Search

Applications challenged by the joint problem of concept drift and class imbalance are attracting increasing research interest. This paper proposes a novel Reinforcement Online Active Learning Ensemble for Drifting Imbalanced data stream (ROALE-DI). The ensemble classifier has a long-term stable classifier and a dynamic classifier group which applies a reinforcement mechanism to increases the weight of the dynamic classifiers, which perform better on the minority class, and decreases the weight of the opposite. When the data stream is class imbalanced, the classifiers will lack the training samples of the minority class. To supply training samples, when creating a new classifier, the labeled instances buffer is used to provide instances of the minority class. Then, a hybrid labeling strategy that combines the uncertainty strategy and imbalance strategy is proposed to define whether to obtain the real label of an instance. An experimental evaluation compares the classification performance of the proposed method with semi-supervised and supervised algorithms on both real-world and synthetic data streams. The results show that the ROALE-DI achieves higher Area Under the ROC Curve (AUC) and accuracy values with even fewer real labels, and the labeling cost dynamically adjusts according to the concept drift and class imbalance ratio.

As the mobile era matures, it is increasingly competitive to market mobile apps, forcing companies to invest heavily on mobile user acquisition campaigns. This has unfortunately given birth to a new form of Internet fraud, which we refer to as ''app distribution fraud''. This new fraud involves collusion between ISPs and fraudulent app distributors where app download is hijacked/redirected. In this paper, we have the unique opportunity to cooperate with a major e-commerce company (with about 0.2 billion active users per month) to take a first peek at this problem. Through the nationwide measurement results, we find that app distribution fraud is ubiquitous yet stealthy --- about 1.55% app downloads are hijacked/redirected, affecting more than 75% of the cities we tested and causing an estimated 7.46 billion U.S. dollars financial loss per year. We follow up with additional measurements on the technical mechanism of the fraud and the scope of the fraud (i.e., what other apps are also affected). Surprisingly, we find that sometimes the original app a user intends to download can be replaced with a completely different app, rendering the user's device at risks.

The unmanned aerial vehicle (UAV) assisted communication can significantly improve the coverage and spectrum efficiency of the Internet of Things (IoT). One main feature of IoT is short packet communication (SPC), in which the data transmission uses finite block-length codewords. The transmission rate and the packet error rate will affect the effective throughput of the IoT. In this paper, we investigate the impact of SPC on the performance of UAV-enabled spectrum sharing network. Specifically, the optimization of energy efficiency (EE) in the UAV communication system is considered since the battery of the UAV is usually limited. We design the packet error rate, the sensing duration, the normalized sensing threshold and the UAV’s transmit power to maximize the EE under the constraint that the primary user is sufficiently protected. Since the system parameters are intertwined with each other, we employ a successive optimization algorithm to solve the optimization problem by dividing it into three subproblems. Then, an efficient iterative algorithm is proposed to obtain the optimal parameters. Simulation results reveal that the proposed algorithm can converge to global optimal value and has better EE performance than other benchmark schemes. And there is a fundamental tradeoff between the throughput and the EE.

An excellent routing protocol is important for wireless sensor network construction and efficient data transmission. With continuous expansion of the application scenarios and scopes of the Internet of Things, the existing wireless sensor network routing protocols are no longer suitable for the complex network structure and the huge demands of communications. Aiming at these issues of existing routing protocols, such as short network lifetime caused by high energy consumption and uneven distribution of surviving nodes, this paper proposes an energy saving clustering protocol based on adaptive Voronoi dividing, named ESCVAD (Energy Saving Clustering by Voronoi Adaptive Dividing) protocol. The innovation of ESCVAD protocol lies in the adaptive clustering algorithm based on Voronoi dividing and cluster head election optimization algorithm based on distance and energy comprehensive weighting. The advantage of proposed algorithms is effectively to balance the energy consumption between cluster head nodes and cluster member nodes. The simulation results show that, compared with the traditional routing protocols, such as LEACH (Low Energy Adaptive Clustering Hierarchy) protocol and SEP (Stable Energy Protocol), the proposed ESCVAD protocol can effectively reduce the clustering frequency and cluster head electing frequency, so as to reduce signaling interaction frequency, finally result in the energy consumption down and the network lifetime up. Among the six protocols for comparation, ESCVAD has the best network lifetime and energy efficiency.

The frequent occurrence of extreme meteorological disasters in urban areas during winter poses great impacts on the secure operation of regional energy infrastructures, leading to cascading failures and load losses in integrated energy systems (IESs). To enhance IES’s capacity to cope with extremely cold disasters, we analyze emergency response strategies in the post-disaster period and establish a top–down and bottom–up restoration model. For the top–down strategy, automatic control of the unit and network topology and human resource deployments are considered. For the bottom–up strategy, the energy consumption patterns are adjusted by the users. Post-disaster restoration greatly improves the working performance of the IES in the event of weather disasters by leveraging these two types of strategies and the complementary characteristic between heat and electrical energy. The entire post-disaster recovery process is modeled as a mixed-whole linear program, which can be solved quickly offline. It is tested on an IES with 9 electrical nodes and 32 heat nodes. The results demonstrate that the proposed model is effective and efficient.

