Network System Research Articles

Neural Network-Based Aggregated Equivalent Modeling of Distributed Photovoltaic External Characteristics of Faults

Distributed power networks have a large number of photovoltaic power sources. The bidirection of power flow, different transient control strategies, and installation locations make the transient characteristics highly complex and unpredictable. The vast network of the distribution system makes it almost impossible to predict the electrical quantities of each branch. Reasonable aggregation modeling of the distribution network can greatly simplify the network topology, facilitating transient control and the setting of relay protection settings. An aggregated equivalent modeling method based on the LSTM neural network for distributed PV fault external characteristics is proposed. This method equates the complex distribution network to a highly nonlinear but controllable current source. The method can output the I–V curves of equivalent PV system parallel points under any output power and is able to predict the fault characteristics of the equivalent system after a voltage drop at the parallel point. Compared to traditional mechanistic modeling, this method does not require specific modeling of complex physical systems and is able to accurately map the strong nonlinear inputs and outputs of distribution networks. The established LSTM model first uses a one-dimensional convolutional layer for feature extraction of the PV power coefficients (input), and then two hidden layers are utilized to process the sequence data; the vectors are mapped into a sequence of external characteristic curves (output) in a fully connected layer. A typical distribution network is built based on the traditional PV power model, and a large number of different output combinations are selected for simulation to provide an effective training set and validation set data for LSTM model training. By using the training set data, the weights and offset coefficients of each layer of the LSTM are continuously optimized until the model with the smallest overall error is obtained, which is the optimal model. Finally, the optimal model is utilized to establish an equivalent distribution network system, different degrees of voltage drops are set up at the grid-connected points, the fault characteristics are compared with those of the complete model, and the simulation results can prove the reliability and practicality of the proposed method.

Research Progress and Suggestions on Natural Gas Industry Technology Revolution in the Context of Energy Revolution

The energy revolution is an important driving force for the progress of productivity and civilization in human society. As a big country in global energy consumption and carbon emission, promoting the energy revolution is a strategic choice to ensure the sustainable development of China's energy industry, which is in line with China's national conditions of the construction of modern energy system. Based on the strategic background of the energy revolution, this paper combines China's natural gas production capacity and consumption demand in the context of national conditions, and from the perspective of technological revolution, we have sorted out the research results of relevant scholars on the natural gas industry and the technological development of the whole industry chain of “production, supply, storage and marketing”, and discussed the technological development of China's natural gas industry in the context of the energy revolution. The results of the study show that: (1) the innovative technology of natural gas industry is diversified, low-carbon and green development, but from the dimension of safety, stability and economy, the performance is relatively weak. (2) The overall synergistic development of the whole industrial chain technology is uneven, the gap between supply and demand is increasing, and the degree of information and intelligence is low. And then for the development of China's natural gas industry technology puts forward proposals: (1) to promote innovative natural gas technology breakthroughs, integration of new areas of technology, to achieve the dual objectives of low-carbon economic development and environmental protection; (2) to increase natural gas exploration and development efforts, breakthroughs in the integration of the digital transformation of traditional exploration technology, and continue to support the increase in storage and production; (3) to accelerate the upgrading of natural gas supply and demand, and the establishment of a synergistic, intelligent, diversified pipeline network system. (4) Boost the construction of gas storage system, pay attention to the standardized gas storage management system, and play the role of valley cutting and peak leveling; (5) Build a new comprehensive energy service system, and build a multi-species, multi-link intelligent technology integration to create a new service system.

Application of deep learning-based Intrusion Detection System (IDS) in network anomaly traffic detection

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, reviewing traditional IDS technology, it analyzes its limitations in dealing with complex network traffic. Then, the design principle of the CNN-Focal model is described in detail, which uses threshold convolution and SoftMax multi-class classification technology to improve abnormal traffic detections accuracy and efficiency effectively. The experimental results show that CNN-Focal performs well on the open data set, demonstrating the potential and advantages of its application in the natural network environment and providing a new perspective and method for further research of deep learning in the field of network security in the future.

