Hunting problems of multi-quadrotor systems via bearing-based hybrid protocols with hierarchical network

Bearing-based hunting protocols commonly adopt a leaderless consensus method,which requests an entire state of the target for each agent and ignores the necessity of collision avoidance.We investigate a hunting problem of multi-quadrotor systems with hybrid bearing protocols,where the quadrotor systems are divided into master and slave groups for reducing the onboard loads and collision avoidance.The masters obtain the entire state of the target,whose hybrid protocols are based on the displacement and bearing constraints to maintain formation and to avoid the collision in the hunting process.However,the slaves’protocols merely depend on the part state of the masters to reduce loads of data transmission.We also investigate the feasibility of receiving the bearing state from machine vision.The simulation results are given to illustrate the effectiveness of the proposed hybrid bearing protocols.

Application and Realization of Hierarchical Network Planning Based on Web

Making use of Microsoft Visual Studio. NET platform, hierarchical network planning is realized in working procedure time-optimization of the construction by TBM, and hierarchical network graph of the construction by TBM is drawn based on browser. Then the theory of system realization is discussed, six components of system that can be reused are explained emphatically. The realization of hierarchical network panning in Internet provides available guarantee for controlling rate of progress in large-scale or middle-sized projects. Key words Web - network graph - hierarchical network planning CLC number TU721 Biography: WU Shi-jing (1963-), male, Professor, research direction: equipments management engineering, mechatronics, state monitoring and malfunction diagnostics of machine and electronics equipments.

Game-theoretic approach to power and admission control in hierarchical wireless sensor networks

Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks （HWSNs）, espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are con- sidered as players. By applying a double-pricing scheme in the definition of OHSs＇ utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effec- tiveness of the proposed algorithm.

Cluster based hierarchical resource searching model in P2P network

For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.

A Novel Handoff Algorithm for Hierarchical Cellular Networks

In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow.Then, according to the mobility classification,network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.

Mixed Hierarchical Networks for Deep Entity Matching

Entity matching is a fundamental problem of data integration.It groups records according to underlying real-world entities.There is a growing trend of entity matching via deep learning techniques.We design mixed hierarchical deep neural networks(MHN)for entity matching,exploiting semantics from different abstract levels in the record internal hierarchy.A family of attention mechanisms is utilized in different periods of entity matching.Self-attention focuses on internal dependency,inter-attention targets at alignments,and multi-perspective weight attention is devoted to importance discrimination.Especially,hybrid soft token alignment is proposed to address corrupted data.Attribute order is for the first time considered in deep entity matching.Then,to reduce utilization of labeled training data,we propose an adversarial domain adaption approach(DA-MHN)to transfer matching knowledge between different entity matching tasks by maximizing classifier discrepancy.Finally,we conduct comprehensive experimental evaluations on 10 datasets(seven for MHN and three for DA-MHN),which illustrate our two proposed approaches1 superiorities.MHN apparently outperforms previous studies in accuracy,and also each component of MHN is tested.DA-MHN greatly surpasses existing studies in transferability.

Dynamics and control strategies of infectious disease under different scenarios on hierarchical geographical networks

Human settlements are embedded in traffic networks with hierarchical structures. In order to understand the spreading mechanism of infectious diseases and deploy control measures, the susceptible-infected-removed spreading process is studied with agents moving globally on the hierarchical geographic network, taking into account agents’ preference for node layers and memory of initial nodes. We investigate the spreading behavior in the case of global infection under different scenarios, including different directions of human flow, different locations of infection source, and different moving behaviors of agents between layers. Based on the above-mentioned analysis, we propose screening strategies based on layer rank and moving distance, and compare their effects on delaying epidemic spreading. We find that in the case of global infection,infection spreads faster in high layers than in low layers, and early infection in high layers and moving to high layers both accelerate epidemic spreading. Travels of high-layer and low-layer residents have different effects on accelerating epidemic spreading, and moving between high and low layers increases the peak value of new infected cases more than moving in the same layer or between adjacent layers. Infection in intermediate nodes enhances the effects of moving of low-layer residents more than the moving of high-layer residents on accelerating epidemic spreading. For screening measures, improving the success rate is more effective on delaying epidemic spreading than expanding the screening range. With the same number of moves screened, screening moves into or out of high-layer nodes combined with screening moves between subnetworks has better results than only screening moves into or out of high-layer nodes, and screening long-distance moves has the worst results when the screening range is small, but it achieves the best results in reducing the peak value of new infected cases when the screening range is large enough. This study probes into the spreading process and control measures under different scenarios on the hierarchical geographical network, and is of great significance for epidemic control in the real world.

