Objective Optimization Research Articles

Blockchain-based 6G task offloading and cooperative computing resource allocation study

In the upcoming era of 6G, the accelerated development of the Internet of Everything and high-speed communication is poised to provide people with an efficient and intelligent life experience. However, the exponential growth in data traffic is expected to pose substantial task processing challenges. Relying solely on the computational resources of individual devices may struggle to meet the demand for low latency. Additionally, the lack of trust between different devices poses a limitation to the development of 6G networks. In response to this issue, this study proposes a blockchain-based 6G task offloading and collaborative computational resource allocation (CERMTOB) algorithm. The proposed first designs a blockchain-based 6G cloud-network-edge collaborative task offloading model. It incorporates a blockchain network on the edge layer to improve trust between terminals and blockchain nodes. Subsequently, the optimization objective is established to minimize the total latency of offloading, computation, and blockchain consensus. The optimal offloading scheme is determined using the wolf fish collaborative search algorithm(WF-CSA) to minimize the total delay. Simulation results show that the WF-CSA algorithm significantly reduces the total delay by up to 42.58% compared to the fish swarm algorithm, wolf pack algorithm and binary particle swarm optimisation algorithm. Furthermore, the introduction of blockchain to the cloud-side-end offloading system improves the communication success rate by a maximum of 14.93% compared to the blockchain-free system.

The planning method of new energy distribution network in plateau area based on local accommodation

Abstract The planning method of new energy distribution network in plateau area based on local accommodation is studied to improve the local accommodation capacity of new energy distribution network in plateau area. The framework of new energy distribution network planning in the plateau area is constructed, and the distribution network equipment suitable for the plateau environment is selected based on the harsh environment in the plateau area; in the framework of new energy distribution network planning in plateau area, a bi-level planning model considering multi-flexible resource coordinated scheduling and energy storage is established. The upper level planning model takes the maximum accommodation of new energy as the objective, and the lower level planning model takes the optimal daily operation benefit of energy storage under the given configuration as the optimization objective; the probabilistic power flow method and C-PSO are used to solve the bi-level model. The experiment shows that this method ensures the stability of the new energy distribution network in the plateau area, reduces the amount of abandoned wind energy and the operating cost, and improves the local accommodation capacity of the core higher than the distribution network.

MTTLA‐DLW: Multi‐task TCN‐Bi‐LSTM transfer learning approach with dynamic loss weights based on feature correlations of the training samples for short‐term wind power prediction

AbstractWind power prediction for newly built wind farms is usually faced with the problem of no sufficient historical data. To efficiently extract the useful features from related wind farms, a novel transfer learning method based on temporal convolutional network (TCN)‐Bi‐long short‐term memory (LSTM) with dynamic loss weights is proposed. Firstly, a novel multi‐task TCN‐Bi‐LSTM model is designed to extract common features. The separate TCNs, and common Bi‐LSTM layers of the proposed model are designed to extract the temporal features from related wind farms. Secondly, in the pre‐training stage, to optimize the training process of the neural networks, a dynamic loss‐weighting strategy is proposed for multi‐task learning (MTL) to select the most related features, which increase the prediction accuracy by providing a suitable optimization object. Thirdly, the multi‐task TCN‐Bi‐LSTM model is re‐trained based on the samples from the target wind farm. Finally, a dataset of seven wind farms was employed to evaluate the efficiency of the proposed MTL structure and the dynamic loss‐weighting strategy. The result shows that the root mean squared error of the 12‐h short‐term prediction can be decreased by 4.19% compared with the traditional single‐task learning model, which verifies the validity of the proposed multi‐task transfer learning method.

Optimizations of structure stiffnesses and sensitivities of Y-type six-axis force/torque sensor

Purpose The six-axis force/torque sensor based on a Y-type structure has the advantages of simple structure, small space volume, low cost and wide application prospects. To meet the overall structural stiffness requirements and sensor performance requirements in robot engineering applications, this paper aims to propose a Y-type six-axis force/torque sensor. Design/methodology/approach The performance indicators such as each component sensitivities and stiffnesses of the sensor were selected as optimization objectives. The multiobjective optimization equations were established. A multiple quadratic response surface in ANSYS Workbench was modeled by using the central composite design experimental method. The optimal manufacturing structural parameters were obtained by using multiobjective genetic algorithm. Findings The sensor was optimized and the simulation results show that the overload resistance of the sensor is 200%F.S., and the axial stiffness, radial stiffness, bending stiffness and torsional stiffness are 14.981 kN/mm, 16.855 kN/mm, 2.0939 kN m/rad and 6.4432 kN m/rad, respectively, which meet the design requirements, and the sensitivities of each component of the optimized sensor have been well increased to be 2.969, 2.762, 4.010, 2.762, 2.653 and 2.760 times as those of the sensor with initial structural parameters. The sensor prototype with optimized parameters was produced. According to the calibration experiment of the sensor, the maximum Class I and II errors and measurement uncertainty of each force/torque component of the sensor are 1.835%F.S., 1.018%F.S. and 1.606%F.S., respectively. All of them are below the required 2%F.S. Originality/value Hence, the conclusion can be drawn that the sensor has excellent comprehensive performance and meets the expected practical engineering requirements.

