RETRACTED ARTICLE: Music singing video teaching and timbre automation analysis based on dynamic programming and surveillance video summary algorithm

Recently, as the market share of electric vehicles (EVs) has increased, how to handle the increased electricity demand for EV charging in the power grid and how to use EV batteries from a grid-operating aspect have become more important. Also, from the perspective of individual EVs, Vehicle-to-Grid (V2G) technologies that reduce the cost for each vehicle’s charging in conjunction with the power grid are significant. In this paper, the V2G control problem at the individual vehicle level is studied using a Dynamic Programming (DP) algorithm that considers EVs’ charging efficiency. The DP algorithm is developed to generate an optimized charging/discharging power profile that minimizes electricity costs, while satisfying the constraints of the initial and final battery states of charge, for given a time-of-use electricity price. To show the effectiveness of the proposed algorithm, simulation is conducted for three different charging scenarios (unidirectional charging, bidirectional charging, and unidirectional charging with cost variations based on electricity usage), and the results showed that DP can achieve significant cost savings of about 30% compared to the normal charging method. Also, the result of DP is compared with that of Linear Programming, demonstrating that DP outperforms Linear Programming in cost savings for the V2G control problem.

In this paper, a dynamic programming (DP) algorithm is proposed to compute the maximum a posteriori probability (MAP) estimate of the random location of multiple sources by passive sensor array. Based on the principle of optimality, the multivariate MAP decision problem can be transformed into a multistage one‐dimensional maximization problem. By a recursive computation and backward optimum searching technique, the MAP solution can be obtained. The computational complexity is much less than that of the direct MAP searching method, especially when the number of sensors and/or the number of sources is large. In addition, the DP computation technique is equally applicable both in the case of correlated sources and the case of uncorrelated sources.

In this work, we study dynamic programming (DP) algorithms for partially observable Markov decision processes with jointly continuous and discrete state-spaces. We consider a class of stochastic systems which have coupled discrete and continuous systems, where only the continuous state is observable. Such a family of systems includes many realworld systems, for example, Markovian jump linear systems and physical systems interacting with humans. A finite history of observations is used as a new information state, and the convergence of the corresponding DP algorithms is proved. In particular, we prove that the DP iterations converge to a certain bounded set around an optimal solution. Although deterministic DP algorithms are studied in this paper, it is expected that this fundamental work lays foundations for advanced studies on reinforcement learning algorithms under the same family of systems.

Routing problems are a class of combinatorial problems with many practical applications. Recently, end-to-end deep learning methods have been proposed to learn approximate solution heuristics for such problems. In contrast, classical dynamic programming (DP) algorithms guarantee optimal solutions, but scale badly with the problem size. We propose Deep Policy Dynamic Programming (DPDP), which aims to combine the strengths of learned neural heuristics with those of DP algorithms. DPDP prioritizes and restricts the DP state space using a policy derived from a deep neural network, which is trained to predict edges from example solutions. We evaluate our framework on the travelling salesman problem (TSP), the vehicle routing problem (VRP) and TSP with time windows (TSPTW) and show that the neural policy improves the performance of (restricted) DP algorithms, making them competitive to strong alternatives such as LKH, while also outperforming most other ‘neural approaches’ for solving TSPs, VRPs and TSPTWs with 100 nodes.KeywordsDynamic ProgrammingDeep LearningVehicle Routing

The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified.

