Real-time Characteristics Research Articles

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Modbus RTU Protocol Timing Evaluation for Scattered Holding Register Read and ModbusE-Related Implementation

In parallel with the development of data transmission in the telecommunications sector to connect peripheral devices, hardware engineers have defined interfaces for independent communication systems. The basic idea of having standardized interfaces for external devices was quickly extended to control and instrumentation equipment, as the real-time characteristic of distributed systems became a challenge in the field. The proposed Modbus Extension (ModbusE) builds on these new techniques introduced in the UART interface, so that the newly introduced extension retains all the characteristics of the Modbus RTU except for the character bit structure. The validation of the ModbusE protocol allowed the proposal of a new communication message structure, a description of Modbus devices, an acquisition cycle to attain a deterministic temporal response behavior and the definition of an architecture for IIoT integration. In this work, practical research is based on data update times in SMARTConvert software when between 1 and 50 registers are accessed. These registers can, in the most favorable case, be at consecutive addresses and can all be read in a single bus query request, or at addresses 1 to 50, or the registers must be read in separate requests. The contributions of this paper are related to highlighting cases where Modbus modules are incorporated, the addressed registers are not consecutive and the Modbus reads 1 or more holding register records and the ModbusE proposal. As these registers must not be on consecutive addresses, it is necessary to make several requests, defining the function of a Modbus acquisition cycle, adapting the equations to the purpose of the tests carried out, measuring the Modbus communication signals and describing the ModbusE communication concept.

Intraoperative Augmented Reality for Vitreoretinal Surgery Using Edge Computing.

Purpose: Augmented reality (AR) may allow vitreoretinal surgeons to leverage microscope-integrated digital imaging systems to analyze and highlight key retinal anatomic features in real time, possibly improving safety and precision during surgery. By employing convolutional neural networks (CNNs) for retina vessel segmentation, a retinal coordinate system can be created that allows pre-operative images of capillary non-perfusion or retinal breaks to be digitally aligned and overlayed upon the surgical field in real time. Such technology may be useful in assuring thorough laser treatment of capillary non-perfusion or in using pre-operative optical coherence tomography (OCT) to guide macular surgery when microscope-integrated OCT (MIOCT) is not available. Methods: This study is a retrospective analysis involving the development and testing of a novel image-registration algorithm for vitreoretinal surgery. Fifteen anonymized cases of pars plana vitrectomy with epiretinal membrane peeling, along with corresponding preoperative fundus photographs and optical coherence tomography (OCT) images, were retrospectively collected from the Mayo Clinic database. We developed a TPU (Tensor-Processing Unit)-accelerated CNN for semantic segmentation of retinal vessels from fundus photographs and subsequent real-time image registration in surgical video streams. An iterative patch-wise cross-correlation (IPCC) algorithm was developed for image registration, with a focus on optimizing processing speeds and maintaining high spatial accuracy. The primary outcomes measured were processing speed in frames per second (FPS) and the spatial accuracy of image registration, quantified by the Dice coefficient between registered and manually aligned images. Results: When deployed on an Edge TPU, the CNN model combined with our image-registration algorithm processed video streams at a rate of 14 FPS, which is superior to processing rates achieved on other standard hardware configurations. The IPCC algorithm efficiently aligned pre-operative and intraoperative images, showing high accuracy in comparison to manual registration. Conclusions: This study demonstrates the feasibility of using TPU-accelerated CNNs for enhanced AR in vitreoretinal surgery.

Development and Implementation of a Biometric Tracking System for Health and Fitness Monitoring

With the development of cheap and powerful microcontrollers and sensors it has become possible to track human activity non-invasively and discreetly. This has led to a revolution in tracking activity for health monitoring, achieving exercise goals, or sports performance analysis. This has developed into to a multimillion-pound industry with many applications. So I want to make an exercise watch that can monitor the wearer’s physical signs to prevent people from going into a dangerous state because of exercise. This project successfully developed a prototype of a biometric tracking system for health and fitness monitoring. The system is capable of monitoring heart rate, exercise, and other biometric features in real time, and the Mbed prototype system integrates sensor data acquisition, processing algorithms, and a user-friendly interface to accurately capture and display data.

