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It has been an active research area to develop robust supervisory control policies for production systems with unreliable resources. So far, most methods for robust deadlock resolution apply only to systems without flexible routes, where each processing step of any part type requires a unique prespecified resource. In this paper, we address deadlock avoidance control problem in production systems with both failure-prone resources and flexible routings, which allow that a part has options when deciding the resource acquisition at each step. This paper presents properties that a controller with robustness must satisfy. Specifically, at any system reachable state, neither the failed resources nor part instances trapped in these resources should have too much detrimental effect on the other portions of the system. Thus, the full range of part types’ production could be assured at all time. After defining the notions of reduced system and reduced state with respect to unreliable resources, we identify and prove conditions for determining whether or not the state resulting from the occurrence of an event is feasible. Subsequently, we develop a method for robust deadlock avoidance, which uses the solutions to state safety checking problem for the reduced production system with only reliable resources. An illustrative example shows the effectiveness of this method. Finally, we conduct a comparison investigation of some representative approaches in the literature about robust supervisory control for deadlock resolution in resource allocation systems with routing flexibility.

Smart grid adopts two-way communication and rich functionalities to gain a positive impact on the sustainability and efficiency of power usage, but on the other hand, also poses serious challenges to customers’ privacy. Existing solutions in smart grid usually use cryptographic tools, such as homomorphic encryption, to protect individual privacy, which, however, can only support limited and simple functionalities. Moreover, the resource-constrained smart meters need to perform heavy asymmetric cryptography in these solutions, and thus unnecessarily increases load on smart grid. In this paper, we present a practical and secure SGX-enabled smart grid system, named SecGrid. Our system leverages trusted hardware SGX to ensure that grid utilities can efficiently execute rich functionalities on customers’ private data, while guaranteeing their privacy. With our well-devised security protocols in SecGrid, only the smart meters need to perform AES encryption. To validate the superiority of our design, we conduct security analysis and experimentation. Security analysis shows that SecGrid can thwart various attacks from malicious adversaries, and the experimental results show that SecGrid is much faster than the existing privacy-preserving schemes in smart grid.

With the fast growth of system complexity, it is increasingly difficult to comprehensively analyze security of such large-scale systems, which is a knowledge-intensive task. Although there are various available security knowledge sources, they are not well-connected with each other due to their heterogeneity and unstructured descriptions. In this paper, we propose a systematic approach to construct a comprehensive and reusable knowledge graph in the field of information security. Specifically, we first investigate heterogeneous security knowledge sources and establish a detailed ontology of information security, integrating various security conceptual models. Then, we train a security entity identifier based on active learning to extract security knowledge from unstructured descriptions. Such extracted knowledge is then fused to establish a comprehensive and reusable security knowledge graph based on the unified ontology. Finally, we illustrate the utility of our established knowledge graph with a set of exemplary queries and reasoning rules in the context of a real security scenario.

The transportation network company (TNC) services efficiently pair the passengers with the vehicles/drivers through mobile applications, such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers’ revenues. From the TNC drivers’ side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings by using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning-based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment, such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep ${Q}$ -network-based route scheduling algorithm for vacant TNC vehicles based on distributed framework, which makes the server closer to the terminal users and accelerates the training speed. Furthermore, we apply the geo-indistinguishability scheme based on differential privacy to preserve the sensitive location information uploaded by the passengers. We evaluate the proposed algorithm’s performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

Device-free activity recognition (DFAR) is a promising technique that detects the activity of a target by analyzing the influence of its existence on surrounding wireless links. It realizes target sensing without the participation or even awareness of the target. The key question of DFAR is how to characterize the influence of the target on wireless links. Existing works mostly utilize statistical features, such as mean and variance in time-domain, and energy as well as entropy in frequency-domain, to characterize the influenced signals. However, statistical features provide only partial information. This paper explores the method on how to characterize the distribution of the signal as a whole. Specifically, we present a novel coherence histogram, which leverages the spatial structural characteristics to better characterize the distribution of the wireless signal. The coherence histogram captures not only the occurrence probability of received signal strength (RSS) measurements, but also the spatial relationship between adjacent RSS measurements as well. Experimental results show that our coherence histogram-based DFAR system could achieve an accuracy of more than 96%, which significantly outperforms other state-of-the-art DFAR systems remarkably.

This paper develops an approach for solving the problem of least-cost transition firing sequences estimation in labeled Petri nets with unobservable transitions. Each transition in the net is labeled as either observable or unobservable, and has a nonnegative cost. Additionally, we assume that the net system is bounded, the unobservable subnet is acyclic, and the cost of each unobservable transition is strictly positive. We propose the method mainly on the basis of the notions of basis marking and basis reachability graph (BRG), which is a compact representation of the reachability graph of the net system. By sacrificing extra storage space to keep the BRG and other necessary information in memory, some time-consuming computations are moved off-line. This makes our proposed approach more feasible for on-line applications. Finally, we present a brief survey and comparison of some representative methods that use labeled Petri nets as a formalism in the literature.

