Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Electroencephalogram Signal Correlations between Default Mode Network and Attentional Functioning

Attentional issues may affect acquiring new information, task performance, and learning. Cortical network activities change during different functional brain states, including the default mode network (DMN) and attention network. We investigated the neural mechanisms underlying attentional functions and correlations between DMN connectivity and attentional function using the Trail-Making Test (TMT)-A and -B. Electroencephalography recordings were performed by placing 19 scalp electrodes per the 10 - 20 system. The mean power level was calculated for each rest and task condition. Non-parametric Spearman’s rank correlation was used to examine the correlation in power levels between the rest and TMT conditions. The most significant correlations during TMT-A were observed in the high gamma wave, followed by theta and beta waves, indicating that most correlations were in the parietal lobe, followed by the frontal, central, and temporal lobes. The most significant correlations during TMT-B were observed in the beta wave, followed by the high and low gamma waves, indicating that most correlations were in the temporal lobe, followed by the parietal, frontal, and central lobes. Frontoparietal beta and gamma waves in the DMN may represent attentional functions.

Topological probability and connection strength induced activity in complex neural networks

Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world （SW） connections. Network elements are described by two-dimensional map neurons （2DMNs） with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied.

A Distributed Newton Method for Processing Signals Defined on the Large-Scale Networks

In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously perform the local computation,which calls for heavy computational and communication costs.Moreover,in many real-world networks,such as those with straggling nodes,the homogeneous manner may result in serious delay or even failure.To this end,we propose active network decomposition algorithms to select non-straggling nodes(normal nodes)that perform the main computation and communication across the network.To accommodate the decomposition in different kinds of networks,two different approaches are developed,one is centralized decomposition that leverages the adjacency of the network and the other is distributed decomposition that employs the indicator message transmission between neighboring nodes,which constitutes the main contribution of this paper.By incorporating the active decomposition scheme,a distributed Newton method is employed to solve the least squares problem in GSP,where the Hessian inverse is approximately evaluated by patching a series of inverses of local Hessian matrices each of which is governed by one normal node.The proposed algorithm inherits the fast convergence of the second-order algorithms while maintains low computational and communication cost.Numerical examples demonstrate the effectiveness of the proposed algorithm.

Optimal Location and Sizing of Distributed Generator via Improved Multi-Objective Particle Swarm Optimization in Active Distribution Network Considering Multi-Resource

In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization,multi-resource penetration in active distribution networks has been advancing fiercely.In particular,distributed generation(DG)based on renewable energy is critical for active distribution network operation enhancement.To comprehensively analyze the accessing impact of DG in distribution networks from various parts,this paper establishes an optimal DG location and sizing planning model based on active power losses,voltage profile,pollution emissions,and the economics of DG costs as well as meteorological conditions.Subsequently,multiobjective particle swarm optimization(MOPSO)is applied to obtain the optimal Pareto front.Besides,for the sake of avoiding the influence of the subjective setting of the weight coefficient,the decisionmethod based on amodified ideal point is applied to execute a Pareto front decision.Finally,simulation tests based on IEEE33 and IEEE69 nodes are designed.The experimental results show thatMOPSO can achieve wider and more uniformPareto front distribution.In the IEEE33 node test system,power loss,and voltage deviation decreased by 52.23%,and 38.89%,respectively,while taking the economy into account.In the IEEE69 test system,the three indexes decreased by 19.67%,and 58.96%,respectively.

Blockchain-Based Power Transaction Method for Active Distribution Network

A blockchain-based power transaction method is proposed for Active Distribution Network(ADN),considering the poor security and high cost of a centralized power trading system.Firstly,the decentralized blockchain structure of the ADN power transaction is built and the transaction information is kept in blocks.Secondly,considering the transaction needs between users and power suppliers in ADN,an energy request mechanism is proposed,and the optimization objective function is designed by integrating cost aware requests and storage aware requests.Finally,the particle swarm optimization algorithm is used for multi-objective optimal search to find the power trading scheme with the minimum power purchase cost of users and the maximum power sold by power suppliers.The experimental demonstration of the proposed method based on the experimental platform shows that when the number of participants is no more than 10,the transaction delay time is 0.2 s,and the transaction cost fluctuates at 200,000 yuan,which is better than other comparison methods.

Multi-Path Routing and Resource Allocation in Active Network

An algorithm of traffic distribution called active multi-path routing (AMR)in active network is proposed. AMR adopts multi-path routing and applies nonlinear optimizeapproximate method to distribute network traffic among multiple paths. It is combined to bandwidthresource allocation and the congestion restraint mechanism to avoid congestion happening and worsen.So network performance can be improved greatly. The frame of AMR includes adaptive trafficallocation model, the conception of supply bandwidth and its' allocation model, the principle ofcongestion restraint and its' model, and the implement of AMR based on multi-agents system in activenetwork. Through simulations, AMR has distinct effects on network performance. The results show AMRisa valid traffic regulation algorithm.