Enhancing VANET Security: An Unsupervised Learning Approach for Mitigating False Information Attacks in VANETs

Vehicular ad hoc networks (VANETs) enable communication among vehicles and between vehicles and infrastructure to provide safety and comfort to the users. Malicious nodes in VANETs may broadcast false information to create the impression of a fake event or road congestion. In addition, several malicious nodes may collude to collectively launch a false information attack to increase the credibility of the attack. Detection of these attacks is critical to mitigate the potential risks they bring to the safety of users. Existing techniques for detecting false information attacks in VANETs use different approaches such as machine learning, blockchain, trust scores, statistical methods, etc. These techniques rely on historical information about vehicles, artificial data used to train the technique, or coordination among vehicles. To address these limitations, we propose a false information attack detection technique for VANETs using an unsupervised anomaly detection approach. The objective of the proposed technique is to detect false information attacks based on only real-time characteristics of the network, achieving high accuracy and low processing delay. The performance evaluation results show that our proposed technique offers 30% lower data processing delay and a 17% lower false positive rate compared to existing approaches in scenarios with high proportions of malicious nodes.

Real-Time Emotion Recognition for Improving the Teaching-Learning Process: A Scoping Review.

Emotion recognition (ER) is gaining popularity in various fields, including education. The benefits of ER in the classroom for educational purposes, such as improving students' academic performance, are gradually becoming known. Thus, real-time ER is proving to be a valuable tool for teachers as well as for students. However, its feasibility in educational settings requires further exploration. This review offers learning experiences based on real-time ER with students to explore their potential in learning and in improving their academic achievement. The purpose is to present evidence of good implementation and suggestions for their successful application. The content analysis finds that most of the practices lead to significant improvements in terms of educational purposes. Nevertheless, the analysis identifies problems that might block the implementation of these practices in the classroom and in education; among the obstacles identified are the absence of privacy of the students and the support needs of the students. We conclude that artificial intelligence (AI) and ER are potential tools to approach the needs in ordinary classrooms, although reliable automatic recognition is still a challenge for researchers to achieve the best ER feature in real time, given the high input data variability.

Detection of Damage on Inner and Outer Races of Ball Bearings Using a Low-Cost Monitoring System and Deep Convolution Neural Networks

Bearings are vital components in machinery, and their malfunction can result in equipment damage and reduced productivity. As a result, considerable research attention has been directed toward the early detection of bearing faults. With recent rapid advancements in machine learning algorithms, there is increasing interest in proactively diagnosing bearing faults by analyzing signals obtained from bearings. Although numerous studies have introduced machine learning methods for bearing fault diagnosis, the high costs associated with sensors and data acquisition devices limit their practical application in industrial environments. Additionally, research aimed at identifying the root causes of faults through diagnostic algorithms has progressed relatively slowly. This study proposes a cost-effective monitoring system to improve economic feasibility. Its primary benefits include significant cost savings compared to traditional high-priced equipment, along with versatility and ease of installation, enabling straightforward attachment and removal. The system collects data by measuring the vibrations of both normal and faulty bearings under various operating conditions on a test bed. Using these data, a deep neural network is trained to enable real-time feature extraction and classification of bearing conditions. Furthermore, an explainable AI technique is applied to extract key feature values identified by the fault classification algorithm, providing a method to support the analysis of fault causes.

Data-driven prediction of soccer outcomes using enhanced machine and deep learning techniques

