Monitoring Data Processing Research Articles

Comparisons of the protein expressions between high myopia and moderate myopia on the anterior corneal stroma in human.

To investigate the differentially expressed proteins (DEP) between high myopia and moderate myopia on the anterior corneal stroma. Tandem mass tag (TMT) quantitative proteomics was utilized to reveal proteins. DEPs were screened by the multiple change of more than 1.2 times or less than 0.83 and the P value < 0.05. The DEPs were functional annotated by Gene Ontology (GO) terms. Proteins and protein interaction (PPI) networks were conducted with String online tool. Parallel reaction monitoring (PRM) data processing was used to verify the TMT proteomics results. There are 36 DEPs between high myopia and moderate myopia on the anterior corneal stroma, of which 11 proteins are upregulated, 25 proteins are downregulated. The GO analysis demonstrated keratinocyte migration and structural constituent of cytoskeleton that are significantly changed with most of the proteins decreased in high myopic corneas. Keratin 16 (KRT16) and erythrocyte membrane protein band 4.1-like protein 4B are the only two proteins involved in both functions. The PPI analysis showed keratin type II cytoskeletal 6A (KRT6A) and KRT16 that have strong connections. Immunoglobulin lambda variable 8-61(IGLV8-61) and nicotinamide phosphoribosyl transferase (NAMPT) have consistent results with the TMT. The high myopic corneas have 36 DEPs compared to the moderate myopic corneas on the anterior corneal stroma. Keratinocyte migrations and structural constituent of cytoskeleton are weakened in high myopic corneas, which may partly account for the lower corneal biomechanics in high myopic eyes. The lower expressed KRT16 plays important roles in high myopic corneas.

Multi-parameter comprehensive early warning of coal pillar rockburst risk based on DNN

A multi-parameter comprehensive early warning method for coal pillar-type rockburst risk based on the deep neural network (DNN) is proposed in this study. By utilizing preprocessed data from the surveillance of coal pillar impact hazards in Yangcheng Coal Mine, this study incorporates training samples derived from three distinct coal pillar-type impact hazard monitoring methodologies: microseismic monitoring, borehole cutting analysis, and real-time stress monitoring. The data characteristics of the monitoring data were extracted, evaluated, classified, and verified by monitoring the data of different working faces. This method was applied to develop the depth of multi-parameter neural network comprehensive early warning software in engineering practice. The results showed that the accuracy of the depth for burst monitoring data processing is improved by 6.89%–16.87% compared to the traditional monitoring methods. This method has a better early warning effect to avoid the occurrence of coal pillar rockburst hazard.

Long-Term Health Monitoring Data Processing on Post-Tensioned Concrete Box-Girder Bridge by Wavelet-Based

The concrete box-girder bridge is designed to have a long service life of around 100 years. To ensure safety and performance degradation during long service life, a Structural Health Monitoring System (SHMS) has been implemented in the box-girder bridge. SHMS can reliably assess structural response due to real-time applied loads, detect anomaly activities and locate the structural damage in the structure. Several sensors have been implemented in the bridge to continuously record the behavior of the bridge in all environmental conditions. Due to real-time natural conditions, false alarms occur frequently in SHM due to the disruption of noises and lead to misunderstanding of who is evaluating. Nevertheless, numerous SHM data that have been collected make it complicated to determine the anomaly of the structures. Therefore, it required signal processing to maximize the potentialities of the massive SHM data, as well as the efficiency of the time work. In this study, wavelet transformation, a rapid and unsupervised signal processing approach, was used to analyze the huge signal data by removing noise, and separating different signal sources as well. Further, with time-frequency analysis and multi-resolution capabilities, the transformation of wavelet is a promising tool for analyzing long-term SHM data. The suggested approach is shown by using long-term strain data from a 40 m concrete box-girder bridge in 24h. The results showed that after the denoising process, the highest discrepancy between the reconstructed and original strain signal is 2.73 μƐ and lost their energy less than 1%. Hence, the strain gauge sensor was successfully able to eliminate the noise through wavelet technology.

