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Application and Optimization of DeepSeek-Based Safe AI Framework in the National Power Grid Multi-Level Knowledge Base for Authority-Task Management

Application and Optimization of DeepSeek-Based Safe AI Frame...

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Green Energy and Power Systems (ICGEPS), International Conference on

作者： Hao Wu Guibin Xu Peng Zhou Yuntao Zou Technology R&D Department Xiaohua (Shanghai) Internet Technology Co. Ltd. Shanghai China Systems Integration Department Hubei Huazhong Electric Power Technology Development Co. Ltd. wuhan China Artificial Intelligence Research Center FutureFront Interdisciplinary Research Institute FFIRI wuhan China LARGE LANGUAGE MODELING RESEARCH CENTER FutureFront Interdisciplinary Research Institute FFIRI wuhan China

ISBN: (数字)9798331510329

ISBN: (纸本)9798331510336

The security-oriented AI framework, built upon DeepSeek and multi-tiered knowledge storage, offers a solution for enterprises to deploy localized proprietary knowledge bases. Unlike cloud-based models, DeepSeek enables local model construction and training, integrating multi-level databases and stringent data management mechanisms to significantly reduce the risk of sensitive information leakage. This feature is particularly critical for high-security enterprises such as State Grid, allowing them to leverage AI for operational efficiency while ensuring data protection. However, the current multi-tiered knowledge base structure, which categorizes knowledge by tasks and access privileges, suffers from low retrieval efficiency due to the vast data volume. This inefficiency hampers timely access to optimal results, diminishing model practicality and potentially leading to misleading outputs or risks. To address this, we propose a hierarchical recommendation algorithm for external knowledge bases in large language models. By dynamically partitioning the knowledge base and leveraging user role-based permissions and historical dialogue records, the algorithm precisely targets relevant knowledge, enhancing retrieval efficiency and accuracy.

关键词： Training Accuracy Large language models Heuristic algorithms Knowledge based systems Information leakage Data models partitioning algorithms Smart grids Optimization

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Discrete-Time Distributed Least Square algorithms for Linear Equations via Layered Coordination

Discrete-Time Distributed Least Square Algorithms for Linear...

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Chinese Control and Decision Conference, CCDC

作者： Bo Wang Jinghao Li College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper is concerned with the problem of designing discrete-time distributed algorithms for the least squares solution of over-determined linear equations via layered coordination. A row-column partition and a column-row partition are provided to partition the augmented matrix associated with the linear equations into several block matrices, respectively. By assigning an equation solver layer to computation and a data integration layer to data exchanges, a row-column partition-based discrete-time distributed algorithm and a column-row partition-based discrete-time distributed algorithm are proposed for solving over-determined linear equations, respectively. It is shown that the distributed algorithms proposed can reach a consensus exponentially on the least squares solution of linear equations. Finally, the effectiveness of the distributed algorithms proposed is validated via a numerical simulation.

关键词： Distributed databases Data integration Numerical simulation Mathematical models partitioning algorithms Distributed algorithms

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Energy-Aware Scheduling for a Cloud Data Center by LSTM Prediction and African Vulture Optimization

Energy-Aware Scheduling for a Cloud Data Center by LSTM Pred...

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IEEE International Conference on Systems, Man and Cybernetics

作者： ZhiYao Zhang QingHua Zhu Yan Hou School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

As data volume increases and data parallelism strengthens, cloud service providers must reduce energy consumption by using effective cloud scheduling methods. This paper investigates scheduling multiple servers in cloud data centers to achieve more balanced energy consumption over the long term while ensuring task completion. We propose a method based on long short-term memory (LSTM) and the African vulture optimization algorithm (AVOA), combining prediction and scheduling. The proposed method is divided into two main modules: the prediction phase and the scheduling phase. The purpose of the prediction is to reserve partial resources on servers for large tasks, thereby reducing energy consumption by minimizing the number of active servers. Therefore, we use a prediction method based on LSTM to predict upcoming resource demands and anticipate server requirements. In the scheduling phase, we adjust the priority of tasks to ensure task completion while maximizing the processing of tasks with larger demands. Experiments are performed to validate the application and effectiveness of the proposed method, which outperforms the benchmark algorithms.