Design of an integrated network order system for main distribution network considering power dispatch efficiency

This study presents a comprehensive review of the primary distribution design of an advanced network control system, emphasizing its evolution from initial requirements to practical applications. The system solves complex problems of power management by combining real-time data analysis, intelligent decision making for resource allocation, rapid fault correction, remote monitoring and complex optimization methods, all aimed at ensuring stable and safe operation of the power grid. Its performance is geared towards fast response, efficient data processing and synchronous processing tasks, ensuring smooth operation even under heavy workloads. Security is enhanced through strict protocols, encryption methods, and controlled access systems. The system is divided into four layers-data collection, communication, decision-making and application management-using innovative tools such as Kalman filters and deep Q networks. The research showcases the integrated network command system’s prowess, achieving an average response time of 0.27 s, 98.5% dispatching accuracy, and 83.2% resource utilization, evidencing exceptional performance. It excels under various tests, including managing high loads with minimal accuracy loss, rapidly adapting to changes with a hydro model response time of 0.22 s, efficiently integrating renewables at 78.0% efficiency, and proving resilient in peak hours, affirming its capability to bolster grid efficiency, reliability, and integration of renewable energy resources. By outlining these specific achievements, this case study not only illustrates the complex design of the system, but also highlights its great potential for improving grid resilience and efficiency, attracting a wide audience interested in the future of energy management.

Co-optimization of sensor fusion and control strategies in electrical and electronic systems

In multi-sensor fusion, resource scheduling and control is responsible for rationally assigning multiple sensors in a networked system to the tasks faced by the system, and is the key to improving the quality of information fusion, increasing the overall system effectiveness, and obtaining the best observation results. Resource scheduling control is mainly to determine which sensors are selected to monitor and track the target according to certain optimization criteria, as well as the working mode, parameters, and measurement process of the sensors, and also to determine how the sensors can cooperate with each other and how the sensors can be adapted to the environment, and so on. This paper is to study the resource scheduling and control algorithm based on covariance control, so as to optimize the sensor resource scheduling and control.

Optimal aggregation and disaggregation for coordinated operation of virtual power plant with distribution network operator

Virtual power plants (VPPs) offer an effective approach for managing distributed energy resources (DERs), including microturbines, distributed generators, demand response aggregators, and energy storage systems. This technology significantly enhances the economic efficiency and flexibility of distribution network systems. This study aims to facilitate a flexible power exchange between the distribution network system and the upper-level grid by aggregating the power flexibility of heterogeneous DERs via a VPP. Additionally, it introduces a methodology for optimal aggregation and disaggregation within a coordinated operation framework between the VPP and distribution network operator (DNO). On one hand, the VPP can determine its day-ahead feasible region and real-time flexible regulation power based on operational constraints of DERs and representative data provided by the DNO, circumventing the need for detailed network information. On the other hand, day-ahead and real-time correction procedures for the DER cost functions are proposed, effectively neutralizing the impact of network operational constraints on these cost functions. Consequently, precise cost functions for both the active power and the flexible regulation power aggregated by the VPP are derived. Employing this aggregated cost function enables the determination of a cost-minimized optimal scheduling solution in real-time by solving a fundamental economic dispatch problem, significantly alleviating computational demands. Finally, case studies demonstrate that the proposed method achieves an error in aggregated power of only 0.77%, compared to the precise computation method that requires comprehensive system information. The proposed optimal VPP disaggregation scheme exhibits power discrepancies of 0.63% and cost discrepancies of 0.91% relative to the precise method. Additionally, when implementing the most cost-effective demand response plan based on the proposed cost function, the average costs for aggregators are reduced by 19.7%.

Improving stability and performance in IoT-Driven networked control systems

With the widespread adoption and benefits of the Internet of Things (IoT), this technology has expanded its reach into the realm of automation and control, giving rise to a branch known as industrial networks. The use of IoT technology is on the rise due to its daily increasing advantages and ease of use. However, it has also introduced new challenges in control systems, prompting researchers to conduct numerous studies to address these challenges and propose innovative solutions. One of these challenges, for example, is the limited bandwidth of the network, which leads to challenging issues such as variable random time delays and packet loss. In light of these challenges, using networked control systems instead of many traditional control methods can significantly improve the performance and stability of closed-loop systems. In this context, this paper aims to enhance system tolerance and reliability, along with permanent state error correction, by presenting methods for designing networked controllers that are influenced by random delays. To achieve this goal, we first propose a predictive control model with reduced prediction horizons, assuming constant delays in the input. This controller not only ensures stability but also tracks the reference input. Simulation results demonstrate that this controller can tolerate some variations in delay despite being designed for constant delays. However, it is not robust against significant variations in delay inherent to network environments. Therefore, in the second approach, a controller based on the Lyapunov function and linear matrix inequalities is designed for a system with time-varying input delays. This controller, in addition to stabilizing the system, tracks the reference input by minimizing the state error.

