Visible to the public Biblio

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2020-07-06
Sheela, A., Revathi, S., Iqbal, Atif.  2019.  Cyber Risks Assessment For Intelligent And Non-Intelligent Attacks In Power System. 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC). :40–45.
Smart power grid is a perfect model of Cyber Physical System (CPS) which is an important component for a comfortable life. The major concern of the electrical network is safety and reliable operation. A cyber attacker in the operation of power system would create a major damage to the entire power system structure and affect the continuity of the power supply by adversely changing its parameters. A risk assessment method is presented for evaluating the cyber security assessment of power systems taking into consideration the need for protection systems. The paper considers the impact of bus and transmission line protection systems located in substations on the cyber physical performance of power systems. The proposed method is to simulate the response of power systems to sudden attacks on various power system preset value and parameters. This paper focuses on the cyber attacks which occur in a co-ordinated way so that many power system components will be in risk. The risk can be modelled as the combined probability of power system impact due to attacks and of successful interruption into the system. Stochastic Petri Nets is employed for assessing the risks. The effectiveness of the proposed cyber security risk assessment method is simulated for a IEEE39 bus system.
Tripathi, Dipty, Maurya, Ashish Kumar, Chaturvedi, Amrita, Tripathi, Anil Kumar.  2019.  A Study of Security Modeling Techniques for Smart Systems. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). :87–92.
The term “smart” has been used in many ways for describing systems and infrastructure such as smart city, smart home, smart grid, smart meter, etc. These systems may lie in the domain of critical security systems where security can be estimated in terms of confidentiality, integrity and some cases may involve availability for protection against the theft or damage of system resources as well as disruption of the system services. Although, in spite of, being a hot topic to enhance the quality of life, there is no concrete definition of what smart system is and what should be the characteristics of it. Thus, there is a need to identify what these systems actually are and how they can be designed securely. This work firstly attempts to describe attributes related to the smartness to define smart systems. Furthermore, we propose a secure smart system development life cycle, where the security is weaved at all the development phase of smart systems according to principles, guidelines, attack patterns, risk, vulnerability, exploits, and defined rules. Finally, the comparative study is performed for evaluation of traditional security modeling techniques for early assessment of threats and risks in smart systems.
Frias, Alex Davila, Yodo, Nita, Yadav, Om Prakash.  2019.  Mixed-Degradation Profiles Assessment of Critical Components in Cyber-Physical Systems. 2019 Annual Reliability and Maintainability Symposium (RAMS). :1–6.
This paper presents a general model to assess the mixed-degradation profiles of critical components in a Cyber-Physical System (CPS) based on the reliability of its critical physical and software components. In the proposed assessment, the cyber aspect of a CPS was approached from a software reliability perspective. Although extensive research has been done on physical components degradation and software reliability separately, research for the combined physical-software systems is still scarce. The non-homogeneous Poisson Processes (NHPP) software reliability models are deemed to fit well with the real data and have descriptive and predictive abilities, which could make them appropriate to estimate software components reliability. To show the feasibility of the proposed approach, a case study for mixed-degradation profiles assessment is presented with n physical components and one major software component forming a critical subsystem in CPS. Two physical components were assumed to have different degradation paths with the dependency between them. Series and parallel structures were investigated for physical components. The software component failure data was taken from a wireless network switching center and fitted into a Weibull software reliability model. The case study results revealed that mix-degradation profiles of physical components, combined with software component profile, produced a different CPS reliability profile.
2020-06-08
Sahabandu, Dinuka, Moothedath, Shana, Bushnell, Linda, Poovendran, Radha, Aller, Joey, Lee, Wenke, Clark, Andrew.  2019.  A Game Theoretic Approach for Dynamic Information Flow Tracking with Conditional Branching. 2019 American Control Conference (ACC). :2289–2296.
In this paper, we study system security against Advanced Persistent Threats (APTs). APTs are stealthy and persistent but APTs interact with system and introduce information flows in the system as data-flow and control-flow commands. Dynamic Information Flow Tracking (DIFT) is a promising detection mechanism against APTs which taints suspicious input sources in the system and performs online security analysis when a tainted information is used in unauthorized manner. Our objective in this paper is to model DIFT that handle data-flow and conditional branches in the program that arise from control-flow commands. We use game theoretic framework and provide the first analytical model of DIFT with data-flow and conditional-branch tracking. Our game model which is an undiscounted infinite-horizon stochastic game captures the interaction between APTs and DIFT and the notion of conditional branching. We prove that the best response of the APT is a maximal reachability probability problem and provide a polynomial-time algorithm to find the best response by solving a linear optimization problem. We formulate the best response of the defense as a linear optimization problem and show that an optimal solution to the linear program returns a deterministic optimal policy for the defense. Since finding Nash equilibrium for infinite-horizon undiscounted stochastic games is computationally difficult, we present a nonlinear programming based polynomial-time algorithm to find an E-Nash equilibrium. Finally, we perform experimental analysis of our algorithm on real-world data for NetRecon attack augmented with conditional branching.
Chugunkov, Ilya V., Ivanov, Michael A., Kliuchnikova, Bogdana V..  2019.  Hash Functions are Based on Three-Dimensional Stochastic Transformations. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :202–205.
The methods are based on injecting unpredictability into means and objects of protection are called stochastic methods of information security. The effective protection can be done only by using stochastic methods against an active opponent. The effectiveness of stochastic protection methods is defined by the quality of the used pseudo-random number generators and hash functions. The proposed hashing algorithm DOZENHASH is based on the using of 3D stochastic transformations of DOZEN family. The principal feature of the algorithm is that all input and output data blocks as well as intermediate results of calculations are represented as three-dimensional array of bytes with 4 bytes in each dimension. Thus, the resulting transformation has a high degree of parallelism at the level of elementary operations, in other words, it is focused on the implementation using heterogeneous supercomputer technologies.
2020-05-18
Nambiar, Sindhya K, Leons, Antony, Jose, Soniya, Arunsree.  2019.  Natural Language Processing Based Part of Speech Tagger using Hidden Markov Model. 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :782–785.
In various natural language processing applications, PART-OF-SPEECH (POS) tagging is performed as a preprocessing step. For making POS tagging accurate, various techniques have been explored. But in Indian languages, not much work has been done. This paper describes the methods to build a Part of speech tagger by using hidden markov model. Supervised learning approach is implemented in which, already tagged sentences in malayalam is used to build hidden markov model.
2020-04-24
Smychkova, Anna, Zhukov, Dmitry.  2019.  Complex of Description Models for Analysis and Control Group Behavior Based on Stochastic Cellular Automata with Memory and Systems of Differential Kinetic Equations. 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA). :218—223.

