飞思卡尔动力总成微控制器提供排放控制 抗击全球变暖

为了减少引发温室效应及全球变暖的汽车排放问题,飞思卡尔半导体现已在32位汽车微控制器（MCU）系列中引入集成的排放控制技术。与飞思卡尔其他动力总成微控制器类似,这些MCU帮助减少二氧化碳废气,为新兴市场提供经济高效且精密的引擎控制设计。     

飞思卡尔先进的动力总成微控制器助力汽车行业

飞思卡尔半导体公司推出了新款QorivvaMPC5777M多核微控制器（MCU）,这是其广泛的Qorivva产品线推出的最新产品。这个先进的MCU系列为新一代引擎管理解决方案提供卓越的性能,将帮助全球汽车制造商满足不同区域的汽车燃油经济性和安全需求。     

飞思卡尔凭借最新Qorivva汽车动力总成微控制器引领未来

飞思卡尔半导体公司推出Qorivva MPC5746M多核微控制器(MCU),旨在满足全球对提高汽车动力总成系统性能日益增长的需求,同时又能符合最新的安全和应用要求。MPC5746M是飞思卡尔首款基于新型200MHz四核Power Architecture平台的高性能32位MCU产品。这种新设计拥有     

飞思卡尔面向汽车应用推出强大的多功能32位微控制器

汽车制造商正在开发新的产品,以满足不断变化的消费需求。汽车行业技术的发展趋势包括主动式车辆安全(除传统的被动安全系统外)和动力总成电气化。新汽车电子系统变得越来越复杂,对MCU的性能要     

飞思卡尔宣布面向汽车应用推出功能强大的多功能32位微控制器

<正>汽车制造商正在开发新的产品选项,以满足不断变化的消费需求。汽车行业技术的发展趋势包括主动式车辆安全(除传统的被动安全系统外)和动力总成电气化。     

飞思卡尔Qorivva微控制器产品提供片上安全功能

汽车电子系统的安全性是汽车OEM、业主及保险业越来越关注的话题。汽车系统的电子控制正在稳步增长,大量数据流过车身控制模块(BCM)/网关。飞思卡尔半导体向其基于Power Architecture技术的Qorivva 32位微     

飞思卡尔提供针对工业市场的Power Architecture微控制器

飞思卡尔半导体宣布在其广泛的工业微控制器(MCU)产品组合中增加Power Architecture MCU新成员。通过推出该产品,飞思卡尔将其Power Architecture技术在汽车和网络基础设施市场获得的成功经验进一步扩展到广泛的工业应用。     

为中国国网电表量身打造的飞思卡尔MZ系列微控制器

随着世界各国政府和企业对于建立智能电网来改进电能分配和节约电能的需求不断增大,智能电网作为下一代电网的发展模式,在全球范围内获得广泛关注。国内对智能电网的建设和发展也愈加重视,为此中国国家电网公司（“中国国网”）对智能电网进行了全新的规划,2010年中国将进入智能电网建设的实质性阶段。     

飞思卡尔最新32位微控制器在苛刻环境下提供出色的性能

<正>飞思卡尔半导体日前宣布推出MPC564xA微控制器(MCU),这是嵌入式32位MCU系列中的一个新成员,是汽车动力和传输应用的理想之选。基于Power Architecture~(?)技术构建的32位双触发内核MCU在恶劣环境下,能够提供实现引擎管理和传输控制所需的性能和精度。与常见的单触发内核相比,MPC564xA在双触发内核中能够承受引擎悬置控制器和流体传输控制应用高达150℃的温度,同时还能在每个周期运行更多指令。与先前产品相比,该产品     

飞思卡尔与罗姆携手为全球市场提供创新汽车解决方案

飞思卡尔半导体日本公司（FreescaleSemiconductorJapanLtd．）与日本半导体厂商罗姆（ROHMCo．,Ltd．）商定,双方将开展汽车业务相关的合作．为日本和全球汽车市场提供全面的解决方案。此次合作标志着汽车半导体行业两家知名企业将实现强强联手。飞思卡尔拥有数十年的行业历史,致力于为全球顶尖汽车厂商和一流供应商提供创新型半导体解决方案,而罗姆则是服务于全球和日本汽车市场的一流供应商,主要供应分立元件、微控制器配套专用标准产品（ASSP）等周边设备。飞思卡尔和罗姆计划在双方产品不存在竞争关系的细分市场和技术领域展开合作。     

