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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Wang, Zibo Li, Honghui Zhang, Junwen Yang, Xiaorui Shen, Nahao School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway of MOE Beijing China

ISBN: (纸本)9798350354010

The structure of railway systems is complex, and accurately and rapidly identifying the root cause of faults post-occurrence is an emerging challenge in the industry. However, railway fault tracing mainly relies on manual experience and traditional data analysis techniques, with most tracing methods suffering from low efficiency and insufficient accuracy. To address these issues, this paper proposes a railway safety equipment fault tracing method based on knowledge graphs and weighted path ranking algorithm. The approach integrates knowledge graphs with Bayesian networks, attention mechanisms, and graph computation, utilizing a weighted calculation to run the weighted path ranking algorithm. Experimental results demonstrate that the proposed method achieves excellent tracing performance in the field of railway equipment. © 2024 IEEE.

关键词： Knowledge graph

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Combining Fourier-based Channel Attention with TCN and Informer for Railway Accident Prediction 15

Combining Fourier-based Channel Attention with TCN and Infor...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Shen, Nahao Li, Honghui Bai, Yanhui Yang, Xiaorui He, Ruiyi School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway of MOE Beijing China

ISBN: (纸本)9798350354010

The rapid expansion of railway networks and technological advancements has introduced significant challenges in ensuring railway safety. To address the critical need for effective prediction and prevention mechanisms, this paper proposes the FCAM-TCN-DSInformer model, a novel approach to railway accident prediction. The model integrates the Fourier-based Channel Attention Mechanism (FCAM), Temporal Convolutional network (TCN), and De-stationary Informer (DSInformer) to capture both temporal and spectral features from time series data effectively. Extensive experiments and ablation studies were conducted to validate the model's performance. The results demonstrate that the FCAM-TCN-DSInformer model significantly outperforms both traditional and advanced models, including ARIMA, LSTM, GRU, Transformer, Autoformer, and FEDformer by achieving the highest R2 score and substantial reductions in error metrics such as RMSE, MAE, and MAPE. These improvements confirm the model's accuracy and robustness, making it a powerful tool for railway safety management. Moreover, the model's adaptability to different univariate time series datasets, such as the Daily Minimum Temperatures Dataset and the US Births Dataset, highlights its strong generalization capabilities. Future work will focus on optimizing the model for real-time predictions and expanding its application to multi-step prediction tasks, thereby fully realizing its potential across diverse forecasting contexts. © 2024 IEEE.

关键词： Railroad accidents

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Bridging the Bandwidth Gap: A Mixed Band Telephonic Urdu ASR Approach with Domain Adaptation for Banking Applications 1

Bridging the Bandwidth Gap: A Mixed Band Telephonic Urdu ASR...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Khalid, Ayesha Adeeba, Farah Sehar, Najm Ul Hussain, Sarmad Center for Language Engineering Al-Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Department of Computer Science University of Engineering and Technology New Campus Pakistan

ISBN: (纸本)9798891762015

The accuracy of Automatic Speech Recognition (ASR) systems is influenced by the quality and context of speech signals, particularly in telephonic environments prone to errors like channel drops and noise, leading to higher Word Error Rates (WER). This paper presents the development of a large vocabulary Urdu ASR system for telephonic speech, based on a corpus of 445 speakers from diverse domains. The corpus, annotated at the sentence level, is used to train and evaluate GMM-HMM and chain Time-Delay Neural network (TDNN) models on a 10-hour test set. Results show that the TDNN model outperforms GMM-HMM. Mixing narrowband and wideband speech further reduces WER. The test sets are also evaluated for the pre-trained model Whisper for performance comparison. Additionally, system adaptation for the banking domain with a specialized lexicon and language model demonstrates the system’s potential for domain-specific applications. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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PSO-CHS Routing Protocol for Energy Consumption Enhancement in Wireless Sensor networks 4

PSO-CHS Routing Protocol for Energy Consumption Enhancement ...

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4th IEEE International Conference on Mobile networks and Wireless Communications, ICMNWC 2024

作者： Fakhri, Israa Sabri Marhoon, Haydar Abdulameer Hussein, Mohsin Hasan College of Computer Science and Information Technology University of Kerbala Department of Computer Science Karbala Iraq Scientific Research Center Al-Ayen University Department of Computer Science Information and Communication Technology Research Group Thi-Qar Iraq

ISBN: (纸本)9798350352931

A region's Wireless sensor networks consist of sensors distributed over a region that collect and send environmental data to a central station. It measures humidity and temperature. The formation is based on many key ideas, including the routing system that optimizes data flow to the base station. Routing introduces limits and obstacles to better results. One challenge is choosing the cluster head, who aggregates data from nearby sensors and sends it to the base station. Many methods are used to choose the cluster head. Instead of GSTEB, DCBRP, and ETSRP, which are laborious and time-consuming, modern cluster head identification methods use artificial intelligence. This study selects the cluster head quickly and accurately using PSO-CHS (particle swarm optimization-Cluster Head Selection Routing Protocol). It improves execution time by 84.1%, delay matrix by 44.3%, and energy matrix by 80.4% over previous protocols. PSO-CHS design promotes energy efficiency, network durability, and packet loss reduction. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Reinforcement Learning Double DQN for Chip-Level Synthesis of Paper-Based Digital Microfluidic Biochips

