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This paper proposes a novel distributed optimal backstepping control method for a class of nonlinear multi-agent systems in strict-feedback form with output constraints. The virtual and actual controls can be locally optimized by designing their cost functions in every backstepping step. Furthermore, a unified barrier Lyapunov function (UBLF) is designed to prevent the outputs violating the constraints, which is still effective whether the constraints exist or not. By constructing a feedforward+feedback composite control framework, the recursive backstepping design has become to solve the coupled Hamilton-Jacobi-Bellman (HJB) equation in every step. Then, a critic network is built to obtain the approximated solution of HJB equation online through designing updating law of critic weight value. Theoretical analysis reveals that the consensus error is uniformly ultimately bounded (UUB) without violating output constraints. Numerical simulation results illustrate the effectiveness of proposed method.

A W -band slot array antenna with low side lobes, fed by a ridge waveguide, is presented in this paper for the applications with strict dimensional limitation. A 94 GHz slotted array antenna was simulated using finite element modeling (FEM) in HFSS. The feed network is analyzed by the equivalent circuit method, and the analysis of side lobe level was based on Taylor distribution. Compared with the theoretical calculations, the simulation results exhibit an excellent performance with high gain of 32 dB and low sidelobes level of −22.9 dB. And the size of this antenna is reduced to 45 % of that of standard waveguide WR10 in the cross-section. It is a promising solution to apply in millimeter wave for high Integration module.

Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation method for plant point clouds using the 3D filtering and facet region growing. In order to separate leaves with different overlapping situations, we develop a new 3D joint filtering operator, which integrates a Radius-based Outlier Filter (RBOF) and a Surface Boundary Filter (SBF) to help to separate occluded leaves. By introducing the facet over-segmentation and facet-based region growing, the noise in segmentation is suppressed and labeled leaf centers can expand to their whole leaves, respectively. Our method can work on point clouds generated from three types of 3D imaging platforms, and also suitable for different kinds of plant species. In experiments, it obtains a point-level cover rate of 97% for Epipremnum aureum, 99% for Monstera deliciosa, 99% for Calathea makoyana, and 87% for Hedera nepalensis sample plants. At the leaf level, our method reaches an average Recall at 100.00%, a Precision at 99.33%, and an average F-measure at 99.66%, respectively. The proposed method can also facilitate the automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering., 24 Pages, 18 Figures, 7 Tables. Intends to submit to an open-access journal

Due to its great learning ability and fast processing speed, deep learning-based Image Compressive Sensing (ICS) methods attract a lot of attention in recent years. However, most existing ICS neural networks ignore the algorithmic structure in iterative optimization-based methods, leading to the lack of insights from traditional ICS study. In this paper, we propose a novel ICS deep network named SPLNet, which is inspired by the well-known Smoothed Projected Landweber (SPL) algorithm for ICS reconstruction. Specifically, we utilize multiple convolution layers to mimic three key steps in SPL algorithm: a) Wiener filter for removal of blocking artifacts; b) Approximation with projection onto the convex set; c) Bivariate shrinkage on transform domain for sparse representation and denoising. In SPLNet, the whole network is trained in end-to-end style, which means all parameters in sampling and reconstruction are learnable. Experimental results indicate that our network performs competitively against state-of-the-art ICS reconstruction methods.

This paper presents a receding path planning method using priority-based artificial potential field (PAPF) for UAV swarms considering moving obstacle and inter-UAV collision avoidance constraint. To enhance computational efficiency, PAPF is proposed to decouple the UAV swarm cooperative path planning problem into a series of single-UAV path planning problems. The receding planning mechanism is used to reduce the horizon of the path planning to further reduce the computational burden. In each planning horizon, when sequentially generating the paths of multiple UAVs, lower priority UAVs treat the path points of higher priority ones as obstacles for collision avoidance. Simulation studies show that PAPF can generally generate swarm paths in around Is on the scenario involving 16 UAVs, which verifies the efficiency merit of PAPF in solving dynamic path planning problems for UAV swarms.

