DP Algorithm Research Articles

Comprehensive Study on Optimizing Inland Waterway Vessel Routes Using AIS Data

Inland waterway transport is an important mode of transportation for many countries and regions. Route planning optimization can reduce navigation time, avoid traffic congestion, and improve transportation efficiency. In actual operations, many vessels determine their navigation routes based on the experience of their shipowners. When the captain fails to obtain accurate information, experience-based routes may pose significant navigation risks and may not consider the overall economic efficiency. This study proposes a comprehensive method for optimizing inland waterway vessel routes using automatic identification system (AIS) data, considering the geographical characteristics of inland waterways and navigation constraints. First, AIS data from vessels in inland waters are collected, and the multi-objective Peak Douglas–Peucker (MPDP) algorithm is applied to compress the trajectory data. Compared to the traditional DP algorithm, the MPDP algorithm reduces the average compression rate by 5.27%, decreases length loss by 0.04%, optimizes Euclidean distance by 50.16%, and improves the mean deviations in heading and speed by 23.53% and 10.86%, respectively. Next, the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm is used to perform cluster analysis on the compressed route points. Compared to the traditional DBSCAN algorithm, the OPTICS algorithm identifies more clusters that are both detailed and hierarchically structured, including some critical waypoints that DBSCAN may overlook. Based on the clustering results, the A* algorithm is used to determine the connectivity between clusters. Finally, the nondominated sorting genetic algorithm II is used to select suitable route points within the connected clusters, optimizing objectives, including path length and route congestion, to form an optimized complete route. Experiments using vessel data from the waters near Shuangshan Island indicate that, when compared to three classic original routes, the proposed method achieves path length optimizations of 4.28%, 1.67%, and 0.24%, respectively, and reduces congestion by 24.15%. These improvements significantly enhance the planning efficiency of inland waterway vessel routes. These findings provide a scientific basis and technical support for inland waterway transport.

Including dynamic capacity impact in an improved model for optimal flood control operation in river-type reservoirs

Flood control operations in river-type reservoirs are significantly affected by both the dynamic reservoir capacity and flood propagation within the reservoir area. However, the traditional reservoir flood control optimal operation (RFCOO) model is usually based on the static capacity method, which may bring large errors in scheduling calculations for river-type reservoirs. To address this issue, an improved model, the reservoir dynamic capacity flood control optimal operation (RDCFCOO) model, that includes the influence of the dynamic reservoir capacity and flood propagation, was developed to improve the accuracy of flood regulation calculations in river-type reservoirs. This improved model includes a 1D unsteady flow simulation and a rederived objective function based on the maximum peak clipping criterion, and expresses the state transformation equations using the de Saint-Venant equations. Furthermore, a multi-core parallel DP (PDP) algorithm that incorporates reduction of state dimensionality (RSD) was developed to effectively derive optimal operation schemes. The improved model and algorithm developed herein were validated through an application to the Xiangjiaba Reservoir, China. The results show that: (1) By adding the item (qiw) to the objective function and substituting the de Saint-Venant equations for the water balance equation, the RDCFCOO model developed in this paper can account for the impact of dynamic capacity and flood propagation. As a result, it exhibits a more accurate and detailed flood control process that was closer to the actual conditions of river-type reservoirs compared to the RFCOO model; (2) By removing the invalid discrete state points from the search space and fully utilizing multi-core processors, the proposed PDP with RSD can significantly improve the computational efficiency of the DP in solving RDCFCOO problems. The improved model and algorithm can effectively improve the calculation accuracy of flood control optimal scheduling in river-type reservoirs and provide more information for decision-makers.

