the continuous proliferation of more complex and various security threats leads to the conclusion that new solutions are required. Intrusion Detection systems can be a pertinent solution because they can deal withthe...
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ISBN:
(纸本)9781479917808
the continuous proliferation of more complex and various security threats leads to the conclusion that new solutions are required. Intrusion Detection systems can be a pertinent solution because they can deal withthe large data volumes of logs gathered from the multitude of systems and can even identify new types of attacks if based on anomaly detection. In this paper we propose an IDS model which includes two stages: feature selection with information gain and detection with Support Vector Machines(SVM). A draw-back of SVM is that its performance results are influenced by its user input parameters. therefore, in order to better the classifier we exploit the advantages of a recent Swarm Intelligence(SI) algorithm, the Bat Algorithm(BA), which we improve by enhancing its randomization with Levy flights. We test our model for the NSL-KDD dataset and prove that it can outperform the original BA, ABC or the popular PSO.
Computational social choice represents a new research area that brings together social choice theory and computerscience. One of the most important topics in computational social choice is the use of the voting theor...
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ISBN:
(纸本)9781479917808
Computational social choice represents a new research area that brings together social choice theory and computerscience. One of the most important topics in computational social choice is the use of the voting theory as part of the solutions for various problems in computerscience. In this paper we propose a formal representation approach for voting methods using complexity functions. Also, using this representation and the Weak theta asymptotic notation, we propose and prove several theorems related to the comparison of seven popular voting methods. the property of a voting method that we study in this paper is the following: a voting method is considered appropriate for a given voting scheme only if the method computes a single winner for that voting scheme;'no winner' or 'more than one winner' are not acceptable results.
High quality entropy data is a very useful asset for a large variety of applications, including computer security, simulations, and gaming. Not only is it important for this type of data to be indistinguishable from t...
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ISBN:
(纸本)9781479917808
High quality entropy data is a very useful asset for a large variety of applications, including computer security, simulations, and gaming. Not only is it important for this type of data to be indistinguishable from true randomness, it must also be possible to generate it in an efficient and scalable manner. Although entropy for local use can be easily obtained from various hardware sources, it does not always suffice, either in terms of quantity or quality. In this paper we explore the benefits of obtaining entropy using a distributed system. the rate at which entropy data is produced can be improved by generating it in parallel on multiple sites. Additionally, because the bits of entropy come from multiple and possibly heterogeneous sources, the quality of the entropy can also be improved. We propose a centralized, distributed system that is able to generate entropy on multiple sites across the network, and aggregate the generated data locally. We show that the system is secure, and that it offers good performance and high quality entropy.
In this paper we propose an effective method of aerial image classification, which combines three types of features: color-based, statistical and fractal information. Two distinct phases were necessary for the CBIR sy...
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ISBN:
(纸本)9781479917808
In this paper we propose an effective method of aerial image classification, which combines three types of features: color-based, statistical and fractal information. Two distinct phases were necessary for the CBIR system, which includes the classification algorithm: the learning phase and the classification phase. In the learning phase 5 different and efficient features were selected: entropy, contrast, homogeneity, mass fractal dimension and lacunarity. Also, three categories (classes) in CBIR were considered. the method of comparison, based on sub-images, improves the texture-based classification. A set of 100 aerial images from UAV was tested for establishing the rate of classification. the rate of 96% accurate classification, obtained as result, confirms the efficiency of the proposed method.
In the last decade Virtual Reality proved to be an efficient alternative for the traditional rehabilitation method for stroke survivors, which is based mainly on kinesiotherapy. A high number of IT systemsthat are fo...
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ISBN:
(纸本)9781479917808
In the last decade Virtual Reality proved to be an efficient alternative for the traditional rehabilitation method for stroke survivors, which is based mainly on kinesiotherapy. A high number of IT systemsthat are focusing on post stroke recovery are available at this moment. this paper contains a short description of two solutions available on the market followed by the technical details of a new idea. the solutions are analyzed, looking at their strong points, areas of improvement and common characteristics. the analysis is based not only on the design of the virtual environment and applicability in rehabilitation and is also based on the dependent libraries regarding their usability, scalability and extensibility. the principles behind the methods used for 3D visualization of the virtual reality environments, in neuromotor rehabilitation, can advance in design and implementation for creating effective solutions to help the post stroke survivors in their rehabilitation process.
this paper presents a non -cooperative distributed model predictive control algorithm used in cyber-physical systems for two agent systems coupled through the inputs. Each agent computes the optimal input trajectory f...
