Average Processing Time Research Articles

CAM-based orchard path detection for developing an unmanned sprayer

This study was conducted to apply a deep learning model to identify and visualise path areas in an orchard. Data was collected by attaching an image capture device to the front of a sprayer and driving it through an orchard. The collected data was classified into four classes: ground, trees, sky and obstacles for pre-processing for training. Sliding window techniques were used on the image dataset for model training and performance. The image was sampled using a sliding window method with 224x224 pixels and divided into train, validation, and test sets. A modified VGG16 algorithm was implemented and used to train the preprocessed image dataset. The performance results of this model showed an accuracy of up to approximately 99% on both the training and validation sets, and after building a confusion matrix using the test set, the classification performance was evaluated and showed an F1 score of 0.96. To visualise the results of this trained learning model, class activation maps were used to detect the paths in the orchard. The implementation of this method resulted in an average processing time of about 0.94 seconds per frame on the orchard footage, which could potentially be beneficial in real-time decision making scenarios in fruit farming where rapid response is important.

The real-time vision measurement of multi geometric information of the rolled ring in the hot ring rolling process based on deep learning

Hot ring rolling (HRR) is an advanced incremental metal-forming technology to manufacture high-performance rings. Nowadays, the mechanical or laser-sensors measurement methods can only obtain one value of the dimensions on the ring circumference per time unit, the measurement data is single and the information is limited. This paper presents a real-time vision measurement method for multi geometric information of the rolled ring such as ring’s diameter, center position, circularity and growth speed in HRR process. Firstly, the deep learning model is constructed to intelligently and quickly identify rolled ring targets from complex image backgrounds, then the adaptive linear gray scale transformation algorithm is proposed to adjust the gray scale contrast of the image according to changes in ring temperature, the interference of oxide scales and flying chips are eliminated by morphology operation. Finally, the least square method is used to fit the contour points, and further the rolled ring’s geometric information and corresponding variation trend are calculated. Several experiments are conducted on a vertical HRR mill, the geometric information of the rolled ring during the HRR is measured in real-time under the interference of harsh working conditions. For the ring with outer diameter of 350 mm, the measurement error of ring’s outer and inner diameter is less than 0.8 mm, the average processing time per image takes about 80 ms. The research provides a basis for accurate measurement of HRR process.

Deep learning based structural damage identification for the strain field of a subway bolster

Strain-based structural health monitoring technology has been widely used in the field of transportation. The existing strain damage identification methods have defects such as complex process, lag in state evaluation, and low intelligence. This paper adopts the deep learning method to establish a network model that uses the strain field information to map directly to the damage information, and takes a subway bolster as the engineering background to realise the end-to-end automatic damage identification. Firstly, the problem of damage identification in strain field is described, combined with the idea of fully convolutional network. The basic structure of damage identification network is modularised, and the overall design framework is proposed. Then, the damage simulation method is determined, and the feasibility of using this method to construct a strain field damage dataset is verified. The batch random damage model generation and the random noise signal addition program are coded, and the datasets of the bolster under static/dynamic force are obtained. Finally, the deep learning model is applied to the bolster damage dataset, and a residual module BolRes_Att that integrates spatial attention and channel attention mechanism is proposed. It has better damage identification performance without an increase in model parameters. The average number of faulty elements on the two bolster test sets of the improved damage identification network is 3.02 and 2.92, respectively, accounting for about 0.016% of all elements. The average processing time for a set of data is only 0.014 s. The results show that the deep learning model constructed in this paper can accurately and quickly identify the damage information of elements according to the strain field information.

Feature information extraction method for narrow gap U-type groove based on laser vision

The precise measurement of weld groove position and geometric dimensions serves as the foundation for predicting and planning welding strategies and process parameters. The steep sidewalls of narrow gap U-type grooves pose challenges for sensing and detection during the welding process. This paper presents a feature information extraction method for narrow gap U-type grooves based on laser vision technology. Firstly, the image acquisition system is calibrated using the incident plane of the laser beam. Subsequently, a digital image processing technique is employed to enhance the collected images, combining a 3D spatial domain enhancement algorithm with a laser stripe image refinement method based on Euclidean distance transformation. Finally, the transverse and vertical positions of the groove, as well as its geometric features such as width, depth, assembly gap, and misalignment, are computed and measured using feature point positioning. The proposed algorithm is experimentally verified for its accuracy, robustness, and real-time performance. The results indicate that the method effectively suppresses unevenness in laser stripe brightness and mitigates random noise in the captured images. The average measurement errors for transverse position, vertical position, width, depth, assembly gap, and misalignment of the groove are 0.0857 mm, 0.0987 mm, 0.1664 mm, 0.2216 mm, 0.0935 mm, and 0.0997 mm, respectively. Furthermore, the average image processing time per frame is 58 ms, meeting the requirements of pre-weld detection in arc welding.