In order to get rid of the chattering problem of sliding mode observer, this article presents an adaptive sliding mode observer (ASMO)-based sensorless control for surface-mounted permanent-magnet synchronous machines (SPMSM). In the conventional sliding mode observer, a large gain is required to ensure the sliding motion stability. However, it leads to undesired chattering and degrades the position estimation accuracy. With the proposed ASMO strategy, the observer gain can be dynamically adjusted according to the back electromotive force, and it is applicable in wide speed rang with improved estimation accuracy. Then, a dual-phase-locked loop (DPLL) is improved to extract accurate rotor position information under dynamic process in both positive and negative rotating directions of the SPMSM. Finally, the effectiveness of the proposed method has been verified by simulation and experiment on a Higale platform with a 0.2-kW SPMSM.

Accurate prediction of surrounding rock grades holds great significance to tunnel construction. This paper proposed an intelligent classification method for surrounding rock based on one-dimensional convolutional neural networks (1D CNNs). Six indicators collected in some tunnel construction sites are considered, and the degree of linear correlation between these indicators has been analyzed. The improved one-hot encoding method is put forward for transforming these non-image indicators into one-dimensional structural data and avoiding the sampling error in the indicators of surrounding rock collected in the field. We found that the 1D CNNs model based on the improved one-hot encoding method can best extract the features of surrounding rock classification indicators (in terms of both accuracy and efficiency). We applied the well-trained classification model of tunnel surrounding rock to a series of expressway tunnels in China, and the results show that our model could accurately predict the surrounding rock grade and has great application value in the construction of tunnel engineering. It provides a new research idea for the prediction of surrounding rock grades in tunnel engineering.

Due to the power interaction between the phase-shifted dual active bridges (PSDABs) and series resonant dual active bridges (SRDABs) in the hybrid modular dc Transformer (HMDCT), it is difficulty to optimize current stress based on the transferred power. An extended-state-observer based model predictive control (ESO-MPC) strategy with the extended phase shift (EPS) is proposed to optimize the current stress of the PSDABs in the HMDCT. The MPC is employed to calculate the optimal transferred power of the PSDABs. Moreover, a first-order ESO is introduced to observe the uncertain output current of the PSDABs. Based on the calculated transferred power, the optimal phase-shifted duty ratios of each PSDAB under EPS, which achieves optimal current stress, are obtained by the Lagrange multiplier method. Because of the ESO-MPC strategy, it enables to achieve the current sensorless control of the HMDCT. Simulations on a four-cell 3kV/500V HMDCT and the extensive experiments of a downscale laboratory platform, using DSP OMPL138 -EZW + FPGA XC7A75T as the core controller, validate the feasibility of the proposed control strategy.

Modern processors often need to switch among different power states based on usage scenarios, energy availability, and thermal conditions. Dynamic voltage and frequency scaling (DVFS) is a commonly used power management strategy to trade off performance and energy. As transistor scaling is reaching its limit, the viable supply voltage range where DVFS can operate is shrinking, which limits its effectiveness. To extend the performance-energy trade-off capabilities in modern processors, this article proposes dynamic core scaling (DCS) that does not rely on voltage scaling. DCS dynamically adjusts the active superscalar datapath resources so that programs run at a given percentage of their maximum speed while minimizing energy consumption at the same time. Since DCS does not need voltage scaling, it can be combined with DVFS to achieve greater energy savings. To effectively manage performance-energy trade-offs using a combination of DCS and DVFS, this article proposes an oracle controller that demonstrates the optimal control strategy, and two practical controllers that are applicable in real implementations. Evaluations using an 8-way superscalar processor implemented in a 45-nm circuit show that DCS is more effective in performance-energy trade-offs than DVFS at the high performance end for a number of SPEC CPU2000 benchmarks. When used together with DVFS, DCS saves an additional 20 percent of a full-size core’s energy on average. At the minimum operating voltage, DVFS hits its limit, while DCS is still able to achieve an average of 46 percent further energy reduction.

The power angle characteristic is not suitable for describing the power transmission character of the droop-controlled grid-connected inverter due to the small inertia and fast response of the power electronic system. Therefore, it is of great significance to study the dynamic model of the power transmission circuit and put forward the strategy to suppress the dynamic power angle oscillation. However, the existing dynamic model only considers the line impedance but ignores the influence of the closed-loop output impedance of the dual-loop control. Also, the existing virtual resistance damping strategy for suppressing dynamic power angle oscillation has the problem of increasing power coupling. This article analyzes the dynamic model of the power transmission circuit considering the influence of the closed-loop output impedance of the dual-loop control, and the dynamic power angle oscillation frequency is extended from the fundamental frequency point to the low-frequency range. Then, a novel virtual impedance damping strategy is proposed, which can suppress the dynamic power angle oscillation without affecting the steady-state performance of the system. Finally, the properties of dynamic power angle oscillation and the effectiveness of transient virtual impedance damping strategy are verified by experiments.

In human-machine interaction systems, continuous movement estimation methods occupy an important position because they are more natural and intuitive than pattern-recognition methods. Essentially, arm position is decided by the shoulder and elbow joint angles. However, the various deformations of muscles around the shoulder and elbow often lead to difficulties in sensor fixation, which results in a loss of synchronization between the surface electromyography (sEMG) signals and joint angles. In order to accurately estimate movement angles using sEMG in situations where the sEMG is not synchronized with joint angles, we utilized a bi-directional long short-term memory (Bi-LSTM) network rather than other deep learning methods to estimate non-dominant arm movements, based on the sEMG signal from the dominant arm. This estimation protocol was designed to avoid a multiplicity of sensors and to simulate more complicated loss of synchronization problems). The performance of the Bi-LSTM was compared with multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and a long short-term memory network (LSTM). The Pearson correlation coefficient (cc) between the estimated and target joint angle sequences was calculated to evaluate the performance of each neural network. The Wilcoxon signed-rank results showed that the Bi-LSTM model significantly outperformed the MLP, CNN, and LSTM models (tested with completely untrained newly recorded free movements).

