Adaptive tracking control of leader–follower systems with unknown dynamics and partial measurements

In this paper, a decentralized adaptive tracking control is developed for a second-order leader–follower system with unknown dynamics and relative position measurements. Linearly parameterized models are used to describe the unknown dynamics of a self-active leader and all followers. A new distributed system is obtained by using the relative position and velocity measurements as the state variables. By only using the relative position measurements, a dynamic output–feedback tracking control together with decentralized adaptive laws is designed for each follower. At the same time, the stability of the tracking error system and the parameter convergence are analyzed with the help of a common Lyapunov function method. Some simulation results are presented to validate the proposed adaptive tracking control.     

Robust adaptive control of a thruster assisted position mooring system

In this paper, robust adaptive control is developed for a thruster assisted position mooring system in the transverse direction. To provide an accurate and concise representation for the dynamic behavior of the mooring system, the flexible mooring lines are modeled as a distributed parameter system of partial differential equations (PDEs). The proposed control is applied at the top boundary of the mooring lines for station keeping via Lyapunov’s direct method. Adaptive control is designed to handle the system parametric uncertainties. With the proposed robust adaptive control, uniform boundedness of the system under the ocean current disturbance is achieved. The proposed control is implementable with actual instrumentations since all the signals in the control can be measured by sensors or calculated by using a backward difference algorithm. The effectiveness of the proposed control is verified by numerical simulations.     

A knowledge and collaboration-based CBR process to improve network performance-related support activities

In a context characterized by a growing demand for networked services, users of advanced applications sometimes face network performance troubles that may actually prevent them from completing their tasks. Therefore, providing assistance for user communities that have difficulties using the network has been identified as one of the major issues of performance-related support activities. Despite the advances network management has made over the last years, there is a lack of guidance services to provide users with information that goes beyond merely presenting network properties. In this light, the research community has been highlighting the importance of User-Perceived Quality (UPQ) scores during the evaluation of network services for network applications, such as Quality of Experience (QoE) and Mean Opinion Score (MOS). However, despite their potential to assist end-users to deal with network performance troubles, only few types of network applications have well established UPQ scores. Besides that, they are defined through experiments essentially conducted in laboratory, rather than actual usage. This paper thus presents a knowledge and Collaboration-based Network Users’ Support (CNUS) Case-Based Reasoning (CBR) Process that predicts UPQ scores to assist users by focusing on the collaboration among them through the sharing of their experiences in using network applications. It builds (i) a knowledge base that includes not only information about network performance problems, but also applications’ characteristics, (ii) a case base that contains users’ opinions, and (iii) a user database that stores users’ profiles. By processing them, CNUS benefits users through the indication of the degree of satisfaction they may achieve based on the general opinion from members of their communities in similar contexts. In order to evaluate the suitability of CNUS, a CBR system was built and validated through an experimental study conducted in laboratory with a multi-agent system that simulated scenarios where users request for assistance. The simulation was supported by an ontology of network services and applications and reputation scheme implemented through the PageRank algorithm. The results of the study pointed to the effectiveness of CNUS, and its resilience to users’ collusive and incoherent behaviors. Besides that, they showed the influence of the knowledge about network characteristics, users’ profiles and application features on computer-based support activities.     

Data-mining based SQL injection attack detection using internal query trees

Detecting SQL injection attacks (SQLIAs) is becoming increasingly important in database-driven web sites. Until now, most of the studies on SQLIA detection have focused on the structured query language (SQL) structure at the application level. Unfortunately, this approach inevitably fails to detect those attacks that use already stored procedure and data within the database system. In this paper, we propose a framework to detect SQLIAs at database level by using SVM classification and various kernel functions. The key issue of SQLIA detection framework is how to represent the internal query tree collected from database log suitable for SVM classification algorithm in order to acquire good performance in detecting SQLIAs. To solve the issue, we first propose a novel method to convert the query tree into an n-dimensional feature vector by using a multi-dimensional sequence as an intermediate representation. The reason that it is difficult to directly convert the query tree into an n-dimensional feature vector is the complexity and variability of the query tree structure. Second, we propose a method to extract the syntactic features, as well as the semantic features when generating feature vector. Third, we propose a method to transform string feature values into numeric feature values, combining multiple statistical models. The combined model maps one string value to one numeric value by containing the multiple characteristic of each string value. In order to demonstrate the feasibility of our proposals in practical environments, we implement the SQLIA detection system based on PostgreSQL, a popular open source database system, and we perform experiments. The experimental results using the internal query trees of PostgreSQL validate that our proposal is effective in detecting SQLIAs, with at least 99.6% of the probability that the probability for malicious queries to be correctly predicted as SQLIA is greater than the probability for normal queries to be incorrectly predicted as SQLIA. Finally, we perform additional experiments to compare our proposal with syntax-focused feature extraction and single statistical model based on feature transformation. The experimental results show that our proposal significantly increases the probability of correctly detecting SQLIAs for various SQL statements, when compared to the previous methods.     