This paper aims to propose a new analytical method of deriving the enveloping macroscopic fundamental diagram (MFD) in the light of the well-defined sufficient condition and the route choice criterion in a general network. The enveloping MFD is defined to describe the boundary of all scatter points relating the flow rate (vehicles per unit time) to the vehicle accumulation (vehicles) in a road network. The theoretical framework consists of two parts. The first part is a static congested traffic assignment model which is based on the congested link performance function and the deterministic user equilibrium principle under the entirely congested condition. The second part is the new analytical method which is proposed based on the static uncongested and congested traffic assignments with the deterministic user equilibrium, in which the well-defined sufficient condition is satisfied by employing the static traffic assignment models, and the route choice criterion is fulfilled by the deterministic user equilibrium condition (Wardrop's principle). The main findings of this paper are summarized as follows: 1) the enveloping MFD delimits a region where all scatter points are located; 2) the existence and reproducibility of the enveloping MFD in a general network are verified through numerical examples; 3) the proposed method is suggested to be applied to transportation planning for describing and evaluating the macro-characteristics and the performance of road networks and evaluating whether the OD pattern is congruous with the network topology.

The aims of this paper are to present a new model to calculate the maximum equilibrium road network capacity and obtain the associated optimal origin-destination (i.e. OD) flow pattern for a general road network with given network topology and link attributes. First, the Static Congested Traffic Assignment (SCTA) is introduced to describe the network where all used routes are in a congested state. Congested Equilibrium Travel Times (CETTs) between OD pairs obtained by the SCTA model are regarded as the corresponding upper limits of actual equilibrium travel times which are called Uncongested Equilibrium Travel Times (UETTs) and derived from the traditional Static Uncongested Traffic Assignment (SUTA) model. The main idea of modeling is that the total OD flows can be maximized by flexibly scaling and adjusting OD flows of each individual OD pairs to make the UETTs of all OD pairs reach their upper limits (i.e. CETTs). Next, a novel equilibrium road network capacity model is built by combining the SUTA and the SCTA models. Then, the equivalency condition and the solution uniqueness of the proposed model are proved. The paper moves on to provide two solution algorithms together with an analysis of the model characteristics. Finally, through three numerical examples, it is demonstrated that the proposed model can obtain the unique equilibrium network capacity with the given network topology and associated link attributes. The optimal OD flow pattern, which leads to the maximum total OD flows, is thus obtained. The findings in the paper can help to improve the utilization of road networks and contribute to land development planning and control.

The large-scale integration of distributed energy resources has resulted in surgical changes in energy trading systems. Traditional centralized trading systems suffer from high management cost and low efficiency. The recent advance of blockchain technology has enabled the invention of distributed energy trading systems, which can overcome the limitations of centralized trading systems. However, the distributed energy trading systems also bring new security and privacy challenges. For instance, transactions on blockchain are publicly visible which can lead to privacy leakage of trading information. Moreover, user privacy can also be leaked during verification of the aggregated energy trading result. In this paper, we propose a privacy-preserving energy trading scheme based on blockchain to meet the security requirements for distributed energy trading. We adopt a stealth transmission approach based on blockchain to ensure data privacy and break the linkage between consumers and providers in the energy trading process. We also use the non-interactive zero-knowledge proof technology to achieve privacy-preserving and trustworthy trading result verification. Security analysis and evaluation results have demonstrated that the proposed scheme can effectively protect the data privacy for distributed energy trading systems.

Under the promotion of Digital China and digital economy, China’s power grid enterprises and other traditional enterprises have carried out digital transformation. Taking the State Grid Jibei Electric Power Company Limited as an example, this paper analyzes the internal and external dynamic factors of the digital transformation of power grid enterprises, and analyzes the dynamic mechanism of digital transformation by using the system dynamics theory. In the process of digital transformation, the external power has the role of promoting and guiding the internal power. The internal power has the reaction and the concrete function to the external power. Finally, the paper puts forward some suggestions for the development of digital transformation, which include establishing inclusive culture, cultivating digital business technical personnel, getting through power grid business data, docking with external data of enterprises, providing data asset support for digital transformation, establishing digital ecology of power grid and social service business of the company, improving the application ability of digital tools, mining data value, and building modular power data commodity, improving the digital service capability of the company.

As the uncertainty of power system increases with each passing day, energy storage facilities become more and more significant and they are widely used to cope with uncertainties. However, the existing power system production simulation methods usually neglect the temporal characteristic of power system and thus difficult to consider the effect of energy storage. To address this limitation, this paper firstly analyzes the charging and discharging process and builds the multi-state model of energy storage. In combination of the energy storage model, the universal generating function based production simulation method is introduced and the production simulation considering energy storage is also put forward. Taking North Hebei Power Grid as an example, this paper finally analyzes the effect of different scales of energy storage on power system reliability and renewable energy curtailment, and thus proposes some development proposals for North Hebei.

Data aggregation plays an important role in the era of big data. Financial institutions aggregate user data to obtain the global information about their users. At the same time, they intend to hide the detailed data from third parties such as fusion center when participating in the aggregation due to concerns on data privacy. Based on the secure multi-party computation and pseudonym, in this paper, we propose a privacy preserving data aggregation mechanism. In this new mechanism, users are grouped into pairs anonymously, and their data is divided and mixed between pairwise users. Compared with the existing methods, this new scheme can achieve lower losses when link failure occurs, and resist the collusion attacks from users.

