在汉越低资源翻译任务中,句子中的实体词准确翻译是一大难点。针对实体词在训练语料中出现的频率较低,模型无法构建双语实体词之间的映射关系等问题,构建一种融入实体翻译的汉越神经机器翻译模型。首先,通过汉越实体双语词典预先获取源...在汉越低资源翻译任务中,句子中的实体词准确翻译是一大难点。针对实体词在训练语料中出现的频率较低,模型无法构建双语实体词之间的映射关系等问题,构建一种融入实体翻译的汉越神经机器翻译模型。首先,通过汉越实体双语词典预先获取源句中实体词的翻译结果;其次,将结果拼接在源句末端作为模型的输入,同时在编码端引入“约束提示信息”增强表征;最后,在解码端融入指针网络机制,以确保模型能复制输出源端句的词汇。实验结果表明,该模型相较于跨语言模型XLM-R(Cross-lingual Language Model-RoBERTa)的双语评估替补(BLEU)值在汉越方向提升了1.37,越汉方向提升了0.21,时间性能上相较于Transformer该模型在汉越方向和越汉方向分别缩短3.19%和3.50%,可有效地提升句子中实体词翻译的综合性能。展开更多
随着电子商务与物流行业的快速发展,仓储配送对拣选效率的要求不断提高,自主移动机器人(autonomous mobile robot,AMR)在“货到人”拣选模式中的应用日益广泛。文章围绕移动机器人履约系统(robotic mobile fulfillment system,RMFS)中AM...随着电子商务与物流行业的快速发展,仓储配送对拣选效率的要求不断提高,自主移动机器人(autonomous mobile robot,AMR)在“货到人”拣选模式中的应用日益广泛。文章围绕移动机器人履约系统(robotic mobile fulfillment system,RMFS)中AMR的订单分配问题展开系统综述。首先明确了订单分配的概念,并构建了包含关键变量、约束条件及优化目标的理论框架,同时根据不同特征对该问题进行分类讨论。其次,为进一步明确相关求解方法,从经典优化方法、启发式与元启发式算法、规则策略与仿真优化算法及人工智能与机器学习方法等多个角度介绍了国内外订单分配及多机器人任务调度方面的研究进展,并详细讨论了影响订单分配效率的多种因素,总结了相关核心性能指标。最后,归纳了当前AMR订单分配研究所面临的实时性、多目标冲突、多机器人协同与路径冲突、动态不确定性以及人因因素等关键挑战,并针对自适应决策、多智能体博弈、深度强化学习结合仿真平台以及绿色能源优化等未来研究方向提出了部分建议。展开更多
The control of gas extraction in coal mines relies on the effectiveness of gas extraction.The main method of gas extraction is to drive drill pipes into the coal seam through a drilling rig and use technologies such a...The control of gas extraction in coal mines relies on the effectiveness of gas extraction.The main method of gas extraction is to drive drill pipes into the coal seam through a drilling rig and use technologies such as hydraulic fracturing to pre-extract gas in the drill holes.Therefore,the real-time detection of the drill pipe status is closely related to the effectiveness of gas extraction.To achieve fast and accurate identification of drill pipes,we propose FSSYOLO,which is a lightweight drill pipe detection method based on YOLOv8n-obb.This method first introduces the FasterBlock module into the C2f module of YOLOv8n-obb to reduce the number of model parameters and decrease the computational cost of the model and redundant feature maps.Next,the SimAM attention mechanism is added to the backbone network to enhance the weight of important features in the feature map and improve the model’s feature extraction capability.In addition,using shared convolution to optimize the detection head,not only lightens the detection head but also enhances its ability to learn features of different scales,improving the model’s generalization ability.Finally,the FSS-YOLO algorithm is validated on the self-built dataset.The results show that compared with the original algorithm,FSS-YOLO achieves improvements of 5.1%in mAP50 and 11.5%in Recall,reduces the number of parameters by 45.8%,and achieves an inference speed of 27.8 ms/frame on Jetson Orin NX.Additionally,the visual detection results for different scenarios demonstrate that the improved YOLOv8n-obb algorithm has promising application prospects.展开更多
随着全球化加速与信息技术迅猛发展,语言交流需求持续增长。机器翻译作为跨越语言障碍的关键工具,对促进不同语言使用者间的信息交流和文化理解意义重大。文章深入探究机器翻译的发展现状、应用场景、传统方法局限、语料库重要性、实验...随着全球化加速与信息技术迅猛发展,语言交流需求持续增长。机器翻译作为跨越语言障碍的关键工具,对促进不同语言使用者间的信息交流和文化理解意义重大。文章深入探究机器翻译的发展现状、应用场景、传统方法局限、语料库重要性、实验研究步骤,剖析当前挑战与改进方向。机器翻译在日常生活中愈发重要,但其仍存在局限,需进一步改进。期望人工智能与翻译深度融合,探索具备真正语言理解能力的通用人工智能(Artificial General Intelligence,AGI)系统,以解决复杂语境和高层次语义问题,推动对各类语言和文化的公平支持,实现普适翻译。展开更多