A BIM-based framework for road construction quality control and quality assurance

ABSTRACT Large-scale data processing for road construction monitoring may significantly improve the efficiency of quality control and quality assurance (QC&QA). However, the traditional construction data storage, management and analysis methods rely on paper documents and limited electronic files, leading to the low data utilisation, fuzzy data features and data value loss. This study proposes a BIM-based road QC&QA framework, which realises the real-time construction data integration in the information model, achieves data analysis and visualisation by the combination between the model and database, and uses structured construction data for QC&QA. Specifically, a BIM-oriented method for transmitting and storing road construction data in real-time, a BIM-oriented process for integrating road construction data, and a road QC&QA model based on AHP, ANN and GA throughout the entire road construction process are involved in the proposed framework. The proposed framework was tested through a case study using data from the Xintai Highway construction project. The results showed a significant improvement in data integration, visualisation and analysis during the QC&QA process. As a result, the overall construction quality of the Xintai project exceeded 95%, providing strong evidence of the effectiveness of the proposed framework.

Avatar-based patient monitoring improves information transfer, diagnostic confidence and reduces perceived workload in intensive care units: computer-based, multicentre comparison study

Patient monitoring is the foundation of intensive care medicine. High workload and information overload can impair situation awareness of staff, thus leading to loss of important information about patients’ conditions. To facilitate mental processing of patient monitoring data, we developed the Visual-Patient-avatar Intensive Care Unit (ICU), a virtual patient model animated from vital signs and patient installation data. It incorporates user-centred design principles to foster situation awareness. This study investigated the avatar’s effects on information transfer measured by performance, diagnostic confidence and perceived workload. This computer-based study compared Visual-Patient-avatar ICU and conventional monitor modality for the first time. We recruited 25 nurses and 25 physicians from five centres. The participants completed an equal number of scenarios in both modalities. Information transfer, as the primary outcome, was defined as correctly assessing vital signs and installations. Secondary outcomes included diagnostic confidence and perceived workload. For analysis, we used mixed models and matched odds ratios. Comparing 250 within-subject cases revealed that Visual-Patient-avatar ICU led to a higher rate of correctly assessed vital signs and installations [rate ratio (RR) 1.25; 95% CI 1.19–1.31; P < 0.001], strengthened diagnostic confidence [odds ratio (OR) 3.32; 95% CI 2.15–5.11, P < 0.001] and lowered perceived workload (coefficient − 7.62; 95% CI − 9.17 to − 6.07; P < 0.001) than conventional modality. Using Visual-Patient-avatar ICU, participants retrieved more information with higher diagnostic confidence and lower perceived workload compared to the current industry standard monitor.

Design of a laser welding plugging system for the steam generators of high-temperature gas-cooled reactor

As fourth-generation nuclear power equipment, high-temperature gas-cooled reactor (HTGR) is attracting more and more attention both in industry and academia. The core outlet temperature of the steam generator (SG) in HTGR is much higher than that of pressurized water reactor steam generators, which is more likely to cause damage. Till now, welding plugging is still one of the important means to prevent the leakage of damaged heat transfer tubes. However, how to improve the automation of welding plugging to reduce the short-range operation time in a radioactive environment is still difficult. This work proposes the design of a laser welding plugging system for the HTGR. Firstly, the design requirements are analyzed and the overall design scheme is given. The whole system consists of a laser generator, a multi-axis robot, and supporting devices. Secondly, the key supporting devices including a laser welding head, a pneumatic clamping jaw, and a single-axis motion platform are explained in detail. The structure simulation based on SolidWorks is adopted, which provides the basis for the design of the single-axis motion platform. Thirdly, the control system which takes the PLC control system as the main control unit and the industrial computer as the monitoring data processing unit is described. Finally, an experimental system is built and the laser welding experiment is carried out to test the feasibility of the designed laser welding plugging system.

Evaluating Characteristics of an Active Coastal Spreading Area Combining Geophysical Data with Satellite, Aerial, and Unmanned Aerial Vehicles Images

The northern region of the Maltese archipelago is experiencing lateral spreading landslide processes. This region is characterized by cliffs with a hard coralline limestone outcropping layer sitting on a thick layer of clay. Such a geological configuration causes coastal instability that results in lateral spreading which predispose to rockfalls and topplings all over the cliff slopes. The aim of this research was to develop a methodology for evaluating cliff erosion/retreat using the integration of geomatics and geophysical techniques. Starting from a 3D digital model of the Selmun promontory, generated by unmanned aerial vehicle (UAV) photogrammetry, it was possible to map the fractures and conduct geophysical measurements such as electrical resistivity tomography and ground penetrating radar for the identification and mapping of vertical fractures affecting the hard coralline limestone plateau, and to create a 3D geological model of the study area. In addition to this, high-accuracy orthophotos from UAV that were captured between 1957 and 2021 were georeferenced into a GIS and compared to aerial and satellite images. The movement and evolution of boulders and cracks in rocks were then vectorized to highlight, track and quantify the phenomenon through time. The results were used to derive a qualitative assessment of the coastal variations in the geometric properties of the exposed discontinuity surfaces to evaluate the volumes and the stop points of the observed rockfalls. The outcomes of this research were finally imported in a GIS which offers an easy approach for the collection and processing of coastal monitoring data. In principle, such a system could help local authorities to address social, economic and environmental issues of pressing importance as well as facilitate effective planning in view of a risk mitigation strategy.