This paper introduces a novel framework for soccer game prediction using advanced machine learning and deep learning techniques, initially focusing on the Dutch Eredivisie League and later expanding to include the Scottish Premiership and the Belgian Jupiler Pro League. The methodology includes data preprocessing, feature engineering, model training, and testing. Various models are evaluated, including enhanced versions of Logistic Regression, XGBoost, Random Forest, SVM, Naive Bayes, Feedforward Neural Network, and Vanilla Recurrent Neural Network. Unlike existing studies that focus on end-of-game features, this research incorporates real-time features like half-time results and goals for in-game decision-making. Advanced data normalization and sampling methods, such as SVM-SMOTE and Near-Miss, are applied to improve model performance. Models are assessed using accuracy, recall, precision, F1-score, and Area under the ROC Curve. Results indicate that the Feedforward Neural Network excels in predicting game results, while Logistic Regression is best for predicting under and over 2.5 goals. The integration of Random Forest and XGBoost in a voting model consistently achieves the highest accuracy across both prediction tasks. The combined use of data from the three leagues further validates the models’ robustness and generalizability. This study demonstrates the potential of machine and deep learning to enhance soccer game predictions through advanced techniques and comprehensive data analysis, making significant contributions to sports analytics.

An Entropy-Based Clustering Algorithm for Real-Time High-Dimensional IoT Data Streams.

The rapid growth of data streams, propelled by the proliferation of sensors and Internet of Things (IoT) devices, presents significant challenges for real-time clustering of high-dimensional data. Traditional clustering algorithms struggle with high dimensionality, memory and time constraints, and adapting to dynamically evolving data. Existing dimensionality reduction methods often neglect feature ranking, leading to suboptimal clustering performance. To address these issues, we introduce E-Stream, a novel entropy-based clustering algorithm for high-dimensional data streams. E-Stream performs real-time feature ranking based on entropy within a sliding time window to identify the most informative features, which are then utilized with the DenStream algorithm for efficient clustering. We evaluated E-Stream using the NSL-KDD dataset, comparing it against DenStream, CluStream, and MR-Stream. The evaluation metrics included the average F-Measure, Jaccard Index, Fowlkes-Mallows Index, Purity, and Rand Index. The results show that E-Stream outperformed the baseline algorithms in both clustering accuracy and computational efficiency while effectively reducing dimensionality. E-Stream also demonstrated significantly less memory consumption and fewer computational requirements, highlighting its suitability for real-time processing of high-dimensional data streams. Despite its strengths, E-Stream requires manual parameter adjustment and assumes a consistent number of active features, which may limit its adaptability to diverse datasets. Future work will focus on developing a fully autonomous, parameter-free version of the algorithm, incorporating mechanisms to handle missing features and improving the management of evolving clusters to enhance robustness and adaptability in dynamic IoT environments.

Direct detection of doxorubicin in whole blood using a hydrogel-protected electrochemical aptamer-based biosensor

Electrochemical aptamer-based biosensors (EABs) have been developed for multiple important biomarkers for their convenient and real-time features. However, the application of EABs in complex biological fluids has been limited by the rapid loss of sensitivity and selectivity due to inactivation and biofouling of aptamer probes and electrodes. To address this issue, we report the preparation of a simple hydrogel-protected aptamer-based biosensor (HP-EAB) for direct detection of Doxorubicin (DOX) in whole blood. The aptamer provides excellent selectivity for the electrochemical sensor, allowing the prepared sensor to accurately detect DOX in a 50-fold diluted whole blood sample. The agarose hydrogel coating on the electrode surface allows the passage of small molecules while hindering the adsorption of biomolecules from the whole blood matrix to the electrode surface. The experimental results show that the prepared HP-EAB has high stability compared with the unprotected EAB, and the HP-EAB maintains excellent detection performance after 7 days of storage. The hydrogel coating can effectively reduce the non-specific response to the whole blood matrix and prolong the life-time of the sensor. When used to detect DOX in rabbit whole blood, the HP-EAB exhibited excellent detection performance with a detection limit of 25.9nM(S/N=3) and a detection range of 0.1μM-50μM. The developed HP-EAB provides an excellent platform for the rapid and accurate determination of important analytes in complex biological fluids.

Integrating Geospatial and Real Time Technologies for Risk zone monitoring in Periyar Tiger Reserve, India.

