 Electrical and Electronic Engineering  Research Publications
Electrical and Electronic Engineering  Research Publications
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ItemOn Eigenvalues of Laplacian Matrix for a Class of Directed Signed GraphsAhmadizadeh, S ; Shames, I ; Martin, S ; Nesic, D ( 20170512)The eigenvalues of the Laplacian matrix for a class of directed graphs with both positive and negative weights are studied. First, a class of directed signed graphs is investigated in which one pair of nodes (either connected or not) is perturbed with negative weights. A necessary condition is proposed to attain the following objective for the perturbed graph: the real parts of the nonzero eigenvalues of its Laplacian matrix are positive. A sufficient condition is also presented that ensures the aforementioned objective for unperturbed graph. It is then highlighted the case where the condition becomes necessary and sufficient. Secondly, for directed graphs, a subset of pairs of nodes are identified where if any of the pairs is connected by an edge with infinitesimal negative weight, the resulting Laplacian matrix will have at least one eigenvalue with negative real part. Illustrative examples are presented to show the applicability of our results.

ItemSecurity Metrics of Networked Control Systems under Sensor Attacks (extended preprint)Murguia, C ; Shames, I ; Ruths, J ; Nesic, D ( 20180914)As more attention is paid to security in the context of control systems and as attacks occur to real control systems throughout the world, it has become clear that some of the most nefarious attacks are those that evade detection. The term stealthy has come to encompass a variety of techniques that attackers can employ to avoid being detected. In this manuscript, for a class of perturbed linear timeinvariant systems, we propose two security metrics to quantify the potential impact that stealthy attacks could have on the system dynamics by tampering with sensor measurements. We provide analysis mathematical tools (in terms of linear matrix inequalities) to quantify these metrics for given system dynamics, control structure, system monitor, and set of sensors being attacked. Then, we provide synthesis tools (in terms of semidefinite programs) to redesign controllers and monitors such that the impact of stealthy attacks is minimized and the required attackfree system performance is guaranteed.

ItemOn Privacy of Quantized Sensor Measurements through Additive NoiseMurguia, C ; Shames, I ; Farokhi, F ; Nesic, D ( 20180910)We study the problem of maximizing privacy of quantized sensor measurements by adding random variables. In particular, we consider the setting where information about the state of a process is obtained using noisy sensor measurements. This information is quantized and sent to a remote station through an unsecured communication network. It is desired to keep the state of the process private; however, because the network is not secure, adversaries might have access to sensor information, which could be used to estimate the process state. To avoid an accurate state estimation, we add random numbers to the quantized sensor measurements and send the sum to the remote station instead. The distribution of these random variables is designed to minimize the mutual information between the sum and the quantized sensor measurements for a desired level of distortion  how different the sum and the quantized sensor measurements are allowed to be. Simulations are presented to illustrate our results.

ItemInformationTheoretic Privacy through Chaos Synchronization and Optimal Additive NoiseMurguia, C ; Shames, I ; Farokhi, F ; Nesic, D ( 20190603)We study the problem of maximizing privacy of data sets by adding random vectors generated via synchronized chaotic oscillators. In particular, we consider the setup where information about data sets, queries, is sent through public (unsecured) communication channels to a remote station. To hide private features (specific entries) within the data set, we corrupt the response to queries by adding random vectors. We send the distorted query (the sum of the requested query and the random vector) through the public channel. The distribution of the additive random vector is designed to minimize the mutual information (our privacy metric) between private entries of the data set and the distorted query. We cast the synthesis of this distribution as a convex program in the probabilities of the additive random vector. Once we have the optimal distribution, we propose an algorithm to generate pseudorandom realizations from this distribution using trajectories of a chaotic oscillator. At the other end of the channel, we have a second chaotic oscillator, which we use to generate realizations from the same distribution. Note that if we obtain the same realizations on both sides of the channel, we can simply subtract the realization from the distorted query to recover the requested query. To generate equal realizations, we need the two chaotic oscillators to be synchronized, i.e., we need them to generate exactly the same trajectories on both sides of the channel synchronously in time. We force the two chaotic oscillators into exponential synchronization using a driving signal. Exponential synchronization implies that trajectories of the oscillators converge to each other exponentially fast for all admissible initial conditions and are perfectly synchronized in the limit only. Thus, in finite time, there is always a “small” difference between their trajectories. To implement our algorithm, we assume (as it is often done in related work) that systems have been operating for sufficiently long time so that this small difference is negligible and oscillators are practically synchronized. We quantify the worstcase distortion induced by assuming perfect synchronization, and show that this distortion vanishes exponentially fast. Simulations are presented to illustrate our results.

