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An MIP-based Force-directed Large Scale Placement Refinement Algorithm 25

An MIP-based Force-directed Large Scale Placement Refinement...

引用

30th Asia and South Pacific Design automation conference, ASP-DAC 2025

作者： Li, Zewen Tang, Ke Feng, Lang Wang, Zhongfeng School of Electronic Science and Engineering Nanjing Univ. Jiangsu Nanjing China School of Integrated Circuits Sun Yat-sen Univ. Guangdong Shenzhen China

ISBN: (纸本)9798400706356

Placement is an important part in the flow of physical design, which can affect the performance of a circuit significantly. Many algorithms have been proposed to refine the placement in the past years and Mixed Integer Programming (MIP) is one of the directions that can further improve the placement quality, since MIP is able to perform a finer-grained placement with precise MIP formulations. Many previous MIP works try to prune the search space for efficiency, but the strategies for selecting valuable search space do not contain enough analysis of the initial placement before refinement. In this work, we propose an MIP-based algorithm that can refine large scale placement by considering more global factors from initial placement, while achieving the trade-off between efficiency and quality. A force-directed displacement technique is proposed, which quantifies multiple metrics in each orientation to assign a potential region for each cell's movement. Meanwhile, we also propose an accurate wirelength prediction method for high-degree nets by introducing the concept of centroid for net breaking. Experiments on benchmarks of ISPD18 and ISPD19 show that our algorithm is able to reduce the wirelength and vias by 1.02% and 0.58% on average and our work outperforms the state-of-the-art related work in wirelength optimization under both its comprehensive mode and wirelength-only mode. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Integer programming

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SISCO: Selective Invariant Sharing, Clustering and Ordering for Effective Multi-Property Formal Verification 25

SISCO: Selective Invariant Sharing, Clustering and Ordering ...

引用

30th Asia and South Pacific Design automation conference, ASP-DAC 2025

作者： Das, Sourav Hazra, Aritra Dasgupta, Pallab Jain, Himanshu Kundu, Sudipta Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India Synopsys Santa ClaraCA United States

ISBN: (纸本)9798400706356

Multi-property formal verification remains a significant challenge in the chip design industry. With hundreds of property goals to verify in a design, several questions arise regarding goal ordering and grouping of properties, sharing of information across proven properties, and other heuristics to improve the verification productivity. This paper introduces SISCO, a novel method for addressing multi-property verification in complex designs. SISCO unfolds properties to a certain depth, clusters them, reorders properties within each cluster, and then uses a modified version of IC3/Property Directed Reachability (PDR) algorithm for efficient verification. The method stores the invariants of proven properties and selectively shares them while solving undecided goals. Additionally, SISCO keeps track of counter-example traces of falsified goals to assist verification. Experimental results demonstrate that clustering and ordering help solve more goals while selective invariant sharing accelerates the process. SISCO achieves a significant average improvement of 4.73× in the runtime of individual goals compared to all invariant sharing. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Formal verification

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Real-Time Analytics for Big Data in Smart City Applications: Transforming Urban Environments with Data-Driven Insights 3

Real-Time Analytics for Big Data in Smart City Applications:...

引用

3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Khanderi, Avinash Rajaram, Gnanajeyaraman Hussein, Ramy Riad Johri, Prashant Mohanraj, T. Balamurugan, M. Walmart Inc. BentonvilleAR United States Simats Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Excel Engineering College Department of Eee Komarapalayam India

ISBN: (纸本)9798331542375

This paper offers an overview of real-time analytics for smart cities proposing a framework for real-time data processing to improve the urban landscape. In the proposed system, large-scale data-streams can be processed using advanced machine learning algorithms, edge computing, and distributed data storage. Analyzing the outcomes of the evaluation procedure evidences reportable performance enhancements in the areas of smart city applications. Average traffic speed in transportation systems in the same period rose by 60%, concurrency and response to incidents likewise cut down by 40% and 53% respectively. The energy management systems responded with a 16% decrease in maximum energy load and the waste reduction was by 50%, while the costs savings was by 25% increase. Public safety applications alone realised a 60% decrease in the time taken to detect a crime and a 28.6% increase in efficiency on disaster resources with an overall spike in public satisfaction of 25.7%. Such findings improve the understanding of how this framework can increase urban efficiency, sustainability, and quality of life, which makes the solution usable in future smart city projects. © 2025 ieee.

