The purpose of this study is to propose a method for rapidly assessing the hit locations and extent of damage to a naval ship attacked by threatening weapons. An integrated method is presented for assessing the vulnerability to the hull and systems caused by the blast or penetrating effects of a weapon at multiple possible impact locations. The proposed method enables the assessment of vulnerability changes for design alternatives during the early design phase of naval vessels. To predict the extent of damage and to visualize the damaged hull and equipment, it is first assumed that a weapon has multiple hit locations. It is shown that a set of possible hit locations was generated by assuming the trajectory for air-explosion (AIREX) weapons by dividing the trajectory into an air-to-ship trajectory and a ship-to-ship trajectory. To account for the non-deterministic nature of weapon hits, a probability distribution approach was used to generate random multiple hit locations for each trajectory while the hit locations of AIREX in all directions were predicted using a multivariate probability distribution that generates three-dimensional random hit points. The extent of damage was then calculated, taking into account the topology of the hull structure and the equipment installed within the hull compartment, along with the damage volume associated with the weapon at each hit point. To identify the damage volume, axis-aligned bounding box (AABB) components were used, which provide a simplified representation of the ship’s geometry, as well as the relative position and dimensions of the hull structure and installed equipment. The damage compartment was defined as the portion of the hull that overlapped the damaged volume. While AABB’s overlap detection algorithm was applied to the damaged hull compartments, the algorithm identified the equipment that overlapped the damaged volume of the hull. Finally, the geometric modeling module, the probabilistic multiple hit location prediction module, and the damage analysis module were developed for damage visualization and vulnerability assessment.
{"title":"Damage visualization and vulnerability assessment of surface ship considering the 3D multihit location of air-explosion threat","authors":"Kwang-Sik Kim, Jang-Hyun Lee, J. Yoon","doi":"10.1093/jcde/qwad051","DOIUrl":"https://doi.org/10.1093/jcde/qwad051","url":null,"abstract":"\u0000 The purpose of this study is to propose a method for rapidly assessing the hit locations and extent of damage to a naval ship attacked by threatening weapons. An integrated method is presented for assessing the vulnerability to the hull and systems caused by the blast or penetrating effects of a weapon at multiple possible impact locations. The proposed method enables the assessment of vulnerability changes for design alternatives during the early design phase of naval vessels. To predict the extent of damage and to visualize the damaged hull and equipment, it is first assumed that a weapon has multiple hit locations. It is shown that a set of possible hit locations was generated by assuming the trajectory for air-explosion (AIREX) weapons by dividing the trajectory into an air-to-ship trajectory and a ship-to-ship trajectory. To account for the non-deterministic nature of weapon hits, a probability distribution approach was used to generate random multiple hit locations for each trajectory while the hit locations of AIREX in all directions were predicted using a multivariate probability distribution that generates three-dimensional random hit points. The extent of damage was then calculated, taking into account the topology of the hull structure and the equipment installed within the hull compartment, along with the damage volume associated with the weapon at each hit point. To identify the damage volume, axis-aligned bounding box (AABB) components were used, which provide a simplified representation of the ship’s geometry, as well as the relative position and dimensions of the hull structure and installed equipment. The damage compartment was defined as the portion of the hull that overlapped the damaged volume. While AABB’s overlap detection algorithm was applied to the damaged hull compartments, the algorithm identified the equipment that overlapped the damaged volume of the hull. Finally, the geometric modeling module, the probabilistic multiple hit location prediction module, and the damage analysis module were developed for damage visualization and vulnerability assessment.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"95 1","pages":"1298-1313"},"PeriodicalIF":4.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86019287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fatemeh Hirbod, Masoud Eshghali, Mohammad Sheikhasadi, F. Jolai, A. Aghsami
Controlling and maintaining public health in the face of diseases necessitates the effective implementation of response strategies, including the distribution of vaccines. By distributing vaccines, vulnerable populations can be targeted, individuals can be protected, and the spread of diseases can be minimized. However, managing vaccine distribution poses challenges that require careful consideration of various factors, including the location of distribution facilities. This paper proposes a novel model that combines location-allocation problems with queueing systems methodologies to optimize the efficiency of vaccine distribution. The proposed model considers factors such as uncertain demand, varying service rates, depending on the system state. Its primary objective is to minimize total costs, which encompass the establishment and adjustment of the service mechanism, travel times, and customer waiting time. To forecast customer demand rates, the model utilizes time series techniques, specifically the seasonal Autoregressive Integrated Moving Average (ARIMA) model. In order to tackle large-scale problems, a total of 16 newly developed Metaheuristic algorithms are employed, and their performance is thoroughly evaluated. This approach facilitates the generation of solutions that are nearly optimal within a reasonable timeframe. The effectiveness of the model is evaluated through a real case study focused on vaccination distribution in Iran. Furthermore, a comprehensive sensitivity analysis is conducted to demonstrate the practical applicability of the proposed model. The study contributes to the advancement of robust decision-making frameworks and provides valuable insights for addressing location-related challenges in health systems.
