Hungarian Algorithm Research Articles

A Mathematical Analysis of Team Impact and Individual Player Contribution in Football

In this paper, we present an important application of the Hungarian Method - a well-known combinatorial optimization tool for solving assignment problems. For our purposes, we consider the assignment of players to specific roles in a football team. It involves the broad classification of team players as defensive, midfield or attacking, while assigning the main roles associated with each of these positions. This provides insight on specific role of each individual player, thereby facilitating an optimal team selection. To illustrate this method, we utilize the average player statistics per game for two teams from the 2016/2017 Premier League Season. In addition, a team rating index is created by identifying six sub-indices. The first is called team contributions - which includes set piece goals, percentage tackles won, percentage take-ons won, percentage aerial duels won, number of interceptions, number of blocked shots, number of clearances, number of red and yellow cards. To visualize the method, a multiple correlation is carried out on team data for the 2016/2017 Premier League season to generate a correlation coefficient for each contribution. The resulting team index can be a useful tool for measuring the overall strengths of competing teams in a football league. Keywords: Hungarian method, football, team rating index, multiple correlations, team comparisons

Fusion of Deep Sort and Yolov5 for Effective Vehicle Detection and Tracking Scheme in Real-Time Traffic Management Sustainable System

In recent years, advancements in sustainable intelligent transportation have emphasized the significance of vehicle detection and tracking for real-time traffic flow management on the highways. However, the performance of existing methods based on deep learning is still a big challenge due to the different sizes of vehicles, occlusions, and other real-time traffic scenarios. To address the vehicle detection and tracking issues, an intelligent and effective scheme is proposed which detects vehicles by You Only Look Once (YOLOv5) with a speed of 140 FPS, and then, the Deep Simple Online and Real-time Tracking (Deep SORT) is integrated into the detection result to track and predict the position of the vehicles. In the first phase, YOLOv5 extracts the bounding box of the target vehicles, and in second phase, it is fed with the output of YOLOv5 to perform the tracking. Additionally, the Kalman filter and the Hungarian algorithm are employed to anticipate and track the final trajectory of the vehicles. To evaluate the effectiveness and performance of the proposed algorithm, simulations were carried out on the BDD100K and PASCAL datasets. The proposed algorithm surpasses the performance of existing deep learning-based methods, yielding superior results. Finally, the multi-vehicle detection and tracking process illustrated that the precision, recall, and mAP are 91.25%, 93.52%, and 92.18% in videos, respectively.

Optimizing Labor Allocation based on Multiobjective Decision Making Using Improved Hungarian Algorithm

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: arial, helvetica, sans-serif;">The aim of this study is to solve the problem of labor allocation in cases where the company has fewer employees than the number of existing jobs based on the evaluation of work quality according to multiple objectives. Optimizing labor allocation not only benefits the company by maximizing the use of human resources, but also saves employees' energy. In addition, employees are assigned to tasks that match their skills and qualifications, maximizing their productivity. The research results show that the multi-objective decision-making algorithm based on the Hungarian algorithm is a suitable method to help leaders of companies solve the aforementioned problem text.</span></p>

Joint Power Control and Resource Allocation for Optimizing the D2D User Performance of Full-Duplex D2D Underlying Cellular Networks

D2D communication is a promising technology for enhancing spectral efficiency (SE) in cellular networks, and full-duplex (FD) has the potential to double SE. Due to D2D’s short-distance communication and low transmittance power, it is natural to integrate FD into D2D, creating FD-D2D to underlay a cellular network to further improve SE. However, the residual self-interference (RSI) resulting from FD-D2D and interference arising from spectrum sharing between D2D users (DUs) and cellular users (CUs) can restrict D2D link performance. Therefore, we propose an FD-D2D underlying cellular system in which DUs jointly share uplink and downlink spectral resources with CUs. Moreover, we present two algorithms to enhance the performance experience of DUs while improving the system’s SE. For the first algorithm, we tackle an optimization problem aimed at maximizing the sum rate of FD-DUs in the system while adhering to transmittance power constraints. This problem is formulated as a mixed-integer nonlinear programming problem (MINLP), known for its mathematical complexity and NP-hard nature. In order to address this MINLP, our first algorithm decomposes it into two subproblems: power control and spectral resource allocation. The power control aspect is treated as a nonlinear problem, which we solve through one-dimensional searching. Meanwhile, spectral resource allocation is achieved using the Kuhn–Munkres algorithm, determining the pairing of CUs and DUs sharing the same spectrum. As for the second algorithm, our objective is to enhance the individual performance of FD-DUs by maximizing the minimum rate among them, ensuring more uniform rate performance across all FD-DUs. In order to solve this optimization problem, we propose a novel spectral resource allocation algorithm based on bisection searching and the Kuhn–Munkres algorithm, whereas the power control aspect remains the same as in the first algorithm. The numerical results demonstrate that our proposed algorithm effectively enhances the performance of DUs in an FD-D2D underlying cellular network when compared to the sum rate maximization design.

