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Practical Byzantine Fault Tolerance (PBFT) consensus mechanism shows a great potential to break the performance bottleneck of the Proof-of-Work (PoW)-based blockchain systems, which typically support only dozens of transactions per second and require minutes to hours for transaction confirmation. However, due to frequent inter-node communications, PBFT mechanism has a poor node scalability and thus it is typically adopted in small networks. To enable PBFT in large systems such as massive Internet of Things (IoT) ecosystems and blockchain, in this article, a scalable multi-layer PBFT-based consensus mechanism is proposed by hierarchically grouping nodes into different layers and limiting the communication within the group. We first propose an optimal double-layer PBFT and show that the communication complexity is significantly reduced. Specifically, we prove that when the nodes are evenly distributed within the sub-groups in the second layer, the communication complexity is minimized. The security threshold is analyzed based on faulty probability determined (FPD) and faulty number determined (FND) models, respectively. We also provide a practical protocol for the proposed double-layer PBFT system. Finally, the results are extended to arbitrary-layer PBFT systems with communication complexity and security analysis. Simulation results verify the effectiveness of the analytical results.

With the development of iris recognition technology, sensors of iris images acquisition are being constantly developed and updated. Re-register users every time a new sensor is deployed is time-consuming and complicated, especially in applications with large-scale registered users. Therefore, it is a challenging problem to choose the common recognition model which is effective for multi-source heterogeneous iris recognition(MSH-IR). The paper proposes a efficient neural network model of stacked Convolutional Deep Belief Networks-Deep Belief Network (CDBNs-DBN) for MSH-IR. The main improvements are two parts: firstly, this model uses the region-by-region extraction method and positions the convolution kernel through the offset of the hidden layer to locate the effective local texture feature structure. Secondly, the model uses DBN as a classifier in order to reduce the reconstruction error through the negative feedback mechanism of the auto-encoder. Experimental results have been implemented on publicly available IIT Delhi iris database, which is captured by three different iris captured sensors. Experiments shows the model performs strong robustness performance and recognition ability.

Taking the perspective of cybernetics theory, enterprise patent management can be described as a controllable information system which includes various activities and performances. This paper aims to analysis the relationship between enterprise patent activities and patent performance, and to help convert technological innovation achievements into patent rights more effectively and consolidate technology superiority. This paper uses System Dynamics method to construct a multiscale nonlinear enterprise patent management model. Validity of this model is tested, simulation is conducted in VENSIM, sensitivity analysis of this model shows that: Technology Disclosure Sufficiency does not have much influence on patent application quantity, but it has strongly positive effect on patent grant rate and patent portfolio diversity, it can also lower the risk of patent circumvention. Simulation shows that technology disclosure sufficiency is a key determinant to reduce information loss and improve output efficiency of enterprise patent management system. Collaboration of R&D personnel, patent engineers and patent agencies should be enhanced to improve TDS, so as to promote enterprise patent management efficiency. This SD model provides a foundation for future research in patent management optimization.

Convolutional neural networks (CNN) have achieved unprecedented results in the fields of pattern recognition and image processing. CNN methods have also been gradually applied to iris recognition. However, in the case of insufficient training samples, training deep CNN models is prone to overfitting. In order to solve the above problem, this paper proposes an iris recognition method based on fine-tune SquIrisNet. When the pre-trained SqueezNet is migrated to the iris dataset, the fully connected layers are used instead of the convolutional layer and the global pooling layer, then the parameters are adjusted using the error back propagation algorithm, and finally the images are classified by the Softmax classifier. The fully connected layers added in this model can play the role of “firewall” in the fine-tune process, retaining the model complexity to a certain extent. The experimental results on the IIT Delhi and SDUMLA-HMT iris databases show that the proposed method has higher correct recognition rate, faster convergence speed and better robustness than AlexNet and VGGNet.

This study analyzed complex causal feedback loops in Regional Technology Innovation (RTI) system and established a system dynamics (SD) model of RTI. Taking China Guangdong Pilot Free Trade Zone as a sample, and running the model in VENSIM, it shows high fitting precision and good extrapolating performance. The simulation gives out the future trends of RTI, FCD, ATD and IPR royalties international trade of the region. This study proposed an effective model of RTI, and more importantly, it points out the type, direction and magnitude of the effects caused by IPR policy in RTI system. This SD model can also be used in policy effect simulation in the future.

