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IEEE Bombay Section Signature Conference (IBSSC)

作者： Trupti Chandak Chaitanya Ghorpade Sanyam Shukla Computer Science and Engineering MANIT Bhopal India

ISBN: (纸本)9781538674765

Malicious activities can harm the security of the system. These activities must be avoided. Network traffic data can be monitored and analyzed by using intrusion detection system. Different data mining classification techniques are used to detect network attacks. Dimensionality reduction performs key role in the Intrusion Detection System, since detecting anomalies is time-consuming. Recently a lot of work has been done in feature selection. But, most of the authors have modified the KDD99 test dataset. Modification of training dataset is valid but modifying test dataset is against the machine learning ethics. This work comprises some of the recently proposed feature selection algorithm such as Information gain, Gain Ratio and Correlation-based feature selection with the objective of determining the reduced feature set. The performance is evaluated using a combination of any two feature selection technique. This study proposes a new heuristic based feature selection algorithm using naive Bayes classifier to detect the important reduced feature set. The results are evaluated on c4.5 decision tree classifier and the results are compared with the existing works. The evaluated results show that the proposed reduced feature set gives the effective and efficient performance.

关键词： Intrusion Detection System (IDS) Feature Reduction KDD99 Dataset Naive Bayes Classifier c4.5 Decision tree INTRUSION DETECTION SYSTEMS Feature extraction Bayesian classifier selection algorithm Accidents,emergencies and safety Dataset Network algorithms

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Auto-Tuning Online POMDPs for Multi-Object Search in Uncertain Environments

Auto-Tuning Online POMDPs for Multi-Object Search in Uncerta...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Wakayama, Shohei Ahmed, Nisar Univ Colorado Boulder CO 80303 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Future exploration missions will require mobile robots to autonomously solve the challenging problem of searching for and localizing multiple objects of interest in uncertain environments. Such problems can be posed as partially observable Markov decision processes (POMDPs), which require approximations to obtain practically implementable policies for sequential decision making under uncertainty. Although online solvers such as Partially Observable Monte Carlo Planning (POMCP) have recently attracted much interest due to their fast anytime closed-loop capabilities, they require extensive hyperparameter tuning before implementation to achieve acceptable performance. However, in missions where robots cannot receive any external aid for long periods of time, they must be able to adjust and adapt hyperparameters to varying operating conditions and environmental contexts. In this paper, we cast this hyperparameter selection problem for search-tree based online solvers a Multi-Armed Bandit (MAB). Our contributions for this problem are as follows. First of all, we evaluate which existing MAB policy approximation strategies are suitable for this problem and examine the definition of utility function for MAB auto-tuning. Then, the performance of auto-tuning robot is evaluated in a simple baseline fully observable Markov Decision Process (MDP) single goal navigation problem using Monte Carlo Tree Search policy approximation. It is shown that the performance of the robot using auto-tuned MCTS planner is shown to be almost the same as the best theoretical performance produced by offline value iteration. Next, the auto-tuning robot is evaluated in a simulated fully observable multi-goal navigation problem in more complex environments, showing that the auto-tuning robot performs equally/better than an offline tuned fixed hyperparameter robot. Finally, the auto-tuning robot is evaluated in simulation for a multi-object search problem in uncertain environment where locations and nu

关键词： Mobile Robots Markov Decision Process Closed Loop selection algorithm Annealing Number of Particles Space Exploration Reinforcement Learning Value Function Probability Distribution

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A Multi-Armed Bandit Approach to Atmospherically-Aware Altitude Optimization

A Multi-Armed Bandit Approach to Atmospherically-Aware Altit...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Bird, John J. Langelaan, Jack W. Univ Colorado Boulder Dept Aerosp Engn Sci Boulder CO 80303 USA Penn State Univ Dept Aerosp Engn University Pk PA 16802 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Small UAS typically fly at low altitudes where the atmospheric environment is complex and uncertain. Typical error in even the best modern weather forecasting systems introduces significant uncertainty into the optimal flight path for a UAS to complete a mission and the energy required. Existing approaches to managing this uncertainty involve creating flight plans whose cost is robust to uncertainty or assimilating aircraft observations into a weather model to eliminate forecast error. Robust approaches cannot respond to the real atmospheric environment and data assimilation requires a high bandwidth data link and significant computational resources be dedicated to the aircraft. This paper proposes a multi-armed bandit inspired approach to altitude optimization. By taking advantage of the spectral characteristics of atmospheric motion this approach enables an aircraft to seek low-cost altitudes while maintaining a model of the atmosphere using in situ sensing and computational resources. In simulation the approach demonstrates performance superior to a collocation based optimal control technique, identifying low cost regions and rejecting poor information provided to the planner by a forecast.

