检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

4,730 篇 会议
650 篇 期刊文献
3 篇 学位论文

馆藏范围

5,383 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,500 篇 工学
- 835 篇 电气工程
- 774 篇 计算机科学与技术...
- 322 篇 信息与通信工程
- 239 篇 控制科学与工程
- 160 篇 软件工程
- 126 篇 机械工程
- 95 篇 仪器科学与技术
- 40 篇 电子科学与技术（可...
- 31 篇 交通运输工程
- 15 篇 航空宇航科学与技...
- 11 篇 测绘科学与技术
- 11 篇 生物医学工程（可授...
- 10 篇 土木工程
- 9 篇 网络空间安全
- 7 篇 材料科学与工程（可...
- 4 篇 动力工程及工程热...
- 4 篇 矿业工程
- 4 篇 石油与天然气工程
166 篇 理学
- 68 篇 系统科学
- 56 篇 数学
- 20 篇 物理学
- 19 篇 生物学
- 9 篇 统计学（可授理学、...
- 5 篇 地球物理学
- 4 篇 地理学
100 篇 管理学
- 98 篇 管理科学与工程(可...
49 篇 医学
- 45 篇 临床医学
- 4 篇 基础医学(可授医学...
38 篇 文学
- 37 篇 新闻传播学
7 篇 艺术学
- 7 篇 设计学（可授艺术学...
6 篇 农学
2 篇 教育学
1 篇 军事学

主题

5,383 篇 inference algori...
540 篇 computational mo...
408 篇 fuzzy systems
400 篇 algorithm design...
386 篇 fuzzy logic
380 篇 bayesian methods
370 篇 computer science
343 篇 training
333 篇 neural networks
304 篇 approximation al...
291 篇 bayes methods
236 篇 optimization
225 篇 clustering algor...
219 篇 signal processin...
211 篇 data mining
207 篇 fuzzy sets
206 篇 accuracy
205 篇 heuristic algori...
192 篇 prediction algor...
185 篇 feature extracti...

机构

15 篇 school of electr...
11 篇 university of ch...
10 篇 school of comput...
10 篇 zhejiang univ co...
9 篇 tsinghua univers...
9 篇 purple mt labs p...
9 篇 national univers...
8 篇 microsoft resear...
8 篇 university of mi...
7 篇 department of co...
7 篇 tianjin univ col...
7 篇 department of el...
7 篇 university of so...
7 篇 department of au...
7 篇 tsinghua univ de...
6 篇 ibm thomas j. wa...
6 篇 nanyang technol ...
6 篇 university of so...
6 篇 department of el...
6 篇 department of co...

作者

8 篇 pushmeet kohli
7 篇 li cheng
7 篇 edward r. doughe...
7 篇 wei li
7 篇 xiaodong wang
7 篇 t.s. huang
7 篇 erik b. sudderth
7 篇 a. taylan cemgil
7 篇 p. baranyi
7 篇 nizar bouguila
6 篇 simon godsill
6 篇 alan s. willsky
6 篇 hui wang
6 篇 wei liu
6 篇 ying wu
6 篇 wang wei
6 篇 s. suresh
6 篇 chen yongxin
6 篇 yang xu
6 篇 luca benini

语言

5,319 篇 英文
51 篇 其他
11 篇 中文
1 篇 西班牙文

检索条件"主题词=inference algorithms"

共 5383 条记录，以下是91-100 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

A Substation Safety Measure Ticket Generation Algorithm based on Neural Network and inference Engine

A Substation Safety Measure Ticket Generation Algorithm base...

引用

Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Yin Wu Huicai Zhao Xingfu Huang Liqiong Huang Yuyang Hu Guangxi Power Grid Power Dispatching and Control Center Nanning China Guangxi Power Grid Limited Liability Company Nanning Power Supply Bureau Nanning China Guangxi Power Grid Limited Liability Company Guilin Power Supply Bureau Guilin China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Intelligent substations use fiber optic as the information transmission medium, transforming the interconnection relationship between secondary devices into fiber optic connections. The corresponding secondary circuits and hardware platforms are replaced with virtual circuits and software templates, resulting in a significant change in maintenance work methods and higher requirements for operators' maintenance work. This article proposes an intelligent substation safety measure ticket generation algorithm based on multi-scale feature convolutional neural network (MSF-CNN) and inference machine. Firstly, a neural network model suitable for safety ticket generation was designed and trained. Combining the structural characteristics of DenseNet, multi-scale feature extraction and logical relationship modeling were achieved through multi-layer convolution, concatenation, and pooling processing. The model uses hyperbolic tangent function as the activation function to improve the accuracy of regression prediction. Secondly, a custom word vector and equipment association modeling method based on SCD file parsing are proposed for the electricity proprietary terms and multi item logical relationships involved in the safety measures ticket text, in order to improve data expression accuracy and model understanding ability. In addition, based on the functional correlation and input-output interaction characteristics of substation equipment, a three-layer neural network-based inference engine was constructed to generate maintenance safety measures for primary and secondary equipment. The experimental results showed that the MSF-CNN model performed the best in accuracy, recall, and F1 score, with an accuracy of 92.5%, recall of 90.8%, and F1 score of 91.6%.