This paper considers the complex of models for the description, analysis, and modeling of group behavior by user actions in complex social systems. In particular, electoral processes can be considered in which preferences are selected from several possible ones. For example, for two candidates, the choice is made from three states: for the candidate A, for candidate B and undecided (candidate C). Thus, any of the voters can be in one of the three states, and the interaction between them leads to the transition between the states with some delay time intervals, which are one of the parameters of the proposed models. The dynamics of changes in the preferences of voters can be described graphically on diagram of possible transitions between states, on the basis of which is possible to write a system of differential kinetic equations that describes the process. The analysis of the obtained solutions shows the possibility of existence within the model, different modes of changing the preferences of voters. In the developed model of stochastic cellular automata with variable memory at each step of the interaction process between its cells, a new network of random links is established, the minimum and the maximum number of which is selected from a given range. At the initial time, a cell of each type is assigned a numeric parameter that specifies the number of steps during which will retain its type (cell memory). The transition of cells between states is determined by the total number of cells of different types with which there was interaction at the given number of memory steps. After the number of steps equal to the depth of memory, transition to the type that had the maximum value of its sum occurs. The effect of external factors (such as media) on changes in node types can set for each step using a transition probability matrix. Processing of the electoral campaign's sociological data of 2015-2016 at the choice of the President of the United States using the method of almost-periodic functions allowed to estimate the parameters of a set of models and use them to describe, analyze and model the group behavior of voters. The studies show a good correspondence between the data observed in sociology and calculations using a set of developed models. Under some sets of values of the coefficients in the differential equations and models of cellular automata are observed the oscillating and almost-periodic character of changes in the preferences of the electorate, which largely coincides with the real observations.

