Dynamic Allocation Method Research Articles

Research on Real-Time Roundup and Dynamic Allocation Methods for Multi-Dynamic Target Unmanned Aerial Vehicles.

When multi-dynamic target UAVs escape, the uncertainty of the formation method and the external environment causes difficulties in rounding them up, so suitable solutions are needed to improve the roundup success rate. However, traditional methods can generally only enable the encirclement of a single target, and when the target is scattered and escaping, this will lead to encirclement failure due to the inability to sufficiently allocate UAVs for encirclement. Therefore, in this paper, a real-time roundup and dynamic allocation algorithm for multiple dynamic targets is proposed. A real-time dynamic obstacle avoidance model is established for the roundup problem, drawing on the artificial potential field function. For the escape problem of the rounding process, an optimal rounding allocation strategy is established by drawing on the linear matching method. The algorithm in this paper simulates the UAV in different obstacle environments to round up dynamic targets with different escape methods. The results show that the algorithm is able to achieve the rounding up of multiple dynamic targets in a UAV and obstacle scenario with random initial positions, and the task UAV, which is able to avoid obstacles, can be used in other algorithms for real-time rounding up and dynamic allocation. The results show that the algorithm is able to achieve the rounding up of multi-dynamic targets in scenarios with a random number of UAVs and obstacles with random locations. It results in a 50% increase in the rounding efficiency and a 10-fold improvement in the formation success rate. And the mission UAV is able to avoid obstacles, which can be used in other algorithms for real-time roundup and dynamic allocation.

Dynamic job allocation method of multiple agricultural machinery cooperation based on improved ant colony algorithm

Contemporary large-scale and systematic agricultural operations demand the collaborative efforts of multiple agricultural machines with distinct functionalities. However, the failure of a single agricultural machine during collaborative operations jeopardizes the entire undertaking. To address this challenge, this paper proposes a multi-machine collaborative dynamic job allocation method based on the improved ant colony algorithm. Initially, the improved ant colony algorithm is employed to determine the optimal solution for harvester scheduling. This solution is then fed into the data conversion algorithm to acquire the necessary unloading point information for transport vehicle scheduling. Subsequently, the improved ant colony algorithm is once again utilized to optimize the transport vehicle scheduling. In cases of agricultural machinery failure or changes in the operating environment, two distinct methods are employed based on the situation. The first involves double-layer rescheduling of both harvester and transport vehicles, while the second employs single-layer rescheduling exclusively for the transport vehicles, yielding the respective rescheduling results. The outcomes demonstrate that the proposed solution method effectively identifies the current optimal scheduling plan for both the harvester and transport vehicle in the event of malfunctions. Moreover, under the premise that the unproductive waiting time of the harvester is reduced to zero, and the number of transport vehicles is minimized, it achieves the minimization of operating time cost and transportation cost. This method exhibits significant potential for seamless integration into the practical application of unmanned farms, providing a foundation for addressing scheduling and management challenges in multi-agricultural machinery collaborative operations within complex farmland operating environments.

Local differential privacy federated learning based on heterogeneous data multi-privacy mechanism

Federated learning enables the development of robust models without accessing users data directly. However, recent studies indicate that federated learning remains vulnerable to privacy leakage. To address this issue, local differential privacy mechanisms have been incorporated into federated learning. Nevertheless, local differential privacy will reduce the availability of data. To explore the balance between privacy budgets and data availability in federated learning, we propose federated learning for clustering hierarchical aggregation with adaptive piecewise mechanisms under multiple privacy-FedAPCA as a way to balance the relationship between privacy preservation and model accuracy. First, we introduce an adaptive piecewise mechanism that dynamically adjusts perturbation intervals based on the data ranges across different layers of the model, ensuring minimized perturbation variance while maintaining the same level of privacy. Second, we propose two dynamic privacy budget allocation methods, which are allocating the privacy budget based on global accuracy and global loss, and allocating the privacy budget based on local accuracy and loss, to ensure that better model accuracy can be achieved under the same privacy budget. Finally, we propose a clustering hierarchical aggregation method in the model aggregation stage, and the model is updated and aggregated after the unbiased estimation of the disturbance in each cluster according to the variance of each layer. FedAPCA improves the balance between privacy preservation and model accuracy. Our experimental results, comparing FedAPCA with the SOTA multi-privacy local differential privacy federated learning frameworks on the MNIST and CIFAR-10 datasets, demonstrate that FedAPCA improves model accuracy by 1%–2%.