关键词： Optical fibers Substations Accuracy Neural networks Training data Data models inference algorithms Safety Maintenance Engines

来源：评论

学校读者我要写书评

暂无评论

Hybrid Offline Passive Grammatical inference and Online Planning for Non-Markovian Tasks

Hybrid Offline Passive Grammatical Inference and Online Plan...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mahyar Alinejad Alvaro Velasquez Yue Wang George Atia Dept. of Electrical and Computer Engineering University of Central Florida Orlando Florida Dept. of Computer Science University of Colorado Boulder Colorado Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida Orlando Florida

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Planning in non-Markovian environments often requires inferring task structures, such as reward machines, through interactions with the environment. Traditional active grammatical inference methods, like Angluin’s L * algorithm, depend on continuous querying to learn task structures for the underlying planning objectives. In contrast, we propose a hybrid approach that combines passive grammatical inference, using the Regular Positive and Negative inference (RPNI) algorithm, with online planning. By leveraging pre-collected positive and negative trajectories, RPNI learns a deterministic finite automaton (DFA) that captures the task structure, significantly reducing the need for real-time interactions. Subsequently, online planning is conducted over the product MDP, which integrates the environment with the learned DFA. This hybrid methodology minimizes the cost of online interactions and improves learning efficiency in complex environments. Our approach outperforms baseline algorithms in terms of runtime and sample complexity, and is well-suited for real-world scenarios where task structures are implicit, and interactions with the environment are expensive.

关键词： Runtime Learning automata Signal processing algorithms Signal processing inference algorithms Real-time systems Planning Complexity theory Trajectory Speech processing

来源：评论

学校读者我要写书评

暂无评论

Anda: Unlocking Efficient LLM inference with a Variable-Length Grouped Activation Data Format

Anda: Unlocking Efficient LLM Inference with a Variable-Leng...

引用

IEEE Symposium on High-Performance Computer Architecture

作者： Chao Fang Man Shi Robin Geens Arne Symons Zhongfeng Wang Marian Verhelst School of Electronic Science and Engineering Nanjing University China ESAT-MICAS KU Leuven Belgium

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

The widely-used, weight-only quantized large language models (LLMs), which leverage low-bit integer (INT) weights and retain floating-point (FP) activations, reduce storage requirements while maintaining accuracy. However, this shifts the energy and latency bottlenecks towards the FP activations that are associated with costly memory accesses and computations. Existing LLM accelerators focus primarily on computation optimizations, overlooking the potential of jointly optimizing FP computations and data movement, particularly for the dominant FP-INT GeMM operations in LLM inference. To address these challenges, we investigate the sensitivity of activation precision across various LLM modules and its impact on overall model accuracy. Based on our findings, we first propose the Anda data type: an adaptive data format with group-shared exponent bits and dynamic mantissa bit allocation. Secondly, we develop an iterative post-training adaptive precision search algorithm that optimizes the bit-width for different LLM modules to balance model accuracy, energy efficiency, and inference speed. Lastly, a suite of hardware optimization techniques is proposed to maximally exploit the benefits of the Anda format. These include a bit-plane-based data organization scheme, Anda-enhanced processing units with bit-serial computation, and a runtime bit-plane Anda compressor to simultaneously optimize storage, computation, and memory footprints. Our evaluations on FP-INT GeMM operations show that Anda achieves a $2.4 \times$ speedup, $4.0 \times$ area efficiency, and $3.1 \times$ energy efficiency improvement on average for popular LLMs including OPT, LLaMA, and LLaMA-2 series over the GPU-like FP-FP baseline. Anda demonstrates strong adaptability across various application scenarios, accuracy requirements, and system performance, enabling efficient LLM inference across a wide range of deployment scenarios.

