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Social media has empowered us to gain immense knowledge, criticism, and positivity from every corner of the world. Accessing social network sites like Twitter and Facebook has slowly become the norm, as people are now inclined towards looking for news and opinions in social media rather than reading traditional newspapers or watching TV news. There are many instances when a piece of news in these sites, irrespective of its reliability, has a significant impact on people's opinions towards it. In this work, we investigated some queried tweets of around 39 weeks of a sensational tragic issue of “Central American migrant caravans” and how the tweets played their critical roles in the overall sentiment on the twittersphere. We used Vader Sentiment Analysis to analyze the sentiment of each tweet before estimating the sentiment on Twitter toward this news. We show that such simple estimates can be very accurate.

As one of the computing paradigms in the process of traditional cloud computing turning to marginalization, edge computing is designed to meet the requirements of edge devices such as real-time response, security, privacy and so on. However, resources in edge computing are generally heterogeneous and dynamic, resulting in lack of trust among devices or a completely untrusted computing resources. Thus, novel and effective methods to evaluate the trust of edge devices in order to increasing the adoption of edge computing resources, have become an urgent issue. In this context the paper proposes a multi-property method to evaluate trust of edge computing, we establish an objective expression of trust property by considering which factors affect trust evaluation in resource request or service application process from the perspective of dynamic change, then the data-driven capsule network is utilized to analyze the correlation between trust properties and gives an objective prediction of trustworthy. The main contribution of this paper is the design of an effective trust evaluation method, which gives the objective reliability of edge devices also coordinates all levels to provide users with trustworthy application services.

Named Data Network (NDN) is an alternative to host-centric networking exemplified by today's Internet. One key feature of NDN is in-network caching that reduces access delay and query overhead by caching popular contents at the source as well as at a few other nodes. Unfortunately, in-network caching suffers various privacy risks by different attacks, one of which is termed timing attack. This is an attack to infer whether a consumer has recently requested certain contents based on the time difference between the delivery time of those contents that are currently cached and those that are not cached. In order to prevent the privacy leakage and resist such kind of attacks, we propose a detection scheme by adopting Long Short-term Memory (LSTM) model. Based on the four input features of LSTM, cache hit ratio, average request interval, request frequency, and types of requested contents, we timely capture more important eigenvalues by dividing a constant time window size into a few small slices in order to detect timing attacks accurately. We have performed extensive simulations to compare our scheme with several other state-of-the-art schemes in classification accuracy, detection ratio, false alarm ratio, and F-measure. It has been shown that our scheme possesses a better performance in all cases studied.

Online reviews are critical in many aspects, for business as well as customers. Yet the accuracy and trustworthiness of these reviews are usually unsubstantiated and little research has been performed to investigate them. In this work, we use a set of Yelp reviews on various topics (food, hotel, etc.) as an example to perform sentiment analysis and investigate the correlation between review comment sentiment and its numeric rating. We use feature selection techniques to statistically remove redundant words from reviews, thus improving run time and accuracy. Our method gives higher weight to those terms/words appearing in reviews with more useful votes. These techniques combined with Naive Bayes approach achieves an overall accuracy of 75%. More interestingly, our method is shown to perform well in 1-star and 5-star reviews, with a 92% accuracy for the latter. With such a strong accuracy, we argue that the proposed sentiment analysis technique can be used to shed light on all online comments, especially those without numerical ratings.

Attention-based deep learning model as a human-centered smart technology has become the state-of-the-art method in addressing relation extraction, while implementing natural language processing. How to effectively improve the computational performance of that model has always been a research focus in both academic and industrial communities. Generally, the structures of model would greatly affect the final results of relation extraction. In this article, a deep learning model with a novel structure is proposed. In our model, after incorporating the highway network into a bidirectional gated recurrent unit, the attention mechanism is additionally utilized in an effort to assign weights of key issues in the network structure. Here, the introduction of highway network could enable the proposed model to capture much more semantic information. Experiments on a popular benchmark data set are conducted, and the results demonstrate that the proposed model outperforms some existing relation extraction methods. Furthermore, the performance of our method is also tested in the analysis of geological data, where the relation extraction in Chinese geological field is addressed and a satisfactory display result is achieved.