Malicious message detection, also known as spam detection, on social networks has long been a critical but challenging problem to be addressed. It has attracted more and more attention from government, industry, and academia, due to an increasing number of recent massive cybercrimes that exploit the dissemination of faked or harmful messages on social networks. Previous work relies on the content of messages or the behavior of user accounts to differentiate and classify malicious and benign messages, which becomes ineffective under evasion tactics from advanced attackers. Different from those solutions, in this work, we leverage the differences in dissemination between benign and malicious messages on social networks and derive features from their dissemination paths among users and social communities to detect malicious messages. Experimental results obtained from real-world datasets demonstrate that our new approach is robust and effective in detecting malicious messages on social networks, which achieves 95% precision and 95% recall.

TRIP is an in-house Computational Fluid Dynamics (CFD) software that can simulate subsonic, transonic, and supersonic flows with complex geometries. With the increase of computation and memory requirement for large-scale CFD simulations, it is an inevitable trend to use massively parallel computers for parallel computing. In this paper, with a dual-level hybrid and heterogeneous programming model using MPI + OpenACC, we port and optimize the TRIP software on the Sunway TaihuLight supercomputer. A series of optimization techniques, including data reconstruction, data packing, loop refactoring and array swapping, are explored. In addition, a grid preprocessing tool is developed for reducing the load imbalance caused by the non-cube shape of sub grids. Scalability tests show that TRIP can achieve 66.9% parallel efficiency of strong scaling and 96% efficiency of weak scaling when the cores are increased from 10,400 to 665,600.

In order to extract the vehicle trajectory on the urban road network and provide data basis and technical support for analyzing the balance of supply and demand of urban traffic, the method to extract the vehicle trajectory which is based on intersection bayonet data will be constructed. Firstly, the daily travel trajectory of the vehicle can be obtained by using the data of intersection bayonet and the method of license plate grouping and time order sorting. Secondly, considering the relationship between link travel time and the vehicle to achieve the separation of the vehicle trajectory chain. Finally, the decision indicator of high-grade road proportion will be used to improve the TOPSIS method, and the approach based on the TOPSIS method will be built to reconstruct vehicle trajectory and infer the trajectory of the lost point which is generated by the heuristic depth-first directional algorithm. The paper chooses Suning County in China as an example to verify the reliability of the above approach. Moreover, it is found that the approach can effectively achieve the extraction, separation, and reconstruction of the vehicle trajectory.

Urban rail transit passenger flow forecasting is an important basis for station design, passenger flow organization, and train operation plan optimization. In this work, we combined the artificial fish swarm and improved particle swarm optimization (AFSA-PSO) algorithms. Taking the Window of the World station of the Shenzhen Metro Line 1 as an example, subway passenger flow prediction research was carried out. The AFSA-PSO algorithm successfully preserved the fast convergence and strong traceability of the original algorithm through particle self-adjustment and dynamic weights, and it effectively overcame its shortcomings, such as the tendency to fall into local optimum and lower convergence speed. In addition to accurately predicting normal passenger flow, the algorithm can also effectively identify and predict the large-scale tourist attractions passenger flow as it has strong applicability and robustness. Compared with single PSO or AFSA algorithms, the new algorithm has better prediction effects, such as faster convergence, lower average absolute percentage error, and a higher correlation coefficient with real values.

Mobile crowdsensing is recognized to be a promising paradigm wherein location-based sensing tasks are outsourced to participants carrying mobile devices. A prominent issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participating devices, which requires the disclosure of participants' locations and leads to potential location privacy threats. In this paper, we aim to develop a privacy-preserving participant recruiting scheme for mobile crowdsensing, which guarantees the crowdsensing coverage while preserving participants' location differential privacy against a semi-honest crowdsensing aggregator. Briefly, based on the differential private geo-indistinguishability method, we enable candidate participants to locally perturb their location data. With the obfuscated location information, we formulate the crowdsensing coverage optimization as an Integer Program (IP), and develop a $1-(1-1/f)^f$-approximation algorithm, which yields a near-optimal participant recruiting solution. Through extensive simulations, we demonstrate the tradeoff between privacy preservation and crowdsensing utility, and show that satisfactory crowdsensing coverage can be achieved while preserving the participants' differential location privacy.

Information Centric Networking (ICN), a new paradigm of Internet infrastructure, aims to better accommodate users' rapid growing demand for content delivery and optimize bandwidth utilization. Although the in-network cache feature of ICN facilitates the dissemination of content to users, it also poses new challenges on access control for content and network resource. Moreover, it is common that the access privilege of content dynamically change over time. However, existing access control mechanisms in ICN cannot support the publication and distribution of such time-sensitive content. In this paper, we propose a time- sensitive, lightweight, and secure access control mechanism, called TSLS, to solve this problem. We introduce broadcast encryption combined with time tokens for content providers to protect content confidentiality, and only authorized users satisfying the time limitation have capability to decrypt and access the content. Besides, a fast lightweight challenge-response verification is implemented at the edge routers to block unauthorized request from injecting into the network. The responses of authorized users are forwarded to content providers for pre-distribute popular content at in-network caches in advance. Our security analysis shows that TSLS possesses the properties of data confidentiality, unforgeability, anonymity, and DoS/DDoS attacks resistance. Our simulation results indicate that our proposed TSLS is an efficient mechanism with low computation cost and network delay.