Application of global optimization methods to model and feature selection

Many data mining applications involve the task of building a model for predictive classification. The goal of this model is to classify data instances into classes or categories of the same type. The use of variables not related to the classes can reduce the accuracy and reliability of classification or prediction model. Superfluous variables can also increase the costs of building a model particularly on large datasets. The feature selection and hyper-parameters optimization problem can be solved by either an exhaustive search over all parameter values or an optimization procedure that explores only a finite subset of the possible values. The objective of this research is to simultaneously optimize the hyper-parameters and feature subset without degrading the generalization performances of the induction algorithm. We present a global optimization approach based on the use of Cross-Entropy Method to solve this kind of problem.     

Immune-inspired incremental feature selection technology to data streams

As data streams are gaining prominence in a growing number of emerging applications, advanced analysis and mining of data streams is becoming increasingly important. In this paper, an immune-inspired incremental feature selection algorithm called ISFaiNET is proposed as a solution for mining data streams, immune network memory antibody set which is far less than the size of data streams is design as a sketch data set. We can get the change features to the most extent by this set. ISFaiNET have the ability of feature extraction of dynamically tracking increasing huge size information by introducing increment strategy such as window mechanism. The empirical results for our algorithm are presented and discussed which demonstrate acceptable accuracy coupled with efficiency in running time.     

Output-feedback global tracking for unknown control direction plants with application to extremum-seeking control

This paper addresses the design of a sliding mode tracking controller for single-input–single-output (SISO) uncertain plants with relative degree one and unknown control direction, i.e., with unknown sign of the high frequency gain (HFG). We demonstrate that, for a class of linear plants with nonlinear output function, it is possible to achieve global exact tracking using only output-feedback by combining a recently introduced periodic switching function with a well-known control parameterization of Model Reference Control (MRC). Simulation results are presented to illustrate the good tracking performance. One significant advantage of the new scheme is its robustness to time-varying control direction which is here theoretically justified for jump variations of the HFG and successfully tested by simulation in more general conditions. This property makes it adequate for solving extremum-seeking problems. Theoretical justification is presented for a class of systems with nonlinear output function using only output-feedback. An application to the wheel slip control in Antilock Braking Systems (ABSs) illustrates the practical viability of the proposed control scheme.     

RFID together with multi-agent systems to control global value chains

Nowadays, the cooperative intelligent transport systems are part of a largest system. Transportations are modal operations integrated in logistics and, logistics is the main process of the supply chain management. The supply chain strategic management as a simultaneous local and global value chain is a collaborative/cooperative organization of stakeholders, many times in co-opetition, to perform a service to the customers respecting the time, place, price and quality levels. The transportation, like other logistics operations must add value, which is achieved in this case through compression lead times and order fulfillments. The complex supplier's network and the distribution channels must be efficient and the integral visibility (monitoring and tracing) of supply chain is a significant source of competitive advantage. Nowadays, the competition is not discussed between companies but among supply chains. This paper aims to evidence the current and emerging manufacturing and logistics system challenges as a new field of opportunities for the automation and control systems research community. Furthermore, the paper forecasts the use of radio frequency identification (RFID) technologies integrated into an information and communication technologies (ICT) framework based on distributed artificial intelligence (DAI) supported by a multi-agent system (MAS), as the most value advantage of supply chain management (SCM) in a cooperative intelligent logistics systems. Logistical platforms (production or distribution) as nodes of added value of supplying and distribution networks are proposed as critical points of the visibility of the inventory, where these technological needs are more evident.     