Simultaneous and proportional control (SPC) methods based on surface electromyogram (sEMG) can provide a more intuitive and natural interaction in rehabilitation assistive robots and prostheses. Recently, an increasing number of researchers have utilized deep learning methods to continuously estimate movements across multiple degrees of freedom (DoFs). It stands for higher dimensional and more complex target space, which requires more comprehensive feature extraction methods. However, the existing feature extraction methods for deep learning approaches can hardly provide adequate information for multiple DoFs complex movement estimation. Therefore, we proposed a novel sEMG feature extraction method for deep learning models to more accurately predict the joint angles simultaneously on ten DoFs and tested it on eight complex movements. The experiment was carried out on four commonly adopted deep learning methods to compare the performance of the proposed feature set with others. For the four deep learning methods, the average Pearson correlation coefficient (CC) value was around 0.85 and 0.61 for the proposed feature set and raw sEMG data, respectively. Besides, the feature extraction only takes 6 μs per 100 ms analysis window. This study suggested that the proposed feature set would improve deep learning method's application prospect on SPC for rehabilitation robotic systems.

Automatic modulation recognition is a critical challenge in the field of cognitive radio. In the process of communication, radio signals are modulated in various modes and are interfered by the complex electromagnetic environment. To cope with these problems and avoid manual selection of complex expert features, we propose a multi-level feature extraction algorithm based on deep learning to adequately exploit the hidden feature information of modulated signals. Our algorithm integrates the correlation between the channels of radio signals with convolutional neural networks and Bidirectional Long Short-term Memory (Bi-LSTM), and adopts the appropriate skip connection, which avoids the loss of valid information and achieves the complementarity between spatial and temporal features. In our model, the one-dimensional convolutional layer is specially utilized to enrich the feature representation of each sample point of in-phase and quadrature (I/Q) signals and emphasize the mutual influence of I channel (in-phase signal) and Q channel (quadrature signal). In addition, the label smoothing technique is used to improve the generalization ability of the model. Our proposed method is also of certain significance for other signal processing methods based on deep learning. Experiment results demonstrate that our algorithm outperforms the popular algorithms and is of higher robustness. Specifically, the proposed method improves the recognition accuracy, reaching 92.68% at high signal-to-noise ratio (SNR). In particular, it also reduces the difficulty of recognition for multiple quadrature amplitude modulation (MQAM) signals and significantly improves the recognition accuracy for 16QAM and 64QAM.

Time series prediction refers to the learning of existing observed data of a parameter and predicting its future evolution. Based on the application of machine/deep learning methods in the field of engineering geology, it is desirable to predict the time series evolution of microseismic parameters in the process of rockburst development. Our study explores key microseismic indices that help describe the development process of rockbursts based on abundant rockburst data obtained from deep underground engineering construction. The integrated process of dynamic moving-window method and improved convolutional neural network (CNN) realizes the evolution prediction of multiple microseismic parameters or their different combinations, and the modified model structures of a univariate, multivariate input and a single-step, multi-step output are established. Various models of the multiple microseismic parameters for the CNN-based time series prediction are innovated, including a univariate prediction model, a multiple parallel series model, a multiple input series model, and a multivariate multi-step prediction model. Model training, testing, and interpretation of the rockburst risk and comparative analyses of the different models are performed for the complete process of multiple rockbursts. The results show that the proposed models can well predict the evolution trends in the various key characteristics during rockbursts. The predicted trend of multiple microseismic parameters provides time labels for rockburst prediction and risk judgement, which is conducive to rockburst early warning. This study provides a new research idea for the prediction and early warning of rockbursts in the field of deep underground and mining engineering.

Human activity recognition using WiFi signals is widespread for smart-environment sensing domain in recent years. Existing researches use learning-based methods to obtain several features of activity data and then recognize human activities. As we know, propagation characteristics of WiFi signals are different for individuals under different place conditions even in the same environment. In this paper, we focus on how to weaken the accuracy differences among individuals on activity recognition and improve the robustness in one indoor environment. Based on this, we design a novel deep learning model called LCED which consists of one LSTM-based Encoder, features image presentation, and one CNN-based Decoder to weaken the accuracy differences among individuals on activity recognition. We first use a low-pass filter to remove high-frequency noise data in time-sequence signal data and design variance-based window method to determine the start and the end of time-sequence signal data corresponding to an activity. After that, we utilize the proposed LCED model to learn informative features space of activity data and improve the accuracy of sixteen activities. Experimental results show that the average accuracy of sixteen activities is high 95 % and the accuracy differences among individuals on activity recognition averagely decreases by 3 % .

