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Dynamic multiobjective optimization problems (DMOPs) challenge multiobjective evolutionary algorithms (MOEAs) because those problems change rapidly over time. The class of DMOPs whose objective functions change over time steps, in ways that exhibit some hidden patterns has gained much attention. Their predictability indicates that the problem exhibits some correlations between solutions obtained in sequential time periods. Most of the current approaches use linear models or similar strategies to describe the correlations between historical solutions obtained, and predict the new solutions in the following time period as an initial population from which the MOEA can begin searching in order to improve its efficiency. However, nonlinear correlations between historical solutions and current solutions are more common in practice, and a linear model may not be suitable for the nonlinear case. In this paper, we present a support vector regression (SVR)-based predictor to generate the initial population for the MOEA in the new environment. The basic idea of this predictor is to map the historical solutions into a high-dimensional feature space via a nonlinear mapping, and to do linear regression in this space. SVR is used to implement this process. We incorporate this predictor into the MOEA based on decomposition (MOEA/D) to construct a novel algorithm for solving the aforementioned class of DMOPs. Comprehensive experiments have shown the effectiveness and competitiveness of our proposed predictor, comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper presents a distributionally robust network-constrained unit commitment (DR-NCUC) model considering AC network modeling and uncertainties of demands and renewable productions. The proposed model characterizes uncertain parameters using a data-driven ambiguity set constructed by training samples. The non-convex AC power flow equations are approximated by convex quadratic and McCormick relaxations. Since the proposed min-max-min DR-NCUC problem cannot be solved directly by available solvers, a new decomposition algorithm with proof of convergence is reported in this paper. The master problem of this algorithm is solved using both primal and dual cuts, while the max-min sub-problem is solved using the primal-dual hybrid gradient method, obviating the need for using duality theory. Also, an active set strategy is proposed to enhance the tractability of the decomposition algorithm by ignoring the subset of inactive constraints. The proposed model is applied to a 6-bus test system and the IEEE 118-bus test system under different conditions. These case studies illustrate the performance of the proposed DR-NCUC model to characterize uncertainties and the superiority of the proposed decomposition algorithm over other decomposition approaches using either primal or dual cuts. [ABSTRACT FROM AUTHOR]

This paper critically analyzes several incoherent model-based decomposition methods for assessing the effect of deorientation in characterization of various land covers. It has been found that even after performing decomposition, ambiguity still occurs in scattering response from various land covers, such as urban and vegetation. Researchers introduced the concept of deorientation to remove this ambiguity. Therefore, in this paper, a critical analysis has been carried out using seven different three- and four-component decomposition methods with and without deorientation and two Eigen decomposition-based methods to investigate the scattering response on various land covers, such as urban, vegetation, bare soil, and water. The comprehensive evaluation of decomposition and deorientation effect has been performed by both visual and quantitative analyses. Two types of quantitative analysis have been performed; first, by observing percentage of scattering power and second, by analyzing the variation in the number of pixels in different land covers for each scattering contribution. The analysis shows that deorientation increases not only the power but also the number of pixels for surface and double bounce scattering. The number of pixels representing volume scattering remain almost the same for all the methods with or without deorientation, whereas volume scattering power reduces after deorientation. Eigen decomposition-based methods are observed to solve the problem of overestimation of volume scattering power. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel modeling framework and decomposition-based solution strategy combining stochastic programming (SP) and robust optimization (RO) to deal with multiplex uncertainties in coordinated mid- and long-term power system planning. The problem is formulated as a multi-year generation and transmission planning problem from an independent system operator (ISO)’s perspective to minimize both expansion and operational costs under binary and continuous uncertainties, ${\bf i.e.}$ , system component contingency and load/generation variation. N- $\bf {k}$ contingencies are captured in RO using the reformulated contingency criteria, while the load/generation uncertainty is considered in SP embedded with RO using operating scenarios generated from the historical data with spatiotemporal correlations. The original hybrid model is highly intractable, but the intractability can be relieved by the proposed decomposition strategy based on the column-and-constraint generation and L-shaped algorithms. We apply our model to perform long-term system planning under extremely high renewable penetration and investigate the case of 100% renewables in long-term planning. Numerical experiments on multi-scale test systems verify the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Control of a large-scale automated manufacturing system is an important and challenging issue. Its discrete event system model represented by Petri nets tends to become highly complicated in structure, especially when there exist uncontrollable or unobservable events. The existing approaches are nontrivial to design both efficient and maximally permissive supervisors to impose constraints on an overall system. In this paper, instead of considering the control problem from an overall system perspective, we intend to transform an overall control problem into the one designing multiple controllers in parallel, each of which is much simpler in structure. A Petri net structure is decomposed via integer linear programming or a polynomial decomposition method to obtain multiple state-machine subnets that constitute a decentralized system. A necessary and sufficient condition for preserving the equivalence in terms of states and behaviors between the overall system and its decentralized version is reported. Constraints representing control requirements are further converted and enforced in the respective subnets. Then, supervisors are generated via a generalized mutual exclusion constraint method. By considering the deviations between the subnet control and overall control, this paper formulates a communication mechanism to guarantee that the decentralized system runs in an appropriate manner. Finally, two examples are presented to demonstrate the proposed approach. [ABSTRACT FROM AUTHOR]

Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and efficiency in solving problems with two or three objectives. However, recent studies show that MOEAs face many difficulties when tackling problems involving a larger number of objectives as their behavior becomes similar to a random walk in the search space since most individuals are nondominated with respect to each other. Motivated by the interesting results of decomposition-based approaches and preference-based ones, we propose in this paper a new decomposition-based dominance relation to deal with many-objective optimization problems and a new diversity factor based on the penalty-based boundary intersection method. Our reference point-based dominance (RP-dominance), has the ability to create a strict partial order on the set of nondominated solutions using a set of well-distributed reference points. The RP-dominance is subsequently used to substitute the Pareto dominance in nondominated sorting genetic algorithm-II (NSGA-II). The augmented MOEA, labeled as RP-dominance-based NSGA-II, has been statistically demonstrated to provide competitive and oftentimes better results when compared against four recently proposed decomposition-based MOEAs on commonly-used benchmark problems involving up to 20 objectives. In addition, the efficacy of the algorithm on a realistic water management problem is showcased. [ABSTRACT FROM AUTHOR]

The decomposition-based evolutionary multiobjective optimization (EMO) algorithm has become an increasingly popular choice for a posteriori multiobjective optimization. However, recent studies have shown that their performance strongly depends on the Pareto front (PF) shapes. This can be attributed to the decomposition method, of which the reference points and subproblem formulation settings are not well adaptable to various problem characteristics. In this paper, we develop a learning-to-decompose (LTD) paradigm that adaptively sets the decomposition method by learning the characteristics of the estimated PF. Specifically, it consists of two interdependent parts, i.e., a learning module and an optimization module. Given the current nondominated solutions from the optimization module, the learning module periodically learns an analytical model of the estimated PF. Thereafter, useful information is extracted from the learned model to set the decomposition method for the optimization module: 1) reference points compliant with the PF shape and 2) subproblem formulations whose contours and search directions are appropriate for the current status. Accordingly, the optimization module, which can be any decomposition-based EMO algorithm in principle, decomposes the multiobjective optimization problem into a number of subproblems and optimizes them simultaneously. To validate our proposed LTD paradigm, we integrate it with two decomposition-based EMO algorithms, and compare them with four state-of-the-art algorithms on a series of benchmark problems with various PF shapes. [ABSTRACT FROM AUTHOR]

In this paper, a theoretical analysis of orbital angular momentum (OAM) wave propagation properties in terms of reflection and refraction is introduced by decomposing OAM waves into infinite plane waves in the spectral domain with different elevation and azimuth angles. Transformations of phases and amplitudes at the interface of two media for different incident angles and elevation angles are numerically analyzed. Similarly, based on the idea of decomposing OAM waves, reflection and refraction properties of OAMwave propagating through slabs are also analyzed and simulated. Finally, experiments on OAM waves with both mode numbers 1 and 2 are conducted to demonstrate the reflection and refraction properties of OAM waves. [ABSTRACT FROM AUTHOR]

Cloud gaming provides a novel service model by hosting game engines in the cloud and delivering real-time gaming videos ubiquitously to the players through the Internet. However, the diversity of end-user devices and frequent changes in network quality of service and cloud responses result in variable quality of experience for game players. This paper presents the design and implementation of a component-based ubiquitous gaming system with cognitive capabilities, which aims to overcome the above-mentioned problem by learning about the game players’ status and cognitively optimizing resource allocations for different software components of a game. Experiments show that well-balanced software components between the cloud and user devices lead to better system performance, e.g., in the overall latency. [ABSTRACT FROM AUTHOR]