Named data networking (NDN) is a promising implementation of information centric networking (ICN), which is based on a publish/subscribe model. In NDN, when an interest packet containing the name of requested content is sent to network, the corresponding data chunk will be returned from a content server or an intermediate router that stores a copy of the content. In order to avoid interest flooding, chunk size usually cannot be set too small and larger chunk will be divided into multiple smaller fragments in transmission. However, in existing NDN designs, the fragmentation, aggregation, and acknowledgment of chunks need to be performed at each hop, which brings huge processing delay. In this paper, we propose a segmented transmission scheme for NDN (STNDN) to address this problem. In STNDN, each fragment of chunk can be independently routed, and chunks will only be aggregated at some selected nodes along the transmission path. We also consider the impact of link state instability. Specifically, w e a ggregate c hunks a t the u pstream of each poor link to avoid retransmissions across multiple hops on these links, thus improving the robustness of STNDN. We further demonstrate the effectiveness of STNDN through experiments in ndnSIM simulator. Compared with other two solutions, NDNLP and FFRD, STNDN has significantly improved both the reliability and efficiency of transmission.

Blockage and deadlock are undesirable in an automated manufacturing system (AMS). Thus, addressing these issues is essential to ensure smooth production evolution via system supervisory control. If an AMS contains unreliable resources which may break down unexpectedly, then the situation is complicated and problems become challenging. This work investigates the robust supervision for such AMSs from the perspective of management and control of resource allocation. The ultimate goal of this study is to develop a controller for allocating buffer space, such that any failure does not propagate through blocking to stall the rest of the system. In other words, the continual production of every part type not requiring any of the failed resources must be guaranteed at all time. We propose a supervisory control policy that satisfies the above desired property by using formal tools of automata and Petri nets. The policy relaxes the assumption made in previous work that each part type requires at most one failure-prone resource in the predefined production route. Meanwhile, the policy execution and implementation do not need any central buffers. We also present a survey and some comparative studies of various approaches in the literature about robust supervision for deadlock resolution in AMSs.

Demand response has been intensively studied in recent years. It can motivate customers to change their consumption patterns according to the dynamic (time-varying) electricity price, which is considered to be the most cost-effective and reliable solution for smoothing the demand curve. However, many existing schemes, based on users’ demand request in each period, require users to consume their requested electricity exactly, which sometimes causes inconvenience and losses to the utility, because customers cannot always be able to consume the accurate electricity demand due to various personal reasons. In this paper, we tackle this problem in a novel approach. Instead of charging after consumption, we adopt the prepayment mechanism to implement power request. Furthermore, we propose a trading market running by the control center to cope with the users’ dynamic demand. It is noteworthy that both users’ original demand and trading records are protected against potential adversaries including the curious control center. Through the numerical simulation, we demonstrate that our scheme is highly efficient in both computation and communication.

Physical space of large high-speed rail (HSR) hub is the foundation of passenger aggregation, distribution and transfer. The spatial layout method of its function areas is the research hotspot in the field of transportation hub design. Aiming at the goal of minimizing the pedestrian walking distance, a spatial layout method for function area based on pedestrian flow of high-speed rail hub has come up. According to the characteristics of passenger activity and movement, the hub space is divided into different function areas macroscopically. Combined with case analysis, the general steps of the spatial layout method of high-speed rail hub and effectiveness of this method are illustrated.

In order to research the influence mechanism of spatial obstacles on evacuation pedestrian flow, simulations of pedestrian evacuation are carried out by using the improved dynamic parameter model based on cellular automata. A retardation coefficient is introduced to describe the effect of obstacles slowing down pedestrian movement. A special technique is adopted to calculate the shortest estimated distance (SED) from cell site to exit considering obstacle layout and retardation coefficient. The repulsion and isolation effect of obstacles on pedestrian flow is manifested by the clusters of evacuation path chains. The movement probability is considered to narrow the gap between the normal speed and simulated evacuation speed caused by spatial discretization. The effectiveness of movement probability is verified by theoretical analysis and simulation results. It is found that in evacuation from a walkway, the influence of retardation coefficient on evacuation time will be gradually weakened and stabilized with walkway width rising. The influence of obstacle layout on the selection of evacuation path will be observed in the classroom. It also shows in the evacuation through an exit with a prepositive obstacle that the evacuation time is determined by the narrowest one in the series-connected bottlenecks. Moreover, the phenomena such as “pedestrians keeping a certain spatial distance from the boundaries of walkway”, “pedestrians preferring to choose a wider movement aisle in classroom” and “evacuation time being dominated by the narrowest one of series-connected bottlenecks” can also be observed during pedestrian evacuation in the simulation.

