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On account of the variable topology and multi-terminal power supply in micro-grid, the fault diagnosis faces more and more challenges. Traditional fault location criteria are unsuitable and fault diagnosis modelling is complex or poor versatility. Further on, the fault reasoning operation is time-consuming. A high transplantable fault diagnosis model aiming at the fault features in micro-grid is established in this paper, and a simple inference algorithm with good error-detecting capability is proposed. Firstly, the fault location criterion based on current magnitude, current phase and Distributed Generation's current direction information is proposed, and the fault transient component is adopted as a supplementary criterion. Secondly, a hierarchical Petri net model utilizing the electrical information, relays' and circuit breakers' state information is accomplished. The model consists of fault location layer and fault clearance layer. In order to increase the portability of the model, the collective processing for the breakers is implemented. Moreover, 'bidirectional arrowhead arc' is introduced to reduce the number of places to optimize the Petri net model well. An improved redundant coding Petri net reasoning algorithm is proposed based on the fault clearance layer of the Petri net model. Finally, the validity of the method is verified through case analysis and comparison. [ABSTRACT FROM AUTHOR]

Low-sampling-rate floating car data (FCD) are more challenging than those with high-sampling-rate FCD for map matching (MM) algorithms. Some MM algorithms for low-sampling-rate FCD lack sufficient efficiency nor accuracy, especially related to complex urban road networks. This paper proposes a new method named the trajectory restoration algorithm, which is based on geometry MM algorithms to ensure efficiency and accuracy. The proposed algorithm adopts the modified A* shortest path algorithm to reduce the number of function calls and fully considers road network topology and historical matched points to improve its accuracy. We test the efficiency and accuracy of the trajectory restoration algorithm with FCD data for the complex urban road networks in Beijing. The results have strong continuity which greatly improves the utilization of FCD. We show that the proposed algorithm outperforms related MM methods in efficiency and accuracy and its robustness to restore trajectories of both high and low sampling rates in complex urban road networks. [ABSTRACT FROM AUTHOR]

This paper focuses on the use of truss topology optimization using an evolutionary approach to find optimized, additively manufactured components through a multi-objective formulation, keeping in mind application areas like 3D printing. Additive manufacturing plays a key role here, as the inherent complexity of most optimized truss structures would not allow for any different manufacturing techniques. The multi-optimization formulation consists of optimizing two conflicting objectives: minimization of structural compliance and minimization of heat flowing from the structure to its supports. A background on truss topology optimization, including a literature overview and the motivation behind the problem, is presented. The physical models and the optimization algorithms are explained and afterward, an example problem is solved and analyzed. [ABSTRACT FROM AUTHOR]

The traditional service restoration methods are experiencing a significant challenge because the performance of such methods is often dominated by very large processing time. Therefore, it is important to develop a fast and an accurate method to solve a complex non-linear optimization problem representing the service restoration problem in a distribution system. In this paper, due to a binary nature of the service restoration problem, the Binary Water Cycle Algorithm (BWCA) is proposed to reduce out-of-service loads and system losses considering operational constraints, the radial topology of the distribution systems, and the influence of the presence of distributed generations (DGs) on the service restoration problem. The proposed method is a modified form of the Water Cycle Algorithm (WCA) that is mainly used for discrete and continuous optimization problems. The effectiveness of the proposed method is validated by comparing with other well-known meta-heuristic algorithms using a modified IEEE 33-node radial distribution system. The simulation results demonstrate the robustness of this method to provide a precise decision with short convergence time for solving service restoration problems. [ABSTRACT FROM AUTHOR]

Map data at smaller scales than their source can result in spatial conflict, whereby map symbols become too close, or overlaid. Server map generalisation operators may be applied to solve this problem, including displacement. In this paper, we show how an optimisation algorithm, the snake algorithm, was used to displace multiple objects in order to resolve spatial conflicts and maintain important spatial relationships between objects during displacement. Two principles based on the snake algorithm are proposed in this paper. First, the truss structure mirroring spatial proximity relationships between buildings and between building and road is formed based on the weighted proximity graph derived from constrained Delaunay triangulations (CDT) in each map partition. In the weighted proximity graph, each connecting line is determined as a snake and as an element unit to assemble the global stiffness matrix in snake algorithm. Second, a buffer method that calculates force between a building and a road (or other linear features) or between pair of buildings is adopted in the snake algorithm. This avoids the imbalance phenomenon caused by different force calculation methods during the displacement. The feasibility of the approach is demonstrated in obtaining real geographic data. Finally, the results are cartographically usable and in particular, the spatial relationships between objects are preserved. [ABSTRACT FROM PUBLISHER]