There are numerous monitoring technologies available today, owing to the rapid advancements in technology and the increasing demand for safety and security in forests. Real-time monitoring with AI cameras, which are commonly utilized for creating and updating real-time features through surveillance, stands out as one of the most effective monitoring solutions. The objective of this current research is to monitor various risk zones within the Periyar Tiger Reserve by integrating real-time AI camera with geographic data. AI cameras were strategically placed using spatial analysis techniques. Leveraging Geographic Information System (GIS) technology, the system facilitates the spatiotemporal management of multiple cameras and their associated data. The spatial distribution and monitoring range of the AI cameras are depicted on the GIS map, along with the layout densities of the cameras and other pertinent information stored in a geospatial database. Additionally, merging various risk areas identified from past incidents with the camera locations enhances the system's capability to establish accurate topological connections between cameras and other points of interest. The results revealed that only 13% of the risk zone was observable from the nine available Real Time Monitoring towers. However, with the addition of 51 more towers, the visibility of the risk zone would increase to 40%. The remaining 15% of the risk zones were not visible through the existing infrastructure. To address this visibility gap, if permitted by the Wildlife Protection Act, wired communication may need to be implemented instead of wireless for monitoring these areas.

Infrared target detection algorithm based on multipath coordinate attention mechanism

Abstract The current generation of infrared target detection algorithms frequently exhibits a high degree of dependency on parameter configurations within complex operational environments. This often results in a reduction in detection accuracy, an increase in the number of model parameters, and a slowing of the detection process. To address these limitations, a new algorithm, CGhostNet-Attention-YOLO (CAY), is proposed in this paper. Firstly, we designed a lightweight backbone network, CGhostNet, with the objective of improving feature extraction efficiency, thereby enabling accurate and real-time feature extraction. Furthermore, we proposed a multipath coordinate attention mechanism, which incorporates both channel and positional information, thereby facilitating enhanced context awareness and the comprehension of relationships between different positions. This effectively enhances the model’s ability to comprehend the overall meaning and addresses the issue of missed detections in infrared targets, significantly improving detection accuracy. Moreover, we employed the Inner-SIoU loss function to accelerate model convergence, reduce loss, and enhance the robustness of the model. Finally, comparative experiments were conducted on our dataset (IFD) as well as publicly available datasets, including FLIR, Pascal VOC, and NEU-DET. The results demonstrate that the CAY algorithm achieved a mean Average Precision (mAP@0.5) of 81.3% on the IFD dataset, 86.1% on the FLIR dataset, 79.2% on the Pascal VOC dataset, and 79.9% on the NEU-DET dataset, with a 27% reduction in the number of parameters. These findings validate the feasibility of the proposed algorithm.

From Movement to Meaning: Real-time Detection of Behavioural Patterns in Human Psychology using Deep Learning and Time Series Sequence

Understanding human behaviour and psychology is complex and involves various fields, including cognitive science, neurology, and artificial intelligence (AI). In this study, we introduce a cutting-edge system that detects both manual (like hand gestures) and non-manual (such as facial expressions or body movements) features in real time, using advanced deep learning techniques and time-series analysis. This system translates these human movements into text, providing a powerful tool to analyse subtle behavioural and psychological signals. By processing real-time data, our system significantly advances psychology by detecting small expressions, involuntary gestures, and behaviour patterns often associated with psychological and neurological conditions. For instance, it can help identify repetitive actions or atypical gestures in people with autism spectrum disorder (ASD), which are critical for early diagnosis. It can also track signs of neurodegenerative diseases like Parkinson’s or Alzheimer’s, detecting tremors, posture changes, or facial expressions, and offer insights for early intervention

Enhancement algorithm for surface electromyographic-based gesture recognition based on real-time fusion of muscle fatigue features

This study aims to optimize surface electromyography-based gesture recognition technique, focusing on the impact of muscle fatigue on the recognition performance. An innovative real-time analysis algorithm is proposed in the paper, which can extract muscle fatigue features in real time and fuse them into the hand gesture recognition process. Based on self-collected data, this paper applies algorithms such as convolutional neural networks and long short-term memory networks to provide an in-depth analysis of the feature extraction method of muscle fatigue, and compares the impact of muscle fatigue features on the performance of surface electromyography-based gesture recognition tasks. The results show that by fusing the muscle fatigue features in real time, the algorithm proposed in this paper improves the accuracy of hand gesture recognition at different fatigue levels, and the average recognition accuracy for different subjects is also improved. In summary, the algorithm in this paper not only improves the adaptability and robustness of the hand gesture recognition system, but its research process can also provide new insights into the development of gesture recognition technology in the field of biomedical engineering.