To actively assist human walking and balance recovery, a unified active assistance control framework of the hip exoskeleton is proposed in this paper. At the beginning of this paper, the condition of active assistance is analyzed. And then, a novel virtual stiffness model is proposed based on the analysis of human hip joint torque during walking and balance recovery. The virtual stiffness model is utilized to generate the desired active assistance torque profile for active walking and balance assistance. Next, the unified active assistance control framework is established based on the virtual stiffness model. Finally, the effectiveness of the proposed control framework is demonstrated by walking experiments. The results of the forward walking experiment show that the muscle effort of iliopsoas is reduced and the amplitude of hip joint motion is enlarged with the assistance of hip exoskeleton. The walking forward pull experiments and the walking backward pull experiments show that when the human suffers from a forward or backward disturbance force during forward walking, exoskeleton can help human regain balance faster and can enlarge the margin of stability (MoS) of the human.

Widely used commercial Android applications (apps) turn to be of complex GUIs and hundreds of activities. Testing this kind of apps is challenging. Existing automated testing tools cannot complete the testing of these complex apps in a short time. However, scaling these tools for parallelism to accelerate the testing is not straightforward. In this paper, we borrow the basic concepts from parallel computing, and introduce the parallel testing platform ParaAim. ParaAim partitions an app testing job into a set of tasks at the activity granularity. Starting from a specific entrance activity, ParaAim explores the UI-states of the app and each newly discovered activity spawns a new task. ParaAim dispatches the new task to an idle device and sets it up to the entrance by replaying an event sequence. In this manner, the independent parts of the app are explored simultaneously. We focus on the efficiency of this parallel GUI exploration schema. ParaAim assigns the tasks that have more possibility to find new activities with high priority for better performance. Event sequence minimization is also studied to reduce the time cost of replay, and we design widget fuzzy match technique to handle the state inconsistency issue. We evaluated ParaAim with 20 popular commercial apps on different settings of Android device cluster. The results show that ParaAim scales well and evidently increases the average speed of exploration to nearly 2 times with two devices, and 3 times with four devices, than a single device.

Wireless Rechargeable Sensor Networks (WRSNs) with perpetual network lifetime have been used in many Internet of Things (IoT) applications, like smart city and precision agriculture. Rechargeable sensors together with Unmanned Aerial Vehicles (UAVs) are collaboratively employed for fulfilling periodic coverage tasks. However, traditional coverage solutions are normally based on static deployment of sensors and not suitable for such coverage requirements. In this paper, we propose a new concept of coverage problem named Period-Area Coverage (PAC) which requires data of the overall area must be collected periodically. We focus on maximizing the energy efficiency of UAVs and propose two heuristic scheduling schemes to balance energy cost. Moreover, we adopt adjustable sensing range to further promote efficiency and develop a charging re-allocation mechanism for UAVs. Test-bed experiments and extensive simulations demonstrate that the proposed schemes can enhance energy efficiency by 18.2% compared to prior arts.

In this paper, a compact on chip lowpass filter with wide stopband and sharp roll-off using multi-stage pi-section low-pass cell circuit is presented. In order to obtain the characteristics of wide stopband and sharp roll-off for lowpass filters (LPFs), the pi-section low-pass cell circuit is analyzed and adopted, which is useful and convenient to produce transmission zeros for LPFs. Finally, a third-order LPF and a fourth-order LPF are manufactured and the measurement results show that the filter it has the −3 dB cut-off frequency at 5 GHz, while its insertion loss is less than 1 dB. In addition, the LPFs achieves more than 35 dB sideband suppression format form 6.3 GHz to 40 GHz. The LPFs size are very compact less than $0.41\ \text{mm}\times 1.1\ \text{mm} (0.02\lambda g\times 0.05\lambda g)$ including the pads. ( $\lambda g$ is the guided wavelength at 5 GHz).