A batch variable learning rate gradient descent algorithm is proposed to efficiently train a neuro-fuzzy network of zero-order Takagi-Sugeno inference systems. By using the advantages of regularization, the smoothing L1/2 regularization is utilized to find more appropriate sparse network. Combining the second-order information of the smoothing error function, a variable learning rate is chosen along the steep descent direction, which avoids line search procedure and may reduce the cost of computation. In order to appropriately adjust the Lipschitz constant of the smoothing error function in the learning rate, a new scheme is proposed by introducing a hyper-parameter. Also the article applies the modified secant equation for estimating the Lipschitz constant, which makes the algorithm greatly reduce the oscillating phenomenon and improve the robustness. Under appropriate assumptions, a convergent result of the proposed algorithm is also given. Simulation results for two identification and classification problems show that the proposed algorithm has better numerical performance and promotes the sparsity capability of the network, compared with the common batch gradient descent algorithm and a variable learning rate gradient-based algorithm.

Due to the lack of selection criteria for the ORC radial inflow turbine's loss models, and information about the models' limitations. This paper presents a comprehensive analysis for performance prediction of the ORC radial inflow turbine, and a novel turbine performance prediction program is proposed. This study can be divided into two parts, during the analysis part, 125 cases of CFD simulation were conducted under 5 rotor structures and R245fa as the working fluid. The performance of 6 loss models and their 18 combinations were compared. And in the examination part, another three organic fluids from different categories were chosen to exam the reliability of the proposed prediction program. The results show that loss model combinations E, O, and Q have a good performance in rotor loss prediction. Besides, Δβ4 has an important influence on prediction reliability and turbine performance, it should be lower than a certain value (about 40° in this paper) during the analysis process to get relatively reliable results under different rotor structures. The proposed program can realize an acceptable accuracy for turbine performance prediction under multiple organic fluids and rotor structures, with an efficiency deviation within ±5% and an output power relative error within ±10%.

Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were 88.15% (kappa index = 0.72) and 85.56% (kappa index = 0.69). The importance of features was evaluated using the occurrence frequency of features at decision nodes. In the six-category classification system, the basic building attributes (22.99%) and POIs (46.74%) contributed most to the classification process; moreover, the building footprint (7.40%) and distance to roads (11.76%) also made notable contributions. The result shows that it is feasible to extract building environments from POI labels and building footprint geometry with a dimensional reduction model using an autoencoder. Additionally, crowdsensing data (e.g., POI and distance to roads) will become increasingly important as classification tasks become more complicated and the importance of basic building attributes declines.

In the current research on software reliability models, the total number of faults and its relevance to imperfect debugging are not sufficiently emphasized or considered. To solve this problem, we firstly classify and analyze the total number of faults in the current reliability models, and then establish a generalized imperfect debugging framework model that considers fault detection and repair. On this basis, we further consider the phenomenon of introducing new faults in debugging and establish concrete imperfect debugging models from the perspective of incremental changes in the total number of faults. By using 12 publicly published failure data sets (FDS) widely used to evaluate the reliability models, we demonstrate the overall performance of the proposed models. The results show that the proposed models have an excellent account in fitting and prediction, which proves their effectiveness and flexibility.

Recently, how to exploit unlabeled data for training face recognition models has been attracting increasing attention. However, few works consider the unlabeled shallow data1 in real-world scenarios. The existing semi-supervised face recognition methods that focus on generating pseudo labels or minimizing softmax classification probabilities of the unlabeled data do not work very well on the unlabeled shallow data. It is still a challenge on how to effectively utilize the unlabeled shallow face data to improve the performance of face recognition. In this paper, we propose a novel face recognition method, named VirFace, to effectively exploit the unlabeled shallow data for face recognition. VirFace consists of VirClass and VirInstance. Specifically, VirClass enlarges the inter-class distance by injecting the unlabeled data as new identities, while VirInstance produces virtual instances sampled from the learned distribution of each identity to further enlarge the inter-class distance. To the best of our knowledge, we are the first to tackle the problem of unlabeled shallow face data. Extensive experiments have been conducted on both the small- and large-scale datasets, e.g. LFW and IJB-C, etc, demonstrating the superiority of the proposed method.

Iris recognition is recognized as one of the most reliable and efficient technique for human identification in the biometric fields. The Gabor filter and local binary pattern (LBP) are widely adopted for feature extraction in face recognition. However, it is difficult to achieve high recognition accuracy when the Gabor filter or LBP is directly applied to iris texture representation. This paper presents an effective iris feature descriptor, which first uses 2D-Gabor filter to extract multi-orientation imaginary (MOI) feature, and then applies uniform LBP for region feature encoding. Thus, the MOI feature-by-point energy is converted into that of the uniform LBP histogram-by-block, during which the distributions of the intra- and inter-class are greatly widened. Such process largely improves distinguishability of MOI features. Finally, the Bhattacharyya distance is adopted for matching. Experimental results on CASIA and JLU iris image databases show that this method performs better for combining MOI features and LBP encoding as compared to their individual function.

