Joint Optimization Research Articles

Game Theory-Based Optimization for Efficient IoT Task Offloading in 6G Network Base Stations

This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. This model involves many base stations and IoT devices. It takes into account factors such as task delay, energy consumption, societal impacts, and economic incentives. The objective of joint optimisation is to maximise the revenues of base stations and the utilities of IoT devices by considering base station price, IoT device base station selection, and job offloading mechanisms. The IoT device base station selection is addressed using a many-to-one matching game model, while pricing and task offloading interactions are managed by a two-stage approach based on Stackelberg game theory. The suggested Optimal Pricing and Best Response Algorithm (OBGT), which is based on Game Theory, successfully reaches equilibrium solutions. It demonstrates rapid convergence in simulations and improves both base station profits and IoT device utility. This study integrates data-driven mobile computing systems assurance for futuristic smart vertical networks, enhancing the efficiency of task offloading and optimisation.

Mechanical Properties of Folding Arch Frame Joints for Unmanned Arch Erection

The application of folding arch frames is deemed crucial for unmanned arch frame erection, with the selection of the joint form being a determining factor in the overall mechanical performance of the folding arch frame, particularly in influencing the primary support safety. In light of the geological conditions of the New Wushaoling Tunnel project, three feasible joint forms for folding arch frames were proposed: buckle, adhesive, and interference-fit joints. Numerical simulations were conducted to analyze the arch’s overall mechanical and the joints’ local mechanical performances, aiming to identify the optimal joint form. On-site construction data were collected, and the effectiveness of unmanned arch frame erection was evaluated. The design requirements for the vertical displacement results of the steel arches with different joints were met. The maximum shear stress of the buckled arch frame was found to be the lowest, whereas that of the interference-fitted arch frame was the highest. The local shear stress of the adhesive joints was the lowest, while that of the interference-fit joint was the highest. Considering the material application limitations and calculation results, buckle joints are recommended. Unmanned arch frame erection, compared with manual arch frame erection, can save 66.6% of human resources and reduce the construction time by 33.3% to 50%. Statistical analysis has confirmed that the quality of automated arch construction can be guaranteed.

Multi-observation points setting problem based on stepwise maximum viewshed approach

Terrain visibility analysis is vital in geospatial research, employing computer geometry and graphics principles for computing and visualizing visibility between observation and target points. The observation point setup problem is crucial for tasks like sentry point selection and signal base station placement. This problem involves choosing the minimum viewpoints on terrain for optimal joint view coverage, presenting a combinatorial optimization challenge. As technology advances, data can be acquired with increasing precision, the number of viewpoints that can be extracted from the same area is gradually increasing as well, obtaining candidate viewpoints and determining optimal combinations become challenging. This paper proposes a novel method, Stepwise Maximum Viewshed (SMV), addressing observation point setup by stepwise filtering the maximum viewshed and can customize the size of the observation point combination. In complex mountainous terrain, the SMV algorithm demonstrates superior joint view coverage compared to Candidate Viewpoints Filtering (CVF) and Simulated Annealing (SA) algorithms. Experimental results reveal up to 5.59% improvement over CVF and a maximum of 12.52% over SA in joint viewshed coverage.

Blockchain design for optimal joint production and maintenance over multiple periods for oil-filling production lines

Joint maintenance and production planning result in enhanced system efficiency and a significant reduction in expensive costs. Therefore, this research proposed an integrated blockchain design of two optimization models for the joint scheduling and sequencing of production orders and maintenance jobs over multiple periods with probabilistic and stochastic parameters for the oil-filling process. In the scheduling model, the objective function was to minimize total production and maintenance costs. Further, the sequencing model aimed to minimize the total overtime cost and the sum start times of jobs and orders. The proposed models were implemented on five production lines of five-liter lube oil bottles. Results showed that the developed optimization models effectively achieved concurrent production and maintenance planning at minimal total cost. In practice, this results in effective resource utilization, and saving costly production and maintenance costs. In conclusion, the developed blockchain design is valuable in developing, managing, and controlling optimal joint production and maintenance planning of oil-filling production lines.