The Bohai A oil and gas field is a natural gas and oil coproduction reservoir in the southern Bohai Sea,with an average gaseoil ratio of approximately 65 m^(3)/m^(3).The oil and gas field has now entered the high wate...The Bohai A oil and gas field is a natural gas and oil coproduction reservoir in the southern Bohai Sea,with an average gaseoil ratio of approximately 65 m^(3)/m^(3).The oil and gas field has now entered the high water-cut stage,and in it,ineffective water circulation has intensified.Meanwhile,the process of adjusting the injection volume of water injection wells is overly complicated and relies on the experience of reservoir engineers.This paper established an automatic allocation method aimed at optimizing injection strategies based on the reservoir injection allocation scheme and utilizing real-time online data from intelligent layered injection wells by combining numerical simulation with artificial intelligence and machine learning algorithms.First,according to the basic parameters of block B in the Bohai A oil and gas field,a reservoir numerical simulation model was established,and historical fitting was carried out.The calculation found that the natural gas production of the A oil field would increase over time,although its oil production showed a decreasing trend.Using this model,finite group calculations were performed to establish an effective dataset.Second,the training and prediction effects of three machine learning prediction modelsdsupport vector machine,BP neural network,and random forestdwere compared,and the BP neural network was selected as the machine learning mathematical model for injection allocation optimization.Third,300 neurons and three hidden layers were used in the optimized neural network.The number of training set samples used was 185,and the number of test set samples was 19.Fourth,the optimized BP neural network model exhibits enhanced prediction accuracy,improved generalization capabilities,and superior dynamic relationshipecapturing abilities.It effectively establishes a relatively accurate complex nonlinear relationship between the injected water volume and the production of natural gas and oil,providing valuable guidance for layered allocation in injection wells.The relative error of the calculation results of the optimized neural network prediction model is approximately±2.3%.This model can be utilized to simulate the injection allocation of injection wells,potentially increasing natural gas and oil production by over 4%.展开更多
文摘在汉越低资源翻译任务中,句子中的实体词准确翻译是一大难点。针对实体词在训练语料中出现的频率较低,模型无法构建双语实体词之间的映射关系等问题,构建一种融入实体翻译的汉越神经机器翻译模型。首先,通过汉越实体双语词典预先获取源句中实体词的翻译结果;其次,将结果拼接在源句末端作为模型的输入,同时在编码端引入“约束提示信息”增强表征;最后,在解码端融入指针网络机制,以确保模型能复制输出源端句的词汇。实验结果表明,该模型相较于跨语言模型XLM-R(Cross-lingual Language Model-RoBERTa)的双语评估替补(BLEU)值在汉越方向提升了1.37,越汉方向提升了0.21,时间性能上相较于Transformer该模型在汉越方向和越汉方向分别缩短3.19%和3.50%,可有效地提升句子中实体词翻译的综合性能。
文摘随着电子商务与物流行业的快速发展,仓储配送对拣选效率的要求不断提高,自主移动机器人(autonomous mobile robot,AMR)在“货到人”拣选模式中的应用日益广泛。文章围绕移动机器人履约系统(robotic mobile fulfillment system,RMFS)中AMR的订单分配问题展开系统综述。首先明确了订单分配的概念,并构建了包含关键变量、约束条件及优化目标的理论框架,同时根据不同特征对该问题进行分类讨论。其次,为进一步明确相关求解方法,从经典优化方法、启发式与元启发式算法、规则策略与仿真优化算法及人工智能与机器学习方法等多个角度介绍了国内外订单分配及多机器人任务调度方面的研究进展,并详细讨论了影响订单分配效率的多种因素,总结了相关核心性能指标。最后,归纳了当前AMR订单分配研究所面临的实时性、多目标冲突、多机器人协同与路径冲突、动态不确定性以及人因因素等关键挑战,并针对自适应决策、多智能体博弈、深度强化学习结合仿真平台以及绿色能源优化等未来研究方向提出了部分建议。
文摘The control of gas extraction in coal mines relies on the effectiveness of gas extraction.The main method of gas extraction is to drive drill pipes into the coal seam through a drilling rig and use technologies such as hydraulic fracturing to pre-extract gas in the drill holes.Therefore,the real-time detection of the drill pipe status is closely related to the effectiveness of gas extraction.To achieve fast and accurate identification of drill pipes,we propose FSSYOLO,which is a lightweight drill pipe detection method based on YOLOv8n-obb.This method first introduces