Engineering Applications of Artificial Intelligence最新文献_第2页

Optimizing and predicting swarming collective motion performance for coverage problems solving: A simulation-optimization approach 优化和预测蜂群集体运动性能以解决覆盖问题：模拟优化方法

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109522

Algorithms using swarming collective motion can solve coverage problems in unknown environments by reacting to unknown obstacles in real-time when they are encountered. However, these algorithms face two key challenges when deployed on real robots. First, hand-tuning efficient collective motion parameters is both time-consuming and difficult. Second, predicting the time required for a swarm to solve a particular problem is not straightforward. This paper introduces a novel evolutionary framework to address both problems by proposing a methodology that autonomously tunes collective motion parameters for coverage problems while predicting the time required for real robots to complete the task. Our approach utilizes a simulation–optimization framework that employs a genetic algorithm to optimize the parameters of a frontier-led swarming algorithm. Results indicate that the optimized parameters are transferable to real robots, achieving 100% coverage while maintaining 84% connectivity between them. Compared to state-of-the-art swarm methods, our system reduced turnaround time by 50% and 57% in different environments while maintaining collective motion. It also achieved a 55% reduction in turnaround time on average across five scenarios compared to budget-constrained path planning, with a 10% increase in coverage. Furthermore, our framework outperformed both hand-tuned and learned collective motion approaches, reducing turnaround time by 73% in non-collective motion scenarios and by 63% while maintaining 85% connectivity in collective motion scenarios. This approach effectively combines the adaptability of swarm behavior with the predictive reliability of planning methods.

使用蜂群集体运动的算法可以在遇到未知障碍物时实时做出反应，从而解决未知环境中的覆盖问题。然而，这些算法在实际机器人上部署时面临两大挑战。首先，手工调整高效的集体运动参数既耗时又困难。其次，预测机器人群解决特定问题所需的时间并不简单。本文介绍了一种新颖的进化框架，通过提出一种方法来解决这两个问题，该方法可针对覆盖问题自主调整集体运动参数，同时预测真实机器人完成任务所需的时间。我们的方法利用仿真优化框架，采用遗传算法来优化前沿引领的蜂群算法参数。结果表明，优化后的参数可应用于真实机器人，在实现100%覆盖率的同时，还能保持机器人之间84%的连通性。与最先进的蜂群方法相比，我们的系统在不同环境中的周转时间分别缩短了 50%和 57%，同时保持了集体运动。与预算受限的路径规划相比，我们的系统在五种情况下平均减少了 55% 的周转时间，覆盖范围增加了 10%。此外，我们的框架还优于人工调整和学习的集体运动方法，在非集体运动场景下，周转时间缩短了 73%，在集体运动场景下，周转时间缩短了 63%，同时保持了 85% 的连接性。这种方法有效地结合了蜂群行为的适应性和规划方法的预测可靠性。

{"title":"Optimizing and predicting swarming collective motion performance for coverage problems solving: A simulation-optimization approach","authors":"","doi":"10.1016/j.engappai.2024.109522","DOIUrl":"10.1016/j.engappai.2024.109522","url":null,"abstract":"<div><div>Algorithms using swarming collective motion can solve coverage problems in unknown environments by reacting to unknown obstacles in real-time when they are encountered. However, these algorithms face two key challenges when deployed on real robots. First, hand-tuning efficient collective motion parameters is both time-consuming and difficult. Second, predicting the time required for a swarm to solve a particular problem is not straightforward. This paper introduces a novel evolutionary framework to address both problems by proposing a methodology that autonomously tunes collective motion parameters for coverage problems while predicting the time required for real robots to complete the task. Our approach utilizes a simulation–optimization framework that employs a genetic algorithm to optimize the parameters of a frontier-led swarming algorithm. Results indicate that the optimized parameters are transferable to real robots, achieving 100% coverage while maintaining 84% connectivity between them. Compared to state-of-the-art swarm methods, our system reduced turnaround time by 50% and 57% in different environments while maintaining collective motion. It also achieved a 55% reduction in turnaround time on average across five scenarios compared to budget-constrained path planning, with a 10% increase in coverage. Furthermore, our framework outperformed both hand-tuned and learned collective motion approaches, reducing turnaround time by 73% in non-collective motion scenarios and by 63% while maintaining 85% connectivity in collective motion scenarios. This approach effectively combines the adaptability of swarm behavior with the predictive reliability of planning methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diophantine spherical vague sets and their applications for micro-technology robots based on multiple-attribute decision-making 基于多属性决策的 Diophantine 球形模糊集及其在微型技术机器人中的应用