ItemOrdinal Optimisation for the Gaussian Copula ModelChin, R ; Rowe, JE ; Shames, I ; Manzie, C ; Nešić, D ( 20191105)We present results on the estimation and evaluation of success probabilities for ordinal optimisation over uncountable sets (such as subsets of R d ). Our formulation invokes an assumption of a Gaussian copula model, and we show that the success probability can be equivalently computed by assuming a special case of additive noise. We formally prove a lower bound on the success probability under the Gaussian copula model, and numerical experiments demonstrate that the lower bound yields a reasonable approximation to the actual success probability. Lastly, we showcase the utility of our results by guaranteeing high success probabilities with ordinal optimisation.

ItemGaussian Processes with Monotonicity Constraints for Preference Learning from Pairwise ComparisonsChin, R ; Manzie, C ; Ira, A ; Nesic, D ; Shames, I (IEEE, 2018)In preference learning, it is beneficial to incorporate monotonicity constraints for learning utility functions when there is prior knowledge of monotonicity. We present a novel method for learning utility functions with monotonicity constraints using Gaussian process regression. Data is provided in the form of pairwise comparisons between items. Using conditions on monotonicity for the predictive function, an algorithm is proposed which uses the weighted average between prior linear and maximum a posteriori (MAP) utility estimates. This algorithm is formally shown to guarantee monotonicity of the learned utility function in the dimensions desired. The algorithm is tested in a Monte Carlo simulation case study, in which the results suggest that the learned utility by the proposed algorithm performs better in prediction than the standalone linear estimate, and enforces monotonicity unlike the MAP estimate.

ItemA machine learning approach for tuning model predictive controllersIra, AS ; Shames, I ; Manzie, C ; Chin, R ; Nesic, D ; Nakada, H ; Sano, T (IEEE, 20180101)Many industrial domains are characterized by MultipleInputMultipleOutput (MIMO) systems for which an explicit relationship capturing the nontrivial tradeoff between the competing objectives is not available. Human experts have the ability to implicitly learn such a relationship, which in turn enables them to tune the corresponding controller to achieve the desirable closedloop performance. However, as the complexity of the MIMO system and/or the controller increase, so does the tuning time and the associated tuning cost. To reduce the tuning cost, a framework is proposed in which a machine learning method for approximating the humanlearned cost function along with an optimization algorithm for optimizing it, and consequently tuning the controller, are employed. In this work the focus is on the tuning of Model Predictive Controllers (MPCs), given both the interest in their implementations across many industrial domains and the associated high degrees of freedom present in the corresponding tuning process. To demonstrate the proposed approach, simulation results for the tuning of an air path MPC controller in a diesel engine are presented.

ItemCorrigendum to "On eigenvalues of Laplacian matrix for a class of directed signed graphs" (vol 523, pg 281, 2017)Ahmadizadeh, S ; Shames, I ; Martin, S ; Nesic, D (Elsevier, 20171001)This note corrects an error in the results of Subsection 3.1 in authors' paper “On Eigenvalues of Laplacian Matrix for a Class of Directed Signed Graphs”, which appeared in Linear Algebra and its Applications 523 (2017), 281–306.

ItemOn eigenvalues of Laplacian matrix for a class of directed signed graphsAhmadizadeh, S ; Shames, I ; Martin, S ; Nesic, D (ELSEVIER SCIENCE INC, 20170615)The eigenvalues of the Laplacian matrix for a class of directed graphs with both positive and negative weights are studied. The Laplacian matrix naturally arises in a wide range of applications involving networks. First, a class of directed signed graphs is studied in which one pair of nodes (either connected or not) is perturbed with negative weights. A necessary and sufficient condition is proposed to attain the following objective for the perturbed graph: the real parts of the nonzero eigenvalues of its Laplacian matrix are positive. Under certain assumption on the unperturbed graph, it is established that the objective is achieved if and only if the magnitudes of the added negative weights are smaller than an easily computable upper bound. This upper bound is shown to depend on the topology of the unperturbed graph. It is also pointed out that the obtained condition can be applied in a recursive manner to deal with multiple edges with negative weights. Secondly, for directed graphs, a subset of pairs of nodes are identified where if any of the pairs is connected by an edge with infinitesimal negative weight, the resulting Laplacian matrix will have at least one eigenvalue with negative real part. Illustrative examples are presented to show the applicability of our results.

ItemCommunication Connectivity in Multiagent Systems with Multiple Uncooperative AgentsJu, Z ; Shames, I ; Nešić, D (IEEE, 20190601)We consider a multiagent system that consists of two group of agents: clients and routers. Clients move according to their own agenda. Routers' control policy needs to be designed to maintain communication connectivity for others. A control policy is designed for the routers to maintain the communication connectivity between clients. The control policy consists of periodically computing desired positions in a distributed manner and then steering the routers to those desired positions. First, desired positions of routers are determined by an optimization problem which minimizes the length of the longest edge connecting a client and a router on a tree corresponding to the communication relationship between agents. Then, the routers are steered to those desired positions by motion control. Simulations are done assuming that all routers are quadrotor drones to illustrate the results.
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