2020-04-20
Lefebvre, Dimitri, Hadjicostis, Christoforos N..  2019.  Trajectory-observers of timed stochastic discrete event systems: Applications to privacy analysis. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). :1078–1083.
Various aspects of security and privacy in many application domains can be assessed based on proper analysis of successive measurements that are collected on a given system. This work is devoted to such issues in the context of timed stochastic Petri net models. We assume that certain events and part of the marking trajectories are observable to adversaries who aim to determine when the system is performing secret operations, such as time intervals during which the system is executing certain critical sequences of events (as captured, for instance, in language-based opacity formulations). The combined use of the k-step trajectory-observer and the Markov model of the stochastic Petri net leads to probabilistic indicators helpful for evaluating language-based opacity of the given system, related timing aspects, and possible strategies to improve them.
2020-02-18
Nasr, Milad, Shokri, Reza, Houmansadr, Amir.  2019.  Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-Box Inference Attacks against Centralized and Federated Learning. 2019 IEEE Symposium on Security and Privacy (SP). :739–753.

Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a comprehensive privacy analysis of deep learning models. We measure the privacy leakage through parameters of fully trained models as well as the parameter updates of models during training. We design inference algorithms for both centralized and federated learning, with respect to passive and active inference attackers, and assuming different adversary prior knowledge. We evaluate our novel white-box membership inference attacks against deep learning algorithms to trace their training data records. We show that a straightforward extension of the known black-box attacks to the white-box setting (through analyzing the outputs of activation functions) is ineffective. We therefore design new algorithms tailored to the white-box setting by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, which is the algorithm used to train deep neural networks. We investigate the reasons why deep learning models may leak information about their training data. We then show that even well-generalized models are significantly susceptible to white-box membership inference attacks, by analyzing state-of-the-art pre-trained and publicly available models for the CIFAR dataset. We also show how adversarial participants, in the federated learning setting, can successfully run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.

2020-01-20
Noma, Adamu Muhammad, Muhammad, Abdullah.  2019.  Stochastic Heuristic Approach to Addition Chain Problem in PKC for Efficiency and Security Effectiveness. 2019 International Conference on Information Networking (ICOIN). :55–59.

This paper shows that stochastic heuristic approach for implicitly solving addition chain problem (ACP) in public-key cryptosystem (PKC) enhances the efficiency of the PKC and improves the security by blinding the multiplications/squaring operations involved against side-channel attack (SCA). We show that while the current practical heuristic approaches being deterministic expose the fixed pattern of the operations, using stochastic method blinds the pattern by being unpredictable and generating diffident pattern of operation for the same exponent at a different time. Thus, if the addition chain (AC) is generated implicitly every time the exponentiation operation is being made, needless for such approaches as padding by insertion of dummy operations and the operation is still totally secured against the SCA. Furthermore, we also show that the stochastic approaches, when carefully designed, further reduces the length of the operation than state-of-the-art practical methods for improving the efficiency. We demonstrated our investigation by implementing RSA cryptosystem using the stochastic approach and the results benchmarked with the existing current methods.