{"title":"A state-dependent M/M/1 queueing location-allocation model for vaccine distribution using metaheuristic algorithms","authors":"Fatemeh Hirbod, Masoud Eshghali, Mohammad Sheikhasadi, F. Jolai, A. Aghsami","doi":"10.1093/jcde/qwad058","DOIUrl":"https://doi.org/10.1093/jcde/qwad058","url":null,"abstract":"\u0000 Controlling and maintaining public health in the face of diseases necessitates the effective implementation of response strategies, including the distribution of vaccines. By distributing vaccines, vulnerable populations can be targeted, individuals can be protected, and the spread of diseases can be minimized. However, managing vaccine distribution poses challenges that require careful consideration of various factors, including the location of distribution facilities. This paper proposes a novel model that combines location-allocation problems with queueing systems methodologies to optimize the efficiency of vaccine distribution. The proposed model considers factors such as uncertain demand, varying service rates, depending on the system state. Its primary objective is to minimize total costs, which encompass the establishment and adjustment of the service mechanism, travel times, and customer waiting time. To forecast customer demand rates, the model utilizes time series techniques, specifically the seasonal Autoregressive Integrated Moving Average (ARIMA) model. In order to tackle large-scale problems, a total of 16 newly developed Metaheuristic algorithms are employed, and their performance is thoroughly evaluated. This approach facilitates the generation of solutions that are nearly optimal within a reasonable timeframe. The effectiveness of the model is evaluated through a real case study focused on vaccination distribution in Iran. Furthermore, a comprehensive sensitivity analysis is conducted to demonstrate the practical applicability of the proposed model. The study contributes to the advancement of robust decision-making frameworks and provides valuable insights for addressing location-related challenges in health systems.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"69 1","pages":"1507-1530"},"PeriodicalIF":4.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86102700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Jia, Chenghao Lu, Di Wu, Changsheng Wen, Honghua Rao, L. Abualigah
In 2021, a meta-heuristic algorithm, Reptile Search Algorithm (RSA), was proposed. RSA mainly simulates the cooperative predatory behavior of crocodiles. Although RSA has a fast convergence speed, due to the influence of the crocodile predation mechanism, if the algorithm falls into the local optimum in the early stage, RSA will probably be unable to jump out of the local optimum, resulting in a poor comprehensive performance. Because of the shortcomings of RSA, introducing the local escape operator can effectively improve crocodiles' ability to explore space and generate new crocodiles to replace poor crocodiles. Benefiting from adding a restart strategy, when the optimal solution of RSA is no longer updated, the algorithm’s ability to jump out of the local optimum is effectively improved by randomly initializing the crocodile. Then joining Ghost opposition-based learning to balance the IRSA’s exploitation and exploration, the Improved RSA with Ghost Opposition-based Learning for the Global Optimization Problem (IRSA) is proposed. To verify the performance of IRSA, we used nine famous optimization algorithms to compare with IRSA in 23 standard benchmark functions and CEC2020 test functions. The experiments show that IRSA has good optimization performance and robustness, and can effectively solve six classical engineering problems, thus proving its effectiveness in solving practical problems.
