Academic literature on the topic 'Decision Fusion in adversarial setup'
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Journal articles on the topic "Decision Fusion in adversarial setup"
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Abrardo, Andrea, Mauro Barni, Kassem Kallas, and Benedetta Tondi. "A message passing approach for decision fusion in adversarial multi-sensor networks." Information Fusion 40 (March 2018): 101–11. http://dx.doi.org/10.1016/j.inffus.2017.06.006.
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Chen, Yong, Senyuan Tian, and Bingnan Sun. "Decision Fusion for Structural Damage Detection: Numerical and Experimental Studies." Advances in Civil Engineering 2010 (2010): 1–12. http://dx.doi.org/10.1155/2010/820762.
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This paper describes a decision fusion strategy that can integrate multiple individual damage detection measures to form a new measure, and the new measure has higher probability of correct detection than any individual measure. The method to compute the probability of correct selection is presented to measure the system performance of the fusion system that includes the presented fusion strategy. And parametric sensitive studies on system performance are also conducted. The superiority of the fusion strategy herein is that it can be extended to deal with the multiresolution subdecision or blind adaptive detection, and corresponding methodologies are also provided. Finally, an experimental setup was fabricated, whereby the vibration properties of damaged and undamaged structures were measured. The experimental results with the undamaged structural model provide information for producing an improved theoretical and numerical model via model updating techniques. Three existing vibration-based damage detection methods with varied resolutions were utilized to identify the damage that occurred in the structure, based on the experimental results. Then the decision fusion strategy was implemented to join the subdecisions from these three methods. The fused results are shown to be superior to those from single method.
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Bali, Jyoti, H. Arpitha, N. Anushree, and Arunkumar Giriyapur. "Power-efficient Strategies for Sensing in Autonomous Mobile Robots, a critical requirement of I4.0 standard." IOP Conference Series: Materials Science and Engineering 1187, no. 1 (September 1, 2021): 012007. http://dx.doi.org/10.1088/1757-899x/1187/1/012007.
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Abstract In a production environment, there are several challenges in meeting the Industry 4.0 (I4.0) standard requirements. Energy efficiency is an essential area of focus. In the production setup, the critical and real-time control systems need to be very efficient while implementing functions, namely, accurate sensing, fast processing and precise actuation. Automated Guided vehicles (AGVs) and Automated Guided Vehicles are an integral part of modern and intelligent manufacturing systems. Power consumption in such systems is directly proportional to the performance level achieved. However, there is a need to evolve strategies to reduce power consumption and attain optimal performance. Field Programmable Gate Array(FPGA) based controller solutions can provide competent performance at optimized power consumption. The proposed work discusses the requirements of I4.0 concerning energy efficiency infrastructures for the intelligent manufacturing setup. The need to develop efficient subsystems for sensing, decision-making and actuation based on FPGA is stressed. Thus the focus is on the FPGA based power-efficient models used for sensor fusion technique in Autonomous Mobile Robots. The fundamentals of sensor fusion technique and the need to fuse sensor data for improved decision making and actuation are emphasized.
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Gul, Noor, Muhammad Sajjad Khan, Su Min Kim, Junsu Kim, Atif Elahi, and Zafar Khalil. "Boosted Trees Algorithm as Reliable Spectrum Sensing Scheme in the Presence of Malicious Users." Electronics 9, no. 6 (June 23, 2020): 1038. http://dx.doi.org/10.3390/electronics9061038.