A P2P Approach to Routing in Hierarchical MANETs

We present an effective routing solution for the backbone of hierarchical MANETs. Our solution leverages the storage and retrieval mechanisms of a Distributed Hash Table (DHT) common to many (structured) P2P overlays. The DHT provides routing information in a decentralized fashion, while supporting different forms of node and network mobility. We split a flat network into clusters, each having a gateway who participates in a DHT overlay. These gateways interconnect the clusters in a backbone network. Two routing approaches for the backbone are explored: flooding and a new solution exploiting the storage and retrieval capabilities of a P2P overlay based on a DHT. We implement both approaches in a network simulator and thoroughly evaluate the performance of the proposed scheme using a range of static and mobile scenarios. We also compare our solution against flooding. The simulation results show that our solution, even in the presence of mobility, achieved well above 90% success rates and maintained very low and constant round trip times, unlike the flooding approach. In fact, the performance of the proposed inter-cluster routing solution, in many cases, is comparable to the performance of the intra-cluster routing case. The advantage of our proposed approach compared to flooding increases as the number of clusters increases, demonstrating the superior scalability of our proposed approach.

High N-doped hierarchical porous carbon networks with expanded interlayers for efficient sodium storage

Sodium-ion batteries (SIBs) have been attracting considerable attention as a promising candidate for large-scale energy storage because of the abundance and low-cost of sodium resources. However, lack of appropriate anode materials impedes further applications. Herein, a novel self-template strategy is designed to synthesize uniform flowerlike N-doped hierarchical porous carbon networks (NHPCN) with high content of N (15.31 at.%) assembled by ultrathin nanosheets via a self-synthesized single precursor and subsequent thermal annealing. Relying on the synergetic coordination of benzimidazole and 2-methylimidazole with metal ions to produce a flowerlike network, a self-formed single precursor can be harvested. Due to the structural and compositional advantages, including the high N doping, the expanded interlayer spacing, the ultrathin two-dimensional nano-sized subunits, and the three-dimensional porous network structure, these unique NHPCN flowers deliver ultrahigh reversible capacities of 453.7 mAh·g^−1 at 0.1 A·g^−1 and 242.5 mAh·g^−1 at 1 A·g^−1 for 2,500 cycles with exceptional rate capability of 5 A·g^−1 with reversible capacities of 201.2 mAh·g^−1. The greatly improved sodium storage performance of NHPCN confirms the importance of reasonable engineering and synthesis of hierarchical carbon with unique structures.

Intrusion response goals and corresponding strategies

The effects of strategy on the network security defense and the related research on intrusion response strategy are briefly presented, with the focus on the status and function of intrusion re- sponse strategy in the intrusion response decision-making. Some specific response strategies for specific response goals are presented as well. The relevant knowledge of the planning, and a classification of response tasks are proposed. The intrusion response planning methods and models based on hierarchical task network （HTN） are described in detail. On this basis, the model of combining the response measure decision-making with the response time decision-making is expounded. The proposed model can integrate response strategy into response decision-making mechanism. In addition, the results of the intrusion response experiments are provided to verify the ability of using different response strategies to achieve different response goals. At last, the application needs of response strategy in network security are analyzed, and the approaches of the response strategy applied in in- trusion response system are summarized.

Learning and fatigue inspired method for optimized HTN planning

Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network （HTN） planning process so as to create better high- quality plans quickly. The process of HTN planning is mapped during a depth-first search process in a problem-solving agent, and the models of learning in HTN planning is conducted similar to the learning depth-first search （LDFS）. Based on the models, a learning method integrating HTN planning and LDFS is presented, and a fatigue mechanism is introduced to balance exploration and exploitation in learning. Finally, experiments in two classical do- mains are carried out in order to validate the effectiveness of the proposed learning and fatigue inspired method.

Hierarchical Neural Networks Method for Fault Diagnosis of Large-Scale Analog Circuits

A novel hierarchical neural networks (HNNs) method for fault diagnosis of large-scale circuits is proposed. The presented techniques using neural networks(NNs) approaches require a large amount of computation for simulating various faulty component possibilities. For large scale circuits, the number of possible faults, and hence the simulations, grow rapidly and become tedious and sometimes even impractical. Some NNs are distributed to the torn sub-blocks according to the proposed torn principles of large scale circuits. And the NNs are trained in batches by different patterns in the light of the presented rules of various patterns when the DC, AC and transient responses of the circuit are available. The method is characterized by decreasing the over-lapped feasible domains of responses of circuits with tolerance and leads to better performance and higher correct classification. The methodology is illustrated by means of diagnosis examples.