Appearance-based gaze estimation techniques have been greatly advanced in these years. However, using a single camera for appearance-based gaze estimation has been limited to short distance in previous studies. In addition, labeling of training samples has been a time-consuming and unfriendly step in previous appearance-based gaze estimation studies. To bridge these significant gaps, this paper presents a new long-distance gaze estimation paradigm: train a camera to perform eye tracking by another eye tracker, named Learning-based Single Camera eye tracker (LSC eye-tracker). In the training stage, the LSC eye-tracker simultaneously acquired gaze data by a commercial trainer eye tracker and face appearance images by a long-distance trainee camera, based on which deep convolutional neural network (CNN) models are utilized to learn the mapping from appearance images to gazes. In the application stage, the LSC eye-tracker works alone to predict gazes based on the acquired appearance images by the single camera and the trained CNN models. Our experimental results show that the LSC eye-tracker enables both population-based eye tracking and personalized eye tracking with promising accuracy and performance.

Human-vehicle collaborative driving (HVCD) has a novel concept of an intermediate stage from the development of a normal smart vehicle to self-driving vehicle, requires the vehicle's decision-making authority equivalent to the human driver's operation. As the self-driving vehicle cannot achieve full-condition driving without human driver yet and still have a long time to go, the collaborative driving can exploit both machine and human specialties, which requires human and vehicle intelligence to control the vehicle simultaneously and work together. However, there were a few studies focusing on the HVCD that experimental environment and evaluation methods are rarely discussed, even under the simulation conditions, which limits the development of collaborative driving. To bridge the significant gaps, this paper designed simple semi-physical simulation system architecture for the HVCD. First, the simple designed system can achieve basic functions, including human control input as the normal driver, vehicle intelligence with local and global sensor data process and decision fusion. The presented system can use and process this multi-source information to achieve the HVCD in the simulated environment. Second, the evaluation method based on vehicle driving safety, stability, and rapidity is proposed and discussed, which matters most for system performance test and feasibility verification. In addition, experiments with different decision fusion conditions were presented and the experimental results show that the proposed simple semi-physical HVCD simulation system can work smoothly and reflect the practical scenario. Furthermore, the performance of different fusion weights experiments shows the advantages of both human driver and vehicle intelligence, which provides a reference for the HVCD related research improvement.

Harris Hawk Optimizer (HHO) is a new algorithm based on population, because of the diversity of its plunder strategy, it has good exploration ability, but there is still room for further improvement of exploitation ability. Because of its unique “explosion” mechanism, Fireworks Algorithm (FWA) has good exploitation ability. In order to make up for the shortcomings of HHO algorithm, this paper proposes an improved HHO algorithm, fireworks Harris hawk algorithm based on dynamic competition mechanism (DCFW-HHO). In the iterative process, taking the escape energy function of HHO algorithm as an index, different competition mechanisms and fireworks explosion operations are performed in different stages of the algorithm. In order to verify the performance of the proposed algorithm, the benchmark function of CEC2005 is optimized by DCFW-HHO, and compared with the marine predator algorithm (MPA), whale optimization algorithm (WOA), lightning search algorithm (LSA), water cycle algorithm (WCA), FWA and HHO, experiments show that the proposed DCFW-HHO algorithm has strong optimization ability.

Achieving ubiquitous ultra-reliable low latency consensus in centralized wireless communication systems can be costly and hard to scale up. The consensus mechanism, which has been widely utilized in distributed systems, can provide fault tolerance to the critical consensus, even though the individual communication link reliability is relatively low. In this article, a widely used consensus mechanism, Raft, is introduced to the Industrial Internet of Things (IIoT) to achieve ultra-reliable and low latency consensus, where the consensus reliability performance in terms of nodes number and link transmission reliability is investigated. We propose a new concept, Reliability Gain, to show the linear relationship between consensus reliability and communication link transmission reliability. We also find that the time latency of consensus is contradictory to consensus reliability. These conclusions can provide guides to deploy Raft protocol in distributed IIoT systems.

Dragonfly Algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous problems. To make DA work in the binary spaces, this paper introduces an angle modulation mechanism on DA (called AMDA) to generate bit strings, that is, to give alternative solutions to binary problems. Instead of running on the original high-dimensional binary spaces, the original AMDA utilizes the four-dimensional trigonometric function. However, the original AMDA has certain limitations, such as poor algorithm stability and slow convergence speed. Therefore, an improved AMDA called IAMDA is proposed. Based on the original generating function, a variable coefficient is added to control the vertical displacement of the cosine function. In this paper, seven high-dimensional zero-one knapsack problems are considered. Experimental results prove that IAMDA has superior convergence speed and quality of solution as compared to AMDA, BDA and BPSO.

