Elevated intraocular pressure(IOP)is one of the causes of retinal ischemia/reperfusion injury,which results in NRP3 inflammasome activation and leads to visual damage.Homerla is repo rted to play a protective role in ...Elevated intraocular pressure(IOP)is one of the causes of retinal ischemia/reperfusion injury,which results in NRP3 inflammasome activation and leads to visual damage.Homerla is repo rted to play a protective role in neuroinflammation in the cerebrum.However,the effects of Homerla on NLRP3inflammasomes in retinal ischemia/reperfusion injury caused by elevated IOP remain unknown.In our study,animal models we re constructed using C57BL/6J and Homer1^(flox/-)/Homerla^(+/-)/Nestin-Cre^(+/-)mice with elevated IOP-induced retinal ischemia/repe rfusion injury.For in vitro expe riments,the oxygen-glucose deprivation/repe rfusion injury model was constructed with M uller cells.We found that Homerla ove rexpression amelio rated the decreases in retinal thickness and Muller cell viability after ischemia/reperfusion injury.Furthermore,Homerla knockdown promoted NF-κB P65^(Ser536)activation via caspase-8,NF-κB P65 nuclear translocation,NLRP3 inflammasome formation,and the production and processing of interleukin-1βand inte rleukin-18.The opposite results we re observed with Homerla ove rexpression.Finally,the combined administration of Homerla protein and JSH-23 significantly inhibited the reduction in retinal thickness in Homer1^(flox/-)Homer1a^(+/-)/Nestin-Cre^(+/-)mice and apoptosis in M uller cells after ischemia/reperfusion injury.Taken together,these studies demonstrate that Homer1a exerts protective effects on retinal tissue and M uller cells via the caspase-8/NF-KB P65/NLRP3 pathway after I/R injury.展开更多
Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions to...Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?展开更多
Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding appro...Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.展开更多
In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and comput...In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has surfaced.Consequently,model training has become more costly and less efficient.To enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language model.Firstly,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is formed.Then,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the performance.Finally,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency.Compared with traditional models,PAL-BERT significantly improves the NLP task’s performance.展开更多
The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challen...The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.展开更多
Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the ...Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.展开更多
以系统总成本最小为目标函数,基于可再生能源发电混合系统优化仿真软件(Hybrid Optimization Model for Electric Renewables,HOMER),针对某工业园区的风光资源数据和负荷情况,进行了设备成本和参数设定。在获得的配置方案中,对比分析...以系统总成本最小为目标函数,基于可再生能源发电混合系统优化仿真软件(Hybrid Optimization Model for Electric Renewables,HOMER),针对某工业园区的风光资源数据和负荷情况,进行了设备成本和参数设定。在获得的配置方案中,对比分析了并网型风光储、风光互补以及风储、光储发电系统的优化配置方案,分析其弃电率和经济性,验证了风光储系统的优越性,最终得到并网型风光储互补发电系统总净现成本最小的配置,并对其各设备的年发电量、系统可靠性及经济性进行了分析,可为可再生能源多能互补微电网风光储系统容量优化配置的研究提供参考。展开更多
Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic interest.Scholars have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the eve...Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic interest.Scholars have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs engender.To clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature review.The goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)users.The final review included 133 articles.Dominant research themes include question quality,answer quality,and expert identification.In terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack Overflow.The scope of most articles was confined to just one platform with few cross-platform investigations.Articles with ML outnumber those with DL.Nonetheless,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.展开更多
Recently,pre-trained language representation models such as bidirec-tional encoder representations from transformers(BERT)have been performing well in commonsense question answering(CSQA).However,there is a problem th...Recently,pre-trained language representation models such as bidirec-tional encoder representations from transformers(BERT)have been performing well in commonsense question answering(CSQA).However,there is a problem that the models do not directly use explicit information of knowledge sources existing outside.To augment this,additional methods such as knowledge-aware graph network(KagNet)and multi-hop graph relation network(MHGRN)have been proposed.In this study,we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers(ALBERT)with knowledge graph information extraction technique.We also propose to applying the novel method,schema graph expansion to recent language models.Then,we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent.Furthermore,we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.展开更多
ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web services.How to model questions and users in the hete...ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web services.How to model questions and users in the heterogeneous content network is critical to this task.Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues.Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling.However,they often fail to distinguish the nodes’personalized preferences and only consider the textual content of a part of the nodes in network embedding learning,while ignoring the semantic relevance of nodes.In this paper,we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more comprehensively.Specifically,we learn topology-based embeddings through a hierarchical attentive network learning strategy,in which the proximity information and the personalized preference of nodes are encoded and preserved.Meanwhile,we utilize the node’s textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram model.In addition,the user’s relative answer quality is incorporated to promote the ranking performance.Experimental results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning together.The performance of the proposed work is analyzed in terms of MRR,P@K,and MAP and is proven to be more advanced than the existing methodologies.展开更多