关键词： Atmospheric Environment Trajectory Optimization Flight Planning Weather Forecasting Energy Consumption Kalman Filter selection algorithm Transport Aircraft Solar Power Markov Decision Process

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Reduction method based structural model updating method via neural networks

Reduction method based structural model updating method via ...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Sung, Heejun Chang, Seongmin Cho, Maenghyo Seoul Natl Univ Dept Mech Engn Seoul 08826 South Korea Kumoh Natl Inst Technol Mech Design Engn Gumi 39177 Gyeongsangbuk D South Korea

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Model updating methods for structural systems have been introduced in various numerical processes. To improve the updating method, the process must require an accurate analyses and minimized experimental uncertainties. In previous study, an identification method has been introduced and verified by measured experiment data of perturbed plate models. Iterated improved reduced system has been employed to the identification and updating method. Perturbed locations and thicknesses were identified accurately through the introduced identification method. To improve the updating method, in this study, we have devised methods to identify more diverse models. In order to detect minute changes in more various models, tetrahedral elements and neural network based updating methods were employed. The previous shell element models were able to identify the thicknesses with relatively thin slenderness. However, the tetrahedral elements can identify the physical properties of the complex models. The accuracy of the updating method for the numerical element models with neural networks was verified through the experimental examples.

关键词： Neural Networks Structural System Tetrahedral Element Structural Modeling Finite Element Modeling Numerical Modeling Mechanical Properties Frequency Response Functions Inverse Problems selection algorithm

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HReplica: A Dynamic Data Replication Engine with Adaptive Compression for Multi-Tiered Storage 8

HReplica: A Dynamic Data Replication Engine with Adaptive Co...

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8th IEEE International Conference on Big Data (Big Data)

作者： Devarajan, Hariharan Kougkas, Anthony Sun, Xian-He IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781728162515

As the diversity of big data applications increases, their requirements diverge and often conflict with one other. Managing this diversity in any supercomputer or data center is a major challenge for system designers. Data replication is a popular approach to meet several of these requirements, such as low latency, read availability, durability, etc. This approach can be enhanced using new modern heterogeneous hardware and software techniques such as data compression. However, both these enhancements work in isolation to the detriment of both. In this work, we present HReplica: a dynamic data replication engine which harmoniously leverages data compression and hierarchical storage to increase the effectiveness of data replication. We have developed a novel dynamic selection algorithm that facilitates the optimal matching of replication schemes, compression libraries, and tiered storage. Our evaluation shows that HReplica can improve scientific and cloud application performance by 5.2x when compared to other state-of-the-art replication schemes.

关键词： data replication dynamic selection algorithm multi-tiered data compression intelligent selection dynamic programming cloud application scientific application big data

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Gene expression feature selection for prostate cancer diagnosis using a two-phase heuristic-deterministic search strategy

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IET SYSTEMS BIOLOGY 2018年第4期12卷 162-169页

作者： Shahbeig, Saleh Rahideh, Akbar Helfroush, Mohammad Sadegh Kazemi, Kamran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

Here, a two-phase search strategy is proposed to identify the biomarkers in gene expression data set for the prostate cancer diagnosis. A statistical filtering method is initially employed to remove the noisiest data. In the first phase of the search strategy, a multi-objective optimisation based on the binary particle swarm optimisation algorithm tuned by a chaotic method is proposed to select the optimal subset of genes with the minimum number of genes and the maximum classification accuracy. Finally, in the second phase of the search strategy, the cache-based modification of the sequential forward floating selection algorithm is used to find the most discriminant genes from the optimal subset of genes selected in the first phase. The results of applying the proposed algorithm on the available challenging prostate cancer data set demonstrate that the proposed algorithm can perfectly identify the informative genes such that the classification accuracy, sensitivity, and specificity of 100% are achieved with only nine biomarkers.

关键词： Feature extraction Prostate Cancer NUMB gene Genes second phase search strategies Biological Markers selection algorithm

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Reduced-Order Modeling for Dynamic Mode Decomposition Without an Arbitrary Sparsity Parameter

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AIAA JOURNAL 2020年第9期58卷 3919-3931页

作者： Graff, John Ringuette, Matthew J. Singh, Tarunraj Lagor, Francis D. SUNY Buffalo Dept Mech & Aerosp Engn Buffalo NY 14260 USA

Dynamic mode decomposition (DMD) yields a linear, approximate model of a system's dynamics that is built from data. This paper seeks to reduce the order of this model by identifying a reduced set of modes that best fit the output. A model selection algorithm from statistics and machine learning known as least angle regression (LARS) is adopted. LARS is modified to be complex-valued, and LARS is used to select DMD modes. The resulting algorithm is referred to as least angle regression for dynamic mode decomposition (LARS4DMD). Sparsity-promoting dynamic mode decomposition (DMDSP), a popular mode-selection algorithm, serves as a benchmark for comparison. LARS4DMD has the advantage that the sparsity parameter required for DMDSP is not needed. Numerical results from a Poiseuille flow test problem show that LARS4DMD yields reduced-order models that have comparable performance to DMDSP. Use of the LARS4DMD algorithm on particle image velocimetry data of a rotating fin confirms this conclusion on experimental data. Results further suggest that LARS4DMD may be slightly more robust to noise in the experimental data.

