Doaa Abouelmagd

HBRC Journal

A place’s personality is beyond the sum of its characteristics. It’s how people perceive it on a human level, hoping for a better understanding and seeking connection. This study aims to understand how people connect with public spaces to enhance the quality of urban life.

A place’s personality extends beyond its physical attributes—it reflects how people emotionally perceive and connect with it. This study investigates the relationship between the perceived personality of urban streets and users’ behaviour to enhance the quality of urban life in Cairo. Focusing on Downtown Cairo and New Cairo, it addresses a gap in destination personality …

Evaluating The Fate And Variability Of Soil Organic Carbon And Nitrogen Species Under Conservation Practices In The Raccoon River Watershed, Zhonglong Zhang, May Wu

Civil and Environmental Engineering Faculty Publications and Presentations

This study evaluates the fate and variability of soil organic carbon (SOC) stocks and nitrogen species using the latest version of the Soil and Water Assessment Tool– Carbon (SWAT-C) and assesses how conservation practices influence their dynamics in the Raccoon River Watershed (RRW). Dominated by intensive agricultural pro- duction, the RRW is a significant contributor of sediment and nutrient loads to local rivers and the Mississippi River. This SWAT-C model simulates the export of SOC and nitrogen species and evaluates their responses under varying management scenarios. Model calibration was performed for streamflow, sediment, nitrate, total nitrogen, and organic carbon with …

Theoretical Analysis Of Mir-Based Differential Photoacoustic Spectroscopy For Noninvasive Glucose Sensing, Tasnim Ahmed, Khan Mahmud, Md Rejvi Kaysir, Shazzad Rassel, Dayan Ban

Electrical and Computer Engineering Faculty Research

Diabetes is a developing global health concern that cannot be cured, necessitating frequent blood glucose monitoring and dietary management. Photoacoustic Spectroscopy (PAS) in the mid-infrared (MIR) region has recently emerged as a viable noninvasive blood glucose monitoring technique. However, MIR-PAS confronts significant challenges: (i) Water absorption, which reduces light penetration, and (ii) interference from other blood components. This paper systematically analyzes the background of photoacoustic signal generation and proposes a differential PAS (DPAS) in the MIR region for removing the background signals arising from water and other interfering components of blood, which improves the overall detection sensitivity. A detailed mathematical …

Polystyrene Nanoplastics As Pfas Carriers And Their Interactions With Zwitterionic Phospholipid Membranes, Jiahuiyu Fang, Tongxuan Qiao, Pranab Sarker, Xiaoxue Qin, Size Zheng, Mark J. Uline, Tao Wei

Faculty Publications

The co-occurrence of per- and polyfluoroalkyl substances (PFAS) and nanoplastics (NPs) poses a synergistic threat to environmental and human health, yet the molecular mechanisms governing PFAS–NP complexation and membrane interactions remain unclear. Using atomistic molecular dynamics simulations, we investigated the adsorption of neutral polytetrafluoroethylene (PTFE) and anionic perfluorinated compounds (perfluorooctanoic acid, PFOA, and perfluorooctanesulfonic acid, PFOS) on polystyrene NPs (3.1 and 6.7 nm) and their interactions with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) membranes. Polystyrene NPs act as carriers, transporting PFAS molecules to the lipid/water interface, where PFAS attachment modifies NP interfacial behavior. PFAS adsorption on the NP surface is driven by …

Cover And Contents

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

Exploitation Prioritization And Residual Risk Assessment Based On Hybrid Mcdm Model, Zibo Wang, Yaofang Zhang, Sicai Lv, Yingzhou Wang, Hongri Liu, Bailing Wang

Turkish Journal of Electrical Engineering and Computer Sciences

Exploitation is one of the most significant ways to launch attacks using vulnerabilities. The increasing number of vulnerabilities and limited allocation of security resources make it impossible to eliminate all exploitations. Because not every vulnerability can be fixed, it is necessary to rank exploitations and subsequently assess the residual risk, which is defined as the remaining threat potential after each elimination. In this paper, a structured and flexible decision support framework based on a hybrid multicriteria decision-making model is proposed for prioritizing exploitations and assessing residual risk. Metrics are treated as criteria in the model. The hybrid model is developed …

A Deep Learning-Based Real-Time No-Reference Image Decolorization Network With Perceptual Preservation, Mengjuan Zhao, Yitao Liang, Weiya Shi, Juan Xia

Turkish Journal of Electrical Engineering and Computer Sciences

Currently, grayscale images are preferred as input data for some specific vision tasks. Decolorization is the transformation of a color image into a grayscale image. Efficient decolorization algorithms can improve the overall task efficiency, while perceptual preservation in decolorization can provide more information for further processing. In recent research, traditional methods focus on preserving contrast or detail information with little attention to perceptual features. Deep-learning methods are beginning to consider perceptual preservation, but they run inefficiently. In addition, the decolorization methods lack the optimal target grayscale images for reference. Therefore, we propose a new deep learning-based real-time no-reference decolorization network …

Noninvasive Condition Monitoring For Eccentricity Fault Detection In Large Hydro Generators, Atena Tazikeh Lemeski, Di̇dem Tekgün, Ozan Keysan, Kemal Leblebi̇ci̇oğlu, Murat Göl

Turkish Journal of Electrical Engineering and Computer Sciences

Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in Türkiye. The effects of DE faults on the SPSG’s magnetic equivalent circuit parameters are then examined under various fault severities. A …