Purpose. To develop a new approach to the design of mining production processes with limited resources. Methods. Combinatorial optimisation methods were used to develop a new approach. The problem of designing technological processes can be solved by interpreting the problem “knapsack problem” with a private solution “backpack 0-1”. Dynamic programming algorithms are used to find the optimal solution. Findings. A new approach to the design of mining production management processes with limited resources has been developed. If the target goal is to obtain the maximum effect at minimum costs, then the direct run method is used. If there are restrictions on bandwidth, energy characteristics, specific cost, then the reverse run method is used. In the final part of the study, the proposed approach was verified. The task consisted in the development of recommendations regarding the choice of the structure of the technological chain of the cleaning pit under the given technological and mining-geological development parameters. The amount of specific production cost was imposed as a limitation. As a result of solving the design problem by the reverse run method, recommendations for replacing the cleaning equipment were developed. This made it possible to ensure the specified level of productivity and reduce the unit cost by 10.6%. The use of combinatorial optimisation methods made it possible to obtain an optimal solution in the field of given parameters. Originality. The scientific novelty of the work consists in establishing regularities in the formation of the efficiency of mineral deposit development processes. For the first time, combinatorial optimization methods were used to design the processes that accompany the development of mineral deposits, and the scope of their application was determined, as well as a number of dynamic programming tasks and algorithms were formalised: backtracking, spanning tree. Practical implication. Verification of the developed approach was carried out in relation to the design of mining production processes with limited resources. The obtained approach makes it possible to find optimal solutions under the constraints set at the design stage. As limitations can be: throughput capacity of the transport chain, energy characteristics of technological processes accompanying mining, specific cost of mining, etc. The design problem can be solved in a direct order – if the objective function is to maximise the parameter, or in the opposite way – if there are restrictions.

The purpose of flight path planning is to find the optimal path from the real-time and conflict-free airspace to meet the targets, according to one or several performance index. Effective avoiding the no-fly zones, such as the areas of martial movement and the areas of rain and thunderstorm, has great significance to the current flight management system (FMS) that is real-time and effective implementation of the flight plan. The dynamic optimization method of level route based on DP (Dynamic Programming) algorithm without no-fly zone constraints is discussed. Quick and effective to find out an optimal path from the waypoints of arbitrary selection and input can be realized. On this basis, the situation of adding no-fly zone constraints is focused on. In order to ensure that the aircraft is able to effectively avoid no-fly zone constraints in actual flight, Gauss Kruger projection method to convert geographic coordinates to plane coordinates is adopted. Simulation results show that the method used can not only effectively avoid no-fly zone constraints, and the path passed is still optimal.

Overview of improved dynamic programming algorithm for optimizing energy distribution of hybrid electric vehicles

Global energy efficiency improvement of redundant hydraulic manipulator with dynamic programming

Conjunctive queries ( CQ s) have been widely used in database systems in which acyclic CQ s can be computed efficiently, whereas cyclic CQ s may not. Here, a CQ is acyclic if its hypergraph representation H is acyclic. In order to find a class of CQ s that are "mildly cyclic", hypertree decompositions (HDs) have been studied. The quality of such HDs is by the so-called hypertree width. The class of acyclic queries is the queries whose hypertree width is 1, and a mildly cyclic CQ can be processed efficiently if its hypertree width is bounded. There are several HDs, such as tree decomposition (TD), generalized hypertree decomposition (GHD), fractional hypertree decomposition (FHD), as well as hypertree decomposition (HD). The minimum hypertree width by FHD is the smallest among all, and it is NP-complete to check if the minimum hypertree width by FHD exists for a given hypertree width at most k. In the literature, there is no dynamic programming ( DP ) algorithm or branch-&-bound algorithm reported to compute FHD. In this paper, we show that there is a DP algorithm for FHD, and we give a branch-&-bound algorithm based on our DP algorithm to compute FHD with upper/lower bounds. We confirm the effectiveness and efficiency of our algorithm by testing all 3,648 hypergraphs given in a benchmark for HDs, and we also confirm our approach in query evaluation in real database systems.

