Search

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load–reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system. [ABSTRACT FROM AUTHOR]

This paper focuses on minimum-time path tracking, a subproblem in motion planning of robotic systems. We generate a time-optimal velocity profile for robotic manipulators taking into account kinematic and dynamic constraints. Based on the special structure of the constraints (called peaked constraints), profile generation is formulated as a linear programming (LP) problem. The LP-based control problem is solved by a sequential optimization method. The presented algorithm has reduced computational time compared to a general LP solver. [ABSTRACT FROM AUTHOR]

In this paper, two different timing-mismatch compensation strategies for two-channel time-interleaved analog-to-digital converters are comprehensively analyzed and compared. In the first strategy (SA), one channel serves as a reference channel, and the other channel is compensated to match the reference channel using the timing mismatch between the channels. In the second strategy (SB), both channels are compensated to match each other, using half the value of the channel mismatch with different signs. For SB, the paper introduces a novel compensation structure that utilizes parallel differentiator-multiplier (PDM) branches. Expressions for the spurious-free dynamic range (SFDR) after compensation are derived for both strategies. These expressions reveal that the novel PDM structure achieves a remarkably greater SFDR than the existing cascaded differentiator-multiplier (CDM) compensation structure which can be used for both SA and SB. This is because, after compensation, the remaining aliasing distortion in the proposed PDM structure is shown to be of higher approximation order (in terms of the mismatch value) and thus substantially smaller than in the CDM structure. Simulations included in the paper validate the theoretical results. [ABSTRACT FROM AUTHOR]

Due to the non-uniform geographic distribution and time-varying characteristics of the ground traffic request, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for beam hopping satellite systems. The conventional greedy-based beam hopping methods do not consider the long-term reward, which is difficult to deal with the time-varying traffic demand. Meanwhile, the heuristic algorithms such as genetic algorithm have a slow convergence time, which can not achieve real-time scheduling. Furthermore, existing methods based on deep reinforcement learning (DRL) only make decisions on beam patterns, lack of the freedom of bandwidth. This paper proposes a dynamic beam pattern and bandwidth allocation scheme based on DRL, which flexibly uses three degrees of freedom of time, space and frequency. Considering that the joint allocation of bandwidth and beam pattern will lead to an explosion of action space, a cooperative multi-agents deep reinforcement learning (MADRL) framework is presented in this paper, where each agent is only responsible for the illumination allocation or bandwidth allocation of one beam. The agents can learn to collaborate by sharing the same reward to achieve the common goal, which refers to maximize the throughput and minimize the delay fairness between cells. Simulation results demonstrate that the offline trained MADRL model can achieve real-time beam pattern and bandwidth allocation to match the non-uniform and time-varying traffic request. Furthermore, when the traffic demand increases, our model has a good generalization ability. [ABSTRACT FROM AUTHOR]

Understanding diffusion processes in networks has emerged as an important research topic because of its wide range of applications. Analysis of diffusion traces can help us answer important questions such as the source(s) of diffusion and the role of each node during the diffusion process. However, in large-scale networks, due to the cost and privacy concerns, it is almost impossible to monitor the entire network and collect the complete diffusion trace. In this paper, we tackle the problem of reconstructing the diffusion history from a partial observation. We formulate the diffusion history reconstruction problem as a maximum a posteriori (MAP) problem and prove the problem is NP-hard. Then, we propose a step-by-step reconstruction algorithm, which can always produce a diffusion history that is consistent with the partial observation. Our experimental results based on synthetic and real networks show that the algorithm significantly outperforms some existing methods. [ABSTRACT FROM AUTHOR]

This paper examines the dynamic aggregation of a group of M same wind turbine generators (WTGs) in a parallel connection. The group of WTGs could either be a part of a grid-connected wind farm or constitute the entire wind farm. Analytical derivation in this paper indicates that the group of M same WTGs is equivalent to M dynamically independent subsystems. Each of the subsystems is comprised of a single WTG. Hence, the dynamic aggregation of a grid-connected group of same WTGs in a parallel connection can be represented by grid-connected single-WTG subsystems for studying the oscillation stability. Further analysis in this paper reveals that when the number of WTGs in a parallel connection increases, it is possible that oscillation modes of the group of WTGs may move into the right half of a complex plane. Thus, the dynamic coupling of the increasing number of WTGs in a parallel connection may likely cause growing oscillations in the wind farm. An example grid-connected DFIG wind farm is used to demonstrate and evaluate the analysis and conclusions made in this paper. The oscillation stability of the example wind farm was examined when the dynamic models of a group of DFIGs in a parallel connection in the example wind farm were identical and different. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics (also known as the Gibbs sampler). The Glauber dynamics is a Markov chain that sequentially updates individual nodes (variables) in a graphical model and it is frequently used to sample from the stationary distribution (to which it converges given sufficient time). Additionally, the Glauber dynamics is a natural dynamical model in a variety of settings. This paper deviates from the standard formulation of graphical model learning in the literature, where one assumes access to independent identically distributed samples from the distribution. Much of the research on graphical model learning has been directed toward finding algorithms with low computational cost. As the main result of this paper, we establish that the problem of reconstructing binary pairwise graphical models is computationally tractable when we observe the Glauber dynamics. Specifically, we show that a binary pairwise graphical model on p nodes with maximum degree d can be learned in time f(d)p^2\log p , for a function f(d) defined explicitly in this paper, using nearly the information-theoretic minimum number of samples. [ABSTRACT FROM AUTHOR]