In this paper an integrated hybrid converter topology, unique DC input as well as at same time of outputs DC and AC that use a hybrid control method. At established integrated converter, accomplish high step up voltage and implement outputs of DC as well as AC to greater reliability, switching topology is utilised. The established hybrid strategy is joint execution of spotted hyena optimiser (SHO) and random forest algorithm (RFA), therefore, it called as SHORFA method. In proposed strategy, SHO is utilised to optimise combination of the controller parameters based on the voltage variation. In light of the optimal datasets, the RFA gives the optimal switching pulses of the driver. The RFA learning process is improved to utilise SHO algorithm at aspect of objective function as minimal error. By the established converter design, voltage stress over diodes as well as power switch is lessened. Additionally, the proposed converter directs the output voltage, power factor correction, reduces the voltage spikes over the switches and restrains the switching losses. The proposed strategy performed at worksite of MATLAB/Simulink platform compared to various existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we review some of the main discrete and finite time average consensus implementations in the literature, discussing their strengths and shortcomings from a theoretical and empirical point of view. In particular, we compare the computational characteristics of the different algorithms, their behaviour considering different underlying network topologies, their ability to withstand packet losses and their robustness to attacks where a malicious node aims to steer the result of the algorithm towards a desired value, without letting the other nodes detect the attack. Specifically, we will discuss synchronous approaches, where the nodes broadcast their messages, and asynchronous approaches, where the nodes need to be able to address their neighbours individually on a point-to-point basis (i.e. by direct communication between a specific sender and a specific receiver). With the aim to overcome some critical aspects of the considered methodologies, in this paper we present an asynchronous consensus algorithm based on a broadcast-only approach. The algorithm is characterised by a good trade-off between the robustness of synchronous approaches and to low computational demands of asynchronous methods. [ABSTRACT FROM AUTHOR]

In this paper, a dimension reduction method via general orthogonal polynomials and multiorder Arnoldi algorithm is proposed, which focuses on the topic of structure-preserving for k-power bilinear systems. The main procedure is using a series of expansion coefficient vectors of each state variables in the space spanned by general orthogonal polynomials that satisfy a recurrence formula to generate a projection based on multiorder Arnoldi algorithm. The resulting reduced-order model not only matches a desired number of expansion coefficients of the original output but also retains the topology structure. Meanwhile, the stability is well preserved under some certain conditions and the error bound is also given. Finally, two numerical simulations are provided to illustrate the effectiveness of our proposed algorithm in the views of accuracy and computational cost. [ABSTRACT FROM AUTHOR]

Uncontrolled network separation (system island formation) is one of the most critical contingencies in power systems. The integrity of the whole power transmission network is a prerequisite for reliable system operation. Therefore, in order to safeguard the system operation and control, it is imperative to quickly identify the topological changes, especially the formation of system islands. In this paper, we developed a spectral clustering-based approach to efficiently detect the existing or the potential network islands. The core of this approach is a graph-algebraic model, which combines the real-power deliverability and topological information of a power transmission network. Based on an improved spectral clustering algorithm, the proposed approach can efficiently identify the critical situations of the network splitting under multiple line outages. This approach has been successfully tested by using the New England 39-bus and 118-bus systems under different scenarios. [ABSTRACT FROM AUTHOR]

This paper focuses on the admissible consensus problem for heterogeneous descriptor multi-agent systems. Based on algebra, graph and descriptor system theory, the necessary and sufficient conditions are proposed for heterogeneous descriptor multi-agent systems achieving admissible consensus. The provided conditions depend on not only the structure properties of each agent dynamics but also the topologies within the descriptor multi-agent systems. Moreover, an algorithm is given to design the novel consensus protocol. A numerical example demonstrates the effectiveness of the proposed design approach. [ABSTRACT FROM AUTHOR]

This paper focuses on the resource allocation problem(RAP) with constraints under a fixed general directed topology by using the distributed sub-gradient algorithm with event-triggered scheme in multi-agent systems, where each agent owns a cost function and its state value is bounded. The distributed sub-gradient algorithm aims to minimise the total cost by a distributed manner while achieving an optimal solution. Unlike centralised methods, the triggering condition and algorithm for each agent are fully decentralised. At each instant of time, each agent updates its state by employing the states which are collected from itself and its neighbouring agents at their last triggering time. In order to illustrate the effectiveness of the proposed sub-gradient algorithm with event-triggered control law, one simulation example is presented before the conclusion. [ABSTRACT FROM AUTHOR]

This paper makes a comparative study about the structural performances of the consolidated AM (additive manufacturing) parts and the multi-piece assemblies. Generally, it is recognised that AM part consolidation can improve the structural performance compared to the traditional multi-piece assembly. However, this may not be true given that the AM materials are directionally weakened, especially in the build direction (BD). Hence, the comparative study performed in this work will provide some conclusions and at the same time, these conclusions could be valuable guidelines for designing AM part consolidation. For implementation details, the level set topology optimisation method will be applied to optimising the multi-piece assembly with rigid joints and the AM part with weakened material properties; and the same design domain will be shared in order to make a fair comparison. A set of different joint positions and material weakening levels will be studied in order to get comprehensive conclusions. [ABSTRACT FROM PUBLISHER]

