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In multiple coordinated views (MCVs), visualizations across views update their content in response to users' interactions in other views. Interactive systems provide direct manipulation to create coordination between views, but are restricted to limited types of predefined templates. By contrast, textual specification languages enable flexible coordination but expose technical burden. To bridge the gap, we contribute Nebula, a grammar based on natural language for coordinating visualizations in MCVs. The grammar design is informed by a novel framework based on a systematic review of 176 coordinations from existing theories and applications, which describes coordination by demonstration, i.e., how coordination is performed by users. With the framework, Nebula specification formalizes coordination as a composition of user- and coordination-triggered interactions in origin and destination views, respectively, along with potential data transformation between the interactions. We evaluate Nebula by demonstrating its expressiveness with a gallery of diverse examples and analyzing its usability on cognitive dimensions.

Planning ideal transit routes in the complex urban environment can improve the performance and efficiency of public transportation systems effectively. However, finding such routes is computationally difficult due to the huge solution space constituted by billions of possible routes. Considering the limited scalability of exact search methods, heuristic search methods were proposed to boost the efficiency and incorporate flexible constraints. Nevertheless, the existing methods conceal multiple criteria in an objective, and thus evaluating the performance of the generated route becomes challenging due to the lack of comparable alternatives. Inspired by the prior study, we formulate the definition of pareto-optimal transit routes based on multiple criteria. However, extracting these routes remains challenging because: A) the sheer volume of possible transit routes; and B) the sparsity of pareto-optimal routes. We address these challenges by developing an efficient search framework: for challenge A, a random search method is developed based on Monte Carlo tree search where the unproductive solution subspaces are pruned progressively to reduce the search cost; and for challenge B, an estimation method is derived to guide the search process by assessing the value for each solution subspace. The superior effectiveness of our approach in approximating the pareto-optimal transit routes was demonstrated by the comprehensive evaluation based on the real-world data.

Rising energy cost and environment-friendly awareness make energy conservation a key issue for metro operation. Reducing traction energy consumption and improving the utilisation of regenerative braking energy are two efficient solutions to conserve energy. Two optimisation models are proposed in this study for metro systems to minimise the energy consumption by exploring energy-efficient speed profile and optimum timetable. An analytical formulation through finding suitable force coefficients and driving regime-switching points is developed to investigate the optimal speed profile of a single train during its movement along complex rail tracks considering gradients, curves, tunnels and speed limits. Moreover, the synchronisation of accelerating and braking trains is much necessarily implemented when a timetable optimisation model is established for the energy-efficient operation by adjusting the dwell times, running times and turnaround times of multiple trains. A combined particle swarm optimisation and genetic algorithm are addressed to solve the model. The case studies from the actual data of a Beijing Metro line are carried out to verify the feasibility and availability of the proposed approach. Their results show that the net energy consumption of trains along the metro line is reduced by 10.99% by combining the optimal speed profiles and timetable.

solar powered UAV, the mass and structural dynamics characteristics of solar powered UAV with different layouts and different structure forms in time and frequency domain are studied in this paper. Firstly, the thin-walled structure models of solar powered UAV with flying wing layout and diamond wing layout are established with the same solar panel laying area. The flutter characteristics are calculated by P-K method in frequency domain, and the response characteristics of the structure under dynamic loads are calculated in time domain. The dynamic characteristics and mass characteristics of solar powered UAV with large aspect ratio with different layouts are compared. Then, the rhombus grid structure is applied to the wing structure, and the finite element model of the rhombus grid structure is established to study the structural dynamics and mass characteristics of the diamond wing layout solar powered UAV. The results show that the frequency domain and time domain methods can accurately obtain the structural dynamic characteristics. The flutter characteristics and gust response characteristics of the solar powered UAV with diamond wing layout are better than those with flying wing layout, but the mass of diamond wing layout solar powered UAV is too large. The rhombus grid structure diamond wing layout solar powered UAV has good structural stiffness and higher flutter speed, and its structural mass is 21.5% lighter than that of the thin-walled diamond wing layout solar poWered UAV.