As an algorithmic framework, message passing is extremely powerful and has wide applications in the context of different disciplines including communications, coding theory, statistics, signal processing, artificial intelligence and combinatorial optimization. In this paper, we investigate the performance of a message-passing algorithm called min-sum belief propagation (BP) for the vertex-disjoint shortest $k$ -path problem ($k$ -VDSP) on weighted directed graphs, and derive the iterative message-passing update rules. As the main result of this paper, we prove that for a weighted directed graph $G$ of order $n$ , BP algorithm converges to the unique optimal solution of $k$ -VDSP on $G$ within $O(n^{2}w_{max})$ iterations, provided that the weight $w_{e}$ is nonnegative integral for each arc $e\in E(G)$ , where $w_{max}=\max \{w_{e}: e\in E(G)\}$. To the best of our knowledge, this is the first instance where BP algorithm is proved correct for NP-hard problems. Additionally, we establish the extensions of $k$ -VDSP to the case of multiple sources or sinks. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at https://github.com/skypitcher/hats. [ABSTRACT FROM AUTHOR]

This paper is concerned with iterative learning control (ILC) algorithms for two-dimensional (2-D) linear discrete systems described by the first Fornasini–Marchesini model (FMMI) with iteration-varying reference trajectories/profiles. The variation of reference trajectories in iteration domain is represented by a high-order internal model (HOIM) formula. Robustness and convergence of two types of HOIM-based ILC laws with different boundary conditions are investigated, respectively. A strategy employed in this paper is to reconstruct the HOIM-based ILC process of the 2-D linear FMMI system into a set of 2-D linear inequalities or a 2-D linear Roesser model such that sufficient robustness/convergence conditions of the HOIM-based ILC laws are obtained. Under random boundary conditions, the designed ILC law (9) is capable to drive the ILC tracking error into a bounded range. Moreover, under the HOIM-based boundary conditions, a perfect tracking to the iteration-varying reference trajectories can be achieved by utilizing the proposed ILC law (32). Two simulation examples are given to validate the effectiveness of the two proposed ILC algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an evolutionary algorithm-based energy-saving control strategy to control a highly nonlinear and time-varying active magnetic bearing (AMB) positioning system that considers energy efficiency and control performance simultaneously. Most AMB control schemes use a bias current with a superimposed control current to improve the linearity and dynamic performance of the system. However, the bias current causes power losses even if no electromagnetic force is required. As such, a recurrent wavelet fuzzy neural network with adaptive differential evolution (RWFNN-ADE)-based dynamic bias current control strategy is proposed in this paper so as to minimize the energy consumed by an AMB without altering its positioning performance and robustness. To begin with, this paper analyzes the operation principle of the AMB positioning system with a differential driving mode. Subsequently, the proposed RWFNN-ADE control scheme, in which the control current and bias current are controlled by the RWFNN and ADE, respectively, is introduced in detail. Finally, the experimental results demonstrate the high-accuracy control and significant energy-saving performances of the proposed RWFNN-ADE-controlled AMB positioning system. In the tests corresponding to operation periods of 10 and 50 s, the energy improvements compared to the baseline values were 20.24% and 17.65%, respectively, in nominal cases, and 18.89% and 18.68%, respectively, in parameter variation cases, for the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments’ movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments. [ABSTRACT FROM AUTHOR]

Basic kinetic process of plasmas is studied by a first principle based N-body simulation. By confirming Coulomb collisional relaxation process, this paper is extended to investigate the dynamics of Debye shielding. Some of the key numerical techniques are discussed, including how to avoid singularities at the near encounter of two charged particles. Electron distribution function in Debye shielding is revealed, which deviates from conventional Maxwellian distribution function. [ABSTRACT FROM AUTHOR]