For widely used real-time applications, encoding and transmitting multiple videos jointly over a limited bandwidth has become a popular topic. Allocating different bitrates for different sources is a better way to meet different demands from applications. In this paper, we focus on providing equal quality to users by minimizing the variance of distortion among sequences, which is denoted as the minVAR problem. The state-of-the-art Look-ahead and Feed-back Allocation Model (LFAM) allocates bitrate by taking both look-ahead complexity measures and feed-back information into consideration. However, LFAM brings additional delay to real-time applications. By taking the bitrate allocation problem as a time-series decision making problem, we propose a Deep-Reinforcement-Learning-based approach to allocate bitrate with only feed-back information to solve the two-source minVAR problem. Afterward, we introduce a binary-tree-based hierarchical approach to apply our model to arbitrary number of sources. Tested with the widely used open-source x264 encoder, our approach decreases the variance compared with LFAM in all experiments under two-, three- and four-source scenarios. Furthermore, the proposed approach also outperforms LFAM in the mean quality. The proposed approach is insensitive to the order of sequences and encoders with different complexities, showing its robustness and generalization capability.

Wireless charging for electric vehicles has been investigated in recent years. One of the critical issues of the research is to improve the weak interoperability between two popular charging pads—rectangular pad (RCP) and double-D pad (DDP). This article intends to model the interoperability under pad misalignment and improve it by adjusting the pads’ relative position. First, the variation of interoperability with pad misalignment is analyzed by the flux model. Full output power and high transmission efficiency can be achieved when the two pads are misaligned to a certain distance. Second, the optimal misalignment is obtained by flux calculation and simulation. Third, an adaptive position adjustment method for the transmitting pad based on the variable-step perturbation and observation algorithm is proposed. By searching the maximum dc input current, the optimal position of the transmitting pad can be found. Finally, in the experiment, a 3.3 kW output with 95% coil-coil efficiency is realized when the RCP and DDP interoperate at the optimal position. Compared with the scenario without position adjustment, the improvement of output power and efficiency proves that the proposal can optimize the interoperability between the RCP and DDP.

We propose a novel multi-mode Gaussian modulated continuous variable measurement-device-independent quantum key distribution (MDI-CVQKD) protocol where Alice and Bob prepare independent and identically distributed Gaussian modulated coherent states in multiple independent modes respectively along with Charlie using a traditional noise homodyne detector to measure. Since it is completely handed over to an untrusted third party (Charlie) to measure, this protocol can effectively eliminate the defects of the actual detector. As well as, we also proved that the multi-mode MDI-CVQKD protocol can reduce electronic noise. The simulation results show that the multi-mode Gaussian modulated MDI-CVQKD protocol can indeed significantly improve the key rate of the original Gaussian modulated MDI-CVQKD protocol, and extend the maximum secure transmission distance of the secret key.

Humans do not have an accurate representation of probability information in the environment but distort it in a surprisingly stereotyped way (“probability distortion”), as shown in a wide range of judgment and decision-making tasks. Many theories hypothesize that humans automatically compensate for the uncertainty inherent in probability information (“representational uncertainty”) and probability distortion is a consequence of uncertainty compensation. Here we examined whether and how the representational uncertainty of probability is quantified in the human brain and its relevance to probability distortion behavior. Human subjects (13 female and 9 male) kept tracking the relative frequency of one color of dot in a sequence of dot arrays while their brain activity was recorded by MEG. We found converging evidence from both neural entrainment and time-resolved decoding analysis that a mathematically derived measure of representational uncertainty is automatically computed in the brain, despite it is not explicitly required by the task. In particular, the encodings of relative frequency and its representational uncertainty, respectively, occur at latencies of ∼300 and 400 ms. The relative strength of the brain responses to these two quantities correlates with the probability distortion behavior. The automatic and fast encoding of the representational uncertainty provides neural basis for the uncertainty compensation hypothesis of probability distortion. More generally, since representational uncertainty is closely related to confidence estimation, our findings exemplify how confidence might emerge before perceptual judgment.SIGNIFICANCE STATEMENTHuman perception of probabilities and relative frequencies can be markedly distorted, which is a potential source of disastrous decisions. But the brain is not just ignorant of probability; probability distortions are highly patterned and similar across different tasks. Recent theoretical work suggests that probability distortions arise from the brain's compensation of its own uncertainty in representing probability. Is such representational uncertainty really computed in the brain? To answer this question, we asked human subjects to track an ongoing stimulus sequence of relative frequencies and recorded their brain responses using MEG. Indeed, we found that the neural encoding of representational uncertainty accompanies that of relative frequency, although the former is not explicitly required by the task.

In the past couple of decades, graphs have been widely used to model complex relationships among various entities in real applications. Shortest path query is a fundamental problem in graphs and has been well-studied. Most existing approaches for the shortest path problem consider that there is only one kind of cost in networks. However, there always are several kinds of cost in networks and users prefer to select an optimal path under the global consideration of these kinds of cost. In this paper, we study the problem of finding the optimal path in the multi-cost networks. We prove this problem is NP-hard and the existing index techniques cannot be used to this problem. We propose a novel partition-based index with contour skyline techniques to find the optimal path. We propose a vertex-filtering algorithm to facilitate the query processing. We conduct extensive experiments on six real-life networks and the experimental results show that our method has an improvement in efficiency by an order of magnitude compared to the previous heuristic algorithms.

