Average Processing Time Research Articles

Automatic loading method for robot guided by 3D vision for scattered and stacked shoe soles

This paper primarily introduces a method for the automated feeding of scattered stacked shoe soles using a 3D visual-guided robot. Initially, addressing issues like slow speed in pose estimation and poor robustness during the robot sorting and feeding process, we introduce an enhanced pose estimation algorithm. This algorithm combines the improved Super 4-Point Congruent Sets (Super 4PCS) with the Truncated Least Squares Semidefinite Relaxation (TEASER) algorithm, significantly boosting the speed and robustness of pose estimation during sole sorting, and achieving precise target pose estimation. Building upon this foundation, we present a sorting strategy for disordered stacked shoe soles. This strategy integrates spatial positional information of each sole, employing multi-objective decision-making and recognition algorithms to determine optimal grasping targets. Finally, an experimental platform for automated sole feeding is designed to validate the proposed method. The experimental results indicate that the pose estimation method proposed in this paper achieves an average distance error of 2.04 mm and an average angular error of 2.72°, with the robot’s average success rate in grasping reaching 97.08%. Moreover, the average processing time of the vision algorithm is only 1.34 s, demonstrating good efficiency, precision, and robustness. This method effectively meets the automated feeding needs of scattered and stacked shoe soles in the actual production processes of shoemaking enterprises.

Prediction model based on artificial intelligence for identifying risk of coronary atherosclerotic heart disease in computed tomography

ObjectiveTo analyze the application value of artificial intelligence (AI) in coronary computed tomography angiography (CCTA) image processing and diagnosis of coronary atherosclerotic heart disease (CHD). MethodsA total of 80 patients with suspected CHD in our hospital were selected for CCTA examination and blood lipid examination. The convolutional neural networks (CNN) model of coronary artery plaque detection was constructed, and the data set was randomly divided into training set and test set after pretreatment of lipid characteristics and image characteristics. The prediction efficiency and accuracy of the model were evaluated. ResultsIn the data set, the lipid indexes LDL-C, TC, and TG of patients in the CHD group were significantly higher than those in the Non-CHD group (P < 0.05). The average processing and diagnosis time of the AI model was (187.19 ± 18.79) s, which was significantly shorter than the average time of doctors (989.07 ± 50.40) s, and the difference was statistically significant (P < 0.05). There was no significant difference in the detection of calcified plaque, non-calcified plaque, and mixed plaque between doctors and AI models (P > 0.05). However, 5 plaques were misdiagnosed in the AI model (3.38%). The area under the curve (AUC) value of the CNN recognition model-based AI and manual recognition of doctors for the CHD were 0.870 (95% CI: 0.698–0.931) and 0.870 (95% CI: 0.691–0.926) (P < 0.001). ConclusionAI integrated with lipid parameters has certain clinical value in CCTA image processing efficiency and plaque diagnosis, and can be used as an effective auxiliary tool to analyze and diagnose CHD.

A hybrid approach for malware detection in SDN‐enabled IoT scenarios

AbstractMalware presents a significant threat to computer systems security, especially in ARM and MIPS architectures, driven by the rise of the internet of things (IoT). This paper introduces Heimdall, a hybrid approach that integrates YARA signatures and machine learning in programmable switches for efficient malware detection in SDN‐enabled IoT environments. The machine learning classifier achieved an accuracy of 99.33% against the IoT‐23 dataset. When evaluated in an emulated environment with real malware samples, Heimdall exhibits a 98.44% detection rate and an average processing time of 0.0217 s.

TMHD: Twin-Bridge Scheduling of Multi-Heterogeneous Dependent Tasks for Edge Computing

As an efficient computing paradigm, Mobile Edge Computing (MEC) is essential in assisting mobile devices with real-time complex tasks such as big data analytics. In MEC, application tasks consist of multiple dependent subtasks, and the way to process tasks ensuring lower response latency through efficient scheduling orders is of relevant importance. Most existing research on scheduling sorts the dependent tasks out by simple topological ordering, which leads to idle and wasteful resources. For such, we propose a Twin-Bridge Scheduling of Multi-Heterogeneous Dependent Tasks for Edge Computing (TMHD), with two state embedding layers for multi-dependent tasks that reconstruct the model of dependent tasks to find the optimal scheduling paths and concurrently make the best offloading decisions. Considering the limitations of local edge-end model training, we upload the trained models to the cloud periodically for local model aggregation update, speeding up the model convergence whilst guaranteeing the training accuracy and demonstrating the convergence of multi-model aggregation. Compared with existing dependent task processing methods, TMHD reduces the average task processing time on four different datasets by 18.68 ms, 71.79 ms, 70.99 ms, and 64.32 ms, which are shown to be effective in reducing the response time of the device task.