The ongoing growth of Internet of Things (IoT) traffic increasesthe emergence of Multi-Access Edge Computing (MEC), which can serve IoT devices to process edge missions properly. The power supply for IoT devices is addressed by energy harvesting (EH) technology. However, the energy arrival model obeys the unknown energy arrival process. In addition, due to the dynamical changes of network state, it is crucial to study the access control in the MEC-based green IoT system. In this paper, we propose an MEC-based access control strategy to maximize the system utility for the green IoT system, considering the spectral efficiency and the successful access ratio from the perspective of operators and users respectively. Specifically, we model the access control problem as a Constrained Markov Decision Process (CMDP), and develop a novel centralized reward-based experience replay deep convolutional Q network algorithm (RCQN) to attain the optimal access control for EH IoT devices. Additionally, to enrich the prior knowledge of access control strategy, we design a Long Short-Term Memory (LSTM) based energy prediction module to predict the energy state of IoT devices by learning the historical energy arrival information, whose output is taken as the state information of RCQN. Simulation results demonstrate that the proposed LSTM-based RCQN access control algorithm significantly improves the system utility with a proper reward design and training mechanism. [ABSTRACT FROM AUTHOR]

Many big data applications produce a massive amount of high-dimensional, real-time, and evolving streaming data. Clustering such data streams with both effectiveness and efficiency are critical for these applications. Although there are well-known data stream clustering algorithms that are based on the popular online-offline framework, these algorithms still face some major challenges. Several critical questions are still not answer satisfactorily: How to perform dimensionality reduction effectively and efficiently in the online dynamic environment? How to enable the clustering algorithm to achieve complete real-time online processing? How to make algorithm parameters learn in a self-supervised or self-adaptive manner to cope with high-speed evolving streams? In this paper, we focus on tackling these challenges by proposing a fully online data stream clustering algorithm (called ESA-Stream) that can learn parameters online dynamically in a self-adaptive manner, speedup dimensionality reduction, and cluster data streams effectively and efficiently in an online and dynamic environment. Experiments on a wide range of synthetic and real-world data streams show that ESA-Stream outperforms state-of-the-art baselines considerably in both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

For several years, the completion time and the decoding delay problems in instantly decodable network coding (IDNC) were considered separately and were thought to act completely against each other. Recently, some works have aimed to balance the effects of these two important IDNC metrics, but none of them studied a further optimization of one by controlling the other. This paper investigates the effect of controlling the decoding delay to reduce the completion time below its currently best known solution in both perfect and imperfect feedback with persistent erasure channels. To solve the problem, the decoding-delay-dependent expressions of the users' and overall completion times are derived in the complete feedback scenario. Although using such expressions to find the optimal overall completion time is NP-hard, this paper proposes two novel heuristics that minimize the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. Afterward, this paper extends the study to the imperfect feedback scenario, in which uncertainties at the sender affect its ability to anticipate accurately the decoding delay increase at each user. This paper formulates the problem in such an environment and derives the expression of the minimum increase in the completion time. Simulation results show the performance of the proposed solutions and suggest that both heuristics achieve a lower mean completion time, as compared with the best known heuristics for completion time reduction in perfect and imperfect feedback. The gap in performance becomes more significant as the erasure of the channel increases. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, namely, maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard, and in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, this paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound CPLEX-based algorithm, and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs. [ABSTRACT FROM PUBLISHER]

Inertial navigation computation is to acquire the attitude, velocity, and position information of a moving body by integrating inertial measurements from gyroscopes and accelerometers. Over half a century has witnessed great efforts in coping with the motion noncommutativity errors to accurately compute the navigation information as far as possible, so as not to compromise the quality measurements of inertial sensors. Highly dynamic applications and the forthcoming cold-atom precision inertial navigation systems demand for even more accurate inertial navigation computation. The paper gives birth to an inertial navigation algorithm to fulfill that demand, named the iNavFIter, which is based on a brand-new framework of functional iterative integration and Chebyshev polynomials. Remarkably, the proposed iNavFIter reduces the noncommutativity errors to almost machine precision, namely, the coning/sculling/scrolling errors that have perplexed the navigation community for long. Numerical results are provided to demonstrate its accuracy superiority over the state-of-the-art inertial navigation algorithms at affordable computation cost. [ABSTRACT FROM AUTHOR]

In this paper, we aim at developing a new collaborative filtering recommender system using soft ratings, which is capable of dealing with both imperfect information about user preferences and the sparsity problem. On the one hand, Dempster–Shafer theory is employed for handling the imperfect information due to its advantage in providing not only a flexible framework for modeling uncertain, imprecise, and incomplete information, but also powerful operations for fusion of information from multiple sources. On the other hand, in dealing with the sparsity problem, community context information that is extracted from the social network containing all users is used for predicting unprovided ratings. As predicted ratings are not a hundred percent accurate, while the provided ratings are actually evaluated by users, we also develop a new method for calculating user–user similarities, in which provided ratings are considered to be more significant than predicted ones. In the experiments, the developed recommender system is tested on two different data sets; and the experiment results indicate that this system is more effective than CoFiDS, a typical recommender system offering soft ratings. [ABSTRACT FROM AUTHOR]