A Variational Surface-Evolution Approach to Optimal Transport over Transitioning Compact Supports with Domain Constraints

We examine the optimal mass transport problem in Rn between densities with transitioning compact support by considering the geometry of a continuous interpolating support boundary Γ in space-time within which the mass density evolves according to the fluid dynamical framework of Benamou and Brenier. We treat the geometry of this space-time embedding in terms of points, vectors, and sets in Rn+1=R×Rn and blend the mass density and velocity as well into a space-time solenoidal vector field W|Ω→Rn+1 over a compact set Ω⊂Rn+1. We then formulate a joint optimization for W and its support using the shaped gradient of the space-time surface Γ outlining the support boundary ∂Ω. This easily accommodates spatiotemporal constraints, including obstacles or mandatory regions to visit.

Joint optimization of the inventory routing problem considering the recycling of broken bikes in the bike-sharing system

Bike-sharing system has become an indispensable element of sustainable urban transportation, effectively resolving the "last mile" transportation challenge for city dwellers. A major daily operational task in these systems is planning a fleet to rebalance the bikes over time, ensuring the optimal availability of bikes and docks to users. Recycling is also a daily work with the an increase in the number of broken bikes. However, rebalancing or recycling operation is always regarded as an independent tasks. They are separately studied in existing papers. Thus, this paper develops an operational strategy for recycling broken bikes during the rebalancing process, and studies the combination of the station inventory and vehicle routing problems. First, an inventory routing model is constructed with the aim of minimizing the total costs including procurement, expected user loss, inventory and transportation costs. Then, a two-stage iterative algorithm is developed with both exact and heuristic algorithms. We use real-world data from Capital Bikeshare to test our proposed model and approach, which shows the two-stage iterative algorithm is efficient and outperforms existing solutions in reducing total costs. Finally, the sensitivity analysis is performed on key parameters such as the vehicle’s capacity, unit penalty costs for customer dissatisfaction events, unit inventory holding costs and the observation period of rebalancing. It shows that enterprises can reduce the total cost by altering vehicle’s capacity, reducing the unit inventory holding costs or changing the observation period of rebalancing.

Deep Reinforcement Learning for Ecological and Distributed Urban Traffic Signal Control with Multi-Agent Equilibrium Decision Making

Multi-Agent Reinforcement Learning (MARL) has shown strong advantages in urban multi-intersection traffic signal control, but it also suffers from the problems of non-smooth environment and inter-agent coordination. However, most of the existing research on MARL traffic signal control has focused on designing efficient communication to solve the environment non-smoothness problem, while neglecting the coordination between agents. In order to coordinate among agents, this paper combines MARL and the regional mixed-strategy Nash equilibrium to construct a Deep Convolutional Nash Policy Gradient Traffic Signal Control (DCNPG-TSC) model, which enables agents to perceive the traffic environment in a wider range and achieves effective agent communication and collaboration. Additionally, a Multi-Agent Distributional Nash Policy Gradient (MADNPG) algorithm is proposed in this model, which is the first time the mixed-strategy Nash equilibrium is used for the improvement in the Multi-Agent Deep Deterministic Policy Gradient algorithm traffic signal control strategy to provide the optimal signal phase for each intersection. In addition, the eco-mobility concept is integrated into MARL traffic signal control to reduce pollutant emissions at intersections. Finally, simulation results in synthetic and real-world traffic road networks show that DCNPG-TSC outperforms other state-of-the-art MARL traffic signal control methods in almost all performance metrics, because it can aggregate the information of neighboring agents and optimize the agent’s decisions through gaming to find an optimal joint equilibrium strategy for the traffic road network.

Finite Element Analysis and Weight Optimization of Knuckle joint for different Materials by using CAE Tools

The current study involved the analysis of a knuckle joint using finite elements. A mechanical joint with two intersecting cylindrical rods whose axes are in the same plane is called a knuckle joint. Knuckle joint should be designed and manufactured in such a way that it can sustain more load without failure. The objective of the present study is to study knuckle joints made of different materials like aluminum, stainless steel, structural steel, magnesium, and gray cast iron using CAE tools. The CAD model of knuckle joint is made in CATIA V5 R20. The finite element analysis of this CAD model using ANSYS 15 has been carried out to investigate maximum stress and deformation. The CAE results compared with the analytical results using conventional approach. The CAE results have been found close to the analytical results, thus validating the model. The knuckle joint of aluminum is found to have highest factor of safety (1.641) at 50 KN load and is the most the most suitable material for knuckle joint. Weight of vehicle plays an important role in overall performance and fuel efficiency of vehicle. Knuckle joint is a part of vehicle. Hence weight optimization of knuckle joint made of aluminum has been performed using ANSYS 15 and it is observed that weight of knuckle joint made of aluminum can be reduced by 5.08% for 50 KN loading condition. Key Words: Knuckle joint, Finite element analysis, Mechanical joint, Load-bearing capacity, Material selection, CAE tools, Deformation analysis etc.