A magnetic-levitation (maglev) Wind Yaw System is proposed to make the nacelle yaw for wind direction without friction. Due to the multi-point suspension nonlinear coupling and higher suspension weight, a double closed-loop control strategy for single-point suspension control is proposed including suspension air gap loop and the current tracking loop. A MDOF dynamic model based on the mechanical geometry and motion dynamics is developed and then transformed into the mult-point suspension model. Then we unify the cross coupling terms and pitching disturbance into synchronous disturbance, and then proposed a composite control strategy to set suspension current reference for current tracking loop, including main tracking controller, adaptive synchronization controller and adaptive disturbance controller. The multi-point suspension simulation platform of maglev wind yawing system is built to carry out the dynamic suspension tracking and pitching disturbance experiment, and the poposed strategy has good dynamic tracking ability, the steady-state air gap error is only 0.003mm, and the reference switching recovery time is 0.445s . As pitching torque of 220Nm is subjected to the nacelle, the maximum air gap fluctuation is only 0.38mm, and the maximum synchronization error is 0.19mm, but these of the PID controller can reach to 2.82mm and 10mm respectively, which validate the proposed composite control and the rationality of four point nacelle suspension.

Signal coordination methods have been widely investigated to improve the performance of the traffic control system in urban arterial. The traditional solution is centralized, which has high computational complexity. To solve the problem, this paper constructs a linear model by assuming that the cycle of each intersection is known. And we proposed a parallel optimization method for arterial signal coordination, which uses the framework of the ADMM algorithm. Consistency constraints are introduced to decompose the centralized problem into multiple independent sub-problems, and parallel solving is realized. Compared with linear programming, the precision of ADMM-based algorithm is 99.5% in the multi-intersection coordination control tasks. Besides, with the increase of arterial road scale, the computational complexity of this method increases linearly, which has the potential to improve the efficiency of large-scale signal coordination.

The splitting strategy over prediction horizon of model predictive control (MPC) has the potential to compute optimal action in a parallel way. However, such time-splitting algorithms often lead to very slow convergence speed because the state consensus only happens in each pair of adjacent nodes, i.e. a point-to-point topology. This paper proposes a generic cross-node consensus method to extend the shortcoming of limiting to point-to-point topology for the purpose of accelerating the convergence of time-splitting MPC. The cross-node consensus is realized by predicting the state transition from one node to another using plant prediction model, which can increase the information exchange efficiency in the prediction horizon. The time-splitting optimization algorithm is implemented by combing with alternating directions method of multipliers (ADMM). Simulations with autonomous driving show that this new algorithm significantly reduces the number of iterations in time-splitting MPC, averagely about 81% compared with classic time-splitting technique.

Noise produced by electrical equipment in the UHV converter station during operation may adversely affect the surrounding environment. In order to identify the contribution of equipment noise in the station from the total noise at the station boundary, it is necessary to conduct an in-depth study on the spectrum characteristics and identification methods of main noise sources in the converter station, so as to control the noise of the equipment in the station in a targeted manner. In this paper, basic characteristics of the source noise spectrum were analyzed based on the measured spectral data around the source equipment. Then, reasonable timbre parameters were selected. Finally, spectral characteristics of main noise sources equipment in the UHV converter station were compared based on the timbre parameters, and characteristic timbre parameters of the main sound sources equipment in the station are obtained. According to these characteristic timbre parameters, the identification method of main noise sources equipment noise in the converter station were given, and the noise source in multiple converter stations is identified by this method. Results show that the noise source noise identification method’s the accuracy of UHV converter station based on timbre parameters is more than 85%, which proves the accuracy of the method. The identification method proposed in this paper can be applied to study the noise of noise sources equipment inside the station of converter station.

In many real-world face restoration applications, e.g., smartphone photo albums and old films, multiple high-quality (HQ) images of the same person usually are available for a given degraded low-quality (LQ) observation. However, most existing guided face restoration methods are based on single HQ exemplar image, and are limited in properly exploiting guidance for improving the generalization ability to unknown degradation process. To address these issues, this paper suggests to enhance blind face restoration performance by utilizing multi-exemplar images and adaptive fusion of features from guidance and degraded images. First, given a degraded observation, we select the optimal guidance based on the weighted affine distance on landmark sets, where the landmark weights are learned to make the guidance image optimized to HQ image reconstruction. Second, moving least-square and adaptive instance normalization are leveraged for {spatial} alignment and illumination translation of guidance image in the feature space. Finally, for better feature fusion, multiple adaptive spatial feature fusion (ASFF) layers are introduced to incorporate guidance features in an adaptive and progressive manner, resulting in our ASFFNet. Experiments show that our ASFFNet performs favorably in terms of quantitative and qualitative evaluation, and is effective in generating photo-realistic results on real-world LQ images. The source code and models are available at https://github.com/csxmli2016/ASFFNet.

