Link Constraints Research Articles

Study on Primary Frequency Regulation Reserve Configuration of High Wind Power System Considering Governor Limiting Links

With the large-scale use of wind turbines, the ability of the power system to resist frequency fluctuations has been greatly weakened, making the contradiction between frequency regulation supply and demand in the power system increasingly prominent. In order to ensure the frequency security of the power system, this paper conducts research on the primary frequency regulation (PR) backup configuration problem of a power system containing a high proportion of wind power. First, according to the dynamic properties of the speed regulator, a system frequency response model considering the limiting link is established. And the system frequency response model is transformed into a time-domain analytical function of PR reserve capacity and frequency maximum value. On this basis, a PR backup configuration model of a power system containing a high proportion of wind power is constructed with the optimization goal of minimizing system operating costs and taking into account the limiting link constraints. It is proposed to use the L-shape algorithm to decompose the model into main problems and sub-problems, which effectively reduces the solution complexity of the model. Finally, the correctness and effectiveness of this model are verified based on the improved IEEE 39-bus system.

Community Preserving Social Recommendation with Cyclic Transfer Learning

Transfer learning-based recommendation mitigates the sparsity of user-item interactions by introducing auxiliary domains. Social influence extracted from direct connections between users typically serves as an auxiliary domain to improve prediction performance. However, direct social connections also face severe data sparsity problems that limit model performance. In contrast, users’ dependency on communities is another valuable social information that has not yet received sufficient attention. Although studies have incorporated community information into recommendation by aggregating users’ preferences within the same community, they seldom capture the structural discrepancies among communities and the influence of structural discrepancies on users’ preferences. To address these challenges, we propose a community-preserving recommendation framework with cyclic transfer learning, incorporating heterogeneous community influence into the rating domain. We analyze the characteristics of the community domain and its inter-influence on the rating domain, and construct link constraints and preference constraints in the community domain. The shared vectors that bridge the rating domain and the community domain are allowed to be more consistent with the characteristics of both domains. Extensive experiments are conducted on four real-world datasets. The results manifest the excellent performance of our approach in capturing real users’ preferences compared with other state-of-the-art methods.

Collision-Free Flocking Control for Satellite Cluster With Constrained Spanning Tree Topology and Communication Delay

In this article, a flocking control strategy combining with artificial potential field is developed for collision-free trajectory tracking of satellite cluster with multiple disturbances and communication delay. Considering the link constraints of each satellite, a degree constrained spanning tree is adopted as the communication topology. Furthermore, a topology optimization method based on Prim algorithm and greedy algorithm is investigated to compute the degree radius constrained minimum spanning tree. By using the optimized communication topology, the satellites in cluster can reduce the communication delay and converge to the desired distances more quickly. Finally, the effectiveness and advantages of the proposed method are verified by numerical simulations.

Game-Theoretical Method Toward Dry Port Multilevel Location Considering Capacity Constraints and Shippers’ Choice Behavior

The high demand for containerized cargo and the low market share of low-carbon transportation modes for hinterland freight systems in China are causing severe traffic congestion and environmental pollution. Dry ports offer an opportunity to reduce carbon emissions, relieve congestion, and expand the hinterland of seaports. Therefore, this paper focuses on the dry port multilevel location problem considering capacity constraints, shippers’ choice behavior, and empty container transportation in a four-mode port-hinterland freight network. A Stackelberg game-theoretical model is built for the government and shippers. In the model, the government determines the number, location, and capacity level of dry ports, and shippers choose seaports and paths according to the constrained nested logit model because of the capacity constraints of nodes and links. To solve the problem, a complete enumeration algorithm and a hybrid genetic algorithm are designed. Numerical experiments in China are performed, and two analyses are conducted on the cost parameters and time parameters. The results indicate that constructing dry ports can decrease carbon emissions and generalized transportation costs, and promote the mode shift from road to railway. However, building a larger number and higher capacity level of dry ports sometimes increases total costs, contrary to expectations. There are also some interesting findings with respect to the effects of costs and time parameters on dry port location results, carbon emissions, modal split, and seaport market share.