Robust scheduling for an integrated electricity and heat system (IEHS) is essential to hedge the uncertainties brought by volatile wind power and heat load that could pose critical security threats to IEHS operation. Because of the distributed structure of different energy sectors, their models are managed locally and private information must be protected. In this context, a decoupled solution is favorable for robust IEHS scheduling. However, due to the specific problem structure with a nonseparable Lagrangian, conventional iterative distributed algorithms are not applicable. In this paper, we make the first attempt for distributed robust IEHS scheduling by providing a non-iterative decoupled solution. A two-stage robust model considering electricity and heat uncertainties is formulated, and the inapplicability of conventional algorithms in decoupling this model is analyzed in depth. To facilitate feasible region projection, a network reduction method considering heat uncerntainties is then devised. Finally, an efficient non-iterative decoupled solution is proposed with guaranteed optimality while preserving the operational independence of different energy sectors. Case studies in two test systems validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Motivated by the increasing need of the real-world patrolling, this paper studies a practical city-scale patrolling (CSP) variant. In CSP, the police are scheduled to patrol city regions, and the objective is not only to protect public security but also to respond to incidents timely. We use an integer program (IP) to formulate the CSP problem, with the objective of maximizing the police visibility rate (PVR) to improve public safety and the additional constraint of response time guarantee to handle incidents timely. For such an NP-hard problem, existing studies either cannot scale-up or do not provide a bound from optimum. To fill the research gap, we propose a decomposition and grafting approach. We first decompose the original CSP into two weakly-coupled subproblems, minimizing police problem (MinP) and maximizing PVR (MaxP) problem. By exploiting the subproblem structures, a polynomial time approximation algorithm is proposed for MinP, and a polynomial time optimal algorithm is proposed for MaxP. We prove that such a decomposition can provide the $1\hspace {-2pt}-\hspace {-2pt}\alpha $ approximation ratio, where $\alpha $ is the percentage of the police used in MinP. To further improve patrolling efficiency, a grafting mechanism is proposed to integrate the two subproblems’ solutions. Finally, we conduct extensive experiments on the real dataset of Foshan, a modern Chinese city. The results demonstrate that compared with benchmarks, our approach scales well to city-scale problem instances with fine-grained periods, hundreds of regions, and hundreds of police officers. [ABSTRACT FROM AUTHOR]

Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary optimization during the last two decades. However, as another famous multiobjective optimization framework, decomposition-based multiobjective optimization has not received sufficient attention from constrained evolutionary optimization. In this paper, we make use of decomposition-based multiobjective optimization to solve constrained optimization problems (COPs). In our method, first of all, a COP is transformed into a biobjective optimization problem (BOP). Afterward, the transformed BOP is decomposed into a number of scalar optimization subproblems. After generating an offspring for each subproblem by differential evolution, the weighted sum method is utilized for selection. In addition, to make decomposition-based multiobjective optimization suit the characteristics of constrained evolutionary optimization, weight vectors are elaborately adjusted. Moreover, for some extremely complicated COPs, a restart strategy is introduced to help the population jump out of a local optimum in the infeasible region. Extensive experiments on three sets of benchmark test functions, namely, 24 test functions from IEEE CEC2006, 36 test functions from IEEE CEC2010, and 56 test functions from IEEE CEC2017, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper investigates an energy-efficient hybrid flowshop scheduling problem with the consideration of machines with different energy usage ratios, sequence-dependent setups, and machine-to-machine transportation operations. To minimize the makespan and total energy consumption simultaneously, a mixed-integer linear programming (MILP) model is developed. To solve this problem, a three-stage multiobjective approach based on decomposition (TMOA/D) is suggested, in which each solution is bound with a main weight vector and a set of its neighbors. Accordingly, a variable direction strategy is developed to ensure each solution along its main direction is thoroughly exploited and can jump to the neighboring directions using a proximity principle. To ensure an active schedule of arranging jobs to machines, a two-level solution representation is employed. In the first phase, each solution attempts to improve itself along its current weight vector through a developed neighborhood-based local search. In the second phase, the promising solutions are selected through the technique for order preference by similarity to an ideal solution. Then, they attempt to update themselves with a proposed global replacement strategy via incorporation with their closing solutions. In the third phase, a solution conducts a large perturbation when it goes through all its assigned weight vectors. Extensive experiments are conducted to test the performance of TMOA/D, and the results demonstrate that TMOA/D has a very competitive performance. [ABSTRACT FROM AUTHOR]