Regulation of the pore structure of carbon nanosheets based electrocatalyst for efficient polysulfides phase conversions

The practical applications of lithium-sulfur(Li-S)batteries are hampered by the sluggish redox kinetics and polysulfides shuttle in the cyclic process,which leads to a series of problems including the loss of active materials and poor cycling efficiency.In this paper,the pore structures of carbon nanosheets based electrocatalysts were precisely controlled by regulating the content of water-soluble KCl template.The relationship between pore structures and Li-S electrochemical behavior was studied,which demonstrates a key influence of pore structure in polysulfides phase conversions.In the liquid-sloid redox reaction of polysulfides,the micropores and small mesopores(d<20 nm)exhibited little impact,while the meso-pores(d>20 nm)and macropores played a decisive role.As a typical exhibition,the nickel-embedded carbon nanosheets(Ni-CNS)with a high content of large mesopores and macropores can aid Li-S batteries in exhibiting stable cycling performance(760.1 mAh g^(-1)at 1 C after 300 cycles)and superior rate capac-ity(847.8 mAh g^(-1)at 2 C).Furthermore,even with high sulfur loading(8 mg cm^(−2))and low electrolyte(E/S is around 6μL mg^(-1)),the high area capacity of 7.7 mAh cm^(−2)at 0.05 C could be achieved.This work can provide a guideline for the design of the pore structure of carbon-based electrocatalysts toward high-efficiency sulfur species redox reactions,and afford a general,controllable,and simple approach to constructing high performance Li-S batteries.

Bio-inspired construction of electrocatalyst decorated hierarchical porous carbon nanoreactors with enhanced mass transfer ability towards rapid polysulfide redox reactions

Li-S batteries are considered as a highly promising candidate for the next-generation energy storage system, attributing to their tremendous energy density. However, the two-dimensional island nucleation-growth process of lithium sulfide leads to a thick insulating film covering the electrode, inducing slow electrons transfer and mass-transfer of ions and liquid sulfur species in working Li-S cells. Here, we demonstrate a bio-inspired strategy of constructing ant-nest-like hierarchical porous ultrathin carbon nanosheet networks with the implants of metallic nanoparticles electrocatalysts (HPC-MEC) as efficient nanoreactors enabling rapid mass transfer, via a simple and green NaCl template. Such nanoreactors with a large active surface area could effectively anchor polysulfides for mitigating the shuttle effect, facilitating uniformly thin Li2S film, and promoting the mass transfer for fast sulfur species conversions. This helps contribute to a continuously high sulfur utilization in Li-S batteries with the HPC-MEC reactors. As a typical exhibition, cobalt embedded hierarchical porous carbon (HPC-Co) could realize to deliver a remarkably high specific capacity of 1,540.6 mAh·g−1, an excellent rate performance of 878.8 mAh·g−1 at 2 C, and high area capacity of 11.6 mAh·cm−2 at a high sulfur load of 10 mg·cm−2 and low electrolyte/sulfur ratio of 5 µL·mg−1.

A novel approach for synthesis of hierarchical mesoporous Nd2O3 nanomaterials

A novel route to the fabrication of hierarchical mesoporous Nd_2O_3 nanostructures including nanospheres and nanoporous network was described. Their structure and morphology evolution of the as-synthesized materials were determined by various techniques such as scanning electron microscopy, transmission electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, energy-dispersive X-ray spectroscopy, Fourier transforminfrared spectra, nitrogen adsorption/desorption isotherm, and a formation mechanism was proposed. The results revealed that the Nd_2O_3 nanospheres had the diameter of 300 nm, which were composed of small primary nanoparticles（NPs） with the size of 10 nm. The nanoporous structure also formed the NPs of ca. 10 nm which were connected with each other to form a three-dimensional（3D） texture. This simple and mild approach to fabricate hierarchical mesoporous Nd_2O_3 nanostructures could be easily scaled up and potentially extended to synthesize other oxide hierarchical structures.

Optimizing oxygen redox kinetics of M-N-C electrocatalysts via an in-situ self-sacrifice template etching strategy

The accessibility and mass transfer between catalytic sites and substrates/intermediates are essential to a catalyst's overall performance in oxygen electrocatalysis based energy devices.Here,we present an“in-situ self-sacrifice template etching strategy”for reconstructing MOF-derived M-N-C catalysts,which introduces micro-meso-macro pores with continuous apertures in a wide range and a central hollowout structure to optimize the electrochemical oxygen redox kinetics.It is realized via one-step pyrolysis of ZIF-8 single crystal epitaxially coating on a multi-functional template of the Fe,Co co-loaded mesoporous ZnO sphere.The ZnO core is reduced during the general pyrolysis of ZIF-8 into M-N-C and acts as a pore former to etch the surrounding ZIF-8 shell into diverse channels anchoring highly exposed Fe and Co-based active sites with edge enrichment.The redesigned catalyst reveals apparent structural benefits towards enhanced oxygen redox kinetics as bifunctional cathode catalysts of rechargeable zinc-air battery compared with the primitive bulk M-N-C catalysts and the mixture of commercial Pt/C and Ir/C.The unique structure-based activity advantages,the omitted template removal step and good template compatibility during synthesis make the strategy universal for the channel engineering of electrocatalysts.