Structural Parameterizations for Two Bounded Degree Problems Revisited

We revisit two well-studied problems, Bounded Degree Vertex Deletion and Defective Coloring , where the input is a graph G and a target degree Δ, and we are asked either to edit or partition the graph so that the maximum degree becomes bounded by Δ. Both problems are known to be parameterized intractable for the most well-known structural parameters, such as treewidth. We revisit the parameterization by treewidth, as well as several related parameters and present a more fine-grained picture of the complexity of both problems. In particular, we present the following: — Both problems admit straightforward DP algorithms with table sizes \((\Delta +2)^\mathrm{tw}\) and \((\chi _\mathrm{d}(\Delta +1))^{\mathrm{tw}}\) , respectively, where tw is the input graph’s treewidth and \(\chi _\mathrm{d}\) the number of available colors. We show that, under the SETH, both algorithms are essentially optimal, for any non-trivial fixed values of \(\Delta , \chi _\mathrm{d}\) , even if we replace treewidth by pathwidth. Along the way, we obtain an algorithm for Defective Coloring with complexity quasi-linear in the table size, thus settling the complexity of both problems for treewidth and pathwidth. — Given that the standard DP algorithm is optimal for treewidth and pathwidth, we then go on to consider the more restricted parameter tree-depth. Here, previously known lower bounds imply that, under the ETH, Bounded Vertex Degree Deletion and Defective Coloring cannot be solved in time \(n^{o(\sqrt [4]{\mathrm{td}})}\) and \(n^{o(\sqrt {\mathrm{td}})}\) , respectively, leaving some hope that a qualitatively faster algorithm than the one for treewidth may be possible. We close this gap by showing that neither problem can be solved in time \(n^{o(\mathrm{td})}\) , under the ETH, by employing a recursive low tree-depth construction that may be of independent interest. — Finally, we consider a structural parameter that is known to be restrictive enough to render both problems FPT: vertex cover. For both problems the best known algorithm in this setting has a super-exponential dependence of the form \(\mathrm{vc}^{\mathcal {O}(\mathrm{vc})}\) . We show that this is optimal, as an algorithm with dependence of the form \(\mathrm{vc}^{o(\mathrm{vc})}\) would violate the ETH. Our proof relies on a new application of the technique of d -detecting families introduced by Bonamy et al. [ToCT 2019]. Our results, although mostly negative in nature, paint a clear picture regarding the complexity of both problems in the landscape of parameterized complexity, since in all cases we provide essentially matching upper and lower bounds.

A fast and highly scalable frequent pattern mining algorithm

As the use of big data and its potential benefits become more widespread, public and private organizations around the world have realized the imperative of incorporating comprehensive and robust technologies into their business processes. In particular, companies are implementing more and more intelligent systems into their business processes, resulting in an exponential increase in the amount of data being collected and used in everyday life on a quarterly basis. Therefore, an algorithm that can efficiently explore the frequent patterns from big data will be able to productively analyze and utilize the generated data to better optimize resources and even develop new business models. Thus, in this urban data era, it is not only essential to make more efficient and accurate decisions to improve business profitability and customer satisfaction, but also a highly challenging core issue. In this study, a new approach, scalable FP mining, is proposed for the first time to achieve a certain level of performance and more efficient memory utilization despite large amounts of data. Experiment results for different data characteristics show that the best performance of the proposed method requires only 48.9% of the DP algorithm's execution time. In further experiments with increased exploration difficulty, the optimal execution time is only 37%. On specific associated databases, the proposed method maintains stable and excellent performance with 12.7% for different data characteristics. The experimental results with increasing exploration difficulty at each stage demonstrate the stability, robustness, and reliability of the proposed SFP, especially under high data complexity and exploration difficulty.

A Fast Granular-Ball-Based Density Peaks Clustering Algorithm for Large-Scale Data.

Density peaks clustering algorithm (DP) has difficulty in clustering large-scale data, because it requires the distance matrix to compute the density and δ -distance for each object, which has O(n2) time complexity. Granular ball (GB) is a coarse-grained representation of data. It is based on the fact that an object and its local neighbors have similar distribution and they have high possibility of belonging to the same class. It has been introduced into supervised learning by Xia et al. to improve the efficiency of supervised learning, such as support vector machine, k -nearest neighbor classification, rough set, etc. Inspired by the idea of GB, we introduce it into unsupervised learning for the first time and propose a GB-based DP algorithm, called GB-DP. First, it generates GBs from the original data with an unsupervised partitioning method. Then, it defines the density of GBs, instead of the density of objects, according to the centers, radius, and distances between its members and centers, without setting any parameters. After that, it computes the distance between the centers of GBs as the distance between GBs and defines the δ -distance of GBs. Finally, it uses GBs' density and δ -distance to plot the decision graph, employs DP algorithm to cluster them, and expands the clustering result to the original data. Since there is no need to calculate the distance between any two objects and the number of GBs is far less than the scale of a data, it greatly reduces the running time of DP algorithm. By comparing with k -means, ball k -means, DP, DPC-KNN-PCA, FastDPeak, and DLORE-DP, GB-DP can get similar or even better clustering results in much less running time without setting any parameters. The source code is available at https://github.com/DongdongCheng/GB-DP.