The discovery of Wireless Power Transfer (WPT) technologies makes charging more convenient and reliable. Among all the existing WPT technologies, directional WPT is more efficient and has been successfully applied to supply energy for wireless rechargeable sensor networks (WRSNs). However, the state-of-the-art methods ignore the anisotropic energy receiving property of rechargeable sensors, resulting in energy wastage. In order to address this issue, in this paper, we point out that the received energy of a sensor is not only relative to the distance, but also relative to the angle between the sensor and the charger’s orientation in directional WPT. Towards this end, we derive a pragmatic energy transfer model verified by experiments. In particular, we focus on a Minimal chArging Delay (MAD) problem to reduce chArging delays. To obtain the optimal solution, we formulate the problem as a linear programming problem. Moreover, we introduce a method of charging power discretization, which significantly reduces the search space and bounds the performance gap to the optimal one with a $\displaystyle \frac{1}{1-\epsilon^{2}}$ approximation ratio. Besides, a merging method is introduced for a more practical application scenario. Finally, we demonstrate that our methods outperform the Set Cover baseline method by an average of 34.2% through simulations and experiments.

According to the jamming principle of SAR (Synthetic Aperture Radar), three different jamming techniques are analyzed. The principle of coherent deception jamming is emphatically discussed. Taking a typical airborne SAR system as an example, the imaging process of SAR and the effect of coherent deception jamming are simulated. The results show that the effect of deception jamming is not only related to the jamming power, but also to the backscattering characteristics of the protected area. Therefore, in order to achieve the same jamming effect, the required deceptive jamming power is different for the regions with different backscattering characteristics.

Ensuring the quality of mobile applications (apps) needs to explore the GUI thoroughly. In practice, exhaustively exploring every GUI widget is unscalable on large real-world apps since it usually suffers from the problem of widgets explosion. To mitigate the problem, many existing testing tools usually detect and group homogeneous widgets heuristicly with different level of model abstraction since these widgets behave the same. However, no heuristic always works well. Heterogeneous widgets with divergent behaviors can be mistakenly grouped, which largely limits the testing effectiveness. This paper proposes a technique to effective GUI testing of Android apps with dynamic feedback-directed widgets grouping. Initially, we group the widgets according to the structure of the GUI. During testing, we observe behaviors of widgets in a group and regroup improperly-grouped widgets dynamically. Then, we apply a feedback-directed strategy to effectively accelerate the GUI exploration. The proposed technique is implemented as a practical tool for Android apps, named WGDroid. We evaluated WGDroid on 17 widely-used Android apps and compared it with the state-of-the-art GUI testing tools, i.e., AimDroid, SAPIENZ, and Monkey on both emulators and real devices. WGDroid outperformed the three tools in all testing coverages and also detected the most unique crashes. In particular, WGDroid discovered 208 more activities on 12 large benchmark apps on real devices and 11 more activities on another 5 benchmark apps on emulators, than the best of the other tools. These results show that WGDroid can significantly accelerate the GUI exploration.

Information propagation in online social networks has drawn a lot of attention from researchers in different fields. While prior works have studied the impact and speed of different information propagation in various networks, we focus on the potential interactions of two hypothetically opposite pieces of information, negative and positive. We experiment the amount of time that is allowed for the positive information to be distributed with wide enough impact after the negative information and different selection strategies for positive source nodes. Our results enable the selection of a set of users based on a limited operating budget to start the spread of positive information as a measure to counteract the spread of negative information. Among different methods, we identify that both eigenvector and betweenness centrality are effective selection metrics. Furthermore, we quantitatively demonstrate that choosing a larger set of nodes for the spread of positive information allows for a wider window of time to respond in order to limit the propagation of negative information to a certain threshold.

Motivated by the proliferation of real-time applications in multimedia communication systems, tactile Internet, networked controlled systems, and cyber-physical systems, supporting delay-constrained traffic become critical for the communication system. In delay-constrained traffic, each packet has a hard deadline and if it cannot be delivered before its deadline, it becomes useless and will be removed from the system. In this work, we consider a slotted ALOHA system where multiple stations need to deliver delay- constrained traffic to a common receiver by accessing a shared channel. We prove that, under the frame- synchronized traffic pattern, the maximum system timely throughput converges to $1/e=36.8\%$ as the number of stations goes to infinity, which is the same as the asymptotic maximum system throughput for delay- unconstrained slotted ALOHA system with saturate traffic. While this is not completely surprising, we further investigate the speed of such a maximum system throughput approaching $1/e$ under borderline traffic.