ADAPTIVE CGF COMMANDER BEHAVIOR MODELING THROUGH HTN GUIDED MONTE CARLO TREE SEARCH

Improving the intelligence of virtual entities is an important issue in Computer Generated Forces （CGFs） construction. Some traditional approaches try to achieve this by specifying how entities should react to predefined conditions, which is not suitable for complex and dynamic environments. This paper aims to apply Monte Carlo Tree Search （MCTS） for the behavior modeling of CGF commander. By look-ahead reasoning, the model generates adaptive decisions to direct the whole troops to fight. Our main work is to formulate the tree model through the state and action abstraction, and extend its expansion process to handle simultaneous and durative moves. We also employ Hierarchical Task Network （HTN） planning to guide the search, thus enhancing the search efficiency. The final implementation is tested in an infantry combat simulation where a company commander needs to control three platoons to assault and clear enemies within defined areas. Comparative results from a series of experiments demonstrate that the HTN guided MCTS commander can outperform other commanders following fixed strategies.

Applied Fault Detection and Diagnosis for Industrial Gas Turbine Systems

The paper presents readily implementable approaches for fault detection and diagnosis （FDD） based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering （HC） and self-organizing map neural networks （SOMNNs） are shown to provide robust and user-friendly tools for application to industrial gas turbine （IGT） systems. HC fingerprints are found for normal operation, and FDD is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, fingerprints of operational behaviour are also obtained using SOMNN based classification maps （CMs） that are initially determined during normal operation, and FDD is performed by detecting changes in their CMs. The proposed methods are shown to be capable of FDD from a large group of sensors that measure a variety of physical quantities. A key feature of the paper is the development of techniques to accommodate transient system operation, which can often lead to false-alarms being triggered when using traditional techniques if the monitoring algorithms are not first desensitized. Case studies showing the efficacy of the techniques for detecting sensor faults, bearing tilt pad wear and early stage pre-chamber burnout, are included. The presented techniques are now being applied operationally and monitoring IGTs in various regions of the world.

Extending HTN to planning and execution control for small combat unit simulation

Modeling how military commanders carry out operations is considered complicated,requiring the capability of not only planning for multiple subordinates but also responding to unexpected events during execution.This paper presents an Hierarchical Task Network(HTN)embedded planning and execution control architecture for small unit commander agents.To be adaptive to dynamic world state changes,the architecture employs a partial planning mechanism and generates actions only applicable to current situations.It is also able to coordinate subordinates’actions and handle execution failures at runtime.We demonstrate the architecture’s use with an infantry company scenario,where the commander orders three platoons assaulting a defined hill.Our approach shows the effectiveness to control multiple entities in dynamic environments,making the architecture well-suited to represent small unit commanders’behavior.

Collective Computation,Information Flow,and the Emergence of Hunter-Gatherer Small-Worlds

Two key features of human sociality are anatomically complex brains with neuron-dense cerebral cortices,and the propensity to form complex social networks with non-kin.Complex brains and complex social networks facilitate flows of fitness-enhancing energy and information at multiple scales of social organization.Here,we consider how these flows interact to shape the emergence of macroscopic regularities in hunter-gatherer macroecology relative to other mammals and non-human primates.Collective computation is the processing of information by complex adaptive systems to generate inferences in order to solve adaptive problems.In hunter-gatherer societies the adaptive problem is to resolve uncertainty in generative models used to predict complex environments in order to maximize inclusive fitness.The macroecological solution is to link complex brains in social networks to form collective brains that perform collective computations.By developing theory and analyzing data,the author shows hunter-gatherers bands of~16 people,or~4 co-residing families,form the largest collective brains of any social mammal.Moreover,because individuals,families,and bands interact at multiple time scales,these fission-fusion dynamics lead to the emergence of the macroscopic regularities in hunter-gatherer macroecology we observe in cross-cultural data.These results show how computation is distributed across spatially-extended social networks forming decentralized knowledge systems characteristic of hunter-gatherer societies.The flow of information at scales far beyond daily interactions leads to the emergence of small-worlds where highly clustered local interactions are embedded within much larger,but sparsely connected multilevel metapopulations.