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A multi-markets biding strategy decision model with grid-side battery energy storage system (BESS) as an independent market operator is proposed in this paper. First, the trading methods of BESS participating in the spot market are analyzed. on this basis, a two-layer transaction decision model is built with comprehensively considering the participation of BESS in the day-ahead market (including the electric energy market and the auxiliary service market) and the intra-day balance market. The upper layer aims to maximize the revenue of BESS participation in spot market transactions under the premise of meeting the operation constraints of BESS, which generates the time-sharing strategy and transmits it to the lower layer. The lower layer conducts joint day-ahead market clearing with the goal of maximizing the whole social benefits, and carry on the intra-day balance market clearing with the goal of minimizing system balance costs. The clearing results are fed back to the upper model. based on particle swarm optimization and CPLEX, the solution method for the two-layer model is designed. Finally, a wind-fire-storage test system is used for simulation analysis, and the results verify the feasibility and effectiveness of the proposed model and method.

In this paper, we propose a fast rendezvous algorithm for a heterogeneous cognitive radio network (CRN), where each user might have more than one radio. One of the well-known problems for most multi-radio rendezvous algorithms in the literature is that they are not backward compatible to users with only one radio. To tackle this backward compatibility problem, our approach is a hierarchical construction that groups several time slots into an interval and proposes a novel algorithm to emulate two radios with a single radio in an interval. By doing so, at the interval level, each user behaves as if it had (at least) two radios. For the two-user rendezvous problem in a CRN with N commonly labelled channels, the interval length is chosen to be 2M time slots, where M=2\lceil \log_2 (\lceil \log_2 N \rceil)\rceil+10. We show that the maximum time-to-rendezvous (MTTR) of our algorithm is bounded above by 18 M \lceil n_1/m_1 \rceil \cdot \lceil n_2/m_2 \rceil time slots, where n_1 (resp. n_2) is the number of available channels to user 1 (resp. 2), and m_1 (resp. m_2) is the number of radios for user 1 (resp. 2). For the setting that each user is equipped with only one radio and two available channels, our MTTR bound is only M and that improves the state-of-the-art bound 16(\lceil \log_2 \log_2 N \rceil+1) in the literature. By conducting extensive simulations, we show that the expected time-to-rendezvous (ETTR) of our algorithm is also better than the two commonly used multi-radio algorithms, JS/Independent and JS/Parallel, in most parameter settings.

In this paper, we conduct centrality analysis for attributed networks. An attributed network, as a generalization of a graph, has node attributes and edge attributes that represent the ``features'' of nodes and edges. Traditionally, centrality analysis of a graph is done by providing a sampling method, such as a random walk, for the graph. To take node attributes and edge attributes into account, the sampling method in an attributed network needs to be twisted from the original sampling method in the underlining graph. For this, we consider the family of exponentially twisted sampling methods and propose using path measures to specify how the sampling method should be twisted. For signed networks, we define the influence centralities by using a path measure from opinions dynamics and the trust centralities by using a path measure from a chain of trust. For attributed networks with node attributes, we also define advertisement-specific influence centralities by using a specific path measure that models influence cascades in such networks. Various experiments are conducted to further illustrate these centralities by using two real datasets: the political blogs and the MemeTracker dataset.

In this paper, we first propose a new iterative algorithm, called the K-sets+ algorithm for clustering data points in a semi-metric space, where the distance measure does not necessarily satisfy the triangular inequality. We show that the K-sets+ algorithm converges in a finite number of iterations and it retains the same performance guarantee as the K-sets algorithm for clustering data points in a metric space. We then extend the applicability of the K-sets+ algorithm from data points in a semi-metric space to data points that only have a symmetric similarity measure. Such an extension leads to great reduction of computational complexity. In particular, for an n × n similarity matrix with m nonzero elements in the matrix, the computational complexity of the K-sets+ algorithm is O((Kn + m)I), where I is the number of iterations. The memory complexity to achieve that computational complexity is O(Kn + m). As such, both the computational complexity and the memory complexity are linear in n when the n × n similarity matrix is sparse, i.e., m = O (n). We also conduct various experiments to show the effectiveness of the K-sets+ algorithm by using a synthetic dataset from the stochastic block model and a real network from the WonderNetwork website.

