不同分析方法对黄土有机碳同位素分析的影响

土壤有机碳同位素研究已在古植被、古气候重建工作中得到了广泛应用,其分析方法传统上采用"密封石英管法"。随着技术的发展,元素分析仪与同位素质谱仪联机在线测试方法("在线法")得到推广。但是,对同一种样品而言,这两种方法的测试结果有多大差别尚不明确,而这一点对不同实验室数据交叉检验和地质记录对比十分重要。本文分别采用"密封石英管法"和"在线法"对宝鸡黄土剖面有机碳同位素样品进行了测定。结果表明,同传统的"封管法"相比较,"在线法"测得的δ^(13)C值要偏正。比如,"在线法"获得的全新世古土壤S_0和末次冰期黄土L_1的有机质δ^(13)C平均值比"封管法"的数值分别偏正1.32‰和0.63‰。通过分析样品制备过程中可能影响实验结果的因素,我们推测,黄土中有机质含量很低,由于"在线法"燃烧时间(2 min)太短,有机质中以有机无机复合体形式存在的低δ^(13)C值的胡敏酸未完全分解,从而导致"在线法"测定结果相对偏正。因此,对于低有机碳含量的地质样品,当以有机无机复合体形式存在的胡敏酸相对比例较高时,采用"在线法"测定有机碳同位素组成时需谨慎。

In-situ measurement and distribution of flue gas mercury for a utility PC boiler system

The in-situ instrumentation technique for measuring mercury and itsspeciation downstream a utility 100 MW pulverized coal (PC) fired boiler system was developed andconducted by the use of the Ontario hydro method (OHM) consistent with American standard test methodtogether with the semi-continuous emissions monitoring (SCEM) system as well as a mobile laboratoryfor mercury monitoring. The mercury and its speciation concentrations including participate mercuryat three locations of before air preheater, before electrostatic precipitator (ESP) and after ESPwere measured using the OHM and SCEM methods under normal operation conditions of the boiler systemas a result of firing a bituminous coal. The vapor-phase total mercury Hg(VT) concentration declinedwith the decrease of flue gas temperature because of mercury species transformation from oxidizedmercury to particulate mercury as the flue gas moved downstream from the air preheater to the ESPand after the ESP. A good agreement for Hg°, Hg^(2+) and Hg( VT) was obtained between the twomethods in the ash-free area. But in the dense particle-laden flue gas area, there appeared to be abig bias for mercury speciation owing to dust cake formed in the filter of OHM sampling probe. Theparticulateaffinity to the flue gas mercury and the impacts of sampling condition to accuracy ofmeasure were discussed.

A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.

Determination of the intrinsic kinetics of iron oxide reduced by carbon monoxide in an isothermal differential micro-packed bed

The intrinsic kinetics of iron oxide reduced by carbon monoxide is evaluated by a method of online measuring concentration of off-gas in an isothermal differential micro-packed bed. Under the condition of getting away from the influence of gas diffusion and gas–solid heat transfer and mass transfer, the reaction of Fe2O3 to Fe3O4, Fe3O4 to Fe O and Fe O to Fe in the process of single reaction can be clearly distinguished from each other, and the relevant activation energy is characterized to be 75.4, 74.4, and 84.0 k J·mol-1, respectively. Therefore, the change of surface area in the reaction process due to losing oxygen could be easily calculated by combining it with pre-exponential parameters of Arrhenius equations. In conclusion, these kinetic parameters are verified by the experimental data for the process of ore reduced by carbon monoxide in a packed bed.

Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation,which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted.Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.

Online algorithms for scheduling with machine activation cost on two uniform machines

In this paper we investigate a variant of the scheduling problem on two uniform machines with speeds 1 and s. For this problem, we are given two potential uniform machines to process a sequence of independent jobs. Machines need to be activated before starting to process, and each machine activated incurs a fixed machine activation cost. No machines are initially activated, and when a job is revealed, the algorithm has the option to activate new machines. The objective is to minimize the sum of the makespan and the machine activation cost. We design optimal online algorithms with competitive ratio of (2s+1)/(s+1) for every s≥1.