A Multi-Object Tracking Approach Combining Contextual Features and Trajectory Prediction

Aiming to solve the problem of the identity switching of objects with similar appearances in real scenarios, a multi-object tracking approach combining contextual features and trajectory prediction is proposed. This approach integrates the motion and appearance features of objects. The motion features are mainly used for trajectory prediction, and the appearance features are divided into contextual features and individual features, which are mainly used for trajectory matching. In order to accurately distinguish the identities of objects with similar appearances, a context graph is constructed by taking the specified object as the master node and its neighboring objects as the branch nodes. A preprocessing module is applied to exclude unnecessary connections in the graph model based on the speed of the historical trajectory of the object, and to distinguish the features of objects with similar appearances. Feature matching is performed using the Hungarian algorithm, based on the similarity matrix obtained from the features. Post-processing is performed for the temporarily unmatched frames to obtain the final object matching results. The experimental results show that the approach proposed in this paper can achieve the highest MOTA.

Intelligent team formation and player selection: a data-driven approach for football coaches

This article presents a data-driven approach called Intelligent Team Formation and Player Selection (ITFPS), aimed at assisting football coaches in making informed decisions about team formation and player selection. The proposed approach utilizes deep neural networks to evaluate the suitability of each player for different positions on the field. The problem is then formulated as finding the maximum weighted matching in a complete bipartite graph, with the objective of achieving the best possible alignment between team members and the positions designated by the coach. The Hungarian algorithm is employed to facilitate this matching process. Furthermore, the approach allows coaches to select a system of distinct representatives for each position, based on the specific qualities expected from players in a given match. The effectiveness of the approach is demonstrated through tests conducted on four teams from the 2021–2022 English Premier League. The results indicate that ITFPS produces decisions comparable to those made by successful coaches. By optimizing team formations and enabling the utilization of rotating formations, this approach not only enhances team performance but also empowers coaches to make strategic decisions while fully leveraging the potential of their players. ITFPS serves as an intelligent assistant for coaches, complementing their strategic perspectives.

Behavioral response of fish under ammonia nitrogen stress based on machine vision

The long-term accumulation of ammonia nitrogen in aquaculture seriously affects the life of fish and even causes large-scale death. Moreover, when the concentration of ammonia nitrogen starts to accumulate, it is a judgment standard to provide early warning through the changes in fish behavior to prevent excessive ammonia nitrogen in water. Therefore, this paper proposes a novel approach to monitoring water quality for aquaculture based on deep learning and three-dimensional movement trajectory. The improved YOLOv8 model was used as the object detection approach to obtain three-dimensional position information of fish by combining Kalman filter, Kuhn Munkres (KM) algorithm, and Kernelized Correlation Filters (KCF) algorithm. The proposed approach was evaluated in the recovery experiment of acute ammonia nitrogen stress of sturgeon, bass, and crucian. The experimental results show that the precision, recall, mAP@0.5, and mAP@0.5:0.95 of the improved YOLOv8 model are 0.964, 0.914, 0.979, and 0.602, respectively. In addition, the proposed three-dimensional positioning approach can qualitatively and quantitatively analyze the fish behavior in different stages and further explores the fish behavior changes through behavior trajectories, volumes of exercise, spatial distribution, and movement velocity. This research provides a new method and idea for studying the abnormal behavior of aquatic animals under ammonia nitrogen stress and has theoretical and practical significance.

QoS-based resource allocation for uplink NOMA networks

Nowadays, several next-generation multiple access technologies are proposed, and non-orthogonal multiple access (NOMA) is one of the candidates. In NOMA systems, since the utilization of successive interference cancellation (SIC), the SIC order is one of the significant factors to be considered. In the literature, the SIC order is usually determined by the channel gains, which is not stable especially when the users are with similar channels. In this paper, we creatively propose the quality-of-service-based SIC order in the uplink NOMA system, and focus on the resource allocation to maximize the rate. Specifically, we first optimize the powers with given user grouping and provide solutions for different cases of channel gains. Furthermore, the Hungarian algorithm is jointly exploited with the optimized powers. Simulation results show that the proposed algorithm outperforms traditional orthogonal multiple access and even achieves similar rate with the exhaustive search.