Interactive ranking techniques have substantially promoted analysts' ability in making judicious and informed decisions effectively based on multiple criteria. However, the existing techniques cannot satisfactorily support the analysis tasks involved in ranking large-scale spatial alternatives, such as selecting optimal locations for chain stores, where the complex spatial contexts involved are essential to the decision-making process. Limitations observed in the prior attempts of integrating rankings with spatial contexts motivate us to develop a context-integrated visual ranking technique. Based on a set of generic design requirements we summarized by collaborating with domain experts, we propose SRVis, a novel spatial ranking visualization technique that supports efficient spatial multi-criteria decision-making processes by addressing three major challenges in the aforementioned context integration, namely, a) the presentation of spatial rankings and contexts, b) the scalability of rankings' visual representations, and c) the analysis of context-integrated spatial rankings. Specifically, we encode massive rankings and their cause with scalable matrix-based visualizations and stacked bar charts based on a novel two-phase optimization framework that minimizes the information loss, and the flexible spatial filtering and intuitive comparative analysis are adopted to enable the in-depth evaluation of the rankings and assist users in selecting the best spatial alternative. The effectiveness of the proposed technique has been evaluated and demonstrated with an empirical study of optimization methods, two case studies, and expert interviews.

In order to increase the potential and trend prediction accuracy of unconventional oil and gas resources, a potential and trend prediction method of unconventional oil and gas resources based on combination forecasting model of variable weight for multi-factor is proposed. First of all, the present situation and trend of oil and gas resources development are analyzed and introduced. It is pointed out that it is influenced and restricted by many factors and external environment, which shows the characteristics of its complexity and non-linear historical evolution trend. Secondly, the optimal variable weights are determined by using the variable weight combination forecasting model and then the potential and trend prediction accuracy of unconventional oil and gas resources is realized to be increased. Finally, the validity of the proposed method is verified by the simulation experiment.

Modern power system can identify component faults, isolate faults and resume operation quickly. In view of the problem that the distribution of line load in distribution network is not reasonable and it is easy to fall into the self-organized critical state, this paper introduces power flow entropy as an evaluation index to measure the robustness of power network reconstruction. Based on the power supply capability and node load level of distributed power supply in the process of network reconstruction, a strategy of island division is proposed. Then, a mathematical model is set up to minimize power flow entropy, network loss and node voltage drop, and the problem of network reconstruction after fault is solved by the improved chaos theory and binary particle swarm optimization algorithm. Finally, an example of IEEE-33 node distribution system is given to verify the feasibility of the proposed strategy and the effectiveness of the algorithm.

Transmission line engineering is a strategic asset with heavy assets and long life. Its normal operation depends on maintenance and maintenance. The initial design of traditional transmission lines mainly considers initial construction cost and operation cost. There are few or no considerations for line maintenance considerations. In the maintenance of transmission lines, power outages are also often used.This paper proposes a calculation method for the the replacement of insulators on the transmission line based on the full life cycle cost, and analyzes the economics by calculating the cost of electrified replacement insulators . electrified replacement insulators. According to the actual engineering examples, it is verified that the convenience of inspection and maintenance improves the economic efficiency of the overall transmission line.

International audience; Comparing provers on a formalization of the same problem is always a valuable exercise. In thispaper, we present the formal proof of correctness of a non-trivial algorithm from graph theory thatwas carried out in three proof assistants: Why3,Coq, and Isabelle.

Transmission line engineering is a strategic asset with heavy assets and long life. Its normal operation depended on maintenance and maintenance. The initial design of traditional transmission lines mainly considers the initial construction cost and operation cost, and the line maintenance was rarely considered, not even considered. This paper proposes a cost calculation method for replacing the insulator string of power transmission line based on the life cycle cost, and considers the calculation method of the power failure replacement cost of the transmission line year by year in the whole life cycle. In the calculation of the line insulator failure rate, the improved gray GM (1,1) model is used to predict the line insulator failure rate in the medium and long term, which makes the line maintenance cost calculation more accurate. According to the actual engineering examples, it is verified that the convenience of inspection and maintenance improves the economic efficiency of the overall transmission line.