297 Impact of Navigated Intraoperative Ultrasound on Brain Shift and Gross Tumor Resection

INTRODUCTION: Intraoperative imaging and neuronavigation aid in achieving maximum tumor resection. However, tissue removal during surgery can cause brain shift, which alters the relative anatomy and “de-calibrates” the standard approaches. While intraoperative MRI (iMRI) is the gold-standard to correct for brain shift, it is expensive, time-consuming, and logistically difficult. As an alternative, we investigated an approach which combines intraoperative ultrasound (ioUS) technology with preoperative imaging to allow for real-time compensation of brain shift and assessment of extent of resection (EOR). METHODS: We prospectively enrolled patients undergoing neurosurgical tumor resection at Massachusetts General Hospital beginning April 2023. Patients were registered with Brainlab Ultrasound Navigation prior to surgery. ioUS sweeps were acquired before cortex incision, mid-resection, and post-resection with bkActiv and fused to preoperative MRIs. Four points around the tumor were longitudinally tracked to assess brain shift. Data included sweep duration, image processing speed, extent of brain shift as determined by intraoperative markers, and EOR assessed from the ioUS compared with postoperative MRIs. RESULTS: Seven patients were enrolled (average age 62 ± 15, 75% male) with frontal (n = 5), temporal (n = 1), and occipital (n = 1) gliomas (n = 4) and metastases (n = 3). All patients exhibiting total resection as determined by ioUS also demonstrated total resection on post-operative MRI, evaluated independently by a neuroradiologist. The average time of scan and image processing (n = 19) was 5.7 ± 1.9 and 27.8 ± 6.5 seconds respectively. CONCLUSIONS: Navigated ioUS offers accurate and efficient assessment of EOR during surgery for tumor resections. Furthermore, when brain shift is detected, navigated ioUS can allow for compensation and continued utilization of navigation via the acquisition of a brand new, navigable, intraoperatively acquired dataset.

Method for GPU-based spectral data cube reconstruction of integral field snapshot imaging spectrometers.

In this paper, the principles of spectral data cube reconstruction based on an integral field snapshot imaging spectrometer and GPU-based acceleration are presented. The primary focus is on improving the reconstruction algorithm using GPU parallel computing technology to enhance the computational efficiency for real-time applications. And the computational tasks of the spectral reconstruction algorithm were transferred to the GPU through program parallelization and memory optimization, resulting in significant performance gains. Experimental results indicate that the average processing time of the GPU-based parallel algorithm is approximately 29.43 ms, showing a substantial acceleration ratio of about 14.27 compared to the traditional CPU serial algorithm with an average processing time of around 420.46 ms. The study aims to refine the GPU parallelization algorithm for continued improvement in computational efficiency and overall performance. The anticipated applications of this research include providing crucial technical support for the perception and monitoring of crop growth traits in agricultural production, contributing to the modernization and advancement of intelligence in the field.

The green window view index: automated multi-source visibility analysis for a multi-scale assessment of green window views

ContextProviding accessible urban green spaces is crucial for planning and ensuring healthy, resilient, and sustainable cities. The importance of visually accessible urban green spaces increases due to inner urban development processes.ObjectivesThis article proposes a new index, the Green Window View Index (GWVI) for analyzing and assessing visible vegetation, that promotes an integrated planning of urban green spaces and buildings at different scales and levels. It is defined as the proportion of visible vegetation area in a field of view when looking out of a specific window with a defined distance to the window.MethodsThe method for estimating GWVI consists of three steps: (a) the modeling of the three-dimensional environment, (b) the simulation of the two-dimensional window views using modern rendering engines for three-dimensional graphics, (c) the computation of the GWVI. The method is proposed and tested through a case study of the urban area of Bonn, Germany, using a Digital Terrain Model (DTM), CityGML-based semantic 3D City Model at level of detail (LoD) 2, airborne Light Detection and Ranging (LiDAR) data, and 2D land use data from the official German property cadaster information system (ALKIS).ResultsWith an average processing time of 0.05 s per window view, an average GWVI of 26.00% could be calculated for the entire study area and visualized in both 2D and 3D.ConclusionThe proposed engine generates multi-scale visibility values for various vegetation shapes. These values are intended for use in participatory citizenship and decision-making processes for analysis by architects, real-estate appraisers, investors, and urban as well as landscape planners.