Bipartite graph-based community-to-community matching in local energy market considering socially networked prosumers

The mutual influences among different energy prosumers caused by the socially connected relationship have not been modeled explicitly in the current local energy market. Hence, we first develop an iterative market clearing framework to bridge the data-driven and model-driven models of heterogeneous market participants. Then, we consider the behavior difficulty, communication channel, depth of information process, social influence, and observed peer effects in the multi-agent deep reinforcement learning (MADRL) framework in the data-driven models. Finally, we calculate the marginal market value of each C2C energy transaction with a linearized distribution network power flow model and formulate the C2C matching as a bipartite graph. We propose a recurrent Hungarian algorithm to solve the best matching schemes of C2C trading. Case studies verify that the distribution network power flow model considered in C2C energy trading successfully constrain the unit values of nodal voltages between 0.9 and 1.1. The power flow values in distribution network are constrained within the upper limits of transmission capacity for each branch. The finite iteration number of MADRL in hybrid market-clearing algorithm guarantees the normal P2P energy market operation. Moreover, the social influences for the P2P energy trading results are verified by comparing P2P trading results in different scenarios. Our proposed model can better depict the trading behaviors in communities.

Multi-pedestrian Tracking Method Fusing Two-stage Matching

Multi-pedestrian tracking is one of the hot topics in computer vision. For an intelligent mobile robot, multi-pedestrian tracking from a first-person perspective can provide information for navigating through a crowd and ensure safety. Most of the existing methods cannot deal with occlusion and trajectory overlap well. In this paper, a multi-pedestrian tracking method fusing two-stage matching is proposed. Firstly, the detection and the corresponding feature values of the pedestrians are obtained by a multi-task learning network based on CenterNet. Then the detected pedestrians are matched with feature values by greedy strategy. When dealing with the reappearance of pedestrians caused by occlusion or trajectory overlap, the sample database is established to update the samples in real time. The color histogram and HOG feature are calculated for each sample. When the pedestrian disappears, the direction of disappearance and the last position is recorded for the selection of trajectory. Finally, the KM algorithm is used for cross-frame matching. Our method is compared with some recent methods on MOT data sets. The result shows that our method has a significant improvement in the main evaluation index MOTA.

Theory and calculation of abelian and non-abelian geometric phase factors with SpinDynamica

Cyclic quantum evolution is accompanied by a systematic change in the phase of the initial state vector. This change only depends upon the path traced out by the system itself. Such effects are collectively known as geometric phase factors. The geometric foundations of these phase factors are most elegantly formulated in terms of fibre bundle theory and differential forms, both of which can represent a significant hurdle to master. We present a derivation of the abelian and non-abelian Berry phase in terms of embedded manifolds of linear vector spaces. Embedded manifolds offer the advantage of being less abstract than fibre bundles, and are well-suited for explicit calculations. Essential features of the derivation reduce to matrix–vector manipulations. We further discuss a numerical strategy for the calculation of abelian and non-abelian phase factors. Our approach is based upon Hungarian method and the polar decomposition, and is made freely available as a SpinDynamica addon. Additionally, all derivations and analytic calculations are supported by Mathematica notebooks.

A New Method for Improving the Fairness of Multi-Robot Task Allocation by Balancing the Distribution of Tasks

This paper presents an innovative task allocation method for multi-robot systems that aims to optimize task distribution while taking into account various performance metrics such as efficiency, speed, and cost. Contrary to conventional approaches, the proposed method takes a comprehensive approach to initialization by integrating the K-means clustering algorithm, the Hungarian method for solving the assignment problem, and a genetic algorithm specifically adapted for Open Loop Travel Sales Man Problem (OLTSP). This synergistic combination allows for a more robust initialization, effectively grouping similar tasks and robots, and laying a strong foundation for the subsequent optimization process. The suggested method is flexible enough to handle a variety of situations, including Multi-Robot System (MRS) with robots that have unique capabilities and tasks of varying difficulty. The method provides a more adaptable and flexible solution than traditional algorithms, which might not be able to adequately address these variations because of the heterogeneity of the robots and the complexity of the tasks. Additionally, ensuring optimal task allocation is a key component of the suggested method. The method efficiently determines the best task assignments for robots through the use of a systematic optimization approach, thereby reducing the overall cost and time needed to complete all tasks. This contrasts with some existing methods that might not ensure optimality or might have limitations in their ability to handle a variety of scenarios. Extensive simulation experiments and numerical evaluations are carried out to validate the method's efficiency. The extensive validation process verifies the suggested approach's dependability and efficiency, giving confidence in its practical applicability.