With the rise of wireless power transfer technology, charging scheduling issue is prevalent in wireless rechargeable sensor networks (WRSNs). Most prior arts focused on two-dimensional (2-D) networks with homogeneous mobile chargers. However, three-dimensional (3-D) networks with collaborations among heterogeneous mobile chargers are more practical. In this paper, we consider 3-D networks in which wireless charging vehicles (WCVs) are employed with unmanned aerial vehicles (UAVs). To prolong network lifetime, we focus on device sleep time and energy usage and propose a 3-D Dynamic Collaborative Scheduling scheme (3DCS). Theoretical values of energy threshold and partition number are determined to assign charging tasks to chargers. Then, scheduling algorithms that include target selection, infeasibility test, and target update, are developed. In addition, a collaborative algorithm is developed to re-assign charging tasks from busy chargers toward their neighboring chargers to further improve charging efficiency. Test-bed experiments and extensive simulations reveal that, compared with several distinguished scheduling schemes, our scheme has a superior performance in charging throughput, energy efficiency, and other characteristics.

The rise of wireless rechargeable sensor networks calls for an analytical study of planned charging trips of wireless charging vehicles (WCVs). Often times, the WCV receives a number of charging requests and form a Hamiltonian cycle and visit these nodes one-by-one. Therefore, it is important to learn the statistics of such cycles. In this work, we use a heuristic algorithm, which we term HQuad, that takes O(N) to generate a Hamiltonian cycle in a 2-D network plane before we analyze its statistics. HQuad is based on a recursive approximation of dividing the region into four quadrants and the non-empty quadrants will be visited one-by-one. Our analysis is based on Poisson point distribution of nodes and models such Hamiltonian cycles surprisingly well in both expected values and the distribution functions of lengths as a function of different network parameters. Numerical results of our analysis model are compared with simulations and demonstrated to be accurate.

Recently, wireless power transfer is emerging as an enabling technology of wireless rechargeable sensor networks. Conventional methods that charge each sensor until its battery is full take unproportionally long time to finish, due to the limitation of charging efficiency and power transfer technologies. In this paper, we propose a mixed partial and full charge (MPF) scheme, including three specialized modules, i.e., evaluation module, adjustment module, and selection module. MPF allows nodes to be replenished "partially" by a mobile charging vehicle (MCV). When executing adjustment module, a concept of power and path adjustment window is proposed for determining a proper power allocation scheme as well as charging path. Then a scheduling strategy termed return mechanism is designed to further utilize the energy of the MCV and improve effective energy utilization. Finally, we build a high-accuracy charging test-bed and evaluate the applicability as well as performance of the proposed scheme. For large-scale networks, we also perform simulations to demonstrate the effectiveness of MPF in promoting survival rate and reducing traveling distance of the MCV.

Benefited from recent breakthrough in wireless power transfer technology, the lifetime of wireless sensor networks (WSNs) can be prolonged significantly, generating the concept of wireless rechargeable sensor networks (WRSNs). While most recent works have been focusing on WRSNs with a single wireless charging vehicle (WCV), we investigate the issue of multiple WCVs' on-line collaborative charging schedules in this work. In our design, termed mTS, the network area is divided into subdomains for designated WCVs. Each WCV schedules its charging scheduling path by responding to the interdependency of temporal and spatial correlations from different charging requests. Higher priorities are given to sensor requests with a mixture of closer charging deadlines and closer distances. We further analyze the system performance with an M/M/n/mTS queueing model. Our further study through simulations revealed that our scheme excels in successful charging rate, sensor survival rate, and other related performance metrics. Our field experiments further confirmed these results and showed some further interesting findings on different charging hardware and methods.

Microturbines (MTs) are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes, which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of MTs. Considering the time-scale difference of various dynamic processes occurring within MTs, the electromechanical subsystem is treated as a fast quasi-linear process, while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 MT show that the proposed modeling method can well capture the system dynamics, and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Twitter is a popular micro-blogging social media platform. For the 2016 US Presidential election, many people expressed their likes or dislikes for a particular presidential candidate. Our work's aim was to calculate the sentiment expressed by these tweets, and then compare this sentiment with polling data to see how much correlation they share. We used a lexicon and Naive Bayes Machine Learning Algorithm to calculate the sentiment of political tweets collected one-hundred days before the election. We used manually labeled tweets as well as automatically labeled tweets based on hashtag content/topic. Our results suggest that Twitter is becoming a more reliable platform in comparison to previous work. By focusing on tweets 43 days before the election (beginning with the first presidential debate), we found a correlation as high as 94% to polling data using a moving average smoothing technique.