With the proliferation of portable devices, the exponential growth of the global mobile traffic brings great challenges to the traditional communication networks and the traditional wireless communication technologies. In this context, converged networks and cache-based data offloading have drawn more and more attention based on the strong correlation of services. This paper proposes a novel popular services pushing and caching scheme by using converged overlay networks. The most popular services are pushed by terrestrial broadcasting networks. And they are cached in ${n}$ router-nodes with limited cache sizes. Each router-node only interconnects with its neighbor nodes. Users are served through the router’s WiFi link. If the services requested are cached in the routers, the user can be immediately responded; otherwise, the requests can be responded through the link from cellular stations to the router. In the proposed scheme, the cache size of the router, the maximum number of requests each router can serve, and the whole-time delay are limited. Three node-selecting and dynamic programming algorithms are adopted to maximize the equivalent throughput. Analytical and numerical results demonstrate that the proposed scheme is very effective. [ABSTRACT FROM AUTHOR]

Partial shading condition drastically reduces the maximum power output of photovoltaic array. Partial shading occurs due to several factors, such as flying birds, trees, and passing clouds. Many ways can be mitigated partial shading problems in photovoltaic (PV) array. One among the way is reconfiguration techniques, namely reconfigure the location of PV modules in PV array based on irradiance levels in order to distribute shading effects and increasing maximum power. This paper proposed an optimal SuDoKu reconfiguration pattern for $9\times 9$ total-cross-tied (TCT) PV array to improve maximum power under partial shading conditions. In this approach, the physical location of PV modules in TCT array are rearranged based on optimal SuDoKu style without altering the electrical connections, so that the shading effects can distribute over the array. Further, the performance of proposed pattern investigated with existing SuDoKu pattern under different shading conditions by comparing the global maximum power point, mismatch losses, fill-factor, and efficiency using MATLAB-Simulink. Based on the results of this paper, it concluded that the proposed optimal SuDoKu reconfiguration arrangement is reducing the wiring arrangement and increasing the shading distribution over the array as compared to SuDoKu arrangement. [ABSTRACT FROM AUTHOR]

Cash-related costs constitute a large portion of management cost of an automated teller machine (ATM) network. Cash should be delivered to or picked-up from ATM devices in certain intervals in order to both meet customer satisfaction and to be able to generate additional revenue from excess cash through daily interest rates. Unlike classical ATMs, new-generation ATMs, also called recycle ATMs, have a single cassette for cash withdrawal and deposit; this property imposes new restrictions on ATM cash management. Moreover, recycle ATMs are costly, and hence their deployment should be planned carefully. In this paper, our aim is to optimize the ATM networks in terms of cash related costs. We formulate an optimization problem as an integer linear program, which jointly decides on when to visit an ATM, how much money to deliver to which ATM and which road should be followed for the distribution of cash to the ATMs. We also decide on which ATMs in the network should be replaced by a recycle ATM. We then propose a polynomial-time heuristic algorithm and compare it with the optimization formulation in terms of cash cost and the recycle ATM decision. We demonstrate through performance evaluation that our heuristic algorithm is suitable for practical implementation. [ABSTRACT FROM AUTHOR]

Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions and out-of-view movements. Despite the attempts to further incorporate global search, prevailing mechanisms that cooperate local and global search are relatively static, thus are still sub-optimal for improving tracking performance. By further studying the local and global search results, we raise a question: can we allow more dynamics for cooperating both results? In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy. In particular, we construct dynamic appearance model that contains multiple target templates, each of which provides its own attention for locating the target in the new frame. Guided by different attention, we maintain diversified tracking results for the target to build multi-trajectory tracking history, allowing more candidates to represent the true target trajectory. After spanning the whole sequence, we introduce a multi-trajectory selection network to find the best trajectory that deliver improved tracking performance. Extensive experimental results show that our proposed tracking strategy achieves compelling performance on various large-scale tracking benchmarks. The project page of this paper can be found at https://sites.google.com/view/mt-track/. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy, such as wind power, has brought great challenges to the power system operation due to its uncertainty. Flexibility, which measures the ability of power system to deal with the uncertainties, is critical for power system to adapt to the new era of renewable energy. The rising electrical demand of the water distribution system (WDS) creates opportunities for power system to leverage the flexibility provided by WDS. This paper investigates the water-energy relationship between these two systems and makes full use of the water pumps and tanks in the WDS to enhance the power system flexibility. Current WDS designs did not consider well the role WDS plays in the power system. This hinders the WDS from being fully used by power system to provide flexibility. An optimization model is proposed to determine the optimal tank size of WDS, which may provide the maximum available flexibility of power system. The effect of tank size on water quality is also investigated to ensure that the supplied water quality is not compromised. Moreover, a Benders-based heuristic algorithm is proposed to find the optimization solution more efficiently and to protect the data of each energy system. Results of case study highlight the merit of the proposed optimization design and the advantage of using WDS to provide flexibility for power system. [ABSTRACT FROM AUTHOR]

