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SSRN 2024年

作者： Sinishaw, Getaye Yeshaneh Tekle, Mekdes Mebratu Mitaw, Abibual Abate Kassa, Abrham School of Electrical and Computer Engineering Industrial Control Woldia Institute of Technology Woldia University Woldia Ethiopia School of Electrical and Computer Engineering Electrical Power Woldia Institute of Technology Woldia University Woldia Ethiopia Faculity of Electrical Engineering Industrial Control Bahir Dar Institute of Technology Bahir Dar Ethiopia

The water level in the boiler drum is crucial for factory operations. controlling it is challenging due to swell and shrink phenomena. If the level is below the set point, overheating of the water wall tubes may cause tube falling-out and serious accidents resulting. Damage can also be a result of several or frequently low drum level conditions. If the level goes above the limits of boiler water carryover toward the super heater or turbine may result in damage resulting in expansive maintenance costs or damage of either the turbine or boiler Various linear and nonlinear controllers have been proposed, such as fractional-order PID, LQR, fuzzy adaptive, and sliding mode controllers, but these cannot handle disturbances in steam and water flow *** paper presents a robust H-infinity-based optimal controller designed for high disturbance rejection and stability under all operating conditions. H-infinity controller can be done by selecting proper weight functions. The weights are tuned to obtain satisfactory performance margins such as rise time, percentage overshoot, settling time and steady state error. Proper weight functions are selected to shape the signal for desired performance and robustness. Weight function W1 is a low-pass filter for good disturbance rejection at low frequencies, while W3 is a high-pass filter. The coefficients of these weights are optimized using Particle Swarm Optimization (PSO).From the simulation result H infinity-based optimal controller is compared with LQR. H infinity controller shows that it has good reference tracking and performance than optimal control LQR The H-infinity controller demonstrates superior reference tracking and performance compared to the LQR. © 2024, The Authors. All rights reserved.

关键词： Disturbance rejection

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Real-Time Bus Driver Monitoring System Using Machine Learning

Real-Time Bus Driver Monitoring System Using Machine Learnin...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Anil Kadu Rajnandini Dharashive Rachit Nimje Arya Rajvaidya Sanket Palkar Vijay Gaikwad Department of Instrumentation and Control Vishwakarma Institute of Technology Pune India Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Electronics and Telecommunication Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

In recent years, the importance of ensuring road safety has intensified, prompting the development of advanced driver monitoring systems. This paper presents a real-time bus driver monitoring system utilizing a Raspberry Pi, OpenCV, a RaspiCam and machine learning techniques. The system aims to enhance passenger safety by detecting driver fatigue and distraction, which are major contributors to road accidents. The proposed solution leverages the Raspberry Pi's processing capabilities and the RaspiCam's imaging features to continuously capture and analyze the driver's facial expressions and eye movements. OpenCV's robust image processing algorithms are employed to identify signs of drowsiness and inattentiveness. The innovative aspects of the proposed solution include the use of Eye Aspect Ratio (EAR) for drowsiness detection and Mouth Aspect Ratio (MAR) for detecting yawning. Both the EAR and MAR are calculated through real-time facial landmark analysis. The system issues alerts when unsafe driving behavior is detected, thereby allowing for timely intervention. The proposed monitoring system is cost-effective and scalable. It offers a promising approach to improving public transportation safety by mitigating the risks associated with driver fatigue and distraction. Experimental results showed an accuracy of 92% for fatigue detection and 88% for distraction detection. This demonstrates the system's effectiveness in real-time detection and its potential for deployment in commercial bus fleets.

关键词： Accuracy Image processing Machine learning Fatigue Real-time systems Road safety Safety Monitoring Public transportation Vehicles

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DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories 62

DevEval: A Manually-Annotated Code Generation Benchmark Alig...

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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Li, Jia Li, Ge Zhao, Yunfei Li, Yongmin Liu, Huanyu Zhu, Hao Wang, Lecheng Liu, Kaibo Fang, Zheng Wang, Lanshen Ding, Jiazheng Zhang, Xuanming Zhu, Yuqi Dong, Yihong Jin, Zhi Li, Binhua Huang, Fei Li, Yongbin Gu, Bin Yang, Mengfei School of Computer Science Peking University China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education China Alibaba Group China Beijing Institute of Control Engineering China China Academy of Space Technology China

ISBN: (纸本)9798891760998

How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs. To address the knowledge gap, we propose a new benchmark named DevEval, which has three advances. 1 DevEval aligns with real-world repositories in multiple dimensions, e.g., code and dependency distributions. 2 DevEval is annotated by 13 developers and contains comprehensive annotations (e.g., requirements, original repositories, reference code, and reference dependencies). 3 DevEval comprises 1,825 testing samples from 115 repositories, covering 10 popular domains (e.g., Internet, Database). Based on DevEval, we propose repository-level code generation and evaluate 8 popular LLMs on DevEval (e.g., gpt-4, gpt-3.5, StarCoder 2, DeepSeek Coder, CodeLLaMa). Our experiments reveal these LLMs' coding abilities in real-world code repositories. For example, the highest Pass@1 of gpt-4 only is 53.04% in our experiments. We also analyze LLMs' failed cases and summarize their shortcomings. We hope DevEval can facilitate the development of LLMs in real code repositories. DevEval, prompts, and LLMs' predictions have been released. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing the transferability of adversarial attacks via Scale Enriching