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109447

We introduce the concept of Diophantine spherical vague set approach to multiple-attribute decision-making. The Spherical vague set is a novel expansion of the vague set and interval valued spherical fuzzy set. Three new concepts have been introduce such as Diophantine spherical vague weighted averaging operator, Diophantine spherical vague weighted geometric operator, generalized Diophantine spherical vague weighted averaging operator and generalized Diophantine spherical vague weighted geometric operator. We provide a numerical example to show how Euclidean distance and Hamming distance interact. Applications of the Diophantine spherical vague number include idempotency, boundedness, commutativity and monotonicity in algebraic operations. They can determine the optimal option and are more well-known and reasonable. Our goal was to identify the optimal choice by comparing expert opinions with the criteria. As a result, the model’s output was more accurate as well as in the range of the natural number

. The weighted averaging distance and weighted geometric distance operators are distance measure that is based on aggregating model. By comparing the models under discussion with those suggested in the literature, we hoped to show their worth and reliability. It is possible to find a better solution more quickly, simply, and practically. Our objective was to compare the expert evaluations with the criteria and determine which option was the most suitable. Because they yield more precise solutions, these models are more accurate and more related to models with

. To show the superiority and the validity of the proposed aggregation operations, we compared it with the existing method and concluded from the comparison and sensitivity analysis that our proposed technique is more effective and reliable. This investigation yielded some intriguing results.

我们在多属性决策中引入了 Diophantine 球形模糊集方法的概念。球形模糊集是模糊集和区间值球形模糊集的新扩展。我们引入了三个新概念，如球面模糊加权平均算子、球面模糊加权几何算子、广义球面模糊加权平均算子和广义球面模糊加权几何算子。我们提供了一个数值示例来说明欧氏距离和汉明距离是如何相互作用的。Diophantine 球模糊数的应用包括代数运算中的幂等性、有界性、交换性和单调性。它们可以确定最优方案，而且更为人熟知和合理。我们的目标是通过比较专家意见和标准来确定最优选择。因此，模型的输出结果更加准确，并且在自然数范围内。加权平均距离和加权几何距离算子是基于聚合模型的距离度量。通过将讨论中的模型与文献中提出的模型进行比较，我们希望证明这些模型的价值和可靠性。这样可以更快、更简单、更实用地找到更好的解决方案。我们的目标是将专家评估与标准进行比较，确定哪种方案最合适。由于这些模型能得出更精确的解决方案，因此它们更准确，与使用 .NET技术的模型更相关。为了证明所提议的聚合操作的优越性和有效性，我们将其与现有方法进行了比较，并通过比较和敏感性分析得出结论，我们提议的技术更加有效和可靠。这项调查得出了一些耐人寻味的结果。

{"title":"Diophantine spherical vague sets and their applications for micro-technology robots based on multiple-attribute decision-making","authors":"","doi":"10.1016/j.engappai.2024.109447","DOIUrl":"10.1016/j.engappai.2024.109447","url":null,"abstract":"<div><div>We introduce the concept of Diophantine spherical vague set approach to multiple-attribute decision-making. The Spherical vague set is a novel expansion of the vague set and interval valued spherical fuzzy set. Three new concepts have been introduce such as Diophantine spherical vague weighted averaging operator, Diophantine spherical vague weighted geometric operator, generalized Diophantine spherical vague weighted averaging operator and generalized Diophantine spherical vague weighted geometric operator. We provide a numerical example to show how Euclidean distance and Hamming distance interact. Applications of the Diophantine spherical vague number include idempotency, boundedness, commutativity and monotonicity in algebraic operations. They can determine the optimal option and are more well-known and reasonable. Our goal was to identify the optimal choice by comparing expert opinions with the criteria. As a result, the model’s output was more accurate as well as in the range of the natural number <figure><img></figure>. The weighted averaging distance and weighted geometric distance operators are distance measure that is based on aggregating model. By comparing the models under discussion with those suggested in the literature, we hoped to show their worth and reliability. It is possible to find a better solution more quickly, simply, and practically. Our objective was to compare the expert evaluations with the criteria and determine which option was the most suitable. Because they yield more precise solutions, these models are more accurate and more related to models with <figure><img></figure>. To show the superiority and the validity of the proposed aggregation operations, we compared it with the existing method and concluded from the comparison and sensitivity analysis that our proposed technique is more effective and reliable. This investigation yielded some intriguing results.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the financial credibility of third-party logistic providers through a novel frank operators-driven group decision-making model with dual hesitant linguistic q-rung orthopair fuzzy information 通过具有双犹豫语言q-rung正交模糊信息的新型坦率经营者驱动群体决策模型评估第三方物流供应商的财务可信度