Time-Varying Topology Formation Reconfiguration Control of the Multi-Agent System Based on the Improved Hungarian Algorithm

Distributed time-varying formation technology for multi-agent systems is recently become a research hotspot in formation control field. However, the formation reconfiguration control technology for agents that randomly appeared to fail during maneuvers is rarely studied. In this paper, the topological relations between intelligence are designed by graph theory to simplify the cooperative interaction between multi-agent systems. Moreover, this paper constructs the time-varying configuration of the target formation based on the rigidity graph theory and leader–follower strategy. Drawing on the establishment of the expert experience database in a collaborative process, we innovatively propose the establishment of a graphic library to help the multi-agent system quickly form an affine transformation as soon as it is disabled. Secondly, the improved Hungarian algorithm is adopted to allocate the target point when the first failure occurs. This algorithm incorporates a gradient weighting factor from the auction algorithm to improve the speed of system reconfiguration with minimum path cost. On this basis, a distributed multi-agent control law based on consistency theory is established, and the system’s stability can be guaranteed via Lyapunov functions. Finally, the simulation results demonstrate the feasibility and effectiveness of the proposed formation reconfiguration control algorithm in a collaborative environment.

Target tracking and detection based on YOLOv5 algorithm

The YOLOv5 algorithm has gained popularity in recent years as an effective solution for real-time object detection in images and videos. This paper explores its potential for solving the problem of target tracking detection by proposing a modified YOLOv5 architecture that integrates object detection and tracking capabilities.The proposed YOLOv5-based tracking system includes three major components: object detection, object tracking, and object association. The object detection component uses a YOLOv5 model to detect and localize the target object in each frame of the video. The object tracking component then tracks the target object across frames using a Kalman filter and a Hungarian algorithm for data association. Finally, the object association component uses a motion model to handle occlusions and re-identifies the target object when it reappears in the field of view.The performance of the proposed YOLOv5-based tracking system is evaluated on several benchmark datasets, and its results are compared to state-of-the-art tracking algorithms. The experimental results show that the system achieves competitive tracking accuracy and real-time processing speed. Additionally, the effectiveness of the proposed motion model for handling occlusions and re-identification of the target object is demonstrated. In conclusion, the YOLOv5 algorithm has promising potential for target tracking detection in real-world scenarios, and it could have various applications in surveillance, robotics, and autonomous driving.

Cooperative vehicular networks over Nakagami-[formula omitted] fading: Joint power control and spectrum scheduling

Facilitated by the rapid development of wireless communication technologies and the broad deployment of cellular networks, vehicular communications have evolved towards ultra-reliable low-latency communications (URLLC). In this paper, we consider a device-to-device (D2D)-enabled vehicular networks, where the uplink from roadside units (RSUs) to the base station (BS) shares spectrum with D2D-enabled vehicle-to-vehicle (V2V) links. The sum capacity of RSUs is maximized by jointly optimizing the transmit power and orthogonal spectrum bands scheduling, with the minimum data rate of RSUs and the reliability of V2V links guaranteed. Specifically, under Nakagami fading channels, the statistical channel state information (CSI) of vehicular communication links is utilized instead of the instantaneous CSI to adapt to the extra overheads resulting from frequent channel estimation. Moreover, user fairness is also considered, through maximizing the minimum ergodic capacity achieved by RSUs. To solve the formulated non-convex problem, we decomposed it into a power allocation subproblem that is analytically solved, and a spectrum bands scheduling subproblem that is tackled by resorting to the Hungarian method. The validity of the proposed methods is confirmed by numerical results.

Integration of government accounting system reform and university infrastructure consolidation based on type two fuzzy sets

Abstract Type II fuzzy set can innovate the accounting method of infrastructure consolidation and integration work. Based on this, this paper constructs a new and feasible accounting system for the integration of government accounting systems and university infrastructure. The specific application is that any point on the financial subordination domain and its corresponding subordination interval constitutes a type two fuzzy set, and the fuzzy set is used as the object for modeling. Using the interval type-two fuzzy set for word calculation, the result of the calculation is output by the decoder and sorted, and the value of the accounting account amount can be derived. The mutual subset measure between the input interval type II fuzzy set and the rule antecedent interval type II fuzzy set is used to check the accounting bills of the rule activation interval and to verify the performance of its use in the capital consolidation work of S universities. The experimental results show that after transferring the balance of $4,982,600 for prepaid construction and $4,956,200 for prepaid provisioning to the prepaid account on the basis of ensuring the consistency of the output KM algorithm, the balance of the account becomes $9,938,800. This result shows that the type II fuzzy set saves 42% of the arithmetic cost for this transfer of university infrastructure accounts, and contributes to the realization of the government accounting system reform and the improvement of financial management of the integration of university infrastructure accounts.