关键词： Energy consumption Cloud computing Prediction algorithms Scheduling partitioning algorithms Servers Data communication Forecasting Long short term memory Optimization

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Clustering algorithms with Balanced Weights for Geographic Data Processing

Clustering Algorithms with Balanced Weights for Geographic D...

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Smart World Congress (SWC), IEEE

作者： Qi Huang Jerome Yen Kenneth B. Kent Wenming Jin Yang Wang Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China Faculty of Science and Technology University of Macau Faculty of Computer Science University of New Brunswick Fredericton NB Canada The SIAT-Suntang Big Data&AI Joint Innovation Laboratory Shenzhen China Shenzhen University of Advanced Technology Shenzhen China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

With the rapid growth of geographic data, generated by various sensors and end equipment, new opportunities for research and practical applications can be found in various applications. However, effective utilization of this data often requires the division of geospatial space into smaller, manageable regions. An important challenge is to ensure that these regions with closed data points are balanced in terms of data size distribution (e.g., population density, resource allocation, etc.), creating a double optimization problem. The contributions of this paper are twofold. First, we propose a balance-driven partitioning algorithm, which is a coordinate-descent based algorithm using a dynamic programming technique. Second, we present a clustering-centric algorithm that improves the classic k-means algorithm with an imbalance-penalized function to allow the geographic data to be clustered together not only in terms of geographic location, but also in terms of the per-cluster total sizes in balance. Finally, to evaluate the efficiency of the proposed algorithms, we conducted experiments based on a trace geographic dataset and compared the results with those of the existing clustering algorithms. Our results demonstrate that the proposed algorithms can not only achieve the competitive clustering effects but also exhibit better performance in terms of data-size balance.

关键词： Shape Heuristic algorithms Clustering algorithms Transforms partitioning algorithms Dynamic programming Sensors Resource management Population density Optimization

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Novel Properties of Hierarchical Probabilistic Partitions and Their Algorithmic Applications

Novel Properties of Hierarchical Probabilistic Partitions an...

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Annual IEEE Symposium on Foundations of Computer Science

作者： Sandip Banerjee Yair Bartal Lee-Ad Gottlieb Alon Hovav IDSIA USI-SUPSI Switzerland The Hebrew University of Jerusalem Israel Ariel University Israel

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

We present a refined construction of hierarchical probabilistic partitions with novel properties, substantially stronger than previously known. Our construction provides a family of hierarchical partitions enabling fast dynamic programming algorithms, by guaranteeing that given a sparse set of balls, each cell of the hierarchical partition intersects only a small number of balls. The number of balls intersecting a cell is bounded solely as a function of the padding parameter of the partition (which is bounded in particular by the doubling dimension). This is in contrast to standard guarantees for probabilistic partitions which holds only in expectation. Additionally, each cell of our partition has a significantly smaller description than in previous constructions. These novel partition properties allow faster dynamic programs for a wide spectrum of fundamental problems defined by inherent or implicit sparsity. Among our main applications highlighting the utility of the novel properties are two well-studied clustering problems: min-sum radii (MSR) and min-sum diameters (MSD) clustering. The input to both these problems is a metric space and an integer $k$ , and the goal is to partition the space into $k$ clusters so as to minimize the sum of radii or diameters of the clusters, respectively. We apply our construction to give dramatically improved exact and approximation algorithms for these problems in Euclidean and doubling spaces, planar graphs, and more general settings. In particular, we obtain for these problems the first PTAS for doubling spaces, improving and generalizing upon the time bounds known for Euclidean space, even achieving linear time algorithms for fixed parameter $k$ . We also obtain the first PTAS for MSR for all metrics of bounded padding parameter, including planar and minor excluded metrics. Moreover, our results extend to constrained variants such as fair MSR and mergeable MSR, dramatically improving upon the best known results on th

关键词： Steiner trees Runtime Power measurement Heuristic algorithms Traveling salesman problems Probabilistic logic Approximation algorithms partitioning algorithms Space exploration Standards

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End-to-End Analysis of Event Chains under the QNX Adaptive partitioning Scheduler

End-to-End Analysis of Event Chains under the QNX Adaptive P...