Coordinated Economic Operation of Hydrothermal Units with HVDC Link Based on Lagrange Multipliers

Coordinated operation of hydrothermal scheduling with HVDC links considering network constraints becomes a vital issue due to their remote location and recent induction in the existing power system. The nonlinear and complex nature of the problem introduces many variables and constraints which results in a heavy computational burden. A widespread approach for handling these complexities is to reformulate the problem by several linearization methods. In this paper, a Lagrange multipliers-based method is proposed for the solution of hydrothermal economic scheduling including HVDC link. This method solves equality constraint optimization problems. The linear programming approach is embedded with the Lagrange method to consider both equality and inequality constraints. The proposed technique has been used on piecewise linear variables and constraints of the system considering generation, water volume, and line power flow limits. The formulated method efficiently minimizes the operational cost of thermal units and maximizes the utilization of hydro units while meeting all generation, water volume, and the HVDC link constraints. The method was successfully implemented in two scenarios of a case study. In the first scenario, hydrothermal scheduling was performed on the typical network without an HVDC line limit and equal nodal prices were found with minimal thermal generation cost of $278,822.3. In the second scenario, the proposed method optimally dispatches units to meet the HVDC line limit and minimizes thermal generation cost to $279,025.4 while satisfying hydro, thermal, and other operating constraints. Both scenarios are implemented for a 24 h period. The results have been presented to illustrate the performance of the proposed method.

Unsupervised Graph-Based Entity Resolution for Complex Entities

Entity resolution (ER) is the process of linking records that refer to the same entity. Traditionally, this process compares attribute values of records to calculate similarities and then classifies pairs of records as referring to the same entity or not based on these similarities. Recently developed graph-based ER approaches combine relationships between records with attribute similarities to improve linkage quality. Most of these approaches only consider databases containing basic entities that have static attribute values and static relationships, such as publications in bibliographic databases. In contrast, temporal record linkage addresses the problem where attribute values of entities can change over time. However, neither existing graph-based ER nor temporal record linkage can achieve high linkage quality on databases with complex entities , where an entity (such as a person) can change its attribute values over time while having different relationships with other entities at different points in time. In this article, we propose an unsupervised graph-based ER framework that is aimed at linking records of complex entities. Our framework provides five key contributions. First, we propagate positive evidence encountered when linking records to use in subsequent links by propagating attribute values that have changed. Second, we employ negative evidence by applying temporal and link constraints to restrict which candidate record pairs to consider for linking. Third, we leverage the ambiguity of attribute values to disambiguate similar records that, however, belong to different entities. Fourth, we adaptively exploit the structure of relationships to link records that have different relationships. Fifth, using graph measures, we refine matched clusters of records by removing likely wrong links between records. We conduct extensive experiments on seven real-world datasets from different domains showing that on average our unsupervised graph-based ER framework can improve precision by up to 25% and recall by up to 29% compared to several state-of-the-art ER techniques.

Distributed Joint Channel Assignment and Power Control for Sum Rate Maximization of D2D-Enabled Massive MIMO System

Device-to-device (D2D) communications underlaid massive multiple-input multiple-output (MIMO) systems have been recognized as a promising candidate technology to achieve the challenging fifth-generation (5G) network requirements. This integration enhances network throughput, improves spectral efficiency, and offloads the traffic load of base stations. However, the co/cross-tier interferences between cellular and D2D communications caused by resource sharing is a significant challenge, especially when dense D2D users exist in an underlay mode. In this paper, we jointly optimize the channel assignment and power allocation to maximize the sum data rate while maintaining the interference constraints of cellular links. Due to the lack of network-wide information in large scale networks, resource management and interference coordination is hard to be implemented in a centralized way. Therefore, we propose a three-stage stable and distributed resource allocation and interference management scheme based on local information and requires little coordination and communication between devices. We model the channel allocation optimization problem in the first stage as a many-to-one matching game. In the second stage, the algorithm adopts a cost charging policy to solve each user’s power control problem as a non-cooperative game. In the third stage, the algorithm search for swap blocking pairs until stable matching exist. It is shown in this paper that the proposed algorithm converges to a stable matching and terminates after finite iterations. Simulation results show that the proposed algorithm can achieve more than 86% of the average transmission rate performance of the optimal matching with lower complexity.

