Although digital changes in power systems have added more ways to monitor and control them,these changes have also led to new cyber-attack risks,mainly from False Data Injection(FDI)attacks.If this happens,the sensors...Although digital changes in power systems have added more ways to monitor and control them,these changes have also led to new cyber-attack risks,mainly from False Data Injection(FDI)attacks.If this happens,the sensors and operations are compromised,which can lead to big problems,disruptions,failures and blackouts.In response to this challenge,this paper presents a reliable and innovative detection framework that leverages Bidirectional Long Short-Term Memory(Bi-LSTM)networks and employs explanatory methods from Artificial Intelligence(AI).Not only does the suggested architecture detect potential fraud with high accuracy,but it also makes its decisions transparent,enabling operators to take appropriate action.Themethod developed here utilizesmodel-free,interpretable tools to identify essential input elements,thereby making predictions more understandable and usable.Enhancing detection performance is made possible by correcting class imbalance using Synthetic Minority Over-sampling Technique(SMOTE)-based data balancing.Benchmark power system data confirms that the model functions correctly through detailed experiments.Experimental results showed that Bi-LSTM+Explainable AI(XAI)achieved an average accuracy of 94%,surpassing XGBoost(89%)and Bagging(84%),while ensuring explainability and a high level of robustness across various operating scenarios.By conducting an ablation study,we find that bidirectional recursive modeling and ReLU activation help improve generalization and model predictability.Additionally,examining model decisions through LIME enables us to identify which features are crucial for making smart grid operational decisions in real time.The research offers a practical and flexible approach for detecting FDI attacks,improving the security of cyber-physical systems,and facilitating the deployment of AI in energy infrastructure.展开更多
The range of memory impairments associated with Alzheimer's disease(AD) has been a focus for psychological and clinical researchers for many years.In addition to investigations of AD patients' veridical memory...The range of memory impairments associated with Alzheimer's disease(AD) has been a focus for psychological and clinical researchers for many years.In addition to investigations of AD patients' veridical memory using traditional recognition memory tasks,a number of recent studies have focused on false memories to reveal the underlying causes of memory impairment in AD.Studies comparing illusory memories between AD patients and healthy older people have revealed various differences in memory deficits between the development of AD and the typical aging processes.Here,we review 3 types of memory illusions tested in AD patients:associative memory illusions,fluency-based false memories and source memory errors.By comparing AD patients with healthy older adults,we sought to analyze the mechanisms underlying AD-related memory impairments at different stages of memory processing,including encoding,retrieval and monitoring.This comparison revealed that AD patients exhibit an impaired ability to establish and utilize gist representations at the encoding stage and impairments in processing on the basis of familiarity and recollection at the retrieval stage.Consequently,patients with AD have access to less information when making memory judgments.As a result,they become more susceptible to the effects of item fluency,which can be manipulated during the retrieval stage.Furthermore,with impaired source memory monitoring abilities,the capacity of AD patients to suppress memory illusions is compromised.Based on these findings,we propose that the study of false memories constitute a critical tool for elucidating the memory impairments involved in AD.Further explorations of these memory impairments will have practical significance for the diagnosis and treatment of AD in the future.展开更多
基金the Deanship of Scientific Research and Libraries in Princess Nourah bint Abdulrahman University for funding this research work through the Research Group project,Grant No.(RG-1445-0064).
文摘Although digital changes in power systems have added more ways to monitor and control them,these changes have also led to new cyber-attack risks,mainly from False Data Injection(FDI)attacks.If this happens,the sensors and operations are compromised,which can lead to big problems,disruptions,failures and blackouts.In response to this challenge,this paper presents a reliable and innovative detection framework that leverages Bidirectional Long Short-Term Memory(Bi-LSTM)networks and employs explanatory methods from Artificial Intelligence(AI).Not only does the suggested architecture detect potential fraud with high accuracy,but it also makes its decisions transparent,enabling operators to take appropriate action.Themethod developed here utilizesmodel-free,interpretable tools to identify essential input elements,thereby making predictions more understandable and usable.Enhancing detection performance is made possible by correcting class imbalance using Synthetic Minority Over-sampling Technique(SMOTE)-based data balancing.Benchmark power system data confirms that the model functions correctly through detailed experiments.Experimental results showed that Bi-LSTM+Explainable AI(XAI)achieved an average accuracy of 94%,surpassing XGBoost(89%)and Bagging(84%),while ensuring explainability and a high level of robustness across various operating scenarios.By conducting an ablation study,we find that bidirectional recursive modeling and ReLU activation help improve generalization and model predictability.Additionally,examining model decisions through LIME enables us to identify which features are crucial for making smart grid operational decisions in real time.The research offers a practical and flexible approach for detecting FDI attacks,improving the security of cyber-physical systems,and facilitating the deployment of AI in energy infrastructure.
基金supported by the National Natural Science Foundation of China (30870763)Beijing Municipal Natural Science Foundation (7093123)the Fundamental Research Funds for the Central Universities
文摘The range of memory impairments associated with Alzheimer's disease(AD) has been a focus for psychological and clinical researchers for many years.In addition to investigations of AD patients' veridical memory using traditional recognition memory tasks,a number of recent studies have focused on false memories to reveal the underlying causes of memory impairment in AD.Studies comparing illusory memories between AD patients and healthy older people have revealed various differences in memory deficits between the development of AD and the typical aging processes.Here,we review 3 types of memory illusions tested in AD patients:associative memory illusions,fluency-based false memories and source memory errors.By comparing AD patients with healthy older adults,we sought to analyze the mechanisms underlying AD-related memory impairments at different stages of memory processing,including encoding,retrieval and monitoring.This comparison revealed that AD patients exhibit an impaired ability to establish and utilize gist representations at the encoding stage and impairments in processing on the basis of familiarity and recollection at the retrieval stage.Consequently,patients with AD have access to less information when making memory judgments.As a result,they become more susceptible to the effects of item fluency,which can be manipulated during the retrieval stage.Furthermore,with impaired source memory monitoring abilities,the capacity of AD patients to suppress memory illusions is compromised.Based on these findings,we propose that the study of false memories constitute a critical tool for elucidating the memory impairments involved in AD.Further explorations of these memory impairments will have practical significance for the diagnosis and treatment of AD in the future.