关键词： Particle Image Velocimetry Poiseuille Flow selection algorithm Machine Learning Singular Value Decomposition Modal Analysis Vortex Shedding Computational Fluid Dynamics Nonlinear Dynamics Navier Stokes Equations

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A Clonal selection algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM

A Clonal Selection Algorithm with Levenshtein Distance based...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Takumi Ichimura Shin Kamada Faculty of Management and Information Systems Prefectural University of Hiroshima 1-1-71 Ujina-Higashi Minami-ku Hiroshima 734-8559 Japan Graduate School of Comprehensive Scientific Research Prefectural University of Hiroshima 1-1-71 Ujina-Higashi Minami-ku Hiroshima 734-8559 Japan

ISBN: (纸本)9781479906505

Mobile Phone based Participatory Sensing (MPPS) system involves a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. Our developed tourist subjective data collection system with Android smartphone can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM. However, the filtering rules related to photograph were not generated, because the extraction of the specified characteristics from images cannot be realized. We propose the effective method of the Levenshtein distance to deduce the spatial proximity of image viewpoints and thus determine the specified pattern in which images should be processed. To verify the proposed method, some experiments to classify the subjective data with images are executed by Interactive GHSOM and Clonal selection algorithm with Immunological Memory Cells in this paper.

关键词： Levenshtein Distance Clonal selection algorithm Image Analysis Immunological Memory Cells Growing Hierarchical SOM Interactive GHSOM Smartphone based Participatory Sensing System Tourist Informatics Knowledge Discovery Levenshtein distance data mining image analysis images selection algorithm Tourist information services Clonal selection Interactive Device Sensor CFC1 gene DATA ACQUISITION SYSTEMS

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High-Definition Map Combined Local Motion Planning and Obstacle Avoidance for Autonomous Driving 30

High-Definition Map Combined Local Motion Planning and Obsta...

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30th IEEE Intelligent Vehicles Symposium (IV)

作者： Jian, Zhiqiang Zhang, Songyi Chen, Shitao Lv, Xin Zheng, Nanning Xi An Jiao Tong Univ Dept Elect & Informat Engn Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781728105604

Local motion planning plays an important role in an autonomous driving system. And applying mature local motion planning methods to real traffic scenarios with regular constraints is one of the keys to the applications of autonomous vehicles. In this paper, we present a local motion planning method combined with High-Definition (HD) maps. Through the HD map defined by OpenStreetMap, the local motion planner can obtain the prior knowledge of traffic scenarios and achieve path planning and optimization accordingly. In order to improve the safety and comfort of the obstacle avoiding process, we also propose an inertia-like path selection algorithm based on this planning method. We evaluated the proposed method on our designed autonomous driving experimental platform 'Pioneer' and participated in the 2018 Intelligent Vehicles Future Challenge. In the competition, the 'Pioneer' successfully completed all the races and won the championship without any manual intervention.

关键词： collision avoidance mobile robots optimisation road vehicles motion planning Collision avoidance road vehicles Mobile robots Obstacle avoidance Land transportation High definition Intelligent vehicles drive train selection algorithm Autonomous Vehicles maps Path planning

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Indoor Experiment of Multi-User MIMO User selection algorithm Based on Chordal Distance

Indoor Experiment of Multi-User MIMO User Selection Algorith...

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IEEE Global Communications Conference

作者： Masato Taniguchi Hidekazu Murata Susumu Yoshida Koji Yamamoto Daisuke Umehara Satoshi Denno Masahiro Morikura Graduate School of Informatics Kyoto University Graduate School of Science and Technology Kyoto Institute of Technology Matsugasaki Graduate School of Natural Science and Technology Okayama University

ISBN: (纸本)9781479913510

A transmission experiment of a multi-user MIMO system with user selection and linear spatial precoding is conducted and the results are reported. As a low-complexity user selection algorithm, the chordal distance-based user selection (CDUS) algorithm is employed. The testbed used in the experiment is implemented by taking full advantage of the software-defined radio. An amplify-and-forward-based simple channel estimation method is adopted. As a result of the experiment in a conference room, the CDUS algorithm improved both the power efficiency and the interference suppression accuracy compared to the round-robin user selection.

关键词： MIMO experiment round-robin user selection multiple input multiple output systems selection algorithm Chord dynamic efficiency multi-user

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