Energy saving and speed control in autonomous electric vehicle using enhanced manta ray foraging algorithm optimized intelligent systems

The integration of autonomous vehicles (AVs) with hybrid electric vehicles (HEVs) aims to address the problem of optimizing energy economy while improving reliability and safety. By using intelligent driver support technologies and sophisticated control algorithms, this method aims to lower emissions and fuel consumption. Therefore, the HEVs develop an intelligent driver aid system that integrates an energy management system (EMS) with adaptive cruise control (ACC). To maximize energy use, the system integrates HEVs with autonomous vehicle technologies. In order to maintain safe distances from the lead vehicle, identify the desired acceleration, and switch between speed and distance control, the ACC uses a deep learning system in conjunction with a tilt integral derivative (TID) controller. This ensures stability. Based on ACC commands, the EMS controls energy usage. An enhanced manta ray foraging optimization (EMRFO) technique is used to significantly enhance speed control and energy economy. Simulink/MATLAB analysis of the suggested model shows notable gains in energy consumption control, particularly with the ACC with deep neural network (DNN) system, and considerable reductions in driving risks. In addition, the system precisely controls engine torque to maintain a 72.75 % State of Charge (SoC) and an average engine energy efficiency of 32.36 %. To validate the performance, the proposed approach is put into practice in real-time configuration. The configuration for the experiment produced an ideal efficiency of 31.99 % with minimal error using the proposed method.

Uncovering leveraging and hindering factors in socio-ecological interactions: Agricultural production in the Yellow River Basin as an example

Agricultural production and sustainable human livelihoods in large river basins are threatened by climate change, human activities, and resource constraints. However, due to the complexity of socio-ecological interactions and agricultural sustainability, current studies are still limited by a priori knowledge and systematic analyses, as well as by the lack of quantification and identification of key factors and valuable pathway structures for agricultural production activities. Here, we combined observation-based causal inference and network analysis to quantify and assess the complex interactions in agricultural production in the Yellow River Basin (YRB) based on data from 12 factors relevant to agriculture over 40 years. We quantitatively assessed the leveraging and hindering roles of the factors in the interacting network system and provided managers with optimization priorities and possible causal pathways to achieve sustainable agriculture in the basin. For example, the fruit yield and income of rural households were identified as leveraging factors that positively affect the agricultural economy. Groundwater was seen as a hindering factor in dampening the negative impacts of the system, highlighting the importance of preventing groundwater depletion. Moreover, the findings suggest that spatially diverse causal interaction structures exist in the YRB and have shaped a variety of distinctive agricultural development modes. Our research ideas and results highlight both systemic considerations and the amplifying or dampening role of factors in interaction pathways, providing valuable quantitative insights into the management and intervention of sustainable agriculture in large river basins. Owing to replaceable and extensible network models, the methodology has the potential to be utilized in a variety of study areas and topics with complex socio-ecological interactions.

Review of Risk Assessment Methods for Cybersecurity Attacks on Road Network and Intelligent Transportation System Applications

One of the most discussed infrastructure issues of our time is cybersecurity. A transportation system that connects not only people but also logistics to make the community around the world closer is one of the critical infrastructures requiring cybersecurity to perform its functions. Transportation systems include aviation, maritime, pipeline, railroad, and road networks. This study focuses only on the roadway network system. Cutting-edge technologies related to highway networks, such as variable message signs, vehicular ad hoc networks, and in-vehicle networks, have been developed to improve safety and efficiency. Those technologies make transportation systems more complex and integrated, bringing many potential vulnerabilities and cyber risks. This can attract an adversary to attack and exploit the system. The number of cybersecurity attacks on transportation systems has been growing for many years. However, it is not feasible to protect against all cybersecurity attacks in the system. The risk assessment concept is proposed to prioritize risk resulting from attacks to support decision-makers in formulating appropriate policies or countermeasures. This study reviews risk assessment methods for cybersecurity attacks on road networks and intelligent transportation system applications. Three potential risk assessment methods are examined for road network systems: the National Institute of Standards and Technology Special Publication 800-30, Attack Potential and Damage Potential, and the fuzzy analytic hierarchy process.