PROJECTION BASED STATISTICAL FEATURE EXTRACTION WITH MULTISPECTRAL IMAGES AND ITS APPLICATIONS ON THE YELLOW RIVER MAINSTREAM LINE DETECTION

Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.

Experimental study by online measurement of the precipitation of nickel hydroxide: Effects of operating conditions

The objective of this work is using the online measurement method to study the process of precipitation of nickel hydroxide in a single-feed semi-batch stirred reactor with an internal diameter ofD = 240mm. The effects of impeller speed, impeller type, impeller diameter and feed location on the mean particle size d43 and particle size distribution （PSD） were investigated, d43 and PSD were measured online using a Malvern Insitec Liquid Pro- cess Sizer every 20 s. It was found that d43 varied between 13 kwh and 26 lain under different operating conditions, and it decreased with increasing impeller diameter. When feeding at the off-bottom distance of D/2 under lower impeller speeds, d43 was significantly smaller than that at D/3. PSDs were slightly influenced by operating conditions.

Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

URI Online Judge： A New Interactive Learning Approach

The URI online judge is a new online tool created with the main purpose of making programming practice more dynamic, interesting and stimulating for those who have just entered into the art of programming. The URI online judge allows problem corrections in real time, interactivity between users, besides it allows flexibility in the choice of the programming language and it makes some supporting materials available. During the short time in which the tool has being used we have observed that it is a very good tool for self-study. As users of programming portals, the authors noticed some details that would be important to be implemented in a new tool, such as the separation of problems by categories. Another fundamental detail is the fact that this tool is available in two languages （Portuguese and English）. This might facilitate the learning process for beginners, both locally and globally.

Improved Generalized Predictive Control Algorithm with Offline and Online Identification and Its Application to Fixed Bed Reactor

An improved generalized predictive control algorithm is presented in thispaper by incorporating offline identification into online identification. Unlike the existinggeneralized predictive control algorithms, the proposed approach divides parameters of a predictivemodel into the time invariant and time-varying ones, which are treated respectively by offline andonline identification algorithms. Therefore, both the reliability and accuracy of the predictivemodel are improved. Two simulation examples of control of a fixed bed reactor show that this newalgorithm is not only reliable and stable in the case of uncertainties and abnormal disturbances,but also adaptable to slow time varying processes.

High Accuracy On-line Measurement Method of Motion Error on Machine Tools Straight-going Parts

Harmonic suppression, non-periodic and non-closing in straightness profile error that will bring about harmonic component distortion in measurement result are analyzed. The countermeasure-a novel accurate two-probe method in time domain is put forward to measure straight-going component motion error in machine tools based on the frequency domain 3-point method after symmetrical continuation of probes' primitive signal. Both straight-going component motion error in machine tools and the profile error in workpiece that is manufactured on this machine can be measured at the same time. The information is available to diagnose the fault origin of machine tools. The analysis result is proved to be correct by the experiment.

An inverse method for online stress monitoring and fatigue life analysis of boiler drums

A method based on solution of the inverse heat conduction problem was presented for online stress monitoring and fatigue life analysis of boiler drums. The mathematical model of the drum temperature distribution is based on the assumptions that the difference of temperature along the longitudinal axis of the boiler drum is negligible with changes only in the radial direction and the circumferential direction, and that the outer surface of drum is thermaUy insulated. Combining this model with the control-volume method provides temperatures at different points on a cross-section of the drum. With the temperature data, the stresses and the life expectancy of the boiler drum are derived according to the ASME code. Applying this method to the cold start-up process of a 300 MW boiler demonstrated the absence of errors caused by the boundary condition assumptions on the inner surface of the drum and testified that the method is an applicable technique for the online stress monitoring and fatigue life analysis of boiler drums.

Understanding and improving online statistical translation method

Companies like Google, MSN and Yahoo provide translation services on their websites, generating translations based on statistical bilingual text corpora. Human translation seems to be inferior in face of huge amount of information and fast development of computer science. Despite the functions and versatility of statistical machine translation, it may never take the place of human effort. Teachers are supposed to guide the students in using online translation system.