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109483

In the relevant literature, there is no study dealing with the financial credibility of third-party logistic providers with the help of decision-making frames. Further, there are no criteria to evaluate the third-party logistics providers' creditworthiness in practice, and decision-makers in the banks consider their judgments and experiences to assess the demand of the logistics firms. This study proposes a multi-criteria group decision-making framework through a dual hesitant linguistic

q

-rung orthopair fuzzy (DHLq-ROF) set to manage uncertainties more effectively and make a theoretical contribution to the academic literature. For ranking, the score function and accuracy function are defined. Additionally, some novel operational laws based on Frank

t

-norms and

t

-conorms are defined for DHLq-ROF numbers. A wide range of generalized aggregation operators, such as DHLq-ROF Frank weighted averaging, DHLq-ROF Frank weighted geometric, DHLq-ROF Frank generalized weighted averaging, and DHLq-ROF Frank generalized weighted geometric operators, are also investigated. Beyond that, several prominent characteristics of the proposed operators are studied. It is applied to a financial credibility problem for a multinational organization to demonstrate the introduced model's applicability. Considering the results obtained regarding the importance of the criteria, the most crucial criterion is market indebtedness, followed by fleet vehicle structure and current rate criteria, respectively. The results indicate that UPS, Kuhne & Nagel and DHL Deutsche Post are the best third-party logistic providers. The sensitivity analysis shows that the framework possesses favourable flexibility and effectiveness. Thanks to the framework's ability to produce practical solutions to challenging decision-making problems, it can be reliably preferred in engineering and other fields.

在相关文献中，还没有借助决策框架对第三方物流供应商的财务信誉进行研究。此外，在实践中也没有评价第三方物流供应商资信的标准，银行决策者仅凭自己的判断和经验来评估物流企业的需求。本研究通过双犹豫语言 q-rung 正对模糊（DHLq-ROF）集提出了一个多标准群体决策框架，以更有效地管理不确定性，并为学术文献做出理论贡献。在排序方面，定义了得分函数和准确度函数。此外，还为 DHLq-ROF 数定义了一些基于 Frank t-norms 和 t-conorms 的新颖运算法则。还研究了一系列广义聚合算子，如 DHLq-ROF 弗兰克加权平均算子、DHLq-ROF 弗兰克加权几何算子、DHLq-ROF 弗兰克广义加权平均算子和 DHLq-ROF 弗兰克广义加权几何算子。除此之外，还研究了所提算子的几个突出特点。将其应用于一个跨国组织的财务可信度问题，以证明所引入模型的适用性。考虑到所获得的有关标准重要性的结果，最关键的标准是市场负债，其次分别是车队车辆结构和现行费率标准。结果表明，UPS、Kuhne & Nagel 和 DHL 德国邮政是最佳的第三方物流供应商。敏感性分析表明，该框架具有良好的灵活性和有效性。由于该框架能够为具有挑战性的决策问题提供切实可行的解决方案，因此可以在工程和其他领域得到广泛应用。

{"title":"Evaluating the financial credibility of third-party logistic providers through a novel frank operators-driven group decision-making model with dual hesitant linguistic q-rung orthopair fuzzy information","authors":"","doi":"10.1016/j.engappai.2024.109483","DOIUrl":"10.1016/j.engappai.2024.109483","url":null,"abstract":"<div><div>In the relevant literature, there is no study dealing with the financial credibility of third-party logistic providers with the help of decision-making frames. Further, there are no criteria to evaluate the third-party logistics providers' creditworthiness in practice, and decision-makers in the banks consider their judgments and experiences to assess the demand of the logistics firms. This study proposes a multi-criteria group decision-making framework through a dual hesitant linguistic <span><math><mrow><mi>q</mi></mrow></math></span>-rung orthopair fuzzy (DHL<em>q</em>-ROF) set to manage uncertainties more effectively and make a theoretical contribution to the academic literature. For ranking, the score function and accuracy function are defined. Additionally, some novel operational laws based on Frank <span><math><mrow><mi>t</mi></mrow></math></span>-norms and <span><math><mrow><mi>t</mi></mrow></math></span>-conorms are defined for DHL<em>q</em>-ROF numbers. A wide range of generalized aggregation operators, such as DHL<em>q</em>-ROF Frank weighted averaging, DHL<em>q</em>-ROF Frank weighted geometric, DHL<em>q</em>-ROF Frank generalized weighted averaging, and DHL<em>q</em>-ROF Frank generalized weighted geometric operators, are also investigated. Beyond that, several prominent characteristics of the proposed operators are studied. It is applied to a financial credibility problem for a multinational organization to demonstrate the introduced model's applicability. Considering the results obtained regarding the importance of the criteria, the most crucial criterion is market indebtedness, followed by fleet vehicle structure and current rate criteria, respectively. The results indicate that UPS, Kuhne & Nagel and DHL Deutsche Post are the best third-party logistic providers. The sensitivity analysis shows that the framework possesses favourable flexibility and effectiveness. Thanks to the framework's ability to produce practical solutions to challenging decision-making problems, it can be reliably preferred in engineering and other fields.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning 利用深度强化学习对考虑双向电力流的插电式电动汽车充电站进行需求管理

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-03 DOI: 10.1016/j.engappai.2024.109585

The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.