Automatically recognizing personality based on historical action logs in online social networks is a promising method to infer a person's behaviors, and it has received a lot of attention lately as it might lead to the construction of a better personal recommendation system. However, very few previous works in the literature put their focus on predicting personality from Chinese texts. As Chinese texts are much more difficult to delimit than English texts, it poses more challenges in recognizing personality from Chinese texts. In this paper, we attempt to classify the personality traits from Chinese texts. We collected a dataset with posts and personality scores of 222 Facebook users who use Chinese as their main written language. Then, we used Jieba, a Chinese text segmentation tool, as the tokenizer for the task of text segmentation, and the Support Vector Machine (SVM) as the learning algorithm for personality classification. Our experimental results show that the performance in precision and recall can be significantly improved with the help of text segmentation. Moreover, exploiting side information, such as the number of friends, could improve the performance further. One interesting finding from our experiments is that extraverts seem to write more sentences and use more common words than introverts. This indicates that extraverts are more willing to share their mood and life with others than introverts.

Two-stage constructions and banyan-type networks play important roles in designing high speed switch fabrics. Switch fabrics designed from two-stage constructions and banyan-type networks cannot realize all the permutations and are known as conditionally nonblocking switches. A renowned property for conditionally nonblocking switches is the closure property, i.e., if all the switches in a two-stage construction can realize a subset of permutations that satisfy a certain property P, then the switch resulted from the two-stage construction can also realize a subset of permutations that satisfy the same property P. However, such a closure property is mostly stated for the sufficient part in the literature and the necessary part of the statement is in general either not true or unknown. Finding such a necessary and sufficient result is of fundamental importance to two-stage constructions as it can completely characterize the permutations that are realizable by two-stage constructions. In this paper, we prove a necessary and sufficient closure property for two-stage constructions. For this, we consider uniform mapping permutations and define uniform mapping switches as switches that can only realize the set of uniform mapping permutations. We show that a switch constructed by a two-stage construction is a uniform mapping switch if and only if all the switches at the first stage and the second stage are uniform mapping switches. Such a necessary and sufficient result provides a complete characterization of the permutations that can be realized by two-stage constructions. Since banyan-type networks are constructed recursively by using two-stage constructions with 2 × 2 switches, we obtain a complete characterization for any realizable permutation of any banyan-type network as a composition of its trace, a uniform mapping permutation and its guide.

In user-item networks, the link prediction problem has received considerable attentions and has many applications (e.g., recommender systems, ranking item popularity) in recent years. Many previous works commonly fail to utilize the dynamic nature of the networks. This paper focuses on dealing with the temporal information and proposes an algorithm to cope with the link prediction problem on bipartite networks. We describe a temporal bipartite projection method that yields a projected item graph, called the temporal projection graph (TPG). Based on the TPG, we propose a scoring function called STEP (Score for TEmporal Prediction) for each user-item pair. STEP leverages the historical behaviors of individual users and the social aggregated behaviors learned from the TPG for the link prediction problem. Furthermore, we use TPG and PageRank to rank the popularity of items. To validate our algorithms, we perform various experiments by using the DBLP author-conference dataset, the Flickr dataset and the Delicious dataset. We show that our results of the link prediction problem for new links are substantially better than other temporal link prediction algorithms. We also find the item rankings generated by our approach match very well with that existed in the real world.

Recommendation systems based on historical action logs between users and items are usually formulated as link prediction problems for user-item bipartite networks, and such problems have been studied extensively in the literature. With the advent of on-line social networks, social interactions can also be recorded and used for predicting user's future actions. As such, the link prediction problem based on the union of a social network and a user-item bipartite network, called a social user-item network in this paper, has been a hot research topic recently. One of the key challenges for such a problem is to identify and compute an appropriate proximity (similarity) measure between two nodes in a social user-item network. To compute such a proximity measure, in this paper we propose using a random walk with two different jumping probabilities toward different neighboring nodes. Unlike the simple random walk, our method is able to assign different weights to different paths and thus can lead to a better proximity measure by optimizing the two jumping probabilities. To test our method, we conduct various experiments on the DBLP dataset [21]. With a 3–5 year training period, our method performs significantly better than random guess in terms of minimizing the root mean squared error.