Several methods have been developed for the computer-aided discovery of new protease substrates and inhibitors. In this paper, we report a novel machine learning implementation to identify high-affinity protease–inhibitor complexes. The implemented proteochemometrics algorithm consists of creating topological autocorrelation descriptors for proteases and inhibitors, and then to develop support vector machine models to relate the feature vectors to the affinity class (high or low) of hypothetical protein–inhibitor complexes based on experimental inhibition constant (Ki) values. The approach based on the autocorrelation features surpassed an atom-centred (AC) approach using AC information of inhibitors. Unique to our approach is that our final classifier could recognise 80% of inhibition-complex of new ligands to be stable or unstable using only chemical connectivity of the ligands and sequence information of the targets. Moreover, the analysis of substructure classification showed a very homogenous behaviour of the model on the whole target–ligand space. The predictor is available online at:http://www.materialsinformatics.net/autoproti.html [ABSTRACT FROM AUTHOR]

This paper presents a randomized planning algorithm for manipulation tasks that require the robot to release and regrasp an object in different robot postures. Such problems arise, for example, in robotic suturing and knot tying, and in assembly tasks where parts must be guided through complex environments. Formulating the problem as one of planning on a foliated manifold, we present a randomized planning algorithm that, unlike existing methods, involves sampling and tree propagation primarily in the task space manifold; such an approach significantly improves computational efficiency by reducing the number of projections to the constraint manifold, without incurring any significant increases in the number of release-regrasp sequences. We also propose a post-processing topological exploration algorithm and path refinement procedure for reducing the number of release-regrasp sequences in a solution path, independent of the algorithm used to generate the path. Experiments involving spatial open chains with up to 10 degrees of freedom, operating in complex obstacle-filled environments, show that our algorithm considerably outperforms existing algorithms in terms of computation time, path length, and the number of release-regrasp operations. [ABSTRACT FROM PUBLISHER]

This paper is devoted to the robust finite-time output consensus problems of multi-agent systems under directed graphs, where all agents and their communication topologies are subject to interval uncertainties. Distributed protocols are constructed by using iterative learning control (ILC) algorithms, where information is exchanged only at the end of one iteration and learning is used to update the control inputs after each iteration. It is proved that under ILC-based protocols, the finite-time consensus can be achieved with an increasing number of iterations if the communication network of agents is guaranteed to have a spanning tree. Moreover, if the information of any desired terminal output is available to a portion (not necessarily all) of the agents, then the consensus output that all agents finally reach can be enabled to be the desired terminal output. It is also proved that for all ILC-based protocols, gain selections can be provided in terms of bound values, and consensus conditions can be developed associated with bound matrices. Simulation results are given to demonstrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, a hierarchical PO-MOESP subspace identification algorithm for directed acyclic graphs (DAGs) is presented. The state of every node and the structure of the graph are assumed to be unknown. The method computes a hierarchical partition of the DAG by using projection matrices typically used in subspace identification methods applied in cascade. Then, the column space of the observability matrix of every node is sequentially estimated by projecting away the input-output data from the previous levels. A past input–output instrumental variable approach is adopted to deal with the noise. The topology of multilevel DAGs is revealed by dedicated projections applied on every level. Moreover, we provide a brief study of a more general class of DAGs that can be accurately represented by multilevel DAGs of reduced interconnection structure. Finally, three simulation examples are provided to show the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

An adaptive gain scheduling technique is investigated to discuss distributed time-varying formation (TVF) control problems for general linear time-invariant multi-agent systems (LTI-MASs), where two types of topologies are considered: (1) the interaction topology is undirected and connected, and (2) the interaction topology is directed. Two fully distributed adaptive TVF control protocols are, respectively proposed, which both assign a time-varying coupling weight to each node in the interaction topology. Two algorithms to design the constructed protocols are presented under the undirected and directed interaction topologies, respectively. A feasible TVF set is provided. The stabilities of two algorithms are, respectively proved based on the Lyapunov functional theory. For both undirected and directed interaction topologies, general LTI-MASs can achieve the given TVF using the designed fully distributed adaptive formation protocol without any global information about the interaction topology when the TVF satisfies the feasible set. Finally, theoretical results are illustrated with numerical simulation examples. [ABSTRACT FROM AUTHOR]