插电式电动汽车技术的最新发展使其更加普及。插电式电动汽车因其环保特性而被广泛使用，并为减少全球变暖做出了贡献。随着插电式电动汽车数量的不断增加，充电协调成为管理充电站需求的关键。插电式电动汽车的随机充电行为使其成为一项艰巨的任务。本文提出了一种新颖的充电站需求管理策略。所提出的策略可在峰值负荷期间支持电网并减轻其负担。该策略采用了基于 Deep-Q 网络的深度强化学习。这是一种基于值的深度强化学习算法，利用深度神经网络逼近 Q 值函数。通过深度强化来调度插电式电动汽车的充电和放电，从而优化成本并管理充电站负荷。深度强化学习可根据实时条件和用户偏好动态优化充电调度，从而提高效率并更好地与电网集成，从而加强充电协调。深度强化学习中的奖励函数是根据充电站的电价和需求设计的。根据服务时间引入折扣系数，以提高充电协调的效率。我们利用动态定价进行了案例研究，以验证所提出的策略。结果证明，所提出的策略优化了充放电成本，并有效管理了充电站需求。此外，研究还发现，所提出的策略运行速度快，计算成本低。

{"title":"Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning","authors":"","doi":"10.1016/j.engappai.2024.109585","DOIUrl":"10.1016/j.engappai.2024.109585","url":null,"abstract":"<div><div>The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time joint recognition of weather and ground surface conditions by a multi-task deep network 多任务深度网络实时联合识别天气和地表状况

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-02 DOI: 10.1016/j.engappai.2024.109543

Climate change and the occurrence of intense and unexpected weather events highlighted the need for real-time weather warning systems, especially in smart roads and isolated scenarios like rural areas. In this work, we propose to jointly recognize the weather and the ground surface conditions using existing video surveillance systems. Previous works separately tackled these two tasks even if they are correlated to each other. We propose a convolutional neural network with shared weights in the lower layers and two separate classification branches on top to exploit the correlation between the tasks and, at the same time, learn diverse high-level features for each task. Moreover, the network architecture implements attention mechanisms allowing the classification branches to focus on diverse image regions. The method is versatile and allows us to train the network on partially labeled data. The experimental analysis on real data demonstrate the effectiveness of the proposed method on both tasks, confirmed by the accuracy comparison with existing methods for the recognition of weather and ground surface conditions. The multi-task solution improves the inference speed (50 frames per second) and reduces the required memory (less than 1 GB) with respect to a system with two different single-task approaches; these results confirm that the proposed solution is ready for video surveillance applications to support smart cities.

气候变化和突发强天气事件的发生凸显了对实时天气预警系统的需求，尤其是在智能道路和农村地区等孤立场景中。在这项工作中，我们建议利用现有的视频监控系统联合识别天气和地面状况。以前的工作是分别处理这两项任务，即使它们之间存在关联。我们提出了一种卷积神经网络，其下层具有共享权重，上层有两个独立的分类分支，以利用任务之间的相关性，同时为每个任务学习不同的高级特征。此外，该网络架构还实现了关注机制，允许分类分支关注不同的图像区域。该方法用途广泛，允许我们在部分标记的数据上训练网络。对真实数据的实验分析表明，所提出的方法在这两项任务上都很有效，与现有的天气和地表条件识别方法的准确性对比也证实了这一点。与采用两种不同单任务方法的系统相比，多任务解决方案提高了推理速度（每秒 50 帧），并减少了所需内存（不到 1 GB）；这些结果证实，所提出的解决方案已为支持智慧城市的视频监控应用做好了准备。

{"title":"Real-time joint recognition of weather and ground surface conditions by a multi-task deep network","authors":"","doi":"10.1016/j.engappai.2024.109543","DOIUrl":"10.1016/j.engappai.2024.109543","url":null,"abstract":"<div><div>Climate change and the occurrence of intense and unexpected weather events highlighted the need for real-time weather warning systems, especially in smart roads and isolated scenarios like rural areas. In this work, we propose to jointly recognize the weather and the ground surface conditions using existing video surveillance systems. Previous works separately tackled these two tasks even if they are correlated to each other. We propose a convolutional neural network with shared weights in the lower layers and two separate classification branches on top to exploit the correlation between the tasks and, at the same time, learn diverse high-level features for each task. Moreover, the network architecture implements attention mechanisms allowing the classification branches to focus on diverse image regions. The method is versatile and allows us to train the network on partially labeled data. The experimental analysis on real data demonstrate the effectiveness of the proposed method on both tasks, confirmed by the accuracy comparison with existing methods for the recognition of weather and ground surface conditions. The multi-task solution improves the inference speed (50 frames per second) and reduces the required memory (less than 1 GB) with respect to a system with two different single-task approaches; these results confirm that the proposed solution is ready for video surveillance applications to support smart cities.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-attention interaction learning network for multi-model image fusion via transformer 通过变换器实现多模型图像融合的交叉注意力交互学习网络

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-02 DOI: 10.1016/j.engappai.2024.109583