A Deep Learning Model For Accurate Tomato Leaf Disease Identification, Maheen Shahzad, Muhammad Abdullah Javed, Erum Ashraf, Hafiz Ishfaq Ahmad, Sabeen Masood

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in machine learning and deep learning have greatly improved how we detect plant diseases, making diagnoses more accurate, faster, and easier to scale. However, many existing solutions depend on large, pretrained models that need powerful hardware, which limits their use in the field, especially in areas with limited resources. To tackle this, we designed a custom lightweight convolutional neural network (CNN) built from scratch using 20,000 carefully selected images from the PlantVillage tomato dataset. Our model uses Squeeze-and-Excitation (SE) blocks and Swish activation functions to boost performance, reaching an accuracy of 97.7% while using far fewer computing resources …

Influence Of Sinr And Noise Variance On Outage Probability For Mimo-Noma System In 5g And Beyond, Sadiq Ur Rehman, Jawwad Ahmed, Muhammad Zubair, Syed Sajjad Hussain Rizvi

Turkish Journal of Electrical Engineering and Computer Sciences

Nonorthogonal multiple access (NOMA) communication presents a promising solution to the limitations of traditional orthogonal multiple access techniques, offering potential improvements in achievable rates. Multiple-input multiple-output (MIMO), when combined with NOMA (MIMO-NOMA), further enhances these benefits by leveraging the diversity advantages of multiple antennas. Looking ahead, the future of wireless communication hinges on deploying heterogeneous networks (HetNets), facilitating the coexistence of various wireless access networks in a hierarchical fashion. However, the advent of 5G and 6G communications brings shorter channel coherence times, rendering channel reciprocity unreliable. Consequently, conventional channel estimation methods relying on uplink (UL) pilots for downlink (DL) transmission …

A Novel Approach To Maximum Weighted Traffic Flow Method For Effective Signal Control, Zülal Hi̇lal Yildiz Budak, Seyi̇t Alperen Çeltek, Aki̇f Durdu

Turkish Journal of Electrical Engineering and Computer Sciences

Traffic signal management is a critical challenge due to its environmental, economic, and public health impacts. The maximum weighted flow method (MaxWeightedFlow) was developed to optimize traffic flow at isolated and coordinated urban intersections. This study proposes a new method, the novel MaxWeightedFlow, which includes two key strategies to enhance the classical approach. The first strategy reduces computational burden by estimating vehicle approach times based on instantaneous speeds, improving real-time performance. The second employs regression analysis to optimize the alpha parameter, representing the vehicle waiting coefficient. The proposed approach, the novel MaxWeightedFlow, was evaluated using real-world traffic data from Kilis, …

Improving Rail System Signaling Efficiency Through Ai-Based Driving Profile Generation: A Comparative Performance Analysis, Mehmet Taci̇ddi̇n Akçay, Abdurrahi̇m Akgündoğdu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a dataset comprising 3600 discrete operational snapshots (rather than continuous time-series data) derived from real-field operations is used to obtain a high-accuracy driving profile equation using a second-degree Polynomial Regression method. This equation demonstrates the model’s interpretability. The performance metrics obtained with the second-degree polynomial regression model’s equation are as follows: a coefficient of determination (R2) of 0.84, a Pearson Correlation Coefficient of 0.91, and an RMSE of 11.13. These results indicate the effectiveness of artificial intelligence-based approaches in improving the efficiency of the railway signaling system. The same dataset is also utilized with other machine learning …

Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts, Kody A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz

Faculty Publications

Fourier-transform spectrometers are widely used for spectral measurements. Changes in the field of view during measurement introduce oscillations into the measured spectra known as scene-change artifacts. Field-of-view changes also introduce uncertainty about which target the measured spectrum represents. Though scene-change artifacts are often present in dynamic data, their significance is disputed in the current literature. This work presents a theoretical framework and experimental validation for scene-change artifacts. Field-of-view changes introduce variable interferogram offsets, which standard processing techniques assume are constant. The error between the interferogram offset and its estimate is Fourier-transformed, yielding scene-change artifacts, often confused with noise, in the …

Distribution System Reliability Evaluation Considering Protection Coordination Using Petri Nets, Rani Kumari, Bhukya Krishna Naick

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining reliable and high-quality power delivery becomes increasingly complex with expanding power grids. The lack of protection coordination poses a significant threat, compromising overall system reliability. This research addresses this challenge by proposing a method for coordinating protective devices within the distribution system, specifically during network faults. The proposed approach utilizes a stochastic timed Petri net (STPN) based methodology to model protective device coordination across various fault scenarios. This technique effectively captures the dynamic behavior and interactions of protective equipment, allowing for the anticipation of potential disturbances. This proactive insight facilitates preventative measures to address prewarning situations, thereby preventing cascading …

Improved Performance Of Wave Energy Converters And Arrays For Wave-To-Onshore Power Grid Integration, Madelyn Veurink, David Wilson, Rush Robinett, Wayne Weaver

Michigan Tech Publications

This paper focuses on power grid integration of wave energy converter (WEC) arrays that minimize added energy storage for maximizing power capture as well as smoothing the oscillatory power inputs into the grid. In particular, a linear right circular cylinder WEC array that implements complex conjugate control is compared and contrasted to a nonlinear WEC array that implements an hourglass buoy shape while both are integrated into the grid utilizing phase control (i.e., relative spacing of the WEC array) on the input powers to the grid. The Hamiltonians of the two WEC systems are derived, enabling a direct comparison of …