Dynamic unbalanced task allocation of warehouse AGVs using integrated adaptive large neighborhood search and Kuhn–Munkres algorithm

Dynamic task allocation poses a complex and challenging decision problem for automated guided vehicles operating within warehouse environments. In this study, we investigate a new method for dynamically allocating unbalanced tasks to a multi-AGV system, with a focus on real-time task arrivals. The research treats this problem as a dynamic vehicle routing problem with pickups and deliveries and proposes the use of a rolling horizon strategy to periodically reallocate tasks by iteratively solving mixed integer programming. To enhance the computational efficiency, a novel metaheuristic is developed, which integrates adaptive large neighborhood search and the Kuhn–Munkres algorithm. Comprehensive numerical experiments are conducted to demonstrate the potential of the proposed approach, in comparison with state-of-the-art heuristics and metaheuristic algorithms, providing insight into the efficiency and effectiveness of the proposed dynamic unbalanced task allocation method for multi-AGV systems in warehouse environments.

Human–machine collaborative optimization method for dynamic worker allocation in aircraft final assembly lines

The allocation of workers with diverse skills on assembly stations significantly impacts the efficiency of aircraft final assembly (AFA). In a real-life assembly process, workers are allowed to move between multiple stations, to avoid bottleneck stations. The arrangement of worker movement is a practical issue coupled with the worker allocation, but is still ignored. This study formulates a dynamic worker allocation problem in AFA (dWA-AFA), focusing on the multiobjective joint optimization of worker allocation and worker movement. The trade-off between objectives depends on the human preferences that reflect assembly requirements. To address this, a human–machine collaborative optimization method (HMC-O) is proposed, involving a bidirectional collaboration: machine-to-human preference adaption, and human-to-machine experience support. Specifically, we design an adaptive dominance operator for integrating human preferences, and combine it with a two-stage nondominated sorting approach to generate initial optimization solutions. An experience-driven neighborhood search is further developed, which uses human experience to improve solutions. The proposed method is evaluated through two scales of real cases. It is observed the dWA-AFA significantly reduces the takt time, particularly by 14.21 % and 9.25 % in two worker-shortage scenarios respectively. Meanwhile, the HMC-O is competitive in terms of convergence and preference satisfaction for solving dWA-AFA.

Carbon footprint accounting method based on dynamic allocation of multi-level carbon data from manufacturing organizations to products

In the process of accounting for product carbon footprints, data quality is a key requirement. To mitigate the impacts of uncertainty, dynamism, and coupling in carbon data during product carbon footprint accounting, a method based on multi-level dynamic allocation of carbon data between organizations and products is proposed. First, considering the complex distribution and merging relationships between organizational carbon emissions and product carbon footprints, an AAC (Accounting Allocation Correlation) model between the two is constructed. Then, through EIS (Enterprise Information System), carbon data is obtained, and a multi-level dynamic allocation method for carbon data, as well as a Monte Carlo-based method for merging carbon data, is proposed for the dynamic accounting of product carbon footprints. Finally, using high-voltage switchgear as a case study, the feasibility and effectiveness of the proposed method are validated.

Dynamic task allocation of hybrid flow shop for machines in parallel with different speeds based on an MLD prediction model

ABSTRACT The current hybrid flow shop task allocation usually assumes a static manufacturing environment, which cannot effectively handle uncertain events in the production process. To address this, the dynamic task allocation problem for machines in parallel with different speeds is studied, and a Mixed Logical Dynamical (MLD) model for predicting the state of the production system is established. A dynamic allocation method based on Model Predictive Control (MPC) is proposed. The proposed novel method integrates dynamic model with rolling optimization to better deal with uncertain events in production process by decomposing the overall planning problem into smaller local planning models. Numerical results show that the proposed method outperform the traditional global planning method and rule-based allocation method in terms of time and job processing rate. In addition, through the Plant Simulation software, the simulation results are consistent with the numerical results, which fully proves the effectiveness of the proposed method.

