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This paper presents a control strategy for multi-machine parallel permanent magnet synchronous generator (PMSG) grid-connected system, which combines the advantages of virtual synchronous generators (VSG) and direct current transmission to achieve large-scale wind power integration. The grid-connected inverter adopts the VSG control strategy to simulate the port characteristics of the traditional synchronous generator, which can actively provide the inertia support for the system when the power system fluctuates. Based on the power-DC voltage drooping characteristics, a machine-side DC droop control strategy is constructed. This control strategy can significantly reduce the number of inverters and realize the collection and transmission of DC-type power, thereby saving economic costs and reducing the cost. Many problems caused by the internal AC collection process of wind farms. Finally, the performance and effectiveness of the proposed method are validated by the simulation results.

Performance of a collaborative task is mostly dependent on the collective effort from participants. To accomplish a participatory task effectively and efficiently, the team formation problem (TFP) outweighs all other considerations. It is even more complicated when social connections among candidates is taken into account. As we can imagine, a large number of tasks require members of the team to be socially close. On the contrary, a portion of tasks, e.g., proposal review, pay more attention to a multidimensional view, and team members should be selected from a variety of cliques. Due to the nature of tasks, it is challenging to find a subset that meets the skill requirement of the task as well as socially diversity demand of team members from a pool of candidates. In this paper, we explore the TFP in a social network. Based on different task objectives, we first formulate the TFP as TFP with strong ties (TFP-ST) and TFP with weak ties (TFP-WT), respectively. Both TFP-ST and TFP-WT are proven to be NP-hard, and we then design corresponding heuristic algorithms to solve the two problems. Through extensive simulations, we show that the solution to TFP-ST can achieve significant improvement in terms of collaboration cost, team size, as well as running time, and the solution to TFP-WT can provide better performance than existing approaches at the same time.

Suitable modeling is the premise and basis for the optimal control of the scroll expander. This paper proposes a modeling method of scroll expander based on deep learning. The method selects the inlet pressure, the rotational speed and the inlet temperature of the scroll expander as the model inputs, and selects the output torque and the outlet volume flow rate as outputs; Processing gross errors in the data by the Butterworth filter; Finally, the long short-term memory (LSTM) neural network is used to train data to obtain the scroll expander neural network model. The LSTM neural network effectively overcomes the vanishing gradient problem in the recurrent neural network. Therefore, the error between the predicted value and the measured value of the model output is small, which satisfies the engineering control requirements.

Time-to-Collision(TTC) algorithm is the key component for collision warning based Vehicle-to-Everything applications. In this paper, a GPS based intersection model is firstly introduced based on the relative position and distance between vehicles or traffic participants. This model avoids the extra computing time required to create a Cartesian coordinate system. Secondly, a novel rectangle model is proposed based on our GPS model in order to represent the shape of the vehicle more accurately. It can also be used to identify different collision situations at the intersection more precisely. Through which, the experiments show that TTC can be calculated more accurately than other shape models, and more effectively in reducing the consumption of calculation for non-collision situations.

With the development of Internet of Things (IoT) technology, the application of IoT is more and more importantly widespread. Meanwhile, how to achieve fast access for various IoT devices, especially for a large-scale of IoT devices, also has been a significant challenge in the development of IoT systems. To address this issue, a fast access protocol for ZigBee-enabled IoT devices is proposed in this paper, in which, the ZigBee module embedded in a registering IoT device could automatically obtain a Personal Area Network (PAN) address and instantly access the target ZigBee wireless network through the signaling interactions among the involved protocol entities. After that, the operation and control of the accessed IoT devices are conducted based on the ZigBee communications. To evaluate the effectiveness and efficiency of this protocol, a prototype system is developed. The experiment and analysis results also have been shown to demonstrate the performance by comparing with the existing access methods.

