Proceedings of the 14th ACM International Conference on Future Energy Systems最新文献_第2页

Effective Risk-limiting Carbon Emission Aware Economic Dispatch: An Algorithmic Perspective 基于算法的有效风险限制碳排放经济调度

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-06-16 DOI: 10.1145/3575813.3576879

Jian Sun, Yaoyu Zhang, Chenye Wu

{"title":"Effective Risk-limiting Carbon Emission Aware Economic Dispatch: An Algorithmic Perspective","authors":"Jian Sun, Yaoyu Zhang, Chenye Wu","doi":"10.1145/3575813.3576879","DOIUrl":"https://doi.org/10.1145/3575813.3576879","url":null,"abstract":"Increasing public concern over climate change calls for high-level penetration of renewable energy sources into the future power grid, which makes the operation of the power grid fragile. One way to enhance the reliability of power grid operation is to equip each renewable generator with uncertainty management facilities such as conventional fast-responding generation units or storage systems. We identify a unified risk-limiting model for these diverse facilities. Specifically, in this paper, we consider two kinds of such facilities. The first one is the storage system, which has been traditionally utilized to enhance system reliability and reduce carbon emissions. We then propose the carbon allowance reserve (CAR), which, in a carbon emission aware economic dispatch, achieves the same goal as storage system does. The key to CAR is that it adopts conventional fast-responding generation units to conduct uncertainty management. We characterize the value of CAR by comparing the two kinds of facilities in the unified risk-limiting model. However, this is challenging because in a multi-period setting, solving the unified model alone is often intractable. Thus, we design an effective algorithm under mild assumptions on the renewable generation distributions. Next, we theoretically examine the robustness of the proposed algorithm, which highlights the practicability of the proposed algorithm. Numerical simulations further verify its effectiveness and provide comprehensive comparisons between the two kinds of uncertainty management facilities.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131002720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the impact of measurement artifacts on energy loss calculation in electrical distribution grids 评估测量伪影对配电网能量损耗计算的影响

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-06-16 DOI: 10.1145/3575813.3576872

Imad Antonios, H. Schwefel

{"title":"Assessing the impact of measurement artifacts on energy loss calculation in electrical distribution grids","authors":"Imad Antonios, H. Schwefel","doi":"10.1145/3575813.3576872","DOIUrl":"https://doi.org/10.1145/3575813.3576872","url":null,"abstract":"Monitoring of electrical distribution grids requires the joint processing of electrical measurements from different grid locations. Such type of processing is influenced by inaccuracies in measurement data originating from measurement errors, non-ideal clocks in measurement devices, and from time averaging of measurands as part of the data collection process. This paper introduces an approach to assess the impact of these three different measurement artifacts in realistic measurement scenarios of electrical distribution grids. A case study of power loss calculation in a real-life medium-voltage grid is presented, covering both technical loss obtained from current measurement and total loss obtained from power measurements. The results show that total loss in general is more robust to aggregation of power measurements over longer measurement intervals, while it is more sensitive to measurement errors and clock offsets. The results of the study are important for quantifying the trustworthiness of the obtained loss values and for the future enhancement of the measurement data collection process.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132173545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalized State of Health Estimation Approach based on Neural Networks for Various Lithium-Ion Battery Chemistries 基于神经网络的各种锂离子电池化学成分健康状态广义估计方法

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-06-16 DOI: 10.1145/3575813.3595207

Steffen Bockrath, M. Pruckner

{"title":"Generalized State of Health Estimation Approach based on Neural Networks for Various Lithium-Ion Battery Chemistries","authors":"Steffen Bockrath, M. Pruckner","doi":"10.1145/3575813.3595207","DOIUrl":"https://doi.org/10.1145/3575813.3595207","url":null,"abstract":"The aging estimation of lithium-ion batteries is a central mission for a safe and efficient handling of lithium-ion batteries over the whole battery lifetime. However, especially the absence of precise diagnostic measurements within real-world applications yields the aging estimation a complex challenge. Moreover, the non-linear aging of lithium-ion batteries is strongly dependent on various operating and environmental conditions and the specific battery cell chemistry. This paper presents a generalized state of health estimation approach based on a neural network that can be used for different lithium-ion battery chemistries. The presented algorithm is able to estimate the aging of lithium-ion batteries by using information obtained from raw sensor data without executing further preprocessing or feature engineering steps. It is firstly shown that the developed temporal convolutional network accurately estimates the state of health for three different lithium-ion battery chemistries by only using high-level parameters from partial charging profiles. In addition, the obtained high-level parameters can provide relevant information needed for a battery passport. The final neural network is trained using transfer learning approaches to model the state of health development of a Lithium-Nickel-Cobalt-Aluminum-Oxide (NCA), a Lithium-Nickel-Cobalt-Manganese-Oxide (NCM) and, an NCM-NCA battery cell. The overall mean absolute percentage error of the generalized state of health estimation is 1.43%.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"253 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132090264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Efficient and Accurate Peer-to-Peer Training of Machine Learning Based Home Thermal Models 基于家庭热模型的机器学习点对点高效准确训练