Global optimisation matching method for multi-representation buildings constrained by road network

Entity matching is one of the key technologies for geospatial data update and fusion. In response to the shortcomings of most spatial entity matching methods that use local optimisation strategies, a global optimisation matching method for multi-representation buildings using road network constraints is proposed. First, the road network is used for region segmentation to obtain candidate matches. Second, the spatial similarity among the candidate matching objects is calculated and the characteristic similarity weights are determined using the entropy weight method. Third, the matching of building entities is transformed into an allocation problem using integer programming ideas, and the Hungarian algorithm is solved to obtain the optimal matching combination with minimum global variance. Finally, two test areas are selected to validate the proposed method, and the precision, recall, and F-measure values of the experiments are 96.35%, 97.11%, and 96.73% versus 95.96%, 97.03%, and 96.49%, respectively. The matching accuracy is greatly improved compared with the local search strategy.

A Novel Ship Speed and Heading Estimation Approach Using Radar Sequential Images

This paper proposes an approach to estimate ship dynamic features (i.e., speed and heading) using radar sequential images, which can be used to assess the ship-ship collision risk in vessel traffic service (VTS) center. The proposed approach estimates the ship speed and heading in three steps, including ship detection, tracking, and data fusion. First, the ship target, together with its center coordinate, is detected by introducing the background subtraction detector in each radar image. Second, the ship position in one frame is mapped to the consecutive frame, and ship trajectory distribution is established using the Kalman filter and Hungarian algorithms. Third, fuzzy logic is introduced to fuse data from radar and AIS. Specifically, input variables include two parameters, which are the ship speed/heading difference in consecutive frames using radar and AIS, respectively. The variance of ship speed distribution is 1.30 at 10s sampling frequency, and the variance of ship heading distribution is 26.53, the experimental results indicate that the proposed approach can accurately estimate ship speed and heading in the radar sequential images of the Yangtze River. The strength of the proposed approach is its capability to estimate the ship speed and heading quantitatively from radar sequential images, which is interactive and interpretable for VTS operators.

Vehicle Detection and Tracking with Roadside LiDAR Using Improved ResNet18 and the Hungarian Algorithm.

By the end of the 2020s, full autonomy in autonomous driving may become commercially viable in certain regions. However, achieving Level 5 autonomy requires crucial collaborations between vehicles and infrastructure, necessitating high-speed data processing and low-latency capabilities. This paper introduces a vehicle tracking algorithm based on roadside LiDAR (light detection and ranging) infrastructure to reduce the latency to 100 ms without compromising the detection accuracy. We first develop a vehicle detection architecture based on ResNet18 that can more effectively detect vehicles at a full frame rate by improving the BEV mapping and the loss function of the optimizer. Then, we propose a new three-stage vehicle tracking algorithm. This algorithm enhances the Hungarian algorithm to better match objects detected in consecutive frames, while time-space logicality and trajectory similarity are proposed to address the short-term occlusion problem. Finally, the system is tested on static scenes in the KITTI dataset and the MATLAB/Simulink simulation dataset. The results show that the proposed framework outperforms other methods, with F1-scores of 96.97% and 98.58% for vehicle detection for the KITTI and MATLAB/Simulink datasets, respectively. For vehicle tracking, the MOTA are 88.12% and 90.56%, and the ID-F1 are 95.16% and 96.43%, which are better optimized than the traditional Hungarian algorithm. In particular, it has a significant improvement in calculation speed, which is important for real-time transportation applications.

Energy-Aware Mode Selection for D2D Resource Allocation in 5G Networks

This paper proposes an energy-aware mode-selection mechanism for device-to-device (D2D) resource allocation in the coexisting 5G network environment of D2D users and traditional cellular users by taking into consideration the mechanisms of mode selection, transmit power, and spectrum resource allocation simultaneously. In the proposed approach, all spectrum resources are fully shared by traditional cellular users and D2D users. We first determine the communication mode of all users after considering the transmission opportunity of traditional cellular users and analyzing the interference degree of different D2D communication modes. Next, we divide all users into three tiers according to the distances between users and the base station to solve the interference problem generated by excessing transmit power in uplink transmission. Afterward, based on the different communication modes, corresponding spectrum resource allocation mechanisms using the Hungarian algorithm are developed. We simulate a single cell environment using Python language and perform several simulations for different pairs of traditional cellular users and D2D users. The simulation results reveal that the proposed approach outperforms the proposed approach by Hou et al. in system throughput. With power control, the energy efficiency of the proposed approach could be enhanced by 20%.