Train Rescheduling of Urban Rail Transit Under Bi-Direction Disruptions in Operation Section

As urban rail transit systems expand rapidly and passenger volumes grow, disruptions in operations increasingly occur, leading to adverse effects such as train delays and passenger retention. This paper introduces an organizational scheme to address train rescheduling in urban rail transit during bidirectional operational disruptions. It uses crossovers for short-turning services on either side of the disruption. Train rescheduling following a disruption is categorized into three stages: the disruption response phase, the disruption duration phase, and the recovery phase. This study presents a cooperative adjustment model for the network train timetable with the optimization objectives of reducing total passenger waiting time and the penalty time incurred from exceeding train capacity. The model considers the interrupted line and other lines connected to the interrupted line, employing a genetic algorithm for solution. Real operational data from a local subway line in a city are used as an example to adjust the train operation of urban rail transit with the bidirectional interruption. The results show that the short-turning strategy can better ensure the service level under the urban rail transit disruptions. Furthermore, the network train timetable collaborative adjustment model outperforms strategies that only modify the timetable of the disrupted line, effectively reducing both total passenger waiting time and penalty time for exceeding train capacity. This approach enhances operational efficiency and service levels in urban rail transit.

Environmental impacts as the key objectives for perovskite solar cells optimization

Environmental impacts as the key objectives for perovskite solar cells optimization

Zero-Resistance-Branch Electrical Drainage System to Solve the Adverse Influence of Stray Current on Buried Pipelines

The buried pipeline's ground potential may be seriously shifted by the stray current leaking from the rail transit, leading to the problem of severe electrochemical corrosion on buried pipeline. For this, a zero-resistance- branch electrical drainage system is proposed to solve the adverse influence of stray current on buried pipeline just like the APF technology in power systems, mainly consisting of a negative resistance compensating converter (NRCC) and a grounding electrode, which can directly drain the stray current to the soil without connecting the rail system. Moreover, the equivalent interference circuit model of stray current is established, and the corrosion rule is analyzed in detail. Furthermore, based on the NRCC, the zero-resistance active drainage control method is proposed to realize the absorption of stray current. As the optimization objective of the control method, the disturbance component of pipe-to-soil potential is regulated to be approximately 0, and the zero-resistance branch at the disturbance frequency can be realized without directly detecting the value of the stray current. Then, the stray current can be transferred to the new drainage branch to mitigate its electrochemical corrosion on buried pipelines. Finally, the experimental results verify the effectiveness of the proposed electrical drainage system.

DeepOTF: Learning Equations-constrained Prediction for Electromagnetic Behavior

DeepOTF: Learning Equations-constrained Prediction for Electromagnetic Behavior

Accurate identification and confidence evaluation of automatic generation control command execution effect based on deep learning fusion model

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces “deep learning” technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0.5 MV, which can support AGC online decision-making.© 2017 Elsevier Inc. All rights reserved.

Multi-Objective Optimization of a Multi-Cavity, Significant Wall Thickness Difference Extrusion Profile Mold Design for New Energy Vehicles

With the rapid development of the new energy vehicle market, the demand for extruded profiles for battery trays, mainly characterized by significant wall thickness differences in multiple chambers, is increasing, posing new challenges to production and quality control. This study examines the multi-objective optimization problem in the design process of aluminum profile dies with multi-cavity profiles and significant wall thickness differences. Using QFORM-extrusion professional aluminum extrusion finite element analysis software and the response surface analysis method, the standard deviation of the velocity (SDV), standard deviation of the pressure (SDP), and thick wall hydrostatic pressure (TWHP) on the profile section at the die exit are optimized. By analyzing the functional relationship between the key die structure parameters (the height of the baffle plates, the length of the bearing, and the height of the false mandrel) and the optimization objective, the optimal combination scheme of die structure parameters was obtained using the NSGA2 (non-dominated sorting genetic algorithm-2) multi-objective genetic optimization algorithm. The results show that, compared with the initial design scheme, the standard deviation of profile section velocity was reduced by 5.33%, the standard deviation of pressure was reduced by 11.16%, and the thick wall hydrostatic pressure was increased by 26.47%. The die designed and manufactured using this scheme successfully completed the hot extrusion production task, and the profile quality met the predetermined requirements, thus verifying the effectiveness of this study in optimizing the design of a multi-cavity aluminum profile die with significant differences in wall thickness for complex structures.