The distribution network restoration problem is by nature a mixed integer and non-linear optimization problem due to the switching decisions and Optimal Power Flow (OPF) constraints, respectively. The link between these two parts involves logical implications modelled through big-M coefficients. The presence of these coefficients makes the relaxation of the mixed-integer problem using branch-and-bound method very poor in terms of computation burden. Moreover, this link inhibits the use of classical Benders algorithm in decomposing the problem because the resulting cuts will still depend on the big-M coefficients. In this paper, a novel decomposition approach is proposed for the restoration problem named Modified Combinatorial Benders (MCB). In this regard, the reconfiguration problem and the OPF problem are decomposed into master and sub problems, which are solved through successive iterations. In the case of a large outage area, the numerical results show that the MCB provides, within a short time (after a few iterations), a restoration solution with a quality that is close to the proven optimality when it can be exhibited. [ABSTRACT FROM AUTHOR]

The current separate transmission and distribution energy management system faces multiple challenges associated with integrating distributed generators (DGs) into future grids. These challenges, such as a voltage rise issue resulting in curtailment of DGs, are difficult to solve via the current separate energy management. Thus, coordination between transmission and distribution is suggested, and a coordinated transmission and distribution AC optimal power flow (TDOPF) is proposed in this paper. A mathematical TDOPF model is established and analyzed in a master-slave structure. A heterogeneous decomposition algorithm (HGD), which is inspired by heterogeneous transmission and distribution characteristics, is proposed to solve the TDOPF in a distributed manner. The HGD is compared to other typical multi-area OPF decomposition algorithms and the differences are discussed. Numerical tests verify the benefit of the TDOPF to both transmission and distribution systems. The TDOPF improves economic operations, mitigates voltage rises, and decreases boundary bus mismatches. Hence, more DGs can be accommodated by the grid. In addition, a series of numerical tests indicate that the HGD competitively solves the TDOPF. [ABSTRACT FROM PUBLISHER]

This paper proposes an optimization method of train scheduling for metro lines with a train dwell time model according to passenger demand. An optimization problem of train scheduling is established with constraints of a headway equation, passenger equation, and train dwell time equation, where the train dwell time is modeled as a function of boarding and alighting passenger volumes. The aim of the optimization problem is to minimize the waiting time of passengers and train operation cost. Lagrangian duality theory is adopted to solve this optimization problem with high dimensionality. Finally, simulation results illustrate that this method is efficient to generate the train schedule, which meets the passengers' exchanging requirements between trains and platforms. The contribution of this paper is that a dwell time model is introduced in train schedule optimization, which provides the possibility of reducing the operation cost in the precondition that the exchanging time of passengers between platforms and trains is assured. [ABSTRACT FROM PUBLISHER]

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a set of scalar optimization subproblems and optimizes them in a collaborative manner. Subproblems and solutions are two sets of agents that naturally exist in MOEA/D. The selection of promising solutions for subproblems can be regarded as a matching between subproblems and solutions. Stable matching, proposed in economics, can effectively resolve conflicts of interests among selfish agents in the market. In this paper, we advocate the use of a simple and effective stable matching (STM) model to coordinate the selection process in MOEA/D. In this model, subproblem agents can express their preferences over the solution agents, and vice versa. The stable outcome produced by the STM model matches each subproblem with one single solution, and it tradeoffs convergence and diversity of the evolutionary search. Comprehensive experiments have shown the effectiveness and competitiveness of our MOEA/D algorithm with the STM model. We have also demonstrated that user-preference information can be readily used in our proposed algorithm to find a region that decision makers are interested in. [ABSTRACT FROM AUTHOR]

Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark (MPB) and show its nonseparable nature irrespective of its number of peaks. We then propose a composite MPB suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of large-scale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decomposition-based coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower-dimensional components. A novel aspect of the framework is its efficient bi-level resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200-D, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems. [ABSTRACT FROM AUTHOR]