关键词： Accuracy Runtime Large language models Computational modeling Organizations Energy efficiency Iterative algorithms inference algorithms Hardware Optimization

来源：评论

学校读者我要写书评

暂无评论

Improved Fuzzy Logic inference Algorithm for Vital Sign Monitoring Using FMCW Radar*

Improved Fuzzy Logic Inference Algorithm for Vital Sign Moni...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Zeping Xu Yan Xiao Yunpeng Wang Ji Li Kailun Song Weixin Zhang School of Computer and Network Security Guilin University of Electronic Science and Technology Guilin China Henan Locaris Intelligent Technology Research Institute Zhengzhou Locaris Technology co.ltd Zhengzhou China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Vital sign monitoring technology based on FMCW radar has become a research hotspot due to its high sensitivity and non-invasive nature. Compared to traditional methods, the fuzzy logic inference methods based on harmonic and intermodulation components proposed in recent years offer higher accuracy. However, these methods are more susceptible to interference from spurious peaks, making them highly sensitive to the configuration of membership functions, which limits the overall performance of the algorithm. To solve this problem, this paper proposes an improved fuzzy logic algorithm based on multi-range bin combination (MRBCIFL). By jointly optimizing the signals from multiple range bins, the proposed algorithm effectively suppresses spurious peak interference and improves the quality of input data for fuzzy logic inference. Experimental results demonstrate that the MGCIFL method significantly improves the accuracy and robustness of heartbeat frequency estimation.

关键词： Fuzzy logic Accuracy Sensitivity Interference inference algorithms Robustness Real-time systems Radar applications Monitoring Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

A radar behavior mode recognition method with Bayesian inference

A radar behavior mode recognition method with Bayesian infer...

引用

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Xingyu Wang China State Shipbuilding Corporation Limited No.723 Research Institute China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

With the increasingly complex electromagnetic signal environment, the traditional radar signal behavior mode recognition method for phased array and radar seeker of this kind of special multi-mode radar mode recognition is more difficult, this paper proposes a based on Bayesian inference, combined with the radar signal pulse stream sliding window processing mechanism, can effectively make up for the traditional radar signal behavior pattern recognition on the shortcomings of the mechanism for phased array and guide head of this kind of special body multi-mode radar pattern recognition method is proposed accordingly. Under this mechanism, a corresponding radar behavior mode recognition method is proposed for the special multi-mode radar of phased array and radar seeker.

关键词： Phased arrays Mechatronics Array signal processing Radar Radar signal processing inference algorithms Bayes methods Pattern recognition Information technology Sorting

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation for Semantic Segmentation Tasks in Autonomous Driving: A Likelihood Active inference Approach

Resource Allocation for Semantic Segmentation Tasks in Auton...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinxin Zhu F. Richard Yu Ying He Biao He College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The latest Segment Anything Model enables realtime scene annotation and understanding for autonomous driving systems, enhancing driving safety. However, effectively allocating resources for real-time performance and accuracy remains challenging in edge-cloud architectures. Traditional reinforcement learning struggles with poor generalization and the explorationexploitation dilemma, making it difficult to define clear reward functions. To address this, we propose a likelihood active inference approach to optimize resource allocation and improve system resource utilization. We use "intelligence" as a high-level indicator to quantify the efficiency of cognition in active inference, evaluating the difference between predicted and actual states during policy exploration. Experimental results show our algorithm outperforms mainstream deep reinforcement learning algorithms, improving sample efficiency and suitability for dynamically changing task workloads.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Signal processing Prediction algorithms inference algorithms Real-time systems Resource management Speech processing Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Optimizing Task Allocation for DNN inference on Edge Devices

Optimizing Task Allocation for DNN Inference on Edge Devices

引用

International Conference on Computing, Networking and Communications (ICNC)

作者： Mark Kotys Yijie Zhang Chase Q. Wu Aiqin Hou Department of Data Science New Jersey Institute of Technology Newark NJ USA School of Information Science and Technology Northwest University Xi’an China

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

As artificial intelligence continues to evolve, the need to deploy models at the network edge becomes increasingly critical. A key challenge in such environments is the effective allocation of tasks across devices. To tackle this challenge, we formulate the execution of DNN inference tasks on edge devices as an optimization problem and design a one-to-one task allocation scheme that optimizes the total execution time for a given set of tasks. Our method is both device-and task-aware, employing a greedy algorithm to create task-device mappings. We demonstrate the viability of this approach through comparisons with random allocation and nearest-device strategies, showing that our scheme consistently outperforms these alternatives.