With the rapid development of social network, friend recommendation algorithm has become an important component of social application. Location-based social network (LBSN) enables users to record and share their locations anytime and anywhere, which is a high quality information source. In order to meet people's demand of expanding social circle and obtaining diversified spatial information when making friends, this paper proposes a potential friend recommendation algorithm based on the similarity of user's check-in behavior and spatial information acquisition level in the real world. Firstly, we employ kernel density estimation and time entropy to solve the problems of data sparsity and low concentration, then employ cosine distance to measure the check-in behavior similarity. Secondly, we analyze users’ spatial distribution of check-in location and cognitive differences on spatial information. Finally, the method mentioned above is tested with dataset called Foursquare. The results of the experiment show that the proposed method has competitive performance.

A key segment in medicinal plant authentication is the establishment of quality markers that embody the intrinsic metabolites difference independent of instruments and experiment conditions. A strategy integrating nontargeted metabolomics and multicriteria decision-making model for robust quality markers discovery is presented and applied to authenticate Ophiopogon japonicus (L. f.) Ker-Gawl. First, an ultra-performance liquid chromatography/quadrupole time-of-flight MSE approach was established for global metabolites profiling and identification. Second, multivariate statistical analysis was performed to explore potential quality markers of different origins of ophiopogonis radix. Third, potential quality markers were ordered and filtered by multicriteria decision-making model to infer robust quality markers and further validated in different instruments and experiment conditions by validation model. Fourth, the validation model using the robust quality markers managed to discriminate the origins of ophiopogonis radix samples procured from the herbal markets. Consequently, two robust quality markers, cixi-ophiopogon B and ophiopogonin D, were discovered and further validated on different instruments and experiment conditions. This integrated strategy provided a practical solution for reliable and convenient authentication of geo-authentic herb.

In rail transit application, the multimode pulsewidth modulation (PWM) is used to take full advantage of the dc-bus voltage in wide speed range. To improve the sensorless control performance of interior permanent magnet synchronous motor (IPMSM) drives in this condition, an overall system delay compensation method is proposed for a hybrid position observer that combines a simple square-wave voltage injection with a nonsingular terminal sliding mode observer. Due to the delay effect, the currents and position estimation performance are deteriorated, such as more severe $dq$ -axis currents coupling and fluctuation of estimated position error. In order to solve these problems, the analysis of delay characteristics in the IPMSM sensorless drives is first presented. For carrier-based modulation, the compensation time is obtained through a self-tuning PI regulator based on predictive $q$ -axis voltage error. Then, the three-phase reference currents are utilized to judge the direction near zero-crossing point and the parameters robustness is also analyzed. For optimal PWM modulation, the voltage vector angle and modulation depth are predicted in a simple way, and the delay effect can be eliminated. Furthermore, the harmonics distribution in sensorless control is described by power spectral density. Finally, the effectiveness of proposed strategy is verified by experimental results with a 3.7-kW IPMSM sensorless drive platform.

Wireless communication with unmanned aerial vehicle (UAV) has attracted much attention due to its flexibility and low-cost in providing wireless connectivity. In this article, we investigate the spectrum sharing between UAV and terrestrial wireless communication. Two scenarios are considered in this work. Scenario 1: the UAV is energy sufficient to transmit the required data. In this case, our objective is to minimize the time that is needed to complete data transmission. Scenario 2: the battery of the UAV is energy insufficient. In this case, the goal becomes maximizing the size of data that can be transmitted. Different scenarios require different placement of the UAV. When the UAV flies towards the primary user (PU), the sensing result will be more accurate and more opportunities can be utilized to transmit data. However, the interference to primary receiver (PR) and the propulsion energy due to UAV’s movement will be increased. In this article, the sensing performance, the power allocation and the UAV’s position are jointly optimized to satisfy different requirements of the UAV system. To efficiently utilize the licensed spectrum, a hybrid transmission mode is proposed for the UAV system, in which sensing and power control are jointly employed to protect the PR. Then, efficient iterative algorithms are proposed to solve the optimization problems. Simulations results show that proposed designs can significantly reduce the time that is required to complete data transmission in energy sufficient scenario and enhance the size of data that can be transferred in energy insufficient scenario.

Aiming at the problem of slow neighbor discovery speed of sector antenna neighbor discovery (SAND) protocol based single token, an asynchronous sector antenna neighbor discovery protocol based double tokens (D-SAND) is proposed. The double tokens include one master token and one slave token. The master token holder (MTH) starts its neighbor nodes discovery process by sending hone-in message through beam scanning, and ends this process when each neighbor node is discovered. Then, MTH passes the master token to the next hop master token holder (nMTH). At the same time, MTH sends the location information of the nMTH node to its parent master token holder (MTP) from which it obtains its token. MTP determines whether to generate a slave token according to that if there exists a neighbor node with a distance greater than 2R from nMTH, where R represents the communication distance. If there exists one such neighbor, MTP generates a slave token and passes it to that neighbor node, who can begin the neighbor discovering process. The time conditions are theoretically derived when these two tokens begin their neighbor discovering process respectively and without interference to each other. Simulation results show that compared to Q-SAND(quick-SAND), the proposed D-SAND protocol reduces the network-wide neighbor discovery time about by 10%.

Recent advances in the cyber-physical smart grid (CPSG) have enabled a broad range of new devices based on the information and communication technology (ICT). However, these ICT-enabled devices are susceptible to a growing threat of cyber-physical attacks. This paper performs a thorough review of the state-of-the-art cyber-physical security of the smart grid. By focusing on the physical layer of the CPSG, this paper provides an abstracted and unified state-space model, in which cyber-physical attack and defense models can be effectively generalized. The existing cyber-physical attacks are categorized in terms of their target components. We then discuss several operational and informational defense approaches that present the current state-of-the-art in the field, including moving target defense, watermarking, and data-driven approaches. Finally, we discuss challenges and future opportunities associated with the smart grid cyber-physical security.

