实模式下获取磁盘文件存储地址的一种捷径

简要分析了实模式下磁盘文件访问与BIOS INT13H直接读写扇区的内在联系，给出了在该模式下获取磁盘文件存储地址的一种捷径和实现方法，最后给出了实例和源代码．     

The three-dimensional X-ray crystal microscope: A new tool for materials characterization

The three-dimensional (3-D) X-ray crystal microscope is a new nondestructive tool for the 3-D characterization of the mesoscopic and nanoscopic materials structure. A prototype microscope is installed on beamline 34-ID at the advanced photon source and has begun initial operation. The prototype microscope has a routine spatial resolution of approximately 0.5 × 0.5 × 1 μm³ and can probe tens to hundreds of microns below a sample surface, depending on the composition of the sample. For each volume element measured, the microscope can determine between 10 and 16 parameters. The measured parameters are the local crystallographic phase (1 deg of freedom), the Eulerian angles of crystal orientation (3 deg of freedom), and the plastic and/or elastic strain-tensor elements (6 to 12 deg of freedom). The time required to collect each volume element varies between 1 and 14 seconds, depending on the precision of the parameters and the sample complexity. Much faster data acquisition and much better spatial resolution are certain in the near future. Some initial results are presented to illustrate how the 3-D X-ray crystal microscope can provide unprecedented information about the 3-D structure of materials. This article is based on a presentation made at the symposium “Characterization and Representation of Material Microstructures in 3-D” held October 8–10, 2002, in Columbus, OH, under the auspices of ASM International’s Phase Transformations committee.     

离子注入聚碳酸酯表面力学性能改变研究

将B^ 和O^ 离子分别注人到高分子化合物聚碳酸酯(PC)中，PC的表面显微硬度和耐磨性能均得到了较大提高。用纳米硬度计测量注人前后PC的显微硬度，硬度提高了7～25倍。用球盘式磨损实验机测量注人前后PC的耐磨性能，磨痕的宽度比未注人的窄，最窄的磨痕(或最窄处)为未注人磨痕宽度的1／3～1／2。傅里叶变换红外光谱(FTIR)测试表明，注人前后高分子的结构发生了变化。     

多路高速信号采集、记录与回放系统设计

为了准确地记录测控系统的测量数据，本文提出了一种基于SCSI硬盘的多路高速信号采集、记录与回放系统的设计方法，给出了系统的总体结构，阐述了采集电路的设计、SCSI硬盘控制的设计和抗干扰方法。本文提出的设计方法能满足多种应用场合，具有广泛的应用前景。     

Hybrid accident simulation methodology using artificial neural networks for nuclear power plants

A hybrid accident simulation methodology for nuclear power plants is proposed to enhance the capabilities of compact simulator by introducing artificial neural networks. Two neural networks are trained with the target values obtained from the analyses of detailed computer codes and trained results are combined with the compact simulator to perform the following roles: (i) compensation for inaccuracies of a compact simulator occurring from simplified governing equation and reduced number of physical control volumes, and (ii) prediction of the critical parameter usually calculated from the sophisticated computer code: the autoassociative neural network improves the computational results of the compact simulator up to the accuracy level of detailed best estimate computer code, while the backpropagation neural network predicts the minimum departure from nucleate boiling ratio (DNBR). Simulations are carried out to verify the applicability of the proposed methodology for the loss of flow accidents and the results show that the neural networks can be used as a complementary tool to improve the results of a compact simulator.     

A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system.

In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.     

Software in the new millennium: a virtual roundtable

What will software look like in the future? To answer this question, we conducted a survey of our editorial and industrial advisory boards, as well as a few outsiders. We constructed a set of 13 questions that we thought reflected the original query. We then circulated these questions to our boards, with a request to respond to the questions, or be extend them with additional are questions as appropriate. The answers were far ranging and sometimes surprising.     

Intelligent leaky bucket algorithms for sustainable-cell-rate usage parameter control in ATM networks

In this paper, we propose two intelligent leaky bucket algorithms for sustainable-cell-rate usage parameter control of multimedia transmission in asynchronous transfer mode networks. One is the fuzzy leaky bucket algorithm, in which a fuzzy increment controller (FIC) is incorporated with the conventional leaky bucket algorithm; the other is the neural fuzzy leaky bucket algorithm, where a neural fuzzy increment controller (NFIC) is added with the conventional leaky bucket algorithm. Both the FIC and the NFIC properly choose the long-term mean cell rate and the short-term mean cell rate as input variables to intelligently determine the increment value. Simulation results show that both intelligent leaky bucket algorithms have significantly outperformed the conventional leaky bucket algorithm, by responding about 160% faster when taking control actions against a nonconforming connection while reducing as much as 50% of the queueing delay experienced by a conforming connection. In addition, the neural fuzzy leaky bucket algorithm outperforms the fuzzy leaky bucket algorithm, in aspects of three performance measures such as selectivity, responsiveness, and queueing delay, especially when the traffic flow is bursty, dynamic, and nonstationary.     

Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge

Provided with plenty of data (experience), data mining techniques are widely used to extract suitable management skills from the data. Nevertheless, in the early stages of a manufacturing system, only rare data can be obtained, and built scheduling knowledge is usually fragile. Using small data sets, this research's purpose is improving the accuracy of machine learning for flexible manufacturing system (FMS) scheduling. The study develops a data trend estimation technique and combines it with mega-fuzzification and adaptive-network-based fuzzy inference systems (ANFIS). The results of the simulated FMS scheduling problem indicate that learning accuracy can be significantly improved using the proposed method involving a very small data set.     

Optimal timeslot and channel allocation considering fairness for multicell CDMA/TDD systems

The CDMA/TDD system is a highly attractive solution to support the next generation cellular mobile systems which provide unbalanced multimedia services between downlink and uplink. In this paper, we analyze the interference for downlink and uplink timeslots in a multicell CDMA/TDD system. We also mathematically formulate an optimal timeslot and channel allocation problem considering capacity fairness among cells, which is to maximize the system capacity under the given traffic unbalance, and propose an efficient algorithm based on the simulated annealing technique. Extensive experimental results show that the proposed scheme yields a good performance, and fairness among cells improves with a decrease in the system capacity.