Flexible assembly systems (FASs) are a class of resource allocation systems with assembly processes as their inevitable components. Deadlock resolution and liveness enforcement play important roles in the supervisory control of FASs. Design of deadlock avoidance policies (DAPs) for FASs has received significant attention recently. The computational feasibility of real-time DAPs is of great importance for the policies’ online implementation in practical applications. However, there are difficulties brought by the existence of assembly processes. So far, nearly all known DAPs with polynomial-time complexity apply only to systems without assembly operations. This study focuses on the deadlock-avoidance control problem of a broader class of FASs as compared to those discussed in the existing work. Specifically, for an FAS addressed in this paper, each resource can be shared by any two operations, and it also allows multiple resource acquisition at each processing step. This work develops a novel DAP f...

Cognitive radio networks (CRNs) have been emerging as a promising technique to improve the spectrum efficiency of wireless and mobile networks, which form spectrum clouds to provide services for unlicensed users. As spectrum clouds, the performance of multi-hop CRNs heavily depends on the routing protocol. In this paper, taking the newly proposed Cognitive Capacity Harvesting network as an example, we study the routing problem in multi-hop CRNs and propose a spectrum-aware anypath routing (SAAR) scheme with consideration of both the salient spectrum uncertainty feature of CRNs and the unreliable transmission characteristics of wireless medium. A new cognitive anypath routing metric is designed based on channel and link statistics to accurately estimate and evaluate the quality of an anypath under uncertain spectrum availability. A polynomial-time routing algorithm is also developed to find the best channel and the associated optimal forwarding set and compute the least cost anypath. Extensive simulations show that the proposed protocol SAAR significantly increases packet delivery ratio and reduces end-to-end delay with low communication and computation overhead, which makes it suitable and scalable to be used in multi-hop CRNs.

Data access control is a challenging issue in public cloud storage systems. Ciphertext-policy attribute-based encryption (CP-ABE) has been adopted as a promising technique to provide flexible, fine-grained, and secure data access control for cloud storage with honest-but-curious cloud servers. However, in the existing CP-ABE schemes, the single attribute authority must execute the time-consuming user legitimacy verification and secret key distribution, and hence, it results in a single-point performance bottleneck when a CP-ABE scheme is adopted in a large-scale cloud storage system. Users may be stuck in the waiting queue for a long period to obtain their secret keys, thereby resulting in low efficiency of the system. Although multi-authority access control schemes have been proposed, these schemes still cannot overcome the drawbacks of single-point bottleneck and low efficiency, due to the fact that each of the authorities still independently manages a disjoint attribute set. In this paper, we propose a novel heterogeneous framework to remove the problem of single-point performance bottleneck and provide a more efficient access control scheme with an auditing mechanism. Our framework employs multiple attribute authorities to share the load of user legitimacy verification. Meanwhile, in our scheme, a central authority is introduced to generate secret keys for legitimacy verified users. Unlike other multi-authority access control schemes, each of the authorities in our scheme manages the whole attribute set individually. To enhance security, we also propose an auditing mechanism to detect which attribute authority has incorrectly or maliciously performed the legitimacy verification procedure. Analysis shows that our system not only guarantees the security requirements but also makes great performance improvement on key generation.

In cognitive radio networks (CRNs), primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, while SUs will in return obtain more spectrum access opportunities, leading to cooperative CRNs (CCRNs). Prior research works in CCRNs mainly focus on providing ubiquitous access and high throughput for users, but have rarely taken energy efficiency into consideration. Besides, most existing works assume that the SUs are passively selected by PUs regardless of SUs’ willingness to help, which is obviously not practical. To address energy issue, this paper proposes an energy-efficient cooperative strategy by leveraging temporal and spatial diversity of the primary network. Specifically, SUs with delay-tolerant packets can proactively make the cooperative decisions by jointly considering primary channel availability, channel state information, PUs’ traffic load, and their own transmission requirements. We formulate this decision-making problem based on the optimal stopping theory to maximize SUs’ energy efficiency. We solve this problem using a dynamic programming approach and derive the optimal cooperative policy. Extensive simulations are then conducted to evaluate the performance of our proposed strategy. The results show significant improvements of SUs’ energy efficiency compared with existing cooperative schemes, which demonstrate the benefits of our proposed cooperative strategy in conserving energy for SUs.

Cognitive radio (CR) technology, which enables unlicensed secondary users to opportunistically access the unused licensed spectrum, has attracted more and more attention from both academia and industry due to its potential to significantly improve the spectrum utilization. Considering both temporal and spatial variations of spectrum availability, this paper focuses on improving the energy efficiency in CR networks by opportunistically serving the delay-tolerant data only when enough spectrum is available. Based on this idea, a stochastic optimization problem is formulated to integrate the power control, link scheduling, and routing, which minimizes the expected power consumption while guaranteeing the system stability. To obtain the solution, we use the Lyapunov optimization technique and design an online algorithm, which solves a sub-problem without future knowledge of the related stochastic models (e.g., random data arrival and spectrum supply). Besides, in view of the NP-hardness of the sub-problem, we also develop a heuristic algorithm based on branch-and-bound framework to obtain the approximate solution with low computing complexity. Theoretical analysis shows that our algorithm offers an explicit tradeoff between energy consumption and delay performance. Numerical results also confirm the effectiveness of our solutions.