{"title":"An Improved Reptile Search Algorithm with Ghost Opposition-based Learning for Global Optimization Problems","authors":"H. Jia, Chenghao Lu, Di Wu, Changsheng Wen, Honghua Rao, L. Abualigah","doi":"10.1093/jcde/qwad048","DOIUrl":"https://doi.org/10.1093/jcde/qwad048","url":null,"abstract":"\u0000 In 2021, a meta-heuristic algorithm, Reptile Search Algorithm (RSA), was proposed. RSA mainly simulates the cooperative predatory behavior of crocodiles. Although RSA has a fast convergence speed, due to the influence of the crocodile predation mechanism, if the algorithm falls into the local optimum in the early stage, RSA will probably be unable to jump out of the local optimum, resulting in a poor comprehensive performance. Because of the shortcomings of RSA, introducing the local escape operator can effectively improve crocodiles' ability to explore space and generate new crocodiles to replace poor crocodiles. Benefiting from adding a restart strategy, when the optimal solution of RSA is no longer updated, the algorithm’s ability to jump out of the local optimum is effectively improved by randomly initializing the crocodile. Then joining Ghost opposition-based learning to balance the IRSA’s exploitation and exploration, the Improved RSA with Ghost Opposition-based Learning for the Global Optimization Problem (IRSA) is proposed. To verify the performance of IRSA, we used nine famous optimization algorithms to compare with IRSA in 23 standard benchmark functions and CEC2020 test functions. The experiments show that IRSA has good optimization performance and robustness, and can effectively solve six classical engineering problems, thus proving its effectiveness in solving practical problems.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"19 1","pages":"1390-1422"},"PeriodicalIF":4.9,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82183127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dongmei Liu, H. Ouyang, Steven Li, Chunliang Zhang, Zhi-hui Zhan
Because of the good performance of convolutional neural network (CNN), it has been extensively used in many fields, such as image, speech, text, etc. However, it is easily affected by hyperparameters. How to effectively configure hyperparameters at a reasonable time to improve the performance of CNNs has always been a complex problem. To solve this problem, this paper proposes a method to automatically optimize CNN hyperparameters based on the local autonomous competitive harmony search (LACHS) algorithm. To avoid the influence of complicated parameter adjustment of LACHS algorithm on its performance, a parameter dynamic adjustment strategy is adopted, which makes the pitch adjustment probability PAR and step factor BW dynamically adjust according to the actual situation. To strengthen the fine search of neighborhood space and reduce the possibility of falling into local optima for a long time, an autonomous decision-making search strategy based on the optimal state is designed. To help the algorithm jump out of the local fitting situation, this paper proposes a local competition mechanism to make the new sound competes with the worst harmonic progression of local selection. In addition, an evaluation function is proposed, which integrates the training times and recognition accuracy. To achieve the purpose of saving the calculation cost without affecting the search result, it makes the training time for each model depending on the learning rate and batch size. In order to prove the feasibility of LACHS algorithm in configuring CNN superparameters, the classification of the Fashion-MNIST dataset and CIFAR10 dataset is tested. The comparison is made between CNN based on empirical configuration and CNN based on classical algorithms to optimize hyperparameters automatically. The results show that the performance of CNN based on the LACHS algorithm has been improved effectively, so this algorithm has certain advantages in hyperparametric optimization. In addition, this paper applies the LACHS algorithm to expression recognition. Experiments show that the performance of CNN optimized based on the LACHS algorithm is better than that of the same type of artificially designed CNN. Therefore, the method proposed in this paper is feasible in practical application.
{"title":"Hyperparameters optimization of convolutional neural network based on local autonomous competition harmony search algorithm","authors":"Dongmei Liu, H. Ouyang, Steven Li, Chunliang Zhang, Zhi-hui Zhan","doi":"10.1093/jcde/qwad050","DOIUrl":"https://doi.org/10.1093/jcde/qwad050","url":null,"abstract":"\u0000 Because of the good performance of convolutional neural network (CNN), it has been extensively used in many fields, such as image, speech, text, etc. However, it is easily affected by hyperparameters. How to effectively configure hyperparameters at a reasonable time to improve the performance of CNNs has always been a complex problem. To solve this problem, this paper proposes a method to automatically optimize CNN hyperparameters based on the local autonomous competitive harmony search (LACHS) algorithm. To avoid the influence of complicated parameter adjustment of LACHS algorithm on its performance, a parameter dynamic adjustment strategy is adopted, which makes the pitch adjustment probability PAR and step factor BW dynamically adjust according to the actual situation. To strengthen the fine search of neighborhood space and reduce the possibility of falling into local optima for a long time, an autonomous decision-making search strategy based on the optimal state is designed. To help the algorithm jump out of the local fitting situation, this paper proposes a local competition mechanism to make the new sound competes with the worst harmonic progression of local selection. In addition, an evaluation function is proposed, which integrates the training times and recognition accuracy. To achieve the purpose of saving the calculation cost without affecting the search result, it makes the training time for each model depending on the learning rate and batch size. In order to prove the feasibility of LACHS algorithm in configuring CNN superparameters, the classification of the Fashion-MNIST dataset and CIFAR10 dataset is tested. The comparison is made between CNN based on empirical configuration and CNN based on classical algorithms to optimize hyperparameters automatically. The results show that the performance of CNN based on the LACHS algorithm has been improved effectively, so this algorithm has certain advantages in hyperparametric optimization. In addition, this paper applies the LACHS algorithm to expression recognition. Experiments show that the performance of CNN optimized based on the LACHS algorithm is better than that of the same type of artificially designed CNN. Therefore, the method proposed in this paper is feasible in practical application.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"89 5 Pt 1 1","pages":"1280-1297"},"PeriodicalIF":4.9,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89404130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nazar K. Hussein, Mohammed Qaraad, Souad Amjad, M. Farag, Saima Hassan, S. Mirjalili, Mostafa A. Elhosseini
