System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks: defense scheme and its optimality

In this paper, we investigate the defense problem against the joint attacks of denial-of-service attacks and data tampering attacks in the framework of system identification with binary-valued observations. By estimating the key parameters of the joint attack and compensating them in the identification algorithm, a compensation-oriented defense scheme is proposed. Then the identification algorithm of system parameter is designed and is further proved to be consistent. The asymptotic normality of the algorithm is obtained, and on this basis, we propose the optimal defense scheme. Furthermore, the implementation of the optimal defense scheme is discussed. Finally, a simulation example is presented to verify the effectiveness of the main results.     

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks: the optimality of attack strategy

With the development of wireless communication technology, cyber physical systems are applied in various fields such as industrial production and infrastructure, where lots of information exchange brings cyber security threats to the systems. From the perspective of system identification with binary-valued observations, we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering attacks. The packet loss rate and the data tampering rate caused by the attack is given, and the estimation error is derived. Then the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with constraints. The explicit expression of the optimal attack strategy is obtained. Simulation examples are presented to verify the effectiveness of the main conclusions.     

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks:the optimality of attack strategy

With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security threats to the systems.From the perspective of system identification with binary-valued observations,we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering attacks.The packet loss rate and the data tampering rate caused by the attack is given,and the estimation error is derived.Then the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with constraints.The explicit expression of the optimal attack strategy is obtained.Simulation examples are presented to verify the effectiveness of the main conclusions.     

Ranking data with ordinal labels: optimality and pairwise aggregation

The paper describes key insights in order to grasp the nature of K-partite ranking. From the theoretical side, the various characterizations of optimal elements are fully described, as well as the likelihood ratio monotonicity condition on the underlying distribution which guarantees that such elements do exist. Then, a pairwise aggregation procedure based on Kendall tau is introduced to relate learning rules dedicated to bipartite ranking and solutions of the K-partite ranking problem. Criteria reflecting ranking performance under these conditions such as the ROC surface and its natural summary, the volume under the ROC surface (VUS), are then considered as targets for empirical optimization. The consistency of pairwise aggregation strategies are studied under these criteria and shown to be efficient under reasonable assumptions. Eventually, numerical results illustrate the relevance of the methodology proposed.     

An iterative multiresolution scheme for SFM with missing data: Single and multiple object scenes

Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach.     

A new modelling and identification scheme for time-delay systems with experimental investigation: a relay feedback approach

In this paper, the conventional relay feedback test has been modified for modelling and identification of a class of real-time dynamical systems in terms of linear transfer function models with time-delay. An ideal relay and unknown systems are connected through a negative feedback loop to bring the sustained oscillatory output around the non-zero setpoint. Thereafter, the obtained limit cycle information is substituted in the derived mathematical equations for accurate identification of unknown plants in terms of overdamped, underdamped, critically damped second-order plus dead time and stable first-order plus dead time transfer function models. Typical examples from the literature are included for the validation of the proposed identification scheme through computer simulations. Subsequently, the comparisons between estimated model and true system are drawn through integral absolute error criterion and frequency response plots. Finally, the obtained output responses through simulations are verified experimentally on real-time liquid level control system using Yokogawa Distributed Control System CENTUM CS3000 set up.     

An addendum and correction to: ‘On identification and adaptive estimation for systems with interrupted observations’

An error in the proof of the Theorem 1 of a previous paper of the author's is corrected. It is also pointed out that an important class of system/signal models, called composite sources, can be cast into the framework of the model considered in the referenced paper. Thus, the parameter estimation/system identification results of the paper are applicable to the composite source system model.     

Closed-loop identification with routine operating data: Effect of time delay and sampling time

In industry, in order to store the reams of data that are collected from all the different flow, level, and temperature sensors, the fast-sampled data is very often downsampled before being stored in a data historian. This downsampled or even compressed data is, then, often used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine operating data. It is shown that if the discrete time delay in a process is sufficiently large or the sampling time is sufficiently small, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine operating data. Using both Monte Carlo simulations and an experimental set-up with a heated tank, the effect of discrete time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model. As well, the models obtained using a PID controller were less sensitive to sampling time than those obtained using a PI controller. However, the PID controller values were more sensitive to the effects of aliasing at larger sampling times.     