Facilitated by the emerging technologies of cloud computing, vehicular cloud computing (VCC) is being expected to be a new paradigm to create a new situation for vehicular data services. However, with the increasing popularity of vehicular cloud services, accessing vehicular terminals and produced mobile data are experiencing tremendous growth, therefore, how to provide enough capacity for all access requirements and handle all the mobile data traffic to address the challenges posed by richer vehicular applications are critical. In future 5G mobile networks, promoted by the advantages of supporting various connectivity and significantly faster transmission performance of 5G communications, VCC will be enabled to provide more realistic vehicular data cloud services. In this article, an innovative VCC platform with the support of 5G communications for providing effective vehicular data cloud services is first proposed. Then, in this context, a vehicular social-based taxi-sharing cloud service is mainly investigated. Finally, some open issues and challenges with possible solutions for future work are discussed.

Addressing the challenges of feeding the burgeoning world population with limited resources requires innovation in sustainable, efficient farming. The practice of precision agriculture offers many benefits towards addressing these challenges, such as improved yield and efficient use of such resources as water, fertilizer and pesticides. We describe the design and development of a light-weight, multi-spectral 3D imaging device that can be used for automated monitoring in precision agriculture. The sensor suite consists of a laser range scanner, multi-spectral cameras, a thermal imaging camera, and navigational sensors. We present techniques to extract four key data products — plant morphology, canopy volume, leaf area index, and fruit counts — using the sensor suite. We demonstrate its use with two systems: multi-rotor micro aerial vehicles and on a human-carried, shoulder-mounted harness. We show results of field experiments conducted in collaboration with growers and agronomists in vineyards, apple orchards and orange groves.

Wireless localization technology has become an important application in wildlife conservation. However, such systems require carrying devices in wildlife unsuited to wildlife conservation, or costing larger energy consumption under the large-scale area, impeding their wide-scale adoption. This poster introduces Trepli, a multi-link animal localization system in large-scale area. Unlike the previous work which needs the target to participate in during the localization, Trepli presents a wireless localization approach using received signal strength indicator (RSSI) without any human or target efforts which called passive localization. Our key contribution is low cost that can turn the large-scale positioning area into the small one using selected random deployment of three links. Finally we obtain the target location using the target localization algorithm on this small area. We evaluate its performance through real-world deployment and simulation experiments. Experimental result shows that Trepli is feasible to achieve target localization with low cost in theory.

In this paper, a variable-length classified template sets based ECG compression algorithm for ECG measurement system is proposed. First of all, apply R-peak detection to the ECG data and the data will be segmented according to the detected R-peak, then cluster the heartbeat vector quantities of different R-R intervals and extract the variable-length heartbeat template sets. Divide the ECG data into three parts including template, residual error and template index by template matching, and use Huffman coding to encode the residual error finally. In decoding algorithm, use the stored template sets, residual error, template index and Huffman coding dictionary to restructure complete ECG data. The performance of algorithm is evaluated by using MIT-BIH Arrhythmia Database. The average of compression ratio is 3.05:1, which is 94.3% higher than Huffman coding.

K-nearest neighbor algorithm is the most widely used classification and clustering algorithm. It is simple, fast, straight and effective. However, the relationship between the nearest items is a partial order. Since it is not a strong conjunction, items could be clustered by force. To that end, in this paper, we propose a concept of k-nearest neighbor clique based on k-nearest neighbors and reversed k-nearest neighbors. First, by measuring the similarity between items, we select the items that form the pairs of mutually k-nearest neighbor and reversed k-nearest neighbor. These items are used to construct k-nearest neighbor cliques. Since the relationship between items in the same clique is a total order, they have a high similarity to each other. Then, we use the cliques as new data to seed clustering in the next round. This process is repeated until some conditions are satisfied. Finally, the experiments on the real-world datasets validate the effectiveness of our proposed algorithm.