An NoC (network on chip) constructed with silicon photonic microrings can be implemented with CMOS technology and integrated with processor cores on the same die. Each microring in the NoC has two switching states, one consuming significantly less power than the other. Different routing schemes can lead to different numbers of microrings in the high-power consumption state, and result in different power consumptions for the NoC. In an earlier work, a looping-based routing algorithm was proposed which exploits the different power consumption characteristics of the two switching states of a microring so as to minimize the power consumption of a Benes-type NoC; but this algorithm cannot provide an optical solution. In this paper, we will show the necessary and sufficient conditions for finding the optimal solution for a Benes-type NoC. We also present a new routing algorithm. Compared with the algorithm of the previous work, the new algorithm can reduce the number of microrings in the high-power-consumption state by as many as $(n - 2)\cdot 2^{n-2}$ in a $2^{n}\times 2^{n}$ Benes-type NoC for certain interconnection patterns. [ABSTRACT FROM AUTHOR]

Data compression is a well-studied (and well-solved) problem in the setup of long coding blocks. But important emerging applications need to compress data to memory words of small fixed widths. This new setup is the subject of this paper. In the problem we consider, we have two sources with known discrete distributions, and we wish to find codes that maximize the success probability that the two source outputs are represented in $L$ bits or less. A good practical use for this problem is a table with two-field entries that is stored in a memory of a fixed width $L$ . Such tables of very large sizes are common in network switches/routers and in data-intensive machine-learning applications. After defining the problem formally, we solve it optimally with an efficient code-design algorithm. We also solve the problem in the more constrained case where a single code is used in both fields (to save space for storing code dictionaries). For both code-design problems we find decompositions that yield efficient dynamic-programming algorithms. With the help of an empirical study we show the success probabilities of the optimal codes for different distributions and memory widths. In particular, this paper demonstrates the superiority of the new codes over existing compression algorithms. [ABSTRACT FROM AUTHOR]

The tridiagonal solver is an important kernel and is widely supported in mainstream numerical libraries. While parallel algorithms have been studied for many-core architectures, the performance of current algorithms and implementations is still hindered by input size sensitivity and cross-platform portability. In this paper, we propose a novel algorithm WM-pGE for the batched solution of diagonally dominant tridiagonal systems. The algorithm balances the key design objectives, including computation complexity, memory complexity, parallelism, and input size sensitivity, better than existing algorithms. Moreover, an elegant formulation is presented to show the implementation and cross-platform optimization without loss of efficiency and generality, by extracting the platform-dependent works into only four vector operators. The results from our batched tridiagonal experiments show that the proposed algorithm outperforms the prior work PCR-pThomas by 25% and 12% on NVIDIA Tesla V100 in single and double precision, respectively. On Intel KNL, our method achieves a 10% improvement in performance over PCR-pThomas in double precision. [ABSTRACT FROM AUTHOR]

This paper studies the allocation of shared resources between vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing algorithms, dynamic vehicular environments and quantization of continuous power become the bottlenecks for providing an effective and timely resource allocation policy. In this paper, we develop two algorithms to deal with these difficulties. First, we propose a deep reinforcement learning (DRL)-based resource allocation algorithm to improve the performance of both V2I and V2V links. Specifically, the algorithm uses deep Q-network (DQN) to solve the sub-band assignment and deep deterministic policy-gradient (DDPG) to solve the continuous power allocation problem. Second, we propose a meta-based DRL algorithm to enhance the fast adaptability of the resource allocation policy in the dynamic environment. Numerical results demonstrate that the proposed DRL-based algorithm can significantly improve the performance compared to the DQN-based algorithm that quantizes continuous power. In addition, the proposed meta-based DRL algorithm can achieve the required fast adaptation in the new environment with limited experiences. [ABSTRACT FROM AUTHOR]

Recently, research on path planning for the autonomous underwater vehicles (AUVs) has developed rapidly. Heuristic algorithms have been widely used to plan a path for AUV, but most traditional heuristic algorithms are facing two problems, one is slow convergence speed, the other is premature convergence. To solve the above problems, this paper proposes a new heuristic algorithms fusion, which improves the genetic algorithm with the ant colony optimization algorithm and the simulated annealing algorithm. In addition, to accelerate convergence and expand the search space of the algorithm, some algorithms like trying to cross, path self-smoothing and probability of genetic operation adjust adaptively are proposed. The advantages of the proposed algorithm are reflected through simulated comparative experiments. Besides, this paper proposes an ocean current model and a kinematics model to solve the problem of AUV path planning under the influence of ocean currents. [ABSTRACT FROM AUTHOR]