Trend predicting of distribution network operation states is an important basis for understanding the safe operation of distribution network. In view of the impacts of measurement data and prediction accuracy, a novel trend predicting method of distribution network operation states is proposed in this paper. It is based on an improved multi-dimensional grey-neural network hybrid coordination prediction model and a branch current forward and backward substitution power flow model considering Phasor Measurement Unit (PMU). This trend predicting method analyzes the errors existing in the historical measurement data and establishes the distribution network measurement database as the input of the trend prediction. Improved multi-dimensional grey-neural network hybrid coordination prediction model is designed to achieve high-precision power prediction in the short term. The result of the power prediction is used as the input of the branch current forward and backward substitution power flow model utilizing PMU to calculate the system trend in the future and to implement the trend predicting of distribution network operation states. The feasibility and effectiveness of the proposed method are verified by analyzing the predicted and true values of IEEE33 system.

To meet the ever increasing demand of wireless services for high data rates and mobility, mobile communication systems have evolved from the first generation cellular network to the current 4G one. The core purpose of a cellular network is to provide users with guaranteed QoS and seamless coverage throughout the service area. In the early stage, this task is achieved by installing cells at candidate sites and configuring their parameters as optimally as possible. Generally, each cell is assigned a dedicated operating frequency, and its adjacent cells operate on different frequencies to avoid inter-cell interference. As the evolution of the cellular network proceeds, advanced signal processing techniques, such as coordinated multipoint transmission and reception, and inter-cell interference coordination, are rising as promising solutions to improve the system performance, which on the other hand inevitably increase the CAPEX and OPEX of the cellular system. Besides, as the deployment of cells becomes denser and denser, it is more and more difficult and expensive to obtain capacity or QoS gains with these signal processing techniques. In this article, we rethink the cellular network planning issue in the context of heterogeneous networking, which is widely accepted as a cost-efficient paradigm to enhance the performance of the cellular system. We point out that the essential objectives of designing a cellular network, coverage and capacity, can also be achieved in heterogeneous networking scenarios using a cutting-edge territory division technique, which divides a service region into multiple subregions with almost equal traffic loads. We develop a dynamic cellular network planning framework that can significantly reduce the CAPEX and OPEX of the system. The QoS for users can also be enhanced while shifting away from employing complex and costly signal processing techniques.

Quantum computing, as an emerging computing paradigm, is expected to tackle problems such as quantum chemistry, optimization, quantum chemistry, information security, and artificial intelligence, which are intractable with using classical computing. Quantum computing hardware and software continue to develop rapidly, but they are not expected to realize universal quantum computation in the next few years. Therefore, the use of quantum hardware to solve practical problems in the near term has become a hot topic in the field of quantum computing. Exploration of the applications of near-term quantum hardware is of great significance in understanding the capability of quantum hardware and promoting the practical process of quantum computing. Hybrid quantum-classical algorithm (also known as variational quantum algorithm) is an appropriate model for near-term quantum hardware. In the hybrid quantum-classical algorithm, classical computers are used to maximize the power of quantum devices. By combining quantum computing with machine learning, the hybrid quantum-classical algorithm is expected to achieve the first practical application of quantum computation and play an important role in the studying of quantum computing. In this review, we introduce the framework of hybrid quantum-classical algorithm and its applications in quantum chemistry, quantum information, combinatorial optimization, quantum machine learning, and other fields. We further discuss the challenges and future research directions of the hybrid quantum-classical algorithm.

After the power distribution system (DS) fails, a single power supply recovery method will cause certain loads to fail to recover in time. To improve the reliability of distribution systems with distributed generation, this paper proposes a two-stage network restoration reliability assessment method considering island division and network reconfiguration. In the process of network fault recovery, firstly, distributed generation is used to supply power to the load point of power loss, islanding of the network after fault with the aim of minimizing power loss. Then the residual network is reconstructed and optimized by considering the network loss and voltage quality. Finally, the improved ieee33 node network is simulated based on Monte Carlo simulation method. The results show that this method is superior and can realize real-time load transfer, reduce power loss, improve the reliability of DS and the economy of system operation, Considering the continuous change of the network structure, the running state of the system can be described more accurately.