This paper proposes a novel control scheme for hybrid energy storage system(HESS) in electric vehicles. The HESS above is composed of two parts: fuel cell (FC) and super capacitor(SC). FC, as the main power supply, provides a large proportion of required power. Whereas, SC, as the auxiliary energy storage source, plays a great role when starting and braking an electric vehicle(EV). Both energy sources are connected to the DC bus with DC-DC converters. Two converters share the very DC bus which is connected to the traction motor with a power inverter. To make sure the performances of the control system, a control method named generic model control is applied. Simulation results indicate the robustness and static performance of the proposed control scheme.

In this paper, an adaptive nonlinear control method based on Lyapunov function is proposed for fuel cell/supercapacitor hybrid energy storage system (HESS) used in electric vehicles. The fuel cell (FC) acts as the main energy storage device, which is connected with a boost converter. And the supercapacitor (SC) acts as the auxiliary energy system, which is connected with a bi-directional DC-DC converter. Based on the two energy storage devices and connected converters, the integral system model is obtained. In the design process of the control scheme, the adaptive controller is utilized to solve the problems of external disturbance and uncertain parts in the system model. The Lyapunov based stability analysis is given to prove the convergence of the proposed control method. And the final simulation results show the effectiveness of the proposed method.

A new synthesis scheme is proposed to effectively generate a random vector with prescribed joint density that induces a (latent) Gaussian tree structure. The quality of synthesis is measured by total variation distance between the synthesized and desired statistics. The proposed layered and successive encoding scheme relies on the learned structure of tree to use minimal number of common random variables to synthesize the desired density. We characterize the achievable rate region for the rate tuples of multi-layer latent Gaussian tree, through which the number of bits needed to simulate such Gaussian joint density are determined. The random sources used in our algorithm are the latent variables at the top layer of tree, the additive independent Gaussian noises, and the Bernoulli sign inputs that capture the ambiguity of correlation signs between the variables.

Activities are the fundamental components of Android applications (apps). However, existing approaches to automated testing for Android apps cannot effectively manage the transitions between activities, e.g., too rarely or too often. Besides, some techniques need to repeatedly restart from scratch and revisit every intermediate activity to reach a specific one, which leads to unnecessarily long transitions and wasted time. To address these problems, we propose AimDroid, a practical model-based approach to automated testing for Android apps that aims to manage the exploration of activities and meantime minimize unnecessary transitions between them. Specifically, AimDroid applies an activity-insulated multi-level strategy during testing and replaying. It systematically discovers unexplored activities and then intensively exploits every discovered individual with a reinforcement learning guided random algorithm. We conduct comprehensive experiments on 50 popular closed-source commercial apps that in total have billions of daily usages in China. The results demonstrate that AimDroid outperforms both Sapienz and Monkey in activity, method and instruction coverage, respectively. In addition, AimDroid also reports more crashes than the other two.

Foot placement plays an important role in human stable walking. Utilizing the linear inverted pendulum model and the capture point theory, the laws of foot placement and two stable walking conditions are derived in this paper. These stable walking conditions are demonstrated by the optical motion capture experiment. The experiment not only shows that the foot placement during walking is within a capture region and locates on an equal velocity point, but also demonstrates the foot placement of human during stable walking can be predicted by the stable condition derived in this paper.

Secret key establishment from common randomness has been traditionally investigated under cartain limiting assumptions, of which the most ubiquitous appears to be that the information available to all parties comes in the form of independent and identically distributed (i.i.d.) samples of some correlated random variables. Unfortunately, models employing the i.i.d assumption are often not accurate representations of real scenarios. A more capable model would represent the available information as correlated hidden Markov models (HMMs), based on the same underlying Markov chain. Such a model accurately reflects the scenario where all parties have access to imperfect observations of the same source random process, exhibiting a certain time dependency. In this paper, we derive a computationally-efficient asymptotic converse bound for the secret key capacity of the correlated-HMM scenario. The main obstacle, not only for our model, but also for other non-i.i.d cases, is the computational complexity. We address this by converting the initial bound to a product of Markov random matrices, and using recent results regarding its convergence to a Lyapunov exponent. The methods developed in the paper are easily extensible to derive a secret-key capacity lower bound.