With the rapid development of digital multimedia technology and Internet technology, the copyright protection of multimedia products has become a very important and challenging research topic at present. Digital watermarking technology is an effective method to realize the copyright protection of digital products, and it has become a hotspot in the field of information protection and information hiding. In this paper, DWT and LSB algorithm are adopted to realize the embedding and extraction of digital watermark through Matlab, after comparing the digital watermarking image under the Gaussian noise, salt and pepper noise, cutting and rotating attacks, the effect of two algorithms to extract digital watermarking image, the results show that: NC values (0.5067, 0.7584, 0.2301, 0.1330) and subjective ratings (3, 4, 2, 2) of DWT algorithm are superior to LSB algorithms (NC values are 0.0965, 0.7005, 0.5507, 0.0567, and subjective ratings are 1, 4, 3, 1). The DWT algorithm has better attack resistance and provides reference for the application of digital watermarking.

Automatic driving vehicles have been developed to provide more convenient and comfortable driving experiences. However, these vehicles failed in satisfying the variance of human intentions. Recently, the strategy of collaborating brain–computer interface (BCI) controlling and automatic driving receives attention. Since the BCI system remained some limitation in real-time controlling, a fusion method has been proposed to explore and verify the feasibility of human-vehicle collaborative driving in this paper. A hybrid BCI was developed to interpret human intentions. In addition, a computer vision-based automatic driving component was developed to maintain the vehicle on the road. A system for fusing these two kinds of vehicle driving decisions was first proposed in this paper. This system can simultaneously obtain the visual data and the hybrid electroencephalograph (EEG) signals. The hybrid EEG signals consist of steady-state visual evoked potentials and motor imagery. The obtained multisource information can be fused to make the final decision to drive a simulated vehicle. The proposed system was evaluated with different destinations. The experimental results verify the feasibility of fusing both human intention and computer vision. The task success rate reached 91.1% and the information transfer rate was 85.80 bit/min.

Environmental sound classification (ESC) is a significant problem in signal processing and machine learning. In this paper, a novel unsupervised learning approach is proposed using topological data analysis. Each sound signal is transformed into a point cloud by time-delay embedding. The persistence diagrams are obtained after computing the persistent homology of Vietoris-Rips filtrations. The sound signals are assigned to different classes using kmeans algorithm based on the bottleneck distance matrix of persistence diagrams.

With the rapid development of augmented reality technology in recent years, the application of mobile AR has become more and more extensive. This paper introduces an AR mobile application based on ARcore, which aims to popularize AR life style, let users experience the fun of AR, combine AR functions such as plane detection, multi image recognition with interesting real scenes, and create new AR playing methods such as transmission door and magic box. The application is divided into four parts, the first part and the third part are presented in a virtual way, the second part and the fourth part are presented in a combination of virtual and reality. Players constantly switch between the virtual world and the real world to experience the ultimate AR fun.

Other than classroom work, recommending learning resource in the extra-curricular activities is also an important factor to improve students' disciplinary study environment. And the learning resource, such as extra-curricular book, supporting video and other learning materials, recommending in extra-curricular activities requires the matching of the requirements of the student and the disciplinary of the resource. Taking the disciplinary book as an example, this paper firstly analyzes the problem of disciplinary-requirement mismatch between the student and the disciplinary book in extra-curricular activities, and describes the student's requirement for disciplinary book recommendation system. Based on requirement investigation, the specific demands for book recommendation system based on android are analyzed. Finally, the disciplinary book recommendation system based on android for disciplinary study in extra-curricular activities is designed.

We extended the existing methodology in Bound-to-Bound Data Collaboration (B2BDC), an optimization-based deterministic uncertainty quantification (UQ) framework, to explicitly take into account model discrepancy. The discrepancy was represented as a linear combination of finite basis functions and the feasible set was constructed according to a collection of modified model-data constraints. Formulas for making predictions were also modified to include the model discrepancy function. Prior information about the model discrepancy can be added to the framework as additional constraints. Dataset consistency, a central feature of B2BDC, was generalized based on the extended framework., 31 pages, 10 figures and 7 tables

Multi-source heterogeneous iris recognition (MSH-IR) has become one of the most challenging hot issues. Iris recognition is too dependent on the acquisition device, causing have large intra-class variations, capture iris duplicate data more and more larger. The paper proposed the application of locality preserving projection (LPP) algorithm based on manifold learning as a framework for MSH-IR. Looking for similar internal structures of iris texture, MSH-IR is performed by measuring similarity. The new solution innovation aspects that LPP algorithm is used to establish the neighboring structure of the similar feature points of the iris texture, and the similarity between the MSH-IR structures is measured after mapping to the low-dimensional space, and using the SVM algorithm to find and establish the optimal classification hyperplane in low-dimensional space to implement the classification of multi-source heterogeneous iris images. The experiment based on the JLU-MultiDev iris database. The experimental results demonstrates the effectiveness of the LPP dimension reduction algorithm for MSH-IR.