In this paper, we propose a multimodal and multiclass semi-supervised image-to-image translation (MM-SSIT) framework to address the dilemma between expensive labeled work and diversity requirement of image translation. A cross-domain adversarial autoencoder is proposed to learn disentangled latent domain-invariant content codes and domain-specific style codes. The style codes are matched with a prior distribution so that we can generate a series of meaningful samples from the prior space. The content codes are embedded into a multiclass joint data distribution by an adversarial learning between a domain classifier and a category classifier so that we can generate multiclass images at one time. Consequently, multimodal and multiclass cross-domain images are generated by joint decoding the latent content codes and sampled style codes. Finally, the networks for MM-SSIT framework are designed and tested. Semi-supervised experiments with comparisons to state-of-art approach show that the proposed framework has the ability to generate high-quality and diversiform images in case of fewer labeled samples. Further experiments in the unsupervised setting demonstrate that MM-SSIT is superior in learning disentangled representation and domain adaption.

As a new energy material, solar cells are difficult to study for the non-destructive testing of their quality. The gray value of the damaged area and the undamaged area of the infrared image of the solar cell obtained by the infrared technology is relatively small, which brings great difficulties to the detection of the solar cell. In order to improve the quality of infrared images of solar cells, a bi-histogram equalization image enhancement algorithm with an influence factor is proposed in this paper. Experiments were conducted using standard images and solar cell infrared images, and compared with the conventional bi-histogram equalization enhancement algorithm. The simulation results showed that under the same image information and contrast evaluation indexes, the algorithm in this paper obtained a larger signal-to-noise ratio.

Inspired by the explosive growth of mobile data traffic, the severe inter-tier interference, and the fierce competition between the total cost of ownership and revenues for mobile operators, the heterogeneous cloud radio access network (H-CRAN) has been proposed as one of the most prominent ways to handle these challenges. The key idea of the H-CRAN is incorporating cloud computing into a heterogeneous network (HetNet) to enhance coordinated multipoint transmission and reception, cooperative radio resource management, and self-organizing networks, which improve both spectral and energy efficiencies of cellular systems. One of the most critical challenges that hinder the implementation of the H-CRAN is the high transmission demand on the backhauls between the baseband unit (BBU) pool and remote radio heads (RRHs). In this article we suggest that we can balance the workload of different RRHs to alleviate the pressure on the transmission links. Our proposed method is different from but compatible with existing compression techniques that have been widely investigated in the literature to lighten the transmission burden of the backhauls. We also describe the technical challenges in existence during the implementation of our proposal and give preliminary ideas of how to address them.

Cell planning has been a long-standing problem since the very starting commercialization of mobile communications, of which power coverage and capacity coverage are two major objectives. In this paper, we develop a novel cell planning scheme that is effective and efficient for both the conventional cellular systems and the arising heterogeneous networks, e.g., the long term evolution advanced (LTE-A) one. The key idea of our proposal is that we redesign the service regions of base stations (BSs) in a traffic-balanced way, making each BS serve a subregion of ALMOST equal throughput requirement. For a given connected polygonal region to be served by a cellular system, we first divide it into compact and connected subregions based on an infinite optimization formulation while keeping the traffic demands of all subregions as equal as possible. Each subregion will be served by a BS located in it. To avoid yielding ill-shaped subregion that is difficult to be covered by a practical BS, a penalty term is introduced to the objective function and it is required that areas of subregions should not differ too much from each other. Then we select the BS from all candidate sites in each subregion to minimize the total power consumption. By using the proposed dividing and selecting algorithms, we update the boundary of each subregion and the location of each BS in an iterative manner until convergence. Numerical results show that our proposal performs quite well for both randomly generated scenarios and real city environment. The proposed cell planning method provides quality of service (QoS) guaranteed performance with lower capital expenditure and operating expenditure.