EEG resting state alpha dynamics predict an individual’s vulnerability to auditory hallucinations

AbstractTask-free brain activity exhibits spontaneous fluctuations between functional states, characterized by synchronized activation patterns in distributed resting-state (RS) brain networks. The temporal dynamics of the networks’ electrophysiological signatures reflect individual variations in brain activity and connectivity linked to mental states and cognitive functions and can predict or monitor vulnerability to develop psychiatric or neurological disorders. In particular, RS alpha fluctuations modulate perceptual sensitivity, attentional shifts, and cognitive control, and could therefore reflect a neural correlate of increased vulnerability to sensory distortions, including the proneness to hallucinatory experiences. We recorded 5 min of RS EEG from 33 non-clinical individuals varying in hallucination proneness (HP) to investigate links between task-free alpha dynamics and vulnerability to hallucinations. To this end, we used a dynamic brain state allocation method to identify five recurrent alpha states together with their spatiotemporal dynamics and most active brain areas through source reconstruction. The dynamical features of a state marked by activation in somatosensory, auditory, and posterior default-mode network areas predicted auditory and auditory-verbal HP, but not general HP, such that individuals with higher vulnerability to auditory hallucinations spent more time in this state. The temporal dynamics of spontaneous alpha activity might reflect individual differences in attention to internally generated sensory events and altered auditory perceptual sensitivity. Altered RS alpha dynamics could therefore instantiate a neural marker of increased vulnerability to auditory hallucinations.

A deep learning based dynamic bandwidth allocation method for XG-PON based mobile fronthaul for CRAN

In the context of 5G and beyond era, the use of Time Division Multiplexed Passive Optical Network (TDM-PON) for mobile fronthaul (MFH) in centralized/cloud radio access network (CRAN) has proven to be an optimal solution for addressing low-latency requirements. While existing literature has predominantly focused on optimizing mobile fronthaul latency rather than end-to-end latency, this paper introduces an end-to-end latency model for TDM-PON, specifically within a 10-gigabit Passive Optical Network (XG-PON) based MFH for CRAN. The paper then proposes an Intelligent Dynamic Bandwidth Allocation (DBA) scheme to minimize end-to-end latency of the network. The proposed scheme predicts the buffer occupancy reports using deep learning techniques at optical network units (ONUs). Thereafter, optical line terminal (OLT) schedules the available bandwidth using conventional DBA schemes (Group-GIANT i.e., Ggiant, Optimized Round Robin, Dynamic Service Interval). Primarily, the proposed DBA scheme transforms conventional DBA schemes (Ggiant, Optimized Round Robin, Dynamic Service Interval) into Intelligent versions (Intelligent Ggiant, Intelligent ORR, Intelligent DSI), showcasing a reduction of 28.63 %, 45.86 %, and 48.60 % in end-to-end latency in the XG-PON-based MFH for CRAN. Further, the analysis of obtained results has confirmed the supremacy of the Intelligent DSI DBA scheme over the Intelligent Ggiant and the Intelligent ORR DBA scheme.

An efficient deep learning mechanisms for IoT/Non-IoT devices classification and attack detection in SDN-enabled smart environment

In recent years, the development of Internet of Things (IoT) applications has increased, resulting in higher demands for sufficient bandwidth, data rates, latency, and quality of service (QoS). In advanced communications, managing network resources for allocating IoT services and identifying the exact IoT devices connected to a network is a major concern. The existing studies have introduced various methods for classifying IoT devices in a network. However, the previous studies faced challenges like limited attributes, low efficiency, inappropriate features, and computational complexities. Also, the existing studies failed to concentrate on IoT/Non-IoT classification along with attack detection. Detecting attacks on IoT devices is critical for making network services more effective. Thus, the proposed study introduces an efficient IoT device classification and attack detection mechanism using software defined networking (SDN)-enabled fiber-wireless access networks internet of things (FiWi IoT) architecture. Initially, an effective resource allocation process is performed to mitigate the delay constraint issues by introducing a hybrid parallel neural network-based dynamic bandwidth allocation (DBA) method. Then, the input traffic information is gathered from the resource-efficient SDN-enabled FiWi IoT network, and the input data is pre-processed to eliminate unwanted noises using min-max normalization and standardization. Next, the essential attributes are extracted to attain enhanced classification performance. To reduce the feature dimensionality problem and thereby solve complexity issues, the most optimal features are selected by a new chaotic seagull optimization (CSO) approach. After that, IoT/non-IoT classification is performed using a transformer-driven deep intelligent model. Finally, the attacks are detected and classified by introducing a novel slice attention-based deep capsule autoencoder (SA_DCAE) model. For experimentation, the Python 3.7.0 tool is used in this work, and the performance of proposed classifiers is measured by evaluating varied matrices. Also, the comparison analysis proves the superiority of the proposed techniques to other existing methods.