Defendroid: Real-time Android code vulnerability detection via blockchain federated neural network with XAI

Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to reduce potential vulnerabilities during development, their effectiveness in detecting vulnerabilities may fall short. To address this, “Defendroid”, a blockchain-based federated neural network enhanced with Explainable Artificial Intelligence (XAI) is introduced in this work. Trained on the LVDAndro dataset, the vanilla neural network model achieves a 96% accuracy and 0.96 F1-Score in binary classification for vulnerability detection. Additionally, in multi-class classification, the model accurately identifies Common Weakness Enumeration (CWE) categories with a 93% accuracy and 0.91 F1-Score. In a move to foster collaboration and model improvement, the model has been deployed within a blockchain-based federated environment. This environment enables community-driven collaborative training and enhancements in partnership with other clients. The extended model demonstrates improved accuracy of 96% and F1-Score of 0.96 in both binary and multi-class classifications. The use of XAI plays a pivotal role in presenting vulnerability detection results to developers, offering prediction probabilities for each word within the code. This model has been integrated into an Application Programming Interface (API) as the backend and further incorporated into Android Studio as a plugin, facilitating real-time vulnerability detection. Notably, Defendroid exhibits high efficiency, delivering prediction probabilities for a single code line in an average processing time of a mere 300 ms. The weight-sharing transparency in the blockchain-driven federated model enhances trust and traceability, fostering community engagement while preserving source code privacy and contributing to accuracy improvement.

Diagnosis of skin lesion using shift-invariant network and an improved grey wolf optimizer

The global incidence of skin cancer has been rising, resulting in increased mortality and morbidity if left untreated. Accurate diagnosis of skin malignancies is crucial for early intervention through excision. While various innovative medical imaging techniques, such as dermoscopy, have improved the way we examine skin cancers, the progress in medical imaging for identifying skin lesions has not kept pace. Skin lesions exhibit diverse visual features, including variations in size, shape, boundaries, and artifacts, necessitating an efficient image-processing approach to assist dermatologists in decision-making. In this research, we propose an automated skin lesion classifier called GreyNet, which utilizes optimized convolutional neural networks (CNNs) or shift-invariant networks (SIN). GreyNet comprises three components: (i) a trained fully deep CNN for semantic segmentation, relating input images to manually labeled standard scans; (ii) an enhanced dense CNN with global information exchange and adaptive feature salvaging module to accurately classify each pixel in histopathological scans as benign or malignant; and (iii) a binary grey wolf optimizer (BGWO) to improve the classification process by optimizing the network’s hyperparameters. We evaluate the performance of GreyNet in terms of lesion segmentation and classification on the HAM10000 database. Extensive empirical results demonstrate that GreyNet outperforms existing lesion segmentation methods, achieving improved dice similarity score, volume error, and average processing time of 1.008±0.009, 0.903±0.009%, and 0.079±0.010 s, respectively. Moreover, GreyNet surpasses other skin melanoma classification models, exhibiting improved accuracy, precision, specificity, sensitivity, false negative rate, false positive rate, and Jaccard similarity score (JSS) of 96.5%, 97%, 96.2%, 92.1%, 3.8%, 3%, and 89.5%, respectively. Based on our experimental analysis, we conclude that GreyNet is an efficient tool to aid dermatologists in identifying skin melanoma.

Line segment detection algorithm in image extraction improvement study

In recent years, image processing technology has been developing and maturing, but due to the influence of many interfering factors in the acquisition process, there is a large amount of redundant information in the images obtained. The line segment detection algorithm in image extraction needs to be improved. This study utilizes computer technology to improve the line segment detection technology, and designs a line segment detection algorithm based on the linear detection improvement. Firstly, based on the basic principle of straight line detection algorithm, for the problems of line segment breakage and missing in straight line detection, RGB three-channel grayscale map is applied to detect line segments. Then the detected line segments are connected, merged and deleted. The test results show that the line segment detection algorithm improved based on straight line detection has the highest accuracy rate of 94.50 %, and the average processing time per image is also the lowest at 0.2 s. The algorithm runs faster at 0.25 s and has a higher F-value. It is able to detect the boundaries of a variety of rectangular targets, using the improved line segment detection algorithm has a wide range of applicability, lower error rate, and strong anti-interference ability. The improved line segment detection algorithm has a greater advantage in rectangular target extraction for document, text and book type images.