Internet of Things (IoT) brings the third development wave of the global information industry, which makes users, network, and perception devices cooperate more closely. However, if IoT has security problems, it may cause a variety of damage and even threaten human lives and properties. To improve the abilities of monitoring, providing emergency response, and predicting the development trend of IoT security, a new paradigm called network security situation awareness (NSSA) is proposed. However, it is limited by its ability to mine and evaluate security situation elements from multi-source heterogeneous network security information. To solve this problem, this paper proposes an IoT network security situation awareness model using a situation reasoning method based on semantic ontology and user-defined rules. Ontology technology can provide a unified and formalized description to solve the problem of semantic heterogeneity in the IoT security domain. In this paper, four key sub-domains are proposed to reflect an IoT security situation: context, attack, vulnerability, and network flow. Furthermore, user-defined rules can compensate for the limited description ability of ontology, and hence can enhance the reasoning ability of our proposed ontology model. The examples in real IoT scenarios show that the ability of the network security situation awareness that adopts our situation reasoning method is more comprehensive and more powerful reasoning abilities than the traditional NSSA methods.

A novel dual-mode ridge waveguide structure is proposed to design dual-band bandpass filter in this paper. This new configuration achieves to realize two orthogonal resonant modes which allows for independent control of the two resonant frequencies. In addition, a transmission zero is introduced in the guard-bands improving the filter selectivity, while keeping a simple and compact inline topology.

In order to enhance the perception ability of unmanned ship's surface environment and solve the problem that the target can not be effectively classified and recognized in radar or sensor-based target detection method, this paper collects and manufactures the data set of surface target detection independently, uses machine learning algorithm to detect and recognize multi-size and multi-type targets in ship image, the default target box parameters are initialized by clustering algorithm, which improves the accuracy of the training model in small target detection rate and target box location. The experimental results show that the mAP of the improved model has been upgraded, which can more accurately accomplish the task of multi-target, multi-type and multi-scale surface object detection and recognition, and provide more abundant and effective surface environment information for the automatic driving of unmanned vehicle.

This paper proposes a collision-free formation control method under the leader-follower control framework for non-holonomic mobile robots. To prevent robots from trapping into locally infeasible states and improve the effectiveness of collision avoidance, a tangential-force-based collision avoidance controller is proposed. The tangential-force-based repulsion field is customized to guide the intruder revolving around the field source with a certain radius. The repulsion field is divided into three areas and repulsion forces are generated in different areas to guide robots to maintain formation and avoid collision efficiently. Simulation results on formation-reconfiguration and formation-crossing scenarios demonstrate that the proposed method can drive all the robots to reach corresponding destinations without collision, which cannot be realized by the standard artificial potential method.

This paper addresses the stability analysis problem for general nonlinear systems. A novel method using parameter-dependent Lyapunov Functions for stability analysis is developed, and less conservative stability conditions in the forms of LMIs are obtained. Then, the stability conditions are extended to LPV systems with parameters that are linear combination of state variables and linear systems subject to actuator saturation. The effectiveness of proposed approaches is illustrated via three numerical examples.

With the continuous economic growth and population migration, the number of mega cities in the world is gradually increasing, and their impact also involves all aspects of the cities. Catering is the basis of human activities, therefore, quantitative analysis of restaurant consumption in mega cities has positive significance for mastering the factors affecting consumption and optimizing urban commercial layout. We select the dining venue within 1 km along the 2nd Ring Road of Beijing as the research target. First, based on the Dianping and Baidu Map, the sample crawling and calculation are performed, and then we establish a system of factors affecting the popularity of the restaurant, including comprehensive star level, per capita, the shortest distance from the nearest subway station, and the number of municipal-level commercial service centers within 1 km. Through the significance test and Box-Cox transformation, a multiple linear regression model affecting the popularity of restaurant is established, and the model is optimized by stepwise regression method. According to the effective samples of 200 restaurants, the results show that in the central area of mega city, the comprehensive experience of the restaurant and group effect have the greatest impact on the popularity of restaurant. Based on the results, it is suggested that the overall planning of the urban commercial district should fully consider the composition of the catering consumption capacity and the improvement of operational management capabilities.