Influence of hysterectomy due to leiomyoma on woman’s psychological status

The objective: to study the clinical-psychopathological features and psycho-emotional symptoms and borderline mental disorders in women of reproductive age after hysterectomy due to leiomyoma.Materials and methods. An assessment of the psychological state of 160 women after hysterectomy for leiomyoma was carried out. 90 patients of them (average age – 45.9±1.3 years old) had transvaginal hysterectomy, both classic and laparoscopically assisted one with opportunistic salpingectomy (I group) and 70 patients (average age – 47.2±1.6 years old) – abdominal hysterectomy with opportunistic salpingectomy (II group). The control group included 50 women 45.7±1.3 years old with asymptomatic leiomyoma. Clinical psychopathological, anamnestic and experimental psychological methods were used. Pathopsychological methods included methods of visual presentation of the personality profile using the Minnesota Multifaceted Personality Questionnaire. The survey was conducted at the pre-operative period, during the adaptation period and during 12 months. Statistical processing of monitoring data was carried out according to the φ-criterion using the statistical analysis software package STATISTICA v. 10 and Microsoft Office Excel application. Results. The results of the questionnaire determined that 44 (48.89 %) patients of the I group and 33 (47.14 %) women of the II group had disorders related to anxiety, and 6 (6.67 %) and 5 (7 .14 %) of the patients of both groups, respectively, were diagnosed depression before surgical treatment. After hysterectomy the number of patients with an anxiety disorder increased to 52 (57.78 %) in the I group and 41 (52.57 %) – II group, with depression – to 10 (11.11 %) and 8 (11.43 %) women, respectively. However, the psychopathological analysis with psychologists in 12 months after hysterectomy determined affective symptoms in 11 (15.7±7.0 %), and latent depression with a somatopsychic correlation, manifested by a feeling palpitation, cardialgias, vegetative disorders was found in 8 (11.4±6.0 %) cases. The type of personality accentuation influenced the development of psychoemotional manifestations. In this structure, the largest rate consisted of psychoasthenic and hysterical character traits. The age of the patient was important when analyzing additional psychological factors. Women under 44 years old associated hysterectomy with loss of femininity with premature aging of the organism. The patients with successful implementation of their role in the family and society had better postoperative psychological adaptation.Conclusions. There is a trend toward an increased number of anxiety and depressive disorders after hysterectomy in women with leiomyoma. After 12 months, affective symptoms were present in 11 (15.7±7.0 %) cases and in 8 (11.4±6.0 %) cases – hidden depression. Postoperative rehabilitation should include a consultation with a psychologist to identify and treat borderline mental disorders, which will lead to the initiation of compensatory and adaptive reactions of the body and the normalization of the psycho-emotional status.

A Data-Driven Approach to W-Beam Barrier Monitoring Data Processing: A Case Study of Highway Congestion Mitigation Strategy

In this paper, a data-driven approach is used to process W-Beam Barrier monitoring data, expecting to achieve online estimation of the number of trucks and accurate identification of barrier impact events. By analyzing the data features, significant noise was found in the original data, hiding the useful information, so this paper proposes an improved wavelet thresholding algorithm to achieve data denoising. As there is no study of the same application, this paper compares three commonly used data fault diagnosis algorithms: Principal Component Analysis (PCA), Partial Least Squares (PLS) and Fisher Discrimination Analysis (FDA). By designing and conducting comparison experiments, the results show that the PCA model is more suitable for estimating the number of trucks and the FDA model is more suitable for identifying barrier impact events. The data processing results are shared with the highway operation management system as a trigger condition to enable the strategy of forbidden truck overtaking. Through long-term application, the results show that highway capacity is improved by 12.7% and the congestion index and emissions are slightly reduced after adopting this paper’s method.