2019-12-02
Elfar, Mahmoud, Zhu, Haibei, Cummings, M. L., Pajic, Miroslav.  2019.  Security-Aware Synthesis of Human-UAV Protocols. 2019 International Conference on Robotics and Automation (ICRA). :8011–8017.
In this work, we synthesize collaboration protocols for human-unmanned aerial vehicle (H-UAV) command and control systems, where the human operator aids in securing the UAV by intermittently performing geolocation tasks to confirm its reported location. We first present a stochastic game-based model for the system that accounts for both the operator and an adversary capable of launching stealthy false-data injection attacks, causing the UAV to deviate from its path. We also describe a synthesis challenge due to the UAV's hidden-information constraint. Next, we perform human experiments using a developed RESCHU-SA testbed to recognize the geolocation strategies that operators adopt. Furthermore, we deploy machine learning techniques on the collected experimental data to predict the correctness of a geolocation task at a given location based on its geographical features. By representing the model as a delayed-action game and formalizing the system objectives, we utilize off-the-shelf model checkers to synthesize protocols for the human-UAV coalition that satisfy these objectives. Finally, we demonstrate the usefulness of the H-UAV protocol synthesis through a case study where the protocols are experimentally analyzed and further evaluated by human operators.
2019-11-12
Vizarreta, Petra, Sakic, Ermin, Kellerer, Wolfgang, Machuca, Carmen Mas.  2019.  Mining Software Repositories for Predictive Modelling of Defects in SDN Controller. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :80-88.

In Software Defined Networking (SDN) control plane of forwarding devices is concentrated in the SDN controller, which assumes the role of a network operating system. Big share of today's commercial SDN controllers are based on OpenDaylight, an open source SDN controller platform, whose bug repository is publicly available. In this article we provide a first insight into 8k+ bugs reported in the period over five years between March 2013 and September 2018. We first present the functional components in OpenDaylight architecture, localize the most vulnerable modules and measure their contribution to the total bug content. We provide high fidelity models that can accurately reproduce the stochastic behaviour of bug manifestation and bug removal rates, and discuss how these can be used to optimize the planning of the test effort, and to improve the software release management. Finally, we study the correlation between the code internals, derived from the Git version control system, and software defect metrics, derived from Jira issue tracker. To the best of our knowledge, this is the first study to provide a comprehensive analysis of bug characteristics in a production grade SDN controller.

2019-03-28
Sahabandu, D., Xiao, B., Clark, A., Lee, S., Lee, W., Poovendran, R..  2018.  DIFT Games: Dynamic Information Flow Tracking Games for Advanced Persistent Threats. 2018 IEEE Conference on Decision and Control (CDC). :1136-1143.
Dynamic Information Flow Tracking (DIFT) has been proposed to detect stealthy and persistent cyber attacks that evade existing defenses such as firewalls and signature-based antivirus systems. A DIFT defense taints and tracks suspicious information flows across the network in order to identify possible attacks, at the cost of additional memory overhead for tracking non-adversarial information flows. In this paper, we present the first analytical model that describes the interaction between DIFT and adversarial information flows, including the probability that the adversary evades detection and the performance overhead of the defense. Our analytical model consists of a multi-stage game, in which each stage represents a system process through which the information flow passes. We characterize the optimal strategies for both the defense and adversary, and derive efficient algorithms for computing the strategies. Our results are evaluated on a realworld attack dataset obtained using the Refinable Attack Investigation (RAIN) framework, enabling us to draw conclusions on the optimal adversary and defense strategies, as well as the effect of valid information flows on the interaction between adversary and defense.
2019-03-22
Liu, Y., Li, X., Xiao, L..  2018.  Service Oriented Resilience Strategy for Cloud Data Center. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :269-274.