This study aims to examine the relations between languages of antiquity and some words in Homer＇s epics, Iliad and Odyssey, that are considered the first written texts and important in terms of the richness of Greek ...This study aims to examine the relations between languages of antiquity and some words in Homer＇s epics, Iliad and Odyssey, that are considered the first written texts and important in terms of the richness of Greek vocabulary. Although the words in Homeric epics carry a value for ancient Anatolia, there are no studies in this field in our eotmtry. For this purpose, with the help of words we have, we will focus on： （a） bond of these words with Hittite, partially （b） some archaic qualities for some Latin words, developed later and also continued of some ground and phrases in the everyday life in the same region for centuries on the language of the people depending on the oral transmission. At the end of the study, we will discuss the changes of the words in philological level as well as in terms of the letter, sounds and meaning by used words in Homer＇s epics as will be explained in this study.展开更多
Solar system design for green hydrogen production has become the most prominent renewable energy research area, and this has also actively fueled the desire to achieve net-zero emissions. Hydrogen is a promising energ...Solar system design for green hydrogen production has become the most prominent renewable energy research area, and this has also actively fueled the desire to achieve net-zero emissions. Hydrogen is a promising energy carrier because it possesses more energy capacity than fossil fuels and the abundant nature of renewable energy systems can be utilized for green hydrogen production. However, the design of an optimized electrical energy system required for hydrogen production is crucial. Solar energy is indeed beneficial for green hydrogen production and this research designed, discussed, and provided high-level research on HOMER design for green hydrogen production and deployed the energy requirement with ASPEN Plus to optimize the energy system, while also incorporating fuzzy logic and PID control approaches. In addition, a promising technology with a high potential for renewable hydrogen energy is the proton exchange membrane (PEM) electrolyzer. Since its cathode (hydrogen electrode) may be operated over a wide range of pressure, a control process must be added to the system in order for it to work dynamically efficiently. This system can be characterized as an analogous circuit that consists of a resistor, capacitor, and reversible voltage. As a result, this research work also explores the Fuzzy-PID control of the PEM electrolysis system. Both the PID and Fuzzy Logic control systems were simulated using the control simulation program Matlab R2018a, which makes use of Matlab script files and the Simulink environment. Based on the circuit diagram, a transfer function that represents the mathematical model of the plant was created, and the PEM electrolysis control system is determined to be highly significant and applicable to the two control systems. The PI controller, however, has a 30.8% overshoot deficit, but when the fuzzy control system is compared to the PID controller, it is found that the fuzzy control system achieves stability more quickly, demonstrating its benefit over PID.展开更多
基金supported by the Youth Development Project of Air Force Military Medical University,No.21 QNPY072Key Project of Shaanxi Provincial Natural Science Basic Research Program,No.2023-JC-ZD-48(both to FF)。
文摘Elevated intraocular pressure(IOP)is one of the causes of retinal ischemia/reperfusion injury,which results in NRP3 inflammasome activation and leads to visual damage.Homerla is repo rted to play a protective role in neuroinflammation in the cerebrum.However,the effects of Homerla on NLRP3inflammasomes in retinal ischemia/reperfusion injury caused by elevated IOP remain unknown.In our study,animal models we re constructed using C57BL/6J and Homer1^(flox/-)/Homerla^(+/-)/Nestin-Cre^(+/-)mice with elevated IOP-induced retinal ischemia/repe rfusion injury.For in vitro expe riments,the oxygen-glucose deprivation/repe rfusion injury model was constructed with M uller cells.We found that Homerla ove rexpression amelio rated the decreases in retinal thickness and Muller cell viability after ischemia/reperfusion injury.Furthermore,Homerla knockdown promoted NF-κB P65^(Ser536)activation via caspase-8,NF-κB P65 nuclear translocation,NLRP3 inflammasome formation,and the production and processing of interleukin-1βand inte rleukin-18.The opposite results we re observed with Homerla ove rexpression.Finally,the combined administration of Homerla protein and JSH-23 significantly inhibited the reduction in retinal thickness in Homer1^(flox/-)Homer1a^(+/-)/Nestin-Cre^(+/-)mice and apoptosis in M uller cells after ischemia/reperfusion injury.Taken together,these studies demonstrate that Homer1a exerts protective effects on retinal tissue and M uller cells via the caspase-8/NF-KB P65/NLRP3 pathway after I/R injury.
文摘Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?
基金Supported by National Nature Science Foudation of China(61976160,61906137,61976158,62076184,62076182)Shanghai Science and Technology Plan Project(21DZ1204800)。
文摘Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.
基金Supported by Sichuan Science and Technology Program(2021YFQ0003,2023YFSY0026,2023YFH0004).
