Pub Date : 2022-04-24DOI: 10.1142/s1793962323410180
B. Padmavathi, B. Muthukumar
{"title":"A deep recursively learning LSTM model to improve cyber security botnet attack intrusion detection","authors":"B. Padmavathi, B. Muthukumar","doi":"10.1142/s1793962323410180","DOIUrl":"https://doi.org/10.1142/s1793962323410180","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"244 1","pages":"2341018:1-2341018:21"},"PeriodicalIF":0.0,"publicationDate":"2022-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78708950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-21DOI: 10.1142/s1793962323410131
N. Girish, C. Nandini
The forgery involved in region duplication is a common type of video tampering, where the traditional techniques used to detect video tampering are ineffective and inefficient for the forged videos under complex backgrounds. To overcome this issue, a novel video forgery detection model is introduced in this research paper. Initially, the input video sequences are collected from Surrey University Library for Forensic Analysis (SULFA) and Sondos datasets. Further, spatiotemporal averaging method is carried out on the collected video sequences to obtain background information with a pale of moving objects for an effective video forgery detection. Next, feature extraction is performed using the GoogLeNet model for extracting the feature vectors. Then, the Unsupervised Feature Selection with Multi-Subspace Randomization and Collaboration (UFS-MSRC) approach is used to choose the discriminative feature vectors that superiorly reduce the training time and improve the detection accuracy. Finally, long short-term memory (LSTM) network is applied for forgery detection in the different video sequences. The experimental evaluation illustrated that the UFS-MSRC with LSTM model attained 98.13% and 97.38% of accuracy on SULFA and Sondos datasets, where the obtained results are better when compared to the existing models in video forgery detection.
{"title":"Inter-frame video forgery detection using UFS-MSRC algorithm and LSTM network","authors":"N. Girish, C. Nandini","doi":"10.1142/s1793962323410131","DOIUrl":"https://doi.org/10.1142/s1793962323410131","url":null,"abstract":"The forgery involved in region duplication is a common type of video tampering, where the traditional techniques used to detect video tampering are ineffective and inefficient for the forged videos under complex backgrounds. To overcome this issue, a novel video forgery detection model is introduced in this research paper. Initially, the input video sequences are collected from Surrey University Library for Forensic Analysis (SULFA) and Sondos datasets. Further, spatiotemporal averaging method is carried out on the collected video sequences to obtain background information with a pale of moving objects for an effective video forgery detection. Next, feature extraction is performed using the GoogLeNet model for extracting the feature vectors. Then, the Unsupervised Feature Selection with Multi-Subspace Randomization and Collaboration (UFS-MSRC) approach is used to choose the discriminative feature vectors that superiorly reduce the training time and improve the detection accuracy. Finally, long short-term memory (LSTM) network is applied for forgery detection in the different video sequences. The experimental evaluation illustrated that the UFS-MSRC with LSTM model attained 98.13% and 97.38% of accuracy on SULFA and Sondos datasets, where the obtained results are better when compared to the existing models in video forgery detection.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"34 1","pages":"2341013:1-2341013:19"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85001332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-18DOI: 10.1142/s1793962322430012
Kunyu Xie, Lin Zhang, Y. Laili, Xiaohan Wang
When Discrete Event System Specification (DEVS) is used as a modeling tool, there is a semantic gap between a DEVS model and the mathematical representation, which may result in understanding difficulties. To provide a more intuitive form of modeling, XDEVS expands the concept of states in DEVS. The continuous state is introduced in XDEVS, which enhances the ability to model hybrid systems. Based on the DEVS simulation framework, a simulation engine is developed to drive the XDEVS model safely and efficiently and avoid the wrong location of state events during the simulation of the continuous model. A hybrid model is constructed and simulated using XDEVS. A comparison between the XDEVS model and models described by DEV&DESS and GDEVS shows that XDEVS can clearly express the structure of the model and reduce the burden on modelers.