关键词： Performance evaluation Greedy algorithms Cloud computing Costs Data transfer inference algorithms Resource management Artificial intelligence Optimization

来源：评论

学校读者我要写书评

暂无评论

Partial inference in Structured Prediction

Partial Inference in Structured Prediction

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chuyang Ke Deepak Maurya Jean Honorio Purdue University CS West Lafayette USA CIS and OPTIMA ARC Training Centre The University of Melbourne Melbourne Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this work, we examine the partial inference problem in the context of structured prediction. Using a generative model approach, we consider the task of maximizing a score function with unary and pairwise potentials in the space of labels on graphs. Employing a two-stage convex optimization algorithm for label recovery, we analyze the conditions under which a majority of the labels can be recovered. We introduce a novel perspective on the Karush-Kuhn-Tucker (KKT) conditions and primal and dual construction, and provide statistical and topological requirements for partial recovery with provable guarantees. The full-length paper with detailed proofs of our novel theoretical claims can be accessed from https://***/abs/2306.03949.

关键词： Signal processing algorithms Signal processing Prediction algorithms inference algorithms Convex functions Acoustics Speech processing

来源：评论

学校读者我要写书评

暂无评论

LAD: Efficient Accelerator for Generative inference of LLM with Locality Aware Decoding

LAD: Efficient Accelerator for Generative Inference of LLM w...

引用

IEEE Symposium on High-Performance Computer Architecture

作者： Haoran Wang Yuming Li Haobo Xu Ying Wang Liqi Liu Jun Yang Yinhe Han Chinese Academy of Sciences Institute of Computing Technology University of Chinese Academy of Sciences

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Large Language Models (LLMs) have emerged as the cornerstone of content generation applications due to their ability to capture relations between newly generated token and the full preceding context. However, this ability stems from the attention mechanism for decoding that retains the entire generation history as key value cache (KV cache). As the generated sequence lengthens, the KV cache expands, causing a substantial memory access bottleneck. In advanced LLM generation systems running on GPUs, the attention mechanism for decoding accounts for more than 50% of the total inference time when the KV cache length reaches 4096. To address this issue, this paper introduces LAD (Locality Aware Decoding), an LLM generation accelerator with algorithm-hardware enhancements that significantly decrease KV cache access, resulting in considerable speedups and energy savings. A key insight underlying LAD is that when the attention score for a specific position remains fixed over the next several decoding steps, it is unnecessary to repeatedly retrieve the associated key and value at each step to reproduce the computation. Our analysis reveals that numerous positions exhibit notable numerical locality in attention scores through multiple decoding steps. Leveraging these insights, we have designed an innovative attention decoding computation method that decreases the frequency of accessing the key and value for positions demonstrating good locality, all while maintaining decoding accuracy. Extensive experiments show that LAD generates sequences with an average ROUGE-1 similarity of 97% compared to those generated by the original model. When the length of KV cache exceeds 2048, the high configuration of LAD accelerator achieves on average (geomean) $10.7 \times$ speedup and $52.4 \times$ energy efficiency for the attention mechanism compared to the A100 GPU. For end-to-end model inference, it also achieves on average $2.3 \times$ speedup and $13.4 \times$ energy efficiency.

关键词： Attention mechanisms Accuracy Large language models Memory management Graphics processing units Energy efficiency inference algorithms Decoding History Optimization

来源：评论

学校读者我要写书评

暂无评论

CLAIRE: Composable Chiplet Libraries for AI inference

CLAIRE: Composable Chiplet Libraries for AI Inference

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Pragnya Sudershan Nalla Emad Haque Yaotian Liu Sachin S. Sapatnekar Jeff Zhang Chaitali Chakrabarti Yu Cao University of Minnesota Twin Cities Arizona State University

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Artificial intelligence has made a significant impact on fields like computer vision, Natural Language Processing (NLP), healthcare, and robotics. However, recent AI models, such as GPT-4 and LLaMAv3, demand significant number of computational resources, pushing monolithic chips to their technological and practical limits. 2.5D chiplet-based heterogeneous architectures have been proposed to address these technological and practical limits. While chiplet optimization for models like Convolutional Neural Networks (CNNs) is well-established, scaling this approach to accommodate diverse AI inference models with different computing primitives, data volumes, and different chiplet sizes is very challenging. A set of hardened IPs and chiplet libraries optimized for a broad range of AI applications is proposed in this work. We derive the set of chiplet configurations that are composable, scalable and reusable by employing an analytical framework trained on a diverse set of AI algorithms. Testing these set of library synthesized configurations on a different set of algorithms, we achieve a $1.99\times-3.99\times$ improvement in non-recurring engineering (NRE) chiplet design costs, with minimal performance overhead compared to custom chiplet-based ASIC designs. Similar to soft IPs for SoC development, the library of chiplets improves flexibility, reusability, and efficiency for AI hardware designs.

关键词： Training Costs Chiplets Computational modeling Libraries inference algorithms Natural language processing Data models Artificial intelligence Testing

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 6 7 8 9 10 11 12 13 14 15 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：