As electronic device feature sizes scale-down, the power consumed due to onchip communications as compared to computations will increase dramatically; likewise, the available bandwidth per computational operation will continue to decrease. Integrated photonics can offer savings in power and potential increase in bandwidth for onchip networks. Classical diffraction-limited photonics currently utilized in photonic integrated circuits (PIC) is characterized by bulky and inefficient devices compared to their electronic counterparts due to weak light–matter interactions (LMI). Performance critical for the PIC is electro-optic modulators (EOM), whose performances depend inherently on enhancing LMIs. Current EOMs based on diffraction-limited optical modes often deploy ring resonators and are consequently bulky, photon-lifetime modulation limited, and power inefficient due to large electrical capacitances and thermal tuning requirements. In contrast, wavelength-scale EOMs are potentially able to surpass fundamental restrictions set by classical (i.e. diffraction-limited) devices via (a) high-index modulating materials, (b) nonresonant field and density-of-states enhancements such as found in metal optics, and (c) synergistic onchip integration schemes. This manuscript discusses challenges, opportunities, and early demonstrations of nanophotonic EOMs attempting to address this LMI challenge, and early benchmarks suggest that nanophotonic building blocks allow for densely integrated high-performance photonic integrated circuits.

Focused on automation management level of distribution network, an evaluation method based on fuzzy matrix is proposed in this paper. First, data extraction efficiency, data quality and automation ratio are selected as evaluation indicators. Then, by establishing an analytical model, each indicator is compared in pairs to construct a judgment matrix. And the consistency test of the matrix is done before the follow step. Next, through the experts’ scores of the influencing factors, fuzzy mathematics is used to make a comprehensive evaluation of the automation management evaluation indicators. Finally, according to the evaluation results, the automation management level of the distribution network is analysed. According to the above method, a certain urban area is taken as an example to obtain the quantitative index of the automation management level of the distribution network. It is of great significance to strengthen the distribution network construction with the cooperation management mode, so as to realize the comprehensive improvement of the distribution network management level.

This paper proposes an optimization control of micro-grid system economy operation model. It coordinates the new energy and storage operation with diesel generator output, so as to achieve the economic operation purpose of micro-grid. In this paper, the micro-grid network economic operation model is transformed into mixed integer programming problem, which is solved by the mature commercial software, and the new model is proved to be economical, and the load control strategy can reduce the charge and discharge times of energy storage devices, and extend the service life of the energy storage device to a certain extent.

Cloud radio access network (CRAN) has been seen as an effective means to address the challenges faced by cellular radio networks, such as high capital expenditure and operating expense, high energy consumption and low spectral efficiency. Especially, CRAN has the potential to equip a cellular network with the load-balancing capability to cope with the non-uniformly distributed traffic in the service area. In this paper, we develop an optimal load balancing scheme for CRAN-based cellular systems by employing an infinite optimization technique. A fairness index is defined to measure the load balancing level of the cellular system and monitored by periodically inspecting the load distribution among all cells. When the fairness index is below a warning threshold, we divide the service zone into compact and connected subregions based on an infinite optimization formulation. Each subregion served by a cell has almost equal area and almost equal throughput requirement. To avoid yielding ill-shaped subregion that is difficult to be covered by a practical cell, a penalty term is introduced to the objective function. Then we update the cell association of each user so that the fairness index return to an acceptable level. Numerical experiments show that our proposal can provide performance-guaranteed load balancing for the cellular network with almost no additional operating expense.

Heterogeneous cloud radio access networks (H-CRANs) have been proposed as a promising architecture for providing high energy efficiency, high spectral efficiency and high data rate at low cost. In this paper, we develop a novel resource allocation scheme among macro base stations (BSs) and remote radio heads (RRHs) for the H-CRANs. The key idea of our proposal is that we design the coverage regions of macro BSs in a seamless and balanced way. Secondly, we calculate the areas that can not rely on macro BSs for reliable high speed data transmission and allocate resources in these areas among RRHs in the balanced way to alleviate the high transmission pressure on fronthauls between RRHs and the centralized baseband unit (BBU) pool. The proposed resource allocation scheme is triggered when severe disbalance is detected. Numerical results show that our proposal can perform quite well for the data traffic distribution in a real city environment. It provides QoS guaranteed performance with lower capital expenditure (CAPEX) and operating expenditure (OPEX).