Android-based framework for condition assessment of existing RC hospital buildings

Rapid visual screening (RVS) is a practically viable tool implemented all over the world to rank structures based on their seismic vulnerability. Seismic assessment of hospital buildings is essential in seismic-prone regions to ensure their structural integrity and readiness to provide emergency care and medical treatment after significant seismic events. Therefore, in the current study, an approach is developed that is similar to the proven methodologies reported in the literature, for the seismic evaluation of structural and non-structural elements of hospital buildings in a metropolitan city like Karachi. Karachi is located in the southwest of Pakistan and is vulnerable to earthquake and tsunami hazards. To this end, an android-based RVS system has been developed for large-scale seismic risk mapping of hospital buildings. The proposed system acquires the physical status of the building features in real time and identifies the building performance obtained from the calculated seismic risk index. Moreover, three case study hospital buildings were employed and analysed through the proposed RVS system. Results illustrate the performance of case study hospital buildings at organisational, structural and non-structural levels based on seismic risk indices.

A Remote Two-Point Magnetic Localization Method Based on SQUID Magnetometers and Magnetic Gradient Tensor Invariants.

In practical application, existing two-point magnetic gradient tensor (MGT) localization methods have a maximum detection distance of only 2.5 m, and the magnetic moment vectors of measured targets are all unknown. In order to realize remote, real-time localization, a new two-point magnetic localization method based on self-developed, ultra-sensitive superconducting quantum interference device (SQUID) magnetometers and MGT invariants is proposed. Both the magnetic moment vector and the relative position vector can be directly calculated based on the linear positioning model, and a quasi-Newton optimization algorithm is adopted to further improve the interference suppression capability. The simulation results show that the detection distance of the proposed method can reach 500 m when the superconducting MGT measurement system is used. Compared with Nara's single-point tensor (NSPT) method and Xu's two-point tensor (XTPT) method, the proposed method produces the smallest relative localization error (i.e., significantly less than 1% in the non-positioning blind area) without sacrificing real-time characteristics. The causes of and solutions to the positioning blind area are also analyzed. The equivalent experiments, which were conducted with a detection distance of 10 m, validate the effectiveness of the localization method, yielding a minimum relative localization error of 4.5229%.

Urban traffic tiny object detection via attention and multi-scale feature driven in UAV-vision

The unmanned aerial vehicle (UAV) city patrol is of great significance in ensuring the safety of residents’ lives and properties, as well as maintaining the normal operation of the city. However, the detection of UAV images faces challenges such as numerous small-scale objects, complex backgrounds, and high requirements for detection speed. In response to these issues, we introduce a Real-time Small Object Detection network in UAV-vision (RTS-Net), tailored for UAV patrols. Initially, we introduce a multiscale feature fusion module (MFFM) designed to augment the expressiveness of features across scales, thereby enhancing the detection of smaller objects. Subsequently, leveraging attention mechanisms, we present the coordinated attention detection module (CADM), which bolsters the detection model’s ability to accurately segregate objects from the background in expansive, complex scenarios. Lastly, a lightweight real-time feature extraction module (RFEM) is crafted to diminish model computational complexity and boost inference speed. On the UAV road patrol image dataset we constructed, our proposed method attains a detection accuracy of 89.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} mAP, breaking previous records. It surpasses all prevailing detection methods, particularly for small-scale objects. Simultaneously, it achieves an inference speed of 163.9 FPS. The experimental results show that RTS-Net can satisfy the accurate and efficient detection of ground objects by various different UAV platforms in different complex scenarios.