An automated process for bulk downloading optical coherence tomography scans

ObjectiveTo develop an automated method for efficiently downloading a large number of optical coherence tomography (OCT) scans obtained using the Heidelberg Spectralis (Heidelberg Engineering, Heidelberg, Germany) platform.MethodsThe electronic medical records and OCT scans were extracted for all patients with age-related macular degeneration treated at the Hadassah University Hospital Retina Clinic between 2010 and 2021. A macro was created using Visual Basic for Applications (VBA) and Microsoft Excel to automate the export process and anonymize the OCT scans in accordance with hospital policy. OCT scans were extracted as proprietary Heidelberg E2E files.ResultsThe VBA macro was used to export a total of 94,789 E2E files from 2807 patient records, with an average processing time of 4.32 min per volume scan (SD: 3.57 min). The entire export process took a total of approximately 202 h to complete over a period of 24 days. In a smaller sample, using the macro to download the scans was significantly faster than manually downloading the scans, averaging 3.88 vs. 11.08 min/file, respectively (t = 8.59, p < 0.001). Finally, we found that exporting the files during both off-clinic and working hours resulted in significantly faster processing times compared to exporting the files solely during working hours (t = 5.77, p < 0.001).ConclusionsThis study demonstrates the feasibility of using VBA and Excel to automate the process for bulk downloading data from a specific medical imaging platform. The specific steps and techniques will likely vary depending on the software used and hospital constraints and should be determined for each application.

Patient-Specific Implant Customization for Treatment of Internal Orbital Fractures Using Office-Based Three-Dimensional Printing.

Three-dimensional (3D) modeling technology aids the reconstructive surgeon in designing and tailoring individualized implants for the reconstruction of complex craniofacial fractures. Three-dimensional modeling and printing have traditionally been outsourced to commercial vendors but can now be incorporated into both private and academic craniomaxillofacial practices. The goal of this report is to present a low-cost, standardized office-based workflow for restoring bony orbital volume in traumatic orbital fractures. Patients with internal orbital fractures requiring open repair were identified. After the virtual 3D models were created by iPlan 3.0 Cranial CMF software (Brainlab), the models were printed using an office-based 3D printer to shape and modify orbital plates to correctly fit the fracture defect. The accuracy of the anatomic reduction and the restored bony orbital volume measurements were determined using postoperative computed tomography images and iPlan software. Nine patients fulfilled the inclusion criteria: 8 patients had unilateral fractures and 1 patient had bilateral fractures. Average image processing and print time were 1.5 hours and 3 hours, respectively. The cost of the 3D printer was $2500 and the average material cost to print a single orbital model was $2. When compared with the uninjured side, the mean preoperative orbital volume increase and percent difference were 2.7 ± 1.3mL and 10.9 ± 5.3%, respectively. Postoperative absolute volume and percent volume difference between the orbits were -0.2 ± 0.4mL and -0.8 ± 1.7%, respectively. Office-based 3D printing can be routinely used in the repair of internal orbital fractures in an efficient and cost-effective manner to design the implant with satisfactory patient outcomes.

Low Latency Signature-Based 4-Step Random Access Procedure for Massive Machine-Type Communications

Massive machine-type communication (mMTC) is one of the major services in the 5G new radio (NR) system. In the mMTC application scenarios, many machine-type communication devices (MTCDs) communicate with each other or through an MTC server. However, how to solve the problem of excessive MTCDs accessing the network simultaneously is an important task. To overcome the large number of random access collisions and retransmission problems, this paper proposes a method to solve the mMTC random access problem. The proposed method consists of two parts, modified Contention Based Random Access (CBRA) with 4-step RA type and 4-step Signature RA type. With the modified CBRA with 4-step RA type, each MTCD will obtain the information to generate the signature. After that, the 4-step Signature RA type is used for MTCDs to proceed with the random access with the gNB through the generated signatures. In our proposed method, the average number of false positives and average process time are evaluated under different numbers of active MTCDs and different parameter values for the signature generation. Besides, the average number of preamble attempts and average process time are used as the metrics to compare the proposed scheme with the 5G NR 4-step RA type. Finally, simulation results show that the proposed scheme performs better than the 5G NR 4-step RA type in the mMTC scenarios.