Multi-view based reconstruction is always focused in computer graphics and many excellent algorithms have been reported these years. According to Middlebury benchmark, PMVS(Patch based Multi-View Stereo) outperforms all the other submitted algorithms (1). In this paper, we propose an improved PMVS algorithm based on quasi-dense matching to save time cost of the original algorithm. Improved algorithm reduces running time for patch expansion through building a quasi-dense set of initial patches and depresses time complexity of the algorithm. The experiments demonstrate effectiveness of improved algorithm. Keywords-Multi-view reconstruction; PMVS; quasi-dense matching

Gesture recognition is of utmost importance in intelligent human-computer interaction HCI. In this paper, an algorithm using moment invariants and BP neural network classifier for hand gesture recognition is proposed. Seveni¾?invariant moments are extracted after the image segmentation, which quantitatively describes the targeted hand gesture, and then the trained BP neural networks are applied to recognize the gesture. The algorithm is validated and achieves a high recognition rate of 99% in experiments, which makes it feasible for practical HCI in some restricted environments.

The intelligent technique is becoming the most important and powerful aided tool to manage the infrastructure information and reduce the urban natural hazards. This paper presents the development in building the smart digital China programs and the application of the digital technique to mitigate natural hazard in Chinese cities. The digital techniques in this paper mean the "3S" technique, which is geographical information system (GIS), globe positioning system (GPS) and remote sensing (RS). As one of the powerful and the most popular tools, 3S is applied to these systems for infrastructure information management and natural hazard reduction. So far, most of Chinese cities have built their own intelligent digital natural hazard systems, which are mainly built based on 3S. Some details techniques in the procedure of building the digital system, including the integration methods, data transferring and data communication among 3S systems, are discussed. The principle, design criteria, structure and functions of these systems are also described in this paper. As an illustration, several successful cases are illustrated.

Due to the influence of nonlinear dynamic characteristic of electronic throttle, time delay, external disturbance and parameter variation, the opening of electronic throttle is hard to control precisely. A fuzzy immune adaptive PID control is presented based on the immune feedback regulating law and the adaptive ability of fuzzy logic ratiocination. The model of the electronic throttle system is built and the controller model is designed based on Matlab/Simulink. Simulation research and practical results show that control effect of the method is superior to general PID control, and it is provided with better robustness and adaptive ability even if parameters are changed and disturbs are happened.

A fuzzy controller of automated mechanical transmission (AMT) clutch in starting phase is developed based on driverpsilas experiences. Focused on the problem that ordinary fuzzy controller design has a lot of subjective factors and the fuzzy controller is difficult to optimize, the fuzzy control model of the AMT clutch was optimized based on adaptive-network-based fuzzy inference system (ANFIS) algorithm and the training, test and simulation analysis were performed for the fuzzy neural network model. The results prove that the membership functions of fuzzy model obtain the satisfactory effect. Applying the optimization model in the simulation, the new control system can avoid the start jerk and better respond to the starting intention of driver.

There are differences in efficiency when pedestrian flows with various directions pass through the bottleneck of a subway in different manners, and the mechanisms of congestion at the bottleneck are distinct as well. The weaving motion of pedestrian flows with various directions in subways is simplified into two crowds with different ODs passing through the bottleneck which connects two parallel channels. The lattice gas model is improved by introducing the floor field so that it is suitable for the description of pedestrian flow under complex situations. It is shown by experiments that different ways lead to the difference in total time when the crowds pass through the field of interest. Then the total time of crowds passing through the field in different ways is investigated numerically, and the effect of bottleneck width is taken into account as well. Numerical simulations confirm the findings from experiments. It is found that the diagonal movement of a pedestrian should be included in the model in order to give a better description of real pedestrian traffic. And the mechanism of congestion near the bottleneck is discussed in detail.