An adaptive aggregate smoothing gradient type algorithm is proposed for training fuzzy neural networks with max-min fuzzy logic operator. The study in this paper extends the work in [Long Li et al. A convergent smoothing algorithm for training max-min fuzzy neural networks. Neurocomputing (2017), https://doi.org/10.1016/j.neucom.2017.04.046]. An adaptive precision parameter adjustment rule is applied to manage the increasing precision parameter. It is shown in numerical experiment that the proposed algorithm can avoid the oscillation phenomenon and improve learning capacity effectively.

In the smart warehousing system adopting cargo-to-person mode, all the items are stored in the movable shelves. There are some warehouse robots transporting the shelves to the working platforms for completing order picking or items replenishment tasks. When the number of robots is insufficient, the task allocation problem of robots is an important issue in designing the warehousing system. In this paper, the task allocation problem of insufficient warehouse robots (TAPIR) is investigated. Firstly, the TAPIR problem is decomposed into three sub-problems: task grouping problem, task scheduling problem and task balanced allocation problem. Then three sub-problems are respectively formulated into integer programming models, and the corresponding heuristic algorithms for solving three sub-problems are designed. Finally, the simulation and analysis are done on the real data of online bookstore. Simulation results show that the mathematical models and algorithms of this paper can provide a theoretical basis for solving the TAPIR problem.

Cloud robotic system needs to learn real time data stream in order to adapt to the new environment for better performance. Incremental learning as a kind of dynamics classification method can avoid sample repeating learning, and develop model for updating itself. In this paper, we proposed an incremental learning-based cloud robot object recognition mechanism. It can learn new objects through human-robot interaction by speech recognition. The proposed incremental learning method was tested with small scale data in domestic indoor environment application, and the cloud robotic system was validated by basic conversation with different testing groups. The experimental results verify the feasibility of the incrementally learning new information algorithm and the proposed cloud robotic system.

Coordination control problem of networked Euler-Lagrange systems with external disturbance is addressed. The sliding mode surface and coordination control law of networked Euler-Lagrange systems with a dynamic leader under a directed graph is first proposed. It is shown that the followers can track the dynamic leader in finite time. Then leaderless consensus law for networked Euler-Lagrange systems is proposed. Based on algebraic graph theory and Lyapunov stability theory, finite-time stability of the closed-loop system is proven. The simulation results show the feasibility and effectiveness of the proposed control law.

Bound-to-Bound Data Collaboration (B2BDC) provides a natural framework for addressing both forward and inverse uncertainty quantification problems. In this approach, QOI (quantity of interest) models are constrained by related experimental observations with interval uncertainty. A collection of such models and observations is termed a dataset and carves out a feasible region in the parameter space. If a dataset has a nonempty feasible set, it is said to be consistent. In real-world applications, it is often the case that collections of experiments and observations are inconsistent. Revealing the source of this inconsistency, i.e., identifying which models and/or observations are problematic, is essential before a dataset can be used for prediction. To address this issue, we introduce a constraint relaxation-based approach, entitled the vector consistency measure, for investigating datasets with numerous sources of inconsistency. The benefits of this vector consistency measure over a previous method of consistency analysis are demonstrated in two realistic gas combustion examples., 31 pages, published in SIAM/ASA Journal on Uncertainty Quantification

In recent years, flying Ad-hoc networks (FANETs), which consist of small unmanned aerial vehicles (UAVs), is being used in the increasing of civilian and military applications. Due to the high mobility of the UAVs nodes, the link between the UAVs may frequently be disrupted. Hence, the existing routing protocols are inability to perform in FANETs. Motivated by this, we propose a new routing protocol named UAV-OLSR for FANETs in this paper. This protocol is based on the well-known protocol called optimized link state routing protocol (OLSR). We focus in our protocol on the lifetime of a communication link between the UAVs nodes and named link live time (LLT). We propose a new multipoint relay (MPR) selection algorithm where the UAVs node with maximum LLT is selected as the MPR. Our emulation results show that UAV-OLSR protocol outperforms OLSR in the packet loss rate, total time delay, the average time delay, and traffic received.