Slope displacement detection in construction: An automated management algorithm for disaster prevention

In construction sites, embankment slope failure has led to catastrophic events, necessitating advanced monitoring for disaster prevention. This study presents a novel Slope Displacement Inspection and Management Algorithm (SDIMA) that utilizes Global Navigation Satellite System (GNSS) data to detect significant slope displacements automatically. SDIMA analyzes GNSS data to discern displacements in various directions (North-South, East-West, Vertical) and separate actual change points from measurement errors by employing a robust mathematical model called Displacement Detector. It successfully detected change points with displacements greater than two times the samples' standard deviation, and thresholds were set for displacements ≥ 25 mm. A comparative analysis showed promising efficiency in North-South detection, while certain limitations were identified. The proposed SDIMA outperforms existing models, achieving a Mean Absolute Error (MAE) of 3.157 mm, Root Mean Square Error (RMSE) of 4.754 mm, Mean Absolute Scaled Error (MASE) of 0.294, and an average processing time of 0.578 min, making it a highly efficient and accurate expert system. The innovation of SDIMA lies in its ability to provide an automated decision support tool for site managers, laying the foundation for a low-cost anomaly detection system for slope failure disaster prevention. The algorithm's proficiency and its potential to integrate with existing practices herald a significant advancement in construction management and safety.

DeepNet model empowered cuckoo search algorithm for the effective identification of lung cancer nodules.

Globally, lung cancer is a highly harmful type of cancer. An efficient diagnosis system can enable pathologists to recognize the type and nature of lung nodules and the mode of therapy to increase the patient's chance of survival. Hence, implementing an automatic and reliable system to segment lung nodules from a computed tomography (CT) image is useful in the medical industry. This study develops a novel fully convolutional deep neural network (hereafter called DeepNet) model for segmenting lung nodules from CT scans. This model includes an encoder/decoder network that achieves pixel-wise image segmentation. The encoder network exploits a Visual Geometry Group (VGG-19) model as a base architecture, while the decoder network exploits 16 upsampling and deconvolution modules. The encoder used in this model has a very flexible structural design that can be modified and trained for any resolution based on the size of input scans. The decoder network upsamples and maps the low-resolution attributes of the encoder. Thus, there is a considerable drop in the number of variables used for the learning process as the network recycles the pooling indices of the encoder for segmentation. The Thresholding method and the cuckoo search algorithm determines the most useful features when categorizing cancer nodules. The effectiveness of the intended DeepNet model is cautiously assessed on the real-world database known as The Cancer Imaging Archive (TCIA) dataset and its effectiveness is demonstrated by comparing its representation with some other modern segmentation models in terms of selected performance measures. The empirical analysis reveals that DeepNet significantly outperforms other prevalent segmentation algorithms with 0.962 ± 0.023% of volume error, 0.968 ± 0.011 of dice similarity coefficient, 0.856 ± 0.011 of Jaccard similarity index, and 0.045 ± 0.005s average processing time.

Deep learning-based framework for the observation of real-time melt pool and detection of anomaly in wire-arc additive manufacturing

ABSTRACT Object detection has become a popular tool of deep learning in the era of digital manufacturing. In this study, the most powerful and efficient object detection algorithm, i.e., You Only Look Once (YOLO) algorithm, was used to detect anomalies in deposited beads of wire-arc additive manufacturing (WAAM) using melt pool images. This study used the latest version of YOLO algorithm to train and validate the custom image dataset of the melt pool obtained by conducting experiments using a robotic-controlled WAAM. The mean average precision (mAP) for the “Regular bead” class and the “Irregular bead” class reached 99% at an Intersection over Union (IoU) threshold of 0.5, for both training and validation. When the model was tested for new or unseen datasets by conducting four new experimental trials, the mAP value for the “Regular bead” class reached 98.47% and for the “Irregular bead” class reached 96.68% at an average processing time of 0.014 s/frame. The object detection algorithm YOLO has shown an excellent processing time of 15 ms per frame, which shows its potential for real-time application in the manufacturing industry.