Nowadays, more and more massive cyber-attacks have been launched over social networks. Using compromised or fake accounts, criminals can exploit the inherent trust between connected users to effectively spread malicious content and perform scams against users. Detecting those malicious accounts on social networks like Twitter has received increasing attention from government, industry, and academia. Traditional methods on malicious account detection often leverage features that are created and selected based on domain knowledge of user data, which is inefficient, time-consuming, and possibly biased due to different understanding and observations of the data. In this paper, we propose a new framework, called MADAFE, for accurate and efficient malicious account detection on social networks like Twitter. To overcome the limitation of existing work on manual feature extraction, MADAFE utilizes an autoencoder to automate feature extraction and selection from unlabeled user data. A softmax regression model is also established and trained with the extracted features for classification of benign and malicious accounts. We test MADAFE on different datasets, and extensive simulation results show that MADAFE is effective in detecting malicious accounts, which outperforms state-of-the-art detection methods.

The dynamics of tremulous motion in the upper limb is complex. We aimed to explore the relationship between the complexity of upper limb tremor when aiming and aiming performance and the influences of physical load on the two outcomes. Fifteen modern pentathlon athletes were recruited and completed two 1000-m treadmill running and three 60-s standard aiming task trials: one at baseline and each of the other two immediately after each running. The time series of light spot trace on the target was measured using a high-speed camera. The complexity of this time series was quantified using multiscale entropy. The effective aiming rate was used to assess the aiming performance. We observed that participants with lower tremor complexity had lower effective aiming rate across three physical load conditions (

Nowadays, multimedia content retrieval has become the major service requirement of the Internet and the traffic of these contents has dominated the IP traffic. To reduce the duplicated traffic and improve the performance of distributing massive volumes of multimedia contents, in-network caching has been proposed recently. However, because in-network content caching can be directly utilized to respond users' requests, multimedia content retrieval is beyond content providers' control and makes it hard for them to implement access control and service accounting. In this paper, we propose an attribute-based accountable access control scheme for multimedia content distribution while making the best of in-network caching, in which content providers can be fully offline. In our scheme, the attribute-based encryption at multimedia content provider side and access policy based authentication at the edge router side jointly ensure the secure access control, which is also efficient in both space and time. Besides, secure service accounting is implemented by letting edge routers collect service credentials generated during users' request process. Through the informal security analysis, we prove the security of our scheme. Simulation results demonstrate that our scheme is efficient with acceptable overhead.

The artificial bee colony-based SLM (ABC-SLM) scheme, which is a novel PAPR reduction scheme, has been proposed to reduce the peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. High PAPR can degrade the efficiency of high-power amplifier and is one of the major disadvantages of OFDM systems. This paper proposes a modified ABC-SLM scheme to further improve the PAPR reduction performance of ABC-SLM scheme. The proposed method, called GA-ABC-SLM, is combined the gene algorithm (GA) with the artificial bee colony-based SLM scheme. The simulation results show that the GA-ABC-SLM scheme has better PAPR reduction performance than the ABC-SLM scheme.

In this paper, we propose to apply deep learning for encrypted data packet classification. Due to the complexity of deep learning, it is inefficient to process the entire data packet, which is usually padded with many zeros. In order to reduce data size for classification, while maintaining high accuracy, we develop data interpolation schemes to process data packets with various lengths to a fixed size. In particular, three data interpolation schemes based on nearest value, bilinear and bicubic interpolation are proposed. Experiments are conducted using an open source dataset. The evaluation results demonstrate that our data interpolation schemes can be applied to process input data for higher computational efficiency without losing classification accuracy.

The advance in mobile communications has escalated the use of transportation network company (TNC) services by residents. The TNC services efficiently pair the passengers with the vehicles/drivers through mobile applications such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings of using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network (DQN) based route scheduling algorithm for vacant TNC vehicles. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

Image quality will greatly affect the acquisition of human visual information, but the image will inevitably cause a certain degree of distortion in the process of transmission, compression, coding and so on. At this time, the accuracy of image quality evaluation (IQA) is particularly important, which also makes IQA an important research direction in the field of image processing. IQA methods are divided into subjective image quality evaluation method and objective image quality evaluation method.there are many subjective evaluation methods, and the international telecommunications union (ITU) has also proposed many subjective evaluation methods and standards. there are three most commonly used methods, namely, the double stimulation continuous quality classification method, the single stimulation continuous quality classification method and the double stimulation damage classification method. Correspondingly, there are three kinds of objective evaluation methods, full reference, half reference and no reference. At present, this paper selects the research focus of IQA field-no reference image quality evaluation method as the main research object, summarizes the advantages and disadvantages of each non-reference evaluation method, and prospects the future development of the non-reference image quality evaluation method.

Coal-fired power has played an important role in the power systems, but its operational characteristics, especially the flexibility characteristics is modelled relatively simple in the current operation simulations. Given the background of a rapid development of renewable energy and a sustainable transformation of power systems in China, it is essential to build a refined model of coal-fired power units that can be incorporated into an operation simulation method. First, this study provides an analysis on the flexibility characteristics of coal-fired power units, including the minimum stable output, ramp limit and minimum up/down time. Subsequently, a multi-state probabilistic model of coal-fired power units is proposed. This study also constructs a sequential operation simulation method based on the universal generation function (UGF) method that incorporates multiple states of coal-fired power units. Finally, this study provides a development and transformation pathway for coal-fired power plants in the North Hebei (NH) region and suggestions for promoting a sustainable development of the power system based on the proposed method and multi-state model.