With the development of mobile internet and multi-media service, advanced techniques need to be applied in wireless network to improve user experience. Long term evolution (LTE) systems, which can offer up to 100Mbps downlink date rates, have been deployed in USA and Korea. However, because plenty of complex physical layer algorithms are utilized, network planning and optimization become heavy burdens for LTE network operators. Self-organizing network (SON) is a promising method to overcome this problem by automatically selecting and adjusting key parameters in LTE systems. In this paper, we present a dynamic adjusting algorithm to improve both handover and load balancing performance by introducing a weighted co-satisfaction factor (CSF). Analysis and system level simulation are conducted to exhibit the performance improvement of the proposed scheme. Results show that the proposed method outperforms the conventional solutions in terms of the network handover success ratio and load balancing gains significantly.

This paper is to design attitude coordination control strategies for multiple spacecraft by state feedback and output feedback. Based on Lyapunov theory and graph theory, first, bounded coordination synchronization controllers are developed by state feedback. Second, output feedback controllers are developed for coordination synchronization(i.e. without angular velocity feedback), it can guarantee spacecraft attitude reach to stable attitude. Finally, simulation results illustrate and visualize the effectiveness and feasibility of the proposed control technique.

Analytical results for the symbol error rate (SER) of M-ary phase shift keying (MPSK) in a slow, flat Rayleigh fading channel for a multiple-input multiple-output (MIMO) system using an Alamouti transmission scheme and generalized selection combining (GSC) scheme are given. Two new receiver selection schemes, generalized space-time sum-of-squares (GSTSoS) selection diversity and generalized space-time sum-of-magnitudes (GSTSoM) selection diversity are proposed. The first provides the same performance as conventional GSC, and the second provides slightly poorer performance, but neither requires channel state information and both have much simpler implementations. The SER of MPSK in Rayleigh fading using these two selection schemes is studied and compared to that of conventional GSC. The effects of channel estimation errors on each selection scheme are examined.

Cell Range Expansion (CRE) technology provide an opportunity for more users to transfer from macro cell to small cell in the Long Term Evolution Advanced (LTE-A) heterogeneous networks. However, these CRE users located at the edge of small cells will be seriously interfered by macro cell, resulting in a sharp decline to their Signal to Interference plus Noise Ratio (SINR). In order to reduce this interference, a method of frequency domain self-organizing interference coordination technique combined with CRE technology is proposed in our paper. In this method, the total spectrum bandwidth is divided into two parts according to the frequency partition ratio which can be adaptively controlled. One part of the bandwidth in which the transmission power of macro cell is reduced. Another band with the transmission power of macro cell do not change. Based on this method, small cell users with low SINR including CRE users and macro cell users with high SINR scheduled in the power reduced band. Our simulation results show that the proposed scheme in this paper can effectively improve the system's throughput and increase the small cell's edge users' performance.

In LTE-A (Long Term Evolution-Advanced) network, CSI-RS (Channel State Information Reference Signal) is substituted for CRS (Common Reference signal) to perform higher order MIMO (Multiple Input Multiple Output). In CoMP (Coordinated Multiple Point Transmission) scenario, the number of REs (Resource Element) occupied by CSI-RS would be multiplied. However, LTE (Long Term Evolution) legacy UEs (User Equipment) cannot distinguish CSI-RS from data, which may deteriorate the LTE UE's demodulation performance. Therefore it could be troublesome for LTE legacy UEs scheduled in LTE-A network with higher order MIMO equipped, and it is even more serious when CoMP technology is used. Investigation results by both link-level and system-level simulation reveal that LTE legacy UEs performance is seriously degraded due to the impact of CSI-RS when coding rate and modulation order go higher. To reduce the performance degeneration, an improved scheduling algorithm named AUP-AMCS (Advanced UE Prioritization combined with Adaptive Modulation Coding Scheme) is proposed in this paper. Compared to existing algorithm, AUP-AMCS algorithm takes UE priority into consideration and is compatible with the established specification. Simulation results indicate that the proposed algorithm can improve system performance obviously.

In the Long Term Evolution Advanced (LTE-A) heterogeneous networks (Het-Nets), smallcell is an emerging and key item, providing improved service coverage and high spectral efficiency with low power. However, the number and location arrangement of smallcell is always changing in reality, the interference scenario is also different. In order to reduce the layer-to-layer interference in uncertain deployment of smallcell, a method of frequency domain self-organizing interference coordination technology is proposed in this paper. In this method, the total spectrum bandwidth is divided into two parts according to a frequency partition ratio. One part of the bandwidth is called protected band where macrocell's transmission power will be reduced in an adaptively controlled way to schedule the edge UEs of smallcell and the center UEs of macrocell, another is named unprotected band to schedule the macro edge UEs and smallcells' center UEs. Thus, the edge UEs of smallcell could be protected under the condition of not losing too much macrocells' performance. The system-level simulation results show that the proposed scheme can achieve superior system performance improvements, and higher cell-edge user data rate.

