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Exploring Freight Loading Management by Deep Learning: a Case Study in Home Furnishing Industry

Annals of data Science 2022年第2期9卷 213-228页

作者： Deng, Wei Patil, Rajvardhan Liu, Fangyao Daji, Ergu Shi, Yong College of Big Data Statistics Guizhou University of Finance and Economics Guizhou550025 China College of Computer and Information Science Arkansas Tech University RussellvilleAR68182 United States College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Research Center on Fictitious Economy and Data Science The Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Nebraska Furniture Mart OmahaNE68124 United States Division of Science Research and Technology Southwest Minzu University Chengdu610041 China

Long short-term memory (LSTM) networks as state-of-the-art Deep Learning models, have achieved remarkable results in time series forecasting. However, they are less commonly applied to the industry of logistics. This paper presents two novel LSTM networks to predict the freight loading of routing areas, and the design of a smart loading management system is introduced. While most existing works on LSTM utilize the power of its prediction very well, our study shows less accurate and instable results if only LSTM network is applied, that arise from the big variation in the dataset. Instead, two constraints are inspired in this paper. The first constraint adds a time factor node to strengthen the correlation between predicted final loading units and booked loading units as close to delivery date. And the second proposal extends to parallel training by groupwise constraint. Experiments with across 3-year records in a national wide home furnishing retail company show that constraint LSTM networks significantly improve both the accuracy and stability of the prediction. Besides, the design of a smart loading management system will be shown, in which LSTM model plays the core rule to predict loading capacity, meanwhile the rule-based system triggers different events based on loading prediction and truck size. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Long short-term memory

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Robust Visual Odometry Using Semantic Information in Complex Dynamic Scenes 5th

Robust Visual Odometry Using Semantic Information in Complex...

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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Wang, Hao Wang, Le Fang, Baofu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230009 China Department of Computer Science and Technology Hefei University of Technology Hefei Anhui230009 China

ISBN: (纸本)9789811623356

Traditional vision-based simultaneous localization and mapping (SLAM) technology cannot obtain the semantic information of the surrounding environment, which will cause the robot to fail to complete intelligent grasping, human-computer interaction, and other advanced decision tasks. There are many challenges to solve this problem, such as obtaining less semantic information of the environment with low precision and being unable to deal with dynamic objects in a real environment quickly and effectively. In this paper, a robust visual odometry using semantic information in complex dynamic scenes was designed. Specifically, we present a refined instance segmentation method based on the contextual information of the frame to improve the accuracy of segmentation. On this basis, a feature detection and elimination algorithm for dynamic objects based on instance-level semantic information is proposed to improve the localization accuracy of camera pose. We extensively evaluate our system on the public TUM data set and compare it with ORB-SLAM2 and other methods. Experiments show that our methods greatly improve the localization accuracy of the camera pose and the robustness of the system, which verifies that our system is effective in complex dynamic scenes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cameras

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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COVID-19 Impact on Global Electricity Generation Structure-Based on Sustainable Development Perspective

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Procedia computer science 2022年 214卷 1206-1213页

作者： Shuyang Peng Xian Yang Haibo Lu Kun Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China. Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China. Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China.

This paper has sorted out the general logic of the impact of COVID-19 on energy consumption. In the short term, the epidemic has forced governments to adopt different levels of lockdown measures. The total electricity consumption decreased and the share of renewable energy has increased. Based on the International Energy Agency's (IEA) data on electricity production and consumption, this paper analyzes the long-term effects: The COVID-19 pandemic does affect overall electricity demand and market structure in the short term, but in the long term, the impact of the pandemic is detrimental to the proportion of electricity generated from renewable sources. Increased, especially in hard-hit countries. The impact on countries with different levels of development is different, and countries with higher levels of economic development are better able to resist such exogenous shocks. The pandemic is still ongoing, how to steadily promote sustainable development? In this paper we suggest that to cope with climate change, to accomplish energy transformation and upgrading or even sustainable development, we should not only rely on reducing human activities, but give more support to countries with low development levels in realizing sustainable development.

关键词： COVID-19 Electricity generation Renewable energy

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Multi-view Feature Augmentation with Adaptive Class Activation Mapping

arXiv

引用

arXiv 2022年

作者： Gao, Xiang Tian, Yingjie Qi, Zhiquan School of Computer Science and Technology University of Chinese Academy of Sciences China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences China

We propose an end-to-end-trainable feature augmentation module built for image classification that extracts and exploits multi-view local features to boost model performance. Different from using global average pooling (GAP) to extract vectorized features from only the global view, we propose to sample and ensemble diverse multi-view local features to improve model robustness. To sample class-representative local features, we incorporate a simple auxiliary classifier head (comprising only one 1×1 convolutional layer) which efficiently and adaptively attends to class-discriminative local regions of feature maps via our proposed AdaCAM (Adaptive Class Activation Mapping). Extensive experiments demonstrate consistent and noticeable performance gains achieved by our multi-view feature augmentation module. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

arXiv

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arXiv 2023年

作者： Ge, Jiake Zhang, Huanchen Shi, Boyu Luo, Yuanhui Guo, Yunda Chai, Yunpeng Chen, Yuxing Pan, Anqun Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China China China and Shanghai Qi Zhi Institute China Tsinghua University China Tencent Inc China

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is learned indexes, which use a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04× compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+. © 2023, CC BY-NC-ND.