Modeling and global trajectory tracking control for an over-actuated MAV

This paper deals with an original micro aerial vehicle (MAV) design, the Omnicopter MAV. It has two central coaxial rotors with fixed-pitch propellers and three perimeter mounted ducted fans with servo motors performing thrust vectoring. Compared with traditional rotary wing MAVs that have inherent underactuation, the Omnicopter possesses some advantages in mobility, for example, lateral translation with zero attitude and hover with nonzero attitude. The trajectory tracking control design, global stability analysis, and control allocation are demonstrated through numerical simulation. The advantage of zero attitude translation is illustrated through experimental results.     

高斯类小波变换的开关电流频域法实现

提出了一种基于频域共享结构的高斯类小波变换的开关电流电路。通过分析在频域具有相似表达的Morlet、Marr和DOG小波函数,基于频域共享单元实现高斯类小波变换频域共享系统。频域共享高斯类小波变换采用开关电流技术实现,不同尺度上的高斯类小波变换可通过调节开关电流电路的时钟频率获得。仿真结果证实了提出方法的有效性。     

A saliency-driven robotic head with bio-inspired saccadic behaviors for social robotics

This paper presents a robotic head for social robots to attend to scene saliency with bio-inspired saccadic behaviors. Scene saliency is determined by measuring low-level static scene information, motion, and object prior knowledge. Towards the extracted saliency spots, the designed robotic head is able to turn gazes in a saccadic manner while obeying eye–head coordination laws with the proposed control scheme. The results of the simulation study and actual applications show the effectiveness of the proposed method in discovering of scene saliency and human-like head motion. The proposed techniques could possibly be applied to social robots to improve social sense and user experience in human–robot interaction.     

Adaptive Neural Control of a Hypersonic Vehicle in Discrete Time

The article investigates the discrete-time controller for the longitudinal dynamics of the hypersonic flight vehicle with throttle setting constraint. Based on functional decomposition, the dynamics can be decomposed into the altitude subsystem and the velocity subsystem. Furthermore, the discrete model could be derived using the Euler expansion. For the velocity subsystem, the controller is proposed by estimating the system uncertainty and unknown control gain separately with neural networks. The auxiliary error signal is designed to compensate the effect of throttle setting constraint. For the altitude subsystem, the desired control input is approximated by neural network while the error feedback is synthesized for the design. The singularity problem is avoided. Stability analysis proves that the errors of all the signals in the system are uniformly ultimately bounded. Simulation results show the effectiveness of the proposed controller.     

Research on the measure method of complaint theme influence on online social network

Consumer complaints on online social network quickly become online groups complaints through many people’s aggregation and looking on, interaction and word-of-mouth communication. Therefore, assessing and managing online complain influence has become a new problem for enterprise to listen to and manage online group complaints. This paper analyzed the complaint information feature of consumer group on online social network, from three-dimensional perspective of complaint text’s quality, transmission timeliness and user interaction degree. We built the influence measure model of online complaint theme based on entropy weight model by monitoring and analyzing real-time the static and dynamic properties of complaint information, explored the measure method of complaint theme influence, employed empirical method to verify the validity and provided scientific decision-making tools and methods for enterprise listening to and managing online group complains.     

Text classification using genetic algorithm oriented latent semantic features

In this paper, genetic algorithm oriented latent semantic features (GALSF) are proposed to obtain better representation of documents in text classification. The proposed approach consists of feature selection and feature transformation stages. The first stage is carried out using the state-of-the-art filter-based methods. The second stage employs latent semantic indexing (LSI) empowered by genetic algorithm such that a better projection is attained using appropriate singular vectors, which are not limited to the ones corresponding to the largest singular values, unlike standard LSI approach. In this way, the singular vectors with small singular values may also be used for projection whereas the vectors with large singular values may be eliminated as well to obtain better discrimination. Experimental results demonstrate that GALSF outperforms both LSI and filter-based feature selection methods on benchmark datasets for various feature dimensions.