Nonlinear bridge deflection monitoring and prediction system based on network communication

Abstract In order to study the bridge deflection monitoring and prediction system based on network communication, first, the development status of the bridge deflection monitoring system, overall demand of the system, hardware composition of the system, realization of the system software, and the timely processing and analysis of monitoring data are discussed. Then, the dynamic prediction of the change trend of the external load and the decay information of the structural resistance contained in the deflection monitoring data is carried out, and the prediction function of the external effect of the structure is established at the same time. Finally, a rapid monitoring system specially designed to deal with accidental bridge disasters was developed, and it was installed and applied in the engineering experiment of Chongqing Gaojia Garden Bridge. The deflection data analysis of Gaojia Huayuan Bridge was carried out, and suggestions for the operation and maintenance of the bridge were put forward, and the safety status assessment of the bridge was realized. The results show that the system has strong practicability, real-time monitoring and accuracy. It provides a convenient and accurate way for bridge managers to supervise bridges and formulate specific bridge maintenance plans in a timely manner. During the radio frequency modulation of communication data, harmonic oscillations occur due to the nonlinear characteristics of oscillating data, so it is difficult to improve the wireless ability to modulate and demodulate transmitted data in communications. The traditional method uses neural network fuzzy control distribution estimation harmonic balance algorithm and nonlinear rolling. The performance of dynamic predictive control is poor in quality, harmonic balance, and stability control. An improved harmonic-based communication network is proposed to balance the stability control model of nonlinear communication system, construct nonlinear communication system model, and extract signal and channel characteristics of the communication system. The channel model is designed and the communication network control method is adopted to improve the control algorithm. The simulation results show that the proposed algorithm can be used to improve the stability of nonlinear communication system, reduce the bit error rate, overcome the interference of coherent component in sidelobe, and autocorrelated the impulse response of receiver. The stability of the cumulative output is good, which can overcome the communication error caused by the harmonic oscillation due to the nonlinear characteristics of the oscillating data, and improve the communication quality.

Stress-deformation monitoring and modelling of an underground marble quarry

Geotechnical instruments have been installed in a marble quarry that are dedicated for the monitoring of the mechanical behavior of rooms and pillars during the successive stages of concurrent surface and underground excavations. To facilitate fast and easy monitoring of stress and relative displacement changes with time, a web-driven platform was created for the collection, processing and presentation of monitoring data on the computer or mobile phone screen on an everyday basis. Since the collected data are local in character they are combined with the full-field three-dimensional solution of a Distinct Element Model. This combination of ground behavior monitoring with 3D numerical modeling data is proposed for the application of the observational or “experimental” method for the proper design and monitoring of safe underground marble quarries.

МАТЕМАТИЧНА МОДЕЛЬ МОНІТОРИНГУ ПОДІЙ В СИСТЕМІ УПРАВЛІНЯ РОЗПОДІЛЕНИМИ БАЗАМИ ДАНИХ НА ОСНОВІ СЕНСОРІВ ПОДІЙ

Methods of increasing the effectiveness of threat detection in distributed databases using the event monitoring system are considered in the work. It is noted that the system works on the basis of the event monitoring model of heterogeneous distributed databases. This model involves three stages of event processing, which are based on appropriate methods. It is highlighted that the mechanisms underlying the functioning of the mentioned methods should be reduced to a single data format to eliminate the possible appearance of incorrect work in future calculations. The event processing method on the monitoring server allows for processing event matrices and transferring the function to control tools, on the basis of which appropriate decisions are made to improve reliability. The article develops a modified method of analyzing and monitoring events in non-relational distributed databases. Event monitoring options for lookup operations in distributed databases are offered. To confirm the theoretical results, mathematical modeling and experimental research on monitoring parameters and data processing speed on the event monitoring server were carried out. The number of detected incidents and the number of incorrectly identified events were studied, which in turn allowed for the protection of information from unauthorized actions or changes. Also, it should be emphasized that the model proposed in the work describes detailed monitoring of requests, transactions, and stored procedures in a database with information about possible incidents in real time and prompts intrusion warnings. In addition, the features of the proposed model make it possible to investigate and monitor the emergence of new vulnerable segments of the database and promptly respond to them, eliminating threats to access channels. The paper compares the speed of data processing based on files and database tables, which allows for fully performing experiments and confirming analytical data experimentally.