DRL-Based Low-Latency Content Delivery for 6G Massive Vehicular IoT

Vehicle-to-everything communication is an indispensable component of 6G networks that could help to facilitate future transportation systems. However, massive vehicles and unstable vehicle-to-vehicle (V2V) links may become bottlenecks for the low-latency delivery of contents, such as safety-critical emergency messages and multimedia. Instead of resolving the problem in a centralized way, we propose a massive vehicular Internet-of-Things system and investigate the approach that would enable each vehicle to decide the transmission mode from three modes, i.e., vehicle-to-network, vehicle-to-infrastructure and V2V sidelinks, and wireless resources. Specifically, a multiagent deep reinforcement learning (RL) framework is formulated by combining the multiagent RL approach, WoLF-PHC, with the techniques from deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning (DQN) to gain the formulated framework with the capability of capturing the effects of interaction between learning agents and states of complex environment. The framework is set to maximize the throughput of vehicles while maintaining the latency and reliability constraints of the vehicle communication links. However, it could be easily extended to other objectives. The simulation results demonstrate that the proposed approach outperforms the compared ones in total traffic capacity and satisfaction rate of the vehicles in communication.

An Efficient Online Trajectory Generation Method Based on Kinodynamic Path Search and Trajectory Optimization for Human-Robot Interaction Safety.

With the rapid development of robot perception and planning technology, robots are gradually getting rid of fixed fences and working closely with humans in shared workspaces. The safety of human-robot coexistence has become critical. Traditional motion planning methods perform poorly in dynamic environments where obstacles motion is highly uncertain. In this paper, we propose an efficient online trajectory generation method to help manipulator autonomous planning in dynamic environments. Our approach starts with an efficient kinodynamic path search algorithm that considers the links constraints and finds a safe and feasible initial trajectory with minimal control effort and time. To increase the clearance between the trajectory and obstacles and improve the smoothness, a trajectory optimization method using the B-spline convex hull property is adopted to minimize the penalty of collision cost, smoothness, and dynamical feasibility. To avoid the collisions between the links and obstacles and the collisions of the links themselves, a constraint-relaxed links collision avoidance method is developed by solving a quadratic programming problem. Compared with the existing state-of-the-art planning method for dynamic environments and advanced trajectory optimization method, our method can generate a smoother, collision-free trajectory in less time with a higher success rate. Detailed simulation comparison experiments, as well as real-world experiments, are reported to verify the effectiveness of our method.

Completion time minimization for UAV enabled data collection with communication link constrained

This paper studies unmanned aerial vehicles (UAV) enabled industrial Internet of Things while a UAV dispatched to collect data of low-power ground sensor nodes (SNs) in multi-obstacle environment. The authors aim to minimize the completion time while satisfying the communication link constraints of each SN and obstacle avoidance, data collection requirements etc. To this end, the authors first formulate the completion time minimization problem by jointly optimizing the UAV trajectory and collection sequence of SNs. The problem is difficult to be optimally solved, as it is non-convex. To tackle this problem, the authors first transform the original problem to a Traveling Salesman Problem-like (TSP-like) problem based on a hover point that can naturally satisfy the communication link constraints of data collection. The dynamic programming (DP) algorithm to figure out the order in which the UAV collects each SN, which gives the initial path of the UAV traversing each SN from the beginning point to the end point. Next, the authors consider the general scenarios of data collection tasks where the UAV also communicates while flying. The authors construct an equivalent problem with integer variable constraints for the original problem with indicative function constraints. The authors rewrite the non-convex constraints of the equal problem by introducing slack variables and leveraging the SCA, and add the discrete region threat constraints for the traditional path discretization method. Finally, the simulation results verify the effectiveness of the proposed algorithm under different parameter configurations.

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Exact Construction of BS-Assisted MSCR Codes With Link Constraints

It is clear that 5G network resources would be consumed by heavy data traffic owing to increased mobility, slicing, and layered/distributed storage system architecture. The problem is elevated when multiple node failures are repaired to address service quality requirements. Typical approaches include individual or cooperative data regeneration to efficiently utilize the available bandwidth. It is observed that storage systems of 5G and beyond technologies shall have a multi-layer architecture in which base stations (BS) would be present. Moreover, communication with each layer would be subject to various communication costs and link constraints. Under limited BS assistance and cooperation, the trade-off between storage per node and communication bandwidth has been established. In this trade-off, two operating points, namely minimum storage, and bandwidth regeneration are particularly important. In this study, we first identify the optimal number of BS use at the minimum storage regeneration point. An explicit code construction is provided subsequently for the exact minimum storage regeneration whereby each layer may help the repair process subject to a communication link constraint.