多智能体协调技术在热连轧中的应用

本文以典型的流程工业过程为对象,结合分布式工业过程控制的特点,分析了多智能体应用于复杂工业过程控制的可行性、必要性;提出了一种基于工业过程控制的多智能体系统的体系结构及其协调控制过程.板带热连轧机过程控制是典型的分布式过程控制对象,文章从综合控制的角度出发提出热连轧机工业生产过程多智能体系统的体系结构,给出了体系结构中各功能模块的组成及其处理过程,并重点分析了加热炉控制的协调过程.     

Energy-efficient train control: From local convexity to global optimization and uniqueness

The optimal driving strategy for a train is essentially a power–speedhold–coast–brake strategy unless the track contains steep grades in which case the speedhold mode must be interrupted by phases of power for steep uphill sections and coast for steep downhill sections. The Energymiser^®  device is used on freight and passenger trains in Australia and the United Kingdom to provide on-board advice for drivers about energy-efficient driving strategies. Energymiser^®  uses a specialized numerical algorithm to find optimal switching points for each steep section of track. Although the algorithm finds a feasible strategy that satisfies the necessary optimality conditions there has been no direct proof that the corresponding switching points are uniquely defined. We use a comprehensive perturbation analysis to show that a key local energy functional is convex with a unique minimum and in so doing prove that the optimal switching points are uniquely defined for each steep section of track. Hence we also deduce that the global optimal strategy is unique. We present two examples using realistic parameter values.     

Exploiting global knowledge to achieve self-tuned congestion control for k-ary n-cube networks

Network performance in tightly-coupled multiprocessors typically degrades rapidly beyond network saturation. Consequently, designers must keep a network below its saturation point by reducing the load on the network. Congestion control via source throttling-a common technique to reduce the network load-prevents new packets from entering the network in the presence of congestion. Unfortunately, prior schemes to implement source throttling either lack vital global information about the network to make the correct decision (whether to throttle or not) or depend on specific network parameters, or communication patterns. This paper presents a global-knowledge-based, self-tuned, congestion control technique that prevents saturation at high loads across different communication patterns for k-ary n-cube networks. Our design is composed of two key components. First, we use global information about a network to obtain a timely estimate of network congestion. We compare this estimate to a threshold value to determine when to throttle packet injection. The second component is a self-tuning mechanism that automatically determines appropriate threshold values based on throughput feedback. A combination of these two techniques provides high performance under heavy load, does not penalize performance under light load, and gracefully adapts to changes in communication patterns.     

图书馆门禁管理系统中的光电通道控制技术的实现

本文通过对通道控制方法的介绍,并经过实际应用,提出能满足图书馆门禁管理系统中的通道控制技术的需要,并对通道控制中可能遇到的一些实际问题给出了一种可行的解决方法.     

Assessment of two methods to monitor wetness to control dust emissions,Owens Dry Lake,California

Legally mandated control of blowing dust from 250 km² Owens Dry Lake, eastern California, USA is accomplished by wetting within shallow flooding basins. Monitoring of surface wetness is required to assess dust-control compliance. The cost, scale, heterogeneity and limited access prevent the use of moderate-resolution satellite data (e.g. Landsat Thematic Mapper (TM), 28.5 m, chosen a priori as the monitoring base) for calibrating directly to surface wetness. Instead, calibration and testing employed controlled spectrometry converted to equivalent broadbands of TM as surrogates.

Spectra, photography and surface assessments were obtained from twenty-nine 0.07 m² undisturbed surfaces varying from flooded to dry and emissive. Two competing broadband methods to assess surface wetting were tested with Landsat TM-equivalent data: TM band 5-equivalent reflectance (TMB5) as a surface wetness analogue; and tasseled cap wetness (TCW), calculated using all six TM-equivalent reflectance bands. Five performance criteria were evaluated: (1) progressive ranking, wet to dry; (2) wet and dry at ends of each index’s distribution; (3) no overlap, wet to dry; (4) large separation, moist/protected to dry/dust-emissive; and (5) clear yes/no dichotomy, whether moist/protected or not. For all criteria, TMB5 outperformed TCW. TMB5 reflectance of 0.19 provided an empirically consistent, verifiable threshold: below this, surfaces are sufficiently wet and protected from windborne dust emission.