This work proposes a full AC optimal power flow (ACOPF) model considering variable impedance-based flexible AC transmission system (FACTS) devices, in which the reactance of lines are introduced as decision variables to minimize system operation costs, power losses, and load shedding costs. This work is motivated by increasing interest in using FACTS and Distributed FACTS (D-FACTS) devices to address system operational and cyber-security concerns in the presence of renewable energy, such as line congestion relief, power loss reduction, load curtailment reduction, and moving target defense. The proposed ACOPF model can be utilized by system operators to achieve economic and cyber-security benefits simultaneously. In addition, we build and make publicly available an open-source MATPOWER-based interior-point solver for the proposed ACOPF model through rigorously deriving the gradient and Hessian matrices of the objective function and constraints. Numerical results on an IEEE 118-bus transmission system and an IEEE 69-bus distribution system show the validity of the proposed ACOPF model as well as the efficacy of the developed interior-point solver.

In order to improve the accuracy and efficiency of Android malware detection, an Android malware detection model based on decision tree (DT) with support vector machine (SVM) algorithm (DT-SVM) is proposed. Firstly, the original opcode, Dalvik opcode, is extracted by reversing Android software, and the eigenvector of the sample is generated by using the n-gram model. Then, a decision tree is generated via training the sample and updating decision nodes as SVM nodes from the bottom up according to the evaluation result of the test set in the decision path. The model effectively combines DT with SVM. Under the premise of maintaining a high-accuracy decision path, SVM is used to effectively reduce the overfitting problem in DT and thus improve the generalization ability, and maintain the superiority of SVM for the small sample training set. Finally, to test our approach, several simulation experiments are carried out, and the results demonstrate that the improved algorithm has better accuracy and higher speed as compared with other malware detection approaches.

In recent years, the large-scale power outages caused by extreme natural disasters have caused considerable economic losses. In this paper, an optimization decision-making method is proposed to allocate emergency power resources in a distribution system ahead of an extreme natural disaster. A resource allocation optimization model is proposed to allocate different resources among microgrids and critical loads in the distribution system. The factors including capacity, rental prices, energy reserves at each stage, and so on are all considered. A heuristic method is proposed to solve the model. By transforming the optimization model into a mixed-integer quadratic program, the resource allocation plan can be obtained efficiently. A case based on the IEEE 123-bus system is performed. The results validate the effectiveness and efficiency of our method.

In distribution networks with arc suppression coil grounding, the fault line can be set as the energy leakage channel of the capacitive current of the entire system. During the period from fault occurrence to the whole action process of arc suppression coil, there is abundant fault information in the transient waves. However, the data of this period are usually ignored in previous methods. In this study, a novel fault section location method based on the leakage energy function is proposed. Only the fault phase voltage and fault phase current are used in the proposed method, to avoid the difficulty of the zero-sequence current measurement. Then, the action characteristics of two types of arc suppression devices are analysed. The influence of the capacitor current on the fault phase voltage and current during the entire process of arc suppression coil operation is deduced. The difference between the leakage energy on both sides of the fault section is used as the criterion for fault section location. Finally, the effectiveness of the method is verified by a case study. The simulation results indicate that the proposed method has high sensitivity and high impedance adaptability, that will have more advantages in the actual situations.

The deployment of unmanned aerial vehicle (UAV)-enabled cognitive radio system in the area covered by primary network is studied in this letter. The UAV shares the spectrum with primary user (PU) and aims to maximize the transmitted bits with limited battery capacity. When the UAV flies towards the primary transmitter, the sensing performance and throughput of the UAV system will be improved. However, the energy consumption increases linearly with the flying distance. The goal of this letter is to find the optimal position of the UAV and optimize the sensing performance as well as the power allocation such that the transmitted bits is maximized while the PU is sufficiently protected. An iterative optimization algorithm is proposed to obtain the system parameters. Simulation results show the efficiency of the proposed approach in terms of transmitted bits as compared to other schemes.

Slicing and cutting processing by inner-diameter saw blade has advantages of low cost and convenient adjustment of machine tool. In small size and batch processing, inner-diameter saw blade slicing and cutting is a common-used processing method. However, the wear of inner-diameter saw blade will seriously affect surface quality of workpiece cross section. It is necessary to monitor and evaluate the wear in real time. Based on the short-time Fourier transform, this article introduces a new method for assessing the wear of inner-diameter saw blade. By measuring and analyzing time–frequency characteristics of vibration and machining noise signals, wear is monitored in real time. The results show that it is a good method to monitor the wear of inner-diameter saw blade based on vibration of machine and machining noise signals. In addition, this method has no interference and influence on cutting work of inner-diameter saw blade, which provides a new idea for other forms of tool wear detection in engineering.