The evolving landscape of the electricity sector along with increasing environmental concerns necessitate modern power grids to be more efficient, sustainable, and adaptive. Microgrids are typically composed of distributed energy sources which have great potential for enabling energy independence, sustainability, and flexibility. However, practical difficulties that deter the widespread deployment of microgrids include the unpredictability of local generation sources (e.g., renewables) and the lack of inertia that is naturally present in systems containing bulk synchronous plants. In this paper, we propose a near real-time microgrid coordination algorithm that allows actuating components to adapt to changing system conditions. We account for the electrical dependencies and limits in microgrid systems by constructing voltage/current balance relations in the $dq0$ frame and applying strategic decompositions to invoke the Schur’s complement and S-procedure that allow for zero duality gap. We highlight the convergence, feasibility, and scalability features of the proposed decentralized algorithm via theoretical and comparative practical simulation studies. [ABSTRACT FROM AUTHOR]

Decomposition of a multiobjective optimization problem (MOP) into several simple multiobjective subproblems, named multiobjective evolutionary algorithm based on decomposition (MOEA/D)-M2M, is a new version of multiobjective optimization-based decomposition. However, it fails to consider different contributions from each subproblem but treats them equally instead. This paper proposes a collaborative resource allocation (CRA) strategy for MOEA/D-M2M, named MOEA/D-CRA. It allocates computational resources dynamically to subproblems based on their contributions. In addition, an external archive is utilized to obtain the collaborative information about contributions during a search process. Experimental results indicate that MOEA/D-CRA outperforms its peers on 61% of the test cases in terms of three metrics, thereby validating the effectiveness of the proposed CRA strategy in solving MOPs. [ABSTRACT FROM AUTHOR]

Frequency filtering is widely used in routine processing of seismic data to improve the signal-to-noise ratio (SNR) of recorded signals and by doing so to improve subsequent analyses. In this paper, we develop a new denoising/decomposition method, DeepDenoiser, based on a deep neural network. This network is able to simultaneously learn a sparse representation of data in the time–frequency domain and a non-linear function that maps this representation into masks that decompose input data into a signal of interest and noise (defined as any non-seismic signal). We show that DeepDenoiser achieves impressive denoising of seismic signals even when the signal and noise share a common frequency band. Because the noise statistics are automatically learned from data and require no assumptions, our method properly handles white noise, a variety of colored noise, and non-earthquake signals. DeepDenoiser can significantly improve the SNR with minimal changes in the waveform shape of interest, even in the presence of high noise levels. We demonstrate the effect of our method on improving earthquake detection. There are clear applications of DeepDenoiser to seismic imaging, micro-seismic monitoring, and preprocessing of ambient noise data. We also note that the potential applications of our approach are not limited to these applications or even to earthquake data and that our approach can be adapted to diverse signals and applications in other settings. [ABSTRACT FROM AUTHOR]

Power system operator executes the unit commitment (UC) program to determine the optimal generation schedule for a daily or weekly horizon. In smart power systems, cyber-attacks such as false data injections (FDIs) may resemble a kind of contingency with only manipulating measurements data and without physical initiating events. This paper proposes a network constrained UC model in the presence of cyber-attacks named cyber-secured UC (CSUC) model. Normally the operating point obtained by UC program is on its conventional schedule, however in case of cyber-attack risk, it can depart from the conventional schedule toward the cyber secured schedule without violating unit and network constraints during such transition. The CSUC schedule is secured against a type of cyber-attacks called load redistribution (LR) attacks. Moving to this new operating point, in case of cyber-attack risk, may incur a small and reasonable additional cost to the cost of conventional generation schedule. In order to guarantee the optimality of solution, the proposed CSUC model is formulated as a mixed integer linear programming (MILP) model. The benders decomposition (BD) method is utilized to solve the model in a master and sub-problem hierarchy. The proposed method is implemented in the IEEE 118-bus test system. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic model predictive control (SMPC) approach to optimally dispatch energy storage and dispatchable generation in the electric energy system under uncertainties introduced by variable energy sources as well as demand. The objective is to minimize the expectation of the sum of the production and ramping costs for generators while satisfying all the system constraints. The uncertainties are represented by scenarios, resulting in a large-scale and computationally demanding optimization problem. We use the optimality condition decomposition (OCD) to decompose the SMPC problem into subproblems which can be solved in parallel thereby reducing the computation time. Both a scenario-based decomposition and a temporal-based decomposition are formulated and numerically evaluated in terms of speed and convergence using a modified IEEE 39-bus test system. Simulation results indicate that the scenario-based decomposition scheme achieves a better trade-off between convergence speed and subproblem size for the considered optimal dispatch problem. [ABSTRACT FROM PUBLISHER]