Dynamic Reactive Power Allocation Method of Power Grid with High-Permeability PV Considering Transient Voltage Stability

Dynamic Reactive Power Allocation Method of Power Grid with High-Permeability PV Considering Transient Voltage Stability

Optimization model of low-carbon technology adoption timing for coal power under decarbonization and flexibility demand: Empirical study in Beijing-Tianjin-Hebei region, China

Substituting renewable energy for coal power under the carbon neutrality goal is an inevitable path for power system transformation, while the high share of renewable energy leads to a rapid rise in system flexibility demand. Currently, coal-fired power is still one of the most economical and reliable large-scale flexible resources in China. Therefore, the issue of how to mitigate the conflict between cleanliness and flexibility for coal-fired power is urgent to be solved. Accordingly, this paper established an optimization framework for the retirement and technology adoption of coal-fired power units with high spatial-temporal resolution, and the regional carbon emission budget is estimated from a top-down perspective. Secondly, a dynamic carbon quota allocation method considering technological innovation and adoption is proposed. The carbon trading cost of the units is evaluated from a bottom-up perspective, to realize the interaction between the obligatory policy constraints of the top-level target and the flexible incentives of the market mechanism. Finally, the empirical analysis was conducted in the Beijing-Tianjin-Hebei region of China. The optimal solution achieved by proposed method results in a substantial 27% reduction in total costs while keeping the increase in carbon emissions below 3%. By analyzing the dynamic evolution path of coal and the scale of diffusion of multi-type technologies, the differences in the roles coal power assuming at various stages of decarbonization are discussed. The robustness of the model and the validity of the proposed method were verified by comparing the dynamic/static carbon quota allocation. Overall, this study provides an effective optimization model for investigating the low-carbon technology adoption timing for coal power in Beijing-Tianjin-Hebei region, and offers valuable insights for other coal-dominated emerging economies seeking for power system transformation roadmap in the forthcoming net-zero carbon age.

Neighbor-based joint spatial division and multiplexing in massive MIMO: user scheduling and dynamic beam allocation

AbstractTwo-stage precoding schemes have been developed to reduce the channel estimation overhead in FDD systems. By integrating user scheduling into these schemes, it becomes possible to meet the quality-of-service requirements of high-density wireless communication systems, despite the limitations on spatial resources and transmit power budget. In this paper, we propose a user scheduling and dynamic beam allocation method for neighbor-based joint spatial division multiplexing (N-JSDM) transmission. The user scheduling problem is formulated as a 0–1 quadratic programming problem to maximize effective spectral efficiency (ESE) using directional channel properties. To address the complexity issue, convex relaxation and linearization methods are employed to transform the problem into a 0–1 mixed integer linear programming, and a dimensionality reduction method is introduced. The proposed user scheduling-aided N-JSDM scheme reduces downlink training length and feedback of channel state information. Additionally, a dynamic configuration form is used for pre-beamforming matrix design based on user distribution, outperforming conventional approaches. Simulation results demonstrate higher ESE achieved by the proposed scheme compared to previous methods.