The paper addresses the limitations of the Moth-Flame Optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. The MFO algorithm, which employs moths' transverse orientation navigation technique, has been used to generate solutions for such problems. However, the performance of MFO is dependent on the flame production and spiral search components, and the search mechanism could still be improved concerning the diversity of flames and the moths' ability to find solutions. The authors propose a revised version called GMSMFO, which uses a Novel Gaussian mutation mechanism and shrink MFO to enhance population diversity and balance exploration and exploitation capabilities. The study evaluates the performance of GMSMFO using the CEC 2017 benchmark and 20 datasets, including a high-dimensional intrusion detection system dataset. The proposed algorithm is compared to other advanced metaheuristics, and its performance is evaluated using statistical tests such as Friedman and Wilcoxon rank-sum. The study shows that GMSMFO is highly competitive and frequently superior to other algorithms. It can identify the ideal feature subset, improving classification accuracy and reducing the number of features used. The main contribution of this research paper includes the improvement of the exploration/exploitation balance and the expansion of the local search. The ranging controller and Gaussian mutation enhance navigation and diversity. The research paper compares GMSMFO with traditional and advanced metaheuristic algorithms on 29 benchmarks and its application to binary feature selection on 20 benchmarks, including intrusion detection systems. The statistical tests (Wilcoxon rank-sum and Friedman) evaluate the performance of GMSMFO compared to other algorithms. The algorithm source code is available at https://github.com/MohammedQaraad/GMSMFO-algorithm.
{"title":"Enhancing feature selection with GMSMFO: A global optimization algorithm for machine learning with application to intrusion detection","authors":"Nazar K. Hussein, Mohammed Qaraad, Souad Amjad, M. Farag, Saima Hassan, S. Mirjalili, Mostafa A. Elhosseini","doi":"10.1093/jcde/qwad053","DOIUrl":"https://doi.org/10.1093/jcde/qwad053","url":null,"abstract":"\u0000 The paper addresses the limitations of the Moth-Flame Optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. The MFO algorithm, which employs moths' transverse orientation navigation technique, has been used to generate solutions for such problems. However, the performance of MFO is dependent on the flame production and spiral search components, and the search mechanism could still be improved concerning the diversity of flames and the moths' ability to find solutions. The authors propose a revised version called GMSMFO, which uses a Novel Gaussian mutation mechanism and shrink MFO to enhance population diversity and balance exploration and exploitation capabilities. The study evaluates the performance of GMSMFO using the CEC 2017 benchmark and 20 datasets, including a high-dimensional intrusion detection system dataset. The proposed algorithm is compared to other advanced metaheuristics, and its performance is evaluated using statistical tests such as Friedman and Wilcoxon rank-sum. The study shows that GMSMFO is highly competitive and frequently superior to other algorithms. It can identify the ideal feature subset, improving classification accuracy and reducing the number of features used. The main contribution of this research paper includes the improvement of the exploration/exploitation balance and the expansion of the local search. The ranging controller and Gaussian mutation enhance navigation and diversity. The research paper compares GMSMFO with traditional and advanced metaheuristic algorithms on 29 benchmarks and its application to binary feature selection on 20 benchmarks, including intrusion detection systems. The statistical tests (Wilcoxon rank-sum and Friedman) evaluate the performance of GMSMFO compared to other algorithms. The algorithm source code is available at https://github.com/MohammedQaraad/GMSMFO-algorithm.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"24 1","pages":"1363-1389"},"PeriodicalIF":4.9,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79754476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Jia, Yongchao Li, Di Wu, Honghua Rao, Changsheng Wen, L. Abualigah
A metaheuristic algorithm that simulates the foraging behavior of remora has been proposed in recent years, called ROA. ROA mainly simulates host parasitism and host switching in the foraging behavior of remora. However, in the experiment, it was found that there is still room for improvement in the performance of ROA. When dealing with complex optimization problems, ROA often falls into local optimal solutions, and there is also the problem of too-slow convergence. Inspired by the natural rule of “Survival of the fittest ”, this paper proposes a random restart strategy to improve the ability of ROA to jump out of the local optimal solution. Secondly, inspired by the foraging behavior of remora, this paper adds an information entropy evaluation strategy and visual perception strategy based on ROA. With the blessing of three strategies, a multi-strategy Remora Optimization Algorithm (MSROA) is proposed. Through 23 benchmark functions and IEEE CEC2017 test functions, MSROA is comprehensively tested, and the experimental results show that MSROA has strong optimization capabilities. In order to further verify the application of MSROA in practice, this paper tests MSROA through five practical engineering problems, which proves that MSROA has strong competitiveness in solving practical optimization problems.