关键词： Urban transportation

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Enhancing Customer Retention: A Federated Machine Learning Framework for Banking Churn Prediction 3

Enhancing Customer Retention: A Federated Machine Learning F...

引用

3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Vanitha, M. Faritha Banu, J. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Bharathi Salai Ramapuram Tamil Nadu Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542375

Customer attrition has become the significant challenge for the bank, making large volume of customers to migrate to other banks, as the banks keeps providing multiple benefits to the incoming customers. The loss due to migration of the existing customer to the competitive bankers creates the banking churn, means of loss of customer relationship with the bank, and affects the development, business and the profitability of the corresponding bank. It is essential to predict the banking churn with a primary objective of retaining the customers, considered as a critical task. To achieve this objective, banking sectors employ the customer behavioral analysis to determine the rate of customer churn, resulting in incorrect diagnosis of the churn rate. To over this concern, this research manuscript proposes a novel federated Machine Learning (ML) framework for the prediction of the customer churn, directly contributes for the enhancement of the customer retention. This proposed novel framework offers significant advancements for the banking prediction on customer retention, thus provides an accurate tool for the analysis of customer relationships. The proposed prediction framework is analyzed in terms of accuracy, precision, recall, F1 score and the performance is compared with the state of the art prediction methodologies. © 2025 ieee.

关键词： Federated learning

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Research on Distributed Management and Early Warning Technology of Food science Laboratory Based on Multi Information Fusion

Research on Distributed Management and Early Warning Technol...

引用

2025 International conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Tong, Yao Wang, Hong Ma, Gaoxing Nanjing University of Finance and Economics College of Food Science and Engineering of NUFE Nanjing China

ISBN: (纸本)9798331506797

Regarding the safety hazards in food science laboratories resulting from a multitude of instruments and equipment, as well as numerous flammable and explosive chemicals, by drawing upon advanced technologies such as the Internet of Things and multi-information fusion, a technology for distributed management, comprehensive health status assessment, and risk early warning of laboratories is proposed. By applying the D-S evidence theory to integrate and analyze information on personnel, environment, and instruments. Not only can this technology promptly detect temporary trigger faults and safety incidents such as fires, waterlogging, and gas leakage, but it can also promptly identify potential safety hazards caused by aging lines and excessive electricity consumption and issue early warnings in the first place. Compared with traditional laboratory monitoring methods, it features higher assessment accuracy and stronger risk control capabilities. © 2025 ieee.

关键词： fault alarm Introduction Laboratory safety monitoring multiple information fusion status assessment

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Modeling Method for Hybrid Ripple Estimation of General PWM Converters Based on KBM Theory

Modeling Method for Hybrid Ripple Estimation of General PWM ...

引用

2025 International conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Sun, Zhaoxuan Chen, Liping Ding, Jianwan National CAD Support Software Engineering Technology Research Center Huazhong University of Science and Technology Wuhan China Organization: National CAD Support Software Engineering Technology Research Center Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798331506797

To address the problem of balancing the accuracy and efficiency of simulation models of PWM converters, a hybrid ripple estimation modeling method for general Pulse-Width-Modulated (PWM) converters is proposed. The method is to apply the Krylov-Bogoliubov-Mitropolsky (KBM) theory to the Piecewise State Space Averaging (PSSA) model of PWM converter, and to address the defects of the first-order ripple estimation model, an upgrade criterion of the ripple estimation model is proposed, so as to realize the variable-order ripple estimation of state variables. Modeling simulations of the three cases are performed based on the system modeling language Modelica, and the results indicate that the hybrid ripple estimation model provides the best fitting effect and the model run efficiency is improved compared with the detailed model. © 2025 ieee.

关键词： component Krylov-Bogoliubov-Mitropolsky Modelica Pulse-Width-Modulated Ripple estimation Simulation model

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Classification of Millet Cultivars Using Image Processing and Machine Learning Techniques

Classification of Millet Cultivars Using Image Processing an...

