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A Hybrid Simulated Annealing with Variable Neighborhood Search for the Heterogeneous Fleet Vehicle Routing Problem with Multiple Forward/Reverse Cross-Docks

SSRN

引用

SSRN 2022年

作者： Yu, Vincent F. Anh, Pham Tuan Gunawan, Aldy Han, Hsun Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Center for Cyber-Physical System Innovation National Taiwan University of Science and Technology Taipei Taiwan School of Computing and Information Systems Singapore Management University Singapore Singapore

This research presents a variant of the Vehicle Routing Problem with cross-docking, named the Heterogeneous Fleet Vehicle Routing Problem with Multiple Forward/Reverse Cross-docks (HF-VRPMFRCD). We consider a heterogeneous fleet of vehicles with different loading capacities and transportation costs. We also consider multiple cross-docks with two different operations: forward and reverse processes. The former focuses on delivering the demand from suppliers to customers while the latter aims at returning unsold products from customers to suppliers. We propose two solution algorithms for HF-VRPMFRCD. The first one is a Simulated Annealing (SA) algorithm and the second is a hybrid Simulated Annealing with Variable Neighborhood Search (h-SAVNS) with five neighborhood moves. Both algorithms outperform the state-of-the-art algorithm in solving the benchmark HF-VRPMCD instances. For HF-VRPMFRCD benchmark instances, the solutions from the two proposed algorithms are optimal for small-scale instances (Sets 1 and 2) and better than those of the GUROBI solver for remaining instances (Sets 3, 4, and 5). In addition, h-SAVNS outperforms SA in terms of the quality of solutions. Lastly, the benefits of the integration of the forward with reverse processes are presented. © 2022, The Authors. All rights reserved.

关键词： Vehicle routing

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KANsformer for Scalable Beamforming

arXiv

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arXiv 2024年

作者： Xie, Xinke Lu, Yang Chi, Chong-Yung Chen, Wei Ai, Bo Niyato, Dusit The School of Computer and Technology Beijing Jiaotong University Beijing100044 China The Institute of Communications Engineering Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan The School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore

This paper proposes an unsupervised deep-learning (DL) approach by integrating transformer and Kolmogorov–Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input-single-output energy efficiency maximization problem subject to the total power budget. The proposed KANsformer first extracts hidden features via a multi-head self-attention mechanism and then reads out the desired beamforming design via KAN. Numerical results are provided to evaluate the KANsformer in terms of generalization performance, transfer learning and ablation experiment. Overall, the KANsformer outperforms existing benchmark DL approaches, and is adaptable to the change in the number of mobile users with real-time and near-optimal inference. © 2024, CC BY.

关键词： Budget control

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Towards Accurate Unified Anomaly Segmentation

Towards Accurate Unified Anomaly Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenxin Ma Qingsong Yao Xiang Zhang Zhelong Huang Zihang Jiang S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) USTC Suzhou Institute for Advance Research Suzhou Jiangsu China Stanford University Palo Alto California United State School of Medicine Shanghai University Shanghai China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for further monitoring. Moreover, this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task, we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine, incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance, attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets, respectively, surpassing previous methods significantly. The codes are shared at https://***/Mwxinnn/UniAS.

关键词： Measurement Image segmentation Computer vision Codes Pipelines Logic gates Transformers Data models Monitoring Anomaly detection

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Webcam-Based OCR System with Text-to-Speech Conversion

Webcam-Based OCR System with Text-to-Speech Conversion

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Rekha R Nair Tina Babu Kishore S Poornima J Madduru Sambasivudu Manchikalapudi Bhargavi Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte(Deemed to be University) Mangalore India Department of Information Science and Engineering Brindavan College of Engineering Bengaluru India Department of Computer Science and Engineering Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields.

关键词： Translation Webcams Scalability Optical character recognition User interfaces Streaming media Real-time systems Text to speech Reliability Testing

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引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jianghao Xiong Jianhuang Lai School of Computer Science and Engineering Sun Yat-Sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Key Laboratory of Video and Image Intelligent Analysis and Applicaiton Technology Ministry of Public Security China

Group re-identification (G-ReID) aims to re-identify a group of people that is observed from non-overlapping camera systems. The existing literature has mainly addressed RGB-based problems, but RGB-infrared (RGB-IR) cross-modality matching problem has not been studied yet. In this paper, we propose a metric learning method Closest Permutation Matching (CPM) for RGB-IR G-ReID. We model each group as a set of single-person features which are extracted by MPANet, then we propose the metric Closest Permutation Distance (CPD) to measure the similarity between two sets of features. CPD is invariant with order changes of group members so that it solves the layout change problem in G-ReID. Furthermore, we introduce the problem of G-ReID without person labels. In the weak-supervised case, we design the Relation-aware Module (RAM) that exploits visual context and relations among group members to produce a modality-invariant order of features in each group, with which group member features within a set can be sorted to form a robust group representation against modality change. To support the study on RGB-IR G-ReID, we construct a new large-scale RGB-IR G-ReID dataset CM-Group. The dataset contains 15,440 RGB images and 15,506 infrared images of 427 groups and 1,013 identi-ties. Extensive experiments on the new dataset demonstrate the effectiveness of the proposed models and the complexity of CM-Group. The code and dataset are available at: https://***/WhollyOat/CM-Group.