{"title":"Multi-strategy Remora Optimization Algorithm for solving multi-extremum problems","authors":"H. Jia, Yongchao Li, Di Wu, Honghua Rao, Changsheng Wen, L. Abualigah","doi":"10.1093/jcde/qwad044","DOIUrl":"https://doi.org/10.1093/jcde/qwad044","url":null,"abstract":"\u0000 A metaheuristic algorithm that simulates the foraging behavior of remora has been proposed in recent years, called ROA. ROA mainly simulates host parasitism and host switching in the foraging behavior of remora. However, in the experiment, it was found that there is still room for improvement in the performance of ROA. When dealing with complex optimization problems, ROA often falls into local optimal solutions, and there is also the problem of too-slow convergence. Inspired by the natural rule of “Survival of the fittest ”, this paper proposes a random restart strategy to improve the ability of ROA to jump out of the local optimal solution. Secondly, inspired by the foraging behavior of remora, this paper adds an information entropy evaluation strategy and visual perception strategy based on ROA. With the blessing of three strategies, a multi-strategy Remora Optimization Algorithm (MSROA) is proposed. Through 23 benchmark functions and IEEE CEC2017 test functions, MSROA is comprehensively tested, and the experimental results show that MSROA has strong optimization capabilities. In order to further verify the application of MSROA in practice, this paper tests MSROA through five practical engineering problems, which proves that MSROA has strong competitiveness in solving practical optimization problems.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"22 1","pages":"1315-1349"},"PeriodicalIF":4.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86367514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A comprehensive analysis of the green hydrogen supply chain is presented in this paper, encompassing production, storage, transportation, and consumption, with a focus on the application of metaheuristic optimisation. The challenges associated with each stage are highlighted, and the potential of metaheuristic optimisation methods to address these challenges is discussed. The primary method of green hydrogen production, water electrolysis through renewable energy, is outlined along with the importance of its optimisation. Various storage methods, such as compressed gas, liquid hydrogen, and material-based storage, are covered with an emphasis on the need for optimisation to improve safety, capacity, and performance. Different transportation options, including pipelines, trucks, and ships, are explored, and factors influencing the choice of transportation methods in different regions are identified. Various hydrogen consumption methods and their associated challenges, such as fuel cell performance optimisation, hydrogen-based heating systems design, and energy conversion technology choice, are also discussed. The paper further investigates multi-objective approaches for the optimisation of problems in this domain. The significant potential of metaheuristic optimisation techniques is highlighted as a key to addressing these challenges and improving overall efficiency and sustainability with respect to future trends in this rapidly advancing area.
