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This paper addresses classification tasks on a particular target domain in which labeled training data are only available from source domains different from (but related to) the target. Two closely related frameworks, domain adaptation and domain generalization, are concerned with such tasks, where the only difference between those frameworks is the availability of the unlabeled target data: domain...
This paper describes a linear multi-armed bandit algorithm that exploits sparsity in the underlying unknown weight vector controlling rewards. In linear multi-armed bandits, a user chooses a sequence of (slot machine) “arms” to pull, and each arm pull results in the user receiving a stochastic reward with mean equal to the inner product between a known feature vector associated with the arm and an...
The Adaptive Fourier Decomposition (AFD) is a novel signal decomposition algorithm that can describe an analytical signal through a linear combination of adaptive basis functions. At every decomposition step of the AFD, the basis function is determined by making a search in an over-complete dictionary. The decomposition continues until the difference between the energies of the original and reconstructed...
This paper presents a novel mixed integer linear programming (MILP) model of the optimal automatic generation control (AGC) strategy under the control performance standard (CPS) for the interconnected power grid. Generally, the orders of AGC regulation can be represented as three-state variables, but the optimization model with three-state variables are difficult to solve. In order to reduce the model's...
Deep learning methods achieve great success recently on many computer vision problems. In spite of these practical successes, optimization of deep networks remains an active topic in deep learning research. In this work, we focus on investigation of the network solution properties that can potentially lead to good performance. Our research is inspired by theoretical and empirical results that use...
Most popular deep models for action recognition split video sequences into short sub-sequences consisting of a few frames, frame-based features are then pooled for recognizing the activity. Usually, this pooling step discards the temporal order of the frames, which could otherwise be used for better recognition. Towards this end, we propose a novel pooling method, generalized rank pooling (GRP), that...
Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to...
Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups. To boost the performance of multi-view clustering, numerous subspace learning algorithms have been developed in recent years, but with rare exploitation of the representation complementarity between different views as well as the indicator consistency among the representations, let alone considering...
Multi-temporal remote sensing imagery has become widely available, which opens up an opportunity to improve the efficiency of supervised classification techniques. While a classifier trained from a previous image (source domain) cannot be directly applied to the current image (target domain) because of changes in imaging conditions and dynamics of land surface spectral properties, domain transfer...
This paper presents a chaotic particle swarm optimization algorithm with random weights to improve the balancing of a LCD panel assembly line considering the coordination of operation elements. Based on the analysis of the constraints, the performance criteria considered are the line balancing rate and the variation of workload. The results of experiments show that the proposed model produced as good...
As an effective software testing technique, combinatorial testing has been gradually applied in various types of test practice. In this case, it is necessary to provide useful combinatorial testing tools to support the application of combinatorial testing technique on industrial scenarios, as well as the academic research for combinatorial testing technique. To this end, on the basis of the research...
Group decision making involves a number of agents verbalizing their testimonies about a collection of options in order to rank them from best to worst. To provide their opinions, the agents feel more comfortable using linguistic values (or words) instead of numbers because words are usually used by humans to interact with others. However, when words are used, they have to be made operational to be...
The dual-drive H gantry is widely used in many industrial processes that require high precision Cartesian motion. The conventional rigid-link version suffers breaking down of joints if any de-synchronization between the two carriages occurs. To prevent above potential risk, a novel biaxial gantry with flexure-links is designed to allow a small rotation angle of the cross-arm. Nevertheless, the chattering...
New Grid and cloud solutions for distributed data mining and data processing are needed for execution of data intensive workflows. In contrast of the standard workflows, in which data between the jobs are exchanged in the form of files and the jobs are finished when they process the input data, data intensive workflows receive data organized in blocks which are streamed on inputs, analyze the data...
Support vector machine (SVM) is a popular machine learning method and has been widely applied in many real-world applications. Since SVM is sensitive to noises, fuzzy SVM (FSVM) has been proposed to relieve the over-fitting problem caused by noises through assigning a fuzzy membership to each sample. Then, different samples make different contributions to the learning of classification hyperplane...
The performance of power electronic converters is sensitive to main design variables such as topology, control algorithm, and modulation scheme as well as details such as manufacturing variability. In this paper, sensitivity index is introduced as an indicator of design quality to measure converter robustness in the presence of manufacturing uncertainty. This paper presents the design and optimization...
Transport sector could play a relevant role in future energy decarbonisation pathways contributing to energy consumption and pollutant emissions reduction. Planning in the transport sector requires that policy makers be supported with scientifically sound tools, which are able to take into account the peculiar aspects of transport system analyses, especially at urban scale. One of these peculiarities...
This paper considers an optimal harvesting strategy problem arising in shrimp culture. The problem is formulated as an optimal control problem of nonlinear impulsive system. Since the impulsive switching constraints is very complex, the impulsive switching instants are unknown, and the objective function is not continuously differentiable, it is difficult to solve this problem by standard optimization...
This paper presents a proposed cuckoo search algorithm with interactive learning and linear decreasing probability strategy (CSIL) to solve the economic dispatch problem with power balance, prohibited operating zones, valve point effect, and ramp rate. In the new approach, interactive learning strategy helps the nest to exchange good information from each other. Meanwhile, the linear decreasing probability...
Nowadays, Linear Parameter Varying (LPV) Model Predictive Control (MPC) represents a consolidated approach to optimally regulate multivariable nonlinear systems imposing constraints on inputs and outputs. The crucial drawback, in particular in embedded LPV-MPC, is represented by the required computational effort. The Quadratic Programming (QP) problem solved by MPC changes at each iteration, and its...
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