Hardening Active Directory Graphs via Evolutionary Diversity Optimization based Policies

Active Directory (AD) is the default security management system for Windows domain networks. An AD environment can be described as a cyber-attack graph, with nodes representing computers, accounts, etc., and edges indicating existing accesses or known exploits that enable attackers to move from one node to another. This paper explores a Stackelberg game model between one attacker and one defender on an AD attack graph. The attacker’s goal is to maximize their chances of successfully reaching the destination before getting detected. The defender’s aim is to block a constant number of edges to minimize the attacker’s chance of success. The paper shows that the problem is #P-hard and, therefore, intractable to solve exactly. To defend the AD graph from cyber attackers, this paper proposes two defensive approaches. In the first approach, we convert the attacker’s problem to an exponential sized Dynamic Program that is approximated by a Neural Network (NN). Once trained, the NN serves as an efficient fitness function for defender’s Evolutionary Diversity Optimization based defensive policy. The diversity emphasis on the defender’s solution provides a diverse set of training samples, improving the training accuracy of our NN for modeling the attacker. In the second approach, we propose a RL based policy to solve the attacker’s problem and Critic network assisted Evolutionary Diversity Optimization based defensive policy to solve defender’s problem. Experimental results on synthetic AD graphs show that the proposed defensive policies are scalable, highly effective, approximate attacker’s problem accurately, and generate good defensive plans.

Perspective: Entropy-stabilized oxide memristors

A memristor array has emerged as a potential computing hardware for artificial intelligence (AI). It has an inherent memory effect that allows information storage in the form of easily programmable electrical conductance, making it suitable for efficient data processing without shuttling of data between the processor and memory. To realize its full potential for AI applications, fine-tuning of internal device dynamics is required to implement a network system that employs dynamic functions. Here, we provide a perspective on multicationic entropy-stabilized oxides as a widely tunable materials system for memristor applications. We highlight the potential for efficient data processing in machine learning tasks enabled by the implementation of “task specific” neural networks that derive from this material tunability.

Box Type Minor Bridge on Small Rivers in Alluvial Region

Since time immemorial, humankind has been using various techniques to cross the rivers, streams or any depression without closing or obstructing the original flow through a structure called bridge. With the span of time and advancements in civil engineering, several types of bridges have come into existence like wooden, steel, masonry arches, RCC and pre stressed concrete bridges. Indian Roads Congress categorizes three types of the bridges on the basis of length i.e. culverts up to 6.0 m, minor bridges above 6.0 m to maximum 60.0 m length and major bridges above 60.0 m. In present scenario, minor bridges over small rivers has become necessary for development and prosperity of nation as most of the roads have to cross small rivers at several places to connect remotest corner of the country. Bridges, though a manmade structure, over a period become an important part of environment because in most of the cases water flowing below is used for drinking, irrigation and underground recharging. The alluvial region of India spread from Punjab to West Bengal has a peculiar nature because soil is almost soft in nature consisting of mainly sand, clay and silt which is fertile for vegetation. Water retention and its movement condition are high throughout the year. Several type of water bodies which exists in this type of region are pond, small drain, small and medium rivers which drains into the big rivers like Ganga, Yamuna, Ghaghara, Gomati and Sai etc. Since long time, road system on the earthen track and pucca has been introduced for traffic like chart, chariot and motorized vehicle. There were little number of bridges over major rivers i.e. bridge alone to cross over the river Ganga except in few places like Allahabad, Kanpur and Varanasi and over the Ghaghra Algine bridge, Maghighat, Bhatni. Some bridges were constructed over small and medium rivers in medevial period by local rulers and businesspersons, which have now become obsolete. It is found that most of the bridges are of masonry arches wooden and trusses having insufficient water and carriageway. After independence, the road network system has been improved to meet out socio economic needs of people. The new bridges have been constructed with standard road width and sufficient waterway. Study has been conducted for existing new constructed bridge system over small river and alluvial region of Uttar Pradesh to set guidance for future course of action in replacing and providing new bridges to optimize the needs of the people. Bridge system being provided over small & minor rivers for the road network for new and replacement of older bridge at different site is varying from place to place. For this purpose, study has been conducted for sustainable option of minor bridges over small rivers discharge up to 300 m3 /s. It is found that the box type minor bridges are best option on small & minor rivers.