As an information hinge of various trades and professions in the era of big data, cloud data center bears the responsibility to provide uninterrupted service. To cope with the impact of failure and interruption during the operation on the Quality of Service (QoS), it is important to guarantee the resilience of cloud data center. Thus, different resilience actions are conducted in its life circle, that is, resilience strategy. In order to measure the effect of resilience strategy on the system resilience, this paper propose a new approach to model and evaluate the resilience strategy for cloud data center focusing on its core part of service providing-IT architecture. A comprehensive resilience metric based on resilience loss is put forward considering the characteristic of cloud data center. Furthermore, mapping model between system resilience and resilience strategy is built up. Then, based on a hierarchical colored generalized stochastic petri net (HCGSPN) model depicting the procedure of the system processing the service requests, simulation is conducted to evaluate the resilience strategy through the metric calculation. With a case study of a company's cloud data center, the applicability and correctness of the approach is demonstrated.

2019-03-06
Liu, Y., Wang, Y., Lombardi, F., Han, J..  2018.  An Energy-Efficient Stochastic Computational Deep Belief Network. 2018 Design, Automation Test in Europe Conference Exhibition (DATE). :1175-1178.

Deep neural networks (DNNs) are effective machine learning models to solve a large class of recognition problems, including the classification of nonlinearly separable patterns. The applications of DNNs are, however, limited by the large size and high energy consumption of the networks. Recently, stochastic computation (SC) has been considered to implement DNNs to reduce the hardware cost. However, it requires a large number of random number generators (RNGs) that lower the energy efficiency of the network. To overcome these limitations, we propose the design of an energy-efficient deep belief network (DBN) based on stochastic computation. An approximate SC activation unit (A-SCAU) is designed to implement different types of activation functions in the neurons. The A-SCAU is immune to signal correlations, so the RNGs can be shared among all neurons in the same layer with no accuracy loss. The area and energy of the proposed design are 5.27% and 3.31% (or 26.55% and 29.89%) of a 32-bit floating-point (or an 8-bit fixed-point) implementation. It is shown that the proposed SC-DBN design achieves a higher classification accuracy compared to the fixed-point implementation. The accuracy is only lower by 0.12% than the floating-point design at a similar computation speed, but with a significantly lower energy consumption.

2019-02-25
Akcay, A., Martagan, T., Corlu, C. G..  2018.  RISK ASSESSMENT IN PHARMACEUTICAL SUPPLY CHAINS UNDER UNKNOWN INPUT-MODEL PARAMETERS. 2018 Winter Simulation Conference (WSC). :3132–3143.
We consider a pharmaceutical supply chain where the manufacturer sources a customized product with unique attributes from a set of unreliable suppliers. We model the likelihood of a supplier to successfully deliver the product via Bayesian logistic regression and use simulation to obtain the posterior distribution of the unknown parameters of this model. We study the role of so-called input-model uncertainty in estimating the likelihood of the supply failure, which is the probability that none of the suppliers in a given supplier portfolio can successfully deliver the product. We investigate how the input-model uncertainty changes with respect to the characteristics of the historical data on the past realizations of the supplier performances and the product attributes.
2019-02-22
Yu, R., Xue, G., Kilari, V. T., Zhang, X..  2018.  Deploying Robust Security in Internet of Things. 2018 IEEE Conference on Communications and Network Security (CNS). :1-9.

Popularization of the Internet-of-Things (IoT) has brought widespread concerns on IoT security, especially in face of several recent security incidents related to IoT devices. Due to the resource-constrained nature of many IoT devices, security offloading has been proposed to provide good-enough security for IoT with minimum overhead on the devices. In this paper, we investigate the inevitable risk associated with security offloading: the unprotected and unmonitored transmission from IoT devices to the offloaded security mechanisms. An important challenge in modeling the security risk is the dynamic nature of IoT due to demand fluctuations and infrastructure instability. We propose a stochastic model to capture both the expected and worst-case security risks of an IoT system. We then propose a framework to efficiently address the optimal robust deployment of security mechanisms in IoT. We use results from extensive simulations to demonstrate the superb performance and efficiency of our approach compared to several other algorithms.