Analysis of the Integration Path of Chinese Traditional Culture in College English Education and Teaching under the Background of Big Data

Abstract The cloud platform teaching system utilizes the optimized load balancing algorithm and DP algorithm to allocate and cluster massive teaching resources respectively, organize massive Chinese cultural knowledge scientifically and rationally into relevant network modules, and present it to English learners in a combination of graphic and text, audio and video. By investigating the selection of resource content and the quality of each functional module on the cloud platform, it can be found that the five primary cultural contents are generally well balanced, with the two that account for the most significant proportion of the content being cultural practices and cultural products, and the proportion of the other three varying little. Most students preferred the functional modules of Learning Center (84.3%), Resource Center, Extracurricular Paradise, and PK Pavilion, but only 12% preferred the Psychological Center module. The correlation coefficient between different factors and the intensity of Chinese culture penetration is the highest, 0.924. The average score of the degree of realization of the five teaching objectives in teaching practice is 3.838, close to “basically realized”. The scores of the four major dimensions of the university students increased significantly (p<0.05). The most significant improvement was seen in the quality of thinking dimension, with a mean increase of 2.678 points.

Predicting the Attacks in IoT Devices using DP Algorithm

Predicting the Attacks in IoT Devices using DP Algorithm

A multi-objective hierarchical energy management strategy for a distributed fuel-cell hybrid electric tracked vehicle

This paper proposes a multi-stack fuel cell system (MFCS) for a distributed fuel cell hybrid electric tracked vehicle. The power distribution results of the DP algorithm under a series of power demand gradients are analyzed for the MFCS, and a basic energy management rule is extracted. To realize the consistency of each fuel cell system (FCS) aging in the MFCS and ensure the battery SOC fluctuates in a healthy range, the energy management rule extracted by the DP algorithm is reconstructed, and a Multi-Objective Hierarchical Energy Management Strategy (MOH-EMS) is proposed. Finally, the performance of the MOH-EMS is analyzed. The results show that the proposed EMS has good performance in terms of fuel economy, with only a 3.8 % and 4.4 % difference from the DP algorithm. The proposed EMS also achieves consistency in the aging of the FCS and improves the lifetime of the FCS.

Research on energy-saving driving control of hydrogen fuel bus based on deep reinforcement learning in freeway ramp weaving area

In the intelligent network traffic scenario, the convenient acquisition of microscopic vehicle states and global traffic states can help solve the problems of vehicle driving and energy management in complex traffic environments. This paper proposes a new energy management method for the hydrogen fuel cell bus based on the double-layer deep deterministic policy gradient (DDPG). Combined with the SUMO simulation platform, a double-layer deep reinforcement learning (D-DRL) architecture based on DDPG is designed to improve control accuracy and training speed. In the upper D-DRL, the Agent handles the effects of complex traffic environments to control the reasonable speed of the vehicle and keep it running smoothly to reduce the loss of energy caused by speed changes, compared with the SUMO-IDM model, the maximum-minimum velocity difference was reduced by 21 % and the acceleration and acceleration change was reduced by 7.9 % and 19 %, After the Agent receives the output speed from the upper layer, it distributes the power between fuel cell and power cell. Compared with the DP algorithm, it keeps the SOC at a higher level, the hydrogen consumption level reaches 93.25 %, and the fluctuation amplitude decreases by 42.09 %, effectively improving fuel cell durability.