The passenger car logistics transportation loading problem of company H is investigated. Based on reasonable assumptions, the passenger car logistics transportation loading problem is transformed into a two-dimensional packing problem. Firstly, under the premise of considering the sizes of two types of car carriers and three kinds of passenger cars, and the stability requirements of car carriers, 36 kinds of possible loading modes of type 1-1 car carriers and 73 kinds of possible loading modes of type 1-2 car carriers are enumerated by using MATLAB program. Then, the integer programming model of passenger car logistics transportation loading problem is constructed to minimize the total transportation cost. A LINGO program is given to solve the model. Using the mathematical model and LINGO program, we obtain the number of two types of car carriers and the loading mode of each car carrier which company H can use to complete the given transport task. The results of this paper can provide a theoretical basis for the logistics company to make passenger car logistics transportation plans.

Nowadays, Voice of LTE (VoLTE) is used to support voice communications in LTE network. As one of the key techniques for VoLTE, semi-persistent scheduling (SPS) is important to improve the capacity of VoLTE users. However with SPS, the resource and modulation and coding scheme (MCS) are usually decided by the initial scheduling and keep unchanged during the active state. Considering the continuously changing channel, it is hard to ensure the performance of the voice packet. In order to solve this problem, a resource allocation algorithm is proposed based on the prediction of channel quality change. Furthermore, the data rate of Adaptive multi Rate(AMR) codec is adaptively adjusted in the resource allocation algorithm to reduce VoLTE's impact on other data services. Simulation results prove that the proposed algorithm can reduce the retransmission of VoLTE users and improve the throughput performance of other traffic businesses.

The task assignment problem of warehouse robots in the smart warehouse (TAWRSW) based on cargo-to-person is investigated. Firstly, the sites of warehouse robots and the order picking tasks are given, and the task assignment problem for picking one order is formulated into a mathematical model to minimize the total operation cost. Then a heuristic algorithm is designed to solve the task assignment problem for picking multiple orders. Finally, simulations are done by using the orders data of online bookstore A. The feasibility and validity of the model and algorithm are verified. The model and algorithm in this paper provide a theoretical basis to solve the TAWRSW.

The bit-error rate (BER) of binary phase-shift keying in Rayleigh fading, using the Alamouti transmission scheme and receiver selection diversity in the presence of channel-estimation error, is studied. Closed-form expressions for the BER of log-likelihood ratio selection, signal-to-noise ratio (SNR) selection, switch-and-stay combining selection, and maximum ratio combining are derived in terms of the SNR and the cross-correlation coefficient of the channel gain and its corrupted estimate. Two new selection schemes, space-time sum-of-squares combining selection diversity and space-time sum-of-magnitudes selection diversity, are proposed and proven to provide almost the same performance as SNR selection, but with much simpler implementations. The effects of channel-estimation errors on each selection scheme are examined.

Heterogeneous networks (HetNets) have been considered as a promising technique for improving spectral efficiency, but the energy efficiency influenced by the fluctuation of user numbers should not be neglected to achieve green communications. We introduce the energy-efficient switch-off mode algorithm for pico cells to reduce power consumption in the self-organizing network (SON). Once the switch-off mode is initiated in some picked pico cells, the users connecting to these cells would face the problem of redistribution. In this paper, we introduce the energy saving (ES) strategy based on the prediction of load capability to select pico cells to enter switchoff mode. Furthermore, network flow based load balancing (LB) is proposed to provide solution of uneven load status caused by ES. System level simulations are conducted to exhibit the performance enhancement that the proposed algorithm can reduce energy consumption as well as improve the balanced degree of resource allocation significantly.

The use of combining pico cell and macro cell, also known as heterogeneous networks (HetNets), is considered as a promising way to solve the limit of system capacity and complement coverage in traditional network HetNets which have changed the infrastructure of traditional cellular networks also bring challenges correspondingly. Mobility management for pico cells is challenging due to different cell size and random deployment, and an enhanced mobility state estimation is desired. In this paper, we propose a self-optimization algorithm for mobility state estimation. The enhancement adapts the weight automatically based on handover performance to weaken the cell crossing counting for pico cells in different network topologies. The simulation results will show the improvements in mobility state estimation (MSE).