Non-Hamiltonian graph is featured by the degree sequence that fails to meet the Chvatal condition. By analyzing this feature, a new function of the edge bound for non-Hamiltonian graph is proposed in this paper. The maximum number of edges in maximal non-Hamiltonian graph is derived from this function with mathematical analysis for extreme values, which not only implies the characters of maximal non-Hamilton graph, but also improves the Erdos condition with a better threshold.

Neurons tuned to binocular disparity in area V1 are hypothesized to be responsible for short latency binocular vergence movements, which align the two eyes on the same object as it moves in depth. Disparity selective neurons in V1 are not only selective to disparity, but also to other visual stimulus dimensions, in particular orientation. In this work, we explore the role of neurons tuned to different orientations in binocular vergence control. We trained an artificial binocular vision system to execute corrective vergence movements based on the outputs of disparity selective neurons tuned to different orientations and scales. As might be expected, we find that neurons tuned to vertical orientations have the strongest effect on the vergence eye movements. The effect of neurons tuned to other orientations decreases as the tuned orientation approaches horizontal. Although adding neurons tuned to non-vertical orientations does not appear to improve vergence tracking accuracy, we find that neurons tuned to non-vertical orientations still play critical roles in binocular vergence control. First, they decrease the time required to learn the vergence control strategy. Second, they also increase the effective range of vergence control.

This paper considers the problem of stabilization of linear switched time-delay systems which contain unstable subsystems. Stability conditions are derived by applying a model transformation and adopting the weighting matrix method in the derivative of the Lyapunov function, such that the system is asymptotically stable. The resulting delay dependent stability criteria are less conservative compared to the work in the literature. Simulation examples illustrate the effectiveness of the proposed method.

In this paper, the stability problem for a class of linear switched systems with time-varying delay is investigated. The purpose of this paper is to obtain a less conservative result. A liner matrix inequality (LMI) approach is adopted to solve the problem. Finally, a numerical example shows that the method is very effective.

Linear projections have been extensively studied for pattern classification. In this paper, we examine them from another perspective: how well they can preserve rejection points. Here rejection points refer to those patterns whose class conditional probabilities are approximately tied. Although such patterns are often rejected in the decision process, due to their unique closeness to the class boundary, they may carry valuable information which we cannot mine from other patterns deep inside the classes. For instance, in support vector machines, the separating hyperplane is completely determined by those support vectors closest to the plane. Along this line, we present an experimental analysis of four commonly used projections with respect to two classical posterior estimators. Empirical results showed that projection robustness depends on the particular posterior estimator used. Finally we discuss the underlying factors that make a robust projection.

This paper describes a new hypergraph formulation for document categorization, where hyperclique patterns, strongly affiliated documents in this case, are used as hyperedges. Compared to frequent itemsets, the objects in a hyperclique pattern have a guaranteed level of global pairwise similarity to one another as measured by the cosine or Jaccard similarity measure. Since hypergraph partitioning is mainly based on vertex similairty on the hyperedge, hypercliques may serve as better quality hyperedges. Besides, due to the additional confidence constraint, we can cover more items in the mined patterns while keep the pattern size reasonable. Hence, the difficulty in partitioning dense hypergraphs, which is often encountered in frequent itemset based hypergraph partitioning, is alleviated considerably. Finally, experiments with real-world datasets show that, with hyperclique patterns as hyperedges, we can improve the clustering results in terms of various external validation measures.

This paper describes a new bipartite formulation for word-document co-clustering such that hyperclique patterns, strongly affiliated documents in this case, are guaranteed not to be split into different clusters. Our approach for pattern preserving clustering consists of three steps: mine maximal hyperclique patterns, form the bipartite, and partition it. With hyperclique patterns of documents preserved, the topic of each cluster can be represented by both the top words from that cluster and the documents in the patterns, which are expected to be more compact and representative than those in the standard bipartite formulation. Experiments with real-world datasets show that, with hyperclique patterns as starting points, we can improve the clustering results in terms of various external clustering criteria. Also, the partitioned bipartite with preserved topical sets of documents naturally lends itself to different functions in search engines.