The ever-increasing integration of non-dispatchable distributed generation, i.e., renewable energy sources (RES), arises new challenges in the field of power system's reliability. Distribution network reconfiguration (DNR) is a cost-effective approach for the distribution system operator (DSO) that wishes to enhance system's reliability without infrastructure upgrades. This paper introduces a novel path-based mixed-integer second-order cone programming model to optimally solve the reliability-oriented DNR problem. The DSO's objectives that are optimized are: a) improvement of distribution system's reliability indices and b) minimization of power losses. The proposed model is enriched with a scenario-based stochastic programming formulation that considers multiple levels of load and RES production. The standard 33-nodes distribution system and a real-world 83-nodes distribution system are employed to prove the efficiency and applicability of the model. Firstly, the multi-objective nature of the reliability-oriented DNR problem is investigated by conducting a sensitivity analysis, which reveals a trade-off region between reliability indices and power losses. Moreover, the obtained results show different global optimal solutions when the variability of load and RES production is considered. This highlights the importance of considering a scenario-based approach for load and RES production when solving the reliability-oriented DNR problem. [ABSTRACT FROM AUTHOR]

Once started, existing hash tables cannot change their pre-defined hash functions, even if the incoming data cannot be evenly distributed to the hash table buckets. In this paper, we present DHash, a type of hash table for shared memory systems, that can change its hash function and rebuild the hash table on the fly, without noticeably degrading its service. The major technical novelty of DHash stems from an efficient distributing mechanism that can atomically distribute every node when rebuilding, without locking the corresponding hash table buckets. This not only enables non-blocking lookup, insert, and delete operations, but more importantly, makes DHash independent of the implementation of hash table buckets, such that DHash allows programmers to select the set algorithms that meet their requirements best from a variety of existing lock-free and wait-free set algorithms. Evaluations show that DHash can efficiently change its hash function on the fly. Moreover, when rebuilding, DHash consistently outperforms the state-of-the-art hash tables in terms of throughput and response time of concurrent operations, at different concurrency levels, and with different operation mixes and average load factors. [ABSTRACT FROM AUTHOR]

Orthogonal/vector approximate message-passing (AMP) is a powerful message-passing (MP) algorithm for signal reconstruction in compressed sensing. This paper proves the convergence of Bayes-optimal orthogonal/vector AMP in the large system limit. The proof strategy is based on a novel long-memory (LM) MP approach: A first step is a construction of LM-MP that is guaranteed to converge systematically. A second step is a large-system analysis of LM-MP via an existing framework of state evolution. A third step is to prove the convergence of state evolution recursions for Bayes-optimal LM-MP via a new statistical interpretation of existing LM damping. The last is an exact reduction of the state evolution recursions for Bayes-optimal LM-MP to those for Bayes-optimal orthogonal/vector AMP. The convergence of the state evolution recursions for Bayes-optimal LM-MP implies that for Bayes-optimal orthogonal/vector AMP. Numerical simulations are presented to show the verification of state evolution results for damped orthogonal/vector AMP and a negative aspect of LM-MP in finite-sized systems. [ABSTRACT FROM AUTHOR]

Multi-scale exposure fusion (MEF) is an efficient way to fuse differently exposed low dynamic range (LDR) images of a high dynamic range (HDR) scene into an information enriched LDR image. In this paper, a new MEF algorithm is proposed to merge the differently exposed LDR images by introducing novel content adaptive edge-preserving smoothing (CAS) pyramids for the weight maps of all the LDR images. With the proposed CAS pyramids, details in the brightest and darkest regions of the HDR scene are preserved better than existing MEF algorithms on top of the Gaussian pyramids and edge-preserving smoothing pyramids. Comparisons experimentally demonstrate the effectiveness of the proposed algorithm to nine state-of-the-art MEF algorithms from both subjective and objective points of view regardless the image sizes. [ABSTRACT FROM AUTHOR]

The Third Generation Partnership Project has standardized cellular vehicle-to-everything (C-V2X) sidelink Mode 4 to support direct communication between vehicles. In Mode 4, the sensing-based semipersistent scheduling (SPS) scheme enables vehicles to autonomously reserve and select radio resources. In particular, SPS has three processes to realize the resource scheduling, including continuously sensing resources, probabilistically reselecting resources, and periodically reserving resources. However, vehicles randomly select resources from the available resource lists in the resource reselection process, resulting in frequent packet collisions especially when radio resources are insufficient. Unlike the traditional SPS, this paper proposes a multiagent deep reinforcement learning-based SPS (RL-SPS) algorithm to help vehicles select appropriate radio resources with the aim of reducing packet collisions. Furthermore, a multi-head attention mechanism is adopted to improve the training efficiency by helping vehicles selectively pay attention to the observations and actions of neighbouring vehicles. It is worth noting that the RL-SPS algorithm fits the characteristics of Mode 4, which selects resources without requiring any global information. Simulation results show that RL-SPS outperforms other decentralized approaches and demonstrate the scalability and robustness of RL-SPS in a dynamic vehicular network. [ABSTRACT FROM AUTHOR]

Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms – including both shallow and deep ones – often model such dynamics independently, i.e., user static and dynamic preferences are not modeled under the same latent space, which makes it difficult to fuse them for recommendation. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences, namely translating sequence to preference. To this end, we formulate the sequential recommendation task as a dictionary learning problem, which learns: 1) a shared dictionary matrix, each row of which represents a partial signal of user dynamic preferences shared across users; and 2) a posterior distribution estimator using a deep autoregressive model integrated with Gated Recurrent Unit (GRU), which can select related rows of the dictionary to represent a user's dynamic preferences conditioned on his/her past behaviors. Qualitative studies on the Netflix dataset demonstrate that the proposed method can capture the user preference drifts over time and quantitative studies on multiple real-world datasets demonstrate that the proposed method can achieve higher accuracy compared with state-of-the-art factorization and neural sequential recommendation methods. [ABSTRACT FROM AUTHOR]

This paper applies a new technique for modal decomposition based solely on measurements to test systems and demonstrates the technique's capability for partitioning a power network, which determines the points of separation in an islanding strategy. The mathematical technique is called the Koopman mode analysis (KMA) and stems from a spectral analysis of the so-called Koopman operator. Here, KMA is numerically approximated by applying an Arnoldi-like algorithm recently first applied to power system dynamics. In this paper we propose a practical data-driven algorithm incorporating KMA for network partitioning. Comparisons are made with two techniques previously applied for the network partitioning: spectral graph theory which is based on the eigenstructure of the graph Laplacian, and slow-coherency which identifies coherent groups of generators for a specified number of low-frequency modes. The partitioning results share common features with results obtained with graph theory and slow-coherency-based techniques. The suggested partitioning method is evaluated with two test systems, and similarities between Koopman modes and Laplacian eigenvectors are showed numerically and elaborated theoretically. [ABSTRACT FROM PUBLISHER]

The low-frequency oscillations (LFOs) inherent in power systems will be impacted by the increasing penetration of renewable energy sources (RESs). This paper investigates the impact of virtual synchronous machine (VSM) based RESs on the LFOs in power systems. A detailed two-machine test-bed has been developed to analyze the LFOs which exists when VSMs replace synchronous generators. The characteristics of the LFO modes, and the dominant states have been comprehensively analyzed. Furthermore, this study analyzes the LFO modes which exists in an all-VSM grid. The role of the power system stabilizers in the all-VSM grid has been comprehensively evaluated. The IEEE benchmark two-area four-machine system has been employed to corroborate the results of the small-signal analysis, and observe the transient performance. The analysis in this paper have been performed in MATLAB/SIMULINK environment. [ABSTRACT FROM AUTHOR]

This paper presents a novel hierarchical cooperative control strategy to solve the problems of unbalanced State of Charge (SoC), unreasonable load current sharing, and unstable bus voltage for battery storage system (BSS) in islanded DC microgrid. In the communication layer, a neighbor-to-neighbor communication network architecture is constructed in which each battery storage unit (BSU) only communicates information with neighboring nodes, and a multi-agent consensus algorithm is used to iteratively estimate the relevant average global variables. In the main control layer, the droop coefficient is dynamically adjusted by constructing a fast SoC convergence function to ensure that the load current is reasonably distributed based on the rated capacity of each BSU. In the secondary control layer, a virtual voltage drop equalization controller and an average bus voltage compensation controller are designed to eliminate the impact of line impedance mismatch and compensate the average bus voltage. Furthermore, an optimized factor is designed to accelerate the later stage of SoC convergence. Finally, a DC microgrid experimental platform based on StarSim HIL is built, and the results demonstrate that the proposed strategy can achieve the control objectives of fast SoC balance, proportional load current sharing, and bus voltage stability. [ABSTRACT FROM AUTHOR]

This paper presents hardware-friendly algorithm for Gini-index (GI) cooperative-spectrum-sensing (CSS) algorithm for the data-fusion based cooperative cognitive-radio network. It simplifies the complex computations of sample-covariance-matrix (SCM) elements and test-statistics value of the conventional GI-based CSS algorithm. It delivers excellent detection performance under the realistic scenario of non-uniform dynamical noise and signal-power. Based on GI-based CSS algorithm, three different VLSI architectures are proposed for the cooperative spectrum sensor (CSR): CSR-VLAR1, CSR-VLAR2, and CSR-VLAR3. Here, CSR-VLAR1 is the first-time reported CSR-architecture for the conventional GI-based CSS algorithm. Subsequently, CSR-VLAR2 represents hardware-architecture of the proposed hardware-friendly GI-based CSS algorithm. Eventually, additional architectural optimization has been applied to CSR-VLAR2 that is transformed into the most hardware-efficient VLSI-architecture of CSR, referred as CSR-VLAR3, which is ASIC chip-fabricated in UMC 130 nm-CMOS technology node. Furthermore, both CSR-VLAR1 and CSR-VLAR2 are synthesized and post-layout simulated in the same technology node. Our ASIC-chip of CSR-VLAR3 occupies 0.35 mm2 of core-area and consumes 8.31 mW of total power at 88.8 MHz of maximum clock frequency, when the supply voltage is 1.2 V. Our CSR ASIC-chip has been functionally verified with the aid of real-world signals, using USRPs and FPGAs based test-setup of cooperative cognitive-radio network. Measured results of our design are compared with reported implementations where the proposed CSR is $4.52\times $ hardware-efficient and $2.8\times $ power-efficient than the state-of-the-art CSR-implementations. Thus, our work addresses the key challenge of designing hardware-efficient CSR that delivers excellent detection performance in the real-world scenario. [ABSTRACT FROM AUTHOR]