文摘In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has surfaced.Consequently,model training has become more costly and less efficient.To enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language model.Firstly,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is formed.Then,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the performance.Finally,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency.Compared with traditional models,PAL-BERT significantly improves the NLP task’s performance.
文摘The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.
基金supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004).
文摘Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.
文摘以系统总成本最小为目标函数,基于可再生能源发电混合系统优化仿真软件(Hybrid Optimization Model for Electric Renewables,HOMER),针对某工业园区的风光资源数据和负荷情况,进行了设备成本和参数设定。在获得的配置方案中,对比分析了并网型风光储、风光互补以及风储、光储发电系统的优化配置方案,分析其弃电率和经济性,验证了风光储系统的优越性,最终得到并网型风光储互补发电系统总净现成本最小的配置,并对其各设备的年发电量、系统可靠性及经济性进行了分析,可为可再生能源多能互补微电网风光储系统容量优化配置的研究提供参考。
文摘Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic interest.Scholars have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs engender.To clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature review.The goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)users.The final review included 133 articles.Dominant research themes include question quality,answer quality,and expert identification.In terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack Overflow.The scope of most articles was confined to just one platform with few cross-platform investigations.Articles with ML outnumber those with DL.Nonetheless,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)(No.2020R1G1A1100493).
文摘Recently,pre-trained language representation models such as bidirec-tional encoder representations from transformers(BERT)have been performing well in commonsense question answering(CSQA).However,there is a problem that the models do not directly use explicit information of knowledge sources existing outside.To augment this,additional methods such as knowledge-aware graph network(KagNet)and multi-hop graph relation network(MHGRN)have been proposed.In this study,we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers(ALBERT)with knowledge graph information extraction technique.We also propose to applying the novel method,schema graph expansion to recent language models.Then,we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent.Furthermore,we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.
文摘ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web services.How to model questions and users in the heterogeneous content network is critical to this task.Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues.Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling.However,they often fail to distinguish the nodes’personalized preferences and only consider the textual content of a part of the nodes in network embedding learning,while ignoring the semantic relevance of nodes.In this paper,we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more comprehensively.Specifically,we learn topology-based embeddings through a hierarchical attentive network learning strategy,in which the proximity information and the personalized preference of nodes are encoded and preserved.Meanwhile,we utilize the node’s textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram model.In addition,the user’s relative answer quality is incorporated to promote the ranking performance.Experimental results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning together.The performance of the proposed work is analyzed in terms of MRR,P@K,and MAP and is proven to be more advanced than the existing methodologies.
文摘This study aims to examine the relations between languages of antiquity and some words in Homer＇s epics, Iliad and Odyssey, that are considered the first written texts and important in terms of the richness of Greek vocabulary. Although the words in Homeric epics carry a value for ancient Anatolia, there are no studies in this field in our eotmtry. For this purpose, with the help of words we have, we will focus on： （a） bond of these words with Hittite, partially （b） some archaic qualities for some Latin words, developed later and also continued of some ground and phrases in the everyday life in the same region for centuries on the language of the people depending on the oral transmission. At the end of the study, we will discuss the changes of the words in philological level as well as in terms of the letter, sounds and meaning by used words in Homer＇s epics as will be explained in this study.
文摘Solar system design for green hydrogen production has become the most prominent renewable energy research area, and this has also actively fueled the desire to achieve net-zero emissions. Hydrogen is a promising energy carrier because it possesses more energy capacity than fossil fuels and the abundant nature of renewable energy systems can be utilized for green hydrogen production. However, the design of an optimized electrical energy system required for hydrogen production is crucial. Solar energy is indeed beneficial for green hydrogen production and this research designed, discussed, and provided high-level research on HOMER design for green hydrogen production and deployed the energy requirement with ASPEN Plus to optimize the energy system, while also incorporating fuzzy logic and PID control approaches. In addition, a promising technology with a high potential for renewable hydrogen energy is the proton exchange membrane (PEM) electrolyzer. Since its cathode (hydrogen electrode) may be operated over a wide range of pressure, a control process must be added to the system in order for it to work dynamically efficiently. This system can be characterized as an analogous circuit that consists of a resistor, capacitor, and reversible voltage. As a result, this research work also explores the Fuzzy-PID control of the PEM electrolysis system. Both the PID and Fuzzy Logic control systems were simulated using the control simulation program Matlab R2018a, which makes use of Matlab script files and the Simulink environment. Based on the circuit diagram, a transfer function that represents the mathematical model of the plant was created, and the PEM electrolysis control system is determined to be highly significant and applicable to the two control systems. The PI controller, however, has a 30.8% overshoot deficit, but when the fuzzy control system is compared to the PID controller, it is found that the fuzzy control system achieves stability more quickly, demonstrating its benefit over PID.