Finding Recently Frequent Items over Online Data Streams

In this paper, a new algorithm HCOUNT ＋ is proposed to find frequent items over data stream based on the HCOUNT algorithm. The new algorithm adopts aided measures to improve the precision of HCOUNT greatly. In addition,HCOUNT ＋ is introduced to time critical applications and a novel sliding windows-based algorithm SL-HCOUNT ＋ is proposed to mine the most frequent items occurring recently.This algorithm uses limited memory （nB · （1 ＋α） · e/ε·In（-M/lnρ）（α〈1） counters）, requires constant processing time per packet （only （1＋α） · ln（-M/lnρ（α〈1）） counters are updated）, makes only one pass over the streaming data,and is shown to work well in the experimental results.

Boosting the Expense and Performance of Ann/Hmm Approch for on-line Handwriting Recognition

This paper focuses on a state sharing method for an artificial neural network (ANN) and hidden Markov model (HMM) hybrid on line handwriting recognition system. A modeling precision based distance measure is proposed to describe similarity between two ANNs, which are used as HMM state models. Limiting maximum system performance loss, a minimum quantification error aimed hierarchical clustering algorithm is designed to choose the most representative models. The system performance is improved by about 1.5% while saving 40% of the system expense. About 92% of the performance may also be maintained while reducing 70% of system parameters. The suggested method is quite useful for designing pen based interface for various handheld devices.

Online split-and-merge expec tation-maximization training of Gaussian mixture model and its optimization

This paper presents a new online incremental training algorithm of Gaussian mixture model （GMM）, which aims to perform the expectation-maximization（EM） training incrementally to update GMM model parameters online sample by sample, instead of waiting for a block of data with the sufficient size to start training as in the traditional EM procedure. The proposed method is extended from the split-and-merge EM procedure, so inherently it is also capable escaping from local maxima and reducing the chances of singularities. In the application domain, the algorithm is optimized in the context of speech processing applications. Experiments on the synthetic data show the advantage and efficiency of the new method and the results in a speech processing task also confirm the improvement of system performance.

IMPROVED FXLMS ALGORITHM BASED ON ADAPTIVE NOTCH FILTER APPROACH

An improved algorithm is presented to identify the secondary path based on the adaptive notch filter approach. Since the interference from the narrow band excitation signal is suppressed by the adaptive notch filter, the convergent speed of the on-line control path identification process is significantly improved. As a result, the controller performance is greatly enhanced. Besides the algorithm development, some important factors, such as the influence of reference signal on the controller convergent speed, are also discussed. The effectiveness of the algorithm is verified by experimental results.

A cellular wireless location algorithm based on RON online RBF neural network

A location and tracking algorithm with NLOS （Non-Line of Sight） errors for MS （Mobile Station） is proposed in this paper. A cellular localization algorithm based on the RON online RBF neural network is proposed. The measurement ofAOA, TOA and TDOA provided by mobile base station is fused to locate mobile. The location performance of RON online RBF neural network is simulated. The simulation results indicate that shrink, attenuation, shift or overlapping phenomenon is avoided when the network redundant hidden nodes appear. It＇ s location accuracy is significantly improved under complicated multi-path environment.

Online Internet Traffic Identification Algorithm Based on Multistage Classifier

Internet traffic classification plays an important role in network management. Many approaches have been proposed to clas-sify different categories of Internet traffic. However, these approaches have specific us-age contexts that restrict their ability when they are applied in the current network envi-ronment. For example, the port based ap-proach cannot identify network applications with dynamic ports; the deep packet inspec-tion approach is invalid for encrypted network applications; and the statistical based approach is time-onsuming. In this paper, a novel tech-nique is proposed to classify different catego-ries of network applications. The port based, deep packet inspection based and statistical based approaches are integrated as a multi-stage classifier. The experimental results demonstrate that this approach has high rec-ognition rate which is up to 98% and good performance of real-time for traffic identifica-tion.