Current image fusion techniques often fail to adequately consider the inherent correlations among different modalities, resulting in suboptimal integration of multi-modal information. Drawing inspiration from inter-modal interactions, this paper introduces a cross-attention interaction learning network, CrossATF, leveraging the transformer architecture. The cornerstone of CrossATF resides in a generator network equipped with dual encoders. The multi-modal encoder incorporates two transformer modules of comparable computational complexity, alongside a meticulously designed cross-modal transformer. This architectural choice empowers the model to effectively extract modality-specific features while simultaneously integrating complementary features from diverse modalities. Furthermore, an auxiliary encoder is enlisted to encode features from the entire input image, thereby enhancing the model's comprehensive understanding of the image. Significantly, the loss function is tailored to selectively preserve a more targeted set of information from the source images, endowing the network with heightened feature extraction capabilities. Comprehensive experimental results across various datasets substantiate the promising performance of the proposed approach when compared to both task-specific methodologies and unified fusion frameworks.

当前的图像融合技术往往未能充分考虑不同模态之间固有的相关性，导致多模态信息融合效果不佳。本文从模态间交互中汲取灵感，利用变压器架构引入了跨注意力交互学习网络 CrossATF。CrossATF 的基石在于一个配备双编码器的生成器网络。多模式编码器包含两个计算复杂度相当的变压器模块，以及一个精心设计的跨模式变压器。这种结构选择使模型能够有效提取特定模态的特征，同时整合来自不同模态的互补特征。此外，辅助编码器还可对整个输入图像的特征进行编码，从而增强模型对图像的全面理解。值得注意的是，损失函数经过定制，可选择性地保留源图像中更有针对性的信息集，从而赋予网络更强的特征提取能力。各种数据集的综合实验结果证明，与特定任务方法和统一融合框架相比，所提出的方法具有良好的性能。

{"title":"Cross-attention interaction learning network for multi-model image fusion via transformer","authors":"","doi":"10.1016/j.engappai.2024.109583","DOIUrl":"10.1016/j.engappai.2024.109583","url":null,"abstract":"<div><div>Current image fusion techniques often fail to adequately consider the inherent correlations among different modalities, resulting in suboptimal integration of multi-modal information. Drawing inspiration from inter-modal interactions, this paper introduces a cross-attention interaction learning network, CrossATF, leveraging the transformer architecture. The cornerstone of CrossATF resides in a generator network equipped with dual encoders. The multi-modal encoder incorporates two transformer modules of comparable computational complexity, alongside a meticulously designed cross-modal transformer. This architectural choice empowers the model to effectively extract modality-specific features while simultaneously integrating complementary features from diverse modalities. Furthermore, an auxiliary encoder is enlisted to encode features from the entire input image, thereby enhancing the model's comprehensive understanding of the image. Significantly, the loss function is tailored to selectively preserve a more targeted set of information from the source images, endowing the network with heightened feature extraction capabilities. Comprehensive experimental results across various datasets substantiate the promising performance of the proposed approach when compared to both task-specific methodologies and unified fusion frameworks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Engineering applications of artificial intelligence a knowledge-guided reinforcement learning method for lateral path tracking 人工智能的工程应用一种用于横向路径跟踪的知识引导强化学习方法

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-02 DOI: 10.1016/j.engappai.2024.109588

Lateral Control algorithms in autonomous vehicles often necessitates an online fine-tuning procedure in the real world. While reinforcement learning (RL) enables vehicles to learn and improve the lateral control performance through repeated trial and error interactions with a dynamic environment, applying RL directly to safety-critical applications in real physical world is challenging because ensuring safety during the learning process remains difficult. To enable safe learning, a promising direction is to make use of previously gathered offline data, which is frequently accessible in engineering applications. In this context, this paper presents a set of knowledge-guided RL algorithms that can not only fully leverage the prior collected offline data without the need of a physics-based simulator, but also allow further online policy improvement in a smooth, safe and efficient manner. To evaluate the effectiveness of the proposed algorithms on a real controller, a hardware-in-the-loop and a miniature vehicle platform are built. Compared with the vanilla RL, behavior cloning and the existing controller, the proposed algorithms realize a closed-loop solution for lateral control problems from offline training to online fine-tuning, making it attractive for future similar RL-based controller to build upon.

自动驾驶车辆的横向控制算法通常需要在真实世界中进行在线微调。虽然强化学习（RL）使车辆能够通过与动态环境的反复试验和错误互动来学习和改进横向控制性能，但将 RL 直接应用于真实物理世界中的安全关键型应用仍具有挑战性，因为在学习过程中确保安全仍然很困难。为了实现安全学习，一个很有前途的方向是利用以前收集的离线数据，这些数据在工程应用中经常可以获得。在此背景下，本文提出了一套知识引导的 RL 算法，不仅可以充分利用先前收集的离线数据，而无需基于物理的模拟器，还能以平稳、安全和高效的方式进一步改进在线策略。为了评估所提出的算法在实际控制器上的有效性，我们构建了一个硬件在环和一个微型车辆平台。与虚RL、行为克隆和现有控制器相比，所提出的算法实现了从离线训练到在线微调的横向控制问题闭环解决方案，这对未来类似的基于RL的控制器具有很大的吸引力。