The topology optimization of continuum structures is investigated to obtain an optimized topology satisfying the fail-safe design principle with suitable redundancy components. An optimization model with a nonlinear objective and linear constraints is established by defining the reciprocal topology variable as design variables and linearly approximating the structural performance. The model is solved by a dual sequential quadratic programming (DSQP) algorithm. Topology optimization with displacement constraints is used as an example. The presented optimization model and solution approach offers the following advantages. (1) A volume constraint, which makes determining reasonable values difficult, need not be specified. (2) Weighted coefficients, which combine multiple compliances under different load cases into a combined compliance, need not be specified as well. (3) The presented optimization model is a type of single-objective programming and thus avoids the challenges in the min-max model. (4) The proposed method shows a strong capability of finding optimum solutions. [ABSTRACT FROM AUTHOR]

The event-based broadcasting containment control problem for both first-order and second-order multi-agent systems under directed topology is investigated. Based on certain event, each agent decides when to transmit its current states to its neighbours and the agents’ distributed control algorithms are based on these sampled state measurements, which can significantly decrease the number of the controllers’ updates. All the agents are divided into two groups, namely, the leaders and the followers. The formation control is introduced. The leaders exchange their information to converge to a formation. The followers utilise the information from both their leader neighbours and their follower neighbours and are driven to the convex hull of the leaders using the proposed control algorithms. Numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results. [ABSTRACT FROM PUBLISHER]

John Maynard Keynes’s phraseLow Animal Spiritsdescribes a paradox found in human nature that locates a herd-like mentality, as well as unpredictability and uncertainty, at the core of international finance: a model of mass behavioural procedures.Low Animal Spiritswas a work made in collaboration with Richard Cochrane that took its cue from the oft-mentioned loss of the referent in both language and the economy that was speculated about wildly after the economic collapse of 2007/08. It employs a high-frequency trading (HFT) algorithm written by Cochrane (a former Vice President of Goldman Sachs), and ‘deals’ in words sourced from global news feeds for virtual ‘profit’, whilst speculating on their usage. The analysis produces new phenomena in the form of speculative headlines tweeted from the twitterbot@LowAnimalSpirit. As various social productions, including news, are increasingly enmeshed within algorithmic procedures, through their production, distribution and reception, it seemed an apt moment to apply an extreme version of algorithmic operation in the form of HFT to global news production. The Associated Press twitter hack of 2013 that brought about a 1% drop in the financial market in 1 minute was displayed during the exhibition as a polished metal graphic:Breaking News – Flash Crash, and generated further investigation into high-speed algorithmic procedures that seemingly conjoin language and the economy. The work developed through procedural experimentation and the following writing traces some of the ideas that came before, after and through the work, often in conversation with others. It is best thought of in terms of a report: a taking stock. [ABSTRACT FROM PUBLISHER]

This work aims at the inference of traffic networks from GPS trajectories. We perform geometry and topology reconstruction of the network in a multistep process. Our main contributions are the formulation of an explicit intersection model with a score function that accounts for consistency with the raw tracking data, as well as for a topology prior and the search for the best model by maximization of this score function using a Markov chain Monte Carlo sampler. We demonstrate the viability of our model-based approach with experiments on GPS data sets of varying size and data quality, followed by a comparison with results achieved by alternative, heuristic approaches. [ABSTRACT FROM AUTHOR]

In this article, a new basic unit for cascaded multi-level inverter is proposed. This inverter is able to increase the number of output voltage levels and reduces the number of power electronic devices. To generate all voltage levels at the output, five different algorithms to determine the magnitude of DC voltage sources are suggested. This inverter is compared with conventional cascaded multi-level inverters. The comparisons show that the proposed topology needs fewer DC voltage sources and power switches, less variety of the magnitude of DC voltage sources, and smaller amounts of blocked voltage by switches. As a result, the installation space and total cost of the inverter decrease. As it is impossible to use charge balance control methods for the asymmetric cascaded multi-level inverters, the developed topology based on the proposed cascaded inverter–the sub-symmetric topology with the usability of charge balance control methods–is proposed. A new algorithm is proposed to determine the magnitude of DC voltage sources. In addition, full-wave and half-wave charge balance control methods are applied in the proposed developed topology. The accurate performance of the proposed topology by applying charge balance control methods is verified through the simulation and experimental results of an 81-level sub-symmetric inverter. [ABSTRACT FROM AUTHOR]

This article proposes a comprehensive approach to computing a navigation graph for an indoor space. It focuses on a single floor, but the work is easily extensible to multi-level spaces. The approach proceeds by using a formal model, based on the combinatorial map but enhanced with geometric and semantic information. The process is almost fully automatic, taking as input the building plans providing the geometric structure of the floors and semantics of the building, such as functions of interior spaces, portals, etc. One of the novel aspects in this work was the use of combinatorial maps and their duals to provide a compact formal description of the topology and connectivity of the indoor structure represented by a connected, embedded graph. While making use of existing libraries for the more routine computational geometry involved, the research develops several new algorithms, including one for computing the local kernel of a region. The process is evaluated by means of a case study using part of a university building. [ABSTRACT FROM AUTHOR]