This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsity that is as broad as possible, so that it generates all the various measures that are useful in practice, but narrow enough that the fundamental properties of generalized sparsity still hold. As we work through the various ways of demonstrating the advantageous properties of sparsity, we illustrate its meaning from geometrical and operational perspectives. Thereafter, we construct specific measures of sparsity which are successfully qualified in complexity analysis and sparse optimization scenarios. Overall, our main objective is to construct measures of sparsity that will facilitate and enhance the design of the next innovative sensing technologies. [ABSTRACT FROM AUTHOR]

In this paper, we present a modular framework for solving a motion planning problem among a group of robots. The proposed framework utilizes a finite set of low-level motion primitives to generate motions in a gridded workspace. The constraints on allowable sequences of motion primitives are formalized through a maneuver automaton. At the high level, a control policy determines which motion primitive is executed in each box of the gridded workspace. We state general conditions on motion primitives to obtain provably correct behavior so that a library of safe-by-design motion primitives can be designed. The overall framework yields a highly robust design by utilizing feedback strategies at both the low and high levels. We provide specific designs for motion primitives and control policies suitable for multirobot motion planning; the modularity of our approach enables one to independently customize the designs of each of these components. Our approach is experimentally validated on a group of quadrocopters. [ABSTRACT FROM AUTHOR]

A fundamental challenge of mobile sensor networks is automated active reconfiguration of sensors in response to environmental stimuli in order to maximize their total sensing quality (or minimize their total sensing cost) of events occurring over an environment. In this paper, given an event distribution over a convex environment, we consider mobile isotropic sensors with adjustable sensing range and propose a new family of provably correct reactive coverage control algorithms for both continuous- and discrete-time sensor dynamics. The proposed coverage control algorithms constantly (re)configure sensor positions and sensing ranges in order to minimize a statistical distance, in particular, an $f$ -divergence, between the event distribution over the environment and the overall event detection probability of sensors. We show that the standard Voronoi-based coverage control law of homogeneous mobile sensor networks is a special case of our framework where the event detection probability of each sensor has a Gaussian form, the statistical distance is set to be the Kullback–Leibler (KL) divergence and sensor allocation is performed based on Voronoi diagrams. To increase the practicality of our framework, we also present its integration with a Voronoi-based collision avoidance strategy for disk-shaped sensor bodies and its extension to differential drive sensor dynamics, while retaining the stability properties. [ABSTRACT FROM AUTHOR]

This paper studies joint uplink (UL) and downlink (DL) resource allocation in user-centric orthogonal frequency division multiple access cloud radio access network (CRAN), where the users select the distributed remote radio heads (RRHs) to cooperatively serve their UL and DL transmissions over different subcarriers (SCs). The goal of this paper is to maximize the system throughput through jointly optimizing UL/DL scheduling, SC assignment, RRH grouping/clustering, and power allocation under the maximum power and fronthaul capacity constraints. The problem is formulated as a mixed integer programming problem, which is nonconvex and NP-hard. We propose an efficient algorithm based on the Lagrange duality method to obtain an asymptotically optimal solution for this problem. A heuristic algorithm is further proposed to reduce the complexity. Simulation results illustrate that the proposed heuristic algorithm also has a close-to-optimal performance, and the proposed algorithms can considerably improve the system throughput compared to other benchmark schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel technique using fuzzy logic controller (FLC) in circulating mode to improve the performance of the International Thermonuclear Experimental Reactor (ITER) poloidal field (PF) converter systems. The accurate mathematical modeling of the complex system is challenging using conventional proportional integral and derivative (PID) controller. The novel approach using FLC overcomes the complexity of the modeling by human expert’s knowledge and experience. The FLC is considered for ITER PF converter system and compared with a conventional PI controller. The dynamic behavior and transient response of the PF converter system in the circulating mode are verified by analysis and simulation. The analysis results determine that the FLC can achieve better operation performance. [ABSTRACT FROM AUTHOR]