The detection of sea-bottom targets has encountered the problems using acoustic arrays with lower resolution on board. Moreover, the uncertainty of moving receivers and sources, the extremely low signal-to-noise ratio due to weak signals from the buried targets, and the computational efficiency requirement of real-time processing present challenges to detection of the targets. For low signal-to-noise ratio, the traditional scenario, detection then tracking is not suitable for this kind of problem. The detection can't be declared unless the signal-to-noise ratio is greater than a reasonable threshold while more information is gathered. Enormous array signal processing methods could enhance the signal-to-noise ratio while the information is gathered spatially. However, the positions of the sensors are not known precisely due to the limitation of underwater navigation technique. Without knowing the spatial distribution of receivers, the results of detection by means of array signal processing cannot be accurate. In contrast, the solution to this problem becomes reversed to traditional detection procedure. A Track-Before-Detection (TBD) algorithm is introduced. Unlike the traditional approaches, the TBD detections are not declared at each ping. Instead, a number of pings of data are processed. This technique integrates the measurements along the possible AUV trajectories. Using the slowly changing environment information or the fixed but unknown target fields, the states of AUV are tracked first. Then, the weak signal detection is declared after confidence of the trajectories estimation is established. However, enormous possible trajectories of AUV needed to be searched while the TBD algorithm is applied, which makes direct online implementation of this technique impossible. A dynamic programming (DP) algorithm is introduced to solve the highly interconnected stochastic network which TBD creates in a much more efficient way. Therefore, together with the DP algorithm, the TBD algorithm is feasible to implement online. This algorithm has been applied in GOATS (Generic Oceanographic Array Technology Sonar) project. Both monostatic and bistatic detection results are demonstrated.

This study proposes an integrated eco‐driving controller, in which speed profile and power split optimisation are simultaneously treated. A speed profile controller is designed based on a multi‐phase dynamic programming (DP) algorithm, which is utilised to optimise speed trajectories with the detailed model of the traction power system, vehicle dynamics system, and signal constraints at intersections. The optimised speed and demand power profiles are designed as the reference of the power split optimal controller. A DP algorithm is presented to determine voltage trajectories of supercapacitors with power plant voltage limitations and dynamic power losses. The performance of the proposed integrated method is evaluated by numerical simulation based on an actual route of Chengdu Tramway Line 2. The energy reduction can be achieved by the speed profiles controller, compared to the eco‐cruising benchmark strategies. Energy efficiency can be further improved by the integrated eco‐driving controller. Moreover, the influences of green signal time windows and trip time on the shape of the optimised solution are investigated. For a given journey, the trip time with minimum energy consumption can be obtained. The integrated eco‐driving control method can be implemented by driver assistant systems or automatic train operation systems.

Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.

In this paper, we propose a novel scheme to supervised nonnegative matrix factorization (NMF). We formulate the supervised NMF as a sparse optimization problem assuming the availability of a set of basis vectors, some of which are irrelevant to a given matrix to be decomposed. The proposed scheme is presented in the context of music transcription and musical instrument recognition. In addition to the nonnegativity constraint, we introduce three regularization terms: (i) a block ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm to select relevant basis vectors, and (ii) a temporal-continuity term plus the popular ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm to estimate correct activation vectors. We present a state-of-the-art convex-analytic iterative solver which ensures global convergence. The number of basis vectors to be actively used is obtained as a consequence of optimization. Simulation results show the efficacy of the proposed scheme both in the case of perfect/imperfect basis matrices.

This study mainly analyses the fuel consumption of plug-in hybrid vehicles during operation. A new control method for automobiles based on energy management strategy and dynamic programming algorithm is proposed. The new method plans and analyses the minimum electricity consumption, and then uses dynamic programming algorithms to analyse this parameter. The research results indicated that the vehicle state was constantly changing with the variation of SOC value during driving. The energy mobilization of the vehicle was more obvious after adding dynamic programming strategy. The efficiency of the vehicle was relatively high in driving state 1, with a minimum value of 70%, which was about 20% higher than in driving state 4. The average fuel consumption in driving state 2 was 1.8L higher than in other driving states. The overall efficiency of automobiles after incorporating dynamic programming was improved, with a shorter time to reach the lowest efficiency point compared with not incorporating dynamic programming algorithms. The highest efficiency value was 7.86% higher than that of not incorporating dynamic programming models. The new control method can reduce energy consumption and improve the energy management and control effect. The study provides a better research direction for energy management and control of hybrid electric vehicles in the future.