Sum-product networks (SPNs) are an expressive deep probabilistic architecture with solid theoretical foundations, which allows tractable and exact inference. SPNs always act as black-box inference machine in many artificial intelligence tasks. Due to their recursive definition, SPNs can also be naturally employed as hierarchical feature extractors. Recently, SPNs have been successfully employed as autoencoder framework in representation learning. However, SPNs autoencoder ignores the model structural duality and trains the models separately and independently. In this work, we propose a Dual-SPNs autoencoder which designs two SPNs autoencoders to compose as a dual form. This approach trains the models simultaneously, and explicitly exploits the structural duality between them to enhance the training process. Experimental results on several multilabel classification problems demonstrate that Dual-SPNs autoencoder is very competitive against with state-of-the-art autoencoder architectures.

DC transformer (DCT) which is composed of dual active bridge (DAB) converters, is capable of fulfiling high-ratio voltage conversion between the medium-voltage dc distribution and low-voltage dc distribution. A model predictive control (MPC) strategy was proposed for the DAB-DCT to improve dynamic performance and achieve a proper power distribution, considering the bidirectional power of DCT. The traditional voltage closed-loop control scheme with the single-phase shift (SPS) and the MPC scheme with SPS are compared on a downscale DAB-DCT laboratory prototype, using PowerPC P2020 + FPGA_6SL X45 as the core controller. The excellent dynamic characteristics of DCT with proposed MPC strategy is verified by extensive experimental results.

Static analysis is known to yield numerous false alarms when used in bug finding, especially for complex vulnerabilities in large code bases like the Linux kernel. One important class of such complex vulnerabilities is what we call "high-order taint style vulnerability", where the taint flow from the user input to the vulnerable site crosses the boundary of a single entry function invocation (i.e., syscall). Due to the large scope and high precision requirement, few have attempted to solve the problem. In this paper, we present SUTURE, a highly precise and scalable static analysis tool capable of discovering high-order vulnerabilities in OS kernels. SUTURE employs a novel summary-based high-order taint flow construction approach to efficiently enumerate the cross-entry taint flows, while incorporating multiple innovative enhancements on analysis precision that are unseen in existing tools, resulting in a highly precise inter-procedural flow-, context-, field-, index-, and opportunistically path-sensitive static taint analysis. We apply SUTURE to discover high-order taint vulnerabilities in multiple Android kernels from mainstream vendors (e.g., Google, Samsung, Huawei), the results show that SUTURE can both confirm known high-order vulnerabilities and uncover new ones. So far, SUTURE generates 79 true positive warning groups, of which 19 have been confirmed by the vendors, including a high severity vulnerability rated by Google. SUTURE also achieves a reasonable false positive rate (51.23%) perceived by users of our tool.

Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein–protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein-protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.

Binders as a bridge in electrodes can bring various components together thus guaranteeing the integrity of electrodes and electronic contact during battery cycling. In this review, we summarize the recent progress of traditional binders and novel binders in the different electrodes of SIBs. The challenges faced by binders in terms of bond strength, wettability, thermal stability, conductivity, cost, and environment are also discussed in details. Correspondingly, the designing principle and advanced strategies of future research on SIB binders are also provided. Moreover, a general conclusion and perspective on the development of binder design for SIBs in the future are presented.

SUMMARYDenoising and onset time picking of signals are essential before extracting source information from collected seismic/microseismic data. We proposed an advanced deep dual-tasking network (DDTN) that integrates these two procedures sequentially to achieve the optimal performance. Two homo-structured encoder–decoder networks with specially designed structures and parameters are connected in series for handling the denoising and detection of microseismic signals. Based on the similarity of data types, the output of the denoising network will be imported into the detection network to obtain labels for the signal duration. The procedures of denoising and duration detection can be completed in an integrated way, where the denoised signals can improve the accuracy of onset time picking. Results show that the method has a good performance for the denoising of microseismic signals that contain various types and intensities of noise. Compared with existing methods, DDTN removes the noise with a minor waveform distortion. It is ideal for recovering the microseismic signal while maintaining a good capacity for onset time picking when the signal-to-noise ratio is low. Based on that, the second network can detect a more accurate duration of microseismic signals and thus obtain more accurate onset time. The method has great potential to be extended to the study of exploration seismology and earthquakes.

In this study, a model-free adaptive sliding mode control method was developed in combination with the prescribed performance method. On this basis, this study attempted to fulfill the joint position tracking trajectory task for the one-degree of freedom (DOF) upper-limb exoskeleton in passive robot-assisted rehabilitation. The proposed method is capable of addressing the defect of the initial error in the controller design and the application by adopting a tuning function, as compared with other prescribed performance methods. Moreover, the method developed here was not determined by the dynamic model parameters, which merely exploit the input and output data. Theoretically, the stability exhibited by the proposed controller and the tracking performance can be demonstrated. From the experimental results, the root mean square of the tracking error is equal to 1.06 degrees, and the steady-state tracking error converges to 1.91 degrees. These results can verify the expected performance of the developed control method.

In recent years, power electronic transformers (PET) that can convert electrical energy have been a research hotspot. In order to better adapt to the requirements of smart grids and high-power applications, a hybrid power electronic transformer (HPET) and its control method are proposed. Firstly, a mathematical model based on the topology of the hybrid power electronic transformer is established. Then a predictive direct power control strategy is designed according to the expected goal. Finally, simulation in MATLAB/Simulink verifies the effectiveness of the proposed control strategy.