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-06-16 DOI: 10.1145/3575813.3597453

Karim Boubouh, Robert Basmadjian, Omid Ardakanian, Alexandre Maurer, Rachid Guerraoui

{"title":"Efficient and Accurate Peer-to-Peer Training of Machine Learning Based Home Thermal Models","authors":"Karim Boubouh, Robert Basmadjian, Omid Ardakanian, Alexandre Maurer, Rachid Guerraoui","doi":"10.1145/3575813.3597453","DOIUrl":"https://doi.org/10.1145/3575813.3597453","url":null,"abstract":"The integration of smart thermostats in home automation systems has created an opportunity to optimize space heating and cooling through the use of machine learning, for example for thermal model identification. Nonetheless, its full potential remains untapped due to the lack of a suitable learning scheme. Traditional centralized learning (CL) and federated learning (FL) schemes could pose privacy and security concerns, and result in a generic model that does not adequately represent thermal requirements and characteristics of each individual home. To overcome these limitations, in this paper we embrace the novel peer-to-peer learning scheme for on-device training of home thermal models. Specifically, we adapt the personalized peer-to-peer algorithm proposed in recent work (called P3) to efficiently train personalized thermal models on resource-constrained devices. Our preliminary experiments with data from 1,000 homes, using the LSTM model, demonstrate that the adapted P3 algorithm produces accurate and personalized thermal models while being extremely energy-efficient, consuming respectively 600 and 40 times less energy than the CL and FL schemes. This result suggests that the P3 algorithm offers a privacy-conscious, accurate, and energy-efficient solution for training thermal models for the many homes in the building stock.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128872183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A distributed approach to privacy-preservation and integrity assurance of smart metering data 一种分布式的智能计量数据隐私保护和完整性保证方法

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-06-16 DOI: 10.1145/3575813.3576876

Gaurav S. Wagh, S. Mishra

{"title":"A distributed approach to privacy-preservation and integrity assurance of smart metering data","authors":"Gaurav S. Wagh, S. Mishra","doi":"10.1145/3575813.3576876","DOIUrl":"https://doi.org/10.1145/3575813.3576876","url":null,"abstract":"Smart grid service providers collect metering data at frequent intervals for providing grid and billing functionalities. Studies have shown that access to the granular metering data can lead to breaches in customers’ privacy. Several aggregation-based privacy-preserving frameworks for smart metering data have been proposed in the literature. However, these frameworks have either a high computational overhead on resource-constrained smart meters and/or are prone to single points of compromise due to centralized designs. Distributed frameworks with outsourced aggregation can provide the desired functionalities while keeping the framework lightweight for the smart meters. However, these distributed frameworks assume an honest-but-curious adversary, which is not a realistic assumption for outsourced aggregation. This work-in-progress paper proposes a distributed aggregation-based privacy-preserving metering data collection framework under a malicious adversarial model (dishonest majority of aggregators). This framework is capable of verifying the integrity of the spatio-temporal metering data while ensuring customers’ privacy. The performance analysis of the proposed framework demonstrates that it outperforms a closely related existing framework with similar customer privacy and integrity verification goals. Our results on the computational overhead on smart meters, end-to-end delay, scalability, and resilience against threats to privacy and integrity are presented in this paper.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116373356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Programming Model for Portable Fault Detection and Diagnosis 便携式故障检测与诊断的编程模型

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-06-16 DOI: 10.1145/3575813.3595190