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Dakshina Dasari Matthias Becker Daniel Casini Tobias Blaß Robert Bosch GmbH Germany KTH Royal Institute of Technology Stockholm Sweden Department of Excellence in Robotics &#x0026 AI TeCIP Institute Scuola Superiore Sant&#x2019 Anna Pisa Italy Saarland Informatics Campus (SIC) Robert Bosch GmbH and Saarland University Germany

ISBN: (数字)9781665499989

ISBN: (纸本)9781665499996

Modern autonomous cars run classic AUTOSAR applications alongside advanced driving assistance systems on a single-vehicle computer. Ensuring safety and predictability in such a complex system is challenging and requires temporal isolation between the various components. A promising solution is the POSIX-compliant QNX operating system: it meets the automotive standards for functional safety at the highest level (ISO 26262 ASIL-D) and provides temporal isolation through the Adaptive partitioning Scheduler (APS), a resource reservation algorithm that guarantees processor bandwidth to groups of threads. These guarantees make it an ideal platform for composing diverse and complex applications on centralized vehicle computers. However, so far, there is no precise description or analysis of the APS reservation mechanism in real-time literature. In this paper, we provide the first description of the behavior of the APS from a real-time point of view and validate the results by running experiments on a real QNX platform. Based on the derived scheduler rules, we develop a response-time analysis to bound the end-to-end latency of event chains under APS. Finally, we evaluate different design strategies on a case study based on a real autonomous construction vehicle.

关键词： Adaptation models Analytical models Operating systems Real-time systems Safety Behavioral sciences partitioning algorithms

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Optimization of Higher Vocational English Teaching Design Based on Optimizing Apriori Algorithm

Optimization of Higher Vocational English Teaching Design Ba...

引用

Industrial IoT, Big Data and Supply Chain (IIoTBDSC), International Conference on

作者： Yutong Jiang Yingkou Vocational and Technical College Yingkou City China

ISBN: (数字)9798331539818

ISBN: (纸本)9798331539825

In the context of rapid advancement in information technology and the explosion of big data, the education sector is facing unprecedented opportunities to improve teaching effectiveness through data-driven approaches. The emergence of big data in education has paved the way for exploring new methods and extracting meaningful insights from vast amounts of teaching data. Analyzing assessment data in vocational English teaching using data mining and analysis techniques has become an urgent need to drive evidence-based improvements in teaching practices. This paper combines the advantages of the traditional Apriori algorithm and introduces the improved O-Apriori algorithm, which addresses the performance issue of full database scanning by adopting a data partitioning approach. The core of this algorithm lies in reducing the number of database scans and candidate sets and introducing a relevance value to address the problem of inaccurate association rules obtained by the original algorithm. It identifies the correlation between the evaluation of vocational English teaching quality and Key Performance Indicator (KPI) factors in teaching content and methods, thereby accurately pinpointing the key areas to enhance teaching quality. This enables teachers to make targeted adjustments to their content and provides robust data support for optimizing vocational English teaching design. The contribution of this paper lies in the development and application of the O-Apriori algorithm, which extracts actionable knowledge from assessment data in vocational English teaching, empowering educators to make data-based adjustments to their teaching methods. The findings of this research have the potential to have a significant impact on the future of vocational English education by providing a systematic and evidence-based approach to optimize teaching design and enhance overall teaching quality.