Price-based demand response (DR) has been widely accepted as playing an important role in the reliable and economic operation of the power system. There are different ways to quantify the electricity consumption experience (i.e., comfort) of customers, which can be divided into positive and negative benefits, that is to say, there is no unified method to evaluate the positive or negative benefits of DR. Based on the self-elasticity and cross-elasticity of price elasticity of demand (PED), this study first addresses a novel approach to evaluate the positive and negative benefits of price-based DR with the consideration of customers’ electricity consumption experience. Then, a unit commitment (UC) model considering priced-based DR is built to analyse the influence of priced-based DR on the power system operation, which is linearized into a mixed integer linear programming problem. Finally, the propose method and model are verified the effectiveness through analysing the influence of self-elasticity and cross-elasticity on the power system operation in the IEEE RTS79 system.

A new heuristics model based on the Voronoi diagram is presented to simulate pedestrian dynamics with the non-crowded state, in which these mechanisms of preference demand evading and surpassing, microscopic anti-deadlock, and site-fine-tuning are considered. The preference demand describes the willingness determination of detouring or following other pedestrians. In the evading and surpassing mechanisms, in order to achieve a balance between avoiding conflicts and minimizing detour distances, a new pair of concepts: “allow-areas and denial-areas” are introduced to divide the feasible region for pedestrians detour behaviors, in which the direction and magnitude of detour velocity are determined. A microscopic anti-deadlock mechanism is inserted to avoid deadlock problem of the counter-directional pedestrian. A site-fine-tuning mechanism is introduced to describe the behavior of avoiding getting too close to the neighbors in pedestrian movement. The presented model is verified through multiple scenarios, including the uni- or bi-direction pedestrian flow in the corridor without obstacles, the uni-direction pedestrian flow in the corridor with obstacles, and the pedestrian evacuation from a room with single-exit. The simulation results show that the velocity–density relationship is consistent with empirical data. Some self-organizing phenomena, such as lanes formation and arching are observed in the simulation. When pedestrians detour an obstacle, the avoiding area before the obstacle and the unoccupied area after the obstacle can be observed. When pedestrians evacuate through a bottleneck without panic, the fan-shaped crowd can be found, which is consistent with the actual observation. It is also found that the behavior of following others in an orderly manner is more conducive to the improvement of the overall movement efficiency when the crowd moves in a limited space.

Pedestrian dynamics under adverse sight conditions is a difficult point in the field of pedestrian flow. In this paper, the simulation of pedestrian evacuation flow is carried out with blind herd mentality under adverse sight conditions. The pedestrian sight radius is introduced to describe adverse sight conditions, based on which blind following movement is adopted to describe the blind herd mentality of pedestrians in the door-invisible area. A special technology is introduced to compute the transition payoff in the dynamic parameter model to crystallize blind following movement, in which the concepts of choice vector, velocity vector, and direction similarity are introduced. Simulation results indicate that evacuation time under adverse sight conditions declines with increases in sight radius and gradually reaches a stable situation. It was found that evacuation time will rely on sight radius, initial density, and exit width. It was also observed that pedestrians blindly walk in herd-like patterns and crowd around the wall in the door-invisible area. Compared with the published model, the improved model is more effective in reducing evacuation time and dependence on sight radius. The extended model with internal layout and heterogeneous movement rules in a room is discussed as a possible future direction.

Sentiment analysis based on the aspects of products or services is designed to explore subjective information such as attitudes and opinions in user-generated reviews. Although a great many of approaches have been proposed in detecting aspects and the relevant aspect-specific sentiments, most of them detect the latent aspects with no proper classifying them or classify them employing unsupervised topic modeling without predicting the sentiment towards these aspects. This paper proposes a novel sentimentaspect analysis probabilistic modeling framework consisting of Seeding words extraction and semi-supervised topic (SST) model based on Sentence-LDA. More specifically, the proposed methodology starts by capturing seeding words from the websites inherent semi-structured information about products or services description. Then, it employs the captured seeding words to instruct the discovery of aspects and relevant sentiment of products or services simultaneously. Experimental results show that significant improvements have been achieved by the proposed method with respect to other state-ofthe-art methods.

Developing the digital economy and promoting the digital transformation of enterprises has been an important direction for the development of enterprises in various countries. This article systematically analyzes the information organization, communication network, digital platform, business application system, information security and information operation and maintenance status of the company in Jibei Electric Power Company. Based on the analysis of the development environment and advantages and disadvantages of digital transformation in Jibei Electric Power Company, the development strategy of establishing unified digital development committee, improving the digitalization ability of power transmission and distribution business, improving the digitalization capability of internal operation and building the toB terminal service platform of integrated energy services are put forward.