This paper presents an effective method for unmanned aerial vehicle (UAV) dynamic path planning considering moving-target and obstacle-avoidance constraints. In the process of dynamic path planning, moving-target positions are predicted using Kalman filtering algorithm on the receding horizon. Then, anytime repairing sparse A* algorithm (AR-SAS) is customized to generate feasible paths from the staring positions to the moving-target positions. By rolling planning, UAVs are able to track the moving targets efficiently. Simulation results demonstrate that the proposed method can generate feasible paths within limited time and has high robustness.

To solve the problem of cooperative target tracking considering ground moving-targets and threats, a novel approach for multiple unmanned aerial vehicles (multi-UAV) cooperative target tracking using sparse $A^{*}$ search and Standoff tracking algorithms is proposed. Firstly, sparse $A^{*}$ search algorithm is introduced to generate multiple paths for UAVs subject to the threats and UAV kinematics constraints. And multi-UAV rendezvous at a predefined formation nearby the moving-target. Then, considering the target tracking distance and relative phase angle constraints, the Standoff tracking algorithm is utilized to generate cooperative tracking paths. Simulation results demonstrate that the multi-UAV cooperative target tracking method can effectively avoid threats and achieve persistent moving-target tracking and monitoring in the mission areas.

By the introduction of advancing blade concept rotor system, the compound high-speed helicopters become a hot research topic in recent years. Due to the novel coaxial rotor and propeller structure of the compound helicopter, the aerodynamic coupling effect between the coaxial rotor and the fuselage has strong influence to the overall performance of the helicopter at low flying speed. In order to accommodate this problem, this paper presents a novel{unidirectional auxiliary surface sliding mode control method for the attitude control of compound helicopters. In the proposed work, the flight dynamics of the novel structured compound helicopter are firstly introduced. Then, expanding the positive invariant sets with 6 unidirectional auxiliary surfaces and an exponential approaching law and a terminal attractor are incorporated into the framework so as to increase the convergence speed and ensure the state error converges within finite time. The stability of the newly developed algorithm is analyzed through the Lyapunov approach. Comparative simulation results have demonstrated that the proposed method is capable of tracking attitude commands and shown strong robustness to the uncertainties.

Evoked tactile sensation (ETS) is induced with stimulation of areas in the projected finger map (PFM) in the stump skin of forearm amputees. The aim of the present study is to further explore the neural correlation of the ETS with central activities in the somatosensory cortex (SI). Two forearm amputees were recruited to participate in this study. The neuroimaging technique of Magnetoencephalography (MEG) was employed to reveal the activities in the somatosensory cortex (SI), while the thumb and little finger areas in the PFM and the thumb and little finger of the contralateral hand were stimulated with a bi-phasic current pulse train. In one subject, local anesthesia was applied to the skin areas of the thumb and little finger of the PFM to examine the effects of blocking peripheral nerve transmission on the central activities in SI. Results obtained in the two subjects indicated that stimulation of the thumb and little finger areas in the PFM of amputated side produced the similar neural activities in the somatosensory cortex as those of stimulating the thumb and little finger of the contralateral hand, both of which were consistent with the homunculus organization of the SI cortex. The intensity of SI cortical activities was proportional to the intensity of the amplitude of peripheral stimulation. In particular, local anesthesia reduced the intensity of central activities in SI as revealed by the MEG response, as well as the sensitivity of ETS as reported by the subject. This neural correlation appears to suggest that the finger areas in the PFM in the stump skin are neuroanatomically connected to the finger areas of the somatosensory cortex. Thus, electrical stimulation of the PFM can induce natural sensation as that of normal fingers. This establishes the neural basis of natural sensory feedback from the prosthetic hand to the forearm amputee with finger-to-finger specificity.