Performance evaluation of the cloud computing application for IoT-based public transport systems

The object of research is cloud computing as an element of the server infrastructure for intelligent public transport systems. Given the increasing complexity and requirements for modern transportation, the application of the Internet of Things concept has a high potential to improve efficiency and passenger comfort. Since the load generated in IoT systems is dynamic and difficult to predict, the use of traditional infrastructure with dedicated servers is suboptimal. This study considers the use of cloud computing as the main server infrastructure for the above systems. The paper investigates the main cloud platforms that can be used to develop such systems and evaluates their advantages and disadvantages. The authors developed the overall architecture of the system and evaluated the performance and scalability of individual components of the server infrastructure. To test the system, a software emulator was developed that simulates the controller module installed in vehicles. Using the developed emulator, stress tests were conducted to analyze and confirm the ability to scale and process input data by the proposed architecture. The test scenarios were developed and conducted on the basis of the existing public transportation system in Kyiv, Ukraine. The experimental results showed that the proposed IoT architecture is able to scale efficiently according to the load generated by the connected devices. It has been found that when the number of incoming messages increases from 40 to 6000, the average message processing time remains unchanged, and the error rate does not increase, which is an indicator of stable system operation. The obtained results can be used in the development of modern public transport systems, as well as for the modernization of existing ones

DETEKSI AREA KERUSAKAN PADA CITRA TERUMBU KARANG AKIBAT CORAL BLEACHING BERBASIS PENGOLAHAN CITRA DIGITAL

Coral reefs have a very large role in marine ecosystems, because they provide rich habitats and support biodiversity. Segmentation on images of coral reefs experiencing coral bleaching is also very important. Coral bleaching is a phenomenon in which coral reefs lose their symbiotic algae pigments, resulting in bleaching and possibly death of coral reefs. By segmenting coral reef images that experience coral bleaching, we can separate areas affected by bleaching from areas that are still healthy. This study aims to detect areas of damage that occur in underwater images of coral reefs that have experienced coral bleaching. By using techniques in digital image processing and segmentation based on colour intensity, satisfactory results are obtained. The test results show that the RAE and ME values are quite low, namely 0.051 and 0.035 respectively with an average processing time of 0.2 seconds. This research is expected to assist in further analysis and modelling related to coral bleaching to understand the causative factors and develop appropriate protection strategies.

A Low-Complexity Accurate Ranging Algorithm for a Switch Machine Working Component Based on the Mask RCNN

According to the intelligent development needs of railway operation and maintenance, turnout maintenance also needs an efficient and intelligent means of detection. It is the main method used to measure the access depth of static contact manually. In order to change the disadvantages of the low efficiency and strong subjectivity of traditional schemes, a low-complexity accurate ranging algorithm of the Mask RCNN is proposed to measure the on–off working parts. Firstly, the Mask RCNN and an interactive iterative method are used to segment the region of interest accurately twice. Secondly, the graph distortion is corrected according to the vertex mapping principle. Finally, the accurate actual distance is calculated through fitting the linear distance transformation equation. Through the secondary segmentation and correction algorithm, the accurate calculation of a small target is completed. The experimental results show that the algorithm can accurately measure the distance of different working parts; the average processing time is 0.8 s/amplitude and the measurement error is ±1 mm.

Lighting system automation using a relay based on radio frequency identification tag input and kiosks’ information access with Telegram application in the modern market

Advancements in technology necessitate the swift and efficient management of systems, particularly concerning electricity consumption and information dissemination. To address this, a prototype was developed to automate lighting systems and display kiosk information in modern markets. Key components utilized include an RFID reader, GPS module, NodeMCU ESP8266, and relays, all of which played crucial roles in the functioning of the prototype. The primary objective was to create a device capable of automatically controlling the lighting system through relays, triggered by RFID inputs, while also relaying kiosk information via the Telegram application. For instance, when a registered RFID tag is tapped while the shop is open and the lamp is initially on, the lamp will be switched off, ensuring energy efficiency and timely response. The GPS module is employed to obtain location data, which, along with kiosk open/close status, can be conveniently accessed through the Telegram app. This integration of the GPS module enhances the prototype's functionality by providing valuable location-based information, making it easier for users to monitor and access the information remotely. Test results demonstrate that the RFID tag can be read from a maximum distance of 4.5 cm, with an average processing time of 2.47 seconds for lamp switching and 5.6 seconds for accessing information. These performance metrics validate the efficacy of the prototype methodology in achieving its intended goals of automation, energy efficiency, and seamless information dissemination in modern markets.