关键词： Scalability

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Automatic Life Event Tree Generation for Older Adults 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： Gui, Fang Wu, Xi Hu, Min Yang, Jiaoyun School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Studies have shown that learning personal stories could help provide individualized eldercare services. However, personal stories are often disordered because of the scattered collection, including informal interviews or daily interactions, which brings difficulties in acquiring valuable information quickly. One solution to this problem is to extract events from personal stories and automatically organize them in chronological order. Events extracted by current methods from social media or news corpus are mainly organized in a linear structure. These works usually focus on the event time and ignore the consistency of event contents when organizing events. This paper aims to organize events into a tree structure based on an event network, with stem nodes representing key event topics and branch nodes representing detailed events. Social workers or caregivers can clarify the life experience of the older adults quickly through the event tree and have a preliminary understanding of them. The experiments show that the event tree generated by our method has a better performance in consistency than current event organization methods. A survey study shows that our method achieves the highest logical coherence for the event tree branches compared with other algorithms. © 2022, Springer Nature Switzerland AG.

关键词： Trees (mathematics)

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Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin

arXiv

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arXiv 2024年

作者： Guo, Tianlin Zhang, Lingling Wang, Jiaxin Lei, Yunkuo Li, Yifei Wang, Haofen Liu, Jun School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China School of Computer Science and Technology Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi’an710049 China College of Design and Innovation Tongji University Shanghai200092 China

Few-shot relation extraction with none-of-the-above (FsRE with NOTA) aims at predicting labels in few-shot scenarios with unknown classes. FsRE with NOTA is more challenging than the conventional few-shot relation extraction task, since the boundaries of unknown classes are complex and difficult to learn. Meta-learning based methods, especially prototype-based methods, are the mainstream solutions to this task. They obtain the classification boundary by learning the sample distribution of each class. However, their performance is limited because few-shot overfitting and NOTA boundary confusion lead to misclassification between known and unknown classes. To this end, we propose a novel framework based on Gaussian prototype and adaptive margin named GPAM for FsRE with NOTA, which includes three modules, semi-factual representation, GMM-prototype metric learning and decision boundary learning. The first two modules obtain better representations to solve the few-shot problem through debiased information enhancement and Gaussian space distance measurement. The third module learns more accurate classification boundaries and prototypes through adaptive margin and negative sampling. In the training procedure of GPAM, we use contrastive learning loss to comprehensively consider the effects of range and margin on the classification of known and unknown classes to ensure the model's stability and robustness. Sufficient experiments and ablations on the FewRel dataset show that GPAM surpasses previous prototype methods and achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Image Orientation Estimation Based On Deep Learning - A Survey

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Procedia Computer Science 2024年 242卷 1193-1197页

作者： Ruijie Xu Yong Shi Zhiquan Qi Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing 00190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska Omaha 68182 USA

In the realm of visual tasks, the estimation of orientation plays a pivotal role yet often remains an understudied aspect in the gamut of image analysis research. This paper endeavors to address this deficiency by conducting a comprehensive review of methods for orientation estimation within 2D imaging contexts. We aim to provide researchers with an exhaustive overview and detailed analysis of the techniques utilized for orientation estimation, thereby enhancing their understanding of this complex field. We introduce an organized classification of orientation estimation methods, segmented into approaches based on classification algorithms and those based on regression models. This classification not only sheds light on the distinct methodologies but also scrutinizes their underlying assumptions, practical applications. Additionally, the study identifies and underscores emerging research opportunities that hold the potential to enrich the discourse and technical advancements in orientation estimation, thereby encouraging deeper academic investigation and technological development.

关键词： Classification-based Orientation Estimation Regression-based Orientation Estimation

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Collision-Aware MPCC Integrated with Neural Augmentation for Quadrotor Trajectory Tracking

Collision-Aware MPCC Integrated with Neural Augmentation for...

引用

International Conference on Mechatronics (ICM)

作者： Chuanqi Hu Jin Wang Yikai Chen Yonghang Chen Zhi Zheng Jun Meng Guodong Lu Polytechnic Institute Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China School of Mechanical Engineering Zhejiang University Hangzhou China National Elite Institute of Engineering Chongqing University Chongqing China College of Electrical Engineering Zhejiang University Hangzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This paper presents a trajectory tracking algorithm designed for autonomous quadrotors to navigate complex dy-namic environments that is resilient to collision disturbances. Since it combines Model Predictive Contouring Control (MPCC) with the capabilities of neural networks, our method is named MPCC Integrated with Neural Augmentation (MINA). By equip-ping the quadrotor with PVDF piezoelectric sensors, we achieve real-time perception of collision forces and utilize S4D-Conv Adaptive Net Architecture (SANA) for state computation, a network that integrates the advantages of S4D and convolutional kernels to enhance the accuracy of state predictions. We incorporate the predicted results into the MPCC framework to optimize the thrust output of the quadrotor for the next time step, ensuring precise trajectory tracking. Experimental validation demonstrates the reliability and effectiveness of our algorithm, providing an innovative solution for autonomous flight and trajectory tracking of quadrotors in complex dynamic environments.

关键词： Trajectory tracking Navigation Heuristic algorithms Neural networks Reliability theory Predictive models Prediction algorithms Real-time systems Sensors Quadrotors

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