Nie, J., Tang, H., Wei, J..  2018.  Analysis on Convergence of Stochastic Processes in Cloud Computing Models. 2018 14th International Conference on Computational Intelligence and Security (CIS). :71-76.
On cloud computing systems consisting of task queuing and resource allocations, it is essential but hard to model and evaluate the global performance. In most of the models, researchers use a stochastic process or several stochastic processes to describe a real system. However, due to the absence of theoretical conclusions of any arbitrary stochastic processes, they approximate the complicated model into simple processes that have mathematical results, such as Markov processes. Our purpose is to give a universal method to deal with common stochastic processes as long as the processes can be expressed in the form of transition matrix. To achieve our purpose, we firstly prove several theorems about the convergence of stochastic matrices to figure out what kind of matrix-defined systems has steady states. Furthermore, we propose two strategies for measuring the rate of convergence which reflects how fast the system would come to its steady state. Finally, we give a method for reducing a stochastic matrix into smaller ones, and perform some experiments to illustrate our strategies in practice.
2018-12-10
Farooq, M. J., Zhu, Q..  2018.  On the Secure and Reconfigurable Multi-Layer Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT). IEEE Transactions on Wireless Communications. 17:2618–2632.

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas, such as smart homes, smart cities, health care, and transportation. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to battlefield specific challenges, such as the absence of communication infrastructure, heterogeneity of devices, and susceptibility to cyber-physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and information dissemination in the presence of adversaries. This paper aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to quantify the information dissemination among heterogeneous network devices. Consequently, a tractable optimization problem is formulated that can assist commanders in cost effectively planning the network and reconfiguring it according to the changing mission requirements.

2018-08-23
Lagunas, E., Rugini, L..  2017.  Performance of compressive sensing based energy detection. 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). :1–5.

This paper investigates closed-form expressions to evaluate the performance of the Compressive Sensing (CS) based Energy Detector (ED). The conventional way to approximate the probability density function of the ED test statistic invokes the central limit theorem and considers the decision variable as Gaussian. This approach, however, provides good approximation only if the number of samples is large enough. This is not usually the case in CS framework, where the goal is to keep the sample size low. Moreover, working with a reduced number of measurements is of practical interest for general spectrum sensing in cognitive radio applications, where the sensing time should be sufficiently short since any time spent for sensing cannot be used for data transmission on the detected idle channels. In this paper, we make use of low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. More precisely, this paper provides new closed-form expressions for accurate evaluation of the CS-based ED performance as a function of the compressive ratio and the Signal-to-Noise Ratio (SNR). Simulation results demonstrate the increased accuracy of the proposed equations compared to existing works.

2018-06-07
Aygun, R. C., Yavuz, A. G..  2017.  Network Anomaly Detection with Stochastically Improved Autoencoder Based Models. 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud). :193–198.

Intrusion detection systems do not perform well when it comes to detecting zero-day attacks, therefore improving their performance in that regard is an active research topic. In this study, to detect zero-day attacks with high accuracy, we proposed two deep learning based anomaly detection models using autoencoder and denoising autoencoder respectively. The key factor that directly affects the accuracy of the proposed models is the threshold value which was determined using a stochastic approach rather than the approaches available in the current literature. The proposed models were tested using the KDDTest+ dataset contained in NSL-KDD, and we achieved an accuracy of 88.28% and 88.65% respectively. The obtained results show that, as a singular model, our proposed anomaly detection models outperform any other singular anomaly detection methods and they perform almost the same as the newly suggested hybrid anomaly detection models.