{"title":"Challenges and opportunities in green hydrogen supply chain through metaheuristic optimization","authors":"Saman A. Gorji","doi":"10.1093/jcde/qwad043","DOIUrl":"https://doi.org/10.1093/jcde/qwad043","url":null,"abstract":"\u0000 A comprehensive analysis of the green hydrogen supply chain is presented in this paper, encompassing production, storage, transportation, and consumption, with a focus on the application of metaheuristic optimisation. The challenges associated with each stage are highlighted, and the potential of metaheuristic optimisation methods to address these challenges is discussed. The primary method of green hydrogen production, water electrolysis through renewable energy, is outlined along with the importance of its optimisation. Various storage methods, such as compressed gas, liquid hydrogen, and material-based storage, are covered with an emphasis on the need for optimisation to improve safety, capacity, and performance. Different transportation options, including pipelines, trucks, and ships, are explored, and factors influencing the choice of transportation methods in different regions are identified. Various hydrogen consumption methods and their associated challenges, such as fuel cell performance optimisation, hydrogen-based heating systems design, and energy conversion technology choice, are also discussed. The paper further investigates multi-objective approaches for the optimisation of problems in this domain. The significant potential of metaheuristic optimisation techniques is highlighted as a key to addressing these challenges and improving overall efficiency and sustainability with respect to future trends in this rapidly advancing area.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"14 1","pages":"1143-1157"},"PeriodicalIF":4.9,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87469949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tunnel construction sites pose a significant safety risk to workers due to the low-light conditions that can affect visibility and lead to accidents. Therefore, identifying personal protective equipment (PPE) is critical to prevent injuries and fatalities. A few research has addressed the challenges posed by tunnel construction sites whose light conditions are lower and images are captured from a distance. In this study, we proposed an improved YOLOX approach and a new dataset for detecting low-light and small PPE. We modified the YOLOX architecture by adding ConvNeXt modules to the backbone for deep feature extraction and introducing the fourth YOLOX head for enhancing multiscale prediction. Additionally, we adopted the CLAHE algorithm for augmenting low-light images after comparing it with eight other methods. Consequently, the improved YOLOX approach achieves an mAP of 86.94%, which is 4.23% higher than the original model and outperforms selected state-of-the-art. It also improves the AP of small object classes by 7.17% on average and attains a real-time processing speed of 22 FPS. Furthermore, we constructed a novel dataset with 8,285 low-light instances and 6,814 small ones. The improved YOLOX approach offers accurate and efficient detection performance, which can reduce safety incidents on tunnel construction sites.
{"title":"An improved YOLOX approach for low-light and small object detection: PPE on tunnel construction sites","authors":"Zijian Wang, Zixiang Cai, Yimin Wu","doi":"10.1093/jcde/qwad042","DOIUrl":"https://doi.org/10.1093/jcde/qwad042","url":null,"abstract":"\u0000 Tunnel construction sites pose a significant safety risk to workers due to the low-light conditions that can affect visibility and lead to accidents. Therefore, identifying personal protective equipment (PPE) is critical to prevent injuries and fatalities. A few research has addressed the challenges posed by tunnel construction sites whose light conditions are lower and images are captured from a distance. In this study, we proposed an improved YOLOX approach and a new dataset for detecting low-light and small PPE. We modified the YOLOX architecture by adding ConvNeXt modules to the backbone for deep feature extraction and introducing the fourth YOLOX head for enhancing multiscale prediction. Additionally, we adopted the CLAHE algorithm for augmenting low-light images after comparing it with eight other methods. Consequently, the improved YOLOX approach achieves an mAP of 86.94%, which is 4.23% higher than the original model and outperforms selected state-of-the-art. It also improves the AP of small object classes by 7.17% on average and attains a real-time processing speed of 22 FPS. Furthermore, we constructed a novel dataset with 8,285 low-light instances and 6,814 small ones. The improved YOLOX approach offers accurate and efficient detection performance, which can reduce safety incidents on tunnel construction sites.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"128 1","pages":"1158-1175"},"PeriodicalIF":4.9,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88161761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ulanbek Auyeskhan, C. Steed, Soohyung Park, Dong-Hyun Kim, Im Doo Jung, Namhun Kim
There is a combinatorial explosion of alternative variants of an assembly design owing to the design freedom provided by Additive Manufacturing. In this regard, a novel Virtual Reality-based decision-support framework is presented herein for extracting the superior assembly design to be fabricated by AM route. It specifically addresses the intersection between human assembly and AM hence combining Design for Assembly, and Design for Additive Manufacturing using Axiomatic Design theory. Several Virtual Reality experiments were carried out to achieve this with human subjects assembling parts. At first, a 2D table is assembled, and the data are used to confirm the independence of nonfunctional requirements such as assembly time and assembly displacement error according to Independence Axiom. Then this approach is demonstrated on an industrial lifeboat hook with three assembly design variations. The data from these experiments are utilized to evaluate the possible combinations of the assembly in terms of probability density based on the Information Axiom. The technique effectively identifies the assembly design most likely to fulfill the nonfunctional requirements. To the authors’ best knowledge, this is the first study that numerically extracts the human aspect of design at an early design stage in the decision process and considers the selection of the superior assembly design in a detailed design stage. Finally, this process is automated using a graphical user interface, which embraces the practicality of the currently integrated framework and enables manufacturers to choose the best assembly design.