In this paper we propose to model the formation of online social networks by a duplication model. In this model vertices are added into the network one at a time. Each vertex is first attached to a randomly selected vertex. Each neighbor of the attached vertex establishes an edge with the new vertex with a probability. A main contribution of this paper is that we derive analytically the clustering coefficient for this model. Numerical studies show that the range of mean degree and the clustering coefficient of the duplication model is quite large. By properly choosing values for the parameters of our model, the mean degree and the clustering coefficient match well with those of popular online social networks.

Establishing control channels in a cognitive radio network (CRN) is an important and challenging problem. To cope with the problem of control channel saturation and the problem of channel blocking by primary users, channel hopping (CH) schemes are commonly used in the literature for control channel establishment in CRNs. There are three metrics that are widely used for evaluating the performance of CH schemes: (i) degree of overlapping (the number of distinct rendezvous channels), (ii) worst case time-to-rendezvous (TTR), and (iii) system load. In this paper, we focus on the symmetric and synchronous setting and propose a novel Cycle-Adjustable Channel Hopping (CACH) scheme that outperforms several existing CH schemes, including SSCH and QCH, in terms of the three metrics. The key idea of CACH is to create an additional layer of logical channels on the top of physical channels so that the cycle of channel hopping sequences can be adjusted to optimize system performance. The mathematic tools for our scheme are based on the operations in Galois fields that are more general than the prime number modular arithmetic used in SSCH. We show that CACH is much more general than SSCH and it can achieve the maximum degree of overlapping while allowing the worst case TTR to be adjustable. It is also much better than QCH in terms of reducing system load while keeping the same degree of overlapping and the same worst case TTR. Our simulation results show that CACH outperforms several existing schemes in many other aspects, including throughput, and robustness to the disturbance of PUs.

Fat-tree networks have been widely used in the field of Network-on-Chip. One of the key issues in a fat-tree network is that the degree of a node has to be increased rapidly from the bottom of the tree to the root. As such, the complexity of implementing the switches near the root could be extremely high, and this poses a serious scalability issue. To cope with the scalability issue in fat-tree networks, many previous works require changing the tree topology and adding buffers in nodes. Unlike the existing arts, we adopt a different approach that can still maintain the original tree topology without adding any buffers in internal nodes. Our key idea is to explore various nice features of the load-balanced Birkhoff-von Neumann switches. Such switches have been shown to achieve 100% throughput for all admissible traffic and have comparable delay performance to the ideal output-buffered switch when traffic is heavy and bursty. We show that the implementation complexity can be greatly reduced if a fat-tree network is only required to realize a set of N permutations needed for the N × N load-balanced Birkhoff-von Neumann switches. For this, we first derive a lower bound on the required degree for each node in a fat-tree network. By using the uniform mapping property of the bit-reverse permutation, we show that there exists a set of N permutations that achieve the lower bound.

The traditional gray model GM (1,1) is a kind of deviation of the exponential model, with fewer required data, forecasting of high precision and no need prior information, etc., but sometimes its forecasting is ineffective. “Poor” information systems are found everywhere. In this paper, a control approach to “poor” information systems, such as social economic systems, is proposed. Firstly, “poor” information processing and SCGM-model will be discussed. Secondly, we will develop an optimal controller of “poor” information systems. The result shows that the improved gray forecasting model SCGM-model, which is simple and practical and has higher accuracy and less error, can provide scientific theoretical basis for the relevant departments to make strategy. A demonstrating example shows the fitting and forecasting results of SCGM (1,h) are rather satisfactory

In order to reduce the influence of wind power to power grid, and to reduce the rotating spare capacity and operation cost of power supply system, it is necessary to predict the wind speed. Because the wind speed has very good succession and randomness, it is quite appropriate to use Auto Regressive Moving Average (ARMA) model of times series to predict the wind speed. In order to improve the prediction precision further, this paper first use wavelet theory to pick up the low frequency parts through the decomposition of the whole wind speed, then use ARMA model to forecast the wind speed on the gentled data. This paper take the wind speed directly measured from a certain wind farm as an example. Practical example shows that: This combination model can effectively improve the wind speed prediction accuracy. It has certain practical value.