DIAGNOSIS OF RICE SEED SECTOR IN COTE DIVOIRE: ASSETS, WEAKNESSES AND STRATEGYFOR ASUSTAINABLE MULTIPLICATION, AND DISSEMINATION OF QUALITY SEEDS

In Cote dIvoire, less than 10% of rice-growing areas are covered by improved varieties seeds of good quality.A diagnosis of the seed sector was carried out to highlight its main constraints and propose a strategy for the sustainable production and dissemination of seeds.The schedule of fixturesindicates that the seed issue is taken into account by policies, strategies and regulations specific to the seed sector and the agricultural sector as a whole.Unfortunately, in its implementation, these seed policies and strategies do not take up their proper place to ensure increased agricultural productivity.Seed production is based on formal and informal seed systems.In the formal system the supply ofquality seeds is negligible. The main constraints of this system coversall links in the value chain. To improve farmers access to seeds of good quality, a strategy that is fully aligned with regional strategies, and especially the revised national rice development strategy, which takes into account the period from 2020 to 2030, has been proposed. The goal of this strategy is to increase the rate ofthe use of seed of improved varieties seedsup to 70% by 2030. This strategy promotes the professionalisation and empowerment of private sector.Its implementation should involve all stakeholders in the countrys seed value chain.

Smooth Stitching Method for the Texture Seams of Remote Sensing Images Based on Gradient Structure Information

Traditional smooth stitching method for the texture seams of remote sensing images is affected by gradient structure information, leading to poor stitching effect. Therefore, a smooth stitching method for the texture seams of remote sensing images based on gradient structure information is proposed in this research. By matching the feature points of remote sensing images and introducing a block link constraint and shape distortion constraint, the modified stitching image is obtained. By using remote sensing image fusion, the smooth stitching image of texture seams is obtained, and the local overlapping area of the texture is optimized. The main direction of texture seams is determined by calculating the gradient structure information of texture seams in horizontal and vertical directions. By selecting the initial point, the optimal stitching line is extracted by using the minimum mean value of the cumulative error of the smooth stitching line. By using the method of boundary correlation constraints, matching the feature points of the texture seams of remote sensing images and selecting the best matching pair, a smooth stitching algorithm for the texture seams of remote sensing image is designed, which realizes the smooth stitching of the texture seams of remote sensing images. Experimental results show that the design method has good performance in stitching accuracy and efficiency in the smooth stitching of the texture seams of remote sensing images. Specifically, the Liu et al. and the Zhang et al. methods that are the benchmark studies in the literature are introduced as a comparison, and the stitching experiment is carried out. The test is carried out according to accuracy and time and the proposed method achieves better results by almost 25%.

Resource Allocation and Computation Offloading for Multi-Access Edge Computing With Fronthaul and Backhaul Constraints

Edge computing is able to provide proximity solutions for the future wireless network to accommodate different types of devices with various computing service demands. Meanwhile, in order to provide ubiquitous connectivities to massive devices over a relatively large area, densely deploying remote radio head (RRH) is considered as a cost-efficient solution. In this work, we consider a vertical and heterogeneous multi-access edge computing system. In the system, the RRHs are deployed for providing wireless access for the users and the edge node with computing capability can process the computation requests from the users. With the objective to minimize the total energy consumption for processing the computation task, a joint radio resource allocation and offloading decision optimization problem is presented under the explicit consideration of capacity constraints of fronthaul and backhaul links. Due to the non-convexity of the formulated problem, we divide the original problem into several sub-problems and address them accordingly to find the optimal solution. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme.

Constrained transformer network for ECG signal processing and arrhythmia classification

BackgroundHeart disease diagnosis is a challenging task and it is important to explore useful information from the massive amount of electrocardiogram (ECG) records of patients. The high-precision diagnostic identification of ECG can save clinicians and cardiologists considerable time while helping reduce the possibility of misdiagnosis at the same time.Currently, some deep learning-based methods can effectively perform feature selection and classification prediction, reducing the consumption of manpower.MethodsIn this work, an end-to-end deep learning framework based on convolutional neural network (CNN) is proposed for ECG signal processing and arrhythmia classification. In the framework, a transformer network is embedded in CNN to capture the temporal information of ECG signals and a new link constraint is introduced to the loss function to enhance the classification ability of the embedding vector.ResultsTo evaluate the proposed method, extensive experiments based on real-world data were conducted. Experimental results show that the proposed model achieve better performance than most baselines. The experiment results also proved that the transformer network pays more attention to the temporal continuity of the data and captures the hidden deep features of the data well. The link constraint strengthens the constraint on the embedded features and effectively suppresses the effect of data imbalance on the results.ConclusionsIn this paper, an end-to-end model is used to process ECG signal and classify arrhythmia. The model combine CNN and Transformer network to extract temporal information in ECG signal and is capable of performing arrhythmia classification with acceptable accuracy. The model can help cardiologists perform assisted diagnosis of heart disease and improve the efficiency of healthcare delivery.