Classifying BISINDO Alphabet using TensorFlow Object Detection API

Indonesian Sign Language (BISINDO) is one of the sign languages used in Indonesia. The process of classifying BISINDO can be done by utilizing advances in computer technology such as deep learning. The use of the BISINDO letter classification system with the application of the MobileNet V2 FPNLite SSD model using the TensorFlow object detection API. The purpose of this study is to classify BISINDO letters A-Z and measure the accuracy, precision, recall, and cross-validation performance of the model. The dataset used was 4054 images with a size of consisting of 26 letter classes, which were taken by researchers by applying several research scenarios and limitations. The steps carried out are: dividing the ratio of the simulation dataset 80:20, and applying cross-validation (k-fold = 5). In this study, a real time testing using 2 scenarios was conducted, namely testing with bright light conditions of 500 lux and dim light of 50 lux with an average processing time of 30 frames per second (fps). With a simulation data set ratio of 80:20, 5 iterations were performed, the first iteration yielded a precision result of 0.758 and a recall result of 0.790, and the second iteration yielded a precision result of 0.635 and a recall result of 0.77, then obtained an accuracy score of 0.712, the third iteration provides a recall score of 0.746, the fourth iteration obtains a precision score of 0.713 and a recall score of 0.751, the fifth iteration gives a precision score of 0.742 for a fit score case and the recall score is 0.773. So, the overall average precision score is 0.712 and the overall average recall score is 0.747, indicating that the model built performs very well.

Multilevel Thresholding of Color Image Segmentation Using Memory-based Grey Wolf Optimizer With Otsu Method, Kapur, and M.Masi Entropy

Determining the optimal threshold value for image segmentation has become more attention in recent years because of its varied uses. Otsu-based thresholding methods, minimum cross entropy, and Kapur entropy are efficient for solving bi-level thresholding image segmentation problems (BL-ISP), but not with multi-level thresholding image segmentation problems (ML-ISP). The main problem is exponentially increasing computational complexity. This study uses the memory-based Gray Wolf Optimizer (mGWO) to determine the optimal threshold value for solving ML-ISP on RGB images. The mGWO method is a variant of the standard grey wolf optimizer (GWO) that utilizes the best track record of each individual grey wolf for the global exploration and local exploitation phases of the problem solution space. The solution candidates are represented by each grey wolf using the image intensity values and optimized according to mGWO characteristics. Three objective functions, namely the Otsu method, Kapur Entropy, and M.Masi Entropy are used to evaluate the solutions generated in the optimization process. The GridSearch method is used to determine the optimal parameter combination of each method based on 10 training images. Evaluation of the performance of the mGWO method was measured using several benchmark images and compared with five standard swarm intelligence (SI) methods as benchmarks. Analysis of the results was carried out qualitatively and quantitatively based on the average PSNR, RMSE, SSIM, UQI, fitness value, and CPU processing time from 30 tests. The results were analyzed further with the Wilcoxon signed-rank test. The experimental results show that the performance of the mGWO method outperforms the benchmark method in most experiments and metrics. The mGWO variant also proved to be superior to the standard GWO in resolving multi-level color image segmentation problems. The mGWO performance results are also compared with other state-of-the-art SI methods in solving ML-ISP on grayscale images and was able to outperform those methods in most experiments.

SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation

As a highly productive rice, ratoon rice is widely planted worldwide, but the rolling of rice stubble in mechanical harvesting severely limits its total yield; based on this, some scholars have proposed rolled rice stubble righting machines. However, limited by the uncertainty of the field environment, the machine’s localization accuracy of the target needs to be improved. To address this problem, real-time detection of rolled rice stubble rows is a prerequisite. Therefore, this paper introduces a deep learning method for the first time to achieve this. To this end, we presented a novel approach to improve a model that is used for the simplification of Mask R-CNN, which does not require any modules to be added or replaced on the original model. Firstly, two branches in the second stage were deleted, and the region proposals output from the stage was used directly as the mask generation region, and segmentation performance was substantially improved after a simple optimization of the region proposals. Further, the contribution of the feature map was counted, and the backbone network was simplified accordingly. The resulting SMR-RS model was still able to perform instance segmentation and has better segmentation performance than Mask R-CNN and other state-of-the-art models while significantly reducing the average image processing time and hardware consumption.

Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion.

Unmanned Aerial Vehicle (UAV) inspection of transmission channels in mountainous areas is susceptible to non-homogeneous fog, such as up-slope fog and advection fog, which causes crucial portions of transmission lines or towers to become fuzzy or even wholly concealed. This paper presents a Dual Attention Level Feature Fusion Multi-Patch Hierarchical Network (DAMPHN) for single image defogging to address the bad quality of cross-level feature fusion in Fast Deep Multi-Patch Hierarchical Networks (FDMPHN). Compared with FDMPHN before improvement, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) of DAMPHN are increased by 0.3 dB and 0.011 on average, and the Average Processing Time (APT) of a single picture is shortened by 11%. Additionally, compared with the other three excellent defogging methods, the PSNR and SSIM values DAMPHN are increased by 1.75 dB and 0.022 on average. Then, to mimic non-homogeneous fog, we combine the single picture depth information with 3D Berlin noise to create the UAV-HAZE dataset, which is used in the field of UAV power assessment. The experiment demonstrates that DAMPHN offers excellent defogging results and is competitive in no-reference and full-reference assessment indices.

Denoising method for weak velocity test signals under laser-induced shock waves based on attention-guided multi-scale convolutional neural network

In the research of characteristics of laser-induced shock waves via the laser interference velocity detection method, noise reduction for weak echo signals still relies on the manual setting of filtering parameters, alongside the problem of the loss of useful signals caused by global filtering and excessive filtering. To solve these problems, a multi-scale network for extraction of sparse features, in combination with the ECA channel module, was established in this study to acquire the features of multi-dimensional and multi-fine-granularity noise signals in a self-adaptive manner. By integrating various channel information of the deep/shallow features through skip connection, the deep features were fully observed without ignoring the impacts of the shallow features. Finally, the attention mechanism was adopted to adjust the weight of the features at different locations in the feature space to acquire an accurate representation of stochastic noise, and noise reduction for weak signals was achieved by reconstructing the module and removing the learned noise features from the noisy data. According to the simulation and test results, the method proposed in this study helped extract the noise information from weak signals in an accurate manner; unlimited by the forms and sizes of the noise and the working conditions and independent from manual setting of parameters, it eliminated the blind-source noise from weak echo signals effectively; with this method, the initial position and the coupled peak value were extracted accurately from the weak signals. In terms of the effects of the method, the phenomena of “peak clipping” and “breakpoint” caused by excessive filtering were avoided; compared to global filtering, time-frequency domain filtering and other methods, the method proposed outperformed the conventional ones for a peak value error of only 0.19%; as for the denoising velocity, simultaneous input of multiple pieces of data as well as simultaneous output of denoising outcomes in a real time manner were also achieved under this method, with the averaged processing time for a single piece of data (0.051 s) far superior to the result under other methods.

Using Lean Management to Improve the Nursing Admission Process for COVID-19 Patients in the Intensive Care Unit

Safe and efficient operational workflows in nursing care can alleviate workloads, enhance quality of care, and improve job satisfaction. A recent survey indicates that the admission process for patients with COVID-19 in nursing care is excessively lengthy due primarily to the waiting time for physicians to enter the ward, external support, and frequent reorganization of medical materials. Inadequate organization measures have resulted in requisite materials not being centralized, leading to increased travel times, interruptions in nursing records maintenance, unprofessional explanations, and time-consuming consent form signing processes. In this project, lean management was implemented to reduce the time spent on the nursing admission process for patients with COVID-19. The nursing admission process and job responsibilities were revised. Furthermore, new policies were implemented, including introducing remote consent form signing, using intelligent digital health education assistants, revising related inventory processes, and planning admission nursing carts to streamline the admission process. The average processing time in the isolation room was reduced by 30.5% from 105 minutes to 73 minutes; the average time spent by nurses on the admission process was reduced by 34.1% from 504 minutes to 332 minutes; and nursing satisfaction levels rose from 55.4% to 82.7%. In this project, lean management was used to investigate the nursing admission process for patients with COVID-19 and a value stream map was compiled to identify low-value activities within the process. Through the implementation and standardization of project measures, processing time was effectively reduced, manual labor was minimized, and job satisfaction improved.

Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing

The explosive growth of users and applications in IoT environments has promoted the development of cloud computing. In the cloud computing environment, task scheduling plays a crucial role in optimizing resource utilization and improving overall performance. However, effective task scheduling remains a key challenge. Traditional task scheduling algorithms often rely on static heuristics or manual configuration, limiting their adaptability and efficiency. To overcome these limitations, there is increasing interest in applying reinforcement learning techniques for dynamic and intelligent task scheduling in cloud computing. How can reinforcement learning be applied to task scheduling in cloud computing? What are the benefits of using reinforcement learning-based methods compared to traditional scheduling mechanisms? How does reinforcement learning optimize resource allocation and improve overall efficiency? Addressing these questions, in this paper, we propose a Q-learning-based Multi-Task Scheduling Framework (QMTSF). This framework consists of two stages: First, tasks are dynamically allocated to suitable servers in the cloud environment based on the type of servers. Second, an improved Q-learning algorithm called UCB-based Q-Reinforcement Learning (UQRL) is used on each server to assign tasks to a Virtual Machine (VM). The agent makes intelligent decisions based on past experiences and interactions with the environment. In addition, the agent learns from rewards and punishments to formulate the optimal task allocation strategy and schedule tasks on different VMs. The goal is to minimize the total makespan and average processing time of tasks while ensuring task deadlines. We conducted simulation experiments to evaluate the performance of the proposed mechanism compared to traditional scheduling methods such as Particle Swarm Optimization (PSO), random, and Round-Robin (RR). The experimental results demonstrate that the proposed QMTSF scheduling framework outperforms other scheduling mechanisms in terms of the makespan and average task processing time.

Sheep Face Detection Based on an Improved RetinaFace Algorithm.

The accurate breeding of individual sheep has shown outstanding effectiveness in food quality tracing, prevention of fake insurance claims, etc., for which sheep identification is the key to guaranteeing its high performance. As a promising solution, sheep identification based on sheep face detection has shown potential effectiveness in recent studies. Unfortunately, the performance of sheep face detection has still been a challenge due to diverse background illumination, sheep face angles and scales, etc. In this paper, an effective and lightweight sheep face detection method based on an improved RetinaFace algorithm is proposed. In order to achieve an accurate and real-time detection of sheep faces on actual sheep farms, the original RetinaFace algorithm is improved in two main aspects. Firstly, to accelerate the speed of multi-scale sheep face feature extraction, an improved MobileNetV3-large with a switchable atrous convolution is optimally used as the backbone network of the proposed algorithm. Secondly, the channel and spatial attention modules are added into the original detector module to highlight important facial features of the sheep. This helps obtain more discriminative sheep face features to mitigate against the challenges of diverse face angles and scale in sheep. The experimental results on our collected real-world scenarios have shown that the proposed method outperforms others with an F1score of 95.25%, an average precision of 96.00%, a model size of 13.20 M, an average processing time of 26.83 ms, and a parameter of 3.20 M.

Optimizing transaction data performance in database management systems

One indicator of the quality of an information system is the speed of data processing. A database's most common data processing operations are adding, displaying, changing, and deleting data. The amount of data stored in the database significantly impacts the performance of data processing and, therefore, the performance of information systems. The update command changes some or all of the data in a table. The update command works by retrieving the data in the table to be changed, entering the new data in a form, and then sending it back to the database. The update command is often combined with a condition specifying which data rows must be changed. This research is an experimental study that compares the use of the update command with a stored procedure to the use of the update command without a stored procedure. The results showed that the average processing time for the update command with the stored procedure was 348.896 milliseconds for the minimum data category, 266.462 milliseconds for the medium data category, and 279.543 milliseconds for the maximum data category. The average processing time for the update command without a stored procedure was 297.132 milliseconds for the minimum data category, 747.670 milliseconds for the medium data category, and 1256.273 milliseconds for the maximum data category. These results suggest that the update command with a stored procedure is more efficient than the one without a stored procedure. This is because the stored procedure can pre-compile the SQL statement, which reduces the time it takes to execute the statement.