The balance between convergence and diversity is the cornerstone of evolutionary multiobjective optimization (EMO). The recently proposed stable matching-based selection provides a new perspective to handle this balance under the framework of decomposition multiobjective optimization. In particular, the one-one stable matching between subproblems and solutions, which achieves an equilibrium between their mutual preferences, is claimed to strike a balance between convergence and diversity. However, the original stable marriage model has a high risk of matching a solution with an unfavorable subproblem, which finally leads to an imbalanced selection result. In this paper, we introduce the concept of incomplete preference lists into the stable matching model to remedy the loss of population diversity. In particular, each solution is only allowed to maintain a partial preference list consisting of its favorite subproblems. We implement two versions of stable matching-based selection mechanisms with incomplete preference lists: one achieves a two-level one-one matching and the other obtains a many-one matching. Furthermore, an adaptive mechanism is developed to automatically set the length of the incomplete preference list for each solution according to its local competitiveness. The effectiveness and competitiveness of our proposed methods are validated and compared with several state-of-the-art EMO algorithms on 62 benchmark problems. [ABSTRACT FROM PUBLISHER]

Because distributed generations are extensively integrated into active distribution grids (ADGs), the transmission and distribution coordinated economic dispatch (TDCED) should be investigated to optimally dispatch the generation resources and evaluate the locational marginal prices (LMPs) of the entire system. In this paper, the TDCED problem is formulated, and a new heterogeneous decomposition (HGD) algorithm is proposed. In the HGD algorithm, the transmission LMP at the boundary bus and ADGs' power are exchanged among transmission and ADGs. The optimality and convergency of HGD are proven and numerically verified. In addition, several related issues are further discussed: a) methods to improve the convergency of HGD, b) considerations regarding security issues and voltage constraints in TDCED and corresponding modified HGD algorithms, and c) the practicality of HGD in either separate or transparent interaction modes of transmission and distribution in future grids. Numerical simulations indicate that the generation resources are optimally utilized and that LMPs are reasonably evaluated in TDCED. Congestion in the traditional isolated dispatch mode can also be prevented. Moreover, simulations on several systems from T6D2 to T300D60 verify that the HGD algorithm is an efficient and robust algorithm with limited communication burdens to solve TDCED. [ABSTRACT FROM PUBLISHER]

Self-paced learning (SPL) is a recently proposed paradigm to imitate the learning process of humans/animals. SPL involves easier samples into training at first and then gradually takes more complex ones into consideration. Current SPL regimes incorporate a self-paced (SP) regularizer into the learning objective with a gradually increasing pace parameter. Therefore, it is difficult to obtain the solution path of the SPL regime and determine where to optimally stop this increasing process. In this paper, a multiobjective SPL method is proposed to optimize the loss function and the SP regularizer simultaneously. A decomposition-based multiobjective particle swarm optimization algorithm is used to simultaneously optimize the two objectives for obtaining the solutions. In the proposed method, a polynomial soft weighting regularizer is proposed to penalize the loss. Theoretical studies are conducted to show that the previous regularizers are roughly particular cases of the proposed polynomial soft weighting regularizer family. Then an implicit decomposition method is proposed to search the solutions with respect to the sample number involved into training. A set of solutions can be obtained by the proposed method and naturally constitute the solution path of the SPL regime. Then a satisfactory solution can be naturally obtained from these solutions by utilizing some effective tools in evolutionary multiobjective optimization. Experiments on matrix factorization and classification problems demonstrate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Traffic surveillance plays an important role in the development of a smart city, and it is a fundamental part in many applications such as security monitoring and traffic analysis. People are thrilled by the abundant data generated from the huge traffic networks but have difficulty in using them. In this paper, we propose a distributed mean-field-type filtering (DMF) framework to handle those noisy, partial-observed, and high-dimensional data. The filter incorporates a mean-field term into the system model and decomposes the state space into highly independent parts; filtering is performed in each part and then integrated. Our approach iterates through four operations: sampling, prediction, decomposition, and correction. Theoretical analysis provides a linear bound for the global error, which is independent of the network’s cardinality. We implemented DMF in aircraft and vehicle tracking scenarios. Performance evaluation on synthetic and real-world data demonstrates the advantage of our approach over traditional mean-field free filters. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Petri Net (PN) decomposition approach to the optimization of route planning problems for automated guided vehicles (AGVs) in semiconductor fabrication bays. An augmented PN is developed to model the concurrent dynamics for multiple AGVs. The route planning problem to minimize the total transportation time is formulated as an optimal transition firing sequence problem for the PN. The PN is decomposed into several subnets such that the subnets are made independent by removing the original shared places and creating its own set of resource places for each subnet with the appropriate connections. The partial solution derived at each subnet is not usually making a feasible solution for the entire PN. The penalty function algorithm is used to integrate the solutions derived at the decomposed subnets. The optimal solution for each subnet is repeatedly generated by using the shortest-path algorithm in polynomial time with a penalty function embedded in the objective function. The effectiveness of the proposed method is demonstrated for a practical- sized route planning problem in semiconductor fabrication bay from computational experiments. [ABSTRACT FROM AUTHOR]

Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing images. The proposed MMCA framework separates a given image into multiple pairs of morphological components (MCs) based on different textural features, with the ultimate goal of improving the signal-to-noise level and the data separability. A distinguishing feature of our proposed approach is the possibility to retrieve detailed image texture information, rather than using a single spatial characteristic of the texture. In this paper, four textural features: content, coarseness, contrast, and directionality (including horizontal and vertical), are considered for generating the MCs. In order to evaluate the obtained MCs, we conduct classification by using both remotely sensed hyperspectral and polarimetric synthetic aperture radar (SAR) scenes, showing the capacity of the proposed method to deal with different kinds of remotely sensed images. The obtained results indicate that the proposed MMCA framework can lead to very good classification performances in different analysis scenarios with limited training samples. [ABSTRACT FROM PUBLISHER]

In response to the computational challenges produced by the integrated dispatch of generation and load (IDGL), this paper proposes a novel and efficient decomposition method. The IDGL is formulated using the mixed-integer quadratic constrained programming (MIQCP) method. To efficiently solve this complex optimization problem, the nodal equivalent load shifting bidding curve (NELSBC) is proposed to represent the aggregated response characteristics of customers at a node. The IDGL is subsequently decomposed into a two-level optimization problem. At the upper level, grid operators optimize load shifting schedules based on the NELSBC of each node. Transmission losses are explicitly incorporated into the model to coordinate them with generating costs and load shifting costs. At the bottom level, customer load adjustments are optimized at individual nodes given the nodal load shifting requirement imposed by the grid operators. The key advantage of the proposed method is that the load shifting among different nodes can be coordinated via NELSBCs without iterations. The proposed decomposition method significantly improves the efficiency of the IDGL. Parallel computing techniques are utilized to accelerate the computations. Using numerical studies of IEEE 30-bus, 118-bus, and practically sized 300-bus systems, this study demonstrates that accurate and efficient IDGL scheduling results, which consider the nonlinear impact of transmission losses, can be achieved. [ABSTRACT FROM PUBLISHER]

This paper introduces a two-stage stochastic program for transmission planning. The model has two dependent random variables, namely, total electric load and available wind power. Given univariate marginal distributions for these two random variables and their correlation coefficient, the joint distribution is modeled using a Gaussian copula. The optimal power flow (OPF) problem is solved based on the linearized direct current (DC) power flow. The Electric Reliability Council of Texas (ERCOT) network model and its load and wind data are used for a test case. A 95% confidence interval is formed on the optimality gap of candidate solutions obtained using a sample average approximation with 200 and 300 samples from the joint distribution of load and wind. [ABSTRACT FROM PUBLISHER]

Data mining offers tools for discovery of relationships, patterns, and knowledge in large databases. The knowledge extraction process is computationally complex and therefore a subset of all data Is normally considered for mining. In this paper, numerous methods for decomposition of data sets are discussed. Decomposition enhances the quality of knowledge extracted from large databases by simplification of the data mining task. The ideas presented are illustrated with examples and an industrial case study. In the case study reported in this paper, a data mining approach is applied to extract knowledge from a data set. The extracted knowledge is used for the prediction and prevention of manufacturing faults in wafers [ABSTRACT FROM PUBLISHER]