Benchmarking a multi-layer approach and neural network architectures for defect detection in PBF-LB/M

The substitution of expensive non-destructive material testing by data-based process monitoring is intensively explored in quality assurance for additive manufactured components. Machine learning show promising results for defect detection but require conceptual adaption to layer wise manufacturing and line scanning patterns in laser powder bed fusion. A multi-layer approach to co-register µ-computer tomography measurements with process monitoring data is developed and a workflow for automatic data set generation is implemented. The objective of this research is to benchmark the volumetric multi-layer approach and specifically selected deep learning methods for defect detection. The volumetric approach shows superior results compared to single slice monitoring. All investigated structured neural network topologies deliver similar performance. • Machine learning identifies correlations between µ-CT porosity scans and melt pool monitoring • 3D-multi-layer-based machine learning outperforms single layer approach in defect detection • Saturation of detection performance is observed beyond optimal number of layers • Results hold independently for all investigated neural network topologies

EFEKTIVITAS DAN EFISIENSI APLIKASI PEMBAYARAN TAGIHAN TERHADAP PENDAPATAN BULANAN INDIHOME PADA PT.TELEKOMUNIKASI,TBK WITEL MEDAN

Payment of receivables collection at PT. Telekomunikasi Indonesia (IndiHome) has a payment system that follows the times. Payment Collection is one part of the company that is in charge of processing monitoring and billing data for consumers who are negligent in making payments and making reports on billing evidence, reports of consumer internet disturbances and then input into the data system which will later be seen by the regional. This study aims to see which is more effective and efficient between cash payments and non-cash payments (digital). This study uses qualitative methods with primay data types. Based on the research results, IndiHome payment transactions, both cash and non-cash, have their respective advantages and disadvantages. IndiHome payments in cash can be said to be effective because customers get payment receipts in detail, clearly and transparently. However, cash payments cannot be said to be efficient because it uses such a long process. One of them is that customers have to come and queue to pay monthly for IndiHome and this can take a long time, requires energy and money. In contrast to non- cash payments (digital). Non-effective payments have not been said to be effective because of frequent errors in making payments such as different bills, bills that do not appear, and excess payments. However, for the efficiency of digital payments, it is said to be quite efficient because the payment process is quite easy and fast. Digital payments can be made anywhere and anytime.

Fatigue life and fatigue reliability assessment for long-span spatial structure based on long-term health monitoring data

Based on the structural health monitoring system of long-span cantilevered spatial structure in Taiyuan Botanical Garden, the welded details strain of the main truss welded tubular joints is monitored for a long time, and the massive monitoring data processing method and the structural fatigue performance evaluation method are studied. First, the key component of the structure is determined through using the measured data, to propose the empirical mode decomposition (EMD) method to compress the load time history, and the rain-flow counting method to extract the stress cycle to obtain the standard daily stress spectrum. Next, through the Palmgren-Miner linear cumulative damage criterion and the S-N curve, the fatigue damage analysis and fatigue life estimation method are established. Consequently, the Monte-Carlo method is used to analyze the fatigue reliability of the key component, on which basis, the influence of considering the load growth effect on the fatigue life and fatigue reliability index is analyzed. The study shows that the weld fatigue damage is mainly caused by the small number of medium amplitude strain cycles. When the load growth effect is considered, the fatigue damage development rate of the weld is significantly accelerated, resulting in a significant decrease in the fatigue life and fatigue reliability index of the weld.

Real-World Application of Open-Path UV-DOAS, TDL, and FT-IR Spectroscopy for Air Quality Monitoring at Industrial Facilities

Open-path spectroscopy is known for its ability to provide real-time measurements of dozens of compounds over sampling paths of up to 1000 meters in length. Advances in open-path monitoring technology and data processing techniques, coupled with new regulatory requirements, have greatly increased the acceptance and widespread application of spectroscopy-based open-path measurements. Large industrial facilities adjacent to residential communities are a particular application of interest, because traditional fixed-point analyzers lack the spatial coverage of the open-path instruments. This work discusses technical and practical considerations for the installation and operation of more than 120 open-path analyzers that are currently providing continuous data at several oil refineries in California. Open-path analyzers include ultraviolet differential optical absorbance spectroscopy (UV-DOAS), Fourier transform infrared (FT-IR), and tunable diode laser (TDL) technologies. We will discuss lessons learned from these projects, including fundamental approaches to compound identification, target species detectability, interferences, and data management.