the FasterBlock module into the C2f module of YOLOv8n-obb to reduce the number of model parameters and decrease the computational cost of the model and redundant feature maps.Next,the SimAM attention mechanism is added to the backbone network to enhance the weight of important features in the feature map and improve the model’s feature extraction capability.In addition,using shared convolution to optimize the detection head,not only lightens the detection head but also enhances its ability to learn features of different scales,improving the model’s generalization ability.Finally,the FSS-YOLO algorithm is validated on the self-built dataset.The results show that compared with the original algorithm,FSS-YOLO achieves improvements of 5.1%in mAP50 and 11.5%in Recall,reduces the number of parameters by 45.8%,and achieves an inference speed of 27.8 ms/frame on Jetson Orin NX.Additionally,the visual detection results for different scenarios demonstrate that the improved YOLOv8n-obb algorithm has promising application prospects.
文摘随着全球化加速与信息技术迅猛发展,语言交流需求持续增长。机器翻译作为跨越语言障碍的关键工具,对促进不同语言使用者间的信息交流和文化理解意义重大。文章深入探究机器翻译的发展现状、应用场景、传统方法局限、语料库重要性、实验研究步骤,剖析当前挑战与改进方向。机器翻译在日常生活中愈发重要,但其仍存在局限,需进一步改进。期望人工智能与翻译深度融合,探索具备真正语言理解能力的通用人工智能(Artificial General Intelligence,AGI)系统,以解决复杂语境和高层次语义问题,推动对各类语言和文化的公平支持,实现普适翻译。
文摘The Bohai A oil and gas field is a natural gas and oil coproduction reservoir in the southern Bohai Sea,with an average gaseoil ratio of approximately 65 m^(3)/m^(3).The oil and gas field has now entered the high water-cut stage,and in it,ineffective water circulation has intensified.Meanwhile,the process of adjusting the injection volume of water injection wells is overly complicated and relies on the experience of reservoir engineers.This paper established an automatic allocation method aimed at optimizing injection strategies based on the reservoir injection allocation scheme and utilizing real-time online data from intelligent layered injection wells by combining numerical simulation with artificial intelligence and machine learning algorithms.First,according to the basic parameters of block B in the Bohai A oil and gas field,a reservoir numerical simulation model was established,and historical fitting was carried out.The calculation found that the natural gas production of the A oil field would increase over time,although its oil production showed a decreasing trend.Using this model,finite group calculations were performed to establish an effective dataset.Second,the training and prediction effects of three machine learning prediction modelsdsupport vector machine,BP neural network,and random forestdwere compared,and the BP neural network was selected as the machine learning mathematical model for injection allocation optimization.Third,300 neurons and three hidden layers were used in the optimized neural network.The number of training set samples used was 185,and the number of test set samples was 19.Fourth,the optimized BP neural network model exhibits enhanced prediction accuracy,improved generalization capabilities,and superior dynamic relationshipecapturing abilities.It effectively establishes a relatively accurate complex nonlinear relationship between the injected water volume and the production of natural gas and oil,providing valuable guidance for layered allocation in injection wells.The relative error of the calculation results of the optimized neural network prediction model is approximately±2.3%.This model can be utilized to simulate the injection allocation of injection wells,potentially increasing natural gas and oil production by over 4%.