In this paper, a single-phase five-level rectifier with coupled inductors is studied. First, a discrete mathematical model of a single-phase five-level rectifier is created in two-phase(αβ). A traditional single vector finite control set model predictive control (FCS-MPC) algorithm is improved to overcome the problems of a varying switching frequency, the large amount of time needed for calculation, and the inaccurate setpoint of current loop tracking. Then, the objective function of the system is established, and a simple objective function is used instead of an iterative optimization of the traditional FCS-MPC algorithm. At the same time, to eliminate the delay error and to reduce harmonic distortion, deadbeat control technology is introduced. Finally, the simulation and experimental results show that the improved model predictive current control algorithm not only retains the fast response of traditional model predictive control, but also has the advantages of fixed switching frequency, small calculation time, and small current steady-state error.

The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability.

In order to overcome the propre disadvantages of FW(Fixed-Wing) and VTOL(Vertical-Taking-Off-and-Landing) UAV (Unmanned Aerial Vehicle) and extend its application, the hybrid drone is invested more in recent years by researchers and several classifications are developed on the part of dual system. In this article, an innovative hybrid UAV is raised and studied by introducing the canard configuration that is coupled with conventional delta wing as well as winglet structure. Profited by Computational Fluid Dynamics (CFD) and Response Surface Method (RSM), a multilevel optimization approach is practically presented and concerned in terms of cruise flight mode: adopted by an experienced-based distribution strategy, the total lift object is respectively assigned into the delta wing (90–95%) and canard wing(5–10%) which is applied into a two-step optimization: the first optimization problem is solved only with the parameters concerned with delta wing afterwards the second optimization is successively concluded to develop the canard configuration considering the optimized delta wing conception. Above all, the optimal conceptual design of the delta and canard wing is realized by achieving the lift goal with less drag performance in cruise mode.

Radio-Frequency Identification (RFID) nowadays has been widely used in a variety of fields such as manufacturing, transportation, and healthcare, for identification, tracking, and information collection due to the simplicity and low cost of RFID tags. Existing RFID information collection protocols focus on information collection at a single reader, which aim to minimize the collision among RFID tags and the execution time for information collection. However, none of them consider reader scheduling to deal with the collision between neighboring readers that exists in multi-reader RFID systems. In this paper, we propose two reader scheduling schemes for time-efficient information collection in multi-reader RFID systems. The two proposed scheduling schemes can coordinate multiple readers to collect information simultaneously and ensure information from all tags is successfully collected with reader-reader collision. Extensive simulation results demonstrate the efficiency of the proposed reader scheduling schemes for multi-reader information collection.

This paper reports the measurements and analysis of propagation characters for some representative aerofoil environments' wireless channels at 4.3GHz, which is used in Wireless Avionics Intra-Communications(WAIC) systems. First of all, We obtained the received power from ANSYS Savant, then calculated the total path loss. Using a least-mean-square fit to model the mean path loss. The shadow fading $X_{\sigma}$ can be obtained from deviations.

Information Centric Networking (ICN) has been regarded as an ideal architecture for the next-generation network to handle users' increasing demand for content delivery with in-network cache. While making better use of network resources and providing better delivery service, an effective access control mechanism is needed due to wide dissemination of contents. However, in the existing solutions, making cache-enabled routers or content providers authenticate users' requests causes high computation overhead and unnecessary delay. Also, straightforward utilization of advanced encryption algorithms increases the opportunities for DoS attacks. Besides, privacy protection and service accountability are rarely taken into account in this scenario. In this paper, we propose a secure, efficient, and accountable access control framework, called SEAF, for ICN, in which authentication is performed at the network edge to block unauthorized requests at the very beginning. We adopt group signature to achieve anonymous authentication, and use hash chain technique to greatly reduce the overhead when users make continuous requests for the same file. Furthermore, the content providers can affirm the service amount received from the network and extract feedback information from the signatures and hash chains. By formal security analysis and the comparison with related works, we show that SEAF achieves the expected security goals and possesses more useful features. The experimental results also demonstrate that our design is efficient for routers and content providers, and introduces only slight delay for users' content retrieval.

As one of open-source codes widely used in computational ocean acoustics, FOR3D can provide a very good estimate for underwater acoustic propagation. In this paper, we propose a performance optimization and parallelization to speed up the running of FOR3D. We utilized a variety of methods to enhance the entire performance, such as using a multi-threaded programming model to exploit the potential capability of the many-core node of high-performance computing (HPC) system, tuning compile options, using efficient tuned mathematical library and utilizing vectorization optimization instruction. In addition, we extended the application from single-frequency calculation to multi-frequency calculation successfully by using OpenMP+MPI hybrid programming techniques on the mainstream HPC platform. A detailed performance evaluation was performed and the results showed that the proposed parallelization obtained good accelerated effect of 25.77X when testing a typical three-dimensional medium-sized case on Tianhe-2 supercomputer. It also showed that the tuned parallel version has a weak-scalability. The speed of calculation of underwater sound field can be greatly improved by the strategy mentioned in this paper. The method used in this paper is not only applicable to other similar computing models in computational ocean acoustics but also a guideline of performance enhancement for scientific and engineering application running on modern many-core-computing platform., Comment: 9 pages, 8 figures, 3 tables. preprint for the International Conference on Computer Science and Application Engineering (CSAE2017). 2017.10.21-23, Shanghai, China