The role of artificial intelligence techniques in scheduling systems

Artificial Intelligence techniques provide good solutions for many of the problems characteristic of scheduling applications. However, scheduling is a large, complex, heterogeneous problem. Different applications will require different solutions. Any individual application will require the use of a variety of techniques, including both Artificial Intelligence (AI) and conventional software methods. The operational context of the scheduling system will also play a large role in design considerations. The key is to identify those places where a specific AI technique is in fact the preferable solution, and to integrate that technique into the overall architecture.     

Failure rate estimation of known failure mechanisms of electronic packages

Product reliability is one of the key factors for a successful product launch. However, electronic components can still fail in various stages of applications due to certain failure mechanisms. A constant failure rate typically describes a majority of non-solder joint related package failures in the accelerated testing or the field application. Historically, the failure rate for a constant failure phenomenon is estimated by using the Chi-square value or the expected number of failures.This paper will discuss the statistical characteristics of the number of failures observed in tests or applications and their confidence bounds. Several methods used to estimate the confidence bounds will be described, and a new approach will be proposed and validated through case studies. The estimation of the acceleration factor (AF) used in the failure rate modeling is also discussed. The conclusion will help engineers to understand the statistical meaning of the failures observed in stress tests or in the field applications, additionally, obtain a meaningful failure rate based on the expected failure data.     

A computationally efficient approach to the estimation of two- and three-dimensional hidden Markov models.

Statistical modeling methods are becoming indispensable in today's large-scale image analysis. In this paper, we explore a computationally efficient parameter estimation algorithm for two-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) and show applications to satellite image segmentation. The proposed parameter estimation algorithm is compared with the first proposed algorithm for 2-D HMMs based on variable state Viterbi. We also propose a 3-D HMM for volume image modeling and apply it to volume image segmentation using a large number of synthetic images with ground truth. Experiments have demonstrated the computational efficiency of the proposed parameter estimation technique for 2-D HMMs and a potential of 3-D HMM as a stochastic modeling tool for volume images.     

Analysis of Incomplete Data in Competing Risks Model

Parametric and nonparametric estimation methods are proposed for reliability characteristics of one failure mode under competing risks with incomplete data; some of the failure times are observed without observing the cause of failure. The efficiencies of these methods are discussed.     

A new evidence updating rule based on conditional event

Updating or conditioning a body of evidence modeled within the DS framework plays an im-portant role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now, most of uncertain reasoning models represent the belief of rule by conditional probability. However, it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper, AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability, then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.     

网络智能化中的AI工程化技术方案

网络智能化是通信行业借助AI技术,对外增强网络赋能能力,对内实现降本增效的重要举措。从AI工程化的视角系统分析网络智能化应用落地的难点,提出了包括数据采集处理、训练计算资源的管理与任务调度、推理部署优化在内的面向生产环境的AI工程化技术方案,探讨网络智能化生态发展的策略。     

数字经济时代人工智能伦理风险及治理体系研究

随着人工智能学习算法的革新、海量数据的积累、计算能力的提升,全球迎来新一轮人工智能发展浪潮。人工智能在为人类生产生活带来诸多便利的同时,也引发了一系列社会伦理风险,为社会治理带来全新挑战。梳理分析全球重点国家人工智能治理进展及特点,提出从治理模式、治理手段、治理评价三方面构建完备的人工智能治理体系,并针对我国人工智能治理体系的构建提出相关发展建议。     

AI大数据在审计管理中的应用研究

通过对业务需求和技术现状的分析，文中深入研究了各种审计模型和关键业务流程，并运用大数据平台建立的算法分析模型，从海量文件和票据中快速定位风险。集成了AI基础能力的系统，可以构建以AI技术实现对业务场景的服务支撑能力，实现AI技术在智慧审计领域的应用落地，为审计业务提供智能化支撑，丰富精细化管理手段的同时减少人工成本，实现降本增效，提升审计人员的工作效率。     

Artificial intelligence for optical transport networks: architecture,application and challenges

Optical network plays an important role in telecommunication networks, which supports high-capacity and long-distance transmission of Internet traffic. However, as the scaling and evolving of optical networks, it faces great challenges in terms of network operation, optimization and maintenance. Artificial intelligence(AI) has been proved to have superiority on addressing complex problems, by mimicking cognitive skills similar with human mind. In this paper, we provide a comprehensive investigat...     