Research on Energy Management of Hydrogen Fuel Cell Bus Based on Deep Reinforcement Learning Considering Velocity Control

In the vehicle-to-everything scenario, the fuel cell bus can accurately obtain the surrounding traffic information, and quickly optimize the energy management problem while controlling its own safe and efficient driving. This paper proposes an energy management strategy (EMS) that considers speed control based on deep reinforcement learning (DRL) in complex traffic scenarios. Using SUMO simulation software (Version 1.15.0), a two-lane urban expressway is designed as a traffic scenario, and a hydrogen fuel cell bus speed control and energy management system is designed through the soft actor–critic (SAC) algorithm to effectively reduce the equivalent hydrogen consumption and fuel cell output power fluctuation while ensuring the safe, efficient and smooth driving of the vehicle. Compared with the SUMO–IDM car-following model, the average speed of vehicles is kept the same, and the average acceleration and acceleration change value decrease by 10.22% and 11.57% respectively. Compared with deep deterministic policy gradient (DDPG), the average speed is increased by 1.18%, and the average acceleration and acceleration change value are decreased by 4.82% and 5.31% respectively. In terms of energy management, the hydrogen consumption of SAC–OPT-based energy management strategy reaches 95.52% of that of the DP algorithm, and the fluctuation range is reduced by 32.65%. Compared with SAC strategy, the fluctuation amplitude is reduced by 15.29%, which effectively improves the durability of fuel cells.

Adaptive equivalent consumption minimization strategy based on road grade estimation for a plug-in hybrid electric truck

Road grade plays an important role in deciding power repartition and improving energy management performances. In this paper, instead of predictive energy management strategies where terrain information is obtained from GIS maps, an adaptive equivalent consumption minimization strategy (A-ECMS) considering current road grade information is proposed, aiming to design an instantaneous feedback supervisory controller based on the estimation of current road grade without using external devices. To achieve real-time control for a plug-in hybrid electric truck (PHET), the bounds of the optimal equivalent factor (EF) are analyzed considering comprehensive performances, then the sensitivity of EF is evaluated towards road grade. According to the effect of different road grade scenarios on control performances, the real-time EF adaptation is divided into two conditions, i.e. SOC-based adaptation and Estimation-based adaption. The proposed A-ECMS can achieve good performances on both fuel economy improvement and emission reduction, which approximates the results obtained from the DP algorithm, and the high computing load can be avoided for real-time implementation.

Peak shaving and short-term economic operation of hydro-wind-PV hybrid system considering the uncertainty of wind and PV power

With uncertain wind and PV power integrated into the grid, the difficulty of peak shaving is exacerbated. Therefore, the peak shaving operation of hydropower has become one of the most important problems in power system. In this paper, an optimal operation strategy of hydro-unit level coordinated peak shaving and economic operation in hydro-wind-PV hybrid system under uncertain conditions of wind and PV power is proposed. Firstly, the uncertainty of wind and PV power is described based on Latin hypercube sampling and scenario reduction methods. Secondly, a two-layer optimization model considering wind and PV uncertainty is established. And then, the synchronous peak shaving strategy and the improved lambda flow iteration strategy are proposed to solve the model. A case study is performed with the HWPHS in the Yarlung Zangbo River basin of China. The results show that (1) The residual load of power system fluctuates steadily under the condition of wind and PV uncertainty, and the peak shaving rate is 95.53%. (2) Compared with the DP algorithm, the proposed algorithm can increase the economic efficiency by 0.89% when solving the lower-layer model. (3) The V3 shaped mapping function proved to be superior to other functions.

Stochastic dynamic programming solution to transmission scheduling: Multi sensor-multi process with wireless noisy channel

We investigate sensor scheduling for remote estimation when multiple smart sensors monitor multiple stochastic dynamical systems. The sensors transmit their measurements to a remote estimator through a noisy wireless communication channel. Such a remote estimator can receive multiple packets simultaneously sent by local sensors. Sensors transmit their measurements if their Signal Interference and Noise Ratio (SINR) is above a threshold. We compute the optimal policy for sensor scheduling by minimizing expected error covariance subject to total signal transmissions from all sensors. We model this problem as Markov Decision Process (MDP) with discounted cost per stage in the finite time horizon framework, then we employ stochastic Dynamic Programming as the optimization method. A novel algorithm based on sampling and machine learning techniques is proposed as the approximation. At each phase of the DP algorithm, samples are collected using a uniform probability distribution. The data is used to feed Neural Network (NN) and Random Forest (RF) models for cost function and policy approximation. The results of the proposed framework are supported by simulation examples comparing RF and NN as Approximate DP (ADP). Note that this idea builds a bridge among the recent advances in the area of data science, Machine Learning, and the ADP.

Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm

The automatic identification system (AIS) provides a massive database for ocean science. The original AIS data are redundant. Direct use will cause a waste of data storage space and computation costs; hence, data compression must be performed. The Douglas–Peucker algorithm (DP) is an effective trajectory compression algorithm that can well preserve the spatial characteristics of a trajectory but has the following shortcomings: first, it has poor track recovery when compressing multi-turn routes; second, it does not consider the ship speed and heading; and third, it may have the wrong result of the compressed trajectory crossing the obstacle. To address these situations, this study proposes a multi-objective peak DP algorithm (MPDP) that adopts a peak sampling strategy, considers three optimization objectives (spatial characteristics, heading and speed) of trajectory and adds an obstacle detection mechanism to realize a compression algorithm more suitable for curved trajectories. The classical DP algorithm is compared with the MPDP algorithm by simulating trajectory and real trajectory experiments. The results show that the MPDP algorithm optimizes the length loss rate, simultaneous Euclidean distance, and average deviations of the speed and the heading while maintaining a high compression rate similar to that of the DP algorithm. Moreover, it can also successfully avoid obstacles. The optimization effect is most obvious for the multi-turn or hovering trajectory. The optimization rate of length loss, synchronous Euclidean distance, and average deviation of the heading can reach 40%.

Information Geometry-Based Track-Before-Detect Algorithm for Slow-Moving Fluctuating Target Detection

To treat the slow-moving fluctuating target detection issues, the information geometry-based track-before-detect algorithms, which combines the performance advantages of information geometry detectors and the multi-frame information integration ability of dynamic programming algorithm, are proposed in this paper. The proposed methods are based on the Hermitian positive definite (HPD) manifold and power spectrum (PS) manifold, that the geometric properties of the target feature track and clutter track on the HPD or PS manifold are exploited for the integration of inter-frame target information. Specifically, the IG-based scoring function is designed to evaluate the candidate trajectories, and a general IG-based DP algorithm is deduced to obtain the trajectories with the maximum integration of scoring functions. Moreover, to avoid the performance degradation resulting from the illegal candidate trajectories, a stricter constraint for the trajectories of slow-moving target is established, and a corresponding efficient IG-based DP algorithm is deduced by utilizing the skipping transition. Experimentally, the superiority of the proposed methods have been validated with the real-recorded ship target echo data and sea clutter data. The experimental results show that the proposed methods can provide a signal-to-clutter ratio improvement of approximately 5.5 dB.

Wireless Play Music Beep Sensor to Assist in Music Fountain Control

Musical water landscape combines landscape design with audiovisual art and is loved by the general public. However, the performance control form of musical water landscape generally adopts the manual offline preprogramming method, which needs to use the human subject’s consciousness to perceive the style, emotion, and other advanced features of the music first and then choreographs the corresponding landscape performance, which is a high-cost design form and low usage rate of the landscape and is not conducive to the expansion of the music library. In this paper, we design and implement a musical water landscape simulation system using OpenGL and 3Dmax technology in combination with wireless music buzzer sensors, extract and analyze the music features based on this system, and study the system control of musical water landscape. Open GL particle system technology is used to realize the dynamic simulation of weather and fountain inside the landscape and improve the drawing scheme from both modeling and rendering aspects. For the fountain, the physical model and the wireless music beep sensor are combined to design a variety of water type actions for a single spout, and the parameter interface to control the water type changes is reserved. We propose a style recognition method based on CRNN and residual network, which has a higher accuracy rate compared with the existing methods; we use the DP algorithm to segment the pitch sequence of the music to reduce the redundancy of information and improve the calculation speed of the system.