Erratum to: An improved multimodal signal-image compression scheme with application to natural images and biomedical data

Multimedia Tools and Applications -     

Monitoring insect migration with radar: the ventral‐aspect polarization pattern and its potential for target identification

Automated vertical‐beam radars employing linear polarization and incorporating a narrow‐angle conical scan provide an effective means for season‐long monitoring of insect migratory activity at altitudes up to about 1 km. As well as detecting and quantifying migration events, these radars provide information about the identity of the targets they detect; such information may provide a means for excluding unwanted taxa from the radar‐observation summaries that are produced for pest managers and other potential users. The copolar‐linear polarization pattern (CLPP), a property of individual targets that is retrieved from the IMR data during routine processing, provides an indication of the shape of the target when viewed from below (i.e. ventrally). The form of the CLPP for insect targets, and the possible extent of its variation, is determined from scattering theory and symmetry considerations. CLPP parameters retrieved from a sample of echoes recorded at a site in inland Australia during a night when two different types of insect were migrating are consistent with the theory, but not all possible parameter combinations occurred. CLPP parameters are correlated with both target size and wingbeat frequency, and on this occasion all three characters discriminated reliably between the two target classes.     

Service robots: System design for tracking people through data fusion and initiating interaction with the human group by inferring social situations

In a situation where a robot initiates conversation with a group of people, questions such as “where is the people group?” and “whether the robot should approach them?” should be addressed. This paper develops a new system that enables a robot to determine whether or not it should approach the aforementioned human group and interact with them after identifying what the current social situation is. The system is mainly to fuse depth-related data to track the positions of a group of people, extract social cues of those people by using depth-related data and a decision network (DN) model, and the main challenge lies in understanding the social cues of the group and the current underlying social situation concerning the relation between the robot and the group. The social cues are based on Proxemics and F-formations, whereas the social situations are categorized as individual-to-individual, individual-to-robot, robot-to-individual, group-to-robot, robot-to-group, confidential discussion and group discussion. Our system proceeds as follows : once a group of people are detected and the social cues of that target group of people are extracted, the corresponding social situation is appropriately inferred, and in turn the robot decides whether it should initiate conversation with the group based on rules to be specified later. The conducted experimental results demonstrate the properness of the system design and the efficacy of the proposed method in recognizing the social cues among individuals of the group as well as the nature of the social situations concerning the group and the robot.     

Use of ICT and its relationship with performance in examinations: a comparison of the ImpaCT2 project's research findings using pupil-level, school-level and multilevel modelling data

Abstract This paper reports a previously unpublished comparative analysis of data from the ImpaCT2 study investigating the relationship between students' performance in England on national tests and their reported use of information technology (particularly networked technology) for school work, at three age levels (11, 14 and 16), in English, Maths and Science (and in additional subjects at age 16). The new analysis compares three separate analyses of the same data set based on approximately 1100 children drawn from 27 primary and 28 secondary schools: the first analysis considers the individual pupil data and looks particularly for pupil‐level (and particularly gender) effects; the second analysis groups the pupils by school, and looks particularly for school‐level effects; the third analysis was a more conservative multilevel modelling analysis, carried out by an independent team at the University of Durham, which split the schools into three groups, based on overall high, medium or low level of use of information and communications technology (ICT) in each school. All three analyses reported findings based on mean achievement measured in terms of standardised residual gain scores, where each student's progress was measured against predicted gains calculated from baseline data on reading and Maths ability gathered 2 years earlier. The results indicated broad agreement between each of the three modes of analysis, though effect sizes were slightly smaller in the case of the multilevel modelling. The findings are interpreted in relation to case study data on ICT use in schools gathered as part of the wider ImpaCT2 investigation, but the case is also made that the common rationale for using multilevel modelling with large data sets, namely that whole class effects are more significant determinants of performance than individual effects, is less applicable in this study, because of wide variation in the use by children of ICT for school work at home.