The objective of this study was to test the hypothesis that electrical stimulation of projected finger map (PFM) in the stump skin of forearm amputees activates concurrently the corresponding finger areas in the primary somatosensory cortex (S1). The technique of magnetoencephalo-graphy (MEG) was used to detect S1 activities, while electrical stimulation pulses or trains were delivered to three sites: (1) the thumb and little finger areas of the PFM on the stump skin of an amputee subject; (2) the thumb and little finger of the contralateral hand of the subject; and (3) the contralateral forearm skin areas matching the PFM in the amputated stump. Result indicated that at single pulse stimulus (1Hz) to the thumb and little fingers in the PFM and contralateral hand, symmetric areas in S1 corresponding to the thumb (more lateral) and little finger (lateral) in both sides could be identified. With stimulation train (50Hz and duty cycle of 50%) to the thumb and little finger in the PFM and contralateral hand, the subject experienced pressure sensations similarly at both sites, and the same symmetric areas of the thumb (more lateral) and little finger (lateral) in S1 were activated. With stimulation at the third site, the activation area in S1 was located towards more medial (elbow) segment. MEG results indicated that PFM stimulation evoked S1 activation consistent with homunculus organization. This may imply that the thumb and little finger areas in the PFM on the stump skin are connected neuroanatomically to the thumb and little finger areas in S1 before amputation.

Vulnerable code reuse in open source software is a serious threat to software security. However, the existing high-efficiency methods for vulnerable code clone detection have a large number of false-negatives when the code is modified, which results in limited application scenarios. In this paper, we present an innovative fingerprint model to describe the vulnerability code and propose VFDETECT, an efficient system to detect vulnerable code clones based on the fingerprints. Firstly, the fingerprint is constructed by applying hash function to appropriate code blocks in the diff which are preprocessed. Then, VFDETECT detects the vulnerable code clone by matching the preprocessed code blocks in target project with the fingerprint, which is mapped to a bitmap so that can be identified efficiently. VFDETECT could maintain better performance and acquire higher robustness under multiple code modification methods such as variable renaming, code sequence changing and redundancy inserting, which is difficult to achieve in existing research work. Our results in real-world datasets detection demonstrate that it is of practical values.

To cope with precision Electrochemical Machining (ECM) of straight teeth face gear, a precise 3D model of the straight teeth face gear is developed, and cathode design and the cathode's working surface model are built based on the gear 3D model accordingly. Taking full account of the convenient maintenance of the cathode, the cathode is designed in a detachable and split structure. The flow channel and the corresponding insulation and sealing device are designed by adopting the reverse flow of electrolyte. The essential design guidelines are developed to enable high quality cathode design to be achieved and so the precision ECM of straight teeth face gear, which are further validated by the machining trials.

The existence of projected finger territories (PFTs) near the stump provides an essential way to realize the tactile sensation of lost fingers by transcutaneous electrical nerve stimulation (TENS). After amputation, the somatosensory cortex corresponding to lost fingers might be invaded by other cortical areas due to cortical plasticity. However, it was seldom observed how the amputees could feel the projected or lost finger tactile sensation in the cerebral cortex under TENS. To answer this question, by using magnetoencephalography (MEG), we investigated the cortical response under TENS of the projected thumb territory and normal thumb with 2 Hz current pulses. One subject with long-term left forearm amputation was recruited. The temporal and spatial characteristics of the activated cortical magnetic signals were analyzed. The Equivalent Current Dipoles (ECDs) corresponding to the strongest strength were mapped in the cerebral cortex, and the current density distribution were clearly illustrated. We found that the latencies at the maximum ECD strength were 60 ± 1.41 ms for the projected thumb and 46 ± 1.25 ms for the normal counterpart. The strongest ECD corresponding to projected thumb was located in the central sulcus near the mirror location of the normal thumb counterpart. And the response strengths of projected thumb cortex were stronger than normal thumb counterpart.

To reduce the radar detection probability of the aircraft during penetration, the sparse A∗ search considering the radar cross section (RCS) distribution characteristic is proposed for route planning. Firstly, the aircraft RCS model and the radar detection probability model are established by considering the attitude and space position of the aircraft. Two kinds of turning modes are studied (the maximum overload mode and the minimum roll angle mode) considering the kinematics constraints of the aircraft. The minimization of radar detection probability and route length are taken as two cost functions. Then the sparse A∗ search algorithm is applied to get the satisfied route. Finally, the results of Monte Carlo simulation show that the route planned by the proposed method can navigate the aircraft to reach the target with low detection probability, and the comparison results show that the penetration performance of the minimum roll angle turning mode is better than that of the maximum overload turning mode.