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-of-charge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is first built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviors are captured, thus offering a comprehensive description of the battery thermal and electrical behavior. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a metaheuristic algorithm and the least-square approach. Further, time-varying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO $_4$ batteries confirm the efficacy of the proposed method.

Battery charging strategy is a key issue in battery management system to ensure good battery performance and safe operation during the charging process. In this paper, a novel battery optimal charging strategy is proposed by applying the TLBO algorithm to a LiFeP04 battery for an optimal charging based on a coupled thermoelectric model. A specific dual-objective function including battery charging time and temperature rise (both battery interior and surface) is formulated first. Then a battery optimal charging strategy is presented in detail by using the TLBO algorithm, aiming at finding a suitable constant-current-constant-voltage (CCCV) current profile to minimize the dual-objective function. Besides, the effects of different weights in dual-objective function on the optimal charging profile are analyzed. Simulation results demonstrate that the presented optimal charging strategy can provide effective and acceptable optimal charge current profile. The strategy can be also easily implemented to other battery types to effectively balance the battery charging time and battery temperature rise during charging process.\ud

Yangtze River is the world's busiest inland waterway. Ships need to be guided when passing through controlled waterways based on their trajectory predictions. Inaccurate predicted trajectories lead to non-optimal traffic signalling which may cause significant traffic jam. For the existing intelligent traffic signalling systems (ITSSs), ships are supposed to travel exactly along the mid-line of the Yangtze River, which has caused many problems and issues. Over the last few years, traffic data have been growing exponentially, and we have gone into big data ear. This trend allows us to predict the ship travel trajectories using big historical data. In this paper, the historical trajectories are grouped by the popular K-Means clustering algorithm firstly, and then Artificial Neural Network (ANN) models are built using the above clustering results and other known factors (i.e. ship speed, loading capacity, self-weight, maximum power and water level) to predict the ships' trajectories. The experimental results show that the developed model is in good agreement with the actual data with more than 70% accuracy. It will also help to generate the optimal traffic commands for Yangtze River traffic control.

In this paper, two kinds of control system architectures for the microgrid are presented. The proposed architectures are based on the communication networks and the network-induced delays are depicted by the inverse Gaussian distribution function. The control strategies are designed respectively according to the corresponding architecture. In every strategy the mechanism of event-triggered is used to deal with the different disturbances such as disconnection of main grid and connection of new load. Simulations are carried out to verify the effectiveness of the proposed control approach under different operating conditions.

Accurate battery internal temperature estimation is a key for safe battery operation of electric vehicles. In this paper, a novel hybrid data-driven method combining a linear neural network (NN) model and an extended Kalman filter (EKF) is developed to estimate the internal temperature of a LiFePo4 battery. In order to select the proper input terms of the linear NN model and estimate the associated parameters, a fast recursive algorithm (FRA) is firstly used. Then an EKF with a battery lumped thermal model as the state function is used to filter out the outliers and reduce the errors in estimating the internal temperature based on the linear NN model. The test results from two different experiment data demonstrate that the hybrid method can achieve good estimation accuracy, and the method can be easily applied to other type of batteries.

The automotive industry is continuously developing technologies and strategies for increasing the efficiency in fuel consumption and reducing the emission of pollutants. The variable valve timing (VVT) system provides such a solution for internal combustion engines. Researches in this area are mainly devoted both to improved layouts and to new operation control techniques. To this aim, modeling, identification and validation are important preliminary steps. This paper focuses on the modelling of an electro-actuated solenoid, fast-acting valve, which is a key component of the VVT system. The valve is used to regulate the pressure that commands the lift of engine intake valves. An identification method is applied to represent the valve dynamics in a complete VVT model. The results are compared to the output from an analytical model and to real data available in certain operating conditions.

Quantized kernel least mean square (QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. With the purpose of compressing the input to further improve learning performance, this paper proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the data set are compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. We conduct the intelligent data analysis experiments to test our proposed EQ-KLMS, and the results demonstrate the desirable performance of our scheme.