关键词： Systematics Correlation Databases Shape Education Key performance indicator Supply chains Big Data partitioning algorithms Optimization

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Smart Substation VLAN Configuration Method Based on Improved Search Tree

Smart Substation VLAN Configuration Method Based on Improved...

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Power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Hongtao Zhang Yuntao Liu Gang Zou Zikai Jiang Wei Huang Kunjun Zhou Urban Power Supply Branch State Grid Chongqing Electric Power Company Chongqing China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

A smart substation VLAN configuration method based on improved search tree is proposed to address the problem of network uncertainty caused by the lack of unified standards for VLAN partitioning and configuration methods. Firstly, a communication network model for VLAN configuration is established based on the topology of the smart substation communication network and the subscription relationship between devices. Then, combined with the VLAN model, the search tree algorithm is improved using the heuristic path cost concept, and it is used to determine the situation where the packet transmission path passes through the switch port. Finally, based on the Tag mechanism, a VLAN partitioning scheme is proposed and switch port setting rules are set, combined with the determined packet transmission path to complete the VLAN configuration. Based on the experimental analysis of a 220kV smart substation, the improved search tree algorithm has an average time and average path length of only 0.25s and 846m, and the obtained VLAN configuration method has lower packet subscription relationship necessity index values and port traffic than other methods, indicating strong practicality.

关键词： Substations Uncertainty Switches Search problems Reliability engineering partitioning algorithms Topology

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deepV:PPO-driven Task Latency Minimization in Volunteer-supported Edge Computing

deepV:PPO-driven Task Latency Minimization in Volunteer-supp...

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Smart World Congress (SWC), IEEE

作者： Xiaojie Zhang Saptarshi Debroy Jiaqi Tong City University of New York USA Hunan First Normal University China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Although the emergence of edge computing has greatly enhanced the efficiency of data processing and reduced latency by bringing computation closer to the data source, one significant drawback of edge computing is the lack of processing capabilities of centralized cloud servers. With the rapid development of Internet of Things (IoT) and Artificial Intelligence (AI) techniques, the computational demand on edge servers is increasing exponentially. To address such shortage of resources at edge servers, this paper proposes deep V, a volunteer-supported edge computing framework. We also formulate a long-term multiobjective problem for joint optimization of resource allocation and volunteer employment. First, driven by deep reinforcement learning (DRL), a novel PPO-based algorithm is designed to configure the volunteer to IoT (V2I) device matching as well as uplink bandwidth allocation; Second, an efficient resource allocation and data partitioning algorithm is proposed utilizing the block coordinate decent (BCD) method. Through extensive simulations, we demonstrate the schedulability and scalability improvements of our work over other related algorithms.

关键词： Computational modeling Channel allocation partitioning algorithms Resource management Servers Internet of Things Uplink Artificial intelligence Optimization Edge computing

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A Novel Approach for Traveling Salesman Problem Via Probe Machine

A Novel Approach for Traveling Salesman Problem Via Probe Ma...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Changfeng Duan Jing Liu Jin Xu Dongdong An Yu Bao Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of High Confidence Software Technologies Peking University Beijing China Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai China

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

The Traveling Salesman Problem is a combinatorial optimization problem that seeks to find the shortest path visiting a set of locations, where each location is visited exactly once, and the path returns to the starting point. Using traditional computing model to solve Traveling Salesman Problem would face the issue of state explosion. This paper proposes a solving method based on the probe machine model, greatly accelerating the solving speed. By iteratively adding probes and performing probe operations in sequence, both optimal and feasible solutions for this problem can be obtained. We developed a solver PROBE4TSP and presented its framework and execution process. Through comparative experiments, we demonstrate that this method is faster than some classical solvers for small-scale Traveling Salesman Problems, especially fewer than thirty nodes.

关键词： Computational modeling Urban areas Transforms Traveling salesman problems Parallel processing Libraries partitioning algorithms

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