{"title":"XDEVS: A hybrid system modeling framework","authors":"Kunyu Xie, Lin Zhang, Y. Laili, Xiaohan Wang","doi":"10.1142/s1793962322430012","DOIUrl":"https://doi.org/10.1142/s1793962322430012","url":null,"abstract":"When Discrete Event System Specification (DEVS) is used as a modeling tool, there is a semantic gap between a DEVS model and the mathematical representation, which may result in understanding difficulties. To provide a more intuitive form of modeling, XDEVS expands the concept of states in DEVS. The continuous state is introduced in XDEVS, which enhances the ability to model hybrid systems. Based on the DEVS simulation framework, a simulation engine is developed to drive the XDEVS model safely and efficiently and avoid the wrong location of state events during the simulation of the continuous model. A hybrid model is constructed and simulated using XDEVS. A comparison between the XDEVS model and models described by DEV&DESS and GDEVS shows that XDEVS can clearly express the structure of the model and reduce the burden on modelers.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"77 1","pages":"2243001:1-2243001:17"},"PeriodicalIF":0.0,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87448813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-12DOI: 10.1142/s1793962322500519
F. Han, Yu Liu, Jianguo Wang
In this paper, a method which reconstructs an H(div)-conforming local equilibrated flux is presented for equilibrated flux-based a posteriori error estimate for the finite element method of the second-order elliptic problem. The flux is reconstructed in the lowest-order Raviart–Thomas spaces for finite element approximation. For a simplicial mesh, the reconstruction which performed on every element rather than on the patch of the elements of the mesh or on the dual mesh is achieved by solving a third (or fourth)-order linear equations on every element and a second-order linear equations on every edge or face. So, the amount of computational work is small. Numerical examples demonstratex the effectiveness and improvements of our method.
{"title":"Reconstruction method of equilibrated flux for a posteriori error estimate of elliptic problems","authors":"F. Han, Yu Liu, Jianguo Wang","doi":"10.1142/s1793962322500519","DOIUrl":"https://doi.org/10.1142/s1793962322500519","url":null,"abstract":"In this paper, a method which reconstructs an H(div)-conforming local equilibrated flux is presented for equilibrated flux-based a posteriori error estimate for the finite element method of the second-order elliptic problem. The flux is reconstructed in the lowest-order Raviart–Thomas spaces for finite element approximation. For a simplicial mesh, the reconstruction which performed on every element rather than on the patch of the elements of the mesh or on the dual mesh is achieved by solving a third (or fourth)-order linear equations on every element and a second-order linear equations on every edge or face. So, the amount of computational work is small. Numerical examples demonstratex the effectiveness and improvements of our method.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"31 1","pages":"2250051:1-2250051:18"},"PeriodicalIF":0.0,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78080359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-05DOI: 10.1142/s1793962322500556
J. Kafle, Gr Acharya, Parameshwari Kattel, Puskar R. Pokhrel
When debris flows, landslides, or any gravitational mass flows hit closed or partially open water sources such as seas, oceans, fjords, hydraulic reservoirs, mountain lakes, bays, and landslide dams, it results in tsunami (impulse water waves) by transforming their impact energy to water body, potentially causing damages of infrastructures and human casualties both near field and the distant coastlines. The intensity of hazard depends on the scale, location and process of the landslide, and also on the reservoir volume and topography that surrounds it. Volume or size of the initial release mass that fails and slides along a slope is one of the dominant factors to determine the degree of splash, propagating speed and the amplitudes of the fluid waves, and potential dam breach or water spill over. Here, we numerically integrate the two-phase mass flow model [Pudasaini S. P., J. Geophysi. Res. 117(F03010), 2012] for quasi-three-dimensional, high-resolution simulation results with variation of size of the two-phase initial landslide or debris both longitudinally and laterally. In our numerical experimental results, we observe fundamentally different solid and fluid evolution and wave structures in the reservoir. There are also significant differences in the flow dynamics of solid under water for different volumes of the release mass by extending or contracting the base area along downslope and/or cross-slope directions. The simulation results show that tsunami amplitudes and run out extents are rapidly increased when the volume of the initial release mass in the form of a triangular wedge is increased by increasing the base area through the increment of the length and breadth of the release base. This study can be useful to develop and implement tsunami hazard mitigation strategies to enhance public safety and reduce potential loss due to landslide-generated wave hazards.