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Cooperative spectrum sensing (CSS) has the ability to accurately identify the activities of the primary users (PUs). As the secondary users’ (SUs) sensing performance is disturbed in the fading and shadowing environment, therefore the CSS is a suitable choice to achieve better sensing results compared to individual sensing. One of the problems in the CSS occurs due to the participation of malicious users (MUs) that report false sensing data to the fusion center (FC) to misguide the FC’s decision about the PUs’ activity. Out of the different categories of MUs, Always Yes (AY), Always No (AN), Always Opposite (AO) and Random Opposite (RO) are of high interest these days in the literature. Recently, high sensing performance for the CSS can be achieved using machine learning techniques. In this paper, boosted trees algorithm (BTA) has been proposed for obtaining reliable identification of the PU channel, where the SUs can access the PU channel opportunistically with minimum disturbances to the licensee. The proposed BTA mitigates the spectrum sensing data falsification (SSDF) effects of the AY, AN, AO and RO categories of the MUs. BTA is an ensemble method for solving spectrum sensing problems using different classifiers. It boosts the performance of some weak classifiers in the combination by giving higher weights to the weak classifiers’ sensing decisions. Simulation results verify the performance improvement by the proposed algorithm compared to the existing techniques such as genetic algorithm soft decision fusion (GASDF), particle swarm optimization soft decision fusion (PSOSDF), maximum gain combination soft decision fusion (MGCSDF) and count hard decision fusion (CHDF). The experimental setup is conducted at different levels of the signal-to-noise ratios (SNRs), total number of cooperative users and sensing samples that show minimum error probability results for the proposed scheme.
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Ichim, Loretta, and Dan Popescu. "Segmentation of Vegetation and Flood from Aerial Images Based on Decision Fusion of Neural Networks." Remote Sensing 12, no. 15 (August 3, 2020): 2490. http://dx.doi.org/10.3390/rs12152490.
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The detection and evaluation of flood damage in rural zones are of great importance for farmers, local authorities, and insurance companies. To this end, the paper proposes an efficient system based on five neural networks to assess the degree of flooding and the remaining vegetation. After a previous analysis the following neural networks were selected as primary classifiers: you only look once network (YOLO), generative adversarial network (GAN), AlexNet, LeNet, and residual network (ResNet). Their outputs were connected in a decision fusion scheme, as a new convolutional layer, considering two sets of components: (a) the weights, corresponding to the proven accuracy of the primary neural networks in the validation phase, and (b) the probabilities generated by the neural networks as primary classification results in the operational (testing) phase. Thus, a subjective behavior (individual interpretation of single neural networks) was transformed into a more objective behavior (interpretation based on fusion of information). The images, difficult to be segmented, were obtained from an unmanned aerial vehicle photogrammetry flight after a moderate flood in a rural region of Romania and make up our database. For segmentation and evaluation of the flooded zones and vegetation, the images were first decomposed in patches and, after classification the resulting marked patches were re-composed in segmented images. From the performance analysis point of view, better results were obtained with the proposed system than the neural networks taken separately and with respect to some works from the references.
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Li, Wei, Jinzhao Yang, and Xin Min. "Next-Day Medical Activities Recommendation Model with Double Attention Mechanism Using Generative Adversarial Network." Journal of Healthcare Engineering 2022 (November 7, 2022): 1–14. http://dx.doi.org/10.1155/2022/6334435.
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Medical activities recommendation is a key aspect of an intelligent healthcare system, which can assist doctors with little clinical experience in clinical decision making. Medical activities recommendation can be seen as a kind of temporal set prediction. Previous studies about them are based on Recurrent Neural Network (RNN), which does not incorporate personalized medical history or differentiate between the impact of medical activities. To address the above-given issues, this paper proposes a Next-Day Medical Activities Recommendation (NDMARec) model. Specifically, our model firstly proposes an inpatient day embedding method based on soft-attention which balances the impact of different medical activities to get a joint representation of medical activities that occurred within the same day. Then, a fusion module is designed to combine features of inpatient day and medical history to achieve personalization. These features are learned by the self-attention mechanism that solves the long-term dependency problem of RNNs. Last, adversarial training is introduced to improve the generalization ability of our model. Extensive experiments on a real dataset from a hospital are conducted to show that NDMARec outperformed both classical and state-of-the-art methods.
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Suawa, Priscile, Tenia Meisel, Marcel Jongmanns, Michael Huebner, and Marc Reichenbach. "Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion." Sensors 22, no. 9 (May 5, 2022): 3516. http://dx.doi.org/10.3390/s22093516.