Optimized Transverse–Longitudinal Hybrid Construction for Sustainable Design of Welded Steel Plate Girders

Optimized Transverse–Longitudinal Hybrid Construction for Sustainable Design of Welded Steel Plate Girders

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope’s working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time–frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope’s output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time–frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope’s output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10−3°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h.

Research on chip flute design of variable pitch variable helix end milling cutter based on manufacturability

In this paper, a design method of chip flute structure of variable pitch variable helix (double variable) end milling cutter based on manufacturability is presented, which aims to improve the dynamic balance and milling performance of the tool. The feasible region for grinding the chip flute of a double variable end milling cutter is obtained by solving the numerical model for the shape of the radial cross-section curve of the chip flute, along with the position and orientation of the grinding wheel. The multiple tool-walking grinding method is proposed to avoid the interference and edge cutting between the grinding wheel profile and the chip flute section, and the chip flute structure design criterion is obtained. The error between the grinding simulation results and the design target parameters is about 1 %, which verifies the effectiveness of the design criteria. With the double variable end milling cutter designed by this criterion, the milling vibration amplitude is reduced by about 21 %, and the average surface roughness of the workpiece is reduced by 9.872 μm. The chip flute structure design method proposed in this paper not only meets the manufacturing feasibility, but also effectively improves the milling performance of the tool. On the premise of considering manufacturability, the design method of chip flute as the optimization objective provides a new idea for tool structure design.

Structural Analysis & Effect of Impingement Cooling Flow in the Internal Surface on Temperature Distribution of a Vane in Gas Turbine Using Taguchi Technique

Abstract: Gas turbine always consider one of the most important systems in the modern engineering applications, because it has continuous ability to generate electric power. In gas turbines the major portion of performance dependency lies upon turbine blade design ,the blades are considered one of the important and expensive parts in the gas turbines , where the blades of first stage from failure .The blades of the gas turbine suffer from tensile stresses due to centrifugal forces resulting from the high rotational speed and because of the loading of densegasses at a high temperature and speed ,as the centrifugal force is one of the problems that thedesigner of the turbine blades faces , as the designer aims to reduce stresses within the permissible limit. CFD study was carried out for evaluating the performance of a utility Steam Turbine. The flow in a turbine blade passage is complex and involves understanding of energy conversion in three dimensional geometries. The performance of turbine depends on efficient energy conversion and analyzing the flow path behavior in the various componentsof Steam Turbine. This study seeks the optimization of an axial turbine from a small gas turbine engine developed by ITA using Computational Fluid Dynamics (CFD) and Multi- Objective Optimization techniques focused on geometry changes to maximize the turbine performance. The simulation process will be done through the use of the commercial software SolidWorks.

Operation optimization and performance evaluation of a ventilated heating floor in a nearly-zero-energy building with mixed power

With the proportion of renewable energy in the construction sector increasing and the structure of energy systems becoming increasingly complex, it is crucial to optimize the operation strategy of building energy systems with thermal energy storage for improving multi-performance. However, conventional heuristic optimization algorithm exhibits low searching speed and converges slowly, which is hard for operation optimization of such systems. In this study, with a binary particle swarm optimization (BPSO) algorithm coupled with experience-based searching guiding strategy, the operation of a ventilated floor heating system in a nearly-zero-energy building was optimized . System performance under different optimization objectives and combinations, including reducing operation cost and carbon emission fee, meanwhile increasing wind power consumption and thermal comfort time, was investigated. Results show that with the experience-based accelerating strategy, the BPSO algorithm converged fast and found more reasonable solutions. In contrast, multi-objective optimization yielded better and diverse feasible solutions were found than single-objective optimization. By optimizing operating costs, wind power consumption, and environmental costs, the best comprehensive solution was obtained. The cost range of energy consumption and carbon emissions within 10 days is 19.24–22.17 yuan and 17.45–20.11 yuan, respectively. The proportion of thermal comfort time is 95.39–95.82%. However, due to high thermal insulation level, thermal comfort time under different optimization objectives showed little difference.

Robust time-consistent strategies of DC pension plans with the return of premiums clauses under inflation

. This article investigates the robust time-consistent investment strategy for a DC pension plan with model uncertainty under the mean-variance optimization objective. For the avoidance of reductions in investment returns due to inflation risk and premature death of members, an inflation-indexed bond is introduced into the financial market and a return of premium clause is incorporated into the model. By establishing extended Hamilton-Jacobi-Bellman (HJB) equations, the explicit solutions of both the robust precommitment strategy and the robust time-consistent strategy are presented by virtue of the robust optimal control theory and the stochastic dynamic programming approach. Furthermore, two degradation cases are derived in detail. Finally, a numerical example demonstrates the analysis of the obtained results.