Fish-inspired tracking of underwater turbulent plumes

Autonomous ocean-exploring vehicles have begun to take advantage of onboard sensor measurements of water properties such as salinity and temperature to locate oceanic features in real time. Such targeted sampling strategies enable more rapid study of ocean environments by actively steering towards areas of high scientific value. Inspired by the ability of aquatic animals to navigate via flow sensing, this work investigates hydrodynamic cues for accomplishing targeted sampling using a palm-sized robotic swimmer. As proof-of-concept analogy for tracking hydrothermal vent plumes in the ocean, the robot is tasked with locating the center of turbulent jet flows in a 13,000-liter water tank using data from onboard pressure sensors. To learn a navigation strategy, we first implemented RL on a simulated version of the robot navigating in proximity to turbulent jets. After training, the RL algorithm discovered an effective strategy for locating the jets by following transverse velocity gradients sensed by pressure sensors located on opposite sides of the robot. When implemented on the physical robot, this gradient following strategy enabled the robot to successfully locate the turbulent plumes at more than twice the rate of random searching. Additionally, we found that navigation performance improved as the distance between the pressure sensors increased, which can inform the design of distributed flow sensors in ocean robots. Our results demonstrate the effectiveness and limits of flow-based navigation for autonomously locating hydrodynamic features of interest.

Personalized prediction of intradialytic hypotension in clinical practice: Development and evaluation of a novel AI dashboard incorporating risk factors from previous and current dialysis sessions

Intradialytic hypotension (IDH) is one of the most common and critical complications of hemodialysis. Despite many proven factors associated with IDH, accurately predicting it before it occurs for individual patients during dialysis sessions remains a challenge. To establish artificial intelligence (AI) predictive models for IDH, which consider risk factors from previous and ongoing dialysis to optimize model performance. We then implement a novel digital dashboard with the best model for continuous monitoring of patients' status undergoing hemodialysis. The AI dashboard can display the real-time probability of IDH for each patient in the hemodialysis center providing an objective reference for care members for monitoring IDH and treating it in advance. Eight machine learning (ML) algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Light Gradient Boosting Machine (LightGBM), Multilayer Perception (MLP), eXtreme Gradient Boosting (XGBoost), and NaiveBayes, were used to establish the predictive model of IDH to determine if the patient will acquire IDH within 60min. In addition to real-time features, we incorporated several features sourced from previous dialysis sessions to improve the model's performance. The electronic medical records of patients who had undergone hemodialysis at Chi Mei Medical Center between September 1, 2020 and December 31, 2020 were included in this research. Impact evaluation of AI assistance was conducted by IDH rate. The results showed that the XGBoost model had the best performance (accuracy: 0.858, sensitivity: 0.858, specificity: 0.858, area under the curve: 0.936) and was chosen for AI dashboard implementation. The care members were delighted with the dashboard providing real-time scientific probabilities for IDH risk and historic predictive records in a graphic style. Other valuable functions were appended in the dashboard as well. Impact evaluation indicated a significant decrease in IDH rate after the application of AI assistance. This AI dashboard provides high-quality results in IDH risk prediction during hemodialysis. High-risk patients for IDH will be recognized 60min earlier, promoting individualized preventive interventions as part of the treatment plan. Our approachis believed to promise an excellent way for IDH management.

Influencing Factors Analysis IoT Adoption in Indonesian State Housing Management Information System

The Internet of Things (IoT) has revolutionized asset management in Smart Homes. In Indonesia, one of the biggest struggles in implementing emerging technology in the e-government system is connecting the users' behavioral intention with the suitability of the type of work and the technological convenience offered. A technology acceptance and use model is proposed to address this issue by analyzing the correlation between IoT adoption in asset management. The model draws insights from two complementary methods: user behavioral intention predicts utilization, and task-technology fit predicts performance with moderating factors analysis. The study finds that social influence, hedonic motivation, and price value factors are compelling users' intentions to adopt the Internet of Things in asset management. The IoT technology is deemed worth the price for asset management, particularly for state residences, due to its automation, accuracy, and real-time features. It also enhances decision-making, as the asset information is more reliable and secure. This study enriches moderating factors analysis to study the effect of technology adoption. Additionally, research on asset management systems still needs to be improved, especially in the government sector. In conclusion, this study provides insights into adopting IoT in the government sector and its potential to transform asset management.