Fusion of Time-of-Flight Based Sensors with Monocular Cameras for a Robotic Person Follower

Human-robot collaboration (HRC) is becoming increasingly important in advanced production systems, such as those used in industries and agriculture. This type of collaboration can contribute to productivity increase by reducing physical strain on humans, which can lead to reduced injuries and improved morale. One crucial aspect of HRC is the ability of the robot to follow a specific human operator safely. To address this challenge, a novel methodology is proposed that employs monocular vision and ultra-wideband (UWB) transceivers to determine the relative position of a human target with respect to the robot. UWB transceivers are capable of tracking humans with UWB transceivers but exhibit a significant angular error. To reduce this error, monocular cameras with Deep Learning object detection are used to detect humans. The reduction in angular error is achieved through sensor fusion, combining the outputs of both sensors using a histogram-based filter. This filter projects and intersects the measurements from both sources onto a 2D grid. By combining UWB and monocular vision, a remarkable 66.67% reduction in angular error compared to UWB localization alone is achieved. This approach demonstrates an average processing time of 0.0183s and an average localization error of 0.14 meters when tracking a person walking at an average speed of 0.21 m/s. This novel algorithm holds promise for enabling efficient and safe human-robot collaboration, providing a valuable contribution to the field of robotics.

Identification of Fencing Athletes Based on Anthropometric Measurements Using MediaPipe Pose

Over time, numerous developments in digital technology have benefited people, including anthropometric measurements that provide information on an athlete’s ability in sports. The use of digital technology in sports must continue, particularly in the National Sports Committee of Indonesia (Komite Olahraga Nasional Indonesia, KONI) of Bandung City. This study proposed a technique for classifying and identifying fencing athletes’ talents. This work developed a methodology for evaluating sports talent based on anthropometric measurements of athletes’ bodies using the posture detection approach. Fencing and nonfencing athletes in KONI Bandung City were categorized using this talent identification. This study used 36 datasets of body posture images from various skills of the sport. These images were in JPEG or JPG format with a resolution of 3,024 × 4,032 and were acquired using a Canon EOS 1300D camera. This study utilized four points landmarks, which are usually used as measurement components in KONI, to categorize fencing athletes. The four points are shoulder (S), elbow (E), index (I), and hip (H) landmarks. The testing was done using three different dataset settings. According to the test results of all scenarios, scenario 2 had the highest accuracy. This scenario was able to categorize fencing and nonfencing athletes with an accuracy rate of 89% and an average processing time of less than 3 s per image.

Observation of Automated Management Use of Self-Sampling Kits

Abstract In the current times of ever-growing prevalence of infectious diseases, it is requisite to explore ways to provide the safest and most effective medical care for our patients. The aim of this study is to explain how the issues raised by the SARS-CoV-2 pandemic were addressed by the E. Gulbis Laboratory in Latvia. The study looks back on the goal to introduce an automated and effective solution for the management of self-collected samples. The study is primarily aimed to formulate the conclusions about the data and use of automation in the self-sample kit collection. Results were collected from 18 automated (contactless) sample collection devices used by E. Gulbis Laboratory. Sixty-four thousand two hundred fifty-seven (64,257) saliva kits for SARSCoV-2 PCR testing were employed. It was found that 3.92% of them were positive (SARS-CoV-2 virus RNA found in saliva sample). The average processing time in automated devices located in the capital city was 11.13 hours, in the suburbs — 15.52 hours, rest of the country — 17.60 hours. The average age of patients that choose an automatic device to hand in their saliva sample kits was 33.94 years. These results suggest that by using the automated device, patient contacts are decreased, and direct communication with medical staff is excluded, which reduces the risk of infection during processing. Automated devices make sample kit distribution available 24 hours. They save workforce resources in the laboratory that are already very limited, especially during a pandemic period.

An unmanned aerial vehicle light detection and ranging Simultaneous Localisation And Mapping algorithm based on factor graph optimisation for tunnel 3D mapping

AbstractThe current mature Simultaneous Localisation And Mapping (SLAM) algorithms, when applied to tunnel scenarios with point cloud degradation and poor lighting conditions, often lead to a sharp increase in the estimated attitude error of the unmanned aerial vehicle (UAV), or even prevent the UAV from moving autonomously due to severe feature degradation. To address the above problems, the authors propose a SLAM algorithm based on factor graph optimisation, Iterative Closest Point and Normal Distributions Transform algorithms. A front‐end point cloud registration module and a back‐end construction algorithm based on filtering and graph optimisation are designed. To verify the effectiveness of the proposed algorithm, experiments are conducted on KITTI dataset and real tunnel scenes, and compared with LiDAR Odometry and Mapping (LOAM) and lightweight and ground optimised (LeGO)‐LOAM algorithms. The results show that the average processing time of the proposed method is about 75 ms, which can meet the real‐time requirements of autonomous aerial vehicles. Compared with LOAM and LeGO‐LOAM in the real tunnel experiment, the proposed method shows the tunnel 3D map construction.