Kang, E. Y., Mu, D., Huang, L., Lan, Q..  2017.  Verification and Validation of a Cyber-Physical System in the Automotive Domain. 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :326–333.
Software development for Cyber-Physical Systems (CPS), e.g., autonomous vehicles, requires both functional and non-functional quality assurance to guarantee that the CPS operates safely and effectively. EAST-ADL is a domain specific architectural language dedicated to safety-critical automotive embedded system design. We have previously modified EAST-ADL to include energy constraints and transformed energy-aware real-time (ERT) behaviors modeled in EAST-ADL/Stateflow into UPPAAL models amenable to formal verification. Previous work is extended in this paper by including support for Simulink and an integration of Simulink/Stateflow (S/S) within the same too lchain. S/S models are transformed, based on the extended ERT constraints with probability parameters, into verifiable UPPAAL-SMC models and integrate the translation with formal statistical analysis techniques: Probabilistic extension of EAST-ADL constraints is defined as a semantics denotation. A set of mapping rules is proposed to facilitate the guarantee of translation. Formal analysis on both functional- and non-functional properties is performed using Simulink Design Verifier and UPPAAL-SMC. Our approach is demonstrated on the autonomous traffic sign recognition vehicle case study.
Hinojosa, V., Gonzalez-Longatt, F..  2017.  Stochastic security-constrained generation expansion planning methodology based on a generalized line outage distribution factors. 2017 IEEE Manchester PowerTech. :1–6.

In this study, it is proposed to carry out an efficient formulation in order to figure out the stochastic security-constrained generation capacity expansion planning (SC-GCEP) problem. The main idea is related to directly compute the line outage distribution factors (LODF) which could be applied to model the N - m post-contingency analysis. In addition, the post-contingency power flows are modeled based on the LODF and the partial transmission distribution factors (PTDF). The post-contingency constraints have been reformulated using linear distribution factors (PTDF and LODF) so that both the pre- and post-contingency constraints are modeled simultaneously in the SC-GCEP problem using these factors. In the stochastic formulation, the load uncertainty is incorporated employing a two-stage multi-period framework, and a K - means clustering technique is implemented to decrease the number of load scenarios. The main advantage of this methodology is the feasibility to quickly compute the post-contingency factors especially with multiple-line outages (N - m). This concept would improve the security-constraint analysis modeling quickly the outage of m transmission lines in the stochastic SC-GCEP problem. It is carried out several experiments using two electrical power systems in order to validate the performance of the proposed formulation.

Hinojosa, V..  2017.  A generalized stochastic N-m security-constrained generation expansion planning methodology using partial transmission distribution factors. 2017 IEEE Power Energy Society General Meeting. :1–5.

This study proposes to apply an efficient formulation to solve the stochastic security-constrained generation capacity expansion planning (GCEP) problem using an improved method to directly compute the generalized generation distribution factors (GGDF) and the line outage distribution factors (LODF) in order to model the pre- and the post-contingency constraints based on the only application of the partial transmission distribution factors (PTDF). The classical DC-based formulation has been reformulated in order to include the security criteria solving both pre- and post-contingency constraints simultaneously. The methodology also takes into account the load uncertainty in the optimization problem using a two-stage multi-period model, and a clustering technique is used as well to reduce load scenarios (stochastic problem). The main advantage of this methodology is the feasibility to quickly compute the LODF especially with multiple-line outages (N-m). This idea could speed up contingency analyses and improve significantly the security-constrained analyses applied to GCEP problems. It is worth to mentioning that this approach is carried out without sacrificing optimality.

2018-05-01
Wang, X., Zhou, S..  2017.  Accelerated Stochastic Gradient Method for Support Vector Machines Classification with Additive Kernel. 2017 First International Conference on Electronics Instrumentation Information Systems (EIIS). :1–6.

Support vector machines (SVMs) have been widely used for classification in machine learning and data mining. However, SVM faces a huge challenge in large scale classification tasks. Recent progresses have enabled additive kernel version of SVM efficiently solves such large scale problems nearly as fast as a linear classifier. This paper proposes a new accelerated mini-batch stochastic gradient descent algorithm for SVM classification with additive kernel (AK-ASGD). On the one hand, the gradient is approximated by the sum of a scalar polynomial function for each feature dimension; on the other hand, Nesterov's acceleration strategy is used. The experimental results on benchmark large scale classification data sets show that our proposed algorithm can achieve higher testing accuracies and has faster convergence rate.