Based on Newman's fast algorithm [13], in this paper we develop a general probabilistic framework for detecting community structure in a network. The key idea of our generalization is to characterize a network (graph) by a bivariate distribution that specifies the probability of the two vertices appearing at both ends of a randomly selected path in the graph. With such a bivariate distribution, we give a probabilistic definition of a community and a definition of a modularity index. To detect communities in a network, we propose a class of distribution-based clustering algorithms that have comparable computational complexity to that of Newman's fast algorithm. Our generalization provides the additional freedom to choose a bivariate distribution and a correlation measure. As such, we obtain significant performance improvement over the original Newman fast algorithm in the computer simulations of random graphs with known community structure.

Load-balanced switches have received a lot of attention lately as they are much more scalable than other existing switch architectures in the literature. One of the most salient features of load-balanced switches is its simplicity of implementing deterministic and periodic connection patterns for the switch fabrics. In particular, for an N x N load-balanced switch, its switch fabric only needs an N x N rotator that is capable of realizing all the powers of the circular shift permutation. In this paper, we consider the problem of incremental update of the number of linecards in load-balanced switches. For this, our idea is to consider a 2^M x 2^M degenerated banyan network that only uses half of the 2M+1 inputs/outputs in the classical 2^{M+1} x 2^{M+1} banyan network. We show how one can use the 2^M x 2^M degenerated banyan network as a p £ p rotator for any 2

Load balanced Birkhoff-von Neumann switches are known to be more scalable than other existing switch architectures that guarantee 100% throughput. However, there are still several design and implementation challenges in prototyping such a switch, including (i) modular design, (ii) synchronization and propagation delay, (iii) fault tolerance, and (iv) buffer management. In this paper, we address these challenges and propose our solutions in our prototype. Specifically, we propose a folded architecture for modular design, a pipelining mechanism for synchronization and propagation delay, a Benes network for fault tolerance, and a caching scheme for buffer management.

Inspired by the recent development of optical queueing theory, in this paper we study a class of multistage interconnection networks (MINs), called {\em twister networks}. Unlike the usual recursive constructions of MINs (either by two-stage expansion or by three-stage expansion), twister networks are constructed {\em directly} by a concatenation of bipartite networks. Moreover, the biadjacency matrices of these bipartite networks are sums of subsets of the powers of the circular shift matrix. Though MINs have been studied extensively in the literature, we show there are several {\em distinct} properties for twister networks, including routability and conditionally nonblocking properties. In particular, we show that a twister network satisfying (A1) in the paper is routable, and packets can be self-routed through the twister network by using the $\cal C$-transform developed in optical queueing theory. Moreover, we define an $N$-modulo distance and use it to show that a twister network satisfying (A2) in the paper is conditionally nonblocking if the $N$-modulo distance between any two outputs is not greater than two times of the $N$-modulo distance between the corresponding two inputs. Such a conditionally nonblocking property allows us to show that a twister network with $N$ inputs/outputs can be used as a $p \times p$ rotator and a $p \times p$ symmetric TDM switch for any $2 \le p \le N$. As such, one can use a twister network as the switch fabric for a two-stage load balanced switch that is capable of providing incremental update of the number of linecards.

Motivated by the design of high speed switching fabrics, in this paper we propose a \emph{bit-stuffing} algorithm for generating forbidden transition codes to mitigate the crosstalk effect between adjacent wires in long on-chip buses. We first model a bus with forbidden transition constraints as a forbidden transition channel, and derive the Shannon capacity of such a channel. Then we perform a worst case analysis and a probabilistic analysis for the bit-stuffing algorithm. We show by both theoretic analysis and simulations that the coding rate of the bit stuffing encoding scheme for independent and identically distributed (i.i.d.) Bernoulli input traffic is quite close to the Shannon capacity, and hence is much better than those of the existing forbidden transition codes in the literature, including the Fibonacci representation.