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Neural Networks 2025年 189卷 107549页

作者： Zhao, Yuhang Zheng, Jun Gao, Xianfeng Liu, Lu Zhang, Yaoyuan Zhang, Quanxin School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

Deep learning models are vulnerable to adversarial attacks. Transfer-based adversarial examples are crafted against surrogate models and transferred to victim models. However, under the black-box settings, most adversaries have poor transferability on models with different input sizes. In this work, we propose the Scale Enriching Method (SEM) to enhance the transferability of adversarial examples using an input scale-enriching framework. By scaling the surrogate model's input in a specific range, our method enriches the attention areas that the surrogate model perceives and enlarges the tolerance of the distinction among different models, significantly improving the transferability. Notably, SEM avoids introducing extraneous noise during perturbation generation, thereby preserving the inherent textural features corresponding to different scales within the original images. Experiments on ImageNet show that our method successfully mitigates the gap of transferability between models with different input sizes. Furthermore, we demonstrate that our method can integrate with existing methods and bypass a variety of defense methods with over 90% success rate. © 2025 Elsevier Ltd

关键词： Adversarial attack Adversarial example Transfer attack

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The Impact of Grid Harmonics on Commutation Failure 7

The Impact of Grid Harmonics on Commutation Failure

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Li, Ming Shen, Xiaolin Wu, Yucheng Liu, Yiqi State Grid Economic and Technology Research Institute Co. Ltd. Beijing China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350359558

Commutation failure is a common fault in Line Commutated Converter High Voltage Direct Current (LCC-HVDC) systems. LCC-HVDC relies on the AC power grid to provide reverse voltage to naturally turn off thyristors. Faults in the AC system on the inverter side can lead to commutation failures. In practical engineering applications, AC network harmonics also affect the commutation of thyristors. This paper further elucidates the impact of AC network harmonics on commutation failure and validates the effect of non-characteristic harmonics of different orders on LCC-HVDC commutation failure using the PSCAD/EMTDC simulation platform. © 2024 IEEE.

关键词： Thyristors

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National institute of Standards and technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Spatiotemporal sequence data mining: Traffic flow prediction algorithm based on Recurrent Neural Networks

Advances in Transportation Studies

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Advances in Transportation Studies 2024年 64卷 25-40页

作者： Liu, Y.P. Liu, B.N. Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan030008 China Department of Traffic Equipment and Control Engineering School of Traffic Electrical and Control Engineering North China University of Technology Beijing100043 China

Traffic congestion is one of the most common problems in the transportation system. In urban planning and construction, traffic congestion increases the difficulty of control and scheduling, hindering the pace of urban modernization development. The development of intelligent transportation systems has to some extent solved these problems, among which traffic flow prediction is particularly crucial. A period processing layer containing concatenation, dimension amplification, and diffusion convolution was studied and designed. On the grounds of this, a graph neural network prediction model combining spatiotemporal period characteristics was designed. And it further introduced a traffic flow prediction method on the grounds of dynamic topology maps. This model combined adaptive topology generation and parameter learning mechanisms, synchronously processing the complex correlation of traffic data and the heterogeneity between nodes through gated recurrent neural networks. The outcomes indicated that the research model is more excellent than other graph learning methods in three evaluation indicators: Mean square error, mean absolute percentage error, and root mean square error. Their values were 15.63%, 9.87%, and 25.08%, respectively. The evaluation on all four datasets showed excellent performance of the research model, confirming the effectiveness of its adaptive topology and parameter learning strategy. This study had important practical significance for improving the traffic management system, alleviating traffic pressure, and enhancing the travel experience of urban residents. © 2024, Aracne Editrice. All rights reserved.

关键词： Mean square error

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Two-Stage DEA Model with Shared Inputs for Measuring Efficiency of the Governance in Energy Transition 17

Two-Stage DEA Model with Shared Inputs for Measuring Efficie...

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17th IEEE International Conference on Application of Information and Communication Technologies, AICT 2023

作者： Ratner, Svetlana V. Lychev, Andrey V. National University of Science and Technology "misis" Economic Dynamics and Innovation Management Laboratory Institute of Control Sciences Russian Academy of Sciences Moscow Russia National University of Science and Technology "misis" College of Information Technologies and Computer Sciences Moscow Russia

ISBN: (纸本)9798350303568

This study applies two-stage Data Envelopment Analysis (DEA) model with shared inputs to the problem of assessment of results of government support of research, development, and demonstration projects in energy sector in 23 countries in 2010-2018. The performance of governments is evaluated in terms of a positive impact on the growth of the energy efficiency of the national economy and the reduction of its carbon intensity. As intermediate results (outputs of the first and inputs of the second stage), patents in the field of energy are considered. The study reveals most and less efficient countries and indicates the target parameters for improvements in energy efficiency in less efficient countries as policy goals. © 2023 IEEE.

关键词： Data envelopment analysis

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model 3

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Elshabrawy, Mahmoud Eid, Marwa M. Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed Delta Higher Institute of Engineering and Technology Dept. of Communications & Electronics Mansoura Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Ain Shams University Faculty of Computer and Information Sciences Dep. of Computer Science Cairo Egypt University Mansoura Faculty of Engineering Computer Engineering and Control Systems Dept. Egypt

ISBN: (纸本)9798350323511

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models. © 2023 IEEE.

关键词： Ant colony optimization

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