Adaptive Control of Flexible Redundant Manipulators Using Neural Networks

An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted. The smart links of the manipulator were synthesized with the flexible links to which were attached piezoceramic actuators and strain gauge sensors. A nonlinear adaptive control strategy named neural networks based indirect adaptive control (NNIAC) was employed to improve the dynamic performance of the manipulator. The mathematical model of the 4-layered dynamic recurrent neural networks (DRNN) was introduced. The neuro-identifier and the neuro-controller featuring the DRNN topology were designed off line so as to enhance the initial robustness of the NNIAC. By adjusting the neuro-identifier and the neuro-controller alternatively, the manipulator was controlled on line for achieving the desired dynamic performance. Finally, a planar 3R redundant manipulator with one smart link was utilized as an illustrative example. The simulation results proved the validity of the control strategy.

Molecular Biological Mechanisms of Yuan Zhi Powder in the Treatment of Alzheimer's Disease: an Analysis Based on Network Pharmacology

Objective To predict the main active ingredients,potential targets and molecular mechanisms of Yuan Zhi powder in treatment of dementia by using network pharmacology.Methods A database of chemical constituents of Yuan Zhi powder was constructed by using databases and literatures.Potential targets were predicted by reverse molecular docking,and then a component-target network was constructed.The target network of Alzheimer's disease(AD)was mapped and analyzed to obtain the“active ingredient-AD target”network.The key targets were screened through network analysis.Finally,the rationality of the prediction was analyzed by comparing with the target reported in the literatures.Results There were180chemical constituents acting on the AD target,and the targets included three key targets(cyclooxygenase-2,muscarinic acetylcholine receptor M1,and muscarinic acetylcholine receptor M2)and an important target(acetylcholine esterase).Alzheimer's disease may be treated by regulating the activity of acetylcholine receptors and the binding toβ-amyloid protein,and its biological process may be concentrated in the acetylcholine receptor signal pathway,negative regulation of synaptic transmission and so on.Conclusion The fact that Yuan Zhi powder can treat AD is consistent with the characteristics of multi-components-multitargets-multiple pathways of traditional Chinese medicine.The important targets obtained from network analysis have a large proportion in literature research,so network analysis have some rationality.

Neural network prediction of solar cycle 24

The ability to predict the future behavior of solar activity has become extremely import due to its effect on the environment near the Earth. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of space weather. The level of solar activity is usually expressed by in- ternational sunspot number （Rz）. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. We predict a solar index （Rz） in solar cycle 24 by using a neural network method. The neural network technique is used to analyze the time series of solar activity. According to our predictions of yearly sunspot number, the maximum of cycle 24 will occur in the year 2013 and will have an annual mean sunspot number of 65. Finally, we discuss our results in order to compare them with other suggested predictions.

GPS Rapid Static and Kinematic Positioning Based on GPS Active Network

This paper presents a data processing strategy for GPS kinematic positioning by using a GPS active network to model the GPS errors in double difference observable.Firstly,the double difference residuals are estimated between the reference stations in the active network.Then the errors at a user station are predicted as the network corrections to user measurements,based on the location of the user.Finally conventional kinematic positioning algorithms can be applied to determine the position of the user station.As an example,continuous 24_hour GPS data in March 2001 has been processed by this method.It clearly demonstrates that,after applying these corrections to a user within the network,both the success rate for ambiguity resolution and the positioning accuracy have been significantly improved.

IMPLEMENTING SECURITY FOR ACTIVE NETWORKS IN INTERNET

Active networks is primarily a Defense Advanced Research Projects Agency(DARPA)-funded project focusing on the research of mechanisms, applications, and operating systems to develop a reconfigurable network infrastructure. This letter proposes an Secure Active Tracing System (SATS) to implementing security for active networking in Internet. Unlike currently existing schemes, SATS reduces the computational overloads by executing the filtering operation on selected packet streams only when needed.

A Cluster-Based Secure Active Network Environment

We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallely processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.