TinyNS: Platform-aware Neurosymbolic Auto Tiny Machine Learning

Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and remote applications. However, deploying interpretable artificial intelligence systems that can perform high-level symbolic reasoning and satisfy the underlying system rules and physics within the tight platform resource constraints is challenging. In this article, we introduce TinyNS , the first platform-aware neurosymbolic architecture search framework for joint optimization of symbolic and neural operators. TinyNS provides recipes and parsers to automatically write microcontroller code for five types of neurosymbolic models, combining the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models. TinyNS uses a fast, gradient-free, black-box Bayesian optimizer over discontinuous, conditional, numeric, and categorical search spaces to find the best synergy of symbolic code and neural networks within the hardware resource budget. To guarantee deployability, TinyNS talks to the target hardware during the optimization process. We showcase the utility of TinyNS by deploying microcontroller-class neurosymbolic models through several case studies. In all use cases, TinyNS outperforms purely neural or purely symbolic approaches while guaranteeing execution on real hardware.

Deblur-GS: 3D Gaussian Splatting from Camera Motion Blurred Images

Novel view synthesis has undergone a revolution thanks to the radiance field method. The introduction of 3D Gaussian splatting (3DGS) has successfully addressed the issues of prolonged training times and slow rendering speeds associated with the Neural Radiance Field (NeRF), all while preserving the quality of reconstructions. However, 3DGS remains heavily reliant on the quality of input images and their initial camera pose initialization. In cases where input images are blurred, the reconstruction results suffer from blurriness and artifacts. In this paper, we propose the Deblur-GS method for reconstructing 3D Gaussian points to create a sharp radiance field from a camera motion blurred image set. We model the problem of motion blur as a joint optimization challenge involving camera trajectory estimation and time sampling. We cohesively optimize the parameters of the Gaussian points and the camera trajectory during the shutter time. Deblur-GS consistently achieves superior performance and rendering quality when compared to previous methods, as demonstrated in evaluations conducted on both synthetic and real datasets.

Finite Element Analysis and weight Optimization of Knuckle joint for different materials by using CAE Tools

The current study involved the analysis of a knuckle joint using finite elements. A mechanical joint with two intersecting cylindrical rods whose axes are in the same plane is called a knuckle joint. Knuckle joint should be designed and manufactured in such a way that it can sustain more load without failure. The objective of the present study is to study knuckle joints made of different materials like aluminum, stainless steel, structural steel, magnesium, and gray cast iron using CAE tools. The CAD model of knuckle joint is made in CATIA V5 R20. The finite element analysis of this CAD model using ANSYS 15 has been carried out to investigate maximum stress and deformation. The CAE results compared with the analytical results using conventional approach. The CAE results have been found close to the analytical results, thus validating the model. The knuckle joint of aluminum is found to have highest factor of safety (1.641) at 50 KN load and is the most the most suitable material for knuckle joint. Weight of vehicle plays an important role in overall performance and fuel efficiency of vehicle. Knuckle joint is a part of vehicle. Hence weight optimization of knuckle joint made of aluminum has been performed using ANSYS 15 and it is observed that weight of knuckle joint made of aluminum can be reduced by 5.08% for 50 KN loading condition. Key Words: Knuckle joint, Finite element analysis, Mechanical joint, Load-bearing capacity, Material selection, CAE tools, Deformation analysis etc.

Joint Optimization Algorithm for Latency-Energy and Privacy Combining Discrete Differential Evolution and Convex Optimization

Joint Optimization Algorithm for Latency-Energy and Privacy Combining Discrete Differential Evolution and Convex Optimization

Frictional Behavior of Chestnut (Castanea sativa Mill.) Sawn Timber for Carpentry and Mechanical Joints in Service Class 2

Wood is poised to become a material of choice for future construction. When appropriately managed, it is a renewable material with unique mechanical properties. Thus, there has been a growing demand for hardwoods, including Castanea sativa Mill., the focal point of this investigation, for structural applications. Albeit in a limited capacity, Eurocode 5-2 offers friction coefficients for softwoods, but it falls short for hardwoods. These coefficients play a critical role in numerical simulations involving friction, enabling the optimization of joints and, by extension, the overall structural integrity. Test samples were evaluated at 15% and 18% moisture content (Service Class 2) for various orientations of timber-to-timber and timber-to-steel friction. The results provide an experimental database for numerical simulations and highlight the influence of moisture on the stick–slip phenomenon, which was absent for the timber-to-timber tests, as well as on the rising friction values. At 18%, the static and kinetic coefficients were 0.70 and 0.48 for timber-to-timber and 0.5 and 0.50 for timber-to-steel. The increase was around 50% for timber-to-timber friction and over 170% for timber-to-steel pairs. Moreover, the findings proved a relationship between both coefficients and the validity of the linear estimation approach within the 12–18% moisture commonly applied to softwoods.