Dimitri Mavrokapnidis, Gabe Fierro, I. Korolija, D. Rovas

{"title":"A Programming Model for Portable Fault Detection and Diagnosis","authors":"Dimitri Mavrokapnidis, Gabe Fierro, I. Korolija, D. Rovas","doi":"10.1145/3575813.3595190","DOIUrl":"https://doi.org/10.1145/3575813.3595190","url":null,"abstract":"Portable applications support the write once, deploy everywhere paradigm. This paradigm is particularly attractive in building applications, where current practice involves the manual deployment and configuration of such applications, requiring significant engineering effort and concomitant costs. This is a tedious and error-prone process which does not scale well. Notwithstanding recent advances in semantic data modelling that allow a unified representation of buildings, we still miss a paradigm for deploying portable building applications at scale. This paper introduces a portable programming model for such applications, which we examine in the context of Fault-Detection and Diagnosis (FDD). In particular, we look at the separation of the FDD logic and the configuration with specific data inputs. We architect a software system that enables their self-configuration and execution across various building configurations, expressed in terms of Brick metadata models. Our initial results from authoring and executing APAR (AHU Performance Assessment Rules) on multiple AHUs of two museums demonstrate the potential of our model to reduce repetitive tasks and deployment costs of FDD applications.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116779664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An Efficient Greedy Algorithm for Real-World Large-Scale Electric Vehicle Charging 现实世界大规模电动汽车充电的一种高效贪心算法

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-06-16 DOI: 10.1145/3575813.3597349

Marius Hegele, Philipp Metzler, Sebastian Beichter, Friedrich Wiegel, V. Hagenmeyer

{"title":"An Efficient Greedy Algorithm for Real-World Large-Scale Electric Vehicle Charging","authors":"Marius Hegele, Philipp Metzler, Sebastian Beichter, Friedrich Wiegel, V. Hagenmeyer","doi":"10.1145/3575813.3597349","DOIUrl":"https://doi.org/10.1145/3575813.3597349","url":null,"abstract":"The increasing use of electric vehicles amplifies the demand for affordable charging infrastructure. By smart charging applications, operators of large-scale facilities of AC chargers can save costs on installation and lighten the load on distribution grids by avoiding high peaks and unbalanced loads. In the present paper, we consider the problem of phase-balancing in the context of non-ideal charging characteristics: some electric vehicles represent unbalanced loads to the grid, and some react to inputs in an unexpected nonlinear fashion. Furthermore, users expect a fair distribution of the limited charging power. In this light, we formally characterize fairness, choose to control load in real time and model smart charging as a time-discrete knapsack problem. In order to guarantee phase symmetry and increase charging efficiency, we develop a real current measurement filter and use it to solve the problem using a branch-and-bound algorithm and to approximate solutions with a greedy algorithm. We compare these solutions in representative simulations based on real charging data. Additionally, we evaluate the greedy algorithm on real charging infrastructure with up to 100 charging points. We conclude from the results that the greedy algorithm using measurements of charging behavior guarantees capacity and symmetry constraints and demonstrates comparatively adequate fair charging efficiency and applicability to computation on resource-constrained hardware.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116825746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CUFF: A Configurable Uncertainty-driven Forecasting Framework for Green AI Clusters 袖口:绿色人工智能集群的可配置不确定性驱动预测框架

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-06-16 DOI: 10.1145/3575813.3595203

P. Mammen, Noman Bashir, Ramachandra Rao Kolluri, Eun Kung Lee, P. Shenoy

{"title":"CUFF: A Configurable Uncertainty-driven Forecasting Framework for Green AI Clusters","authors":"P. Mammen, Noman Bashir, Ramachandra Rao Kolluri, Eun Kung Lee, P. Shenoy","doi":"10.1145/3575813.3595203","DOIUrl":"https://doi.org/10.1145/3575813.3595203","url":null,"abstract":"AI applications are driving the need for large dedicated GPU clusters, which are highly energy- and carbon-intensive. To efficiently operate these clusters, operators leverage workload forecasts that inform resource allocation decisions to save energy without sacrificing performance. The traditional forecasting methods provide a single-point forecast and do not expose the uncertainty about their predictions, which can lead to an unexpected loss in performance. In this paper, we present an uncertainty-driven GPU demand forecasting framework that exposes the uncertainty in its predictions and provides a mechanism to configure the trade-off between energy savings and performance. We evaluate our approach using multiple GPU workload traces and demonstrate that the forecasting framework, called CUFF, outperforms state-of-the-art point predictions. CUFF predictor meets performance goals 83% of the time compared to 7.6% for the point predictions under high GPU demand. Furthermore, CUFF knob enables users to configure up to 98% performance target while providing 26% energy savings, comparable value to point forecasts that only ensure 68% performance target.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133928016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward Model-Assisted Safe Reinforcement Learning for Data Center Cooling Control: A Lyapunov-based Approach 数据中心冷却控制的模型辅助安全强化学习:一种基于lyapunov的方法