通信网络级联失效建模与仿真分析

通信网络的级联失效会导致网络的鲁棒性下降,严重时会导致网络的大面积瘫痪, 对其进行理论建模是研究通信网络级联失效的基础和关键。在分析通信网络级联失效机理的基础上,建立了通信网络级联失效模型,并针对通信网络的级联失效模型进行了仿真分析,验证了模型的科学性和有效性,从而为研究级联失效对通信网络的影响提供了新的研究思路和方法。     

A New Training Approach for Parametric Modeling of Microwave Passive Components Using Combined Neural Networks and Transfer Functions

This paper presents a novel technique to develop combined neural network and transfer function models for parametric modeling of passive components. In this technique, the neural network is trained to map geometrical variables onto coefficients of transfer functions. A major advance is achieved in resolving the discontinuity problem of numerical solutions of the coefficients with respect to the geometrical variables. Minimum orders of transfer functions for different regions of geometrical parameter space are identified. Our investigations show that varied orders used for different regions result in the discontinuity of coefficients. The gaps between orders are bridged by a new order-changing module, which guarantees the continuity of coefficients and simultaneously maintains the modeling accuracy through a neural network optimization process. This technique is also expanded to include bilinear transfer functions. Once trained, the model provides accurate and fast prediction of the electromagnetic behavior of passive components with geometrical parameters as variables. Compared to conventional training methods, the proposed method allows better accuracy in challenging applications involving high-order transfer functions, wide frequency range, and large geometrical variations. Three examples including parametric modeling of slotted patch antennas, bandstop microstrip filters, and bandpass coupled-line filters are examined to demonstrate the validity of this technique.      

A New Discretization Approach With Application in Reliability Estimation

This paper introduces a new discretization technique that retains the basic structure of the failure rate function of the original life distribution. Earlier approaches for discretizing a continuous random variable, reported in the literature so far, are moment equalization, and discrete concentration techniques. This proposed approach has been used for approximating the reliability of complex systems where exact determination of survival probability is analytically intractable. These applications show a reasonable degree of closeness between this new method and the simulated results. Admissibility of the proposed method over the methods of discrete concentration and numerical integration has also been established.     

电子目标智能处理架构研究

随着现代战场电磁环境日益复杂,相似电子设备大量增加,电子目标情报处理的容量、时效和准确性等要求也越来越高。分析了现有处理流程存在的缺陷,在智能处理技术的基础上。提出了电子目标智能处理架构,分析了主要智能处理技术的主要功能、流程和技术性能,为相关功能系统设计提供架构和技术参考。     

Accurate Estimation of SRAM Dynamic Stability

In this paper, an accurate approach for estimating SRAM dynamic stability is proposed. The conventional methods of SRAM stability estimation suffer from two major drawbacks: 1) using static failure criteria, such as static noise margin (SNM), which does not capture the transient and dynamic behavior of SRAM operation and 2) using quasi-Monte Carlo simulation, which approximates the failure distribution, resulting in large errors at the tails where the desired failure probabilities exist. These drawbacks are eliminated by employing a new distribution-independent, most-probable-failure-point search technique for accurate probability calculation along with accurate simulation-based dynamic failure criteria. Compared to previously published techniques, the proposed technique offers orders of magnitude improvement in accuracy. Furthermore, the proposed technique enables the correct evaluation of stability in real operation conditions and for different dynamic circuit techniques, such as dynamic write-back, where the conventional methods are not applicable.      

面向视频云微服务系统的智能运维技术

提出了一种基于人工智能(AI)的保障视频云体验质量(QoE)的系统架构。该系统针对多个维度创建运维知识图谱,例如运行数据、运行环境、运维数据,以用于建模、感知、映射和分析。在对系统的微服务保障中,运用了图神经网络(GNN)等方法进行分类和预测。通过知识图谱和机器学习,该系统可实现实时监控、自愈恢复、智能预测和主动运维,从而实现QoE的智能保障。     

实际AANC系统误差通道建模的分析

以滤波－XLMS(FLMS)算法为代表的多种AANC算法用到了误差通道模型来实现对输入参考信号的滤波,而对误差通道建模的准确与否直接影响到算法的实际效果.本文分析了两种不同建模方式对FLMS算法收敛性的影响,并提出了实际AANC系统误差通道的一种建模方法,对实测数据的建模结果表明,该建模方法更符合实际.     