Risk sharing for internal resources conflict in two-stage process with slacks-based measure

Taking the two-stage process that the outputs from the first stage are taken as the inputs for the second stage as example, internal resources conflict refers to the mismatch in amount of intermediate products between two efficient stages. Although the existing centralized models can eliminate this conflict by maximizing the efficiency of entire system. In many real-world situations, however, because of decentralized organizational structure of system and independent operational mechanism of stages, each decentralized stage wants to maximize its own efficiency. This leads to the internal resources conflict, which is called the risk of mismatch in this paper. Therefore, this paper proposes the risk sharing strategy to eliminate such conflict by sharing this risk between two decentralized stages, and the slacks-based measure mixed integer linear program (MILP) model is developed to ensure that the risk allocated to each decentralized stage is minimal. Finally, the proposed method is illustrated through two examples. The results show that the proposed method can not only identify the target intermediate products to solve the potential conflict, but also make the efficiency score of inefficient system higher than that of inefficient system measured by the centralized method of “free” link constraint.

Educational Robotics to Foster and Assess Social Relations in Students' Groups

Robotics has gained, in recent years, a significant role in educational processes that take place in formal, non-formal, and informal contexts, mainly in the subjects related to STEM (science, technology, engineering, and mathematics). Indeed, educational robotics (ER) can be fruitfully applied also to soft skills, as it allows promoting social links between students, if it is proposed as a group activity. Working in a group to solve a problem or to accomplish a task in the robotics field allows fostering new relations and overcoming the constraints of the established links associated to the school context. Together with this aspect, ER offers an environment where it is possible to assess group dynamics by means of sociometric tools. In this paper, we will describe an example of how ER can be used to foster and assess social relations in students' group. In particular, we report a study that compares: (1) a laboratory with robots, (2) a laboratory with Scratch for coding, and (3) a control group. This study involved Italian students attending middle school. As the focus of this experiment was to study relations in students' group, we used the sociometric tools proposed by Moreno. Results show that involving students in a robotics lab can effectively foster relations between students and, jointly with sociometric tools, can be employed to portrait group dynamics in a synthetic and manageable way.

An Energy Efficient QoS Supported Optimized Transmission Rate Technique in WBANs

The Wireless Body Area Networks (WBANs) provide an unprecedented opportunity for ubiquitous real-time health-care and fitness monitoring without impairing the activities of the user. Furthermore, meeting the quality of service (QoS) requirements, i.e., throughput, delay and packet loss rate in WBAN, is a challenge due to the lossy channel, resource-restricted nodes and the transmission of data in the highly dynamic situations. In the proposed work, an optimized hybrid technique of Genetic algorithm (GA) with BAT algorithm (GABAT) is presented to achieve QoS metrics. The hybrid GABAT meticulously adjusts the transmission rates at each sensor node for each posture taking into account both the QoS metric constraint and the dynamic link constraint. The proposed scheme gives higher priority to the emergency packets than the normal packets to support the QoS requirements. The results exhibit that the proposed GABAT attains maximum fitness value in less iterations as compared to GA and BAT optimizations.

A Reinforcement Learning Method for Joint Mode Selection and Power Adaptation in the V2V Communication Network in 5G

A 5G network is the key driving factor in the development of vehicle-to-vehicle (V2V) communication technology, and V2V communication in 5G has recently attracted great interest. In the V2V communication network, users can choose different transmission modes and power levels for communication, to guarantee their quality-of-service (QoS), high capacity of vehicle-to-infrastructure (V2I) links and ultra-reliability of V2Vlinks. Aiming atV2V communication mode selection and power adaptation in 5G communication networks, a reinforcement learning (RL) framework based on slow fading parameters and statistical information is proposed. In this paper, our objective is to maximize the total capacity of V2I links while guaranteeing the strict transmission delay and reliability constraints of V2V links. Considering the fast channel variations and the continuous-valued state in a high mobility vehicular environment, we use a multi-agent double deep Q-learning (DDQN) algorithm. Each V2V link is considered as an agent, learning the optimal policy with the updated Q-network by interacting with the environment. Experiments verify the convergence of our algorithm. The simulation results show that the proposed scheme can significantly optimize the total capacity of the V2I links and ensure the latency and reliability requirements of the V2V links.