{"title":"Impact of variation of size of the initial release mass in the dynamics of landslide generated tsunami","authors":"J. Kafle, Gr Acharya, Parameshwari Kattel, Puskar R. Pokhrel","doi":"10.1142/s1793962322500556","DOIUrl":"https://doi.org/10.1142/s1793962322500556","url":null,"abstract":"When debris flows, landslides, or any gravitational mass flows hit closed or partially open water sources such as seas, oceans, fjords, hydraulic reservoirs, mountain lakes, bays, and landslide dams, it results in tsunami (impulse water waves) by transforming their impact energy to water body, potentially causing damages of infrastructures and human casualties both near field and the distant coastlines. The intensity of hazard depends on the scale, location and process of the landslide, and also on the reservoir volume and topography that surrounds it. Volume or size of the initial release mass that fails and slides along a slope is one of the dominant factors to determine the degree of splash, propagating speed and the amplitudes of the fluid waves, and potential dam breach or water spill over. Here, we numerically integrate the two-phase mass flow model [Pudasaini S. P., J. Geophysi. Res. 117(F03010), 2012] for quasi-three-dimensional, high-resolution simulation results with variation of size of the two-phase initial landslide or debris both longitudinally and laterally. In our numerical experimental results, we observe fundamentally different solid and fluid evolution and wave structures in the reservoir. There are also significant differences in the flow dynamics of solid under water for different volumes of the release mass by extending or contracting the base area along downslope and/or cross-slope directions. The simulation results show that tsunami amplitudes and run out extents are rapidly increased when the volume of the initial release mass in the form of a triangular wedge is increased by increasing the base area through the increment of the length and breadth of the release base. This study can be useful to develop and implement tsunami hazard mitigation strategies to enhance public safety and reduce potential loss due to landslide-generated wave hazards.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"263 1","pages":"2250055:1-2250055:27"},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76247761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-05DOI: 10.1142/s1793962322500404
A. Sedelnikov
The hatching model of Lymantria dispar caterpillars is being constructed. The variable parameter of the model is the duration of the diapause. The annual and semi-annual laboratory populations of L. dispar are studied. The statistical analysis of 2000 eggs from 20 clutches was carried out. The main features of the development of laboratory populations of L. dispar are described and compared with the studies of other authors. The results of this work can be used to study the evolution of L. dispar in the framework of laboratory growing conditions.
{"title":"The dependence modeling of the caterpillar eggs yield of the laboratory population of Lymantria dispar on the duration of diapause","authors":"A. Sedelnikov","doi":"10.1142/s1793962322500404","DOIUrl":"https://doi.org/10.1142/s1793962322500404","url":null,"abstract":"The hatching model of Lymantria dispar caterpillars is being constructed. The variable parameter of the model is the duration of the diapause. The annual and semi-annual laboratory populations of L. dispar are studied. The statistical analysis of 2000 eggs from 20 clutches was carried out. The main features of the development of laboratory populations of L. dispar are described and compared with the studies of other authors. The results of this work can be used to study the evolution of L. dispar in the framework of laboratory growing conditions.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"14 1","pages":"2250040:1-2250040:10"},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80305131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-28DOI: 10.1142/s1793962322500507
A. Baruah, S. Baruah
Student performance calculation is an essential process in online learning scheme, which intends to afford students along with admittance to active learning. Student performance forecast is most concerning problem in education and training field, particularly in educational data mining (EDM). The prediction process provisions the students to choose courses and intend suitable training strategies for themselves. Furthermore, student performance calculation permits lecturers and educational supervisors to designate which students should be observed and maintained to finish their plans with finest outcomes. These provisions can decrease the official notices and exclusions from universities because of students’ poor performance. In this paper, Political Fractional Competitive Multi-verse Optimization enabled Deep Neuro fuzzy network (PFCMVO enabled DNFN) uses spark framework for student performance calculation. Moreover, Yeo–Johnson transformation is applied for transforming the input data for effectual student performance prediction. In addition, Damerau–Levenshtein (DL) distance is applied to select appropriate features. The DNFN classifier is utilized to execute student performance prediction where the classifier is trained by PFCMVO algorithm. The developed student performance prediction model outperforms than the other existing techniques with respect to Precision, Recall, [Formula: see text]-measure, and Prediction accuracy of 0.9259, 0.9321, 0.9290, and 0.9372 for dataset-1 and 0.9126, 0.9271, 0.9198, and 0.9248 for dataset-2, respectively.