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ISBN:
(纸本)9781479917808
this paper presents a non -cooperative distributed model predictive control algorithm used in cyber-physical systems for two agent systems coupled through the inputs. Each agent computes the optimal input trajectory for its corresponding subsystem as the minimum of a local optimization problem. the input trajectory of the neighbor, which is used in the local optimization problem, is obtained based on the previous optimal control sequence and is received in a communication session. After that, the optimization problem is solved and the optimal input is sent to the process. this approach is computationally efficient because the communication between the two agents is reduced at minimum (only one session each sampling period) and the optimal input is obtained solving one optimization problem thus diminishing the overall computational time. the algorithm was implemented in Matlab and the obtained performances were compared with a cooperative distributed model predictive control strategy. Both methods were tested in simulation on a quadruple tanks process and the results recommend the noncooperative strategy which has similar results with less computational requirements.
the design of dictionaries for sparse representations is typically done by iterating two stages: compute sparse representations for the fixed dictionary and update the dictionary using the fixed representations. Most ...
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ISBN:
(纸本)9781479917808
the design of dictionaries for sparse representations is typically done by iterating two stages: compute sparse representations for the fixed dictionary and update the dictionary using the fixed representations. Most of the innovation in recent work was proposed for the update stage, while the representation stage was routinely done with Orthogonal Matching Pursuit (OMP), due to its low complexity. We investigate here the use of other greedy sparse representation algorithms, more computationally demanding than OMP but still with convenient complexity. these algorithms include a new proposal, the projection-based Orthogonal Least Squares. It turns out that the effect of using better representation algorithms may be more significant than improving the update stage, sometimes even leveling the performance of different update algorithms. the numerous experimental results presented here suggest which are the best combinations of methods and open new ways of designing and using dictionaries for sparse representations.
It is well-known that high-purity distillation columns are difficult to control due to their ill-conditioned and strongly nonlinear behaviour. the fact that these processes are operated over a wide range of feed compo...
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ISBN:
(纸本)9781479917808
It is well-known that high-purity distillation columns are difficult to control due to their ill-conditioned and strongly nonlinear behaviour. the fact that these processes are operated over a wide range of feed compositions and flow rates makes the control design even more challenging. this paper proposes the most suitable control strategies applicable to a series of cascaded distillation column processes. the conditions for control and input-output relations are discusssed in view of the global control strategy. the increase in complexity with increased number of series cascaded distillation column processes is tackled. Uncertainty in the model parameters is discussed with respect to the dynamics of the global train distillation process. the main outcome of this work is insight into the possible control methodologies for this particular class of distillation processes.
In the last decades, social choice theory has gained a significant popularity. Its main application areas are social sciences, political sciences, economic sciences and computerscience. Computational social choice is...
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ISBN:
(纸本)9781479917808
In the last decades, social choice theory has gained a significant popularity. Its main application areas are social sciences, political sciences, economic sciences and computerscience. Computational social choice is a new research area situated at the intersection of social choice theory and computerscience. Another popular and relatively new research area is swarm intelligence that aims to propose and use bio-inspired algorithms for solving optimization problems. In this paper we propose a methodology of comparing various swarm intelligence algorithms using voting methods (an important topic in social choice theory). Also, as a case study, we use our methodology to compare three swarm intelligence algorithms (Particle Swarm Optimization, Cat Swarm Optimization, and Artificial Bee Colony) on several minimization functions. For the interpretation of our comparison results, we use two important theorems: No Free Lunch theorem (from optimization theory) and Arrow's Impossibility theorem (from voting theory).
the effects of choosing among different control strategy when electric drives with BLDC PM motors are involved, lead inevitably to different heat losses even if the error performances are accepted as similar. A compar...
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ISBN:
(纸本)9781479917808
the effects of choosing among different control strategy when electric drives with BLDC PM motors are involved, lead inevitably to different heat losses even if the error performances are accepted as similar. A comparative study based on unconventional infrared thermal imaging is proposed to evaluate heat losses. Two cases are considered for medium power BLDC PM speed control. First control structure revises the classic P control loop and its practical implementation advantages. the second control algorithm present a modified PID control derived from state space approach. thermal infrared captures are externally made, at the housing level of the BLDC PM motor. When using current and speed feedback, the captured external temperature results higher, in comparison to the case when using only speed as correction signal. A trade off must be established between torque requirements, heat losses and tracking reference signal.
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