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Only with new sensor concepts in a network, which go far beyond what the current state-of-the-art can offer, can current and future requirements for flexibility, safety, and security be met. The combination of data from many sensors allows a richer representation of the observed phenomenon, e.g., system degradation, which can facilitate analysis and decision-making processes. This work addresses the topic of predictive maintenance by exploiting sensor data fusion and artificial intelligence-based analysis. With a dataset such as vibration and sound from sensors, we focus on studying paradigms that orchestrate the most optimal combination of sensors with deep learning sensor fusion algorithms to enable predictive maintenance. In our experimental setup, we used raw data obtained from two sensors, a microphone, and an accelerometer installed on a brushless direct current (BLDC) motor. The data from each sensor were processed individually and, in a second step, merged to create a solid base for analysis. To diagnose BLDC motor faults, this work proposes to use data-level sensor fusion with deep learning methods such as deep convolutional neural networks (DCNNs) for their ability to automatically extract relevant information from the input data, the long short-term memory method (LSTM), and convolutional long short-term memory (CNN-LSTM), a combination of the two previous methods. The results show that in our setup, sound signals outperform vibrations when used individually for training. However, without any feature selection/extraction step, the accuracy of the models improves with data fusion and reaches 98.8%, 93.5%, and 73.6% for the DCNN, CNN-LSTM, and LSTM methods, respectively, 98.8% being a performance that, according to our reading, has never been reached in the analysis of the faults of a BLDC motor without first going through the extraction of the characteristics and their fusion by traditional methods. These results show that it is possible to work with raw data from multiple sensors and achieve good results using deep learning methods without spending time and resources on selecting appropriate features to extract and methods to use for feature extraction and data fusion.
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Syed, Muhammad Shehram Shah, Elena Pirogova, and Margaret Lech. "Prediction of Public Trust in Politicians Using a Multimodal Fusion Approach." Electronics 10, no. 11 (May 25, 2021): 1259. http://dx.doi.org/10.3390/electronics10111259.
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This paper explores the automatic prediction of public trust in politicians through the use of speech, text, and visual modalities. It evaluates the effectiveness of each modality individually, and it investigates fusion approaches for integrating information from each modality for prediction using a multimodal setting. A database was created consisting of speech recordings, twitter messages, and images representing fifteen American politicians, and labeling was carried out per a publicly available ranking system. The data were distributed into three trust categories, i.e., the low-trust category, mid-trust category, and high-trust category. First, unimodal prediction using each of the three modalities individually was performed using the database; then, using the outputs of the unimodal predictions, a multimodal prediction was later performed. Unimodal prediction was performed by training three independent logistic regression (LR) classifiers, one each for speech, text, and images. The prediction vectors from the individual modalities were then concatenated before being used to train a multimodal decision-making LR classifier. We report that the best performing modality was speech, which achieved a classification accuracy of 92.81%, followed by the images, achieving an accuracy of 77.96%, whereas the best performing model for text-modality achieved a 72.26% accuracy. With the multimodal approach, the highest classification accuracy of 97.53% was obtained when all three modalities were used for trust prediction. Meanwhile, in a bimodal setup, the best performing combination was that combining the speech and image visual modalities by achieving an accuracy of 95.07%, followed by the speech and text combination, showing an accuracy of 94.40%, whereas the text and images visual modal combination resulted in an accuracy of 83.20%.
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.., Abedallah Z., and Rasha Almajed. "Deep Neural Network-based Fusion and Natural Language Processing in Additive Manufacturing for Customer Satisfaction." Fusion: Practice and Applications 3, no. 1 (2021): 70–90. http://dx.doi.org/10.54216/fpa.030105.
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Modern Machine learning fusion approaches tend to extract features depending on two techniques (hand-crafted feature and representation learning). Hand-crafted features can waste time and are not sufficient for downstream tasks. Unlike representation learning, we automatically learn features with minimum time and effort and are suitable for downstream tasks. In our paper, we provide work on graph neural network methods with details on classical graph embedding approaches and the different methods in neural graph networks such as graph filtering, graph pooling, and the learning parameter for graph following each technique with a general framework or mathematical proof for customer satisfaction. To satisfy customer's feel, this research employs NLP techniques. We describe the adversarial attacks and defenses on graph representation approaches. Also, advanced application of neural graph networks is reviewed, such as combinational optimization, learning program representation, physical system modeling, and natural language processing. Finally, the challenges in geometric neural networks and future research work have been introduced.