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第8期43卷 2465-2478页

作者： Li, Katherine Shu-Min Wu, Fang-Chi Li, Jian-De Wang, Sying-Jyan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan National Chung Hsing University Department of Computer Science and Engineering Taichung402 Taiwan Realtek Semiconductor Corporation Corporate Technology Center Hsinchu300 Taiwan

Digital microfluidic biochips (DMFBs) can effectively reduce the cost of biochemical analysis and improve experimental efficiency, as they are easy to carry, use fewer reagent samples and have high precision. Paper-based DMFBs (PB-DMFBs) are a branch of microfluidic biochips. This technology prints ink containing carbon nanotubes on special paper to form electrodes and control wire, so the manufacturing cost and time required are far less than the traditional digital microfluidic chip, in which droplets move between two control layers. However, the chip-level synthesis of PB-DMFBs becomes more challenging because all circuits of PB-DMFBs are printed on a single paper layer. Furthermore, current PB-DMFB designs must address various issues, including fabrication cost, reliability, and safety. Therefore, a more flexible method for the chip-level synthesis of PB-DMFBs is needed. In this article, we propose a chip-level synthesis method of PB-DMFBs based on reinforcement learning. Double deep Q -learning networks (Double DQN) are suitable for agents to select actions and estimate actions, and then obtain optimized comprehensive results. Experimental results demonstrate that the proposed method is not only effective and efficient for chip-level synthesis but also scalable to applications with high reliability and safety requirements. © 1982-2012 IEEE.

关键词： Electrodes

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Landslide Susceptibility Mapping in Gilgit-Baltistan: Leveraging Machine Learning for Risk Assessment

Landslide Susceptibility Mapping in Gilgit-Baltistan: Levera...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ashfaq, Nazish Khalid, Adnan Shabir, Mohamed Ali, Aitizaz Ahmad, Wasim Tariq, Mehreen University of the Punjab Department of Computer Science Lahore Pakistan Bahria University Department of Computer Science Lahore Pakistan Network Security Forensic Group School of Technology Asia Pacific University Malaysia College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350369106

This study examined the challenging issues of landslide detection along Gilgit-Baltistan's Karakoram Highway (KKH), where seismic activity and a dearth of data were significant issues161 characters. The study selected support vector machine (SVM), random forest (RF), and XGBoost algorithms to balance algorithm performance with ease of implementation. Finally, the study assessed the performance of the algorithms by fine-tuning them and optimizing their parameters. This was done by evaluating metrics such as the Area Under the Curve (AUC) and accuracy. Surprisingly, this comprehensive approach resulted in an average accuracy of 90% for all algorithms. These findings provide valuable insights for improving disaster prevention strategies and guiding land-use planning efforts along the KKH highway. © 2024 IEEE.

关键词： Landslides

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openGauss:An Enterprise-Grade Open-Source Database System

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Journal of computer science & technology 2024年第5期39卷 1007-1028页

作者： Guo-Liang Li Jiang Wang Guo Chen Department of Computer Science Tsinghua UniversityBeijing 100084China Huawei Technologies Co. LtdBeijing 100088China China Mobile Information Technology Center Beijing 100027China

We have built openGauss,an enterprise-grade open-source database *** has fulfilled its design goal of high performance,high availability,high security,and high *** high performance,it leverages NUMA(non-uniform memory access)-aware data access among multiple cores to enable efficient concurrent transaction processing,and symmetric multi-processing to make use of parallel processing resources ***,memory-optimized tables(MOTs)are designed to put everything in *** high availability,a three-tier pooling architecture that shares storage among the master and standby instances is proposed to achieve availability at 99.99%,containing both a distributed memory service(DMS)and a distributed storage service(DSS).For high security,it is a fully encrypted database with safe storage features,efficient complex querying,and *** high intelligence,an AI-based optimizer in the kernel and a self-driving platform named DBMind are demonstrated to achieve better performance and greater *** has served over 150 enterprises and institutions since its release in *** share the lessons we learned from its development and operation,and our customers.

关键词： database system open-source high performance storage engine

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Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking 38

Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Fo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xi Qin, Chuan Fang, Chuyu Wang, Chao Zhu, Chen Zhuang, Fuzhen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Computer Network Information Center Chinese Academy of Sciences China Baidu Inc. China Institute of Artificial Intelligence Beihang University China SKLSDE School of Computer Science Beihang University China AI Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://***/Job-SDF/benchmark. © 2024 Neural information processing systems foundation. All rights reserved.

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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