关键词：

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junyi Bian Rongze Jiang Weiqi Zhai Tianyang Huang Xiaodi Huang Hong Zhou Shanfeng Zhu School of Computer Science Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China School of Computing Mathematics and Engineering Charles Sturt University New South Wales Australia Atypon Systems LLC UK Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Zhangjiang Fudan International Innovation Center Fudan University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available 1 .

关键词： Training Dictionaries Codes Semantics Named entity recognition Complexity theory Labeling Data mining Bioinformatics

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Inkjet Printed Passive RFID Sensor for Wetness Detection of Diapers

Inkjet Printed Passive RFID Sensor for Wetness Detection of ...

引用

Microwave and Antenna Symposium (IMAS), International

作者： Abubakar Sharif Sidrah Liaqat Kamran Arshad Khaled Assaleh Naeem Ramzan Yangtze Delta Region Institute University of Electronic Science and Technology of China (UESTC) Huzhou China School of Computing Engineering and Physical Sciences University of the West of Scotland Paisley United Kingdom Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman United Arab Emirates

Infants, the elderly, people with disabilities, and patients in hospitals are among those who use disposable diapers. Event-based alerting regarding the wetness level of diapers can improve care for this population by improving incontinence management, decreasing the risk of getting cold, rashes, and infections. Therefore, this paper presents an inkjet printed ultra-high frequency (UHF) radio frequency identification (RFID) sensor for wetness detection of diapers. The proposed sensor design consists of an L-shaped radiator, a T-shaped loop, and two small stubs. The loop is connected to Impinj R6 RFID chip and joined at the middle of L-shaped radiator. This sensor design covers a bandwidth ranging from 900–935 MHz along with a read range of 2.5 m. The tag antenna is optimized to work on water filled slab with a low conductivity value (0.05). However, the realized gain of this sensor reduced drastically after increasing conductivity. An experiment is conducted to record the difference between received signal strength indicator (RSSI) of the empty, water and urine-filled diaper. So, the proposed sensor can be useful for sensing the wetness level of both adult and baby diapers.

关键词： Microwave antennas Pediatrics Bandwidth Conductivity Distance measurement Received signal strength indicator Slabs

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A Study on English Front Vowels of Tibetan College Students Based on information technology 23

A Study on English Front Vowels of Tibetan College Students ...

引用

Proceedings of the 2023 International Conference on information Education and Artificial Intelligence

作者： Fu Zhang Naqin Ma Gezhi Duojie Jieben Dao School of Foreign Studies of Gansu University of Political Science and Law China and Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University China School of Foreign Languages Northwest Minzu University China Tibetan Information Technology Research Center of Hainan Prefecture China The Computer College of Qinghai Minzu University China

ISBN: (纸本)9798400716157

Based on the acoustic speech experiment, this study took English front vowels of Maqu Tibetan college students as the object, and took Maqu Tibetan college participants’ and GB native participants’ F1, F2 and the duration of English front vowels as the basis for a comparative analysis of bias. The results showed that Maqu Tibetan college students had a good command of [i:]; [I] was confused with [i:]; the tongue position of [e] was slightly higher than GB native participants; in the acquisition of [æ], there was a big deviation from GB native participants’ tongue position, which was too high and forward. At the level of phonetic perception, they perceived English front vowel [I] as [i:] and [æ] as [e].

关键词：

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Opinion Mining using Machine Learning Approaches: A Critical Study

Opinion Mining using Machine Learning Approaches: A Critical...

引用

2020 International Conference on computing and information technology, ICCIT 2020

作者： Ramakrishnan, Jayabrabu Mavaluru, DInesh Srinivasan, Karthik Mubarakali, Azath Narmatha, C. Malathi, G. Jazan University College of Computer Science and Information Technology Department of Information Technology and Security Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Information Technology Saudi Arabia King Khalid University College of Computer Science Department of CNE Saudi Arabia University of Tabuk Faculty of Computer and Information Technology Department of Computer Science Tabuk Saudi Arabia Vellore Institute of Technology School of Computing Science and Engineering Chennai India

ISBN: (纸本)9781728126807

With the advancement of internet technology, there is a large volume of information exists on the web for users of the internet. These users not just utilize the accessible resources on the web yet, in addition, give their feedback, consequently creating more useful data. Opinion mining (OM) is contemplated as a subfield of NLP, information retrieval, and data mining. It is the way toward extricating people's thoughts and observations from unformed content, which with respect to the development of online social media and the huge amount of user feedback, has gotten an effective, attractive and furthermore complex issue. The fundamental in OM is classifying the polarity of text as far as positive (good), neutral (surprise) or negative (bad). This paper is proposed to review the methods and applications of opinion mining. This paper presents a review of OM and related strategies and additionally discusses the applications and difficulties for OM with knowledge into previous researchers. © 2020 IEEE.

关键词： Sentiment analysis

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Multi-Graph Fusion Networks for Urban Region Embedding

arXiv

引用

arXiv 2022年

作者： Wu, Shangbin Yan, Xu Fan, Xiaoliang Pan, Shirui Zhu, Shichao Zheng, Chuanpan Cheng, Ming Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Department of Computer Science and Technology Xiamen University China Department of Data Science and AI Faculty of Information Technology Monash University Australia University of Chinese Academy of Sciences China

Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. First, we integrate the graphs with spatio-temporal similarity as mobility patterns through a mobility graph fusion module. Then, in the mobility pattern joint learning module, we design the multi-level cross-attention mechanism to learn the comprehensive embeddings from multiple mobility patterns based on intra-pattern and inter-pattern messages. Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 13.11% improvement. https://***/wushangbin/MGFN. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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