The detection of pig growth and monitoring of abnormal behaviors are key steps in pig breeding management. Using conventional methods to obtain information on growth and abnormal behavior causes stress to pigs, directly affects the number of live pigs for market, and decreases the quality of the pork. Moreover, this approach requires considerable labor, reduces economic returns, and does not meet the requirements of high-welfare farming. Compared to the conventional methods for obtaining growth parameters and data on abnormal behaviors, modern information technology provides a new method for stress-free growth detection and behavior monitoring in farmed pigs. This article first summarizes the importance of body size, body mass, and abnormal behaviors as well as the correlations among these factors. For the research on growth detection and behavior monitoring based on computer vision, radio frequency identification (RFID) and sensor technology, methods of detecting increases in body size and body mass and methods of monitoring abnormal behaviors are summarized separately. Through computer-computer vision technology, we found that the data sampling for growth and abnormal behaviors of the pigs was achieved without contact monitoring but, rather, occurred at the expense of complex data calculation and a higher illumination requirement during data collection. However, with the development of depth camera technology and improved product performance, technology based on high-precision depth cameras reduces the amount of data processing and complexity, making it possible to obtain real-time data on pig growth and abnormal behaviors. Moreover, with the advantages of no contact and no stress, the method conforms to the requirements of welfare farming. Keywords: Abnormal behaviors, Stress-free detection, Welfare farming.

The overhead caused by broadcast schemes cannot be neglected in mobile ad hoc networks. It is known that the probabilistic broadcast scheme is an effective approach to reduce overhead, in which nodes forward packets with a probability to reduce redundant retransmissions. In hostile environments, malicious nodes degrade network efficiency, and they should be prevented from route discovery and data transmission. In this case, routing packets forwarded by malicious nodes are redundant. This paper presents a Trust-based Probabilistic Broadcast scheme (TPB) focusing on reducing overhead, especially caused by malicious nodes, in which rebroadcast probability is determined based on nodes' trustworthiness. In order to obtain nodes' trust level, a light-weight trust management model is designed based on direct and recommended trust evidence. According to nodes' trust level, rebroadcast delay is calculated for rearranging the rebroadcast order of routing packets. Furthermore, a novel method based on nodes' trustworthiness is proposed to calculate rebroadcast probability, which prevents untrusted nodes from route discovery. The proposed trust-based probabilistic broadcast scheme is incorporated with existing routing protocol and its performance is evaluated by simulations. The numerical results show that the proposed scheme can secure network communication and reduce the routing overhead.

Summary Responsive and shape-memory materials allow stimuli-driven switching between fixed states. However, their behavior remains unchanged under repeated stimuli exposure, i.e., their properties do not evolve. By contrast, biological materials allow learning in response to past experiences. Classical conditioning is an elementary form of associative learning, which inspires us to explore simplified routes even for inanimate materials to respond to new, initially neutral stimuli. Here, we demonstrate that soft actuators composed of thermoresponsive liquid crystal networks “learn” to respond to light upon a conditioning process where light is associated with heating. We apply the concept to soft microrobotics, demonstrating a locomotive system that “learns to walk” under periodic light stimulus, and gripping devices able to “recognize” irradiation colors. We anticipate that actuators that algorithmically emulate elementary aspects of associative learning and whose sensitivity to new stimuli can be conditioned depending on past experiences may provide new routes toward adaptive, autonomous soft microrobotics., Graphical Abstract, Highlights • A synthetic material that emulates algorithmically associative learning is disclosed • The present material learns to respond to an initially neutral stimulus by bending • A soft thermoresponsive strip can be conditioned to walk upon irradiation • Gripping devices can be conditioned to differentiate irradiation colors, Progress and Potential As described by Eric R. Kandel in his Nobel lecture, habituation, sensitization, and classical conditioning are the elementary forms of biological learning, which involves prohibitive complexity. Trying to capture even the slightest elements thereof in artificial materials is a grand challenge. We demonstrate soft actuators based on liquid crystal networks that show programmed associative learning behavior, inspired by the classical conditioning experiments on dogs. The thermoresponsive network evolves to respond to light upon conditioning, whereby the initially neutral light stimulus is applied together with heating. The conditioned actuator enables soft robotic devices that “learn” to translocate, or grippers that can be conditioned to differentiate irradiation colors. A modular artificial dog is also demonstrated that salivates upon a neutral stimulus. These findings suggest generalization from association of stimuli to soft systems that evolve depending on their history., Aiming toward bioinspired materials whose responsivity evolves depending on their history, we disclose programmable liquid crystal polymer networks that “learn” to respond to an initially neutral stimulus (light) after association with an intrinsically effective stimulus (heating). The concept is inspired by the Pavlovian conditioning and enables soft robots that learn to walk, grippers that recognize different irradiation colors, and an artificial Pavlov's dog. This is a step toward actuators that algorithmically mimic elementary aspects of learning.

In this paper, we investigate the spectrum access for energy harvesting cognitive radio sensor network (EH-CRSN), where sensor nodes (SNs) connected to different sinks share several channels. To maximize the sum of system energy efficiency (EE) and spectrum efficiency (SE), different from traditional works modeling the interference relationship by binary graph and unweighted hypergraph, we apply a novel weighted directed hypergraph (WDH) to accurately characterize the degree of interference among neighbor SNs and formulate the channel access problem in multi-sink network as a WDH game. We analyze the existence of Nash equilibrium (NE) and design a spectrum access algorithm to achieve the optimal solution which is one of NE. Moreover, simulation results are presented to verify the effectiveness of the proposed algorithm.