Dynamic Traveling salesman problem (DTSP) is a theoretical mathematical model and has been widely applied in dynamic problems in reality. Most of the existing methods used to model DTSP lack a realistic foundation, and cannot provide convenient and polytrophic operations to simulate real-world scenarios. In this paper, a reality-based method is proposed to model DTSP. The model features good controllability and reality by changing the weight of some edges and the nodes needed to be visited at different moments. Besides, for DTSP, some edges are actually added to or deleted from the optimal solution because of environmental changes. A reuse strategy (Rs) is proposed for DTSP so as to excavate the useful information from the historical search experience and reuse them in the new environment to avoid overlapping search. Based on the reuse strategy, the two state-of-the-art optimizers in discrete space, i.e. S-CLPSO and ACS, are extended to SRs-CLPSO and Rs-ACS respectively. By reusing the historical search experience, SRs-CLPSO and Rs-ACS can find a better solution with faster convergence speed. The performance of SRs-CLPSO is compared with Rs-ACS algorithm in fifty instances of TSPLIB. Experimental results indicate that the proposed Rs is efficient for DTSP, and the SRs-CLPSO algorithm is a better solver for DTSP.

This paper addresses the robust consensus reliable control protocol design problem against actuator faults for linear parameter-varying multi-agent systems. First, the actuator faults are modeled via a polytopic uncertainty method. Second, a distributed observer is designed for the single agent by sharing the communication network sensors to estimate the state information. Then by these estimated information, a robust consensus reliable control protocol against actuator faults is obtained and desired disturbance rejection performance can be guaranteed by this proposed protocol. Third, the non-convexity conditions of the consensus control protocol can be translated into a LMI optimization problem via simple matrix calculation. Finally, the effectiveness of the proposed reliable controller scheme is illustrated by the example.

In view of the data sparseness problem existed in the traditional collaborative filtering recommendation algorithm, this paper proposes a hybrid collaborative filtering recommender framework integrated FCM clustering and Slope One algorithm and FSUBCF algorithm. Firstly this algorithm use the Slope One algorithm based on FCM cluster to predict item ratings that users have not rated in matrix, and then, to implement recommendation by the collaborative filtering recommendation algorithm based on user. The experimental results show that this algorithm can improved the prediction accuracy compared to the original Slope One algorithm and can adapt to the data sparser recommendation system. Compared with other traditional collaborative filtering algorithms, the recommendation accuracy also has obvious advantages.

3D integration is one of the promising solutions to overcome the interconnect bottleneck with vertical interconnect through-silicon vias (TSVs). This paper investigates the crosstalk in 3D IC designs, especially the capacitive crosstalk in TSV interconnects. We propose a novel ω-LAT coding scheme to reduce the capacitive crosstalk and minimize the power consumption overhead in the TSV array. Combining with the Transition Signaling, the LAT coding scheme restricts the number of transitions in every transmission cycle to minimize the crosstalk and power consumption. Compared to other 3D crosstalk minimization coding schemes, the proposed coding can provide the same delay reduction with more affordable overhead. The performance and power analysis show that when ω is 4, the proposed LAT coding scheme can achieve 38% interconnect crosstalk delay reduction compared to the data transmission without coding. By reducing the value of ω, further reduction can be achieved1.

This paper addresses the mixed H − /H ∞ fault detection observer design problem for linear parameter-varying systems. The fault detection filter design is an optimal Luenberger observer synthesized which ensures guaranteed levels of fault detection in finite frequency domain and disturbance attenuation. With the aid of GKYP Lemma, extended Bounded Real Lemma, performance indexes are translated into a convex linear matrix inequality (LMI) optimization problem to avoid the complexity of system associated with weight functions. An iterative linear matrix inequality algorithm is given to obtain the solutions. The effectiveness of the filter is illustrated via a numerical example.

