The widespread adoption of renewable energy sources presentssignificant challenges for power system *** paper proposes a dynamic optimal power flow(DOPF)method based on reinforcement learning(RL)to ad-dress the dispa...
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The widespread adoption of renewable energy sources presentssignificant challenges for power system *** paper proposes a dynamic optimal power flow(DOPF)method based on reinforcement learning(RL)to ad-dress the dispatching *** proposed method consid-ers a scenario where large-scale offshore wind farms are inter-connected and have access to an onshore power grid through multiple points of common coupling(PCCs).First,the opera-tional area model of the offshore power grid at the PCCs is es-tablished by combining the prediction results and the transmis-sion capacity limit of the offshore power *** upon this,a dynamic optimization model of the power system and its RL en-vironment are constructed with the consideration of offshore power dispatching ***,an improved algorithm based on the conditional generative adversarial network(CGAN)and the soft actor-critic(sAC)algorithm is *** analyzing an improved IEEE 118-node system,the proposed method proves to have the advantage of economy over a longer *** resulting strategy satisfies power system opera-tion constraints,effectively addressing the constraint problem of action space of RL,and it has the added benefit of faster so-lution speeds.
Prior research argues that the characteristics of crowdfunding campaigns affect their success rate. We examine this further to understand whether success in funding projects can be predicted by associated project char...
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support Vector Machine(sVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these paramete...
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support Vector Machine(sVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of sVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+sVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the sVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four sVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental resultsshow the high ability of the proposed algorithm to find the appropriate sVM’s parameters,and its acceptable performance to deal with feature selection problem.
Permissioned blockchains can provide high security and reliability for various Internet of Things (IoT) systems, such assmart healthcare and vehicular networks. However, the performance issues of permissioned blockch...
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With the advancement of new infrastructures,the digitalization of the substation communication network has rapidly increased,and its information security risks have become increasingly *** and reliable substation comm...
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With the advancement of new infrastructures,the digitalization of the substation communication network has rapidly increased,and its information security risks have become increasingly *** and reliable substation communication network flow models and flow anomaly detection methods have become an important means to prevent network security problems and identify network *** existing substation network analyzers and flow anomaly detection algorithms are usually based on threshold determination,which cannot reflect the inherent characteristics of substation automation flow based on IEC 61850 and have low detection *** effectively detect abnormal traffic,this paper fully explores the substation network traffic rules,extracts the frequency domain features of the station level network,and designs an abnormal traffic identification model based on the ResNest convolutional neural *** learning is used to solve the problem of insufficient abnormal traffic labeled samples in the ***,a new method of abnormal traffic detection in smart substation station level communication networksbased on deep transfer learning is *** T1-1 substation communication network is constructed on OPNET for abnormal simulations,and the actual network traffic in a 110kV substation is fused with CIC DDos2019 and KDD99 data sets for the algorithm performance test,*** accuracy reached is 98.73%and 98.95%,indicating that the detection model proposed in this paper has higher detection accuracy than existing algorithms.
With the advancement of deep learning techniques,the number of model parameters has been increasing,leading to significant memory consumption and limits in the deployment of such models in real-time *** reduce the num...
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With the advancement of deep learning techniques,the number of model parameters has been increasing,leading to significant memory consumption and limits in the deployment of such models in real-time *** reduce the number of model parameters and enhance the generalization capability of neural networks,we propose a method called Decoupled MetaDistil,which involves decoupled *** method utilizes meta-learning to guide the teacher model and dynamically adjusts the knowledge transfer strategy based on feedback from the student model,thereby improving the generalization ***,we introduce a decoupled loss method to explicitly transfer positive sample knowledge and explore the potential of negative samples *** experiments demonstrate the effectiveness of our method.
Ensuring the precise anticipation of a driver’s attention is crucial for upholding safety in diverse human-centric transportation scenarios. This capability proves invaluable for discerning and evaluating accident ri...
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Human–robot interface(HRI)electronics are critical for realizing robotic ***,we report graphene-based dual-function acoustic transducers for machine learning-assisted human–robot interfaces(GHRI).The GHRI functions ...
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Human–robot interface(HRI)electronics are critical for realizing robotic ***,we report graphene-based dual-function acoustic transducers for machine learning-assisted human–robot interfaces(GHRI).The GHRI functions both an artificial ear through the triboelectric acoustic sensing mechanism and an artificial mouth through the thermoacoustic sound emission *** success of the integrated device is also attributed to the multifunctional laser-induced graphene,as either triboelectric materials,electrodes,or thermoacoustic *** systematically optimizing the structure parameters,the GHRI achieves high sensitivity(4500 mV Pa^(–1))and operating durability(1000000 cycles and 60 days),capable of recognizing speech identities,emotions,content,and other information in the human *** the assistance of machine learning,30 speech categories are trained by a convolutional neural network,and the accuracy reaches 99.66%and 96.63%in training datasets and test ***,GHRI is used for artificial intelligence communication based on recognized speech *** work shows broad prospects for the development of robotic intelligence.
Correct and timely medication plays an important role in the treatment and recovery of a patient. Poor health outcomes are associated with the nonadherence to medication which also increases health care costs for pati...
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With the increasing number of smart devices and the development of machine learning technology,the value of users’personal data is becoming more and more *** on the premise of protecting users’personal privacy data,...
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With the increasing number of smart devices and the development of machine learning technology,the value of users’personal data is becoming more and more *** on the premise of protecting users’personal privacy data,federated learning(FL)uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original ***,since FL can still leak the user’s original data by exchanging gradient *** existing privacy protection strategy will increase the uplink time due to encryption *** is a huge challenge in terms of *** there are a large number of devices,the privacy protection cost of the system is *** on these issues,we propose a privacy-preserving scheme of user-based group collaborative federated learning(GrCol-PPFL).Our scheme primarily divides participants into several groups and each group communicates in a chained transmission *** groups work in parallel at the same *** server distributes a random parameter with the same dimension as the model parameter for each participant as a mask for the model *** use the public datasets of modified national institute of standards and technology database(MNIsT)to test the model *** experimental resultsshow that GrCol-PPFL not only ensures the accuracy of themodel,but also ensures the security of the user’s original data when users collude with each ***,through numerical experiments,we show that by changing the number of groups,we can find the optimal number of groups that reduces the uplink consumption time.
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