{"title":"Engineering applications of artificial intelligence a knowledge-guided reinforcement learning method for lateral path tracking","authors":"","doi":"10.1016/j.engappai.2024.109588","DOIUrl":"10.1016/j.engappai.2024.109588","url":null,"abstract":"<div><div>Lateral Control algorithms in autonomous vehicles often necessitates an online fine-tuning procedure in the real world. While reinforcement learning (RL) enables vehicles to learn and improve the lateral control performance through repeated trial and error interactions with a dynamic environment, applying RL directly to safety-critical applications in real physical world is challenging because ensuring safety during the learning process remains difficult. To enable safe learning, a promising direction is to make use of previously gathered offline data, which is frequently accessible in engineering applications. In this context, this paper presents a set of knowledge-guided RL algorithms that can not only fully leverage the prior collected offline data without the need of a physics-based simulator, but also allow further online policy improvement in a smooth, safe and efficient manner. To evaluate the effectiveness of the proposed algorithms on a real controller, a hardware-in-the-loop and a miniature vehicle platform are built. Compared with the vanilla RL, behavior cloning and the existing controller, the proposed algorithms realize a closed-loop solution for lateral control problems from offline training to online fine-tuning, making it attractive for future similar RL-based controller to build upon.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new spatiotemporal long-term prediction method for Continuous Annealing Processes 连续退火过程的时空长期预测新方法

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-01 DOI: 10.1016/j.engappai.2024.109514

Accurately and consistently predicting the heating state is essential to maintain stable operation in continuous annealing processes (CAPs). However, long-term prediction biases often arise due to unmodeled dynamics associated with high-dimensional, time-varying, and strongly coupled variables. This study introduces a spatiotemporal-based forecast model designed to extend the prediction horizon while significantly reducing bias accumulation. The model leverages the spatial characteristics derived from classified process parameters by analyzing the internal structure and dynamics of the process. Additionally, it captures the temporal features of each parameter type through deep learning techniques that preserve and learn from historical data, enabling the model to account for the autocorrelation of multiple variables, including the output, and their correlation with the output. We conducted experiments with real process data, confirming the model’s accuracy and consistency in real-world settings. Additionally, ablation experiments validated the need to integrate both temporal and spatial features for long-term prediction accuracy. Compared to existing methods, the proposed model significantly reduces prediction bias and enhances forecast robustness.

要保持连续退火过程（CAPs）的稳定运行，就必须准确一致地预测加热状态。然而，由于与高维、时变和强耦合变量相关的未建模动态，往往会出现长期预测偏差。本研究介绍了一种基于时空的预测模型，旨在延长预测期限，同时显著减少偏差累积。该模型通过分析过程的内部结构和动态，利用从分类过程参数中得出的空间特征。此外，它还通过保存和学习历史数据的深度学习技术来捕捉每种参数类型的时间特征，使模型能够考虑包括输出在内的多个变量的自相关性及其与输出的相关性。我们利用真实过程数据进行了实验，证实了模型在现实世界环境中的准确性和一致性。此外，消融实验还验证了整合时间和空间特征以提高长期预测准确性的必要性。与现有方法相比，所提出的模型大大减少了预测偏差，增强了预测的稳健性。

{"title":"A new spatiotemporal long-term prediction method for Continuous Annealing Processes","authors":"","doi":"10.1016/j.engappai.2024.109514","DOIUrl":"10.1016/j.engappai.2024.109514","url":null,"abstract":"<div><div>Accurately and consistently predicting the heating state is essential to maintain stable operation in continuous annealing processes (CAPs). However, long-term prediction biases often arise due to unmodeled dynamics associated with high-dimensional, time-varying, and strongly coupled variables. This study introduces a spatiotemporal-based forecast model designed to extend the prediction horizon while significantly reducing bias accumulation. The model leverages the spatial characteristics derived from classified process parameters by analyzing the internal structure and dynamics of the process. Additionally, it captures the temporal features of each parameter type through deep learning techniques that preserve and learn from historical data, enabling the model to account for the autocorrelation of multiple variables, including the output, and their correlation with the output. We conducted experiments with real process data, confirming the model’s accuracy and consistency in real-world settings. Additionally, ablation experiments validated the need to integrate both temporal and spatial features for long-term prediction accuracy. Compared to existing methods, the proposed model significantly reduces prediction bias and enhances forecast robustness.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous smart palm tree harvesting with deep learning-enabled date fruit type and maturity stage classification 利用深度学习对枣果类型和成熟阶段进行分类，实现自主智能棕榈树采摘

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-01 DOI: 10.1016/j.engappai.2024.109506