Electronic Patient Record (EPR) is the start of medical treatment information and the core of a Hospital Information System (HIS). First, the data flow at a hospital is presented. Then, based on B/S system architecture, a three-layer EPR management system is presented. Case history database stores and manages EPR data. The middle layer consists of application server and Web server where transaction logics are realized. At the client layer, the system can be navigated with a browser by different users. And then, the development of the server is discussed in detail. Its expandable, maintainable and secure B/S system architecture is cost-effective and is developed with Eclipse platform. The server is developed with the components registered in the Struts-config.xml file. At the client, JSP pages are created with Dreamweaver. The prototype system is tested by test run and its correctness is validated.

In order to improve the efficiency of symbolic execution in software vulnerability detection, we propose the sensitive point oriented test method for parallel approach in the paper. Static analysis is used for identification and location of sensitive points. Then based on dynamic symbolic execution, the algorithm of parallel approach to sensitive point is designed, in order to select for the preferential path and realize iterative approach test. Moreover, we present search algorithm of test cases to reuse forward information and reduce communication redundancy. Finally, the experiment results verify the improvement of symbolic execution efficiency and effectiveness of exception detection.

Through program dependence analysis, the method of parallel symbolic execution based on reduction about program execution path family was proposed. Firstly, instruction statements which affect on the symbolic value were extracted according to data dependency. then the key branch conditions controlling these statements were located and connected. Thus the reduction algorithm from multiple paths of which symbolic values are all the same to path families was was designed and implemented. Finally, path family chain was built and parallel symbolic execution was realized. the experimental results show that the method promotes parallel processing ability and test efficiency, compared with common parallel scheduling policy of allocating program execution path.

At present, the support vector machine (SVM) has been successfully applied in the field of electric load forecasting, but most of the load forecasting models are based on the day characteristics of meteorological factors, without considering a real-time weather factors which are valuable real-time information, while the prediction accuracy and generalization ability are influenced by the sample set of input variables. This paper aims to propose a selection strategy based on a sample of real-time weather factors. Firstly, data processing methods deal with abnormal points, secondly, then we use the day meteorological feature vectors to reduce the sample set, and use FP-Growth algorithm to select the training sample similar to prediction day based on real-time weather factors, finally prediction model based on SVM is established. The practical application shows that the text of the prediction models and processing strategies can be more accurate predictions.

The multinomial logit (MNL) consumer choice rules employed in the extant product design research assume that a product always has a market share even though the product has a very low utility value. In this paper, a new consumer choice rule integrating the dollar-scaled utility with piecewise logit function is proposed to avoid estimation bias caused by this problem. A profit maximizing model under the proposed rule is established in a multi-segment product market. It has been proven that in an unpartitioned product market, a local optimal solution of the model is also a global optimal solution, and hence standard nonlinear programming algorithms can be applied. An algorithm based on interval analysis is developed to solve the model under a multi-segment market. Finally, an example of selection of new product scheme is given to illustrate the feasibility and effectiveness of the proposed model and algorithm.

In this paper a new LMI method is proposed for the strong γ k -γ cl H ∞ stabilization problem. Due to the introduction of a new slack variable, it is expected to be less conservative than the authors' previous work. Furthermore, several recently developed methods can be seen as its special cases. Meanwhile the extended bounded real lemma proposed lately can be adopted to improve the LMI design results iteratively. And for the possible order problem, the nested construction method can also be applied. The effectiveness is demonstrated by two numerical examples.

Consumer credit prediction is considered as an important issue in the credit industry. The credit department often makes decision which depends on intuitive experience with large risk. This study proposed a new model that hybridized the support vector machine (SVM) and particle swarm optimization (PSO) to evaluate the new consumer's credit score. The hybrid model simultaneously optimizes the SVM kernel function parameters and the input feature subset in order to achieve a high accuracy. Two UCI credit data sets are selected as the experimental data to evaluate the prediction performance of the hybrid model. The experimental results are compared with other existing methods which imply that the PSO-SVM model is a promising approach for credit scoring.

WebGIS system needs to interact with the server frequently, dynamic refresh technology can greatly reduce the system response time, Ajax is the representing technology of dynamic refresh, and at the same time, JSON as a kind of new data transmission technology, with its superiority in data construction and analysis, has become the mainstream technology of data transmission in Ajax technique. While Dojo as an excellent open-source JavaScript tool set, can provide a better and more efficient platform to support Web development.