Data-Driven-Based Intelligent Alarm Method of Ultra-Supercritical Thermal Power Units

In order to ensure the safe operation of the ultra-supercritical thermal power units (USCTPUs), this paper proposes an intelligent alarm method to enhance the performance of the alarm system. Firstly, addressing the issues of slow response and high missed alarm rate (MAR) in traditional alarm systems, a threshold optimization method is proposed by integrating kernel density estimation (KDE) and convolution optimization algorithm (COA). Based on the traditional approach, the expected detection delay (EDD) indicator is introduced to better evaluate the response speed of the alarm system. By considering the false alarm rate (FAR), and EDD, a threshold optimization objective function is constructed, and the COA is employed to obtain the optimal alarm threshold. Secondly, to address the problem of excessive nuisance alarms, this paper reduces the number of nuisance alarms by introducing an adaptive delay factor into the existing system. Finally, simulation results demonstrate that the proposed method significantly reduces the MAR and EDD, improves the response speed and performance of the alarm system, and effectively reduces the number of nuisance alarms, thereby enhancing the quality of the alarms.

Control of Discrete Event Systems by Using Symbolic Transition Model: An Application to Power Grids

AbstractIn this paper, a new symbolic modeling framework is proposed for the control of discrete event systems (discrete controller synthesis). Reactive infinite-state systems are generally dynamic systems with inputs and outputs. Modeling the outputs in existing symbolic modeling methods for reactive infinite-state systems has not been extensively discussed in the literature. It is crucial to realistically model the outputs and obtain reliable controllers. One of the main contributions and innovations in this study is the ability to symbolically model their output. In this direction, symbolic transition models are offered, where events are defined as guarded symbolic transitions. The framework considers input/output reactive infinite-state systems to satisfy safety and optimization objectives. Effective algorithms and safety control policies are offered to solve the safety control problem (i.e., satisfy the safety objective) for input/output infinite-state systems. Another contribution in this work provides optimization targets within a given cost function based on state transitions, without specifying the target states. The framework is then applied to power grids to demonstrate the effectiveness of the symbolic transition models and experimentally evaluate the new control algorithms. The results showed that the approach is very effective compared to other approaches found in the literature. The computation times required to achieve both safety and optimization objectives are obtained relatively quickly compared to other deterministic methods found in the literature.

A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions

In the semiconductor manufacturing industry, the Dynamic Flexible Job Shop Scheduling Problem is regarded as one of the most complex and significant scheduling problems. Existing studies consider the dynamic arrival of jobs, however, the insertion of urgent jobs such as testing chips poses a challenge to the production model, and there is an urgent need for new scheduling methods to improve the dynamic response and self-adjustment of the shop floor. In this work, deep reinforcement learning is utilized to address the dynamic flexible job shop scheduling problem and facilitate near-real-time shop floor decision-making. We extracted eight state features, including machine utilization, operation completion rate, etc., to reflect real-time shop floor production data. After examining machine availability time, the machine's earliest available time is redefined and incorporated into the design of compound scheduling rules. Eight compound scheduling rules have been developed for job selection and machine allocation. By using the state features as inputs to the Double Deep Q-Network, it is possible to acquire the state action values (Q-values) of each compound scheduling rule, and the intelligent agent can learn a reasonable optimization strategy through training. Simulation studies show that the proposed Double Deep Q-Network algorithm outperforms other heuristics and well-known scheduling rules by generating excellent solutions quickly. In most scenarios, the Double Deep Q-Network algorithm outperforms the Deep Q-Network, Q-Learning, and State-Action-Reward-State-Action (SARSA) frameworks. Moreover, the intelligent agent has good generalization ability in terms of optimization for similar objectives.

How to Achieve Comprehensive Carbon Emission Reduction in Ports? A Systematic Review

Under the mounting pressure to make changes to become more environmentally friendly and sustainable, port authorities have been exploring effective solutions to reduce CO2 emissions. In this regard, alternative fuels, innovative technology, and optimization strategies are key pathways for ports to transition toward a low-carbon pattern. In this review work, the current development status and characteristics of renewable and clean energy in ports were meticulously analyzed. The CO2 emission reduction effects and limitations of port microgrids, carbon capture, and other technological operations were thoroughly examined. Lastly, the emission reduction optimization strategies ports could adopt under different scenarios were evaluated. The research findings showed that (1) combining the characteristics of the port and quantifying the properties of different renewable energy sources and low-carbon fuels is extremely necessary to select suitable alternative energy sources for port development; (2) technological advancements, multi-party interests, and policy impacts were the primary factors influencing the development of emission reduction technology methods; and (3) the coordinated optimization of multiple objectives in cross-scenarios was the main direction for ports to achieve sustainable development. This study provides theoretical guidance to ports that are transitioning to a greener pattern, as well as pointing out future research directions and development spaces for researchers.