This work proposes an innovative autonomous system based on intelligent deep-transfer learning for the sustainable harvesting of palm trees. The machine learning-based autonomous robot detects and captures the date fruit bunches on palm trees in the natural farm environment using the lightweight you only look once (YOLO)v8 algorithm. Five different types of fruit bunches are further classified using a deep transfer learning system based on the type (Khalas, Barhi, Sullaj, Meneifi, and Naboot Saif) and the maturity stage of date fruit (Immature, Khalal, Khalal with Rutab, Pre-Tamar, and Tamar) for their efficient, faster, and accurate harvesting. Five deep convolutional neural network (CNN) models, the Alex Krizhevsky network (AlexNet), the visual geometry group (VGG-16), the residual network (ResNet-50), Inception-v3 and Efficient Net, were trained on around 12,000 images at the bunch level for the two classification tasks. The findings of various performed experiments suggested that the VGG-16 network outperforms the compared models with maximum achieved testing accuracies of 98.89% and 98.17% for date type and maturity stage classification, respectively. The obtained testing accuracies of AlexNet, ResNet-50, Efficient Net, and Inception-v3 models are 97.33%, 97.87%, 98.39%, 96.61% 98%, 93%, and 86.5% for both date type/maturity stage predictions. These obtained accuracies are superior than the state-of-the-art legacy models. Autonomous robotic vehicle front and top cameras are used to localize the quick response (QR)-labeled palm trees using canny edge detection and hough transformation, and date bunch detection and capturing using the trained YOLOv8 algorithm. The robotic vehicle transfers all captured images using Firebase after the completion of the farm journey. The developed and integrated front-end user interface (UI) provides ease to farmers for the two classification tasks of the retrieved images, along with the harvesting decision for each image. The use of proposed sustainable smart harvesting robots to classify and analyze date bunches in the natural environment can significantly improve the yield and global supply chain of this fruit.

本作品提出了一种基于智能深度传输学习的创新型自主系统，用于棕榈树的可持续收获。基于机器学习的自主机器人采用轻量级的 "你只看一次（YOLO）v8 "算法，在自然农场环境中检测并捕捉棕榈树上的红枣果穗。根据枣果的类型（Khalas、Barhi、Sullaj、Meneifi 和 Naboot Saif）和成熟阶段（未成熟、Khalal、Khalal with Rutab、Pre-Tamar 和 Tamar），使用深度传输学习系统对五种不同类型的果穗进行进一步分类，以便高效、快速、准确地收获。为了完成这两项分类任务，我们在约 12,000 幅果穗级别的图像上训练了五个深度卷积神经网络（CNN）模型，即 Alex Krizhevsky 网络（AlexNet）、视觉几何组（VGG-16）、残差网络（ResNet-50）、Inception-v3 和 Efficient Net。各种实验结果表明，VGG-16 网络在枣类和成熟期分类方面的测试准确率最高，分别达到 98.89% 和 98.17%，优于其他比较模型。AlexNet、ResNet-50、Efficient Net 和 Inception-v3 模型在日期类型/成熟阶段预测方面的测试准确率分别为 97.33%、97.87%、98.39%、96.61% 98%、93% 和 86.5%。这些准确度均优于最先进的传统模型。自主机器人车辆的前置摄像头和顶部摄像头利用边缘检测（canny edge detection）和霍夫变换（hough transformation）对快速反应（QR）标记的棕榈树进行定位，并利用训练有素的 YOLOv8 算法对枣束进行检测和捕捉。在完成农场之旅之后，机器人车辆使用 Firebase 传输所有捕获的图像。开发和集成的前端用户界面（UI）可方便农民对检索到的图像进行两次分类，并对每张图像做出收割决定。使用建议的可持续智能收获机器人对自然环境中的枣串进行分类和分析，可以显著提高这种水果的产量和全球供应链。

{"title":"Autonomous smart palm tree harvesting with deep learning-enabled date fruit type and maturity stage classification","authors":"","doi":"10.1016/j.engappai.2024.109506","DOIUrl":"10.1016/j.engappai.2024.109506","url":null,"abstract":"<div><div>This work proposes an innovative autonomous system based on intelligent deep-transfer learning for the sustainable harvesting of palm trees. The machine learning-based autonomous robot detects and captures the date fruit bunches on palm trees in the natural farm environment using the lightweight you only look once (YOLO)v8 algorithm. Five different types of fruit bunches are further classified using a deep transfer learning system based on the type (Khalas, Barhi, Sullaj, Meneifi, and Naboot Saif) and the maturity stage of date fruit (Immature, Khalal, Khalal with Rutab, Pre-Tamar, and Tamar) for their efficient, faster, and accurate harvesting. Five deep convolutional neural network (CNN) models, the Alex Krizhevsky network (AlexNet), the visual geometry group (VGG-16), the residual network (ResNet-50), Inception-v3 and Efficient Net, were trained on around 12,000 images at the bunch level for the two classification tasks. The findings of various performed experiments suggested that the VGG-16 network outperforms the compared models with maximum achieved testing accuracies of 98.89% and 98.17% for date type and maturity stage classification, respectively. The obtained testing accuracies of AlexNet, ResNet-50, Efficient Net, and Inception-v3 models are 97.33%, 97.87%, 98.39%, 96.61% 98%, 93%, and 86.5% for both date type/maturity stage predictions. These obtained accuracies are superior than the state-of-the-art legacy models. Autonomous robotic vehicle front and top cameras are used to localize the quick response (QR)-labeled palm trees using canny edge detection and hough transformation, and date bunch detection and capturing using the trained YOLOv8 algorithm. The robotic vehicle transfers all captured images using Firebase after the completion of the farm journey. The developed and integrated front-end user interface (UI) provides ease to farmers for the two classification tasks of the retrieved images, along with the harvesting decision for each image. The use of proposed sustainable smart harvesting robots to classify and analyze date bunches in the natural environment can significantly improve the yield and global supply chain of this fruit.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A robust accent classification system based on variational mode decomposition 基于变模分解的鲁棒口音分类系统

