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Explosive naval mines pose a threat to ocean and sea faring vessels, both military and civilian. This work applies deep neural network (DNN) methods to the problem of detecting minelike objects (MLO) on the seafloor in side-scan sonar imagery. We explored how the DNN depth, memory requirements, calculation requirements, and training data distribution affect detection efficacy. A visualization technique...
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on...
The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen...
This study is motivated by the problem of evaluating reliable false alarm (FA) rates for sinusoid detection tests applied to unevenly sampled time series involving colored noise, when a (small) training data set of this noise is available. While analytical expressions for the FA rate are out of reach in this situation, we show that it is possible to combine specific periodogram standardization and...
Distribution of bleach and chlorine rice makes some people uneasy because it is dangerous to health. The author conducted a simulation for implemantation the method of Principal Component Analysis to detect bleach and chlorine on rice with Matlab R2013a. This study focuses on the shape, texture, color, and position Pandanwangi, Rojolele, and Setra Ramos Rice intact. The result is an Euclidean value...
We propose a novel data-driven approach for automatically detecting and completing gaps in line drawings with a Convolutional Neural Network. In the case of existing inpainting approaches for natural images, masks indicating the missing regions are generally required as input. Here, we show that line drawings have enough structures that can be learned by the CNN to allow automatic detection and completion...
In this paper, we propose an Extreme Learning Machine (ELM) approach for solving large and complex data problems. In contrast to existing approaches, we embed hidden nodes that are designed using Restricted Boltzmann machine (RBM) into the classical ELM, exhibiting excellent generalization performances. To overcome the high computational complexity involved especially on large datasets, hidden nodes...
Extracting intuitive and useful information from the high-dimensional, fuzzy and complex operational simulation training data is in urgent need. In this paper, the operational simulation training data refers to the quantitative, numeric data that is usually used as the simulation results. The traditional statistical analysis methods have some limitations in clustering, visualizing and evaluating the...
Soft sensors are used to infer the quality variable from easy-to-measure process variables. The conventional static soft sensor is incapable of handling the dynamic of processes. For data-based soft sensor development, with abundance of the raw sensor data, the problem of variable correlations and large number of sample are encountered. This work presents a latent variable model (LVM) based active...
Kernel methods have been used to effectively tackle nonlinear or nonparametric machine learning problems. However, their computational and memory complexity grows at least quadratically with the number of training samples. This issue has made these methods difficult to use for medium to large-sized datasets and hindered practical applications. A common approach involves the use of only a selected...
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a neural network using synthetic data can be viewed as learning a proposal distribution generator for approximate inference in the synthetic-data generative model. We demonstrate this connection in a recognition task where...
Classification is at the very center of the supervised learning. In this work, we propose a novel algorithm to classify the test data set with the aid of a vector field, emanating from the training data set. In particular, the vector field is constructed such that the location of each training data point becomes a local minimum of the potential. The test data points are allowed to evolve under the...
State space models, such as Kalman filters or Particle filters, have been applied to improve the accuracy of radio-wave-based localization. However, these models can drift radically when assumptions of the models are violated, and they do not have a mechanism to fix errors. Therefore, we propose an approach to apply supervised learning to pedestrian localization, which is based on the Inference Machines...
The interaction between the cyber domain and the physical domain components and processes can be leveraged to enhance the security of the cyber-physical system. In order to do so, we must first analyze various cyber domain and physical domain information flows, and characterize the relation between them using model functions. In this paper, we present a notion of cross-domain security of cyber-physical...
Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern data via random finite sets (RFS). In particular, we propose appropriate likelihood...
This paper addresses the problem of modeling long-range motion patterns of a 3D human skeleton performing an activity. This problem is important, as such a model can be used in many applications, including person tracking via 3D pose estimation, and probabilistic sampling of realistic 3D skeleton sequences conducting different activities with different motion styles. To this end, we formulate a new...
We propose two simple methods to improve the performance of a keyword spotting system. In our application, the users are allowed to change the keywords anytime if they want. Thus we focused on phone-based GMM-HMM models since they do not require keyword-specific training data. However, the GMM-HMM based models usually have very high false alarm rate, i.e., a keyword is not present but the system gives...
In the Gaussian mixture model based online writer identification system, the writer specific models are usually learned by adapting the universal background model. However, among all the possible adapting plans, which one performs best is still an unsolved problem, as well as the underlying principles. Towards finding the answer, this paper analyses all the combinations of the parameter adaptation...
Today, enterprise integration and cross-enterprise collaboration is becoming evermore important. The Internet of things, digitization and globalization are pushing continuous growth in the integration market. However, setting up integration systems today is still largely a manual endeavor. Most probably, future integration will need to leverage more automation in order to keep up with demand. This...
This paper presents a computational strategy for condition monitoring of multi-mode processes on the example of real data from a photovoltaic system. The concept uses a new type of fuzzy models with partially activated set of fuzzy rules on a pre-determined grid, called partial fuzzy grid models. Each such model represents one specific operating condition of the process, which is saved in the Model...
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