Research on Networked Rapid Product Development Process

Today the cycle time of the product develop is requ ir ed to be shortened. At the same time the requirement of the customers becomes mo re and more diverse and complex. The capability of the develop unit is limited b ecause of the existence of heterogeneous systems and distributed environments. I n this paper, we bring forward a new approach to solve the problem in product de velopment process. We also settle part key technologies in it. A great deal of information from all kinds of sources in the distributed develop ment process is interweaved. The solution to organize the workflow and manage th e information in the process is called for anxiously. We use a new approach that is asynchronous and synchronous coupling product development approach based on the network. The approach extends the develop process from the time axis. Then t he activities in the process are organized from the asynchronous and synchronous aspects. The state of every activity projects at the ASN (active semantic netwo rk). The ASN includes decision system, intelligent agent, user interface and net work. The ASN decides the types and states of the activities and deals with the couple relationship among them. The knowledge stored in ASN is open to all users through the relative interfaces. Every specialist keeps contact with their user s relying on collaborative platform implements CSCW (computer support collaborat ive work) that integrated product/process design and development. The lack of gl obal communication in product development process can be prevented in the most d egree. The key technologies that exist in the asynchronous and synchronous coupling pro duct develop approach include: integrated development structure, orderly organiz ation of information, transparent management of process, agile transfer of infor mation and rapid prototype. The development process can be completed quickly by these technologies. The technologies involve wide content. In this paper, we dis cuss some key technologies. We validate the approach by the projectrapid response manufacturing a pplication in the distributed environment. The expensive device, high technology and low using lead to RE (Rapid engineering) and RP (Rapid prototype) service a pplication by the network. RE and RP develop rapidly due to the accelerated prod uct development process. RE and RP application service platform is built in the project.

A Novel Active Network Architecture Based on Extensible Services Router

Active networks are a new kind of packet-switched networks in which packets have code fragments that are executed on the intermediary nodes (routers). The code can extend or modify the foundation architecture of a network. In this paper, the authors present a novel active network architecture combined with advantages of two major active networks technology based on extensible services router. The architecture consists of extensible service router, active extensible components server and key distribution center (KDC). Users can write extensible service components with programming interface. At the present time, we have finished the extensible services router prototype system based on Highly Efficient Router Operating System (HEROS), active extensible components server and KDC prototype system based on Linux.

HSCP:a hardware and software combined platform to improve precision in active network measurement

In this paper,an active network measurement platform is proposed which is a combination of hardware and software.Its innovation lies in the high performance of hardware combined with features that the software is easy to program,which retains software flexibility at the same time.By improving the precision of packet timestamp programmable hardware equipment,it provides packet sending control more accurately and supports the microsecond packet interval.We have implemented a model on the NetMagic platform,and done some experiments to analyze the accuracy difference of the user,the kernel and hardware timestamp.

DEAON： dynamically-evolving active overlay networks for scalable information retrieval

In this paper, we propose the dynamically-evolving active overlay network （DEAON）, which is an efficient, scalable yet simple protocol to facilitate applications of decentralized information retrieval in P2P networks. DEAON consists of three novel components ： a Desirable Topology Construction and Adaptation algorithm to guide the evolution of the overlay topology towards a small-world-like graph; a Semantic-based Neighbor Selection scheme to conduct an online neighbor ranking; a Topology-aware Intelligent Search mechanism to forward incoming queries to deliberately selected neighbors. We deploy and compare DEAON with other several existing distributed search techniques over static and dynamic environments. The results indicate that DEAON outperforms its competitors by achieving higher recall rate while using much less network resources, in both of the above environments.

Construction and the Second-order Nonlinear Optics of a New Chiral Coordination Network Based on Two Types of Homo-chiral Helices

A new 2D chiral cadmium coordination network [Cd（TCBA）2]？2EtOH （1） was prepared by the reaction of achiral angular asymmetric bridging ligand 2-（（3,5-dimethyl- 4H-1,2,4-triazol-4-yl）-carbamoyl）-benzoic acid （TCBA） with cadmium acetate, which was structurally characterized by IR, elemental analysis, thermogravimetric analysis and single-crystal X-ray diffraction. Complex 1 crystallizes in the chiral space group P21 and features a 2D chiral network consisting of two types of homo-chiral helices, which are further extended into a 3D chiral framework along the a-axis via hydrogen bonds. Moreover, powder second-order nonlinear optical （NLO） measurements reveal complex 1 has a modest second-harmonic-generation （SHG） efficiency at room temperature.

In Silico Investigation of Agonist Activity of a Structurally Diverse Set of Drugs to hPXR Using HM-BSM and HM-PNN

The human pregnane X receptor（hPXR） plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards h PXR. Heuristic method（HM）-Best Subset Modeling（BSM） and HM-Polynomial Neural Networks（PNN） were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain（AD） of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved（for HM-BSM, r^2=0.881, q^2_（LOO）=0.797, q^2_（EXT）=0.674; for HM-PNN, r^2=0.882, q^2_（LOO）=0.856, q^2_（EXT）=0.655）. The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to h PXR.

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.