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Santos, J. M., E. Ricardo, F. J. da Silva, T. Ribeiro, S. Heuraux, and A. Silva. "A 3D CAD model input pipeline for REFMUL3 full-wave FDTD 3D simulator." Journal of Instrumentation 16, no. 11 (November 1, 2021): C11013. http://dx.doi.org/10.1088/1748-0221/16/11/c11013.
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Abstract The use of advanced simulation has become increasingly more important in the planning, design, and assessment phases of future fusion plasma diagnostics, and in the interpretation of experimental data from existing ones. The design cycle of complex reflectometry systems, such as the ones being planned for next generation machines (IDTT and DEMO), relies heavily on the results produced by synthetic diagnostics, used for system performance evaluation and prediction, both crucial in the design process decision making. These synthetic diagnostics need realistic representations of all system components to incorporate the main effects that shape their behavior. Some of the most important elements that are required to be well modelled and integrated in simulations are the wave launcher structures, such as the waveguides, tapers, and antennas, as well as the vessel wall structures and access to the plasma. The latter are of paramount importance and are often neglected in this type of studies. Faithfully modelling them is not an easy task, especially in 3D simulations. The procedure herein proposed consists in using CAD models of a given machine, together with parameterizable models of the launcher, to produce a description suited for Finite Difference Time Domain (FDTD) 3D simulation, combining the capabilities of real-world CAD design with the power of simulation. However, CAD model geometric descriptions are incompatible with the ones used by standard FDTD codes. CAD software usually outputs models in a tessellated mesh while FDTD simulators use Volumetric Pixel (VOXEL) descriptions. To solve this interface problem, we implemented a pipeline to automatically convert complex CAD models of tokamak vessel components and wave launcher structures to the VOXEL input required by REFMUL3, a full wave 3D Maxwell FDTD parallel code. To illustrate the full procedure, a complex reflectometry synthetic diagnostic for IDTT was setup, converted and simulated. This setup includes 3 antennas recessed into the vessel wall, for thermal protection, one for transmission and reception, and two just for reception.
Dissertations / Theses on the topic "Decision Fusion in adversarial setup"
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KALLAS, KASSEM. "A Game-Theoretic Approach for Adversarial Information Fusion in Distributed Sensor Networks." Doctoral thesis, Università di Siena, 2017. http://hdl.handle.net/11365/1005735.
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Every day we share our personal information through digital systems which are constantly exposed to threats. For this reason, security-oriented disciplines of signal processing have received increasing attention in the last decades: multimedia forensics, digital watermarking, biometrics, network monitoring, steganography and steganalysis are just a few examples. Even though each of these fields has its own peculiarities, they all have to deal with a common problem: the presence of one or more adversaries aiming at making the system fail. Adversarial Signal Processing lays the basis of a general theory that takes into account the impact that the presence of an adversary has on the design of effective signal processing tools.
By focusing on the application side of Adversarial Signal Processing, namely adversarial information fusion in distributed sensor networks, and adopting a game-theoretic approach, this thesis contributes to the above mission by addressing four issues. First, we address decision fusion in distributed sensor networks by developing a novel soft isolation defense scheme that protects the
network from adversaries, specifically, Byzantines. Second, we develop an optimum decision fusion strategy in the presence of Byzantines. In the next step, we propose a technique to reduce the complexity of the optimum fusion by relying on a novel nearly-optimum message passing algorithm based on factor graphs. Finally, we introduce a defense mechanism to protect decentralized networks running consensus algorithm against data falsification attacks.
Book chapters on the topic "Decision Fusion in adversarial setup"
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Abrardo, Andrea, Mauro Barni, Kassem Kallas, and Benedetta Tondi. "Adversarial Decision Fusion: A Heuristic Approach." In Information Fusion in Distributed Sensor Networks with Byzantines, 45–55. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-32-9001-3_4.
Full textConference papers on the topic "Decision Fusion in adversarial setup"
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Fan, Bo, and Jiexin Pu. "Multi-Agent Decision Fusion and Its Application in Adversarial Multi-robot System." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.329.
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