{"title":"PFCMVO: Political fractional competitive multi-verse optimization enabled deep neuro fuzzy network for student performance estimation in spark environment","authors":"A. Baruah, S. Baruah","doi":"10.1142/s1793962322500507","DOIUrl":"https://doi.org/10.1142/s1793962322500507","url":null,"abstract":"Student performance calculation is an essential process in online learning scheme, which intends to afford students along with admittance to active learning. Student performance forecast is most concerning problem in education and training field, particularly in educational data mining (EDM). The prediction process provisions the students to choose courses and intend suitable training strategies for themselves. Furthermore, student performance calculation permits lecturers and educational supervisors to designate which students should be observed and maintained to finish their plans with finest outcomes. These provisions can decrease the official notices and exclusions from universities because of students’ poor performance. In this paper, Political Fractional Competitive Multi-verse Optimization enabled Deep Neuro fuzzy network (PFCMVO enabled DNFN) uses spark framework for student performance calculation. Moreover, Yeo–Johnson transformation is applied for transforming the input data for effectual student performance prediction. In addition, Damerau–Levenshtein (DL) distance is applied to select appropriate features. The DNFN classifier is utilized to execute student performance prediction where the classifier is trained by PFCMVO algorithm. The developed student performance prediction model outperforms than the other existing techniques with respect to Precision, Recall, [Formula: see text]-measure, and Prediction accuracy of 0.9259, 0.9321, 0.9290, and 0.9372 for dataset-1 and 0.9126, 0.9271, 0.9198, and 0.9248 for dataset-2, respectively.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"30 1","pages":"2250050:1-2250050:25"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80772049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-28DOI: 10.1142/s1793962322500544
Linjiang Xie, Feilu Hang, W. Guo, Zhenhong Zhang, Hanruo Li
Information technology services for businesses and consumers can be delivered via the Internet using cloud computing (CC) because it is agile, cost-effective, and time-tested. For many real-world applications, the data are kept in the cloud by a third-party service and accessible through the Internet as needed through CC approaches. Risks associated with CC involve the data security and network security account for real-time systems. This paper discusses different security threats in CC and suggests a solution by designing a network security analysis scheme with machine learning (NSA-ML). The ML classifier predicts the network vulnerabilities and prevents insecure communication in a CC environment. The proposed NSA-ML presents a data authentication scheme with a novel encryption methodology to ensure data security. The experimental results show that the proposed NSA-ML outperforms the existing cloud security approaches by gaining an efficiency of 95.4%.
{"title":"Network security analysis for cloud computing environment","authors":"Linjiang Xie, Feilu Hang, W. Guo, Zhenhong Zhang, Hanruo Li","doi":"10.1142/s1793962322500544","DOIUrl":"https://doi.org/10.1142/s1793962322500544","url":null,"abstract":"Information technology services for businesses and consumers can be delivered via the Internet using cloud computing (CC) because it is agile, cost-effective, and time-tested. For many real-world applications, the data are kept in the cloud by a third-party service and accessible through the Internet as needed through CC approaches. Risks associated with CC involve the data security and network security account for real-time systems. This paper discusses different security threats in CC and suggests a solution by designing a network security analysis scheme with machine learning (NSA-ML). The ML classifier predicts the network vulnerabilities and prevents insecure communication in a CC environment. The proposed NSA-ML presents a data authentication scheme with a novel encryption methodology to ensure data security. The experimental results show that the proposed NSA-ML outperforms the existing cloud security approaches by gaining an efficiency of 95.4%.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"64 1","pages":"2250054:1-2250054:20"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82356627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-28DOI: 10.1142/s1793962322500532
Weiwei Wei, C. Sivaparthipan, P. Kumar
Technological advancement in a modern environment is approaching universality. The realm and uses of information technology (IT) and Blockchain Security have been extensively broadened. Any business wishing to boost its market prospects would certainly keep records of the perspectives and behaviors of its consumers. The firms employ advanced technological concepts, skills, and methods to comprehend their applicants. Additional information and facts are analyzed to improve judgment. Business analytics and Blockchain Security professionals have a positive opinion. Online shopping behavior analysis (OSBA) is proposed in this research. It addresses a smooth transition from a prediction method to a gradual information strategy that learns the clients’ needs and achieves their electronic trading revenues. Any commercial enterprise must have limitless entry to information. That contains population revenues, industrial patterns, competition and customer information, productivity measurements, computations, and much more. Corporate information has a significant role in this undertaking. Experimental information is collected periodically to evaluate the evidence and provide fresh discoveries and operations that provide fresh perspectives. The major consideration principal element assessment approach is utilized with big data analytics, Blockchain Security, and fuzzy interference system to assess the essential purchasing variables for customers. It achieves an accuracy of 89% and an [Formula: see text] score of 87%. Models like support vector machine, convolutional neural network, deep neural network, random forest, fuzzy logic, and decision tree (DT) are compared with the OSBA model’s simulation results (DT). Fuzzy interference, big data, and Blockchain Security analytics improve the OSBA model’s performance.