Automatic arrival-time picking of P- and S-waves of microseismic events based on object detection and CNN

Automatic, quick and accurate picking of the arrival-times of both the P- and S-waves of microseismic (MS) events is essential to real-time data processing for MS monitoring. This study proposes a two-stage arrival-time picking method that involves extracting the region of interest of the waveform and P- and S-wave arrival-time prediction. The dataset collects 6107 MS signals with low and high signal-to-noise ratios to train and test the proposed method. The YOLO network is employed to extract the MS signal region to narrow the range of arrival-time picking and significantly improve the problem of serious interference caused by excessive negative samples in the MS signal. Then, the convolutional neural network is utilised further to precisely pick the arrival-times of both the P- and S-waves. The results showed that the proposed two-stage method is more accurate than the one-stage method in picking the arrival-time. The effects of different convolutional kernel sizes and fully connected layers on the two-stage model are also discussed. Furthermore, a new multi-index comprehensive evaluation method that considers both the accuracy and the size of the model is proposed to further evaluate the proposed model's practicality. This study presents a new method suitable for both P- and S-wave arrival-time picking to enable MS monitoring, which is valuable for various rock engineering applications.

Data-Driven Approach toward Long-Term Equipment Condition Assessment in Sterile Drug Product Manufacturing.

A two-stage data-driven methodology for long-term equipment condition assessment in drug product manufacturing is presented with a case study for a commercially operating aseptic filling line. The methodology leverages process monitoring data. Sensor measurements are partitioned using process information and maintenance schedules that are available on different databases. Data is processed to tackle heterogeneity in sources and formats. The data is cleaned to remove the effects of short-term variabilities and to enhance underlying long-term trends. Two approaches are presented for data analysis: first, anomaly detection using independent component analysis (ICA), where clusters of outliers are identified. The frequency and timing of such outliers yield important insights regarding maintenance schedules and actions. The second approach enables condition monitoring using principal component analysis (PCA). Long-term operational baselines are identified and shifts therein are linked with different process and equipment faults. This approach highlights the impact of equipment deterioration on shifting operational data baselines and shows the potential for the combined application of ICA and PCA for equipment condition monitoring. It can be applied within predictive maintenance applications where the installation of new specialized sensors is difficult, like in the pharmaceutical industry.

Statistical Methods for Handling Nondetected Results in Food Chemical Monitoring Data to Improve Food Risk Assessments

Chemical risk assessment is important for risk management, and estimates of chemical exposure must be as accurate as possible. Chemical concentrations in food below the limit of detection are known as nondetects and result in leftcensored data. During statistical analysis, the method used for handling values below the limit of detection is important. Many risk assessors employ widely used substitution methods to treat left-censored data, as recommended by international organizations. The National Institute of Food and Drug Safety Evaluation of South Korea also recommend these methods, which are currently used for chemical exposure assessments. However, these methods have statistical limitations, and international organizations recommend more advanced alternative statistical approaches. In this study, we assessed the validity of currently used statistical methods for handling nondetects. The best method was determined based on a simulation study. In three case studies, we compared the various methods based on the root mean squared error. The data for all case studies were from the same source, to avoid heterogeneity. Across various sample sizes and nondetection rates, the mean and 95th percentile values for all treatment methods were similar. However, “log normal maximum likelihood estimation” method was not suitable for estimating the mean. Risk assessors should consider statistical processing of monitoring data to reduce uncertainty. Currently used substitution methods are effective and easy to apply to large data sets with nondetection rates &lt; 80%. However, advanced statistical methods are required in some circumstances, and national guidelines are needed regarding their use in risk assessments.

Development of Automatic Real Time Inventory Monitoring System using RFID Technology in Warehouse

RFID technology is one of the technologies in logistics as an important application in logistics operations and supply chain management. The application of RFID technology can be applied to the inventory control monitoring system in real-time. The inventory monitoring information system can replace the manual system with a computerized system so that the processing of monitoring data is more efficient, effective, and can be controlled directly and accurately. This study presents a case study of a real stock monitoring system based on RFID technology. The design of a real-time stock monitoring system is transitioning from manual to technology by involving computerization in its implementation. This study aims to design an RFID-based real-time stock monitoring system and integrate warehousing systems in the company. The real-time inventory stock monitoring system is still developing, so a simulation is carried out to compare the existing data with the data from the RFID system. We used the existing warehouse layout to try the efficiency of the RFID stock monitoring. Based on the research results, the RFID system increases the efficiency and effectiveness of inventory control. In further research, it is necessary to integrate the inventory optimization model with real-time inventory control with RFID. The integration of real-time monitoring technology can be used as input to the inventory optimization model to be more accurate in providing purchasing policies.