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

With the increasing dissemination of the Internet of Things and 5G, mobile edge computing has become a novel scheme to assist terminal devices in executing computation tasks. To elevate the coverage and computation capability of edge computing, a collaborative computation offloading and resource management architecture was proposed in space–air–ground integrated networking (SAGIN). In this manuscript, we established a novel model considering the computation offloading cost constraints of the communication, computing and cache model in the SAGIN. To be specific, the joint optimization problem of collaborative computation offloading and resource management was modeled as a mixed integer nonlinear programming problem. To address this issue, this paper proposed a computation offloading and resource allocation strategy based on deep reinforcement learning (DRL). Differing from traditional methods, DRL does not need a well-established formulation or previous information, and it is capable of revising the strategy adaptively according to the environment. The simulation results demonstrate the proposed approach can achieve the optimal reward values in the case of different terminal device numbers. Furthermore, this manuscript provided the analysis with variant parameters of the proposed approach.

Space and time joint optimization for vertical takeoff and landing aircraft in dense obstacles and interference environments

ABSTRACT The Vertical Takeoff and Landing Aircraft (VTOL) is capable of both low-speed hovering and high-speed flight. This paper proposes an autonomous motion planning and anti-wind disturbance control algorithm for the VTOL, implemented spatial and temporal joint motion planning for VTOL, under the limitation of maximum flight speed of the aircraft, VTOL can achieve fully autonomous flight in complex environments, which can realize the autonomous flight of a single aircraft in a complex and dense environment, achieve real-time perception and avoidance of static and dynamic obstacles. The information generated by motion planning includes the position, velocity, and acceleration information of the aircraft, and proposes a wind disturbance estimation and active control strategy in a wind disturbance environment, accurate estimation of wind disturbance is achieved through a state observer, and the wind disturbance was suppressed through nonlinear error feedback control law, implemented wind disturbance estimation and suppression for aircraft in wind disturbance environments, and achieved complete autonomous motion planning and trajectory tracking of VTOL in a complex and wind disturbance environment. Our work can provide a basis for the collaborative control of multiple UAVs.

Artificial Neural Networks and Experimental Analysis of the Resistance Spot Welding Parameters Effect on the Welded Joint Quality of AISI 304.

The automobile industry relies primarily on spot welding operations, particularly resistance spot welding (RSW). The performance and durability of the resistance spot-welded joints are significantly impacted by the welding quality outputs, such as the shear force, nugget diameter, failure mode, and the hardness of the welded joints. In light of this, the present study sought to determine how the aforementioned welding quality outputs of 0.5 and 1 mm thick austenitic stainless steel AISI 304 were affected by RSW parameters, such as welding current, welding time, pressure, holding time, squeezing time, and pulse welding. In order to guarantee precise evaluation and experimental analysis, it is essential that they are supported by a numerical model using an intelligent model. The primary objective of this research is to develop and enhance an intelligent model employing artificial neural network (ANN) models. This model aims to provide deeper knowledge of how the RSW parameters affect the quality of optimum joint behavior. The proposed neural network (NN) models were executed using different ANN structures with various training and transfer functions based on the feedforward backpropagation approach to find the optimal model. The performance of the ANN models was evaluated in accordance with validation metrics, like the mean squared error (MSE) and correlation coefficient (R2). Assessing the experimental findings revealed the maximum shear force and nugget diameter emerged to be 8.6 kN and 5.4 mm for the case of 1-1 mm, 3.298 kN and 4.1 mm for the case of 0.5-0.5 mm, and 4.031 kN and 4.9 mm for the case of 0.5-1 mm. Based on the results of the Pareto charts generated by the Minitab program, the most important parameter for the 1-1 mm case was the welding current; for the 0.5-0.5 mm case, it was pulse welding; and for the 0.5-1 mm case, it was holding time. When looking at the hardness results, it is clear that the nugget zone is much higher than the heat-affected zone (HZ) and base metal (BM) in all three cases. The ANN models showed that the one-output shear force model gave the best prediction, relating to the highest R and the lowest MSE compared to the one-output nugget diameter model and two-output structure. However, the Levenberg-Marquardt backpropagation (Trainlm) training function with the log sigmoid transfer function recorded the best prediction results of both ANN structures.