Robust phase extraction from complex signals/fringe patterns for optical communications in 5G and beyond through continuous wavelet ridge technique

AbstractThe rapid progress in high‐precision optical instruments necessitates sophisticated image processing techniques for extracting vital information from generated images. Most of these instruments output images comprising RGB pixels. Deciphering these pixel values into phase information poses a significant challenge, especially in the presence of background noise. This study focuses on utilizing Two‐Dimensional Continuous Wavelet Transforms (2‐D CWT) for analyzing fringe patterns with varying noise levels and fringe alignments, crucial for high‐precision optical systems pivotal in enhancing the performance and reliability of optical communication systems in advanced 5G networks. The simulation results demonstrate that 2‐D CWT efficiently extracts phase information from complex and highly noisy fringes while requiring less computational time. Furthermore, the algorithm effectively handles noise disturbances with a commendable degree of accuracy, ensuring robust performance in 5G‐enabled optical systems critical for supporting ultra‐high‐speed data transmission and low‐latency communication requirements. This research contributes to optimizing image analysis techniques for 5G optical systems, facilitating their integration into next‐generation communication networks.

Fault-tolerant security-efficiency combined authentication scheme for manned-unmanned teaming

Manned-unmanned teaming (MUM-T) is an emerging network system which interconnects manned aerial vehicles (MAVs) with unmanned aerial vehicles (UAVs) to enhance mission effectiveness and reduce workloads. Like other wireless systems, MUM-T is prone to attacks from the open communication channel. Therefore, an authentication scheme is required to establish secure and trusted communication between the MAV and the UAV. However, existing schemes fail to provide adequate security features and necessary efficiency, mostly due to their incomprehensive threat modeling and improper use of authentication techniques. Moreover, traditional centralized architecture of the authentication server leads to the single point of failure (SPOF) problem, which jeopardizes the robustness and scalability of MUM-T. In this paper, we propose a fault-tolerant authentication scheme for MUM-T that will solve these problems. To enhance its security, we construct a new threat model consisting of adversary's capabilities, security features, and security challenges to ensure the security of a scheme under combination attacks. To preserve the highest possible efficiency, we propose the design principle of using lightweight primitives for authentication and applying public key operations in key establishment. To address the SPOF problem, we employ a distributed fault-tolerant mechanism to share registration information within authentication servers and defend against faulty nodes. As is demonstrated by the security proof and performance comparison, our scheme succeeds in improving security and reducing overall costs, which provides a better solution than existing schemes.

Improved Stability Analysis of Neural Networks with Time Delay Based on Variable Augmented Free Weight Matrix

This article investigates the stability of time-delay neural network system by the free-weighting matrices based on variable-augmentation.In the process of accurately evaluating the stability of delayed neural network systems, the conservative simplification of stability criteria has received widespread attention. In this paper, a variable-augmented-based free-weighting matrix method is applied to time-varying delayed neural network systems, and unnecessary free weighting matrices are removed. Some results with fewer free-weighting matrices are obtained, while maintaining their stability and conservatism. Finally, a specific neural network numerical example was used to demonstrate the effectiveness of this method.

Exploring the Interplay of Dataset Size and Imbalance on CNN Performance in Healthcare: Using X-rays to Identify COVID-19 Patients.

Convolutional Neural Network (CNN) systems in healthcare are influenced by unbalanced datasets and varying sizes. This article delves into the impact of dataset size, class imbalance, and their interplay on CNN systems, focusing on the size of the training set versus imbalance-a unique perspective compared to the prevailing literature. Furthermore, it addresses scenarios with more than two classification groups, often overlooked but prevalent in practical settings. Initially, a CNN was developed to classify lung diseases using X-ray images, distinguishing between healthy individuals and COVID-19 patients. Later, the model was expanded to include pneumonia patients. To evaluate performance, numerous experiments were conducted with varied data sizes and imbalance ratios for both binary and ternary classifications, measuring various indices to validate the model's efficacy. The study revealed that increasing dataset size positively impacts CNN performance, but this improvement saturates beyond a certain size. A novel finding is that the data balance ratio influences performance more significantly than dataset size. The behavior of three-class classification mirrored that of binary classification, underscoring the importance of balanced datasets for accurate classification. This study emphasizes the fact that achieving balanced representation in datasets is crucial for optimal CNN performance in healthcare, challenging the conventional focus on dataset size. Balanced datasets improve classification accuracy, both in two-class and three-class scenarios, highlighting the need for data-balancing techniques to improve model reliability and effectiveness. Our study is motivated by a scenario with 100 patient samples, offering two options: a balanced dataset with 200 samples and an unbalanced dataset with 500 samples (400 healthy individuals). We aim to provide insights into the optimal choice based on the interplay between dataset size and imbalance, enriching the discourse for stakeholders interested in achieving optimal model performance. Recognizing a single model's generalizability limitations, we assert that further studies on diverse datasets are needed.