Music Data Feature Analysis and Extraction Algorithm Based on Music Melody Contour

Music is a way to reflect people’s real-life emotions, and listening to music has become an inseparable habit of the daily life. Text-based music information retrieval is still the main way for people to find music, but this method has obvious shortcomings and deficiencies, and it is a relatively cumbersome and inefficient method. In order to solve this problem, this paper proposes a feature extraction LAM algorithm based on the contour of music melody. Melody is the most important extraction feature in content-based music retrieval. Users can hum a song according to their own memory and then extract the rhythm, melody, and other information of the hummed audio information to match and identify the rhythm and melody features of the original song stored in the database. The retrieval method is based on the melody, rhythm, and other musical features of music and involves many issues such as the expression of musical melody, feature extraction of musical melody, user query construction, music melody matching, and music database construction. With the help of the customized query interface, media information can be retrieved. Finally, the experiment proves that the top-ten hit rate of the LAM algorithm after clustering is 91.3%, the top-three hit rate is 78.8%, and the first hit rate is 71.2%. The approximate symbol matching DP algorithm has a top-ten hit rate of 83.6%, a top-three hit rate of 66.4%, and a first hit rate of 63.6%. The method proposed in this paper has a high retrieval hit rate.

Defective Coloring on Classes of Perfect Graphs

In Defective Coloring we are given a graph $G$ and two integers $\chi_d$, $\Delta^*$ and are asked if we can $\chi_d$-color $G$ so that the maximum degree induced by any color class is at most $\Delta^*$. We show that this natural generalization of Coloring is much harder on several basic graph classes. In particular, we show that it is NP-hard on split graphs, even when one of the two parameters $\chi_d$, $\Delta^*$ is set to the smallest possible fixed value that does not trivialize the problem ($\chi_d = 2$ or $\Delta^* = 1$). Together with a simple treewidth-based DP algorithm this completely determines the complexity of the problem also on chordal graphs. We then consider the case of cographs and show that, somewhat surprisingly, Defective Coloring turns out to be one of the few natural problems which are NP-hard on this class. We complement this negative result by showing that Defective Coloring is in P for cographs if either $\chi_d$ or $\Delta^*$ is fixed; that it is in P for trivially perfect graphs; and that it admits a sub-exponential time algorithm for cographs when both $\chi_d$ and $\Delta^*$ are unbounded.

A survey of adaptive optimal control theory.

This paper makes a survey about the recent development of optimal control based on adaptive dynamic programming (ADP). First of all, based on DP algorithm and reinforcement learning (RL) algorithm, the origin and development of the optimization idea and its application in the control field are introduced. The second part introduces achievements in the optimal control direction, then we classify and summarize the research results of optimization method, constraint problem, structure design in control algorithm and practical engineering process based on optimal control. Finally, the possible future research topics are discussed. Through a comprehensive and complete investigation of its application in many existing fields, this survey fully demonstrates that the optimal control algorithms via ADP with critic-actor neural network (NN) structure, which also have a broad application prospect, and some developed optimal control design algorithms have been applied to practical engineering fields.

Polynomial-time algorithms to solve the single-item capacitated lot sizing problem with a 1-breakpoint all-units quantity discount

This study investigates the single-item capacitated lot sizing problem considering a 1-breakpoint all-units quantity discount. Assuming the fixed ordering cost and the undiscounted unit purchase price to be non-increasing over the periods, the lot sizing problem under study is a special case of that with piecewise concave production cost function investigated by Koca et al. (2014). Koca’s DP algorithm solves our problem with a time complexity of O(T7), where T denotes the number of periods. It, however, fails in the case of large-sized instances in acceptable runtimes due to its higher complexity. Hence, the present study is a response to the need for more efficient algorithms to overcome this shortcoming. First, some properties of the optimal solution are proved. Second, these properties are exploited in an implicit enumeration exact algorithm. Third, certain speed-up techniques are employed to reduce the time complexity of the proposed algorithm from O(T5) to O(T4). Finally, a heuristic algorithm is presented for the problem. The proposed algorithms are compared with Koca’s DP algorithm and the commercial solver used to solve some test problems. Based on the runtimes observed, the exact algorithms proposed in this study are found to outperform both Koca’s DP one and the commercial solver. Moreover, the heuristic algorithm developed herein is found to be faster than both the exact algorithms in finding optimal solutions to most problem instances.