{"title":"Virtual reality-based assembly-level design for additive manufacturing decision framework involving human aspects of design","authors":"Ulanbek Auyeskhan, C. Steed, Soohyung Park, Dong-Hyun Kim, Im Doo Jung, Namhun Kim","doi":"10.1093/jcde/qwad041","DOIUrl":"https://doi.org/10.1093/jcde/qwad041","url":null,"abstract":"\u0000 There is a combinatorial explosion of alternative variants of an assembly design owing to the design freedom provided by Additive Manufacturing. In this regard, a novel Virtual Reality-based decision-support framework is presented herein for extracting the superior assembly design to be fabricated by AM route. It specifically addresses the intersection between human assembly and AM hence combining Design for Assembly, and Design for Additive Manufacturing using Axiomatic Design theory. Several Virtual Reality experiments were carried out to achieve this with human subjects assembling parts. At first, a 2D table is assembled, and the data are used to confirm the independence of nonfunctional requirements such as assembly time and assembly displacement error according to Independence Axiom. Then this approach is demonstrated on an industrial lifeboat hook with three assembly design variations. The data from these experiments are utilized to evaluate the possible combinations of the assembly in terms of probability density based on the Information Axiom. The technique effectively identifies the assembly design most likely to fulfill the nonfunctional requirements. To the authors’ best knowledge, this is the first study that numerically extracts the human aspect of design at an early design stage in the decision process and considers the selection of the superior assembly design in a detailed design stage. Finally, this process is automated using a graphical user interface, which embraces the practicality of the currently integrated framework and enables manufacturers to choose the best assembly design.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"6 1","pages":"1126-1142"},"PeriodicalIF":4.9,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78869884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobina Mousapour Mamoudan, A. Ostadi, Nima Pourkhodabakhsh, A. M. F. Fard, Faezeh Soleimani
Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical methods to identify fake signals obtained from technical analysis indicators in the precious metals market. In this paper, we analyze these indicators in different ways based on the recorded signals for ten months. The main novelty of this research is to propose hybrid neural network-based metaheuristic algorithms for analyzing them accurately while increasing the performance of the signals obtained from technical analysis indicators. We combine a convolutional neural network and a bidirectional gated recurrent unit whose hyperparameters are optimized using the firefly metaheuristic algorithm. To determine and select the most influential variables on the target variable, we use another successful recently-developed metaheuristic, namely, the moth-flame optimization algorithm. Finally, we compare the performance of the proposed models with other state-of-the-art single and hybrid deep learning and machine learning methods from the literature. Finally, the main finding is that the proposed neural network-based metaheuristics can be useful as a decision support tool for investors to address and control the enormous uncertainties in the financial and precious metals markets.
{"title":"Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market","authors":"Mobina Mousapour Mamoudan, A. Ostadi, Nima Pourkhodabakhsh, A. M. F. Fard, Faezeh Soleimani","doi":"10.1093/jcde/qwad039","DOIUrl":"https://doi.org/10.1093/jcde/qwad039","url":null,"abstract":"\u0000 Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical methods to identify fake signals obtained from technical analysis indicators in the precious metals market. In this paper, we analyze these indicators in different ways based on the recorded signals for ten months. The main novelty of this research is to propose hybrid neural network-based metaheuristic algorithms for analyzing them accurately while increasing the performance of the signals obtained from technical analysis indicators. We combine a convolutional neural network and a bidirectional gated recurrent unit whose hyperparameters are optimized using the firefly metaheuristic algorithm. To determine and select the most influential variables on the target variable, we use another successful recently-developed metaheuristic, namely, the moth-flame optimization algorithm. Finally, we compare the performance of the proposed models with other state-of-the-art single and hybrid deep learning and machine learning methods from the literature. Finally, the main finding is that the proposed neural network-based metaheuristics can be useful as a decision support tool for investors to address and control the enormous uncertainties in the financial and precious metals markets.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"69 1","pages":"1110-1125"},"PeriodicalIF":4.9,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81332766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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