5G network model development in AnyLogic environment

Problem statement. The development of mobile communication networks does not stop for a minute, new standards and various new technologies are emerging. The opportunities provided by mobile technologies have long gone beyond voice services. The sharp growth of Internet traffic in networks around the world is explained by the widespread use of devices connected to mobile networks. With the development of mobile networks, new and diverse requirements are being imposed on them. Technology development is aimed at increasing productivity and expanding opportunities. Therefore, it becomes necessary to study the influence of the characteristics of additional parameters (delays, station loads) on the quality of data packet processing in order to optimize the operation of 5G networks and ensure more efficient use of resources. Many scientists who conducted research on 5G networks using modeling did not take into account various variants of queue processing algorithms, did not consider how the values of data transmission delays and the number of lost packets would change with an increase or decrease in the number of base stations, models consisting of several base stations were not considered. Based on this, we will conclude that it is advisable to develop a 5G network model, with the help of which various studies can be carried out, which will take into account the above parameters. The goal is to investigate the resource model of data transmission in 5G networks, to analyze the operation of the 5G network model in the AnyLogic environment. Results. During the study of traffic maintenance in the 5G network, the optimal system parameters were determined, allowing the most efficient use of network resources. It turned out that the use of methods of dynamic resource allocation and automatic change of base station parameters reduces the load on the network and increases its performance. The developed model allows us to assess the reliability of the system and its ability to process user requests, because the probability of losing an application or non-fulfillment of an application is high, this can lead to user dissatisfaction and a decrease in the quality of service. Practical significance. The developed model can be used in practice to study and evaluate the parameters of the 5G network, as well as to analyze various characteristics that affect the quality of the network in order to improve it. This can help telecom operators optimize their networks and ensure more efficient use of resources.

A Dynamic Task Allocation Method for Unmanned Aerial Vehicle Swarm Based on Wolf Pack Labor Division Model

A Dynamic Task Allocation Method for Unmanned Aerial Vehicle Swarm Based on Wolf Pack Labor Division Model

Task reallocation of human-robot collaborative production workshop based on a dynamic human fatigue model

As collaborative robots become more popular in industry, they are used to collaborate with human workers and free human workers from repeated and heavy work and reduce the risk of physical injuries. However, human workers need to finish tasks that require high cognitive abilities, which increases the risk of mental fatigue. Fatigue can negatively impact workers and even threaten the safety of workers. Currently, the dynamics of human fatigue model are rarely considered. Hence, it is crucial to develop a dynamic allocation method for human-robot collaborative tasks that considers dynamic human fatigue to ensure efficient and safe human-robot collaboration. This paper proposes a task reallocation method for human-robot collaborative production workshop based on a dynamic human fatigue model. A fatigue feature extraction method from multimodal data and a fatigue evaluation network are proposed to evaluate the current fatigue status of human workers. Based on the evaluation results, the human fatigue model is dynamically updated. If the worker’s fatigue state changes, a task reallocation scheme is generated by an improved genetic algorithm based on reinforcement learning and the dynamic human fatigue model. The experimental results show that the proposed model and algorithm are effective and efficient, and can provide innovative insights for realizing a human-centered manufacturing workshop.

The Japan‐multimodal intervention trial for prevention of dementia (J‐MINT): a multi‐center, randomized, 18‐month controlled trial

AbstractBackgroundThe Japan‐multimodal intervention trial for prevention of dementia (J‐MINT) aimed to examine the efficacy of the multi‐domain intervention in the prevention of cognitive decline in older adults at high risk of dementia.MethodThe J‐MINT is an 18‐month, multi‐centered, randomized controlled trial. Participants aged 65‐85 years with mild cognitive deficits were recruited and randomized into multi‐domain intervention (management of vascular risk factors, group‐based physical exercise, nutritional counseling, and cognitive training) and control groups (health‐related information in writing every 2 months) at a 1:1 allocation ratio using the dynamic allocation method. The primary outcome was a change from baseline to 18‐month follow‐up in a composite Z score combining several neuropsychological tests, which include tests of global cognitive function (MMSE); memory (Logical memory I and II subset of the Wechsler Memory Scale‐Revised and the Free and Cued Selective Reminding Test); attention (Digit Span); executive function/processing speed (Trail Making Test, Digit Symbol Substitution Test, and Letter word fluency test).ResultBetween November 26, 2019, and December 28, 2020, 1677 participants were screened and 531 were randomly assigned to the multi‐domain intervention group (n = 265) or control group (n = 266). Four hundred and three (75.9%) participants completed the trial. The full analysis set included 433 patients who received the intervention program/health‐related information at least once. In the primary efficacy analysis, there were no significant differences in the change of composite Z score between the intervention group and the control group (mean difference 0.047 [95% CI ‐0.029 to 0.124]). In the analysis for each neuropsychological test, the score of the letter word fluency test in the intervention group significantly improved by a mean of 0.827 (95% CI = 0.356 to 1.298) points, whereas the mean difference between the intervention group and control group was not significant (mean difference 0.625 [95% CI ‐0.039 to 1.289]). In subgroup analyses, the intervention effect on composite Z score was significant among older adults with the apolipoprotein‐E (APOE) ε4 allele (mean difference 0.164 [95% CI 0.011 to 0.317]).ConclusionThe multi‐domain intervention showed significant efficacy for the prevention of cognitive decline among Japanese older adults with the APOE ε4 allele.