APPLICATION OF THE FLOYD-WARSHALL ALGORITHM TO OPTIMIZE ELECTRICITY DISTRIBUTION IN THE CITY OF CIMAHI

Efficiency in the use of electricity is a necessity for the sustainability of a better life. Almost all human activities utilize electrical energy, such as household appliances, industry, and even vehicles. This research contains the efficiency of electricity use in a housing by applying graph theory in optimizing the electricity distribution network system. The purpose of this study is to optimize the electricity distribution network system in the residential area of Kota Mas Cimahi, so that the best network model is obtained. Network data and information were obtained from PT. PLN UP3 Cimahi, West Java. The method used is to determine the minimum spanning tree in the electricity distribution network model by applying the Floyd-Wish algorithm. The initial model of the power grid was constructed by data on the position of substations and power poles, the number of power poles, and the length of the power cables connecting the poles. The data used is data on a line from several electricity distribution lines in this housing. The result of the minimum cable length on this line is 349 meters. The results of this study are expected to provide alternative solutions and additional information in planning and structuring the power grid optimally. Furthermore, the results of this research can also be used by PLN in order to support the government's program towards the efficiency of using electrical energy to serve the community because they get electricity services faster and cheaper

Space optical communication system for space optical networks and deep space exploration

The acquisition time of tens to hundreds of seconds in the optical link between satellites makes it difficult to meet the needs of constructing spatial optical networks. In addition, as a basic requirement for deep space explorers, autonomous attitude determination and autonomous navigation demand the installation of separate, expensive, and complex inertial devices, and the communication data rate is too low to meet the timely transmission of large amounts of data. In this paper, we proposed and developed a multifunctional fusion space optical communication system for space optical networks and deep space exploration, which has the functions of autonomous attitude determination, autonomous navigation, and high-speed optical communication. The sub-second acquisition time can meet the requirements of space optical network construction, and the ability of autonomous attitude determination and autonomous navigation significantly reduce the amount of R&D expenses of the explorer; decrease the volume, weight, and power consumption of the explorer; and improve the reliability and autonomous survival ability of the explorer. The paper provides the structure, working principle, and main algorithm models and conducts a feasibility analysis and field experiments. The experimental results showed that the average open-loop pointing accuracy of the optical terminal is 95.8 µrad (attitude determination accuracy), which can be improved to 39.1 µrad after filtering, and the acquisition time is less than 1 s. For deep space exploration, the navigation accuracy is less than 67.6 km in the cruise phase and 10 km in the acquisition phase, and field experiments have also proven its feasibility. The significance of our research work lies in proposing what we believe to be a new system operation scheme and design method for optical communication systems, and its results can be widely applied in all fields of space optical communication.

Scenario-based robust reachability analysis for networked airport delay dynamics

This work introduces a novel scheme to represent delay dynamics in airport networks. A key feature of the proposed scheme is that uncertainty affecting network operation is accounted for via a robust reachability analysis approach. In particular, the dynamical system is adopted to describe the propagation of outbound and inbound delays between airport nodes. A probabilistic reach set for this networked system is formulated as a chance-constrained optimization program. Then, we resort to a data-driven randomized computation method called scenariooptimization , where the chance constraint is replaced by a set of scenario-based deterministic constraints. Besides, the robustness of the reach set is explicitly derived through a posterior algorithm. Moreover, we conduct the extensive experiments on a dataset of real-world airport delays in China and provide some interesting results. Additionally, the case study at a refined scenario level shows the potential of the proposed approach in application.