Design of Multisectional Driver and Field-Oriented Modeling of the Axial-Flux Linear Brushless Motor for Railway Transportations

A multisectional power driver design for the two-phase linear brushless motor is proposed in this paper. The field-oriented modeling of the axial-flux linear brushless motor (AFLBM) is also developed by using stationary and cosecant similarity transformation and singular value decomposition. Stationary and cosecant coordinate models were derived based on the proposed approach. Decoupled relation and constant torque property were obtained from the proposed models. Switching logic table for the linear motor operation is provided to drive the AFLBM properly. The switching-mode analysis for the power driver is also analyzed in detail. In order to increase the efficiency and the effectiveness of the electric motor applications, a multisectional driver circuit, which features the power driver with the so-called N + 2 structure, is investigated and compared. Experimental results show that the proposed circuit design can achieve better performance with the higher efficiency. It is believed that the proposed driver circuit technique and system modeling can be very helpful to the control of railway transportations.     

SRAM dynamic stability estimation using MPFP and its applications

In this paper, an accurate approach for estimating the dynamic stability of static random access memory (SRAM) is proposed. The conventional methods of SRAM stability estimation suffer from two major drawbacks: (1) using static failure criteria, such as SNM, which does not capture the transient and dynamic behavior of SRAM operation, and (2) using quasi-Monte-Carlo simulation, which approximates the failure distribution, resulting in large errors at the tails where the desired failure probabilities exist. These drawbacks are eliminated by employing accurate simulation-based dynamic failure criteria along with a new distribution-independent, Most-probable-failure-point search technique for accurate probability calculation. Compared to previously published techniques, the proposed dynamic stability technique offers orders of magnitude improvement in accuracy. Furthermore, the proposed dynamic stability technique enables the correct evaluation of stability in real operation conditions and for different dynamic circuit techniques, such as dynamic write back, where the conventional methods are not applicable.     

On Estimation of Covariance Matrices With Kronecker Product Structure

The estimation of signal covariance matrices is a crucial part of many signal processing algorithms. In some applications, the structure of the problem suggests that the underlying, true covariance matrix is the Kronecker product of two valid covariance matrices. Examples of such problems are channel modeling for multiple-input multiple-output (MIMO) communications and signal modeling of EEG data. In applications, it may also be that the Kronecker factors in turn can be assumed to possess additional, linear structure. The maximum-likelihood (ML) method for the associated estimation problem has been proposed previously. It is asymptotically efficient but has the drawback of requiring an iterative search for the maximum of the likelihood function. Two methods that are fast and noniterative are proposed in this paper. Both methods are shown to be asymptotically efficient. The first method is a noniterative variant of a well-known alternating maximization technique for the likelihood function. It performs on par with ML in simulations but has the drawback of not allowing for extra structure in addition to the Kronecker structure. The second method is based on covariance matching principles and does not suffer from this drawback. However, while the large sample performance is the same, it performs somewhat worse than the first estimator in small samples. In addition, the Cramer-Rao lower bound for the problem is derived in a compact form. The problem of estimating the Kronecker factors and the problem of detecting if the Kronecker structure is a good model for the covariance matrix of a set of samples are related. Therefore, the problem of detecting the dimensions of the Kronecker factors based on the minimum values of the criterion functions corresponding to the two proposed estimation methods is also treated in this work.     

Parametric inference of incomplete data with competing risks among several groups

We develop parametric inferential methods for the competing risks problem where data arise due to multiple causes of failure in several groups with censoring and possibly missing causes. We provide the general likelihood method and the closed-form maximum-likelihood estimators for the exponential model. Parametric tests are given for comparing different causes and groups. An extensive numerical and graphical investigation is presented to substantiate the proposed methods. A real-data example is illustrated.