Reversible data hiding of JPEG images based on block sorting and segmented embedding

Existing JPEG image reversible data hiding (RDH) schemes based on quantized DCT (Discrete cosine transform) modification usually use non-zero or zero AC (Alternating current) coefficients as peak points for data embedding. However, this approach tends to limit the embedding capacity or result in significant file size growth. This paper proposed an RDH scheme for JPEG images based on DCT block sorting and segmented embedding. This scheme can take into account the dual advantages of having a non-zero or zero AC coefficient as the peak point, so it has both high embedding capacity and low file growth, and good visual quality. Firstly, a new DCT block sorting method is designed, the smoothness of the block is estimated by the number of zero coefficients in the EOB (End of Block) and the fluctuation of the DC coefficient in the block, and the zero coefficients in the EOB in the block have not embedded data. Then, the sorted DCT block sequence is divided into three segments, the peak points of the front two segments are (−1, 1) and (−1, 0) respectively, and the third segment may be empty but it always remains unchanged. Finally, the dynamic allocation method is used to quickly solve the optimal segmentation point to achieve the optimal embedding performance. The experimental results show that the overall performance of this scheme is better than the existing state-of-the-art algorithms.

Study on development methods of different types of gas wells in tight sandstone gas reservoirs

Reasonable production allocation of tight sandstone gas reservoirs is an important basis for efficient development of gas wells. Taking Block XX in Ordos Basin as an example, the modified flowing material balance equation was established considering the variation of gas viscosity and compression coefficient, the advantages and disadvantages of the method were discussed, and a reasonable production allocation process for gas wells was developed. The results show that: ① The commonly used flow material balance method ignores the change of natural gas compression coefficient, viscosity and deviation coefficient in the production process. The slope of the relationship curve between bottom hole pressure and cumulative production and the slope of the relationship curve between average formation pressure and cumulative production are not equal After considering this change. Compared with the results calculated by the material balance method, the results calculated by the flow material balance method are smaller. ② The production of 660 gas wells in the study area during stable production period is verified. Compared with the open flow method, the dynamic reserve allocation method is better, with an error of 0.06%. ③ The new method in this paper is used to allocate production for different types of gas wells. The cumulative production of different types of gas wells shows different degrees of increase. The I, II, III and IV types of gas wells increase by 32.26%, 30.29%, 23.58% and 25.07% respectively. This study provides technical support for dynamic reserve calculation and reasonable production allocation of gas wells in the study area, and has important guiding significance for the formulation of reasonable development plan and economic and efficient development of tight sandstone gas reservoirs.

Evaluation of resource efficiencies for the non-symmetric dynamic wavelength allocation method applied in the P2MP WDM-PON network design

At present days, metropolitan and access communication networks are markedly developing utilizing a variety of technologies. Their bearer communication infrastructures will be mostly exploiting the optical transmission medium where wavelength division multiplexing techniques will play a key role. Wavelength-Division Multiplexed Passive Optical Networks (WDM-PON) attract considerable attention for the next generation optical metropolitan and access networked systems. Simultaneously, the utilization of common optical network resources in wavelength domains will be more important. The main purpose of this paper is presented by the evaluation of resource efficiencies for the non-symmetric dynamic wavelength allocation method applied in the WDM-PON network design with aconventional topology. This contribution introduces a novel non-symmetric Dynamic Wavelength Allocation (DWA) method for a selected network design with the utilization only passive optical splitters in remote nodes. This novel wavelength allocation method is oriented on minimizing anumber of working wavelengths that will be used for compliance of customers’ requests for data transmitting in the common point-to-multipoint WDM–PON network design. For evaluation of wavelength scheduling, a simulation model related to the conventional WDM-PON network design is realized in the MATLAB programming environment is presented with results. Finally, different traffic loading scenarios are determined and, subsequently, resource efficiencies of the presented non-symmetric DWA method are evaluated from aviewpoint of application in future WDM-PON network designs.