Intraoperative artificial intelligence system identifying liver vessels in laparoscopic liver resection: a retrospective experimental study.

The precise recognition of liver vessels during liver parenchymal dissection is the crucial technique for laparoscopic liver resection (LLR). This retrospective feasibility study aimed to develop artificial intelligence (AI) models to recognize liver vessels in LLR, and to evaluate their accuracy and real-time performance. Images from LLR videos were extracted, and the hepatic veins and Glissonean pedicles were labeled separately. Two AI models were developed to recognize liver vessels: the "2-class model" which recognized both hepatic veins and Glissonean pedicles as equivalent vessels and distinguished them from the background class, and the "3-class model" which recognized them all separately. The Feature Pyramid Network was used as a neural network architecture for both models in their semantic segmentation tasks. The models were evaluated using fivefold cross-validation tests, and the Dice coefficient (DC) was used as an evaluation metric. Ten gastroenterological surgeons also evaluated the models qualitatively through rubric. In total, 2421 frames from 48 video clips were extracted. The mean DC value of the 2-class model was 0.789, with a processing speed of 0.094s. The mean DC values for the hepatic vein and the Glissonean pedicle in the 3-class model were 0.631 and 0.482, respectively. The average processing time for the 3-class model was 0.097s. Qualitative evaluation by surgeons revealed that false-negative and false-positive ratings in the 2-class model averaged 4.40 and 3.46, respectively, on a five-point scale, while the false-negative, false-positive, and vessel differentiation ratings in the 3-class model averaged 4.36, 3.44, and 3.28, respectively, on a five-point scale. We successfully developed deep-learning models that recognize liver vessels in LLR with high accuracy and sufficient processing speed. These findings suggest the potential of a new real-time automated navigation system for LLR.

Intelligent intrusion detection for optical fiber perimeter security system based on an improved high efficiency feature extraction technique

The automated analysis of optical fiber vibration sensing data has been highly demanded in engineering applications. Therefore, intrusion analysis, which aims at detecting, recognizing, and classifying intrusions, holds great importance for optical fiber vibration sensing. In this work, an intelligent intrusion detection scheme employing an improved high-efficiency feature extraction technique and utilizing a dual Mach–Zehnder interferometer (DMZI)-based optical fiber perimeter security system is proposed. So, the DMZI-based perimeter security system in practical settings can be successfully established. Specifically, time-frequency feature vectors with nine features are firstly constructed using a maximal overlap discrete wavelet transformation approach and a zero crossing rate method. Then, the feature vectors are classified into corresponding categories using a radial basis function neural network. The effectiveness of the proposed scheme has been validated using six types of human intrusions, such as knocking, climbing, waggling, cutting, crashing and kicking the fence. The results show that the given intrusions can be accurately and rapidly recognized by the proposed scheme. The average recognition rate of 95.0% is achieved, and the average processing time for each sample data is only 0.033 s, which is significantly lower than the sampling interval (0.3 s) in our experiment. It is believed that the proposed scheme holds promising potential in the field of optical fiber perimeter security systems.

Proactive scheduling for steel plants with unrelated parallel machines and time uncertainty

This paper deals with the steel plant proactive scheduling problem with unrelated parallel machines and time uncertainty to enhance the rationality of machine assignment and processing path, improve the schedule robustness and guarantee capability of energy. First, a scheduling model is formulated with the objectives of average process time, matching degree and oxygen consumption fluctuation. Then, an improved preference-based NSGA-II is designed to solve the problem. The algorithm introduces the preference information into the optimization process and designs an individual sorting method based on the technique for order preference by similarity to ideal solution (TOPSIS) to guide the evolution direction. Besides, to improve the local search ability, variable neighborhood search is incorporated to generate new solutions with high quality. Computational experiments prove that the algorithm can effectively address the proactive scheduling problem. The sensitivity analysis and results of different scheduling models verify the robustness and superiority in machine matching and energy synergy of the model.