引用

2025 ieee International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Bharambe, Dewendra Aher, Pushpalata Sandip University Department of Computer Science and Engineering Nashik India

ISBN: (纸本)9798331512965

India is one of the leading producers of various millet cultivars. Indian millets are a class of highly nutritious, drought-resistant crops that are mostly cultivated in the country's semi-arid and drought-prone regions. The most popularly cultivated millet varieties in India are Sorghum (Jowar), Pearl millet (Bajra), Barnyard Millet (Bhagar), Foxtail millet (Kangi), Amaranth (Rajgira), Red sorghum (Chikani), Finger millet (Nachani), and White sorghum (Dadar). In addition to being a wholesome dietary source, millets offer several health advantages. Since they don't contain gluten, they're an excellent alternative for anyone with gluten sensitivity or coeliac disease. Additionally low in glycemic index;millets are a great meal choice for diabetics. Each cultivar has a different nutritional composition and uses. Also, the majority of the time the farmers adapted the millet seeds obtained from their crop hence to preserve the purity of seeds and quality of produce it is very important to classify different millet cultivars. However, the classification of millet cultivars is a complex and time-consuming process. automation in millet cultivar classification becomes greatly beneficial with improved accuracy and efficiency. This study presents the effective classification of eight different millet cultivars using different machine-learning classifiers along with image processing techniques, based on the derived color features. © 2025 ieee.

关键词： Seed

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Electro Magnetic Compatibility Analysis of Signal Interference on Ultra-Low Power Four-Level ECT

Electro Magnetic Compatibility Analysis of Signal Interferen...

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2025 International conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Zhang, Yong He, Zhichao Shen, Xiangyu Xie, Nan School of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (纸本)9798331506797

To enhance the electromagnetic protection performance of ultra-low power Electronic Current Transformer (ECT), a damping oscillatory wave (DOW) disturbance test is conducted on it. A method for generating parallel resonant DOWs is proposed, which involves two-stage following and amplification of the initial waveform, enabling a maximum voltage of over 12V. Finally, considering the characteristics of the output signal from the four-level encoding module of ultra-low power ECT, electromagnetic interference countermeasures combining Transient Voltage Suppressor diodes with RC low-pass filters are proposed. Experimental results show that the four-level encoding module is extremely sensitive to voltage fluctuations due to the nonlinear relationship between the voltage and current of the LED. The normal operation of the four-level encoding module can be ensured through anti-interference measures. © 2025 ieee.

关键词： damping oscillatory wave electro magnetic compatibility electronic current transformer four-level encoding

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A New Method for One-Pedal Driving With Fuzzy-Based Efficiency-Optimization Algorithm

A New Method for One-Pedal Driving With Fuzzy-Based Efficien...

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2025 ieee International conference on Mechatronics, ICM 2025

作者： Heydrich, Marius Hammer, Tim Mitsching, Thomas Ivanov, Valentin Technische Universitä t Ilmenau Dept. of Computer Science and Automation Ilmenau Germany Technische Universitä t Ilmenau Dept. of Mechanical Engineering Ilmenau Germany

ISBN: (纸本)9798331533892

This paper presents a case study on adaptive one pedal driving for a battery electric sport utility vehicle with in wheel based rear-wheel drive that is able to adjust the drive pedal curve automatically. In addition, a fuzzy logic observes the state of the electric machines to adapt the motor torque to the most efficient region. The control method was tested in a co-simulation using a validated vehicle model and a driver-in-the-loop strategy. Within the experiments, an improvement in energy recovery of up to 4.8 % was achieved compared to the classic brake blending. In addition, the total number of brake pedal applications is ten times lower, which translates into less driving stress and greater driving comfort, especially on long distance trips, proving the effectiveness of the controller. © 2025 ieee.

关键词： Vehicle wheels

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Graph Contrastive Learning with Decoupled Augmentation

Graph Contrastive Learning with Decoupled Augmentation

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gao, Shihao Li, Caoshuo Mao, Cunli Zhang, Xulong Qu, Xiaoyang Jin, Taisong Wang, Jianzong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China Ping An Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9798350368741

Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, this joint approach may deviate from the expectation of semantically similar before and after augmentation. The propagation of attribute information in graphs usually occurs through their structure, meaning that structural and attribute augmentations can interfere with each other and potentially distort the graph's semantics. To address this, we propose a decoupled augmentation framework for graph contrastive learning, which eliminates the mutual interference between the two levels of augmentation while fully exploring graph information. Specifically, our framework employs separate encoders to learn data invariance under different augmentation levels, and it considers the positive gains generated between these levels. Experimental results on five public datasets show that the proposed method is more competitive than state-of-the-art approaches. © 2025 ieee.

关键词： Contrastive learning Graph representation Self-supervised learning

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