Micro-location is the key factor to determine the value of urban housing. In view of the practical situation of researches on the selection of China's urban residential micro-location, this paper analyzes the factors affecting the selection of urban residential micro-location, sets up evaluation index system and puts forward evaluation procedure with the tool of combining linearity method with entropy method, which provide a theoretical reference for rational distribution of China's urban residential housing and sustainable development of the domestic estate industry. Keywords-urban housing; micro-location; AHP; Entropy Law I. INTRODUCTION Housing is the basic material requirement of mankind, and is also the most basic investment and consumer behavior. Foreign experts and scholars all agreed that among the factors that affect the real estate value, location is of utmost importance. Choosing urban residential areas has become an important factor in residential investment, and researching on evaluation method of micro-urban residential location selection can not only be able to improve the present system theory, but also to provide reference for government departments charging for urban housing, investors and consumers, which has important theoretical and practical value. II. MAIN PROBLEMS EXISTING IN THE SELECTION OF CHINA'S URBAN RESIDENTIAL LOCATION From China's urban residential developing situation, we can see that China's housing industry has been restricted by the level of economic development and macro-policy, and there are still some problems compared with the western developed countries.

A Combined Input and Crosspoint Queueing (CICQ) switch is a switch that has both buffers at the crosspoints of the switch fabric and buffers at the inputs. Inspired by the fixed frame based algorithm for an input-buffered switch and the smooth scheduling algorithm for a CICQ switch, in this paper we propose using a dynamic frame sizing algorithm for a CICQ switch. It is formally shown that such a CICQ switch indeed achieves 100% throughput for certain Poisson-like traffic models. This is done without using the framed Birkhoff-von Neumann decomposition needed. Moreover, such a CICQ switch only requires a two-cell buffer at each crosspoint when there is only unicast traffic. Unlike input-buffered switches, the dynamic frame sizing algorithm also achieves 100% throughput in the setting of multicast traffic. This is done at the cost of increasing the buffer size at each crosspoint.

Despite all the recent advances in the mathematical theories for constructing optical queues by optical Switches and fiber Delay Lines (SDL), there are still many problems that need to be resolved. In this paper, we tackle the following problems: (i) is it possible to construct optical queues with switches of arbitrary sizes? (ii) is there a general theory that unifies many constructions of optical queues with known packet delays? and (iii) under what conditions can a concatenation of optical queues allow overtaking? For the first problem, we propose a new class of optical memory cells that can be made by switches of arbitrary sizes. Moreover, we propose the generalized C -transform for routing packets through such optical memory cells. For the second problem, we introduce a new class of optical queues, including FIFO, LIFO and absolute contractors. We show that both linear compressors in [13] and FIFO multiplexers (with multiple inputs) in [5], [7] are special cases of contractors. An interesting finding is that overtaking can occur in LIFO and absolute contractors.

In this paper we propose to construct an flexible delay line with maximum delay d by parallel non-overtaking delay lines. We show that for a fixed number of non-overtaking delay lines, an optimal policy to minimize packet losses is to assign arriving packets to the non-overtaking delay line that has the largest residual service time while maintaining the FIFO order for each non-overtaking delay lines. Based on this optimal policy we show that to exactly emulate an flexible delay line, one needs [(d + 1)/2] non-overtaking delay lines. We also show that if one can tolerate a small packet loss probability, one just needs O(radic(d)) non-overtaking delay lines. In this case, we show that the residual service times of the non-overtaking delay lines behaved as if they followed the order statistics of uniform random variables.

In the light of the development of energy performance contracting (EPC) in our country, the writer systematically studies the policy, market, financing, running and benefit risk which are existing in the process of the development of EPC. On the basis of it, the target system of risk evaluation on EPC is discussed, quantitative evaluation is conducted by applying theory of indistinct integrated evaluation, and the guiding ideas and the model are provided.