This paper compares the three levels of data fusion with the goal of determining the optimal level of data fusion for multi-sensor human activity data. Using the data processing pipeline, gyroscope and accelerometer data was fused at the sensor-level, feature-level and decision-level. For each level of data fusion four different techniques were used with varying levels of success. This analysis was performed on four human activity publicly-available datasets along with four well-known machine learning classifiers to validate the results. The decision-level fusion (Acc = 0.7443±0.0850) outperformed the other two levels of fusion in regards to accuracy, sensor level (Acc = 0.5934 ± 0.1110) and feature level (Acc = 0.6742 ± 0.0053), but, the processing time and computational power required for training and classification were far greater than practical for a HAR system. However, Kalman filter appear to be the more efficient method, since it exhibited both good accuracy (Acc = 0.7536 ± 0.1566) and short processing time (time = 61.71ms ± 63.85); properties that play a large role in real-time applications using wearable devices. The results of this study also serve as baseline information in the HAR literature to compare future methods of data fusion. [ABSTRACT FROM AUTHOR]

The expansion of Advanced Metering Infrastructure (AMI) has provided building operators and researchers detailed information on building energy consumption. The majority of AMI systems, however, record data at relatively low resolutions of 15, 30, or 60 minutes, due to cost, storage and bandwidth limitations. Emerging applications in power flow analysis, Quasi-Static Time-Series Simulation (QSTS), smart grid integration and load matching, however, require data at higher resolutions. Short-term energy demand can deviate significantly from long-term averages, with an unknown magnitude and frequency when only low-resolution load profile data is available. This paper presents a novel data-driven approach to predict characteristics of the missing high-resolution information in a low-resolution signal, applicable to both measured and modeled building load profile data, utilizing machine learning regression algorithms. In the proposed framework, the relationship between characteristics of high-resolution and low-resolution signals is learned from the decomposition and characterization of a subset of high-resolution building data. This paper validates the underlying hypotheses and methodology of this approach through a single-building case study, training a variety of machine learning models on one year of data, and using the resulting models to predict high-resolution characteristics in a different year. An Ensemble Tree regression model demonstrates a high predictive accuracy (R2 of 0.79-0.92) for several statistical metrics of the high-resolution load profile. These results support the broader potential for leveraging low-resolution information to accurately constrain predictions of missing high-resolution information in building load profiles, which may greatly increase the utility of both measured and modeled data in many practical and research applications. Generalizing such models will require analysis of high-resolution data from a diverse set of building types.

Economic transmission switching has been proposed as a new control paradigm to improve the economics of electric power systems. In practice, the transmission switching operation itself is a disruptive action to the system. Frequently switching lines into or out of service can create undesirable effects on the security and reliability of power systems and may require new investments in the automation and control systems. In this paper, we formulate an economic seasonal transmission switching model where transmission switching occurs once at the beginning of a time period (season) and then the transmission topology remains unchanged during that period. The proposed seasonal transmission switching model is a large-scale mixed integer programming problem. The objective of the optimization model is to minimize the total energy generation cost over the season subject to loads and N-1 reliability requirements. We develop a novel decomposition method that decomposes the seasonal problem into one-hour problems which are then solved efficiently. We demonstrate our model and the decomposition approach on the 14-bus, 39-bus, and 118-bus power systems and show potential cost savings in each case. [ABSTRACT FROM PUBLISHER]

This paper presents, for the first time, a directional coupler that allows for arbitrary power division ratios as well as arbitrary phase differences at dual frequencies of interest. Explicit design equations in terms of dual-band power-dividing ratios and phase differences will be given here and were derived based on the even- and odd-mode decomposition analysis. To illustrate the design procedure, examples will be provided and the relevant studies on the coupler's electrical parameters for a wide range of dual-band specifications and frequency ratios were conducted, by which the graphical solutions can be readily used as the further design tool. In addition, the resulting operational bandwidths are included and discussed for completeness. To validate our idea, four coupler prototypes were carried out according to the given guidelines. Excellent agreement is obtained between the measured and full-wave calculated results. The functional versatility of the proposed simple structure is well suited to applications in dual-band integrated modules, such as the beam-forming networks, for both loss and size reduction. [ABSTRACT FROM AUTHOR]