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-01 DOI: 10.1016/j.engappai.2024.109512

State-of-the-art automatic speech recognition models often struggle to capture nuanced features inherent in accented speech, leading to sub-optimal performance in speaker recognition based on regional accents. Despite substantial progress in the field of automatic speech recognition, ensuring robustness to accents and generalization across dialects remains a persistent challenge, particularly in real-time settings. In response, this study introduces a novel approach leveraging Variational Mode Decomposition (VMD) to enhance accented speech signals, aiming to mitigate noise interference and improve generalization on unseen accented speech datasets. Our method employs decomposed modes of the VMD algorithm for signal reconstruction, followed by feature extraction using Mel-Frequency Cepstral Coefficients (MFCC). These features are subsequently classified using machine learning models such as 1D Convolutional Neural Network (1D-CNN), Support Vector Machine (SVM), Random Forest, and Decision Trees, as well as a deep learning model based on a 2D Convolutional Neural Network (2D-CNN). Experimental results demonstrate superior performance, with the SVM classifier achieving an accuracy of approximately 87.5% on a standard dataset and 99.3% on the AccentBase dataset. The 2D-CNN model further improves the results in multi-class accent classification tasks. This research contributes to advancing automatic speech recognition robustness and accent-inclusive speaker recognition, addressing critical challenges in real-world applications.

最先进的自动语音识别模型往往难以捕捉重音语音中固有的细微特征，从而导致基于地区口音的说话人识别效果不尽如人意。尽管自动语音识别领域取得了长足进步，但确保对口音的鲁棒性和跨方言泛化仍是一项长期挑战，尤其是在实时环境中。为此，本研究引入了一种利用变异模式分解（VMD）来增强重音语音信号的新方法，旨在减轻噪声干扰，提高对未见重音语音数据集的泛化能力。我们的方法采用 VMD 算法的分解模式进行信号重建，然后使用梅尔-频率倒频谱系数（MFCC）进行特征提取。随后使用机器学习模型对这些特征进行分类，如一维卷积神经网络（1D-CNN）、支持向量机（SVM）、随机森林和决策树，以及基于二维卷积神经网络（2D-CNN）的深度学习模型。实验结果表明，SVM 分类器在标准数据集上的准确率约为 87.5%，在 AccentBase 数据集上的准确率为 99.3%，表现出色。2D-CNN 模型进一步提高了多类口音分类任务的结果。这项研究有助于提高自动语音识别的鲁棒性和口音包容性，解决实际应用中的关键挑战。

{"title":"A robust accent classification system based on variational mode decomposition","authors":"","doi":"10.1016/j.engappai.2024.109512","DOIUrl":"10.1016/j.engappai.2024.109512","url":null,"abstract":"<div><div>State-of-the-art automatic speech recognition models often struggle to capture nuanced features inherent in accented speech, leading to sub-optimal performance in speaker recognition based on regional accents. Despite substantial progress in the field of automatic speech recognition, ensuring robustness to accents and generalization across dialects remains a persistent challenge, particularly in real-time settings. In response, this study introduces a novel approach leveraging Variational Mode Decomposition (VMD) to enhance accented speech signals, aiming to mitigate noise interference and improve generalization on unseen accented speech datasets. Our method employs decomposed modes of the VMD algorithm for signal reconstruction, followed by feature extraction using Mel-Frequency Cepstral Coefficients (MFCC). These features are subsequently classified using machine learning models such as 1D Convolutional Neural Network (1D-CNN), Support Vector Machine (SVM), Random Forest, and Decision Trees, as well as a deep learning model based on a 2D Convolutional Neural Network (2D-CNN). Experimental results demonstrate superior performance, with the SVM classifier achieving an accuracy of approximately 87.5% on a standard dataset and 99.3% on the AccentBase dataset. The 2D-CNN model further improves the results in multi-class accent classification tasks. This research contributes to advancing automatic speech recognition robustness and accent-inclusive speaker recognition, addressing critical challenges in real-world applications.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0