{"title":"Online shopping behavior analysis for smart business using big data analytics and blockchain security","authors":"Weiwei Wei, C. Sivaparthipan, P. Kumar","doi":"10.1142/s1793962322500532","DOIUrl":"https://doi.org/10.1142/s1793962322500532","url":null,"abstract":"Technological advancement in a modern environment is approaching universality. The realm and uses of information technology (IT) and Blockchain Security have been extensively broadened. Any business wishing to boost its market prospects would certainly keep records of the perspectives and behaviors of its consumers. The firms employ advanced technological concepts, skills, and methods to comprehend their applicants. Additional information and facts are analyzed to improve judgment. Business analytics and Blockchain Security professionals have a positive opinion. Online shopping behavior analysis (OSBA) is proposed in this research. It addresses a smooth transition from a prediction method to a gradual information strategy that learns the clients’ needs and achieves their electronic trading revenues. Any commercial enterprise must have limitless entry to information. That contains population revenues, industrial patterns, competition and customer information, productivity measurements, computations, and much more. Corporate information has a significant role in this undertaking. Experimental information is collected periodically to evaluate the evidence and provide fresh discoveries and operations that provide fresh perspectives. The major consideration principal element assessment approach is utilized with big data analytics, Blockchain Security, and fuzzy interference system to assess the essential purchasing variables for customers. It achieves an accuracy of 89% and an [Formula: see text] score of 87%. Models like support vector machine, convolutional neural network, deep neural network, random forest, fuzzy logic, and decision tree (DT) are compared with the OSBA model’s simulation results (DT). Fuzzy interference, big data, and Blockchain Security analytics improve the OSBA model’s performance.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"41 1","pages":"2250053:1-2250053:22"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76480459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-28DOI: 10.1142/s1793962322500441
Andrew J. Collins, Erika F. Frydenlund, Christopher J. Lynch, R. M. Robinson
Advances in computing allow for the construction of increasingly large and complex models and simulations. Exhaustive error checking of these intricate, large computational simulation models is daunting and potentially impractical. This paper explores an approach to error-checking simulation model components using an Acceptance Sampling methodology from the field of industrial manufacturing. We propose a systematic process in which a simulation inspector examines only a fraction of the computational model elements to measure the errors present. Our proposed process could support established verification processes by sampling the simulation components to identify whether the model is acceptably error free and which components require correcting. The proposed methodology relies on several statistical constraints but serves the interests of simulation professionals as part of the overall verification process. We illustrate the application and usefulness of our methodology through a real-world case study of a citywide microscopic transportation model.
{"title":"Acceptance sampling to aid in the verification of computational simulations","authors":"Andrew J. Collins, Erika F. Frydenlund, Christopher J. Lynch, R. M. Robinson","doi":"10.1142/s1793962322500441","DOIUrl":"https://doi.org/10.1142/s1793962322500441","url":null,"abstract":"Advances in computing allow for the construction of increasingly large and complex models and simulations. Exhaustive error checking of these intricate, large computational simulation models is daunting and potentially impractical. This paper explores an approach to error-checking simulation model components using an Acceptance Sampling methodology from the field of industrial manufacturing. We propose a systematic process in which a simulation inspector examines only a fraction of the computational model elements to measure the errors present. Our proposed process could support established verification processes by sampling the simulation components to identify whether the model is acceptably error free and which components require correcting. The proposed methodology relies on several statistical constraints but serves the interests of simulation professionals as part of the overall verification process. We illustrate the application and usefulness of our methodology through a real-world case study of a citywide microscopic transportation model.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"79 1","pages":"2250044:1-2250044:28"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81892483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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