Kernel least-mean mixed-norm (KLMMN) algorithm as a special kernel adaptive filter method achieves good performance when the measured noises are distributed with a linear combination of long-tails and short-tails. In order to reduce the computational efforts and improve the accuracy, this paper proposes a novel entropy optimized kernel learning algorithm, called E-KLMMN, on the basis of information entropy and KLMMN. The first step of E-KLMMN algorithm is to calculate the entropy weights of input vectors in the training set which contains the linear combination of long-tailed and short-tailed distribution noises. Then we remove the input vectors and their corresponding outputs whose entropy weights are less than the average value. Finally, using the modified training set to train KLMMN model, the following data points thus could be predicted. Through the use of information entropy, the proposed algorithm E-KLMMN has the advantages of high precision and low cost, while employing it to noise environment. We use the actual data to conduct the experiment, and the comparisons among E-KLMMN, KLMS, and KLMMN demonstrate the effectiveness and superiority of our algorithm.

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Although various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministicprobabilistic method where a temporally local moving window technique is used in Gaussian process (GP) to examine estimated forecasting errors. This temporally local GP employs less measurement data with faster and better predictions of wind power from two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while it is more likely to generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

In speech processing, speech signal is usually processed frame by frame due to the non-stationary characteristic of speech. In this paper, a frequency-domain averaging based frame smoothing method is proposed. Besides the conventional frame shift, we introduce a short time shift to create several frames around current frame. Then we take the average of power spectrum for these frames. The average will be treated as a new frame instead of current frame. The new frame is considered to retain more integrated phonetic information than conventional frames. An experiment on speaker verification task showed that this method could improve the performance of speaker verification. The evaluation tasks performed on the NIST SRE 2008 database showed that our proposed method could achieve a better verification performance when compared with the conventional framing methods.

Wireless power transfer technique provides new alternatives for solving the limited power capacity problem for so many popular mobile wireless devices, and makes wireless rechargeable sensor networks (WRSNs) promising. However, mainly due to the underestimate of the unbalanced influences of spatial and temporal constraints posed by charging requests, traditional scheduling strategies for on-demand WRSNs architecture achieve rather low charging request throughput or successful rate, posing as a major bottleneck for further improvement. In this paper, we propose a Double Warning Thresholds with Double Preemption (DWDP) charging scheme, in which double warning thresholds are used when residual energy levels of sensor nodes fall below certain thresholds. By introducing specific comparison rules, warning thresholds can be used to adjust charging priorities of different sensors, warn the upcoming recharge deadlines, as well as support preemptive scheduling. We perform extensive simulations to manifest the advantages of DWDP. Simulation results reveal that DWDP can achieve better scheduling performance, in guaranteeing the scheduling success of the high-priority task and improving stability of the system.

Extensive works have been undertaken to develop efficient statistical inference algorithms based on graphical models. However, there still lacks sufficient understanding about how topological properties affect certain information related metrics for certain graphs. In this paper, we are particularly interested in finding out how topological properties of rooted polytrees for Gaussian random variables determine its security robustness, which is measured by our proposed max-min information (MaMI) metric. MaMI is defined as the maximin value of the conditional mutual information between any two random variables (nodes) in a given DAG, conditioned on the value of a third random variable, which is at full disposal of an eavesdropper, under a constraint of a given fixed joint entropy. We show some general topological properties which the desired max-min solutions satisfy. Under such properties, we prove the superior max-min feature of the linear topology for a simple but non-trivial case. The results not only help us understand the security strength of different rooted polytree type DAGs, which is critical when we evaluate the information leakage issues for various jointly Gaussian distributed measurements in networks, but also provide us another algebraic and analysis perspective in grasping some fundamental properties of such DAGs.

Due to the huge popularity of online social networks, many researchers focus on adding links, e.g., link prediction to help friend recommendation. So far, no research has been performed on link cuts. However, the spread of malware and misinformation can cause havoc and hence it is interesting to see how to cut links such that malware and misinformation will not run rampant. In fact, many online social networks can be modeled as undirected graphs. In this paper, we investigate different strategies to cut links among different users in undirected graphs so that the speed of virus and misinformation spread can be slowed down the most or even cut off. Two measures are chosen to evaluate the performance of these strategies: Average Inverse of Shortest Path Length (AIPL) and Rumor Saturation Rate (RSR). AIPL measures the communication efficiency of the whole graph while RSR checks the percentage of users receiving information within a certain time interval.