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

In the near future, fully autonomous vehicles may revolutionise mobility and contribute to the development of the smart city concept. In this work, we assume that vehicles are not only fully autonomous but also centrally controlled by a single operator, who can also define the traffic light control parameters at intersections. With the aim of optimising the system to achieve a global optimum, the operator can define both the routes of the fleet of vehicles and the traffic light control parameters. This paper proposes a model for the joint optimisation of traffic light control parameters and autonomous vehicle routes to achieve the system optimum. The model, which is solved using a gradient algorithm, is tested on networks of different sizes. The results obtained show the validity of the proposed approach and the advantages of centralised management of vehicles and intersection control parameters.

WCPNet: Jointly Predicting Wirelength, Congestion and Power for FPGA Using Multi-Task Learning

To speed up the design closure and improve the QoR of FPGA, supervised single-task machine learning techniques have been used to predict individual design metric based on placement results. However, the design objective is to achieve optimal performance while considering multiple conflicting metrics. The single-task approaches predict each metric in isolation and neglect the potential correlations or dependencies among them. To address the limitations, this article proposes a multi-task learning approach to jointly predict wirelength, congestion and power. By sharing the common feature representations and adopting the joint optimization strategy, the novel WCPNet models (including WCPNet-HS and WCPNet-SS) cannot only predict the three metrics of different scales simultaneously, but also outperform the majority of single-task models in terms of both prediction performance and time cost, which are demonstrated by the results of the cross design experiment. By adopting the cross-stitch structure in the encoder, WCPNet-SS outperforms WCPNet-HS in prediction performance, but WCPNet-HS is faster because of the simpler parameters sharing structure. The significance of the feature image pinUtilization on predicting power and wirelength are demonstrated by the ablation experiment.

Traffic-Aware Energy-Efficient Resource Allocation for RSMA Based UAV Communications

Traffic measurement will play an essential role in future networks to reveal the traffic requirements of the users, which will support network operations like resource allocation. In this paper, we study the traffic-aware resource allocation problem for downlink rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) communications. Specifically, with the help of traffic measurement, user requirements on achievable rate are known as prior knowledge to the UAV. Considering user requirements, we maximize the energy efficiency of the UAV by jointly optimizing the UAV deployment, beamforming, rate allocation, and subcarrier allocation. To overcome the non-convexity of the above problem, a joint optimization is developed to solve it iteratively. First, a heuristic approach is proposed to find the three-dimensional location of the UAV. Then, successive convex approximation approach is utilized to optimize RSMA parameters. Moreover, we formulate the subcarrier allocation problem as a many-to-one two-sided matching game, which is tackled by the swap matching algorithm. Numerical results suggest that the proposed subcarrier allocation scheme outperforms benchmark schemes, and RSMA based schemes perform close to that based on non-orthogonal multiple access, which all outperform orthogonal frequency division multiple access based scheme.

Joint optimization of train timetable and passing facility: A capacity allocation strategy

To addreess the challenge of overcrowding in the urban rail transit system, this paper proposes a novel joint optimization strategy that optimizes the timetable and the passing facility’s working status (PFWS) simultaneously according to the passenger demands. To this end, a mixed-integer nonlinear programming (MINLP) model is formulated to jointly minimize overcrowding on trains and platforms, as well as the passenger travel time. The passing facility setting and the passenger flow activities depiction are particularly taken into account by the rigorous constraints. Subsequently, we design a simulation-based hybrid variable neighborhood search (SHVNS) algorithm to efficiently generate near-optimal solution. This algorithm incorporates a variable neighborhood search (VNS) algorithm, a randomly reset strategy, and a simulation-based algorithm. The VNS algorithm includes five neighborhood operators to enhance search efficiency. Finally, various experiments with the real-world data from Chongqing rail transit line 6 in China are tested to prove the applicability and the performance of our proposed model and algorithm. The computational results show that the joint optimization strategy outperforms a current operation strategy in terms of the overcrowded situation on trains and platforms, often by more than 60%, and some management implications and theoretical support can be obtained for the urban rail transit operator.