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-06-16 DOI: 10.1145/3575813.3597343

Zhi-Ying Cao, Ruihang Wang, Xiaoxia Zhou, Yonggang Wen

{"title":"Toward Model-Assisted Safe Reinforcement Learning for Data Center Cooling Control: A Lyapunov-based Approach","authors":"Zhi-Ying Cao, Ruihang Wang, Xiaoxia Zhou, Yonggang Wen","doi":"10.1145/3575813.3597343","DOIUrl":"https://doi.org/10.1145/3575813.3597343","url":null,"abstract":"This paper considers intelligent data center cooling control via the Deep Reinforcement Learning (DRL) approach to improve data center sustainability. Existing DRL-based controllers are trained with a simplified data hall thermodynamic model which assumes uniform room temperature distribution. This assumption is not valid for a real-world data center with highly nonuniform temperature distribution. Furthermore, most of them cannot guarantee thermal safety during the DRL learning process. To bridge these gaps, we propose LyaSafe, a model-assisted safe DRL approach for data center cooling control. To address the safety evaluation issue, we develop a coupled model that combines a differentiable surrogate data hall thermodynamics model with the energy model. It can simulate both data hall temperature distribution and the facility energy consumption. To address safe learning, we introduce a novel constrained Markov Decision Process (CMDP) formulation for data center cooling control by considering the Rack Cooling Index (RCI), the best-practice metric for evaluating compliance with ASHRAE data center thermal guidelines. The objective is to minimize data center carbon footprints while regulating the RCI within a threshold. We first derive the safety set based on the concept of the virtual queue and Lyapunov stability theory. Next, we rectify unsafe actions from the DRL agent by projecting them to the safety set. We evaluate LyaSafe in a data center hosting 20 racks and 299 servers. Evaluation results show that LyaSafe can ensure strict safety during the DRL learning while achieving up to 50 metric tons of annual carbon emission savings using Singapore’s statistics. Moreover, we conduct root cause analysis for the savings, revealing the importance of joint control of the data hall and the chiller plant.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114583077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A GNN-based Day Ahead Carbon Intensity Forecasting Model for Cross-Border Power Grids 基于gnn的跨境电网日前碳强度预测模型

Proceedings of the 14th ACM International Conference on Future Energy Systems Pub Date : 2023-06-16 DOI: 10.1145/3575813.3597346

Xiaoyang Zhang, Dan Wang

{"title":"A GNN-based Day Ahead Carbon Intensity Forecasting Model for Cross-Border Power Grids","authors":"Xiaoyang Zhang, Dan Wang","doi":"10.1145/3575813.3597346","DOIUrl":"https://doi.org/10.1145/3575813.3597346","url":null,"abstract":"Carbon intensity forecasting of power grids is critical to the optimization of demand-side consumers. Recently, cross-border power grids have emerged, i.e., those allowing electricity to be transmitted across different national transmission systems. Cross-border power grids substantially increase the sharing of highly variable renewable energy sources (VRE), leading to greater economic benefits and increased reliability. In Europe, the total volume of cross-border electricity that is exchanged comprises 13% of the annual net electricity that is generated. Current studies on carbon intensity forecasting, however, apply to individual regional power grids. In cross-border grids, the carbon intensity of a regional grid depends not only on that of its own electricity but also on the carbon intensity from the electricity exchanged with cross-border grids. Thus, if the cross-border electricity exchange is not captured appropriately, significant forecasting errors can occur. In this paper, we formulate a new Carbon Intensity Forecasting for Cross-border Grids (CFCG) problem by proposing and integrating carbon flows generated by cross-border electricity exchanges. The challenge is to capture the complex spatial and temporal dependencies that are involved. We propose a CFCG model based on a Graph Neural Network (GNN) submodel to learn the spatial dependencies and a Long Short Term Memory (LSTM) submodel to learn the temporal dependencies. We evaluate the CFCG model using real-world data from the cross-border power grids in Europe involving 28 member countries. We compare five baseline models. Our results show that the CFCG model achieves an average improvement of 26.46% or 20.34% as compared to state-of-the-art forecasting models based on regional grids or one-hop neighbor grids, respectively.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125515623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1